The goals of the project are to develop automated quality control systems for mountain forest production using multi-sensor models. Work Package 4 involves developing various quality indices using technologies like 3D scanning, near infrared spectroscopy, hyperspectral imaging, stress wave measurements, and analysis of cutting power to optimize log and biomass segregation. The resources planned and utilized, as well as any problems and solutions, are monitored for each method.
This document outlines the goals and tasks of Project SLOPE Work Package 4, which aims to develop automated quality control systems for mountain forest production using multi-sensor modeling. The goals are to improve log segregation, support efficient supply chain management, and refine growth models. The work package involves developing quality indices using 3D modeling, near infrared spectroscopy, hyperspectral imaging, stress wave analysis, and measuring cutting power to determine wood quality. Tasks include sensor calibration and optimization, data collection, model development and validation, and integrating results into a grading system.
Work Package 4, Task 2 aims to evaluate near infrared (NIR) spectroscopy as a tool for determining log and biomass quality indices in mountain forests. The task leader CNR will coordinate partners to collect NIR spectra at different stages of the harvesting chain and develop guidelines for proper data collection. CNR will also develop a "NIR quality index" and evaluate NIR spectroscopy for characterizing forest resources. Partners BOKU, KESLA, GREIFENBERG, and FLYBY will support CNR by providing laboratory measurements, spectra collection in the field, and calibration transfers between lab and portable equipment. The task will establish chemometric models to predict quality indicators from spectra and classify logs based on quality.
The document outlines work to be done for Project SLOPE Work Package 4, which aims to develop quality control of mountain forest production using multi-sensor modeling. Specific tasks for T4.4 include developing reports and models on using stress wave measurements, testing these on standing and felled trees and equipment, defining quality thresholds, and determining optimal sensor setup. The resources planned were 17 person-months and there was a delay in processor access that impacted work, but collaboration helped conclude the tasks.
This work package involves developing methods for quality control of mountain forest products using multi-sensor models. It has six subtasks: 1) using 3D modeling and sensors to develop a quality index for standing and felled trees, 2) evaluating near infrared spectroscopy to determine quality indexes, 3) using hyperspectral imaging to evaluate quality, 4) analyzing stress wave propagation to determine quality thresholds, 5) measuring cutting power to develop quality models, and 6) implementing the quality control system and algorithms.
The document describes work done in Task 4.4 to optimize acoustic measurement protocols and develop prediction models for characterizing wood quality using stress wave tests, with the goals of determining two quality indices: an index (SW#1) relating stress wave velocity to overall log quality, and an index (SW#2) relating free vibration frequency to external log quality. Sensors were integrated with a forest harvester to measure stress waves and vibrations, and algorithms were developed to compute the quality indices from the acoustic data.
This document summarizes work from Project SLOPE on collecting and analyzing forest information using remote sensing techniques. It describes using a UAV to acquire RGB and multispectral imagery of a test site in Annaberg, Austria. Terrestrial laser scanning was also used to generate digital terrain models, detect individual trees, and analyze tree characteristics like volume estimations. Field measurements were taken and compared to remote sensing data. The integration of remote sensing with field data helped improve forest inventory and management techniques.
This document discusses Project SLOPE's Work Package 7, which focuses on piloting the SLOPE demonstrator. Task 7.1 involves defining an evaluation methodology for testing two forest supply chains. Task 7.2 prepares demonstrators by developing experimental designs and guidelines. Task 7.3 conducts trials and validation, evaluating data collection methods, processes, and the overall performance of the supply chains. Task 7.4 provides training to operators. The goals are to demonstrate models, systems, stakeholder involvement and on-the-job training. Partners will trial and validate the framework in Austria, Italy and Norway between 2014-2016.
This document outlines the goals and tasks of Project SLOPE Work Package 4, which aims to develop automated quality control systems for mountain forest production using multi-sensor modeling. The goals are to improve log segregation, support efficient supply chain management, and refine growth models. The work package involves developing quality indices using 3D modeling, near infrared spectroscopy, hyperspectral imaging, stress wave analysis, and measuring cutting power to determine wood quality. Tasks include sensor calibration and optimization, data collection, model development and validation, and integrating results into a grading system.
Work Package 4, Task 2 aims to evaluate near infrared (NIR) spectroscopy as a tool for determining log and biomass quality indices in mountain forests. The task leader CNR will coordinate partners to collect NIR spectra at different stages of the harvesting chain and develop guidelines for proper data collection. CNR will also develop a "NIR quality index" and evaluate NIR spectroscopy for characterizing forest resources. Partners BOKU, KESLA, GREIFENBERG, and FLYBY will support CNR by providing laboratory measurements, spectra collection in the field, and calibration transfers between lab and portable equipment. The task will establish chemometric models to predict quality indicators from spectra and classify logs based on quality.
The document outlines work to be done for Project SLOPE Work Package 4, which aims to develop quality control of mountain forest production using multi-sensor modeling. Specific tasks for T4.4 include developing reports and models on using stress wave measurements, testing these on standing and felled trees and equipment, defining quality thresholds, and determining optimal sensor setup. The resources planned were 17 person-months and there was a delay in processor access that impacted work, but collaboration helped conclude the tasks.
This work package involves developing methods for quality control of mountain forest products using multi-sensor models. It has six subtasks: 1) using 3D modeling and sensors to develop a quality index for standing and felled trees, 2) evaluating near infrared spectroscopy to determine quality indexes, 3) using hyperspectral imaging to evaluate quality, 4) analyzing stress wave propagation to determine quality thresholds, 5) measuring cutting power to develop quality models, and 6) implementing the quality control system and algorithms.
The document describes work done in Task 4.4 to optimize acoustic measurement protocols and develop prediction models for characterizing wood quality using stress wave tests, with the goals of determining two quality indices: an index (SW#1) relating stress wave velocity to overall log quality, and an index (SW#2) relating free vibration frequency to external log quality. Sensors were integrated with a forest harvester to measure stress waves and vibrations, and algorithms were developed to compute the quality indices from the acoustic data.
This document summarizes work from Project SLOPE on collecting and analyzing forest information using remote sensing techniques. It describes using a UAV to acquire RGB and multispectral imagery of a test site in Annaberg, Austria. Terrestrial laser scanning was also used to generate digital terrain models, detect individual trees, and analyze tree characteristics like volume estimations. Field measurements were taken and compared to remote sensing data. The integration of remote sensing with field data helped improve forest inventory and management techniques.
This document discusses Project SLOPE's Work Package 7, which focuses on piloting the SLOPE demonstrator. Task 7.1 involves defining an evaluation methodology for testing two forest supply chains. Task 7.2 prepares demonstrators by developing experimental designs and guidelines. Task 7.3 conducts trials and validation, evaluating data collection methods, processes, and the overall performance of the supply chains. Task 7.4 provides training to operators. The goals are to demonstrate models, systems, stakeholder involvement and on-the-job training. Partners will trial and validate the framework in Austria, Italy and Norway between 2014-2016.
This document provides an introduction to multivariate image analysis (MIA). It describes how MIA analyzes images with multiple variables at each pixel, such as hyperspectral images, and discusses tools for visualizing and extracting information from such images. TrendTool allows investigating multivariate data through simple univariate measurements, while Image Manager facilitates manipulating and analyzing image groups. Factorial techniques aid in enhancing signal-to-noise when many variables are present.
The document summarizes the work completed for Task 1.3 of defining the human-machine interfaces for the SLOPE system. It describes the process undertaken which included analyzing existing interfaces from consortium partners and defining requirements based on user needs. Interface designs were then created for desktop, mobile, in-vehicle, and ERP systems with the desktop interface having tools for analytics, operations, and forest management. The interfaces were designed based on usability principles and to integrate with existing partner systems.
- Weather
- Production Targets
- Contingency Plans
Harvesting Head
Control Interface
Production
Statistics
Machine
Parameters
Tree Detection
& Recognition
SLOPE
In-Vehicle
Interface
Machine
Monitoring
Route
Planning
Cable Crane
Control
Risks and Mitigation Actions
Technical Meeting
2-4/Jul/2014
Risks:
- Integration with existing systems (MHG, TREE) not seamless
- Mobile/In-Vehicle interfaces not robust enough for field conditions
- User acceptance of new interfaces
Mitigation Actions:
- Early prototyping and testing with end users
- Modular design allowing independent development
Multispectral imaging is a digital imaging technique that adds depth to understanding cultural heritage collections. It is a unique technology combining components from cross-disciplinary fields, thus requiring diverse standards, practices, and workflows. This poses challenges when integrating this system into a digitization lab with pre-established methods of operation, but is nonetheless feasible, as demonstrated by the collaboration between the Preservation Research and Testing Division of the Library of Congress (USA) and the digitization labs of The John Rylands Library, University of Manchester (UK).
2+3D Photography 2017 – INV 7 The 3D Image Capture Moonshot: Managing the Ene...rijksmuseum
Spectral data has arrived with the new iccMAX specification. This will lead to significant improvements in multi-ink printing where color separation will be based on spectral matching. It also provides a framework for combining scientific and studio photography for image archiving. Multispectral photography bridges hyperspectral imaging, used in conservation science, and RGB imaging, used in the studio. Two approaches will be described. The first is dual-RGB where an RGB camera and two colored filters (or lights) combine to form a five-channel multispectral camera. The second is a conventional system consisting of a monochrome camera and filter wheel. This new seven-filter multispectral system was designed with three quality criteria: high color accuracy, moderate spectral accuracy, and low color transformation noise.
Task 2.3 – on field digital survey systems (by tre)SLOPE Project
This document summarizes a terrestrial laser scanning (TLS) forest measurement system called AutoStem Forest. The system provides automated 3D measurement of forests to better predict log distributions. It was validated in trials in Sweden where it measured individual tree volumes with high accuracy compared to manual measurements, with biases mostly under 3% and standard deviations around 10%. The system integrates field data collection via TLS with online analytics and services to provide detailed forest inventories and valuations to improve harvest planning.
Implementation Cost Analysis of the Interpolator for the Wimax Technologyiosrjce
The design of the multirate filter (programmable) has been proposed which can be used in digital
transceivers that meets 802.16d/e (wimax) standard in the wireless communication system. Wimax is a
technology emerging in the wireless communication system in order to increase the broadband wireless internet
access. As there is wide spread need of the digital representation of the signal for the transmission and storage
which create the challenges in DSP [1]. In this paper, analysis of the implementation cost of interpolator for the
wimax technology, and cost of interpolator is analyzed on the basis of number of adders and multiplier. The
Filters are designed using the FDA (filters design and analysis) tool in MATLAB.
The document provides a mid-term review of Project SLOPE, which aims to develop innovative technologies for forestry operations in mountainous areas. It summarizes the status and results of Work Package 8 on dissemination and engagement activities over the first 18 months of the 36 month project. Key activities included developing dissemination strategies and materials, establishing a website and social media presence, organizing conferences and workshops, and initiating an Industrial Advisory Board with members from the forestry industry. The review indicates that dissemination goals have been achieved so far in raising awareness of the project and that focus should now turn to specific technical areas and maintaining engagement over the remainder of the project.
This document summarizes an update on Project SLOPE's Task 3.6 on data management and backup. The task aims to develop a system for exchanging data between field hardware and a central computer, and provide a data backup strategy. It is led by CNR and involves several partners. The current status is 50% complete. A key output is a prototype portable and internally powered "black box" for daily/weekly data backups and transmitting data from areas without network coverage, due by Month 25.
The document summarizes work being done for Task 7.02 of the Project SLOPE, which involves preparing demonstrators to assess the technical and economic feasibility of the proposed SLOPE timber harvesting system compared to current methods. Activities being defined for the demonstrators include forest inventory, harvest planning, harvest operations, and logistics/storage/sale. Data will be collected from pilot studies on time consumption, productivity, costs, and other metrics to enable comparison between the innovative SLOPE methods and conventional approaches. Flow charts are provided as an example of how work cycles will be documented for analysis.
The Task 5.3 aims to design and develop an online purchasing and invoicing platform for industrial timber and biomass. Activities completed include benchmarking existing e-trading solutions in Finland and globally, and identifying key elements for the new platform such as material identification, negotiation, bidding, and market analysis functions. The platform will facilitate trade between forest owners and buyers in a digital environment.
Project SLOPE is developing a forest information system to optimize timber harvesting and supply chain operations. The system will integrate real-time data on tree sizes, product distributions, and harvesting machine positions. It aims to develop modules for inventory data, real-time supply chain control, online purchasing and invoicing, and short and long-term optimization. Partners will utilize existing solutions like MHG Biomass Manager and develop new applications to track harvesting data, manage transportation logistics, and facilitate online commerce between producers and buyers. The system seeks to strengthen industry linkages and competitiveness through information sharing.
This document summarizes the mid-term review of Task 5.2 to develop a platform for near real-time control of forest operations. The task is on schedule and involves designing a module to integrate harvesting plan data with real-time data from an intelligent processing head to analyze harvested versus predicted timber and enable adjustments to storage and logistics. Key activities completed include defining the workflow and involved actors. Upcoming activities are agreeing the module architecture, developing the near real-time control platform, and testing it in a demonstration area.
The document discusses dissemination activities for Project SLOPE, including:
1. An overview of the dissemination plan with activities like brochures, website, social media, newsletters, and workshops.
2. Updates on dissemination tasks completed in the last period like the dissemination plan, project website, and first newsletter.
3. Upcoming dissemination events and deadlines like the next press release in April 2015 and workshops planned for 2015-2016.
WP 1 of the Project SLOPE was completed and focused on defining requirements for the system. It identified user needs through questionnaires, defined the necessary hardware, equipment and sensors, specified the user interface guidelines for desktop, mobile and in-vehicle access, developed a data and metadata model for storing forest information, and designed a scalable system architecture based on service-oriented principles. All deliverables were finalized and submitted on schedule, though some partners left the project early on. The work specified what was needed to develop the SLOPE Forest Information System.
The document discusses the development of a forest information system called Project SLOPE. It involves 5 work packages, including the development of a database to support novel inventory data (WP5.1), a platform for near real-time control of forest operations (WP5.2), and an online purchasing/invoicing system for industrial timber and biomass (WP5.3). It will also include the development of modules for short-term optimization (WP5.4) and mid-long term optimization/strategic planning (WP5.5). The system will integrate data on timber quality, quantities, and origin to optimize procedures and avoid delays. It will also facilitate long-term forest planning, simulations, optimization, and
This document details the development of an intelligent transport truck as part of Project SLOPE. Technologies like RFID, GPS, and GPRS will be added to trucks to identify harvested trees and transmit location data. Partners will develop a control unit over months 12-24 to interface sensors with a database. A communication scheme will be tested and the system installed in pilot trucks to evaluate performance.
SLOPE Final Conference - innovative cable yarderSLOPE Project
This document discusses innovations in cable yarding machinery developed through the SLOPE project, which received EU funding. It describes new automated machines like the TECNO self-propelled carriage, which can transport loads of up to 3.2 tons at 4.5 meters/second and automatically unload. An automatic chocker system and rope launcher are also presented, which aim to increase efficiency and safety in cable logging operations. The overall goal of these new technologies is to automate processes and facilitate communication within the logging workflow.
The document summarizes work from Project SLOPE Task 2.1 on remote sensing and multispectral analysis of forest sites in Ireland and Italy. Key points include:
- Participants in Task 2.1 defined approaches to monitor tree growth and health using different vegetation indexes derived from satellite, UAV, and ground instrumentation data.
- Analysis of vegetation indexes at different scales (satellite, UAV, laser scanner) allowed estimation of biological parameters and increasingly detailed information.
- A case study in Ireland using RapidEye satellite imagery to calculate NDVI, NDRE, and CCCI showed relationships between the indices and chlorophyll levels over time.
- UAV and terrestrial laser scanner data provided more
This document summarizes discussions from a July 2014 meeting of the Project SLOPE working group on openness with other activities, dissemination, and exploitation of results (WP8). Key discussion points included: overall guidelines for awareness, networking and dissemination activities; contributing to social networking platforms like LinkedIn, Facebook, and Twitter; a dissemination plan and calendar; and linking with other projects. Partners provided updates on dissemination tasks including developing a brochure, launching the project website and social media channels, releasing the first newsletter, and distributing initial press releases. An overview of relevant conferences and trade fairs for disseminating project results was also presented.
This document summarizes a technical meeting that took place on July 5th, 2016 for Project Slope. It discusses the status of several work packages and tasks. Task 3.6 involving data management backup is still ongoing with a prototype under development. For task 3.4, the team has integrated all hardware and software components on the processor head. Connection to the central database and uploading of required data has been accomplished. The team also worked on connecting the processor head and cable crane to verify data transfer. For task 3.3, the Tecno carriage hardware and software are complete pending RFID tag testing in the forest. Chokers and rope launchers are also ready for use pending further testing.
The document discusses dissemination activities for the SLOPE project from January 2016. It provides an overview of dissemination activities planned from 2014-2016, including brochures, newsletters, conferences, and trade fairs. It also summarizes dissemination activities and results from the last 6 months of 2015, including publications, conferences attended, and trade fairs/demonstrations participated in by various project partners.
This document provides an introduction to multivariate image analysis (MIA). It describes how MIA analyzes images with multiple variables at each pixel, such as hyperspectral images, and discusses tools for visualizing and extracting information from such images. TrendTool allows investigating multivariate data through simple univariate measurements, while Image Manager facilitates manipulating and analyzing image groups. Factorial techniques aid in enhancing signal-to-noise when many variables are present.
The document summarizes the work completed for Task 1.3 of defining the human-machine interfaces for the SLOPE system. It describes the process undertaken which included analyzing existing interfaces from consortium partners and defining requirements based on user needs. Interface designs were then created for desktop, mobile, in-vehicle, and ERP systems with the desktop interface having tools for analytics, operations, and forest management. The interfaces were designed based on usability principles and to integrate with existing partner systems.
- Weather
- Production Targets
- Contingency Plans
Harvesting Head
Control Interface
Production
Statistics
Machine
Parameters
Tree Detection
& Recognition
SLOPE
In-Vehicle
Interface
Machine
Monitoring
Route
Planning
Cable Crane
Control
Risks and Mitigation Actions
Technical Meeting
2-4/Jul/2014
Risks:
- Integration with existing systems (MHG, TREE) not seamless
- Mobile/In-Vehicle interfaces not robust enough for field conditions
- User acceptance of new interfaces
Mitigation Actions:
- Early prototyping and testing with end users
- Modular design allowing independent development
Multispectral imaging is a digital imaging technique that adds depth to understanding cultural heritage collections. It is a unique technology combining components from cross-disciplinary fields, thus requiring diverse standards, practices, and workflows. This poses challenges when integrating this system into a digitization lab with pre-established methods of operation, but is nonetheless feasible, as demonstrated by the collaboration between the Preservation Research and Testing Division of the Library of Congress (USA) and the digitization labs of The John Rylands Library, University of Manchester (UK).
2+3D Photography 2017 – INV 7 The 3D Image Capture Moonshot: Managing the Ene...rijksmuseum
Spectral data has arrived with the new iccMAX specification. This will lead to significant improvements in multi-ink printing where color separation will be based on spectral matching. It also provides a framework for combining scientific and studio photography for image archiving. Multispectral photography bridges hyperspectral imaging, used in conservation science, and RGB imaging, used in the studio. Two approaches will be described. The first is dual-RGB where an RGB camera and two colored filters (or lights) combine to form a five-channel multispectral camera. The second is a conventional system consisting of a monochrome camera and filter wheel. This new seven-filter multispectral system was designed with three quality criteria: high color accuracy, moderate spectral accuracy, and low color transformation noise.
Task 2.3 – on field digital survey systems (by tre)SLOPE Project
This document summarizes a terrestrial laser scanning (TLS) forest measurement system called AutoStem Forest. The system provides automated 3D measurement of forests to better predict log distributions. It was validated in trials in Sweden where it measured individual tree volumes with high accuracy compared to manual measurements, with biases mostly under 3% and standard deviations around 10%. The system integrates field data collection via TLS with online analytics and services to provide detailed forest inventories and valuations to improve harvest planning.
Implementation Cost Analysis of the Interpolator for the Wimax Technologyiosrjce
The design of the multirate filter (programmable) has been proposed which can be used in digital
transceivers that meets 802.16d/e (wimax) standard in the wireless communication system. Wimax is a
technology emerging in the wireless communication system in order to increase the broadband wireless internet
access. As there is wide spread need of the digital representation of the signal for the transmission and storage
which create the challenges in DSP [1]. In this paper, analysis of the implementation cost of interpolator for the
wimax technology, and cost of interpolator is analyzed on the basis of number of adders and multiplier. The
Filters are designed using the FDA (filters design and analysis) tool in MATLAB.
The document provides a mid-term review of Project SLOPE, which aims to develop innovative technologies for forestry operations in mountainous areas. It summarizes the status and results of Work Package 8 on dissemination and engagement activities over the first 18 months of the 36 month project. Key activities included developing dissemination strategies and materials, establishing a website and social media presence, organizing conferences and workshops, and initiating an Industrial Advisory Board with members from the forestry industry. The review indicates that dissemination goals have been achieved so far in raising awareness of the project and that focus should now turn to specific technical areas and maintaining engagement over the remainder of the project.
This document summarizes an update on Project SLOPE's Task 3.6 on data management and backup. The task aims to develop a system for exchanging data between field hardware and a central computer, and provide a data backup strategy. It is led by CNR and involves several partners. The current status is 50% complete. A key output is a prototype portable and internally powered "black box" for daily/weekly data backups and transmitting data from areas without network coverage, due by Month 25.
The document summarizes work being done for Task 7.02 of the Project SLOPE, which involves preparing demonstrators to assess the technical and economic feasibility of the proposed SLOPE timber harvesting system compared to current methods. Activities being defined for the demonstrators include forest inventory, harvest planning, harvest operations, and logistics/storage/sale. Data will be collected from pilot studies on time consumption, productivity, costs, and other metrics to enable comparison between the innovative SLOPE methods and conventional approaches. Flow charts are provided as an example of how work cycles will be documented for analysis.
The Task 5.3 aims to design and develop an online purchasing and invoicing platform for industrial timber and biomass. Activities completed include benchmarking existing e-trading solutions in Finland and globally, and identifying key elements for the new platform such as material identification, negotiation, bidding, and market analysis functions. The platform will facilitate trade between forest owners and buyers in a digital environment.
Project SLOPE is developing a forest information system to optimize timber harvesting and supply chain operations. The system will integrate real-time data on tree sizes, product distributions, and harvesting machine positions. It aims to develop modules for inventory data, real-time supply chain control, online purchasing and invoicing, and short and long-term optimization. Partners will utilize existing solutions like MHG Biomass Manager and develop new applications to track harvesting data, manage transportation logistics, and facilitate online commerce between producers and buyers. The system seeks to strengthen industry linkages and competitiveness through information sharing.
This document summarizes the mid-term review of Task 5.2 to develop a platform for near real-time control of forest operations. The task is on schedule and involves designing a module to integrate harvesting plan data with real-time data from an intelligent processing head to analyze harvested versus predicted timber and enable adjustments to storage and logistics. Key activities completed include defining the workflow and involved actors. Upcoming activities are agreeing the module architecture, developing the near real-time control platform, and testing it in a demonstration area.
The document discusses dissemination activities for Project SLOPE, including:
1. An overview of the dissemination plan with activities like brochures, website, social media, newsletters, and workshops.
2. Updates on dissemination tasks completed in the last period like the dissemination plan, project website, and first newsletter.
3. Upcoming dissemination events and deadlines like the next press release in April 2015 and workshops planned for 2015-2016.
WP 1 of the Project SLOPE was completed and focused on defining requirements for the system. It identified user needs through questionnaires, defined the necessary hardware, equipment and sensors, specified the user interface guidelines for desktop, mobile and in-vehicle access, developed a data and metadata model for storing forest information, and designed a scalable system architecture based on service-oriented principles. All deliverables were finalized and submitted on schedule, though some partners left the project early on. The work specified what was needed to develop the SLOPE Forest Information System.
The document discusses the development of a forest information system called Project SLOPE. It involves 5 work packages, including the development of a database to support novel inventory data (WP5.1), a platform for near real-time control of forest operations (WP5.2), and an online purchasing/invoicing system for industrial timber and biomass (WP5.3). It will also include the development of modules for short-term optimization (WP5.4) and mid-long term optimization/strategic planning (WP5.5). The system will integrate data on timber quality, quantities, and origin to optimize procedures and avoid delays. It will also facilitate long-term forest planning, simulations, optimization, and
This document details the development of an intelligent transport truck as part of Project SLOPE. Technologies like RFID, GPS, and GPRS will be added to trucks to identify harvested trees and transmit location data. Partners will develop a control unit over months 12-24 to interface sensors with a database. A communication scheme will be tested and the system installed in pilot trucks to evaluate performance.
SLOPE Final Conference - innovative cable yarderSLOPE Project
This document discusses innovations in cable yarding machinery developed through the SLOPE project, which received EU funding. It describes new automated machines like the TECNO self-propelled carriage, which can transport loads of up to 3.2 tons at 4.5 meters/second and automatically unload. An automatic chocker system and rope launcher are also presented, which aim to increase efficiency and safety in cable logging operations. The overall goal of these new technologies is to automate processes and facilitate communication within the logging workflow.
The document summarizes work from Project SLOPE Task 2.1 on remote sensing and multispectral analysis of forest sites in Ireland and Italy. Key points include:
- Participants in Task 2.1 defined approaches to monitor tree growth and health using different vegetation indexes derived from satellite, UAV, and ground instrumentation data.
- Analysis of vegetation indexes at different scales (satellite, UAV, laser scanner) allowed estimation of biological parameters and increasingly detailed information.
- A case study in Ireland using RapidEye satellite imagery to calculate NDVI, NDRE, and CCCI showed relationships between the indices and chlorophyll levels over time.
- UAV and terrestrial laser scanner data provided more
This document summarizes discussions from a July 2014 meeting of the Project SLOPE working group on openness with other activities, dissemination, and exploitation of results (WP8). Key discussion points included: overall guidelines for awareness, networking and dissemination activities; contributing to social networking platforms like LinkedIn, Facebook, and Twitter; a dissemination plan and calendar; and linking with other projects. Partners provided updates on dissemination tasks including developing a brochure, launching the project website and social media channels, releasing the first newsletter, and distributing initial press releases. An overview of relevant conferences and trade fairs for disseminating project results was also presented.
This document summarizes a technical meeting that took place on July 5th, 2016 for Project Slope. It discusses the status of several work packages and tasks. Task 3.6 involving data management backup is still ongoing with a prototype under development. For task 3.4, the team has integrated all hardware and software components on the processor head. Connection to the central database and uploading of required data has been accomplished. The team also worked on connecting the processor head and cable crane to verify data transfer. For task 3.3, the Tecno carriage hardware and software are complete pending RFID tag testing in the forest. Chokers and rope launchers are also ready for use pending further testing.
The document discusses dissemination activities for the SLOPE project from January 2016. It provides an overview of dissemination activities planned from 2014-2016, including brochures, newsletters, conferences, and trade fairs. It also summarizes dissemination activities and results from the last 6 months of 2015, including publications, conferences attended, and trade fairs/demonstrations participated in by various project partners.
The document discusses progress on Work Package 7 of the Project SLOPE, which involves piloting the SLOPE timber harvesting demonstrator. Key points discussed include:
- Potential harvesting sites have been identified in Austria and Italy for demonstrating the SLOPE system.
- Tasks include developing process flow charts, identifying bottlenecks, selecting evaluation methods, and planning demonstration activities from 2015-2018.
- Process/data flow charts will be created to visualize and compare the conventional and SLOPE timber supply chains. This will help identify strengths and risks of the new system.
The document summarizes a technical meeting to discuss the status and plans for Work Package 6 of the Project SLOPE, which aims to integrate various forest management systems through three main tasks, and provides updates on the progress of Tasks 6.02 involving the integration of forest inventory and harvesting systems and 6.03 on the integration of forest management systems.
The document provides an overview of activities for the Project SLOPE trials and validation cycle. It describes two survey sites in Austria and Italy where the SLOPE system will be piloted and tested. Activities performed at the sites so far include UAV and TLS surveys, tree marking, and data collection. Plans for upcoming harvesting demonstrations at each site in autumn 2016 are presented, including extraction and processing scenarios. Metrics that will be used to evaluate the efficiency of the new SLOPE system are also discussed.
SLOPE Final Conference - intelligent machinesSLOPE Project
This document discusses the design and development of sensor systems to be installed on a harvester head as part of the SLOPE project. It describes taking measurements of an existing harvester head using scanning and 3D modeling techniques. The document outlines plans to design new subsystems that include a scanning bar for cameras, sensors to measure cutting and debarking forces, systems for evaluating stress waves in wood using lasers and accelerometers, and an RFID tagging system. The goal is to integrate these new sensor systems and hardware into the harvester head to enable machine control and data collection for research.
The document summarizes the status of Project SLOPE's Task 6.1 on system integration. It describes the development of an integrated forest management system through three main integration steps. The first step focuses on connecting forest inventory and harvesting software and hardware. It discusses the development of alpha versions 1.0 through 1.2 and plans to validate the initial integration and fill the system's database with real-time pilot data. Overall, the integration work is progressing well and establishing guidelines for efficient continuous integration of the various project components.
Task 4.5 – Evaluation of cutting process (CP) for the determination of log/bi...SLOPE Project
This document summarizes Task 4.5, which aims to develop a system for estimating log quality by analyzing cutting resistance during harvesting. CNR and Kesla will work to select sensors for the processor head to measure cutting forces, develop methods to correlate these measurements with wood properties, and create a "Cutting Process quality index". The task will establish a protocol for collecting cutting data from chainsaws and delimbers by July 2015 and deliver a prototype quality index method by November 2015. The index is intended to support real-time grading of logs based on estimated density and resistance from cutting force analyses.
This work package aims to develop an automated grading system using multi-sensor data to improve log segregation and supply chain efficiency in mountain forests, including using near infrared spectroscopy, hyperspectral imaging, acoustic measurements, and cutting power analysis to estimate log quality and classify logs into quality classes.
This document outlines Work Package 4, which aims to develop an automated quality control system for wood from mountain forests using multiple sensors. The goals are to improve log sorting, support efficient supply chain management, and provide data to refine growth models. Several partners will collaborate on tasks to characterize trees using sensors like NIR, hyperspectral imaging, acoustics and cutting power analysis. Models will be developed and validated to estimate log quality classes. The work package supports other work packages and considers risks like ensuring a practical focus and integrating sensors with machinery.
Webinar: What's New in Pipeline Pilot 8.5 Collection Update 1?BIOVIA
The document discusses new features in Pipeline Pilot 8.5 Collection Update 1. It introduces protocol comparison capabilities, updates to the documents and text collection including new visualization and search components. The Accelrys Query Service allows unified searching across data sources. Imaging components now include curvature analysis and color deconvolution. The NGS collection includes performance updates and new viewers. Additional resources and services are available to assist with the upgrade.
Air monitoring sensors and advanced analytics in exposure assessmentDrew Hill
https://doi.org/10.6084/m9.figshare.12354866.v2
We are in the middle of a movement in environmental sensors that is taking the world by storm— Californian governments and public health practitioners, in particular, are leading the nation in exploring and implementing environmental sensors in the production of highly granular, realtime air quality information. As this movement matures, we are seeing improved understanding of ambient exposures and insights that are truly actionable — for example informing community emissions reduction plans under the recent Assembly Bill 617. This innovation in air quality sensor science can be leveraged to improve measurements in the industrial and occupational spaces. This movement has also lead to innovations in analysis methods that facilitate exposure insights not feasible with standard filter, adsorbent, and general integrated samples. This presentation discusses recent advancements in these spaces and offer brief examples of their implementation and potential applicability toward the industrial and occupational hygiene spaces.
The document describes the FC 500 Flow Cytometer. It has:
- Two lasers (488nm and 633nm) for excitation of various fluorochromes across 5 detectors.
- An elliptical laser profile for consistent illumination.
- High sensitivity across all detectors allowing detection of dim populations.
- Wide dynamic range of 6 decades for analysis of both bright and dim markers.
- Intuitive software for acquisition and analysis with features like automated gating and reporting.
DesCOTS-SL: A Tool for the Selection of Software ProductsCarme Quer
The paper describes DesCOTS-SL, a tool for selecting commercial off-the-shelf (COTS) components. The tool helps users identify their requirements, formalize them, prioritize needs, and select COTS products that meet the formalized requirements based on quality models and evaluations stored in the DesCOTS system. Current work involves improving the usability of defining requirements and reusability of quality metrics to evaluate more types of software domains and products.
Apache Carbondata: An Indexed Columnar File Format for Interactive Query with...Spark Summit
Realtime analytics over large datasets has become an increasing wide-spread demand, over the past several years, Hadoop ecosystem has been continuously evolving, even complex queries over large datasets can be realized in an interactive fashion with distributed processing framework like Apache Spark, new paradigm of efficient storage were introduced as well to facilitate data processing framework, such as Apache Parquet, ORC provide fast scan over columnar data format, and Apache Hbase offers fast ingest and millisecond scale random access.
In this talk, we will outline Apache Carbondata, a new addition to open source Hadoop ecosystem which is an indexed columnar file format aimed for bridging the gap to fully enable real-time analytics abilities. It has been deeply integrated with Spark SQL and enables dramatic acceleration of query processing by leveraging efficient encoding/compression and effective predicate push down through Carbondata’s multi-level index technique.
James b. williamson rd eng long version 6 1 16James
James B. Williamson is an experienced R&D engineer with expertise in optical and optoelectronic device design and development. Over his career, he has invented several leading-edge products including a vertical see-thru document scanner, efficient scanner illuminator, and polymer waveguide scanhead. He holds multiple patents and has published papers on topics like photodetector arrays and resolution improvement techniques. Williamson currently works as an optical engineer and has previously held roles at Technical Optics LLC and HP Laboratories, contributing to projects in areas such as biomedical illumination, LEDs, and optical test systems.
Image Retrieval Based on its Contents Using Features ExtractionIRJET Journal
This document proposes a content-based image retrieval system using ordered-dither block truncation coding. The system extracts two image features - color co-occurrence features and bit pattern features - directly from the encoded image data to represent images. Experiments show the system retrieves similar images with promising accuracy compared to other methods. The system decomposes images into color quantizers and bitmaps using ordered-dither block truncation coding for low complexity feature extraction to represent images in the database. Queries return similar images based on similarity distances between feature vectors.
The document discusses spatial data harmonization based on experiences from two European projects - PLAN4ALL and HUMBOLDT. PLAN4ALL focused on defining data models for several INSPIRE themes related to spatial planning. Through analyzing country-specific models and stakeholder needs, conceptual models were developed. HUMBOLDT contributed to the European Spatial Data Infrastructure by integrating diverse spatial data through addressing issues like classifications, formats, and coordinate systems. An example use case harmonized forest data to update the Corine Land Cover dataset. Harmonization supports better integration, classification, data combination, and cross-border cooperation.
The document summarizes research done at the Barcelona Supercomputing Center on evaluating Hadoop platforms as a service (PaaS) compared to infrastructure as a service (IaaS). Key findings include:
- Provider (Azure HDInsight, Rackspace CBD, etc.) did not significantly impact performance of wordcount and terasort benchmarks.
- Data size and number of datanodes were more important factors, with diminishing returns on performance from adding more nodes.
- PaaS can save on maintenance costs compared to IaaS but may be more expensive depending on workload and VM size needed. Tuning may still be required with PaaS.
The document discusses the H.264/MPEG-4 AVC video compression standard. It provides an overview of the evolution of video coding standards that led to H.264/AVC, describes key features of H.264/AVC such as enhanced motion compensation, transform coding, and entropy coding, and compares H.264/AVC's compression performance to prior standards. The document concludes that H.264/AVC provides up to 50% better compression efficiency than previous standards through improvements like smaller block sizes and an adaptive deblocking filter, though it also increases computational complexity.
What LTE Parameters need to be Dimensioned and OptimizedHoracio Guillen
How to Dimension user Traffic in 4G networks
What is the best LTE Configuration
Spectrum analysis for LTE System
MIMO: What is real, What is Wishful thinking
LTE Measurements what they mean and how they are used
How to consider Overhead in LTE Dimensioning and What is the impact
How to take into account customer experience when Designing a Wireless Network
Evaluation of near infrared (NIR) spectroscopy as a tool for determination of...SLOPE Project
This document summarizes a task to evaluate near infrared (NIR) spectroscopy for determining log and biomass quality in mountain forests. Several organizations will work together on the task, led by CNR. CNR will coordinate, evaluate NIR spectroscopy along the harvesting chain, and develop a "NIR quality index." Boku will support CNR with lab measurements and calibration transfer. Other partners will help collect NIR spectra in the field. The objectives are to evaluate NIR spectroscopy for characterizing resources along the harvesting chain and provide guidelines for collection and analysis of NIR spectra. Activities will include feasibility studies, developing chemometric models to predict quality indicators from spectra, and transferring calibrations between lab and portable instruments. Del
The document describes the Porto Living Lab project in Porto, Portugal which involves three sensing infrastructures: BusNet, HarbourNet, and UrbanSense. UrbanSense is an environmental sensor platform that collects data from over 500 sensors across the city to understand phenomena and their impacts. It involves sensor units that collect data and send it via various wireless networks to a cloud database. The sensors measure air quality, noise, weather and other environmental factors.
Task 4.6 – Implementation of the log/biomass grading system (by CNR)SLOPE Project
Task 4.6 involves implementing a log/biomass grading system using data from multiple sensors along the harvesting chain. CNR will coordinate the research and develop software tools to integrate data and grade quality. Partners will provide material parameters. GRAPHITECH will link grades to market prices. MHG will track material along the value chain. BOKU will validate the system. The goals are to reliably predict grades, optimize resource use, and harmonize grading practices. Quality indexes from different sensors and data will be merged to define thresholds for grades based on end-uses. A database of existing grading rules will be created. The system will be validated for performance, reliability, and flexibility. Challenges include selecting optimal sensors,
This presentation was provided by Paul Needham of Cranfield University and Johan Bollen of Indiana University, during the NISO webinar "Measuring Use, Assessing Success, Part Two: Count Me In: Measuring Individual Item Usage," which was held on September 15, 2010.
LoCloud - D5.4: Analysis and Recommendationslocloud
The document analyzes and makes recommendations for local content in Europeana's cloud based on the LoCloud project. It describes 8 use cases for typical local collection holders to understand their needs. It identifies 7 common issues faced by small institutions that the LoCloud services aim to address, such as lack of technical expertise and standards. The services developed in LoCloud are then described and evaluated based on the use cases. Overall, the analysis finds that while the services try to help small institutions, their needs may not be fully met due to limited resources, requiring good documentation and support.
An Overview of the iMicrobe Project and available tools in the iPlant Cyberinfrastructure. This talk was given at a workshop at ASLO in Granada, Spain focused on applications in Oceanography and Limnology.
The document summarizes the results of WP8 Task 8.1 on dissemination planning and publications. It describes the dissemination activities carried out, including developing dissemination materials, maintaining a project website and social media presence, organizing workshops and conferences, engaging with other related projects, publishing scientific papers and articles, and issuing press releases to promote the project results. The task was completed over the full 36-month project period and ensured visibility of the project activities and wide dissemination of the technical results.
Slope Final Review Meeting - Introduction SLOPE Project
This document summarizes the agenda and objectives of a final review meeting for the SLOPE project. The SLOPE project aims to develop integrated processing and control systems to improve sustainability in mountain forest production. The meeting agenda covers reviewing progress on tasks in areas like requirements analysis, forest data collection, intelligent harvesting systems, quality control, and system integration. The objectives of the meeting are to evaluate fulfillment of deliverables, continued relevance of objectives, resource use, contributions of partners, and plans for impact and results dissemination. The review involves 10 partners across several European countries working for 36 months on developing and testing new forest monitoring and harvesting technologies.
WP7 tested the SLOPE harvesting system across two pilot sites in Italy and Austria. At the Italian site in Sover, RFID-tagged trees were felled and extracted using cable yarding. Some technical issues were encountered but valuable lessons were learned. The system was improved and demonstrated again at the Austrian site in Annaberg, where the whole supply chain was tested and productivity was higher. Comprehensive data was collected across operations and sites to validate system performance and identify areas for further improvement.
The document summarizes the work done in Project SLOPE for system integration (WP6). It discusses the three main integration tasks: 1) integrating forest inventory and harvesting systems, 2) integrating forest management systems, and 3) validating the integrated system. Each integration task involved defining components, timelines, and test scenarios. Functional and non-functional requirements were tested across nine software versions, with over 90% of tests passed. The work package developed an integrated SLOPE system ready for pilot demonstrations and field testing.
The document summarizes work from Project SLOPE's Work Package 5 on developing a forest information system. It discusses five tasks related to creating different modules for the system. The tasks focused on developing a database, real-time operations platform, online purchasing platform, and short and long-term optimization modules. All prototypes were completed and delivered by month 36. The system integrates forest data and provides tools to support planning, operations management, sales and optimization across timescales. It represents an innovative approach to developing a modern, integrated digital system for the forest sector.
The document summarizes the work done for Task 3.4 of the SLOPE project. An intelligent processor head was developed that can perform grading and marking of logs. Sensors were added to measure stress waves, cutting forces, near infrared spectra and hyperspectral images. The processor head can determine wood properties and mark each log with an RFID tag containing collected information. All systems were designed, implemented and tested on the processor head prototype, fulfilling the objectives of the task.
This document summarizes the final meeting of the WP2 Slope Project in Brussels on February 1, 2017. It discusses the completion of deliverables, data collection from various partners, tree classification and detection methods, estimation of environmental parameters, combining data sets from different sources, logistics modeling, and analytics. The meeting highlights that the project has proven the concept of combining data from remote sensing, UAVs, and TLS to map up to 1,000 hectares in a single flight and provide useful data for both harvesting and long-term forest management - providing a solution beyond the state of the art.
1) Researchers evaluated RFID UHF tags for electronically marking standing trees by testing different tag models attached using screws, staples, or other methods. Tags attached using staples on the underside of bark performed best, with all tags still functioning after one year.
2) The study examined how factors like tag and reader position, moisture content, and dynamic reading influence RFID readability. Distance between tag and reader, moisture content, and angle had significant effects on readability, while tag position on or within the tree also impacted performance.
3) Preliminary results found that moisture content reduction to 40% improved readability by 20%, and tag readability decreased with increasing distance between tag and reader, but
Researchers tested the durability of RFID UHF tags during timber harvesting operations. They applied two types of tags - Wintag Flexytag and Smartrac Shortdipole - using single or double stapling. The tags were applied to trees and logs in three sites undergoing cable yarding and tractor transport. They found an overall 97% survival rate for tags, with 91% surviving in the site with the steepest terrain. Tag survival was slightly lower for shortdipole tags stapled singly compared to tags stapled doubly or Wintag tags. The researchers concluded RFID tags can successfully endure forest operations and provide traceability, though visibility may decrease over longer transport distances.
SLOPE Final Conference - sensors for timber grading in forestSLOPE Project
The document discusses a project that aims to develop an automated timber grading system using multiple sensors. The goals are to optimize log segregation, improve supply chain efficiency, and provide data to refine growth models. Sensors will assess quality indexes for properties like density, knots, and decay. Models will predict grade based on sensor data. A combined quality index will determine suitability for different end uses, integrating indexes from different sensors. The system will help match logs to their best uses and increase value recovered from forests.
SLOPE Final Conference - remote sensing systemsSLOPE Project
This document discusses Coastway's involvement in the SLOPE project, which tested the use of unmanned aerial vehicles (UAVs) and remote sensing to support forest inventory work. As part of Work Package 2, Coastway captured aerial imagery using fixed-wing drones across several test sites in Ireland. The drones tested different camera payloads, including RGB, multispectral, and near-infrared cameras. The goal was to create digital terrain models, digital elevation models, and digital canopy models, and to combine the UAV data with terrestrial laser scans and satellite imagery. The document describes the flight planning, site preparation, and data processing methods used to generate orthomosaics, point clouds, and models of the
This document discusses using near infrared (NIR) spectroscopy to characterize bio-materials. It outlines how NIR can be used to determine the chemical composition, physical properties, and anatomical features of materials like wood and paper. Specific applications mentioned include identifying wood species and origin, assessing virgin wood, characterizing particleboards, selecting biomass for conversion processes, and developing calibration models to predict the chemical composition of willows. The document highlights the benefits of NIR including its non-destructive nature, speed, and ability to determine multiple components simultaneously.
The document provides an overview of near infrared (NIR) spectroscopy and its applications for wood science and technology. It discusses the history and principles of NIR spectroscopy. Specific topics covered include the electromagnetic spectrum, molecular vibrations, instrumentation, sample presentation, calibration and validation strategies, and applications for measuring various wood characteristics. The document serves to introduce NIR technology and its use for analyzing wood.
The document discusses processor heads for logging machines and quality assessment of logs. Processor heads are used for felling, delimbing, and crosscutting trees. Sensors can measure diameter, length, and position. Marking and sorting by volume, length, and assortment allows for simplified logistics. Quality is assessed visually in the forest and with sensors in sawmills. A concept for an intelligent processor head includes sensors like NIR, hyperspectral imaging, RFID, and stress waves to interact with databases, measure loads and volumes, and provide full traceability and quality assessment of individual logs. RFID marking associates quality indexes to logs for full traceability of timber products.
The SLOPE project aims to optimize forest production through a 3D virtual forest system to support harvesting operations in mountainous areas. The system will integrate data from forest surveys, digital terrain and forest models, and real-time sensor data to create a digital model of the forest. This model will then support various harvesting planning and monitoring tasks, such as cableway deployment planning, working area setup, and tree felling monitoring. The system is being developed as a web-based application to allow easy access and integration with other services. It will track the entire process from tree tagging to sawmill processing.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Leveraging Generative AI to Drive Nonprofit Innovation
3rd Technical Meeting - WP4
1. Project SLOPE
WP 4 – Multi-sensor model-based quality
control of mountain forest production
2. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
The goals of this WP are:
• to develop an automated and real-time grading (optimization)
system for the forest production, in order to improve
log/biomass segregation and to help develop a more efficient
supply chain of mountain forest products
• to design software solutions for continuous update the pre-
harvest inventory procedures in the mountain areas
• to provide data to refine stand growth and yield models for
long-term silvicultural management
3. Work Package 4: work to be done T4.1
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
4. T4.1: Data mining and model integration of stand
quality indicators from on-field survey
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
draft: October 2014
accepted: July 2015
OCtober 2015
the resources planned: 9 M/M
the resources utilized:
PROBLEMS: Not reported
5. Work Package 4: work to be done T4.2
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
6. T4.2: Evaluation of NIRS as a tool for determination
of log/biomass quality index
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
the resources planned: 13 M/M
the resources utilized:
PROBLEMS: Delay in access to sensor (sensor arrived Oct 2015, software Dec 2015)
SOLUTIONS: intensify efforts, working meetings with BOKU and COMPOLAB
draft: Dec 2015
draft: October 2014
accepted: July 2015
7. Work Package 4: work to be done T4.3
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
8. T4.3: Evaluation of hyperspectral imaging for the
determination of log/biomass quality index
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
the resources planned: 17 M/M
the resources utilized:
PROBLEMS: Delay in access to sensor (sensor arrived Oct 2015)
SOLUTIONS: intensify efforts, working meetings with BOKU and COMPOLAB
Jan 2016
draft: May 2014
accepted: July 2015
9. Work Package 4: work to be done T4.4
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
10. T4.4: Data mining and model integration of
log/biomass quality indicators from stress-wave
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
the resources planned: 5.5 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016), change of Task Leader
SOLUTIONS: intensify efforts, change in staff involved
draft: Jan 2016
draft: December 2014
accepted: July 2015
11. Work Package 4: work to be done T4.5
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
12. T4.5: Evaluation of cutting process (CP) for the
determination of log/biomass CP quality index
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
the resources planned: 6.0 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016)
SOLUTIONS: intensify work, close collaboration with COMPOLAB
draft: Jan 2016
draft: January 2014
accepted: July 2015
13. Work Package 4: work to be done T4.6
Quality rules &specifications
CNR,TRE:
Develop tool Harvest Simulator
TRE:
Develop models of trees
GRA,TRE:
Compare models with real data
TRE,GRA, TRE:
Link automatic system with visual
TRE,CNR:
Develop 3D qualityindex
TRE, CNR:
Measurement of standing trees
CNR,TRE:
Measurement of felled trees
CNR:
T4.1 3D quality
D03.01
D01.04
D04.07
TRE
D04.02
TRE
D01.04
Determine optimalprotocol
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Measure NIR on standing trees
TRE,CNR, FLY:
Measure NIR on felled trees
CNR,GRE:
Measure NIR on processor head
CNR,COM:
Measure NIR on pale of logs
CNR,BOK:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop NIR quality index
CNR,BOK:
Develop provenance NIR models
CNR,BOK:
Design data base of NIR spectra
BOK,CNR:
T4.2 NIR quality
D04.03
CNR
D04.08
CNR
Determine usability
CNR:
Calibration transfer
BOK,CNR:
Develop models for lab
CNR,BOK:
Imaging standing trees
BOK,FLY, TRE:
Imaging fallen trees
BOK,GRE:
Imaging on processor head
BOK,COM:
Imaging on pale of logs
BOK,CNR:
Develop models for in field
CNR,BOK:
Compare models with lab data
CNR,BOK:
Develop hyperspectral index
CNR,BOK:
Design data base of hyperspectra
BOK,CNR:
T4.3 hyperspectral quality
D04.04
D04.09
BOK
BOK
Determine optimalset-up for the
hyperspectral camera, illumination,
and sample holding
BOK,CNR:
D01.04
Determine optimalset-up for the
NIR sensor, illumination, and
sample holding
CNR,BOK:
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
14. T4.6: Implementation of the log/biomass grading
system
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
the resources planned: 8.0 M/M
the resources utilized:
PROBLEMS: Delay related to other tasks – difficulties with implementation
SOLUTIONS: LAB scanner + prototype software developed in lab + algorithms ready
31.06.2016
draft: October 2014
accepted: July 2015
15. fulfillment of the project work plan:
related deliverables (M25)
WP4 M17
task
delive
rable
title
type of
deliverable
lead
particip
ant
due date
foreseen or actual
delivery date
comment
T4.1
D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 accepted
D4.7
estimation of log/biomass quality by external tree shape
analysis
software tool TRE 31.05.2015 18.12.2015
Waiting for final
approval
T4.2
D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 accepted
D4.8 estimation of log/biomass quality by NIR software tool CNR 30.09.2015 March 2016
T4.3
D4.4 establisghing hyperspectral imaging measurement protocol report BOK 30.11.2014 05.05.2015 accepted
D4.9 estimation of log/biomass quality by hyperspectral imaging software tool BOK 31.10.2015 April 2016
T4.4
D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 05.05.2015 accepted
D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.11.2015 May 2016
T4.5
D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 31.01.2015 accepted
D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.12.2015 April 2016
T4.6
D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 accepted
D4.12
implementatio and callibration of prediction models for
log/biomass quality classes
software tool CNR 31.06.2016 June 2016 NO
16. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Planning actions for all activities and deliverables to be executed
in M25-30:
Finalize + close: D04.8, D04.9, D04.10, D04.11
Deliver + finalize + close: D04.12
Initiate + deliver: -
Assemble sensors + control system
Install sensors in the processor head
Continue field tests with portable instruments
Calibrate system in the lab (“model tree”)
Collaborate with WP3 (and others) in hardware development
17. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Risks and mitigating actions:
Significant delay related to DoW amandment:
• the purchase and delivery of sensors delayed set-up of the system
in the lab (laboratory scanner) as well as on the processor head;
intensify efforts for all involved partners, direct collaboration and
working group meetings, involve additional staff for
developments, testing and implementation
Technologies provided will not be appreciated by “conservative”
forest users; demonstrate financial (and other) SLOPE advantages
Limited reliability of some sensors when implemented on the forest
machinery; careful planning, collaboration with SLOPE (+outside)
engineers
18. Sensors and electronics (WP3 & WP4) in progress
MicroNIR
Hamamatsu
C11708
Hamamatsu
C12666
Accelerometers
time of flight
Mechanical excitator
Accelerometers
free vibration LDS correction
Laser Displacement Sensor
AE sensor + amplifier
Tensionmeters 1/4 bridge
Dynamic load cell
Hydraulic pressure sensor
Hydraulic flow sensor
Absolute encoders
Hamamatsu
C11351
NI 9234
NI 9223
NI 9235/NI 9236
NI 9220
Port #8
CompactDaq
SENSORS
Port #7
Port #6
Port #5
Port #4
Port #3
Port #2
Port #1
LAN port #2
Industrial PC
LAN port #1
Port #6
Port #5
Video output + USB port #4
USB port #3
USB port #2
USB port #1
NI 9403 (Digital I/O)
Custom line scan camera
Port #8
CRio (real time?)
MACHINE CONTROL
Port #7
Port #6
Port #5
Port #4
Port #3
Port #2
Port #1
SEA 9744 (GSM + GPS)
Joystic(s)
RFID reader
Hydraulic actuators
???
???
???
???
LAN port #5
LAN port #4
LAN port #3
Touch screen
T4.2+T4.3T4.4T4.5T4.5T4.4WP3
WP3WP3
NI 9220
Temperatures of oil and air
19. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Thank you! – Grazie!
20. 4.2 Deliverables status
Deliverable D.4.03 Establishing NIR measurement protocol
evaluating the usability of NIR spectroscopy for characterization of bio-resources
along the harvesting chain, providing guidelines for proper collection and analysis
of NIR spectra.
Delivery Date M10, October 2014 - accepted
Estimated person Month = 5
Deliverable D.4.08 Estimation of log/biomass quality by NIR
Set of chemometric models for characterization of different “quality indicators” by
means of NIR and definition of “NIR quality index”
Delivery Date M21, September 2015 – draft presenting protocol validation
uploaded in dropbox, the deliverable are the models currently improved
Estimated Man/Month = 8
21. Detailed procedure related to measurement of NIR
along the whole harvesting scenario
Forest modeling
NIR quality index #1 will be related directly to the
health status, stress status and to the productivity
capabilities of the tree(s) foreseen for harvest –
images from FLYBY to be analyzed
Tree marking
Direct measurement of the NIR spectra by means
of portable instruments will be performed in
parallel to the tree marking operation (NIR quality
index #2) – first trials done in December
Cutting of tree
testing the possibility of collecting sample of wood
in a form of the triangular slice being a part of the
chock cut-out from the bottom of the log (NIR
quality index #3) – first trials done in December
(optional)
prepare samples #1
measurement of infrared
spectra (wet state)
prepare samples #2
condition samples
chemometric models for wet
wood and/or in field
chemometric models for
dry/conditioned wood (lab)
measurement of infrared
spectra
collect sample #1:
chip of axe
collect sample #2:
core ~30mm deep
collect sample #3:
chips after drilling core
collect sample #4:
triangular slices
measurement NIR profile
or hyperspectral image
measurement profile
of infrared spectra
consider approach: max
slope, pith position, WSEN
compute NIR
quality index#2
compute NIR
quality index#3
compute NIR
quality index#4
measurement profile
of infrared spectra
consider approach:
pith position, defects
compute NIR
quality index#5
tree marking
cutting tree
processor head
pile of logs
expert system & data base
refresh sample surface
measurement of infrared
spectra (dry state)
compute dry wood
NIR quality index#6compute the log quality
class (optimize cross-cut)
estimated tree quality
forest models
update the forest database
compare results of wet and
dry woods
combine all available char-
acteristics of the log
lab
Calibration transfer
f(MC, surface_quality)
3D tree
quality index
hyperspectral
HI quality index
stress wave
SW quality index
cutting force
CF quality index
compute NIR
quality index#1
22. Detailed procedure related to measurement of NIR
along the whole harvesting scenario
Processor head
NIR sensors will be integrated with the processor head (NIR quality index #4). The first
trials are foreseen for mid January on the lab scanner during measurement of the model
tree
Pile of logs
The cross section of logs stored in piles is easily accessible for direct measurement. Such
measurements will be repeated periodically in order to monitor the quality depreciation
and to determine the most optimal scanning frequency. The result of measuring NIR
spectra of logs stored in piles will be NIR quality index #5 – first trials done on 60 logs
Laboratory
Samples collected in the forest will be measured instantaneously after arrival in the
laboratory (at the wet state and with rough surface) by using the bench equipment
(NIR quality index #6). Campaign done by BOKU with FT instrument, recently parallel
measurement at IVALSA with MicroNIR
25. Scheduled activity
activity responsible status schedule
Determination of measurement conditions of MicroNIR CNR On going
Measurement discs from BOKU CNR On going
Calibration transfer BOKU February/March 2016
Measurement trees in field CNR On going
Data base of spectra for QI CNR/BOKU On going
Report from in field measurement CNR done
Chemometric models in PLS toolbox CNR/BOKU February 2016
Installation of MicroNIR on processor head COMPOLAB March 2016
Implementation of the software in the system CNR May 2016
Report of NIR traceability CNR March 2016
Use existing models for prediction of calorific value CNR February/March 2016
26. Project SLOPE
WP4: Multi-sensor model-based quality control of
mountain forest production
T4.3– Evaluation of hyperspectral imaging (HI) for the
determination of log/biomass “HI quality index”
Cork, January 19th-21st, 2016
Andreas Zitek, Katharina Böhm, Jakub Sandak, Anna Sandak,
Barbara Hinterstoisser
BOKU & CNR
Technical Meeting, Cork
19.01.2016
27. Mid-term Review
2/Jul/15
Task 4.3 – Output
D4.04 Establishing hyperspectral measurement protocol
• Methodology, laboratory setup and field transfer
D4.09 Estimation of log quality by hyperspectral imaging
• Labscale investigations ((visible)/near infrared hyperspectral cameras)
• Validation by NIR measurements
• Application of chemometric approaches for data evaluation and
multivariate image analysis
• Identification of most relevant spectral information
• Development of transfer options to (harsh) field conditions
• Development of the “HI quality index” for quality grading
• Technological implementation on prototype
28. Fulfillment of the project work plan:
related deliverables (M25)
WP4
task
delive
rable
title
type of
deliverable
lead
particip
ant
due date
foreseen or actual
delivery date
comment
T4.1
D4.2 on field survay data for tree characterization report TRE 31.10.2014 31.10.2014 accepted
D4.7
estimation of log/biomass quality by external tree shape
analysis
software tool TRE 31.05.2015 18.12.2015
Waiting for final
approval
T4.2
D4.3 establisghing NIR measurement protocol report CNR 31.10.2014 31.10.2014 accepted
D4.8 estimation of log/biomass quality by NIR software tool CNR 30.09.2015 March 2016
T4.3
D4.4
Establishing hyperspectral imaging measurement
protocol
report BOK 30.11.2014 05.05.2015 accepted
D4.9
Estimation of log/biomass quality by hyperspectral
imaging
software tool BOK 31.10.2015 April 2016
T4.4
D4.5 establishing acoustic-based measurement protocol report CNR 31.12.2014 05.05.2015 accepted
D4.10 estimation of log/biomass quality by acoustic methods software tool CNR 31.11.2015 May 2016
T4.5
D4.6 establisghing cutting power measurement protocol report CNR 31.01.2015 31.01.2015 accepted
D4.11 estimation of log/biomass quality by cutting power analysis software tool CNR 30.12.2015 April 2016
T4.6
D4.1 existing grading rules for log/biomass report CNR 31.10.2014 31.10.2014 accepted
D4.12
implementatio and callibration of prediction models for
log/biomass quality classes
software tool CNR 31.06.2016 June 2016 NO
PROBLEMS: Delay in access to sensors, that produce the data to develop
the model and implement the system (=D 4.09)
SOLUTIONS: intensify efforts, working meetings with CNR, sharing and
transfer of samples measured at BOKU with NIR and HSI to CNR for
MicroNIR and Hamamatsu measurements, meeting in February at BOKU
to produce models, implement system and finalize D4.09 in April 2016
29. Task 4.3 – Field transfer options
Implementation of the hyperspectral imaging in the field:
• Hyperspectral imaging using new technologies
Optimal accuracy and spatial resolution
Rigidity of sensors (not suitable for harsh conditions)
Relatively high cost
• Mono/multi spectral imaging the log cross-section
Optimal spatial resolution
Reasonable cost
Poor spectral accuracy
Challenges with implementation
• Several simple spectrometers installed on the scanning bar &
measuring the log cross-section
Optimal spectral accuracy and sufficient spatial resolution
Reasonable cost
Difficulties with implementation
Mid-term Review
2/Jul/15
T3.4 Intelligent processor head
30. Task 4.3 HSI – general setups
• Whiskbroom imaging : During whiskbroom imaging the sample is scanned pixel
per pixel in the x–y–spatial direction in a sequential manner.
• Staring (staredown) imaging: Staring imaging is done by a two-dimensional
camera capturing the spectral information in each pixel x-, y-plane at once.
• Implementation of the Pushbroom imaging idea: as a line scanning system with
multiple sensors acquiring the information for a reduced set of pixels in the line
at once – subsequent interpolation planned and possible.
Project meeting
19-22/jan/2015
From: BOLDRINI, B.,
KESSLER, W., REBNER, K.
& KESSLER, R. W., 2012.
Hyperspectral imaging: a
review of best practice,
performance and pitfalls
for inline and online
applications. Journal of
Near Infrared
Spectroscopy, 20 (5):
438-508.
31. Task 4.3 HSI – general setups
Software #1: simulation of the NIR sensor results on the scanning bar.
It is possible to simulate the timing of scan by changing the integration time.
Cycle time of software (including integration of signal and acquisition of data by USB +
display on monitor.) = 0.25 s (4 Hz)
Project meeting
19-22/jan/2015
32. Task 4.3 HSI – general setups
Software #2: simulation of the hyperspectral sensor results on the scanning bar (only single
wavelength). Image on the left is an input showing all points measured with hyperspectral
system. Image on the right is reconstructed image by using simple interpolation (part of the
code is shown, used for reconstruction only).
Project meeting
19-22/jan/2015
33. settings of scanning density – rotation (degree)
settings of scanning density – pixels on the scan bar
size of the probe / measured area (pixels, ROI)
resolution of interpolated image
34. LabView code for reconstruction
raw data from scanner 2D interpolation
40. Task 4.3 Model development
Collection of
training
samples with
different
deficits
Measurements
with NIR and HSI
Laboratory
equipment
Detection of
most significant
wavelength
regions for
deficits
First models, lab
equipment
Measurements with
NIR and HSI with
sensors that will be
on Processor Head
MicroNIR
Hamamatsu
Model development and export
with PLS model exporter
Models can be directly used for
data from scanning bar and the
Labview software installed on PC
incl. preprocessing and statistical
methods
Models sensor arm equipment
Workflow
Lab (scientific basis,
calibration transfer)
Calibration & field
transfer
41. Task 4.3 Sensor wavelength range
comparison
Visible & near infrared range (VNIR)
400 nm
• Visible wavelength range ~ 390 - 700 nm
• Near IR wavelength range ~ 700 nm - 2500 μm
2500 nm
FT NIR (lab) 800 – 2400 nm
Hyperspectral (lab)
900 – 1700 nm
MicroNir (sensor)
900 – 1700 nm
Hamamatsu
C12666MA
340 – 780 nm
Hamamatsu
C11708MA
640 – 1050 nm
Range covered by sensors on processor head
340 – 1700 nm
42. Mid-term Review
2/Jul/15
Task 4.3 – 25 samples (spruce, Picea
abies) with defects
resin pockets
eccentric pith + compression wood + rot eccentric pith + rot + knot
shakes, checks, splitsknots
Measured with NIR
and hyperspectral imaging at
BOKU, and MicroNIR and
Hamamatsu at CNR
43. NIR-Spectroscopic measurements –
BOKU - laboratory
• 14 out of 25 samples wood discs were measured using a FT-NIR
with a fibre optic probe at BOKU
Meeting
19/Jan/2016
54. Task 4.3 – Hyperspectral imaging of 23 logs – example
resin pockets intensity slabs, final explorations ongoing
Brussels
3/jul/2015
1190 nm 1377 nm
55. Task 4.3 Status of the sensor & model
development & implementation (D 4.09)
NIR measurements of BOKU samples with MicroNIR
Prototype of sensor arm
HSI measurements of BOKU samples - Hamamatsu
Pototype of LabView software
Focus lenses mounted on Hamatsu sensors
Integration of sensors, soft- & hardware, models
Model development & quality index
Implementation of full system on sensor arm with
hard- and software
UntilFebruary/March
D4.09inApril
56. NIR-Spectrocopic measurements
Scientific publication in prep.
Principal component analysis for wood and resin (resin pockets)
Scores Loadings
Meeting
19/Jan/2016
Böhm, Zitek et al., in prep, Assessing resin pockets on freshly cut wood logs of spruce
by NIR and hyperspectral imaging, European Journal of Wood and Wood Products
58. The objectives of this task is to optimize testing procedures and
prediction models for characterization of wood along the harvesting
chain, using acoustic measurements (i.e. stress-wave tests).
A part of the activity will be dedicated to the definition of optimal
procedures for the characterization of peculiar high-value assortments,
typically produced in mountainous sites, such as resonance wood.
Task Leader: CNR
Task Participants: Greifenberg, Compolab
WP4:T 4.4 Data mining and model integration of log/biomass
quality indicators from stress-wave (SW) measurements, for the
determination of the “SW quality index”
Objectives
59. WP4:T 4.4 Deliverables
D4.05) Establishing acoustic-based measurement protocol: This deliverable
contains a report and protocol for the acoustic-based measurement procedure
Starting Date: August 2014 - Delivery Date: December 2014
D4.10) Estimation of log quality by acoustic methods: Numerical procedure for
determination of “SW quality index” on the base of optimized acoustic velocity
conversion models.
Starting Date: January 2015 - Delivery Date: August 2015
Estimated person Month= 6.00
60. T4.4: Data mining and model integration of
log/biomass quality indicators from stress-wave
Develop report on using SW
CNR:
Develop models for SW quality
CNR:
Test on standing trees
CNR,GRE:
Tests on fallen trees
CNR,GRE:
Tests on processor head
CNR,COM:
Imaging on pale of logs
CNR:
Develop SW quality index
CNR:
Define qualitythresholds
CNR:
Analyze material dependant
factors
CNR:
T4.4 stress wave quality
D04.05
D04.10
CNR
CNR
Determine optimalset-up for the
stress wave measurement,
including time of flight and free
vibrations sensor
CNR:
D01.04
Determine quality requirements for
high-end assortments
CNR:
the resources planned: 5.5 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016), change of Task Leader
SOLUTIONS: intensify efforts, change in staff involved
draft: Jan 2016
draft: December 2014
accepted: July 2015
61. D: 4.5 Establishing acoustic-based measurement
protocol: hardware
Hardware design has been made by COMPOLAB in
collaboration with CNR
Two approaches for measuring stress waves on the
processor are considered:
• ToF (time of flight)
• FV (free vibrations)
63. D: 4.5 Establishing acoustic-based measurement
protocol
Time of Flight in SLOPE
l1 l2
t0
t1
t2
01
1
10
tt
l
v
−
=−
02
21
20
tt
ll
v
−
+
=−
12
2
21
tt
l
v
−
=−
64. D: 4.5 Establishing acoustic-based measurement
protocol:ToF challenges
•Preliminary tests highlighted great
problem with coupling of
accelerometers and wood, especially
due to bark
•Wet wood attenuates a lot stress
wave – hardly measurable, especially
with ultrasound…
•Several properties of log/wood are
not known during test (such as MC,
density)
•What does the value of velocity
means? (regarding quality)
Special design of hardware on
the processor head
The QI is (may be) computed
after processing of log
Experimental campaign is
foreseen & self learning system
on the base of historic data
65. D: 4.5 Establishing acoustic-based measurement
protocol
Free vibrations
if:
f1 = f2 - machine vibrations
f3 <> f1 - free vibrations of log,
fundamental frequency
D1
l
D2
time
time
frequency
f2 f3
FFT
f1
frequencyFFT
66. D: 4.5 Establishing acoustic-based measurement
protocol: FV challenges
•Laser displacement sensor’s spot is
absorbed by rough surface
•Are we measuring free vibrations of
log or processor head?
•What is the noise of signal?
•Several properties of log/wood are
not known during test (such as MC,
density, diameters, length)
•What does the value of frequency
means? (regarding quality)
Special sensor with enlarged
spot size (Keyence LK-G87)
The QI is (may be) computed
after processing of log and
related later by RFID
identification
Experimental campaign is
foreseen & self learning system
on the base of historic data
Compensation of LDS results
with additional acclerometer
67. Conclusions
Many factors influence SW propagation in wood.
Parameters measured with the other NDT methods will be incorporated in the SW
prediction models
Multiple linear regression analysis will be implemented for the definition of the
importance of the different parameters (regression t-values) for the model.
The further development of Task 4.4 is based on the implementation of the lab scanner
(i.e. purchase of sensors)
For the implementation of the methodology in the real case scenario, some practical issues
(e.g. coupling-decoupling of sensors, etc.) have to be considered in combination with
activity of Task 3.4
Sensors arrived: work will be done… and is ongoing
68. Task 4.5: cutting process quality index
Objectives
The goals of this task are:
• to develop a novel automatic system for measuring of the
cutting resistance of wood processed during harvesting
• to use this information for the determination of log/biomass
quality index
69. Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNR
Task Partecipants: Compolab
Starting : October 2014
Ending: January 2016
Estimated person-month = 4.00 (CNR) + 2.00 (Compolab)
CNR : will coordinate the research necessary, develop the knowledge base linking process and wood
properties, recommend the proper sensor, develop software tools for computation of the CP quality
index
Compolab: will provide expertise in regard to sensor selection and integration with the processor head
+ extensive testing of the prototype
70. Task 4.5: cutting process quality index
Deliverables
D.4.06 Establishing cutting power measurement protocol
Report: This deliverable will contain a report and recommended protocol for collection of
data chainsaw and delimbing cutting process.
Delivery Date: January 2015 (M.13) DONE
D.4.11 Estimation of log quality by cutting power analysis
Prototype: Numerical procedure for determination of “CP quality index” on the base of
cutting processes monitoring
Delivery Date: January 2016 (M.25)
71. T4.5: Evaluation of cutting process (CP) for the
determination of log/biomass CP quality index
Laboratory scale tests for
delimbing energy needs
CNR:
Develop CP qualityindex
CNR:
T4.5 cutting power quality
D04.11
D04.06
CNR
CNR
Determine optimalset-up for the
measurement of cutting forces on
the processor head
CNR:
D01.04
Laboratory scale tests for
chain saw energy needs
CNR:
Develop models linking CP in
delimbing and quality
CNR:
Develop models linking CP in chain
sawing and quality
CNR:
Develop report on using CP
CNR:
the resources planned: 6.0 M/M
the resources utilized:
PROBLEMS: delay with access to sensors (arrived January 2016)
SOLUTIONS: intensify work, close collaboration with COMPOLAB
31.01.2016
draft: January 2014
accepted: July 2015
72. working time of the cutting tools (knifes and chain):
estimation of the tool wear and correction of the cutting forces
position of the saw bar while cross-cutting:
monitoring of the cutting progress
correction factors related to the determination of the cutting forces and material
characteristics
log diameter (combined with position of the saw bar):
determination of the cutting length at each moment of the cross-cutting
position of the main hydraulic actuator while cutting-out branches:
monitoring of the de-limbing progress
determination/mapping of the detailed knot position
Task 4.5: cutting process quality index
other sources of information
74. Task 4.5: cutting process quality index
working plan
activity responsible status (end of task)
Assemble sensors and controllers in lab CNR Ongoing (Feb 2016)
Design solutions for sensors placement COMPOLAB Ongoing (Feb 2016)
Report from lab measurements CNR Ongoing (Mar 2016)
Installation of sensor on processor head COMPOLAB (Mar 2016)
Testing of sensors in the shop COMPOLAB (Mar 2016)
Implementation of the software for QI CNR (April 2016)
Final adjustments + callibrations CNR + COM (May 2016)
Processor ready for pilot: June 2016
75. hydraulic pressure sensors , hydraulic flow sensor , termometer ,
linear gauge
Task 4.5: cutting process quality index
schematic of the log cross-cutting system of the ARBRO1000
76. Task 4.5: cutting process quality index
cross-cutting with the chain saw
Hydraulic flow (l/min)
Oil pressure (MPa)
Oil temperature (°C)
Position of the saw (mm)
+
Total working time of tool (min)
Log diameter (mm)
time of one sawing stroke/cycle
cutting resistance log diameter quality Index
“easy” “small” “low” (0,2)
“easy” “small” “very low” (0,0)
“difficult” “small” “very high” (1,0)
“difficult” “big” “high” (0,8)
77.
hydraulic pressure sensor , load cell
Task 4.5: cutting process quality index
schematic of the instrumented de-branching system of the ARBRO1000
78. Task 4.5: cutting process quality index
de-branching
Load cell#1 (N)
Load cell#2 (N)
Oil pressure (MPa)
Oil temperature (°C)
Position of the feed piston (mm)
+
Total working time of tool (min)
time of one debranching stroke/cycle
map of knots
CF quality index#2
79. Task 4.5: cutting process quality index
de-branching
time of one debranching stroke/cycle
80. Task 4.5: cutting process quality index
de-branching
map of knots – displayed for operator
CF quality index#2
81. two quality indexes (numbers in the range from 0 to 1) associated to wood/log properties
are determined:
CP quality index #1: reflects the estimation of the “wood density” as related to the
cutting resistance during cross-cutting of log by chain saw. The quality index #1 value
is unique for the whole log.
CP quality index #1 = f(wood moisture content, tool wear, cutting
speed, feed speed, log diameter, ellipsoid shape, presence of
defects)
CP quality index #2: reflects the “brancheness” of the log along its length and is
estimated by means of signals associated with cutting out branches. The quality
index #2 is spatially reolved.
CP quality index #2 = f(hydraulic pressure changes along the log
length, changes of cutting forces in time, number of AE events or
sound pressure level)
Task 4.5: cutting process quality index
algorithms for data mining
82. Task 4.5: cutting process quality index
Challenges
Important delay with prototype developing:
the equipment just now ready for testing
How to physically install sensors on the processor?
How reliable will be measurement of cutting forces in forest?
What is an effect of tool wear?
How to link cutting force (wood density) with recent quality sorting
rules?
Delimbing or debarkining?
84. Task 4.6: Implementation of the log/biomass grading system
Task Leader: CNR
Task Participants: GRAPHITECH, COMPOLAB ,MHG, BOKU, GRE, TRE
Starting : June 2014
Ending: July 2016
Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (COMPOLAB) + 1.00
(MHG) + 1.00 (BOKU), 0.50 (GRE) + 1.00 (TRE)
CNR: will coordinate the research necessary, develop the software tools (expert systems)
and integrate all available information for quality grading
TRE, GRE, COMPOLAB: incorporate material parameters from the multisource data
extracted along the harvesting chain
GRAPHITECH: integration with the classification rules for commercial assortments, linkage
with the database of market prices for woody commodities
MHG: propagate information about material characteristics along the value chain (tracking)
and record/forward this information through the cloud database
BOKU: validation of the grading system
85. Task 4.6: Implementation of the grading system
Objectives
The goals of this task are:
• to develop reliable models for predicting the grade (quality
class) of the harvested log/biomass.
• to provide objective/automatic tools enabling optimization of
the resources (proper log for proper use)
• to contribute for the harmonization of the current grading
practice and classification rules
• provide more (value) wood from less trees
86. Task 4.6: Implementation of the grading system
Deliverables
D.4.01 Existing grading rules for log/biomass
Report: This deliverable will contain a report on existing log/biomass grading criteria and
criteria gap analyses
Delivery Date: October 2014 (M.10) DONE
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes
and report on the validation procedure
Prototype: This deliverable will contain a report on the validation procedure, and results of
the quality class prediction models, and integration in the SLOPE cloud data base
Delivery Date: June 2016 (M.30)
87. T4.6: Implementation of the log/biomass grading
system
Link in-field data with cloud
database
CNR:
Compare automatic and visual
grading results
BOK,CNR:
Determine threshold values
CNR:
Develop grading expert system
CNR:
Develop algorithm for data fusion
CNR, COM, TRE:
In field visual qualityassessment
CNR,BOK:
Develop data base for prices of
woody commodities
CNR,BOK:
Reliabilitystudies
BOK:
Economic advantage studies
BOK,CNR:
T4.6 quality implementation
D04.01
CNR
D04.12
CNR
Identifygrading rules for standard
and niche products
CNR:
Prepare state-of-the-art report on
grading rules
CNR:
the resources planned: 8.0 M/M
the resources utilized:
PROBLEMS: Delay related to other tasks within WP4
SOLUTIONS: intensify efforts, implement ready theoretical solutions developed up-to-data
31.06.2016
draft: October 2014
accepted: July 2015
88. Task 4.6: Implementation of the grading system
The concept (logic)
3D quality index (WP 4.1)
NIR quality index (WP 4.2)
HI quality index (WP 4.3)
SW quality index (WP 4.4)
CP quality index (WP 4.5)
Data from harvester
Other available info
Quality class
Threshold values and
variability models of
properties will be
defined for the
different end-uses
(i.e. wood processing
industries, bioenergy
production).
(WP5)
89. Task 4.6: Implementation of the grading system
implementation#1: Quality index concept
Each index can be between:
0 – bad, not suitable, low, , …
and
1 – good, proper, perfect, appreciated, , …
Computed for:
Suitability modeled separately for different destination fields:
resonance wood, structural timber, pulp/paper, chemical conversion…
Presence of various defects, such as:
Rotten wood, knottiness, compression wood, eccentric pith…
Compatibility with standard quality classes
For each task of WP4 series of quality indexes will be computed as default
90. Task 4.6: Implementation of the grading system
implementation#2: Quality index computation
Set of experimental samples
with characteristics representing
poor quality QI = “0”
Set of experimental samples
with characteristics representing
superb quality QI = “1”
PLS models for prediction
validation of models
implementation of models
for routine data processing
never
ending
tuning
process
91. Task 4.6: Implementation of the grading system
implementation#3: summary of QI + weights
weight for each quality aspect
range
construct.
wood
biomass
/fuel pulp plywood class A class D
T4.2 moisture 0 - 1 0,2 1
density 0 - 1 1 1 1 1 1
carbohydrate content 0 - 1 1
lignin content 0 - 1 1 1
calorific value 0 - 1 1
rotten wood progress 0 - 1 -100 1 1 1
early/late wood ratio 0 - 1 0,2 1
width of sapwood 0 - 1 0,1
pith eccentricity 0 - 1 0,5 0,8 1
width of bark 0 - 1 0,2 1 1 1
presence of reaction wood 0 or 1 1 1 1 1
presence of resin 0 or 1 0,2 1 1
presence of rot 0 or 1 -100 0,7 1
presence of bark 0 or 1 -0,5 0,2 1 1
presence of contamination –soil 0 or 1 -0,1 -0,1
presence of contamination – oil 0 or 1 1
T4.3 ovalness 0 - 1 1 2 1
ratio of knot area 0 - 1 0,2 1
knot count 0 - 1 0,2 1
T4.4 velocity 0 - 1 1 0,8 1
homogenity velocity 0 - 1 1 1 1
density 0 - 1 1 0,8 1
elasticity 0 - 1 1 0,3 1
suitability for pales 1
T4.5 knotines 0 - 1 0,5 0,6 1
knots size 0 - 1 2 0,6 1
knot spatial distribution 0 - 1 1 1 1
log density 0 - 1 1 1 1 1
easy for processing 0 - 1 1 1 1 1
92. Task 4.6: Implementation of the grading system
implementation#4: maths behind
For each log:
∑
∑ ⋅
=
i
ii
market
w
QIw
Q
where:
Qmarket – log quality for specific use/market
wi – weight of quality index
QIi – quality index assessed by sensor
)( ii wtresholdQI >∀
where:
treshold(wi) – minumum value of QIi
AND/OR*
* - depending on application
93. Task 4.6: Implementation of the grading system
implementation#4: quality map
Map of knots
Map of quality
QIT4.4
QIT4.1
QIT4.2
QIT4.3
QIT4.5
94. Task 4.6: Implementation of the grading system
The concept (diagram)
Measure 3D shape of
several trees
Measure NIR spectra of
tree X in forest
Extract 3D shape of
tree X
Compute 3D quality in-
dexes for log X.1 … X.n
Measure NIR spectra of
tree X on processor
Measure NIR spectra of
tree X on the pale
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Data base for Forest In-
formation System
Determine quality grade
for log X.1 … X.n
T4.1
T4.2
Measure hyperspectral
image of tree X in forest
Measure cross section
image of log X.1 … X.n
Measure NIR spectra of
tree X on the pale
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in-
dexes for log X.1 … X.n
T4.3
Measure stress waves on
tree X in forest
Measure stress waves of
tree X on processor
Measure stress waves of
log X.1 …X.n on the pale
Compute SW quality in-
dex for tree X
Compute SW quality in-
dexes for log X.1 … X.n
Compute SW quality in-
dexes for log X.1 … X.n
T4.4
Measure delimbing force
on log X.1 … X.n
Measure cross-cutting
force on log X.1 … X.n
Compute CF quality in-
dexes for tree X
Compute CF quality in-
dexes for log X.1 … X.n
T4.5
95. Task 4.6: Implementation of the grading system
The concept (diagram)#1
Measure 3D shape of
several trees
Measure NIR spectra of
tree X in forest
Extract 3D shape of
tree X
Compute 3D quality in-
dexes for log X.1 … X.n
Measure NIR spectra of
tree X on processor
Measure NIR spectra of
tree X on the pale
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Determine quality
for log X.1 …
T4.1
T4.2
Measure hyperspectral
image of tree X in forest
Measure cross section
image of log X.1 … X.n
Measure NIR spectra of
tree X on the pale
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in-
dexes for log X.1 … X.n
T4.3
96. Task 4.6: Implementation of the grading system
The concept (diagram)#2
Measure stress waves on
tree X in forest
Measure stress waves of
tree X on processor
Measure stress waves of
log X.1 …X.n on the pale
Compute SW quality in-
dex for tree X
Compute SW quality in-
dexes for log X.1 … X.n
Compute SW quality in-
dexes for log X.1 … X.n
T4.4
Measure delimbing force
on log X.1 … X.n
Measure cross-cutting
force on log X.1 … X.n
Compute CF quality in-
dexes for tree X
Compute CF quality in-
dexes for log X.1 … X.n
T4.5
97. Task 4.6: Implementation of the grading system
The concept (diagram)#3
Compute 3D quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dex for tree X
Compute NIR quality in-
dexes for log X.1 … X.n
Compute NIR quality in-
dexes for log X.1 … X.n
Data base for harvest
data
Data base for Forest In-
formation System
Determine quality grade
for log X.1 … X.n
Compute HI quality index
for tree X
Compute HI quality in-
dexes for log X.1 … X.n
Compute HI quality in
98. Task 4.6: Implementation of the grading system
data flow & in-field hardware
NI CompactRio master
Database
NI CompactRio client
FRID
weight
fuel
???
Data storage
CP
NIR
HI
SW
camera
kinect
99. Task 4.6: Implementation of the grading system
Challenges
What sensors set is optimal (provide usable/reliable information)?
How to merge various types of indexes/properties?
Can the novel system be accepted by “conservative” forest (and
wood transformation) industry?
How the SLOPE quality grading will be related to established
classes?
the final answer possible only after demonstrations