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.
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 provides an overview of activities for piloting the SLOPE demonstrator. It summarizes preparations for demonstrators in Sover, Italy in spring 2016 and Annaberg, Austria in autumn 2016. It describes the survey site in Annaberg including the characteristics of the forest stand and outlines activities already performed at the site. It then presents prospective plans for the harvesting demo in Annaberg, including marking trees with RFIDs, felling, extracting and processing trees. An agreement with Austrian Federal Forests to support the demo is also summarized.
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.
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.
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 discusses progress on Task 2.1 of Project SLOPE. It outlines the participants in the task and their roles in collecting and analyzing forest information using remote sensing. Work completed so far includes acquiring satellite imagery of test sites, conducting trials combining aerial imagery and laser scanning in Ireland, and identifying additional test sites in Trento and Austria. The next steps are to get permission to fly in Italy, test equipment at new sites, finalize the methodology, and disseminate results.
This document summarizes a kick-off meeting for Project SLOPE. The project involves developing methods for remote sensing-based forest inventory and 3D modeling to evaluate harvesting technologies in mountain forests. Task 2.1 will design an automatic method for satellite-based forest inventory. Task 2.4 will generate a detailed 3D model of the forest to simulate harvesting plans and evaluate equipment. The model will integrate remote sensing, field measurements, and a web-based planning system to optimize harvesting in mountain areas.
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 provides an overview of activities for piloting the SLOPE demonstrator. It summarizes preparations for demonstrators in Sover, Italy in spring 2016 and Annaberg, Austria in autumn 2016. It describes the survey site in Annaberg including the characteristics of the forest stand and outlines activities already performed at the site. It then presents prospective plans for the harvesting demo in Annaberg, including marking trees with RFIDs, felling, extracting and processing trees. An agreement with Austrian Federal Forests to support the demo is also summarized.
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.
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.
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 discusses progress on Task 2.1 of Project SLOPE. It outlines the participants in the task and their roles in collecting and analyzing forest information using remote sensing. Work completed so far includes acquiring satellite imagery of test sites, conducting trials combining aerial imagery and laser scanning in Ireland, and identifying additional test sites in Trento and Austria. The next steps are to get permission to fly in Italy, test equipment at new sites, finalize the methodology, and disseminate results.
This document summarizes a kick-off meeting for Project SLOPE. The project involves developing methods for remote sensing-based forest inventory and 3D modeling to evaluate harvesting technologies in mountain forests. Task 2.1 will design an automatic method for satellite-based forest inventory. Task 2.4 will generate a detailed 3D model of the forest to simulate harvesting plans and evaluate equipment. The model will integrate remote sensing, field measurements, and a web-based planning system to optimize harvesting in mountain areas.
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.
The document describes Project SLOPE which aims to develop intelligent systems for tree marking, felling, hauling, and processing in mountain forests. It outlines the tasks, participants, goals, challenges, and timeline for Task 3.1 which focuses on developing an intelligent system for tree marking using RFID tags, GPS, and a rugged tablet computer to store and access forest inventory data and mark trees efficiently in mountainous terrain. The key challenges are ensuring the systems are ergonomic for mountain forest conditions and have high tag survival and reading rates to enable full traceability.
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.
This document summarizes a review meeting for Project SLOPE Work Package 2 on forest information collection and analysis. The task involved defining a methodology to characterize forest status using remote sensing data from multiple sensors. Partners completed the task of determining useful vegetation indices from satellite, UAV, and laser scanning data to estimate biological parameters. The group analyzed parameters with increasing detail and resolved issues related to selecting case study sites with comparable satellite and UAV data. They concluded that the work established an integrated system to monitor forests and provided detailed tree-level information for management using different data sources.
The document outlines tasks related to defining requirements for Project SLOPE. Task 1.1 involves identifying user requirements through questionnaires. Task 1.2 defines hardware and equipment needs based on user requirements. Task 1.3 focuses on defining human-machine interfaces for different scenarios like planning, harvesting, and resource management. The tasks involve various partners contributing expertise in areas like 3D modeling, inventory, harvesting, and enterprise resource planning.
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.
- 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
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.
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 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.
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.
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 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.
This document summarizes the progress of various tasks under Work Package 3 of the Project SLOPE, which aims to integrate novel intelligent harvesting systems operating in mountain areas.
Task 3.1 on intelligent tree marking has tested RFID tags on trees and developed a roadmap for the tagging process. Equipment for tagging and reading tags is available but the GPS capacity may need improvement.
Task 3.2 on processor head selection has defined requirements, requested offers from manufacturers, and selected a model, but the processor head has not yet been purchased. Re-engineering work is planned.
Task 3.5 on intelligent transport trucks is adding RFID reading, GPS, and data transmission capabilities to trucks to track timber and optimize
The document provides a mid-term review of work being done on Project SLOPE. It summarizes the status of tasks relating to developing an intelligent cable crane system. The TECNO self-propelled carriage has been completed mechanically and is stored, with only the electric box and wiring remaining. Work is ongoing to integrate sensors into the software over the next few months to develop deliverables due. Chokers and a synthetic rope launcher are also being developed as part of creating an intelligent cable harvesting system for steep terrain forest operations.
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
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.
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.
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 - 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.
SLOPE Final Conference - electronic marking of treesSLOPE Project
This document discusses electronic marking of trees and timber using RFID tags for traceability purposes. It examines different RFID tag types and their suitability for long-term exposure to forest environments. Testing showed that UHF RFID tags attached using staples survived well on trees for over two years. Tag and reader positioning tests demonstrated the influence of moisture content, distance, angle and other factors on readability. RFID tags were also found to survive logging and transport processes with high reliability, making them suitable for traceability from standing trees to end products.
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.
The document describes Project SLOPE which aims to develop intelligent systems for tree marking, felling, hauling, and processing in mountain forests. It outlines the tasks, participants, goals, challenges, and timeline for Task 3.1 which focuses on developing an intelligent system for tree marking using RFID tags, GPS, and a rugged tablet computer to store and access forest inventory data and mark trees efficiently in mountainous terrain. The key challenges are ensuring the systems are ergonomic for mountain forest conditions and have high tag survival and reading rates to enable full traceability.
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.
This document summarizes a review meeting for Project SLOPE Work Package 2 on forest information collection and analysis. The task involved defining a methodology to characterize forest status using remote sensing data from multiple sensors. Partners completed the task of determining useful vegetation indices from satellite, UAV, and laser scanning data to estimate biological parameters. The group analyzed parameters with increasing detail and resolved issues related to selecting case study sites with comparable satellite and UAV data. They concluded that the work established an integrated system to monitor forests and provided detailed tree-level information for management using different data sources.
The document outlines tasks related to defining requirements for Project SLOPE. Task 1.1 involves identifying user requirements through questionnaires. Task 1.2 defines hardware and equipment needs based on user requirements. Task 1.3 focuses on defining human-machine interfaces for different scenarios like planning, harvesting, and resource management. The tasks involve various partners contributing expertise in areas like 3D modeling, inventory, harvesting, and enterprise resource planning.
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.
- 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
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.
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 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.
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.
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 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.
This document summarizes the progress of various tasks under Work Package 3 of the Project SLOPE, which aims to integrate novel intelligent harvesting systems operating in mountain areas.
Task 3.1 on intelligent tree marking has tested RFID tags on trees and developed a roadmap for the tagging process. Equipment for tagging and reading tags is available but the GPS capacity may need improvement.
Task 3.2 on processor head selection has defined requirements, requested offers from manufacturers, and selected a model, but the processor head has not yet been purchased. Re-engineering work is planned.
Task 3.5 on intelligent transport trucks is adding RFID reading, GPS, and data transmission capabilities to trucks to track timber and optimize
The document provides a mid-term review of work being done on Project SLOPE. It summarizes the status of tasks relating to developing an intelligent cable crane system. The TECNO self-propelled carriage has been completed mechanically and is stored, with only the electric box and wiring remaining. Work is ongoing to integrate sensors into the software over the next few months to develop deliverables due. Chokers and a synthetic rope launcher are also being developed as part of creating an intelligent cable harvesting system for steep terrain forest operations.
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
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.
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.
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 - 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.
SLOPE Final Conference - electronic marking of treesSLOPE Project
This document discusses electronic marking of trees and timber using RFID tags for traceability purposes. It examines different RFID tag types and their suitability for long-term exposure to forest environments. Testing showed that UHF RFID tags attached using staples survived well on trees for over two years. Tag and reader positioning tests demonstrated the influence of moisture content, distance, angle and other factors on readability. RFID tags were also found to survive logging and transport processes with high reliability, making them suitable for traceability from standing trees to end products.
The document summarizes a technical meeting for Project SLOPE to discuss system integration tasks and timelines. It outlines the goals of Work Package 6 to build an integrated forest management system through three stages: integrating inventory and harvesting systems; adding forest management; and validating the full system. Task 6.2 aims to integrate forest inventory with harvesting measurement and planning tools over 14 months. Testing shows progress but some requirements and use cases remain untested. An action plan was defined to complete integration and address delays.
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 Task 5.5 of Project SLOPE, which aims to develop a mid-long term optimization and strategic planning module for the Forest Information System (FIS). It will utilize the IPTIM software tool to produce optimal harvesting and sales plans over 1-10 years. Key points discussed include: using growth models and stand simulations to create realistic plans; setting goals to minimize costs or maximize profits; including spatial and temporal clustering to improve efficiency; and enabling continuous adaptation of plans based on new supply chain data integrated into the FIS. The results will include demo harvesting plans, process models, planning indicators and guidelines to aid mid-long term optimization.
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.
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 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 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.
SLOPE Final Conference - online purchase of timber and biomassSLOPE Project
Wuudis is an online marketplace that allows buyers and sellers to connect for the purchase of timber and biomass. The marketplace provides a simple process where sellers can create offer requests that buyers can browse, bid on, and eventually create trade contracts for accepted offers. The system also includes features for managing forestry data and real-time operations at the stand level. The goal of the Wuudis platform is to provide an easy to use system that facilitates timber and biomass trading online.
The document summarizes progress on Project SLOPE's Work Package 5, which involves developing a forest information system. It discusses the status of three tasks: 1) developing a database to support novel inventory data, which is 35% complete; 2) developing a platform for near real-time control of operations using an existing system called MHG Biomass Manager; and 3) planning online purchasing/invoicing of timber and biomass. It outlines actions taken and planned for each task to develop prototypes and integrate different modules by established deadlines.
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 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.
This document summarizes dissemination activities for the Project SLOPE from July 4-5, 2016 in Trento, Italy. It describes the dissemination plan, including a timeline of activities such as brochures, newsletters, conferences, and trade fairs planned through the project. It provides updates on the project website, social media, recent events attended, and upcoming events. It also discusses cooperation with other related projects and plans for four technical workshops to disseminate project results.
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 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.
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.
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,
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.
Remote sensing and mapping tool development of NFA Project in VietnamFAO
The document summarizes remote sensing and mapping tool development by the National Forest Administration (NFA) project in Vietnam to support national forest inventories, statistics programs, and REDD+ monitoring requirements. Key activities included:
1) Developing land use and forest type classification methods using SPOT-5 and DMC satellite imagery to map resources on national and local levels.
2) Creating forest distribution maps and a centralized forestry database to provide statistical data.
3) Developing change detection tools to monitor land use and forest type changes over time to support reporting under NFI Cycle 5 and REDD+.
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 summarizes work package 1, task 1.5 on defining the system architecture for Project SLOPE. The task leader defined the system architecture to integrate various partner applications and technologies. Key elements included specifying design principles based on service-oriented architecture, and defining integration technologies and components like Liferay, web services, and GeoServer. The system architecture overview and component diagram were included to illustrate how the different partner systems would integrate on a deployment platform.
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.
T.2.1 – remote sensing and multispectral analysis (by fly)SLOPE Project
This document summarizes a kick-off meeting for Task 2.1, which involves using remote sensing and multispectral analysis to conduct forest inventories. The task will design an automatic method using satellite imagery and NDVI calculations to monitor forests. It will provide a first-level inventory to guide more accurate UAV and field measurements, and fuse satellite data with other sources for improved accuracy. Participants include Flyby S.r.l., CNR, Coastway, and TreeMetrics. The expected output is a report on the data, methodologies, algorithms, and results in August 2014.
Some sensing technologies for food and agriculture from VTT Ltd, the Technical Research Centre of Finland, as presented at the Sensors in Food and Agriculture 2016 conference in Cambridge UK.
This document discusses using machine learning techniques to build models for predicting agricultural crop yields. It first provides background on the importance of feature selection and engineering for accurate modeling. It then outlines the proposed system, which would acquire dataset on weather, soil parameters, and past yields, preprocess the data, perform clustering, feature selection, and classification to predict future crop yields. The goal is to help farmers choose optimal crops and improve farm management. The document reviews several related works applying data mining and machine learning to agricultural data and concludes the proposed approach could effectively optimize feature selection and model performance.
Task 3.1 intelligent tree marking (by cnr)SLOPE Project
This document outlines tasks related to intelligent tree marking (Task 3.1) as part of a larger project. It involves developing a system using RFID tags, a programming tool, and insertion device to effectively mark trees in a forest for harvesting. The system aims to be easy to use, robust, and integrate with other tasks like felling and processing trees. Challenges include developing a non-cumbersome system that can reliably mark and identify trees over time as part of improving traceability in the forestry process.
The document outlines the tasks and timeline for the development of a forest information system from August 2014 to January 2016. It involves 6 tasks: 1) developing a database to support novel inventory data, 2) a platform for near real-time control of operations, 3) online purchasing and invoicing of timber and biomass, 4) short-term optimization of operational planning, 5) mid-long term optimization of strategic and tactical planning, and 6) communication and risk management. Key deliverables are due between months 8 to 28 with bi-monthly project meetings planned.
The document outlines the tasks and timeline for the development of a forest information system from August 2014 to January 2016. It involves 6 tasks: 1) developing a database to support novel inventory data, 2) a platform for near real-time control of operations, 3) online purchasing and invoicing of timber and biomass, 4) short-term optimization of operational planning, 5) mid-long term optimization of strategic and tactical planning, and 6) communication and risk management. Key deliverables are due between months 8 to 28 with bi-monthly project meetings to ensure the strict schedule is maintained through open communication and information sharing.
Building on iMarine for fostering Innovation, Decision making, Governance and...Blue BRIDGE
BlueBRIDGE - Building Research environments fostering Innovation, Decision making, Governance and Education - is funded under H2020 and provides data services to scientists, researchers and data managers delivering a solid foundation for informed advice to competent authorities. A complete set of web-based data and computational resources will enable them to address key challenges related to the Blue Growth long term strategy with a strong focus on sustainable growth. BlueBRIDGE services will be built on top of the iMarine infrastructure (www.i-marine.eu) in order to capitalize on the previous investments made by the European Commission and as a first step towards their sustainability after the end of the project. www.bluebridge-vres.eu | @BlueBridgeVREs
From sensor readings to prediction: on the process of developing practical so...Manuel Martín
Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.
Forest-Fires Surveillance System Based On Wireless Sensor NetworkIJERA Editor
We present the design and evaluation of a wireless sensor network for early detection of forest fires. Wild fires
cause to damage on forest and a mountain which have valuable natural resources during the dry winter season
Where it becomes very paramount to cover the area caused by fire by the forest fighters. Current surveillance
systems utilize a camera, an infrared sensor system and a satellite system. These systems cannot support
authentic time surveillance, monitoring and automatic alarm. Even though it gives information about fire caused
area,but asthe forest looks same in all areas as it is covered with dense trees it is very hard to recognize the exact
area andimage transmition through the transmitter to the officers computer takes too much time .It takes too
much time to load the image. Which in turns waste the time and fire caused area goes on increasing. Taking in
toconsideration all this faults of the prior system in the forest we have designed our modified project.In our
project, we develop a forest fires surveillance system.
Towards an e-infrastructure in agriculture?Blue BRIDGE
Donatella Castelli, CNR-ISTI & BlueBRIDGE Coordinator, gave an introductive talk in the "Towards an e-infrastructure in agriculture?" session at the Euragri workship in Inra, Paris discussing leading an e-infrastructure project in marine research e-Infrastructure and how it refers to a combination of digital technologies (hardware and software), resources (data, services, digital libraries), communications (protocols, access rights and networks), and the people and organisational structures needed to manage them.
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.
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.
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.
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.
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.
This document summarizes a hyperspectral camera and its applications for precision agriculture. It describes the camera as the world's smallest and lightest hyperspectral imaging sensor. The camera provides high resolution spectral and spatial data to help farmers monitor crop health and development, identify issues like diseases or nutrient deficiencies, optimize resource use, and forecast yields. It also helps foresters with tasks like species identification and inventory, detecting diseases or water stress, estimating timber volume, and mapping clear-cut or burned areas. The document outlines the camera's data acquisition and processing methodology, as well as examples of vegetation indices and applications for precision agriculture and forestry management.
EVK produces hyperspectral imaging cameras and systems for real-time sorting and process analysis applications in recycling, mining, food processing, and pharmaceuticals. Their HELIOS camera uses near-infrared spectroscopy to identify and classify materials and quantify chemical properties. EVK has implemented successful projects in applications like plastic and metal recycling, recovered paper sorting, potato grading, mineral analysis, and measuring active pharmaceutical ingredients in tablets. Hyperspectral imaging allows both removal of contaminants and quantitative chemical analysis for 100% inline process monitoring, combining laboratory precision with high-volume sampling.
This document summarizes research on using hyperspectral imaging to analyze wood. It discusses using hyperspectral cameras to capture spectral information from wood samples across multiple wavelengths. Researchers are aiming to identify chemical properties of wood, like lignin and cellulose content, and monitor changes over time, such as the development of mould on wood samples in the lab. The document also discusses challenges with applying this technique outdoors to analyze painted and weathered wood samples, as well as logs in forest environments, where varying moisture, light and surface roughness conditions exist. The goal is to select important bands of wavelengths that provide useful information and could enable hyperspectral cameras to be used for in-line monitoring applications.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
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.)
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
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 ...
2. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Before starting…
1. The forest in mountains is peculiar, and very different than such
of flat lands!!!
2. Trees in mountains are (mostly) BIG…
3. Big/old tree may be or superior quality, or “fuel wood”
4. Trees from mountains might be of really high value
5. We do support “PROPER LOG FOR PROPER USE”
6. The quality of wood/log/tree is an issue!!!!!
7. The quality of wood is not only external dimensions, taper and
diameter…
3. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood might not be perfect…
4. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Wood from mountains might be priceless…
5. 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
7. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Interim delivery stages (with dates):
D.4.01 R: Existing grading rules for log/biomass (December 2014)
D.4.02 R: On-field survey data for tree characterization (March 2015)
D.4.03 R: Establishing NIR measurement protocol (April 2015)
D.4.04 R: Establishing hyperspectral imaging measurement protocol (May 2015)
D.4.05 R: Establishing acoustic-based measurement protocol (June 2015)
D.4.06 R: Establishing cutting power measurement protocol (July 2015)
D.4.07 P: Estimation of log/biomass quality by external tree shape analysis (July 2015)
D.4.08 P: Estimation of log/biomass quality by NIR (August 2015)
D.4.09 P: Estimation of log quality by hyperspectral imaging (September 2015)
D.4.10 P: Estimation of log quality by acoustic methods (October 2015)
D.4.11 P: Estimation of log quality by cutting power analysis (November 2015)
D.4.12 P: Implementation and calibration of prediction models for log/biomass quality
classes and report on the validation procedure (July 2016)
8. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Task 2.3
4.1.
on-field forest survey
GPS
PC/PAD
3D scanner
3D vision
Tasks 3.1
4.2-4.3
Mark tree
Confirm route of cable crane
GPS
PC/PAD
RFID TAG
RFID reader
Tasks 3.2
4.4
Tree felling
Database
NIR QI
H QI
RFID reader
RFID TAG
(if cross cut)
Portable NIR
Hyperspectral
Accellerometers
Oscilloscope
SW QI
Tasks 3.3
Cable crane
Techno carriage
GPRS
RFID reader
WIFI
Skyline launcher
Load sensor
Intelligent chookers
GPS
PC/PAD
Data logger
Black box access
Control system
M/M interface
Tasks 3.4
4.2-4.3-4.4-4.5-4.6
Processor
de-brunch, cut to length, measures, mark
Load cell for cutting force
Cutting feedsensor
Feed forcesensor
Diameterdigital caliper
Length
RFID reader
RFID TAG
PC control comp.
GPRS/WIFI
Hyperspectral
NIR scanner
Kinect® (or similar 3D vision)
Microphone/accellerometer
Data logger
Black box access
CodePrinter
Control system
M/M interface
ID backup
Database
NIR QI + H QI + SW QI+ CF QI
Tasks 3.5
Truck
RFID tags are only used for identifying trees/logs along the supply chain, not to store information.
Material parameters from sensors are stored in the database
GPS
GPRS
RFID antenna
BUSCAN
Load cell
Logistic Software
ID backup
ID backup
Weight, time
Quality class
9. Work Package 4: Multi-sensor model-based
quality control of mountain forest production
Risks and mitigating actions:
Significant delay related to changes in the consortium (lack of the
practical expertise of the processor head engineers); technical
meetings, new partners/collaborators
Properly define real user expectations; contribution of the
development of WP1, discussions with stake holders, foresters,
users of forest resources
Technologies provided will not be appreciated by “conservative”
forest users; demonstrate financial (and other) SLOPE advantages
Difficulties with integration of some sensors with forest machinery;
careful planning, collaboration with SLOPE engineers
26. Forest Mapper - First In The World – Online Forest
Mapping & Analysis - Data Management System
27. Forest Mapper: Automated net area calculation,
stratification and Location for ground sample plots
to be collected
Sample
Plots
Net Area
Stratification
(Inventory
Planning)
28. Terrestrial Laser Scanning Forest Measurement System
(AutoStem Forest)
Automated 3D Forest
Measurement System
34. Task 4.2
Evaluation of near infrared (NIR) spectroscopy
as a tool for determination of log/biomass
quality index in mountain forests
Task leader: Anna Sandak (CNR)
35. Task 4.2: Partners involvement
Task Leader: CNR
Task Partecipants: KESLA, BOKU, FLY, GRE
CNR: Project leader,
•will coordinate all the partecipants of this task
•will evaluate the usability of NIR spectroscopy for characterization of bio-
resources along the harvesting chain
•will provide guidelines for proper collection and analysis of NIR spectra
•will develop the “NIR quality index”; to be involved in the overall log and biomass
quality grading
Boku: will support CNR with laboratory measurement and calibration transfer
Kesla, Greifenberg and Flyby: will support CNR in order to collect NIR spectra at
various stages of the harvesting chain
36. 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
The raw information provided here are near infrared
spectra, to be later used for the determination of
several properties (quality indicators) of the sample
4.2 Objectives
37. 4.2 Deliverables
Kick-off Meeting
8-9/jan/2014
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 M16 April 2015
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 M20 August 2015
Estimated person Month= 3.45
38. 4.2Timing
Kick-off Meeting
8-9/jan/2014
Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass quality index in mountain forests
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.2
D.4.03
D.4.08
test sensors avaliable on the market
finalize concept
design/adopt to the processor
test electronic system
assemble hardware
collect reference samples
analyse reference samples
test hardware + software
calibrate system
develop algorithm for NIR qualityindex
integrate NIR quality index with quality grading/optymization (T4.6) D.4.12
D.4.03 Establishing NIR measurement protocol
D.4.08 Estimation of log/biomass quality by NIR
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
41. NIR spectra will be collected at various stages of the harvesting chain
measurement procedures will be provided for each field test
In-field tests will be compared to laboratory results
4.2 Activities: Feasibility study and specification of the
measurement protocols for proper NIR data acquisition
42.
the scanning bar #1 with NIR sensor
4.2 sensor position in the intelligent processor head
44. • spectra pre-processing, wavelength selection, classification,
calibration, validation, external validation (sampling –
prediction – verification)
• prediction of the log/biomass intrinsic “quality indicators”
(such as moisture content, density, chemical composition,
calorific value) (CNR).
• classification models based on the quality indicators will be
developed and compared to the classification based on the
expert’s knowledge.
• calibrations transfer between laboratory instruments
(already available) and portable ones used in the field
measurements in order to enrich the reliability of the
prediction (BOKU).
4.2 Activities: Development and validation of
chemometric models.
45. Development of “provenance models”. The set of
spectra collected from selected samples (of known
provenance and silvicultural characteristics) along the
supply chain will be also processed in order to verify
applicability of NIR spectroscopy to traceability of
wood (CNR).
4.2 Additional deliverable
46. Wood provenance & NIRS
2163 trees of Norway spruce
from 75 location
in 14 European countries
2163 samples measured
x 5 spectra/sample
= 10815 spectra
49. WP4: Multi-sensor model-based quality control of mountain
forest production
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”
Task leader: Mariapaola Riggio (CNR)
50. WP4:T 4.4 Role of partners involved
Kick-off Meeting
8-9/jan/2014
Task Leader: CNR
Task Participants: Kesla, Greifenberg
CNR: will coordinate all the participants to this task and in particular will define the
testing procedures and develop the prediction models for characterization of wood along
the harvesting chain, using acoustic measurements
Greifenberg: will provide expertise and assistance for the collection for in field
measurements of acoustic data on the felled/delimbed stems
Kesla: will provide expertise, in field assistance and product components (mainly sensors)
to be tested for the harvester head integration, for in-field acoustic measurements on the
logs
51. WP4:T 4.4 Deliverables
Kick-off Meeting
8-9/jan/2014
D4.05) Establishing acoustic-based measurement protocol: This deliverable will contain 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
52. WP4:T 4.4 Timing
Kick-off Meeting
8-9/jan/2014
Data mining and model integration of log/biomass quality indicators from stress-wave (SW) measurements, for the determination of the “
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.4
D.4.05
D.4.10
finalize concept
field tests
design/adopy to the processor
test electronic system
assemble hardware
test hardware + software
callibrate system
develop algorithm for CP Q_index
integrate CP quality index with quality grading/optimization (T4.6) D.4.12
D.4.05 Establishing acoustic-based measurement protocol
D.4.10 Estimation of log quality by acoustic methods
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
53. WP4:T 4.4 A premise
Kick-off Meeting
8-9/jan/2014
Stress-waves
Parameters
SW velocity or time-of-flight (SW-TOF)
Acoustic impendance
Damping
Resonance frequency
54. WP4:T 4.4 Objectives
Kick-off Meeting
8-9/jan/2014
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.
55. WP4:T 4.4 Objectives
Kick-off Meeting
8-9/jan/2014
Task 4.4 does not aim at defining a procedure for the estimation of
specific properties (e.g. dynamic moduli, etc.) of the harvested material.
The aim of Task 4.4 is to define a procedure for determination of “SW
quality index” that will support final grading of logs.
“SW quality index” will be used in combination with the other
implemented “quality indices” developed from the multisource data
extracted along the harvesting chain.
56. WP4:T 4.4 Interactions
Kick-off Meeting
8-9/jan/2014
WP4: interaction with all other tasks
tasks 4.1, 4.2, 4.3: Information about
material characteristics (such as diameter,
length, moisture content and density),
estimated through the other non-
destructive tests implemented in WP4 and
propagated along the harvesting chain,
will be incorporated into prediction
models.
task 4.6: “SW quality index” will be used
in combination with the other
implemented “quality indices” developed
from the multisource data extracted along
the harvesting chain. SW quality index
Density,
MC, …
geometrical
data
TOF,
resonance
frequency
57. Kick-off Meeting
8-9/jan/2014
WP4:T 4.4 manual measurement of the log mechanical properties
Task 4.4 will start from recent developments of acoustic-based diagnostics for
forest resource segregation.
58.
the scanning bar #1 with free vibrations sensor
WP4:T 4.4 sensor position in the intelligent processor head
59. Kick-off Meeting
8-9/jan/2014
WP4:T 4.4
For many years, the sawmilling industry has utilized acoustic technology for lumber
assessment and devices such as the in- line commercialized stress-wave grade sorter
METRIGUARD®
VISCAN®
61. The stress wave velocity measuring system for determination of the mechanical properties of
the log; ultrasound transducer and ultrasound receiver
WP4:T 4.4 sensor position in the intelligent processor head (2)
63. WP4:T4.4 Activities
Kick-off Meeting
8-9/jan/2014
Available acoustic measurement procedures will
be tested in the field:
on the delimbed stem: CNR – Greifenberg
on the cut logs: CNR – KESLA
Additionally measurements will be taken by operators
along the whole supply chain
Acquisition time of measurement, influence of
obstacles and factors limiting instrument performance,
reliability/quality of recorded signals and overall
validation of measurement procedures will be provided
for each field test.
64. Kick-off Meeting
8-9/jan/2014
WP4:T4.4 Challenges
Cope with the factors that might influence acoustic data:
• tree structure :
Anisotropy, local variability, heterogeneity, presence/absence of branches, bark,
etc.
• MC dependent on growing season (sap flow variation), time of measurement from
the felling time, weather and environmental conditions, etc
• Type of sensors/coupling/acquisition setup
• Embodiment of acoustic instruments on a mechanized harvester head
Provide reliable data to be coupled with acoustic data:
i.e. Density, geometrical data, defects, MC, etc.
66. TASK 4.5
Evaluation of cutting process (CP) for the
determination of log/biomass “CP quality index”
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
67. Task 4.5: Cutting Process (CP) for the determination of
log/biomass “CP quality index”
Task Leader: CNR
Task Partecipants: Kesla
Starting : October 2014
Ending: November2015
Estimated person-month = 4.00 (CNR) + 2.00 (Kesla)
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
Kesla : will provide expertise in regard to sensor selection and integration with the processor head +
extensive testing of the prototype
68. 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: July 2015 (M.19)
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: November 2015 (M.23)
69. Task 4.5: cutting process quality index
Timing
Evaluation of cutting process (CP) for the determination of log/biomass “CP quality index”
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.5
D.4.06
D.4.11
finalize concept
design/adopr to the processor
test electronic system
assemble hardware
test hardware + software
callibrate system
develop algorithm for CP Q_index
integrate CP quality index with quality grading/optymization (T4.6) D.4.12
D.4.06 Establishing cutting power measurement protocol
D.4.11 Estimation of log quality by cutting power analysis
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
70. 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
71. Task 4.5: cutting process quality index
Principles
The indicators of cutting forces:
• energy demand
• hydraulic pressure in the saw feed piston
• power consumption
will be collected on-line and regressed to the known log
characteristics.
http://www.youtube.com/watch?v=M3Pm9B5xXaI (ARBRO)
http://www.youtube.com/watch?v=XzaPvftspg0 (KESLA)
72. Task 4.5: cutting process quality index
Delimbing system
Schematic of the de-branching system; cutting knives and hydraulic actuator
73. Task 4.5: cutting process quality index
Chainsaw
the scanning bar #1 and the chain saw in the working positions
74. Task 4.5: cutting process quality index
control system
CRio
cutting force
saw “push” force
feed force
75. Task 4.5: cutting process quality index
Comments
The working principles of the selected processor head (ARBRO
1000) allows direct measurement of the cutting/feed force as
related to (just) the cutting-out branches.
The average density and mechanical resistance will be a result of the
analysis of the chainsaw cutting process.
Estimation of the “CP-branch indicator” will be computed only in
the case of delimbing on the processor head. In this case, it will be
correlated to the “3D-branch indicator” determined from the 3D
stem model of the original standing tree (T4.1).
The information will be forwarded to the server in real-time and will
support final grading of logs.
76. Task 4.5: cutting process quality index
Challenges
What sensors are appropriate for measuring cutting forces in
processor head?
load cell? tensometer? oil pressure? electrical current?
How to 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?
78. TASK 4.6
Implementation of the log/biomass grading
system
Work Package 4: Multi-sensor model-based
quality control of mountain forest production
79. Task 4.6: Implementation of the log/biomass grading
system
Task Leader: CNR
Task Participants: GRAPHITECH, KESLA, MHG, BOKU, GRE, TRE
Starting : June 2014
Ending: July 2016
Estimated person-month = 1.50 (GRAPHITECH) + 2.0 (CNR) + 1.00 (Kesla) + 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, KESLA: 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
80. 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: December 2014 (M.12)
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: July 2016 (M.31)
81. Task 4.6: Implementation of the grading system
Timing
Implementation of the log/biomass grading system
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
T4.6
D.4.01
D.4.12
surveys
literature research
test quality measuring systems
develop software for integration of quality indexes
test software
calibrate system
validate the algorithm/system
D.4.01 Existing grading rules for log/biomass
D.4.12 Implementation and calibration of prediction models for log/biomass quality classes and report on the validation procedure
82. 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
83. Task 4.6: Implementation of the grading system
The concept
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)
84. color cameras for color mapping of log’s sides
Task 4.6: Implementation of the grading system
Other avaliable info (1)
85. multisensor system for 3D/color mapping of logs
Task 4.6: Implementation of the grading system
Other avaliable info (2)
86. Task 4.6: Implementation of the grading system
Results
Several grading rules are in use in different regions and/or niche
products: a systematic database of these rules will be developed for
this purpose.
• The performance
• Reliability
• Repetability
• Flexibility
of the grading system will be carefully validated in order to quantify
advantages from both economic and technical points of view.
at different stages of the value chain.
87. 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?
88. NI CompactRio master
Database
NI CompactRio client Wifi (in field)
FRID
weight
fuel
???
Wifi (home)
Wifi (home)
HD
or
GPRMS
Black box
CP
NIR
HI
SW
camera
kinect
Wifi (in field)
Wifi (home)
Wifi (home)