NUMERICAL METHOD
•Download as PPTX, PDF•
2 likes•442 views
The document discusses a presentation about numerical methods and their real-life applications. It provides two example problems to estimate population and cotton production from census and production data. It then lists several applications of numerical analysis, including modeling ocean currents, combustion flow, aircraft design, and more. Finally, it outlines the bisection method for finding roots of equations, listing the key steps of defining the function, selecting endpoints, calculating the midpoint, and iteratively narrowing the range until the root is found.
Report
Share
Report
Share
Recommended
Real-world Applications of Symbolic Regression
In symbolic regression, we try to find a simple formula to predict the values of a dependent variable from known values of independent variables. Machine learning methods for symbolic regression search the space of possible expressions which can be constructed using basic mathematical operators and functions and optimize numerical parameters of expression based on given input data. Most commonly genetic programming is used to solve this task.
However, when we try to apply this method in real-world applications we find that this approach is limited in several ways (non-determinism, high computational effort, bloated formulas, bad interpolation and even worse extrapolation). In this talk I will discuss possible solutions to these issues which we have developed in our recent research.
Crop identification using geo spatial technologies
Geo spatial technologies can be used to identify crop fields using satellite imagery. Data is collected using satellite images and GPS coordinates of fields. Images are analyzed using techniques like cloud masking, true/false color composites, NDVI, and MSAVI to understand vegetation levels. Thresholding is applied to NDVI and MSAVI values to identify areas as paddy or sugarcane fields. Graphs show the crops' values decrease or increase over months in ways that can distinguish between them. Crop identification through geo spatial analysis is faster and cheaper than field surveys, and helps estimate crop areas for agricultural decision making.
07 a70110 remotesensingandgisapplications
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes questions about rainfall-runoff relationships and models, scanner systems for remote sensing, key aspects of making effective maps from geospatial data, GIS workflow processes and cognitive models, disadvantages of remotely sensed data and physics concepts related to electromagnetic radiation, and questions about photogrammetry applications, raster data models, and comparing aerial photographs to topographic maps.
Real-Time Visual Simulation of Smoke
1) The document describes a real-time GPU implementation of visual smoke simulation using the incompressible Navier-Stokes equations.
2) Key steps in the simulation algorithm include adding forces, advecting velocity and scalar fields, solving for pressure, projecting the velocity field, and applying boundary conditions.
3) Volume rendering is achieved by slicing the 3D grid from the viewer's perspective and compositing the slices using the "under" operator, implementing shadows using half-angle slicing.
geo.admin.ch at the EuroCloud Congress in Luxembourg
geo.admin.ch at the EuroCloud Congress in Luxembourggeoportal of the federal authorities of the Swiss Confederation
swisstopo, the early cloud adopter within the Swiss Public Administration, has started usind cloud services in 2008 and successfully deployed "geo.admin.ch: the Swiss Geoportal" in 2010. The presentation depicts swisstopo's 5 years experience in using cloud services and explains why the "Public Cloud Only" approach has to be extended to an "Open Hybrid Cloud" approach to enable further continued growth and sustainability.
On 15th October 2013 geo.admin.ch, the geoportal of the Swiss Confederation has achieved the 2nd place in the EuroCloud Award 2013 in the category of «Best Cloud Service Use Case Public Sector» (the award ceremony took place in the context of the EuroCloud Congress in Luxembourg on October 15th 2013). This award honors the most innovative cloud solution characterized by originality, innovation, creativity and efficiency.
2017 ASPRS-RMR Big Data Track: A Geospatial Picture of 1959 Albuquerque, New ...
This document summarizes the process of constructing a historical GIS resource from 1959 aerial photography of Albuquerque, New Mexico. It describes scanning over 300 prints and 800 film exposures from 1959 to create digital images. Photo control points were used to georeference the images. Image processing techniques like dodging were used to improve the scans. Stereo triangulation was used to match tie points between photos and adjust them to an absolute coordinate system. A digital elevation model from 2010 LiDAR was used for elevation control in orthorectifying the historical photos. The rectified photos were mosaicked together to create a seamless aerial view of Albuquerque from 1959 within a GIS framework.
Munich Airport
The document contains numbers that appear to represent forecast and observed values over multiple years. It includes a title indicating it relates to low visibility forecasts at Munich Airport from 2009 to 2018, with the numbers potentially representing the number of forecasts issued and actual occurrences.
TEAM 3: Open Land Use for Africa (OLU4Africa)
The document discusses creating an open land use map for Africa using machine learning and satellite imagery. It describes how land use maps help with spatial planning and management, and that open data is scarce in Africa compared to Europe. The project aims to classify satellite imagery into land use classes using TensorFlow and convolutional neural networks (CNNs). Sample images were collected for classes like airports and residences from Sentinel-2. Models were trained but did not perform well, needing more diverse training data to correctly identify land uses in new images. Improving data collection and selecting a more suitable CNN model are next steps.
Recommended
Real-world Applications of Symbolic Regression
In symbolic regression, we try to find a simple formula to predict the values of a dependent variable from known values of independent variables. Machine learning methods for symbolic regression search the space of possible expressions which can be constructed using basic mathematical operators and functions and optimize numerical parameters of expression based on given input data. Most commonly genetic programming is used to solve this task.
However, when we try to apply this method in real-world applications we find that this approach is limited in several ways (non-determinism, high computational effort, bloated formulas, bad interpolation and even worse extrapolation). In this talk I will discuss possible solutions to these issues which we have developed in our recent research.
Crop identification using geo spatial technologies
Geo spatial technologies can be used to identify crop fields using satellite imagery. Data is collected using satellite images and GPS coordinates of fields. Images are analyzed using techniques like cloud masking, true/false color composites, NDVI, and MSAVI to understand vegetation levels. Thresholding is applied to NDVI and MSAVI values to identify areas as paddy or sugarcane fields. Graphs show the crops' values decrease or increase over months in ways that can distinguish between them. Crop identification through geo spatial analysis is faster and cheaper than field surveys, and helps estimate crop areas for agricultural decision making.
07 a70110 remotesensingandgisapplications
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes questions about rainfall-runoff relationships and models, scanner systems for remote sensing, key aspects of making effective maps from geospatial data, GIS workflow processes and cognitive models, disadvantages of remotely sensed data and physics concepts related to electromagnetic radiation, and questions about photogrammetry applications, raster data models, and comparing aerial photographs to topographic maps.
Real-Time Visual Simulation of Smoke
1) The document describes a real-time GPU implementation of visual smoke simulation using the incompressible Navier-Stokes equations.
2) Key steps in the simulation algorithm include adding forces, advecting velocity and scalar fields, solving for pressure, projecting the velocity field, and applying boundary conditions.
3) Volume rendering is achieved by slicing the 3D grid from the viewer's perspective and compositing the slices using the "under" operator, implementing shadows using half-angle slicing.
geo.admin.ch at the EuroCloud Congress in Luxembourg
geo.admin.ch at the EuroCloud Congress in Luxembourggeoportal of the federal authorities of the Swiss Confederation
swisstopo, the early cloud adopter within the Swiss Public Administration, has started usind cloud services in 2008 and successfully deployed "geo.admin.ch: the Swiss Geoportal" in 2010. The presentation depicts swisstopo's 5 years experience in using cloud services and explains why the "Public Cloud Only" approach has to be extended to an "Open Hybrid Cloud" approach to enable further continued growth and sustainability.
On 15th October 2013 geo.admin.ch, the geoportal of the Swiss Confederation has achieved the 2nd place in the EuroCloud Award 2013 in the category of «Best Cloud Service Use Case Public Sector» (the award ceremony took place in the context of the EuroCloud Congress in Luxembourg on October 15th 2013). This award honors the most innovative cloud solution characterized by originality, innovation, creativity and efficiency.
2017 ASPRS-RMR Big Data Track: A Geospatial Picture of 1959 Albuquerque, New ...
This document summarizes the process of constructing a historical GIS resource from 1959 aerial photography of Albuquerque, New Mexico. It describes scanning over 300 prints and 800 film exposures from 1959 to create digital images. Photo control points were used to georeference the images. Image processing techniques like dodging were used to improve the scans. Stereo triangulation was used to match tie points between photos and adjust them to an absolute coordinate system. A digital elevation model from 2010 LiDAR was used for elevation control in orthorectifying the historical photos. The rectified photos were mosaicked together to create a seamless aerial view of Albuquerque from 1959 within a GIS framework.
Munich Airport
The document contains numbers that appear to represent forecast and observed values over multiple years. It includes a title indicating it relates to low visibility forecasts at Munich Airport from 2009 to 2018, with the numbers potentially representing the number of forecasts issued and actual occurrences.
TEAM 3: Open Land Use for Africa (OLU4Africa)
The document discusses creating an open land use map for Africa using machine learning and satellite imagery. It describes how land use maps help with spatial planning and management, and that open data is scarce in Africa compared to Europe. The project aims to classify satellite imagery into land use classes using TensorFlow and convolutional neural networks (CNNs). Sample images were collected for classes like airports and residences from Sentinel-2. Models were trained but did not perform well, needing more diverse training data to correctly identify land uses in new images. Improving data collection and selecting a more suitable CNN model are next steps.
2018 GIS in the Rockies Vendor Showcase (Th): ERDAS Imagine What's New and Ti...
This presentation will cover the latest release highlights as well as tips and tricks for processing LiDAR data, ERDAS Imagine modeling capabilities and a roadmap for cloud based processing.
The session will highlight exploiting the full spectrum of LiDAR from viewing and measurements to surface and terrain modeling as well as extraction of point clouds from imagery.
In addition we will discuss the migration of our image exploitation capabilities from the desktop to the cloud.
Multiple Quantile Fourier Neural Network
1. The document presents a new Multiple Quantile Fourier Neural Network approach for probabilistic time series forecasting. It estimates composite quantiles using a Fourier neural network that models trends, cycles and seasonality.
2. The goals are to demonstrate modeling of periodic and non-periodic components, conduct multi-step probabilistic forecasts, and validate the approach through experiments.
3. Case studies on various time series datasets show the Quantile Fourier Neural Network outperforms other methods like QRNN, SARIMA and ETS in reliability and sharpness of probabilistic forecasts.
Spatial presentation of prognosis models in plant protection
This document discusses the development and use of risk maps for plant protection models. Models are enhanced using geostatistics to interpolate meteorological data across 1,367 virtual stations, allowing risk maps with grids of 1 km2. These maps ease the interpretation of model results across large regions. New technologies bring high resolution spatial meteorological data and mobile internet access to enable location-based decision support services online. Risk maps and online access optimize the provision of millions of model runs to farmers and advisors.
Karnaugh map program Assignment
The document describes a C++ program that implements a Karnaugh map to simplify boolean algebra expressions. The program takes in 16 binary inputs from the user, arranges them in a 4x4 Karnaugh map table, and checks for combinations of 1s in order to output a simplified boolean expression. If no combinations are found, it outputs 0.
G6 m3-c-lesson 17-s
This document contains a lesson on drawing coordinate planes and plotting points on them. It includes 4 examples of drawing coordinate planes with different scales for the x- and y-axes and plotting points. The lesson emphasizes properly scaling the axes to show all points based on their ranges. Students are asked to label coordinate planes and plot points for 2 problems.
Leland Wilkinson, H2O.ai - The Grammar of Graphics and the Future of Big Data...
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/1X93Sum_SyM
The Grammar of Graphics and the Future of Big Data Visualization
This year marks the twentieth anniversary of the first edition of The Grammar of Graphics (GG). The book laid the foundation for a major visualization component at the statistical software company SPSS. Since that edition, a commercial company, Tableau, evolved from a Stanford seminar and dissertation devoted to the book. Not long after, the widely used open-source visualization package, ggplot2, arose from a dissertation at Iowa State University. GG not only provided for the first time a formal mathematical foundation for generating statistical charts, but also introduced a wider range of graphics than seen in previous graphical systems. This talk will briefly review that history and then outline recent efforts at H2O to apply GG to very large datasets where classical rendering and analytic methods are infeasible.
Bio: Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.
Processing Large ToF-SIMS Datasets
This document discusses processing large time-of-flight secondary ion mass spectrometry (ToF-SIMS) datasets. ToF-SIMS spectrometers can generate hyperspectral image datasets containing millions of voxels and spectral channels. Non-negative matrix factorization (NMF) was performed on an unbinned ToF-SIMS dataset containing over 8 trillion data points from a fingerprint contamination sample. Subsampling was used to address memory limitations. NMF separated fingerprint components from silicon peaks and identified systematic peak misalignments. MapReduce processing is proposed for even larger datasets to distribute computations across nodes.
I²: Interactive Real-Time Visualization for Streaming Data
This is our poster for the demonstration "I²: Interactive Real-Time Visualization for Streaming Data" which was awarded as best demonstration at EDBT 2017.
The paper and the source code are available on GitHub: https://github.com/TU-Berlin-DIMA/i2
Integrating eo with official statistics using machine learning in mexico geo ...
Integrating eo with official statistics using machine learning in mexico geo ...Abel Alejandro Coronado Iruegas
Sharing the experience and results of using georeferenced 2010 Census data in Mexico and EO to train algorithms in order to detect urban growth and generate useful information for estimating population for non-census years.Seminar psu 21.10.2013 financial bubble diagnostics based on log-periodic p...
This document discusses methods for identifying and modeling financial bubbles using a log-periodic power law model. It provides background on several historical financial bubbles, including the Dutch tulip bubble in the 1630s, the US stock market crash of 1929, and others. It then introduces the log-periodic power law model developed by Sornette and others to model speculative bubbles. The document explains the mathematical components of the LPPL model, including the power law and log-periodic components, and describes methods for estimating the parameters of the LPPL model to identify bubbles and obtain a predicted crash time.
Machine learning and Satellite Images
We use the Georeferenced results of the 2010 Census in Mexico to train machine learning algorithms to detect growth in cities and contribute new information to estimate the total population.
KatRisk RAA 2018 Highlights
Presentation about our probabilistic coupled inland flood, hurricane wind and storm surge model. We discuss Harvey losses, SpatialKat runtimes, and show for the first time industry wide EP curves for all perils combined and by themselves. Highlights also include our climate sensitivity and climate variability modeling, another industry first. Comments welcome.
AI in Aerospace
Artificial intelligence can help improve fuel efficiency in aerospace in three key ways:
1) AI tools can optimize climb profiles for pilots to save up to 6% of fuel during climbs.
2) AI algorithms can help designers explore design options faster to integrate more functionality while lowering fuel consumption and carbon footprints.
3) Virtual reality and AI-enabled cockpit systems can help improve pilot training and ensure safety by monitoring fuel, system status, and weather conditions.
Lppl models MiFIT 2013: Vyacheslav Arbuzov
This document discusses log-periodic power law (LPPL) models for analyzing financial bubbles and crashes. It provides background on the development of LPPL models, including their origins in the study of phase transitions in physics. Several examples are given of different LPPL models and the parameters used in the models. Estimation techniques for LPPL models including filtering and optimizing parameters are also summarized.
KatRisk SpatialKat US Flood Model
These are our introduction slides for the Ariel Re / Lloyd's / Argo US flood model comparison workshop.
Single Line Diagram
This single line diagram shows the electrical infrastructure for the Scholl Canyon Landfill Power Plant. It includes 4 generators labeled GEN-1 and connects them to the Scholl Canyon Substation. The diagram is included in Appendix C of the document.
SFScon21 - Daniel Frisinghelli - The Cost of Traditional Machine Learning and...
SFScon21 - Daniel Frisinghelli - The Cost of Traditional Machine Learning and...South Tyrol Free Software Conference
A Case Study of the AI4EBV Project
Machine learning and deep learning methods are widely used in the field of Earth observation, both for image classification and image segmentation tasks. However, the implementation of these methods is often limited by the available computational resources, especially when applied to large-scale high-resolution imagery. Within the scope of the AI4EBV project (https://ai4ebv.eurac.edu/), funded by the Group on Earth Observation Biodiversity Observation Network (https://geobon.org/) and Microsoft, we are assessing the computational cost and accuracy of different machine learning and deep learning models for the task of multispectral image segmentation. We are integrating open digital elevation model and multitemporal Earth observation data, to derive general land cover at a spatial resolution of 30m for the entire European Alps throughout the years 2016-2020. We focus on comparing the accuracy and cost of advanced feature extracting deep learning algorithms (e.g., deep convolutional neural networks) with traditional feature-based machine learning algorithms (e.g., random forests, support vector machines). The algorithms are written in Python-3 and are based on existing open-source frameworks for scalable data analytics (numpy, xarray, and dask) and machine learning (scikit-learn and PyTorch). The developed algorithms are highly optimized, allowing for large-scale image analysis on both multiple CPUs and GPUs in parallel.STARS_AGU_2015_Poster_jnagol_Final
This document summarizes a study using multi-temporal UAV data to map crop types for smallholder farms in Tanzania. UAVs with RGB and NIR cameras were used to collect imagery over 1km2 plots in different regions of Tanzania on a monthly basis during the 2015 growing season. An object-based image analysis workflow including segmentation, classification and accuracy assessment was able to map maize fields with an overall accuracy of 97.1% and kappa of 86.2%. The study demonstrates the potential of low-cost UAVs to support crop monitoring, but challenges include cloud shadows, coregistering mosaics from uncalibrated sensors, and time required for data harmonization.
DSD-INT 2018 The new 3D Subsurface tool - Vermeulen
Presentation by Peter Vermeulen (Deltares) at the iMOD International User Day 2018, during Delft Software Days - Edition 2018. Tuesday 13 November 2018, Delft.
07 a70401 remotesensingandgisapplications
1. The document contains exam questions for a Remote Sensing and GIS Applications course. It includes questions on topics like the basic components of a remote sensing system, geographical techniques for groundwater exploration, photogrammetric activities, and using remote sensing for drought assessment and monitoring.
2. Other questions cover topics such as vector data models, data translation and transformation in GIS, instruments used for visual image interpretation, and the differences between file structures like simple lists and ordered sequential files.
3. The document provides exam questions for students testing their knowledge of key concepts and applications in remote sensing and GIS.
RJW Odyssey the 1980s
This document summarizes the career and work of Dr. Bob Williams from 1980 to the present. It outlines his educational background and qualifications in fields like cartography, surveying, and geospatial science. It then describes several projects he worked on in the 1980s that demonstrated innovative and visionary ideas for automated cartography and geographic information systems. These included early digital mapping software called MAPPACK, YAMPI, MISTI, and WIMS that allowed for automated map generation and querying of geographic databases. The document discusses how these projects foreshadowed future applications in areas like navigation, infrastructure management, and command/control systems.
History
- The document provides a brief history of computing from ancient times to modern computers. It discusses early calculating devices like the abacus and how they evolved over time.
- Important figures that advanced computing are mentioned, like Charles Babbage who designed analytical engines, Ada Lovelace who wrote about programming the analytical engine, and Herman Hollerith who developed tabulating machines.
- Milestones like the first general purpose electronic computer (ENIAC), stored program concept, and first commercial computers are summarized. The evolution of computers from mainframes to minicomputers to microprocessors is covered at a high level.
More Related Content
What's hot
2018 GIS in the Rockies Vendor Showcase (Th): ERDAS Imagine What's New and Ti...
This presentation will cover the latest release highlights as well as tips and tricks for processing LiDAR data, ERDAS Imagine modeling capabilities and a roadmap for cloud based processing.
The session will highlight exploiting the full spectrum of LiDAR from viewing and measurements to surface and terrain modeling as well as extraction of point clouds from imagery.
In addition we will discuss the migration of our image exploitation capabilities from the desktop to the cloud.
Multiple Quantile Fourier Neural Network
1. The document presents a new Multiple Quantile Fourier Neural Network approach for probabilistic time series forecasting. It estimates composite quantiles using a Fourier neural network that models trends, cycles and seasonality.
2. The goals are to demonstrate modeling of periodic and non-periodic components, conduct multi-step probabilistic forecasts, and validate the approach through experiments.
3. Case studies on various time series datasets show the Quantile Fourier Neural Network outperforms other methods like QRNN, SARIMA and ETS in reliability and sharpness of probabilistic forecasts.
Spatial presentation of prognosis models in plant protection
This document discusses the development and use of risk maps for plant protection models. Models are enhanced using geostatistics to interpolate meteorological data across 1,367 virtual stations, allowing risk maps with grids of 1 km2. These maps ease the interpretation of model results across large regions. New technologies bring high resolution spatial meteorological data and mobile internet access to enable location-based decision support services online. Risk maps and online access optimize the provision of millions of model runs to farmers and advisors.
Karnaugh map program Assignment
The document describes a C++ program that implements a Karnaugh map to simplify boolean algebra expressions. The program takes in 16 binary inputs from the user, arranges them in a 4x4 Karnaugh map table, and checks for combinations of 1s in order to output a simplified boolean expression. If no combinations are found, it outputs 0.
G6 m3-c-lesson 17-s
This document contains a lesson on drawing coordinate planes and plotting points on them. It includes 4 examples of drawing coordinate planes with different scales for the x- and y-axes and plotting points. The lesson emphasizes properly scaling the axes to show all points based on their ranges. Students are asked to label coordinate planes and plot points for 2 problems.
Leland Wilkinson, H2O.ai - The Grammar of Graphics and the Future of Big Data...
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/1X93Sum_SyM
The Grammar of Graphics and the Future of Big Data Visualization
This year marks the twentieth anniversary of the first edition of The Grammar of Graphics (GG). The book laid the foundation for a major visualization component at the statistical software company SPSS. Since that edition, a commercial company, Tableau, evolved from a Stanford seminar and dissertation devoted to the book. Not long after, the widely used open-source visualization package, ggplot2, arose from a dissertation at Iowa State University. GG not only provided for the first time a formal mathematical foundation for generating statistical charts, but also introduced a wider range of graphics than seen in previous graphical systems. This talk will briefly review that history and then outline recent efforts at H2O to apply GG to very large datasets where classical rendering and analytic methods are infeasible.
Bio: Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.
Processing Large ToF-SIMS Datasets
This document discusses processing large time-of-flight secondary ion mass spectrometry (ToF-SIMS) datasets. ToF-SIMS spectrometers can generate hyperspectral image datasets containing millions of voxels and spectral channels. Non-negative matrix factorization (NMF) was performed on an unbinned ToF-SIMS dataset containing over 8 trillion data points from a fingerprint contamination sample. Subsampling was used to address memory limitations. NMF separated fingerprint components from silicon peaks and identified systematic peak misalignments. MapReduce processing is proposed for even larger datasets to distribute computations across nodes.
I²: Interactive Real-Time Visualization for Streaming Data
This is our poster for the demonstration "I²: Interactive Real-Time Visualization for Streaming Data" which was awarded as best demonstration at EDBT 2017.
The paper and the source code are available on GitHub: https://github.com/TU-Berlin-DIMA/i2
Integrating eo with official statistics using machine learning in mexico geo ...
Integrating eo with official statistics using machine learning in mexico geo ...Abel Alejandro Coronado Iruegas
Sharing the experience and results of using georeferenced 2010 Census data in Mexico and EO to train algorithms in order to detect urban growth and generate useful information for estimating population for non-census years.Seminar psu 21.10.2013 financial bubble diagnostics based on log-periodic p...
This document discusses methods for identifying and modeling financial bubbles using a log-periodic power law model. It provides background on several historical financial bubbles, including the Dutch tulip bubble in the 1630s, the US stock market crash of 1929, and others. It then introduces the log-periodic power law model developed by Sornette and others to model speculative bubbles. The document explains the mathematical components of the LPPL model, including the power law and log-periodic components, and describes methods for estimating the parameters of the LPPL model to identify bubbles and obtain a predicted crash time.
Machine learning and Satellite Images
We use the Georeferenced results of the 2010 Census in Mexico to train machine learning algorithms to detect growth in cities and contribute new information to estimate the total population.
KatRisk RAA 2018 Highlights
Presentation about our probabilistic coupled inland flood, hurricane wind and storm surge model. We discuss Harvey losses, SpatialKat runtimes, and show for the first time industry wide EP curves for all perils combined and by themselves. Highlights also include our climate sensitivity and climate variability modeling, another industry first. Comments welcome.
AI in Aerospace
Artificial intelligence can help improve fuel efficiency in aerospace in three key ways:
1) AI tools can optimize climb profiles for pilots to save up to 6% of fuel during climbs.
2) AI algorithms can help designers explore design options faster to integrate more functionality while lowering fuel consumption and carbon footprints.
3) Virtual reality and AI-enabled cockpit systems can help improve pilot training and ensure safety by monitoring fuel, system status, and weather conditions.
Lppl models MiFIT 2013: Vyacheslav Arbuzov
This document discusses log-periodic power law (LPPL) models for analyzing financial bubbles and crashes. It provides background on the development of LPPL models, including their origins in the study of phase transitions in physics. Several examples are given of different LPPL models and the parameters used in the models. Estimation techniques for LPPL models including filtering and optimizing parameters are also summarized.
KatRisk SpatialKat US Flood Model
These are our introduction slides for the Ariel Re / Lloyd's / Argo US flood model comparison workshop.
Single Line Diagram
This single line diagram shows the electrical infrastructure for the Scholl Canyon Landfill Power Plant. It includes 4 generators labeled GEN-1 and connects them to the Scholl Canyon Substation. The diagram is included in Appendix C of the document.
SFScon21 - Daniel Frisinghelli - The Cost of Traditional Machine Learning and...
SFScon21 - Daniel Frisinghelli - The Cost of Traditional Machine Learning and...South Tyrol Free Software Conference
A Case Study of the AI4EBV Project
Machine learning and deep learning methods are widely used in the field of Earth observation, both for image classification and image segmentation tasks. However, the implementation of these methods is often limited by the available computational resources, especially when applied to large-scale high-resolution imagery. Within the scope of the AI4EBV project (https://ai4ebv.eurac.edu/), funded by the Group on Earth Observation Biodiversity Observation Network (https://geobon.org/) and Microsoft, we are assessing the computational cost and accuracy of different machine learning and deep learning models for the task of multispectral image segmentation. We are integrating open digital elevation model and multitemporal Earth observation data, to derive general land cover at a spatial resolution of 30m for the entire European Alps throughout the years 2016-2020. We focus on comparing the accuracy and cost of advanced feature extracting deep learning algorithms (e.g., deep convolutional neural networks) with traditional feature-based machine learning algorithms (e.g., random forests, support vector machines). The algorithms are written in Python-3 and are based on existing open-source frameworks for scalable data analytics (numpy, xarray, and dask) and machine learning (scikit-learn and PyTorch). The developed algorithms are highly optimized, allowing for large-scale image analysis on both multiple CPUs and GPUs in parallel.STARS_AGU_2015_Poster_jnagol_Final
This document summarizes a study using multi-temporal UAV data to map crop types for smallholder farms in Tanzania. UAVs with RGB and NIR cameras were used to collect imagery over 1km2 plots in different regions of Tanzania on a monthly basis during the 2015 growing season. An object-based image analysis workflow including segmentation, classification and accuracy assessment was able to map maize fields with an overall accuracy of 97.1% and kappa of 86.2%. The study demonstrates the potential of low-cost UAVs to support crop monitoring, but challenges include cloud shadows, coregistering mosaics from uncalibrated sensors, and time required for data harmonization.
DSD-INT 2018 The new 3D Subsurface tool - Vermeulen
Presentation by Peter Vermeulen (Deltares) at the iMOD International User Day 2018, during Delft Software Days - Edition 2018. Tuesday 13 November 2018, Delft.
What's hot (19)
2018 GIS in the Rockies Vendor Showcase (Th): ERDAS Imagine What's New and Ti...
2018 GIS in the Rockies Vendor Showcase (Th): ERDAS Imagine What's New and Ti...
Spatial presentation of prognosis models in plant protection
Spatial presentation of prognosis models in plant protection
Leland Wilkinson, H2O.ai - The Grammar of Graphics and the Future of Big Data...
Leland Wilkinson, H2O.ai - The Grammar of Graphics and the Future of Big Data...
I²: Interactive Real-Time Visualization for Streaming Data
I²: Interactive Real-Time Visualization for Streaming Data
Integrating eo with official statistics using machine learning in mexico geo ...
Integrating eo with official statistics using machine learning in mexico geo ...
Seminar psu 21.10.2013 financial bubble diagnostics based on log-periodic p...
Seminar psu 21.10.2013 financial bubble diagnostics based on log-periodic p...
SFScon21 - Daniel Frisinghelli - The Cost of Traditional Machine Learning and...
SFScon21 - Daniel Frisinghelli - The Cost of Traditional Machine Learning and...
DSD-INT 2018 The new 3D Subsurface tool - Vermeulen
DSD-INT 2018 The new 3D Subsurface tool - Vermeulen
Similar to NUMERICAL METHOD
07 a70401 remotesensingandgisapplications
1. The document contains exam questions for a Remote Sensing and GIS Applications course. It includes questions on topics like the basic components of a remote sensing system, geographical techniques for groundwater exploration, photogrammetric activities, and using remote sensing for drought assessment and monitoring.
2. Other questions cover topics such as vector data models, data translation and transformation in GIS, instruments used for visual image interpretation, and the differences between file structures like simple lists and ordered sequential files.
3. The document provides exam questions for students testing their knowledge of key concepts and applications in remote sensing and GIS.
RJW Odyssey the 1980s
This document summarizes the career and work of Dr. Bob Williams from 1980 to the present. It outlines his educational background and qualifications in fields like cartography, surveying, and geospatial science. It then describes several projects he worked on in the 1980s that demonstrated innovative and visionary ideas for automated cartography and geographic information systems. These included early digital mapping software called MAPPACK, YAMPI, MISTI, and WIMS that allowed for automated map generation and querying of geographic databases. The document discusses how these projects foreshadowed future applications in areas like navigation, infrastructure management, and command/control systems.
History
- The document provides a brief history of computing from ancient times to modern computers. It discusses early calculating devices like the abacus and how they evolved over time.
- Important figures that advanced computing are mentioned, like Charles Babbage who designed analytical engines, Ada Lovelace who wrote about programming the analytical engine, and Herman Hollerith who developed tabulating machines.
- Milestones like the first general purpose electronic computer (ENIAC), stored program concept, and first commercial computers are summarized. The evolution of computers from mainframes to minicomputers to microprocessors is covered at a high level.
History of computer
- The document provides a brief history of computing from ancient times to modern computers. It discusses early computing devices like the abacus and how they evolved over time.
- It describes pioneers in computing like Charles Babbage and Ada Lovelace and their work on early mechanical computers in the 1800s. It also covers the development of electromechanical computers in the 1900s and early electronic digital computers in the 1940s.
- The summary highlights the development of stored-program architecture and programming languages as well as the emergence of mainframe, mini, and microcomputers that drove computing innovation from the 1940s through the 1970s.
Geography fm 4
This 3-hour exam consists of 4 sections - A, B, C, and D. Section A contains 10 multiple choice questions on physical and mathematical geography. Section B contains 3 questions on statistics, research, and surveying. Section C contains 2 questions requiring analysis of a map extract and photograph. Section D contains 2 parts with 2 questions each, on regional focal studies and environmental issues. Calculators are not allowed. The instructor wishes all students the best of luck on the exam.
Remotesensingandgisapplications
This document contains exam questions for a Remote Sensing and GIS Applications course. It includes 8 questions related to topics like remote sensing systems, GIS components and data structures, photogrammetry, natural disasters, and watershed drainage patterns. Students are instructed to answer 5 of the 8 questions, which vary in length from short answer to longer explanations and include diagrams/flowcharts. Key remote sensing and GIS concepts covered include raster vs vector data, active vs passive systems, interpolation methods, and using the technologies for applications like flood mapping and groundwater analysis.
2nd midterm gla university
This document contains questions for a mid-term exam on various topics related to soft computing.
The questions are divided into three sections:
Section A contains 4 multiple choice questions related to fuzzy set theory and operations. Section B contains 3 short answer questions on fuzzy sets, membership functions, and fuzzification.
Section C contains 2 longer theoretical questions, one involving sketching membership functions and defining 1-cut sets, and another involving fuzzy relations composition and cut sets.
The document provides guidance on answering questions from each section and notes that parts of questions should be answered together and diagrams can be used when needed. It also mentions that missing data can be assumed with justification.
Mst 1 question paper(bt401) cse
This document contains a mid-semester examination for the subject of Engineering Mathematics - III. The exam contains 4 questions worth a total of 28 marks. Question 1 has two parts that involve finding real roots of equations using the Regula Falsi and Newton-Raphson methods. Question 2 has two parts involving Newton's interpolation formula and Newton's divided difference formula. Question 3 has two parts, one requiring finding a cubic polynomial and the other evaluating a definite integral using different numerical methods. Question 4 has two parts involving solving equations using Crout's method and Gauss-Jacobi method.
DATA ANALYSIS
DATA ANALYSIS, CORE DATA ANALYSIS, 1D ANALYSIS, 2D ANALYSIS, MULTIVARIATE CORRELATION, PRINCIPAL COMPONENT ANALYSIS, CLUSTER ANALYSIS, QUANTITATIVE ANALYSIS, QUALITATIVE ANALYSIS, etc.
Application of Morphological Representation to Enhance the Visualization of D...
"Application of Morphological Representation to Enhance the Visualization of Dynamic Weather Maps" - Presentation at the 2007 American Association of Geographers Conference.
Similar to NUMERICAL METHOD (10)
Application of Morphological Representation to Enhance the Visualization of D...
Application of Morphological Representation to Enhance the Visualization of D...
Recently uploaded
UNLOCKING HEALTHCARE 4.0: NAVIGATING CRITICAL SUCCESS FACTORS FOR EFFECTIVE I...
The Fourth Industrial Revolution is transforming industries, including healthcare, by integrating digital,
physical, and biological technologies. This study examines the integration of 4.0 technologies into
healthcare, identifying success factors and challenges through interviews with 70 stakeholders from 33
countries. Healthcare is evolving significantly, with varied objectives across nations aiming to improve
population health. The study explores stakeholders' perceptions on critical success factors, identifying
challenges such as insufficiently trained personnel, organizational silos, and structural barriers to data
exchange. Facilitators for integration include cost reduction initiatives and interoperability policies.
Technologies like IoT, Big Data, AI, Machine Learning, and robotics enhance diagnostics, treatment
precision, and real-time monitoring, reducing errors and optimizing resource utilization. Automation
improves employee satisfaction and patient care, while Blockchain and telemedicine drive cost reductions.
Successful integration requires skilled professionals and supportive policies, promising efficient resource
use, lower error rates, and accelerated processes, leading to optimized global healthcare outcomes.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting
The Python for beginners. This is an advance computer language.
Python language is very important language at this time. we can easily understand this language by these notes.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
Comparative analysis between traditional aquaponics and reconstructed aquapon...
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Recently uploaded (20)
UNLOCKING HEALTHCARE 4.0: NAVIGATING CRITICAL SUCCESS FACTORS FOR EFFECTIVE I...
UNLOCKING HEALTHCARE 4.0: NAVIGATING CRITICAL SUCCESS FACTORS FOR EFFECTIVE I...
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
john krisinger-the science and history of the alcoholic beverage.pptx
john krisinger-the science and history of the alcoholic beverage.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptx
gray level transformation unit 3(image processing))
gray level transformation unit 3(image processing))
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
NUMERICAL METHOD
- 2. My situation Class start time After mid Before final
- 3. Presentration topic NUMERICAL METHOD(The application of numerical methods in real life)
- 4. Some problem 1. The population of a village in the last six censuses was recorded as follows.Estimate the population for the year 1945. 2. Estimate the production of cotton in the year 1935 from the data given below: YAER 1941 1951 1961 1971 1981 1991 Popul ation 2500 2800 3200 3700 4350 5225 Year (x) 1931 1932 1934 1936 1938 Produc tion (y) 17.1 13.0 14 9.6 12.4
- 5. Real life application of Mathmatics in Numerical Analysis Estimation of ocean currents Modeling combustion flow in a coal power plant Shuttle/tank separation Scientific programming Modeling of airflow over airplane bodies Electromagnetics analysis for detection by radar Design and analysis of control systems for aircraft Electromagnetics Large scale shock wave physics code development Curve fitting of tabular data
- 6. Bisection method 1. Define F(x) 2. Read ‘a’ 3. Read ‘b’ 4. Set k=1 5. Calculate x=(A+B)/2 6. Calculate f=f(x) 7. Print k,x,f 8. 0001.0|| 1 kk xxif 0001.0|| 1 kk xxif
- 7. Then Goto Step 11 Else if F(a). f<0 then b=x Else F(b). F<0 then a=x Endif 9. Set k=k+1 10. Goto step 5 11. Print x 12. stop
- 10. What are the advantages of numerical method ?
- 11. Thank you sir