CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
"Outlier detection of point clouds generating from low-cost UAVs for bridge i...TRUSS ITN
Using an Unmanned Aerial Vehicle (UAV) for documentation and inspection of civil infrastructures has become increasingly popular. One area of interest is in bridge inspection as it holds the potential of being safer, more economical, and less disruptive, with respect to traffic flow. With 3D reconstruction method, structural deficiencies and 3D models can be obtained from a 3D point cloud generated from UAV imagery data. However, shadows and water reflectivity may affect the quality of the point cloud generated from images, which causes difficulty in data processing. This paper presents a detailed workflow of removing outlier data points through the statistical filter and geometric-based filter. The experimental results showed that the statistical filter gives the best performance.
Machine learning for decentralized and flying radio devicesITU
This presentation discusses matters of machine learning for decentralized and flying radio devices. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx
Join our upcoming forums and workshops here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/Pages/default.aspx
https://www.slideshare.net/ITU/ai-and-machine-learning
Context Driven Delivery of Aeronautical InformationWen Zhu
Presentation at Air Transportation Information Exchange Conference
Today, mission planners and pilots must access and process an overwhelming volume of information (e.g. NOTAMs, special activity airspace data, airport data and weather), of which only a small portion of that information is relevant to the mission. In this presentation, we will demonstrate a Semantic Decision Support Tool (SDST) that pulls together information from disparate sources and delivers context-specific information to an end user (e.g. a pilot or an air traffic controller) based on, among other things, flight mission, phase of flight, and type of aircraft.
Dear Colleagues,
Call for papers for another Machine Learning special issue of SEG/AAPG Journal of Interpretation focusing on the Seismic Data Analysis has been announced.
We look forward to your contribution.
Vikram Jayaram
Special Section Editor
Interpretation
EDF2014: Talk of Axel Polleres, Full Professor, WU - Vienna University of Eco...European Data Forum
Selected Talk of Axel Polleres, Full Professor, WU - Vienna University of Economics and Business, Austria at the European Data Forum 2014, 19 March 2014 in Athens, Greece: City Data Pipeline - A report about experiences from using Open Data to gather indicators of city performance
The document discusses the goals and key requirements of scalable trajectory data analysis systems. It provides an overview of state-of-the-art technologies for analyzing large transportation datasets including Elastic Stack, Geomondrian, Leaflet, and Neo4j. It proposes using a graph database approach combined with Apache Spark for scalable processing and analyzing trajectory patterns from large datasets. Future work includes benchmarking graph databases and combining multiple datasets.
Geographical Map Annotation With Social Metadata In a Surveillance EnvironmentElena Roglia
The document discusses annotating geographical maps with social metadata from a surveillance environment. It proposes extracting significant tags from maps to enrich them using a statistical method. A metadata retrieval and search module is developed to allow operators to view historical metadata and suggest new annotations. Case studies applying the tag extraction method to maps of Turin and the Everest area are analyzed. Future work involves standardizing tags, relating similar annotations, and using data mining to integrate web resources for tagging maps from sensor data.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
"Outlier detection of point clouds generating from low-cost UAVs for bridge i...TRUSS ITN
Using an Unmanned Aerial Vehicle (UAV) for documentation and inspection of civil infrastructures has become increasingly popular. One area of interest is in bridge inspection as it holds the potential of being safer, more economical, and less disruptive, with respect to traffic flow. With 3D reconstruction method, structural deficiencies and 3D models can be obtained from a 3D point cloud generated from UAV imagery data. However, shadows and water reflectivity may affect the quality of the point cloud generated from images, which causes difficulty in data processing. This paper presents a detailed workflow of removing outlier data points through the statistical filter and geometric-based filter. The experimental results showed that the statistical filter gives the best performance.
Machine learning for decentralized and flying radio devicesITU
This presentation discusses matters of machine learning for decentralized and flying radio devices. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx
Join our upcoming forums and workshops here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/Pages/default.aspx
https://www.slideshare.net/ITU/ai-and-machine-learning
Context Driven Delivery of Aeronautical InformationWen Zhu
Presentation at Air Transportation Information Exchange Conference
Today, mission planners and pilots must access and process an overwhelming volume of information (e.g. NOTAMs, special activity airspace data, airport data and weather), of which only a small portion of that information is relevant to the mission. In this presentation, we will demonstrate a Semantic Decision Support Tool (SDST) that pulls together information from disparate sources and delivers context-specific information to an end user (e.g. a pilot or an air traffic controller) based on, among other things, flight mission, phase of flight, and type of aircraft.
Dear Colleagues,
Call for papers for another Machine Learning special issue of SEG/AAPG Journal of Interpretation focusing on the Seismic Data Analysis has been announced.
We look forward to your contribution.
Vikram Jayaram
Special Section Editor
Interpretation
EDF2014: Talk of Axel Polleres, Full Professor, WU - Vienna University of Eco...European Data Forum
Selected Talk of Axel Polleres, Full Professor, WU - Vienna University of Economics and Business, Austria at the European Data Forum 2014, 19 March 2014 in Athens, Greece: City Data Pipeline - A report about experiences from using Open Data to gather indicators of city performance
The document discusses the goals and key requirements of scalable trajectory data analysis systems. It provides an overview of state-of-the-art technologies for analyzing large transportation datasets including Elastic Stack, Geomondrian, Leaflet, and Neo4j. It proposes using a graph database approach combined with Apache Spark for scalable processing and analyzing trajectory patterns from large datasets. Future work includes benchmarking graph databases and combining multiple datasets.
Geographical Map Annotation With Social Metadata In a Surveillance EnvironmentElena Roglia
The document discusses annotating geographical maps with social metadata from a surveillance environment. It proposes extracting significant tags from maps to enrich them using a statistical method. A metadata retrieval and search module is developed to allow operators to view historical metadata and suggest new annotations. Case studies applying the tag extraction method to maps of Turin and the Everest area are analyzed. Future work involves standardizing tags, relating similar annotations, and using data mining to integrate web resources for tagging maps from sensor data.
IRJET- Smart Railway System using Trip Chaining MethodIRJET Journal
This document proposes a smart railway system using trip chaining and big data analysis of passenger information from smart cards. The system would collect data like passenger name, age, travel time, source and destination stations from smart cards. It would then use k-means clustering to group passengers by age and travel patterns. A naïve bayes classifier would predict passenger counts at each station. This analysis of passenger data could help the railway department improve infrastructure and services based on demand.
CARTO Cloud Native – An Introduction to the Spatial Extension for BigQueryCARTO
In this practical webinar, we'll walk through some of the key location intelligence functions that are now available in BigQuery with our Spatial Extension.
(Slides) A demand-oriented information retrieval method on MANETNaoki Shibata
Enomoto, M., Shibata, N., Yasumoto, K., Ito, M. and Higashino, T.: A demand-oriented information retrieval method on MANET, International Workshop on Future Mobile and Ubiquitous Information Technologies (FMUIT'06).
http://ito-lab.naist.jp/themes/pdffiles/060510.makoto-e.fmuit06.pdf
In urban areas including shopping malls and stations
with many people, it is important to utilize various information
which those people have obtained. In this paper, we
propose a method for information registration and retrieval
in MANET which achieves small communication cost and
short response time. In our method, we divide the whole application
field into multiple sub-areas and classify records
into several categories so that mobile terminals in an area
holds records with a category. Each area is associated with
a category so that the number of queries for the category
becomes the largest in the area. Thus, mobile users search
records with a certain category by sending a query to nodes
in the particular area using existing protocol such as LBM
(Location-Based Multicast). Through simulations supposing
actual urban area near Osaka station, we have confirmed
that our method achieves practical communication
cost and performance for information retrieval in MANET.
The Seaport Service Rate Prediction System: Using Drayage Truck Trajectory Da...Meditya Wasesa
This file elaborates how drayage (truck) operators can apply predictive analytic techniques to their internal data assets to extract better insights and improve their operational decision making. For more detail please find our Decision Support Systems journal article at http://dx.doi.org/10.1016/j.dss.2016.11.008
A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cam...RoopTeja Muppalla
Imagery-based Traffic Sensing Knowledge Graph (ITSKG) framework utilizes the stationary traffic camera information as sensors to understand the traffic patterns. This system extracts image-based features from traffic camera images, adds a semantic layer to the sensor data for traffic information, and then labels traffic imagery with semantic labels such as congestion. This framework adds a new dimension to existing traffic modeling systems by incorporating dynamic image-based features as well as creating a knowledge graph to add a layer of abstraction to understand and interpret concepts like congestion to the traffic event detection system.
This work is presented at the Industrial Knowledge workshop co-located with the 9th International ACM Web Science Conference 2017 on 25th June 2017.
This document presents an approach for generating valuable traffic density data to simulate route planning for patrol cars. It involves extracting location data from GPS and tracking devices of patrol cars over time. This data is used to calculate route frequencies, which are then encoded with color to represent density on a map. The route density data is then correlated with crime hotspot information to propose a new route planning simulation for law enforcement. This aims to more efficiently dispatch patrol cars by considering both traffic patterns and crime trends.
The document is a resume for Dejan Neskovic, who has over 16 years of experience as a senior data scientist and system engineer working on projects for the FAA and DHS. He has led teams developing predictive models, geospatial analysis tools, and other innovative solutions to support air transportation and border security. Current areas of focus include predictive threat modeling, radar coverage assessment, and statistical analysis to improve departure time predictions.
Enhancing Traffic Prediction with Historical Data and Estimated Time of ArrivalIRJET Journal
This document proposes a methodology to enhance traffic prediction accuracy by combining historical traffic data, real-time traffic updates, and estimated time of arrival (ETA) information. The methodology utilizes machine learning techniques, ARIMA modeling, nonparametric methods, and deep neural networks to analyze the data. While the methodology lays out a framework for collecting raw traffic congestion data from online maps and transportation departments, the research focuses on establishing a theoretical model rather than conducting empirical experiments. The goal is to develop a comprehensive solution for traffic prediction by leveraging different data sources and analytical techniques.
MACHINE LEARNING TECHNIQUES FOR ANALYSIS OF EGYPTIAN FLIGHT DELAYIJDKP
Flight delay has been the fiendish problem to the world's aviation industry, so there is very important
significance to research for computer system predicting flight delay propagation. Extraction of hidden
information from large datasets of raw data could be one of the ways for building predictive model. This
paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt
Airline’s Flight dataset. In this work, four decision tree classifiers were evaluated and results show that the
REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based
classifiers were compared and results show that PART provides best accuracy among studied rule-based
classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work
concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the
current work is extended to apply of Apriori association technique to extract some important information
about flight delay. Association rules are presented and association technique is evaluated.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
This document discusses recent research on using computer vision and machine learning techniques for person detection in maritime search and rescue operations from images and video captured by drones. Specifically, it summarizes 12 research papers on this topic, covering approaches such as training convolutional neural networks on bird's eye view datasets to detect people from aerial images, using multiple detection methods like sliding windows and precise localization, combining data from multiple drones and sensors to optimize search efforts, and evaluating models on both RGB and thermal image datasets. The goal of this research is to automate part of the search process to make maritime rescue operations more efficient and effective.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
1) The document discusses using machine learning and computer vision techniques for person detection in maritime search and rescue operations using drones/UAVs. It aims to automatically detect people in images/videos captured by drones to help with search efforts.
2) A key challenge is that people appear small in drone footage and are often obscured by vegetation or terrain. The models need to be trained on similar bird's eye view data to achieve high accuracy. The document reviews different person detection models and their use in search and rescue.
3) It discusses recent work involving using efficient neural networks like MobileNet for object detection from drones. Other work involves using depth sensors and pose estimation for person tracking, as well as using distributed deep learning
The document discusses security issues in cyber-physical systems and proposes enforcing sensor network theory, information flow-based theory, and control theory to develop security policies and mechanisms for cyber-physical systems. It categorizes different types of attacks based on these three approaches and discusses mitigations to improve cyber-physical system security and allow their continued growth. The document defines cyber-physical systems as integrated computational and physical processes that sense, interact with, and control physical entities using communication, computation, and feedback control.
Spatio-Temporal Data Analysis using Deep LearningIRJET Journal
This document provides a literature review of spatio-temporal data analysis using deep learning techniques. It discusses applications in domains such as transportation, social media, and environmental issues. For transportation, examples of deep learning models for traffic forecasting and accident detection are discussed. For social media, models for event detection from social media data and sentiment analysis of disaster tweets are described. For environmental issues, applications discussed include wind and rainfall prediction, land use classification from satellite imagery, and crop yield modeling from aerial imagery. The document provides an overview of different deep learning techniques used for spatio-temporal data analysis across these application domains.
The ICARUS Aviation Data Sharing and Intelligence framework was presented by Mr. Nikolaos Papagiannopoulos from Athens International Airport (AIA) at the 27th ACRIS Meeting, which was held in London on February 25th-27th, 2020.
This document describes a major project to develop an E-Pilots system to predict hard landings during commercial flights. The system will use machine learning algorithms to analyze flight data and identify patterns that precede hard landings, providing pilots with real-time warnings. The objectives are to enhance aviation safety and reduce hard landings. The proposed system offers advantages over existing systems such as more accurate predictions and cost savings. The project will involve collecting and analyzing flight data, developing and testing machine learning models, and integrating the system into existing flight systems.
This document describes a major project to develop an E-Pilots system to predict hard landings during commercial flights. The system will use machine learning algorithms to analyze flight data and identify patterns that precede hard landings, providing pilots with real-time warnings. The objectives are to enhance aviation safety and reduce hard landings. The proposed system offers advantages over existing systems such as more accurate predictions and cost savings. The project will involve collecting and analyzing flight data, developing and testing machine learning models, and integrating the system into existing flight systems.
TRAFFIC FORECAST FOR INTELLECTUAL TRANSPORTATION SYSTEM USING MACHINE LEARNINGIRJET Journal
1. The document discusses using machine learning techniques like random forests and support vector machines to predict traffic patterns using large datasets from intelligent transportation systems.
2. It proposes predicting traffic using an SVM algorithm with Euclidean distance metrics on traffic data derived from online sources, aiming to improve accuracy and reduce errors compared to existing systems.
3. The system would take in historical vehicle movement data to be trained via machine learning, allowing it to process large amounts of real-time sensor data and better predict traffic conditions, which could help minimize congestion and carbon emissions from transportation.
This document presents a system for predicting airfare prices using machine learning algorithms. The system was developed to help travelers book flight tickets at cheaper prices by predicting prices for flights on different dates. It uses a flight price dataset containing over 1000 records and 13 features to build random forest models for price prediction. Data preprocessing, feature extraction and selection are performed before training the models. The preliminary results show that machine learning can accurately predict airfare prices based on historical data and help reduce uncertainty for travelers.
This document describes a study that uses Gradient Boosted Decision Trees (GBDT) to predict flight delays. The researchers applied GBDT to flight on-time performance data from the US Department of Transportation to predict departure and arrival delays. They preprocessed the data, selected important features, then used GBDT to build a predictive model. The model was more accurate than other methods at predicting delays based on features like day of week, carrier, origin/destination airports, and scheduled departure/arrival times.
IRJET - Airplane Crash Analysis and Prediction using Machine LearningIRJET Journal
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
the hybrid cloud[1] World Pipeline MagazineLayne Tucker
1. The document discusses a pilot project funded by the US Department of Transportation to test whether cloud and mobile technologies could improve pipeline risk management processes like damage prevention and integrity management.
2. The pilot project implemented ProStar's cloud-based geospatial solution called Transparent Earth to capture precise location data of buried pipelines using mobile devices, GPS, and pipe locators. This allowed real-time sharing of pipeline location and attribute data with field workers.
3. The pilot was successful, improving data collection, quality, and accessibility. Using cloud and mobile technologies enhanced workflows and supported compliance with new regulations.
The document presents a system for real-time tracking of aircraft in crowdsourced air traffic networks. The system utilizes data from the OpenSky Network to track aircraft locations reported by onboard GPS sensors. It develops models to predict aircraft trajectories and times of arrival based on historical data and weather impacts. The system is built on Apache Spark for real-time and historical data analysis. It includes APIs for flight tracking and predicting missing location data using simple localization estimates. The goals are to enable cleaner, safer and more efficient air traffic control.
IRJET- Smart Railway System using Trip Chaining MethodIRJET Journal
This document proposes a smart railway system using trip chaining and big data analysis of passenger information from smart cards. The system would collect data like passenger name, age, travel time, source and destination stations from smart cards. It would then use k-means clustering to group passengers by age and travel patterns. A naïve bayes classifier would predict passenger counts at each station. This analysis of passenger data could help the railway department improve infrastructure and services based on demand.
CARTO Cloud Native – An Introduction to the Spatial Extension for BigQueryCARTO
In this practical webinar, we'll walk through some of the key location intelligence functions that are now available in BigQuery with our Spatial Extension.
(Slides) A demand-oriented information retrieval method on MANETNaoki Shibata
Enomoto, M., Shibata, N., Yasumoto, K., Ito, M. and Higashino, T.: A demand-oriented information retrieval method on MANET, International Workshop on Future Mobile and Ubiquitous Information Technologies (FMUIT'06).
http://ito-lab.naist.jp/themes/pdffiles/060510.makoto-e.fmuit06.pdf
In urban areas including shopping malls and stations
with many people, it is important to utilize various information
which those people have obtained. In this paper, we
propose a method for information registration and retrieval
in MANET which achieves small communication cost and
short response time. In our method, we divide the whole application
field into multiple sub-areas and classify records
into several categories so that mobile terminals in an area
holds records with a category. Each area is associated with
a category so that the number of queries for the category
becomes the largest in the area. Thus, mobile users search
records with a certain category by sending a query to nodes
in the particular area using existing protocol such as LBM
(Location-Based Multicast). Through simulations supposing
actual urban area near Osaka station, we have confirmed
that our method achieves practical communication
cost and performance for information retrieval in MANET.
The Seaport Service Rate Prediction System: Using Drayage Truck Trajectory Da...Meditya Wasesa
This file elaborates how drayage (truck) operators can apply predictive analytic techniques to their internal data assets to extract better insights and improve their operational decision making. For more detail please find our Decision Support Systems journal article at http://dx.doi.org/10.1016/j.dss.2016.11.008
A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cam...RoopTeja Muppalla
Imagery-based Traffic Sensing Knowledge Graph (ITSKG) framework utilizes the stationary traffic camera information as sensors to understand the traffic patterns. This system extracts image-based features from traffic camera images, adds a semantic layer to the sensor data for traffic information, and then labels traffic imagery with semantic labels such as congestion. This framework adds a new dimension to existing traffic modeling systems by incorporating dynamic image-based features as well as creating a knowledge graph to add a layer of abstraction to understand and interpret concepts like congestion to the traffic event detection system.
This work is presented at the Industrial Knowledge workshop co-located with the 9th International ACM Web Science Conference 2017 on 25th June 2017.
This document presents an approach for generating valuable traffic density data to simulate route planning for patrol cars. It involves extracting location data from GPS and tracking devices of patrol cars over time. This data is used to calculate route frequencies, which are then encoded with color to represent density on a map. The route density data is then correlated with crime hotspot information to propose a new route planning simulation for law enforcement. This aims to more efficiently dispatch patrol cars by considering both traffic patterns and crime trends.
The document is a resume for Dejan Neskovic, who has over 16 years of experience as a senior data scientist and system engineer working on projects for the FAA and DHS. He has led teams developing predictive models, geospatial analysis tools, and other innovative solutions to support air transportation and border security. Current areas of focus include predictive threat modeling, radar coverage assessment, and statistical analysis to improve departure time predictions.
Enhancing Traffic Prediction with Historical Data and Estimated Time of ArrivalIRJET Journal
This document proposes a methodology to enhance traffic prediction accuracy by combining historical traffic data, real-time traffic updates, and estimated time of arrival (ETA) information. The methodology utilizes machine learning techniques, ARIMA modeling, nonparametric methods, and deep neural networks to analyze the data. While the methodology lays out a framework for collecting raw traffic congestion data from online maps and transportation departments, the research focuses on establishing a theoretical model rather than conducting empirical experiments. The goal is to develop a comprehensive solution for traffic prediction by leveraging different data sources and analytical techniques.
MACHINE LEARNING TECHNIQUES FOR ANALYSIS OF EGYPTIAN FLIGHT DELAYIJDKP
Flight delay has been the fiendish problem to the world's aviation industry, so there is very important
significance to research for computer system predicting flight delay propagation. Extraction of hidden
information from large datasets of raw data could be one of the ways for building predictive model. This
paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt
Airline’s Flight dataset. In this work, four decision tree classifiers were evaluated and results show that the
REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based
classifiers were compared and results show that PART provides best accuracy among studied rule-based
classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work
concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the
current work is extended to apply of Apriori association technique to extract some important information
about flight delay. Association rules are presented and association technique is evaluated.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
This document discusses recent research on using computer vision and machine learning techniques for person detection in maritime search and rescue operations from images and video captured by drones. Specifically, it summarizes 12 research papers on this topic, covering approaches such as training convolutional neural networks on bird's eye view datasets to detect people from aerial images, using multiple detection methods like sliding windows and precise localization, combining data from multiple drones and sensors to optimize search efforts, and evaluating models on both RGB and thermal image datasets. The goal of this research is to automate part of the search process to make maritime rescue operations more efficient and effective.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
1) The document discusses using machine learning and computer vision techniques for person detection in maritime search and rescue operations using drones/UAVs. It aims to automatically detect people in images/videos captured by drones to help with search efforts.
2) A key challenge is that people appear small in drone footage and are often obscured by vegetation or terrain. The models need to be trained on similar bird's eye view data to achieve high accuracy. The document reviews different person detection models and their use in search and rescue.
3) It discusses recent work involving using efficient neural networks like MobileNet for object detection from drones. Other work involves using depth sensors and pose estimation for person tracking, as well as using distributed deep learning
The document discusses security issues in cyber-physical systems and proposes enforcing sensor network theory, information flow-based theory, and control theory to develop security policies and mechanisms for cyber-physical systems. It categorizes different types of attacks based on these three approaches and discusses mitigations to improve cyber-physical system security and allow their continued growth. The document defines cyber-physical systems as integrated computational and physical processes that sense, interact with, and control physical entities using communication, computation, and feedback control.
Spatio-Temporal Data Analysis using Deep LearningIRJET Journal
This document provides a literature review of spatio-temporal data analysis using deep learning techniques. It discusses applications in domains such as transportation, social media, and environmental issues. For transportation, examples of deep learning models for traffic forecasting and accident detection are discussed. For social media, models for event detection from social media data and sentiment analysis of disaster tweets are described. For environmental issues, applications discussed include wind and rainfall prediction, land use classification from satellite imagery, and crop yield modeling from aerial imagery. The document provides an overview of different deep learning techniques used for spatio-temporal data analysis across these application domains.
The ICARUS Aviation Data Sharing and Intelligence framework was presented by Mr. Nikolaos Papagiannopoulos from Athens International Airport (AIA) at the 27th ACRIS Meeting, which was held in London on February 25th-27th, 2020.
This document describes a major project to develop an E-Pilots system to predict hard landings during commercial flights. The system will use machine learning algorithms to analyze flight data and identify patterns that precede hard landings, providing pilots with real-time warnings. The objectives are to enhance aviation safety and reduce hard landings. The proposed system offers advantages over existing systems such as more accurate predictions and cost savings. The project will involve collecting and analyzing flight data, developing and testing machine learning models, and integrating the system into existing flight systems.
This document describes a major project to develop an E-Pilots system to predict hard landings during commercial flights. The system will use machine learning algorithms to analyze flight data and identify patterns that precede hard landings, providing pilots with real-time warnings. The objectives are to enhance aviation safety and reduce hard landings. The proposed system offers advantages over existing systems such as more accurate predictions and cost savings. The project will involve collecting and analyzing flight data, developing and testing machine learning models, and integrating the system into existing flight systems.
TRAFFIC FORECAST FOR INTELLECTUAL TRANSPORTATION SYSTEM USING MACHINE LEARNINGIRJET Journal
1. The document discusses using machine learning techniques like random forests and support vector machines to predict traffic patterns using large datasets from intelligent transportation systems.
2. It proposes predicting traffic using an SVM algorithm with Euclidean distance metrics on traffic data derived from online sources, aiming to improve accuracy and reduce errors compared to existing systems.
3. The system would take in historical vehicle movement data to be trained via machine learning, allowing it to process large amounts of real-time sensor data and better predict traffic conditions, which could help minimize congestion and carbon emissions from transportation.
This document presents a system for predicting airfare prices using machine learning algorithms. The system was developed to help travelers book flight tickets at cheaper prices by predicting prices for flights on different dates. It uses a flight price dataset containing over 1000 records and 13 features to build random forest models for price prediction. Data preprocessing, feature extraction and selection are performed before training the models. The preliminary results show that machine learning can accurately predict airfare prices based on historical data and help reduce uncertainty for travelers.
This document describes a study that uses Gradient Boosted Decision Trees (GBDT) to predict flight delays. The researchers applied GBDT to flight on-time performance data from the US Department of Transportation to predict departure and arrival delays. They preprocessed the data, selected important features, then used GBDT to build a predictive model. The model was more accurate than other methods at predicting delays based on features like day of week, carrier, origin/destination airports, and scheduled departure/arrival times.
IRJET - Airplane Crash Analysis and Prediction using Machine LearningIRJET Journal
This document discusses research on analyzing and predicting airplane crashes using machine learning techniques. The researchers conducted an analysis of airplane crash data, correlating it with accident factors. They used supervised machine learning algorithms like SVM, K-NN, AdaBoost and XGBoost for classification and prediction. Feature selection was used to choose the most relevant features for improving accuracy. The algorithms were trained and tested on datasets, with the most accurate one used for prediction to determine if a flight was "safe" or at "crash" risk based on input specifications. The goal was to help the aviation industry improve safety by better understanding factors that contribute to crashes.
the hybrid cloud[1] World Pipeline MagazineLayne Tucker
1. The document discusses a pilot project funded by the US Department of Transportation to test whether cloud and mobile technologies could improve pipeline risk management processes like damage prevention and integrity management.
2. The pilot project implemented ProStar's cloud-based geospatial solution called Transparent Earth to capture precise location data of buried pipelines using mobile devices, GPS, and pipe locators. This allowed real-time sharing of pipeline location and attribute data with field workers.
3. The pilot was successful, improving data collection, quality, and accessibility. Using cloud and mobile technologies enhanced workflows and supported compliance with new regulations.
The document presents a system for real-time tracking of aircraft in crowdsourced air traffic networks. The system utilizes data from the OpenSky Network to track aircraft locations reported by onboard GPS sensors. It develops models to predict aircraft trajectories and times of arrival based on historical data and weather impacts. The system is built on Apache Spark for real-time and historical data analysis. It includes APIs for flight tracking and predicting missing location data using simple localization estimates. The goals are to enable cleaner, safer and more efficient air traffic control.
Christopher Guidice is a professional engineer and analyst with over 25 years of experience performing simulation modeling, analysis, and data mining related to airspace and airport operations. He has worked for the MITRE Corporation since 2001 analyzing historical and simulated operational data to develop simulations and draw conclusions about airport and airspace design. Prior to MITRE, he worked for Innovative Solutions International where he managed a team developing aviation simulation tools.
A Transfer Learning Approach to Traffic Sign RecognitionIRJET Journal
This document presents a study on traffic sign recognition using transfer learning with three pre-trained convolutional neural network models: InceptionV3, Xception, and ResNet50. The models were trained on the German Traffic Sign Recognition Benchmark dataset containing 43 classes of traffic signs. InceptionV3 achieved the highest test accuracy of 97.15% for traffic sign classification, followed by Xception at 96.79%, while ResNet50 performed poorly with only 60.69% accuracy. Transfer learning with InceptionV3 is shown to be an effective approach for traffic sign recognition tasks.
This document discusses the COMP4DRONES project, which aims to develop key technologies to provide secure and autonomous drones for complex applications. The project will run from 2019-2022 with a budget of €29.76 million and involve 49 participants from 8 countries. It will work to enable drones to make safe autonomous decisions, ensure trusted communications, and ease the integration of drone systems. The project objectives are demonstrated through 9 pilots involving logistics, infrastructure inspection, and agriculture. The expected impacts are strengthening AI integration in drones, reinforcing drone industries, and enabling new drone services in Europe.
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The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
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You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
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Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
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A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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1. DART. Data-driven AiRcraft Trajectory
prediction research
José Manuel Cordero García
jmcordero@e-crida.enaire.es
Data Driven ATM: Going Digital!
8th March 2018
2. DART Consortium
SESAR 2020 - Exploratory Research 2
University of Piraeus Research Center - UPRC
Reference Centre for Research, Development and
Innovation in ATM (CRIDA)
Fraunhofer Gesellschaft zur Förderung der
Angewandten Forschung e.V. (IAIS)
Boeing Research & Technology Europe
S.L.U. (BR&T-E)
3. DART Objectives and Impact
SESAR 2020 - Exploratory Research 3
Data Driven
Trajectory
Prediction
Improved
Network
Management
Improved
airport
operations
Collaborative
decision
making
Challenge of exploring the applicability of data science and
complexity science techniques to the ATM domain with the aim of
improving aircraft trajectory prediction capabilities of the ATM system
It is expected that data-driven techniques help to improve the
accuracy of predictions by complementing classical model-based
prediction approaches.
4. Problem addressed by DART
SESAR 2020 - Exploratory Research 4
Aircraft Operators
ANSPs
Network Manager
Minimizing the cost throught
maximizing the adherence to the
airlines preferred FPs.
Decide which flights to modify to
resolve sector imbalances and
potential conflicts.
Minimizing the sector
imbalances and potential
conflicts.
DART aims to develop a collaborative decision making process that would support
multi-objective optimization taking into account stakeholders’ expectations.
5. CTP
Collaborative Trajectory Prediction
Imbalances and
conflicts
detection (ANSP)
AOspreferredFPs
Iterative Optimization
process
Select the flights
to modify (NM)
STP
Single Trajectory
Prediction
Trajectory predictions
from an individual
trajectory perspective
STP predict new single
trajectories for these flights
CTP detection of imbalances
and conflicts, selection of
flight to modify and
proposition of new FPs
Optimaltrajectories
AOs
propose
new FP for
these
flights
Network Manager Air Navigation
Service Providers
Solution proposed: Single and
Collaborative Trajectory Prediction
SESAR 2020 - Exploratory Research 5
DART contributions
Collaborative
Trajectory Prediction
Individual
Trajectory Prediction
Aircraft Operators
6. DART Concept
SESAR 2020 - Exploratory Research 6
Data-driven algorithm
for single trajectory
predictions:
- Hidden Markov Models (HMM)
- Similarity-based retrieval
(HMM/clustering)
- Reinforcement Learning
Models
Collaborative
reinforcement
learning algorithms for
agent based modelling
of ATM
Visual Interfaces for
Interactive exploration
of solutions and
decision making
DART explores the applicability of a collection of machine learning and agent- based
models and algorithms to derive a data-driven trajectory prediction capability.
Advanced visual analytics techniques will be used to facilitate data exploration,
quality assessment, and algorithms parameters and features selection.
7. DART Data Sources
SESAR 2020 - Exploratory Research 7
DARTSurveillance
Data
Weather Data:
NOAA
forecasts,
SIGMET, TAF
Flight Plans
DDR -2 Spanish
Operational Data
Airspace Structure
DDR-2 Spanish
Operational Data
Reconstructed
Trajectories
BR&T-E Trajectory
Predictor using
Flight Plans
Aircraft Intent
Descriptions
BR&T-E
The datasets used for training and testing the algorithms are a key component
of the project.
8. Visually supported Trajectory
Prediction
SESAR 2020 - Exploratory Research 8
Visually supported detection of clusters of trajectories and flight plans from Madrid to Barcelona
Historical data provide recurrent patterns
of trajectories (enriched with contextual
information – e.g. weather) that data-
driven methods learn.
Prediction of a single trajectory
involves choosing the pattern
that fits better a flight plan
w.r.t. contextual data.
9. DART
Expected Achievements & Conclusions
Expected Achievements (Maturity: TRL-1)
• Data-driven algorithms for single trajectory predictions;
Implementation and evaluation of the following algorithms:
(a) Hidden Markov Models (HMM),
(b) Similarity-based retrieval (HMM/clustering),
(c) Reinforcement Learning Models applied on Aircraft Intent.
• Collaborative reinforcement learning algorithms for agent based modelling of
ATM; Agents correspond to aircraft in specific trajectories.
Implementation and evaluation of collaborative reinforcement learning algorithms
• Visual Interfaces for Interactive exploration of solutions and decision making,
providing an overview of modeling results in space and time.
SESAR 2020 - Exploratory Research 9
10. This project has received
funding from the SESAR
Joint Undertaking under
the European Union’s
Horizon 2020 research
and innovation
programme under grant
agreement No [number]
The opinions expressed herein reflect the author’s view only.
Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information
contained herein.
Thank you
DART. Data-driven AiRcraft Trajectory prediction research
11. Collaborative Trajectory Prediction
• Reinforced Learning approach considered (work in progress):
1. Independent Reinforcement Learners approach
Each agent (flight) is self-interested and
learns by itself to resolve the DCB problem,
by measuring its own reward after each
Decision
2. Edge-Based Collaborative Reinforcement Learners approach
Reward received from global state and joint action of all agents (global
optimum)
3. Agent-based Collaborative Reinforcement Learners approach
Variant of the previous approach. Agents are not flights, but
flights+sectors. Would allow rerouting extension
11International Workshop on Uncertainty and Air Traffic Management
13. Collaborative Trajectory Prediction
13
• Early results (Method 2, Sparse Collaborative Q-Learning – Agent-
based decomposition – Edge based update):
International Workshop on Uncertainty and Air Traffic Management
Editor's Notes
Understanding what can be achieved today by data-driven trajectory prediction models, also accounting for ATM network effects.
Improved network management and airport operations due to reduction of uncertainty factors, focusing on delays
Advanced collaborative decision making processes at the pre-tactical stage leading to more efficient ATM procedures
The aim is to develop a collaborative decision making process that would support multi-objective optimization taking the requirements of the different actors in the ATM system into account at the planning phase (i.e. few days before operation):
Aircraft Operators (AOs) : Minimizing the cost thought maximizing the adherence to the airlines preferred FPs.
Network Manager (NM): Decide which flights to modify to resolve sector imbalances and potential conflicts.
Air Navigation Service Providers (ANSPs): Minimizing the sector imbalances and potential conflicts.
Major research issues:
• What are the supporting data required for robust and reliable trajectory predictions?
• What is the potential of data-driven machine learning algorithms to support high-fidelity aircraft trajectory prediction?
• How the complex nature of the ATM system impacts the trajectory predictions?