This document proposes using neural networks to train flight data from a human-piloted unmanned aerial vehicle (UAV) in order to develop a robust autonomous flight controller. Flight data including roll, pitch, and yaw will be collected and used to train a feedforward multilayer perceptron neural network. The network will be trained and tested to output flight positions that can be integrated into the UAV's controller. Results show the network was able to be trained to minimize error and accurately model the UAV's pitch dynamics, demonstrating neural networks are a viable method for understanding and training UAV flight behavior.
This document provides a literature review on beamforming techniques for 5G mobile communication systems. It discusses the objectives of focusing on beamforming techniques using a combination of the MUSIC and LMS algorithms in a new MLMS algorithm. It then reviews various beamforming algorithms such as LMS, TVLMS, MUSIC, and MLMS and compares their advantages and disadvantages. The document outlines future work plans to improve the MLMS algorithm by reducing iterations and convergence parameters to increase stability. It provides a work plan and references various literature on adaptive beamforming algorithms.
This document summarizes a research paper that implemented Levenberg-Marquardt artificial neural network training using graphics processing unit (GPU) hardware acceleration. The key points are:
1) This appears to be the first description of implementing artificial neural networks using the Levenberg-Marquardt training method on a GPU.
2) The paper describes their approach for implementing the Levenberg-Marquardt algorithm on a GPU, which involves solving the matrix inversion operation that is typically computationally expensive.
3) Results show that training networks using the GPU implementation can be up to 10 times faster than using a CPU-only implementation on the same hardware.
SERENE 2014 Workshop: Paper "Advanced Modelling, Simulation and Verification ...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 3: Advanced Modelling, Simulation and Verification for Future Traffic Regulation Optimisation
Data mining projects topics for java and dot netredpel dot com
This document discusses several papers related to data mining and machine learning techniques. It begins with a brief summary of each paper, discussing the key contributions and findings. The summaries cover topics such as differential privacy-preserving data anonymization, fault detection in power systems using decision trees, temporal pattern searching in event data, high dimensional indexing for similarity search, landmark-based approximate shortest path computation, feature selection for high dimensional data, temporal pattern mining in data streams, data leakage detection, keyword search in spatial databases, analyzing relationships on Wikipedia, improving recommender systems using user-item subgroups, decision trees for uncertain data, and building confidential query services in the cloud using data perturbation.
A machine learning model for average fuel consumption in heavy vehiclesVenkat Projects
The document describes a machine learning model that uses artificial neural networks to predict average fuel consumption in heavy vehicles. Seven features are extracted from vehicle dataset to train the ANN model, including number of stops, time stopped, average speed, etc. The trained model is then used to predict fuel consumption for new test vehicles based on their feature values. Screenshots of a software implementation of this ANN model for fuel consumption prediction are also included.
Review and Comparisons between Multiple Ant Based Routing Algorithms in Mobi...IJMER
Along with an increase in the use and development of various types of mobile ad hoc and
wireless sensor networks the necessity for presenting optimum routing in these networks is a topic yet to
be discussed and new algorithms are presented. Using ant colony optimization algorithm or ACO as a
routing method because of its structural similarities to these networks’ model, has had acceptable results
regarding different parameters especially quality of service (QoS). Considering the fact that many
articles have suggested and presented various models for ant based routing, the need for studying and comparing them can be felt. There are about 17 applied ant based routings, this article studies and compares the most important ant based algorithms so as to indicate the quality and importance of each of them under different conditions
This document discusses simulating a photovoltaic (PV) energy conversion system using MATLAB/Simulink and PLECS. It first describes the graphical environments of MATLAB/Simulink and PLECS. It then presents the simulation of a PV inverter system, developing models of the control system in MATLAB/Simulink and models of the power electronics plant using both MATLAB/Simulink transfer functions and PLECS circuit components. Simulation results are provided to compare the two approaches.
M phil-computer-science-artifical-neural-networks-projectsVijay Karan
List of Artifical Neural Networks IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Artifical Neural Networks for M.Phil Computer Science students.
This document provides a literature review on beamforming techniques for 5G mobile communication systems. It discusses the objectives of focusing on beamforming techniques using a combination of the MUSIC and LMS algorithms in a new MLMS algorithm. It then reviews various beamforming algorithms such as LMS, TVLMS, MUSIC, and MLMS and compares their advantages and disadvantages. The document outlines future work plans to improve the MLMS algorithm by reducing iterations and convergence parameters to increase stability. It provides a work plan and references various literature on adaptive beamforming algorithms.
This document summarizes a research paper that implemented Levenberg-Marquardt artificial neural network training using graphics processing unit (GPU) hardware acceleration. The key points are:
1) This appears to be the first description of implementing artificial neural networks using the Levenberg-Marquardt training method on a GPU.
2) The paper describes their approach for implementing the Levenberg-Marquardt algorithm on a GPU, which involves solving the matrix inversion operation that is typically computationally expensive.
3) Results show that training networks using the GPU implementation can be up to 10 times faster than using a CPU-only implementation on the same hardware.
SERENE 2014 Workshop: Paper "Advanced Modelling, Simulation and Verification ...SERENEWorkshop
SERENE 2014 - 6th International Workshop on Software Engineering for Resilient Systems
http://serene.disim.univaq.it/
Session 3: Verification and Validation
Paper 3: Advanced Modelling, Simulation and Verification for Future Traffic Regulation Optimisation
Data mining projects topics for java and dot netredpel dot com
This document discusses several papers related to data mining and machine learning techniques. It begins with a brief summary of each paper, discussing the key contributions and findings. The summaries cover topics such as differential privacy-preserving data anonymization, fault detection in power systems using decision trees, temporal pattern searching in event data, high dimensional indexing for similarity search, landmark-based approximate shortest path computation, feature selection for high dimensional data, temporal pattern mining in data streams, data leakage detection, keyword search in spatial databases, analyzing relationships on Wikipedia, improving recommender systems using user-item subgroups, decision trees for uncertain data, and building confidential query services in the cloud using data perturbation.
A machine learning model for average fuel consumption in heavy vehiclesVenkat Projects
The document describes a machine learning model that uses artificial neural networks to predict average fuel consumption in heavy vehicles. Seven features are extracted from vehicle dataset to train the ANN model, including number of stops, time stopped, average speed, etc. The trained model is then used to predict fuel consumption for new test vehicles based on their feature values. Screenshots of a software implementation of this ANN model for fuel consumption prediction are also included.
Review and Comparisons between Multiple Ant Based Routing Algorithms in Mobi...IJMER
Along with an increase in the use and development of various types of mobile ad hoc and
wireless sensor networks the necessity for presenting optimum routing in these networks is a topic yet to
be discussed and new algorithms are presented. Using ant colony optimization algorithm or ACO as a
routing method because of its structural similarities to these networks’ model, has had acceptable results
regarding different parameters especially quality of service (QoS). Considering the fact that many
articles have suggested and presented various models for ant based routing, the need for studying and comparing them can be felt. There are about 17 applied ant based routings, this article studies and compares the most important ant based algorithms so as to indicate the quality and importance of each of them under different conditions
This document discusses simulating a photovoltaic (PV) energy conversion system using MATLAB/Simulink and PLECS. It first describes the graphical environments of MATLAB/Simulink and PLECS. It then presents the simulation of a PV inverter system, developing models of the control system in MATLAB/Simulink and models of the power electronics plant using both MATLAB/Simulink transfer functions and PLECS circuit components. Simulation results are provided to compare the two approaches.
M phil-computer-science-artifical-neural-networks-projectsVijay Karan
List of Artifical Neural Networks IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Artifical Neural Networks for M.Phil Computer Science students.
The document summarizes the system architecture of the Global Hawk unmanned aerial vehicle. It describes Global Hawk as a high-altitude, long-endurance aircraft system used for intelligence, surveillance, and reconnaissance missions. The key components of the Global Hawk system are the unmanned air vehicle, a common ground segment for command and control, and support systems. The air vehicle carries sensor payloads and has autonomous flight and navigation capabilities. The common ground segment includes a mission control element and launch/recovery element to monitor the vehicle and payload data and control missions.
The document discusses the lifecycle of stars from their formation to death. It begins by defining what stars and constellations are. It then focuses on our sun as a medium-sized, yellow star at the center of our solar system. Stars are classified based on temperature and brightness as young dwarf stars or older, larger supergiant stars. The document outlines the typical stages a star like the sun will progress through over billions of years from birth from clouds of dust and gas, burning hydrogen through nuclear fusion as a main sequence star, and eventual death through expansion and explosion at the end of its life. Various nebulae and supernova remnants are used as examples of star birth and death.
ADS-B es un sistema que permite que las aeronaves envíen automáticamente datos como posición, altitud y velocidad a estaciones en tierra y a otras aeronaves. El protocolo ADS-B puede implementarse a través de dos tecnologías y existen posibles riesgos para la seguridad debido a que la posición depende del GPS. Se puede recibir el tráfico ADS-B usando un receptor SDR con GNU/Linux o accediendo a sitios web que recopilan datos de receptores en todo el mundo.
This document discusses ADS-B (Automatic Dependent Surveillance - Broadcast), a technology that will replace radar as the primary means for air traffic controllers to track aircraft. It operates using two modes: ADS-B Out broadcasts data from aircraft, while ADS-B In receives data in the aircraft. By 2020, all aircraft will be required to have ADS-B Out equipment installed. ADS-B uses GPS and broadcasts aircraft's location, speed, and other data to any aircraft or ground station equipped to receive it. This allows pilots and controllers to see the same information simultaneously.
There are different life cycle stages for stars depending on their original mass. Low mass stars progress through the stages of nebula, main sequence, red giant, planetary nebula, white dwarf, and black dwarf. High mass stars go through nebula, main sequence, red supergiant, supernova, and either become a neutron star or black hole. The main sequence stage can last billions of years for low mass stars but only millions for high mass stars.
The document discusses the design and development of quadcopter unmanned aerial vehicles (UAVs). It describes the prototypes created, including improvements made to reduce weight and increase lift. Sensors and controllers are discussed, including sensors for position, proximity, and navigation. The final prototype achieved stable hovering with a weight of 43 grams and incorporated an inertial measurement unit, ultrasonic sensors, GPS, and radio frequency transmission for control and data transmission.
Stars are formed from clouds of dust and gas collapsing under gravity. They spend most of their life fusing hydrogen into helium in their cores, glowing from the heat and pressure of nuclear fusion. When stars run out of fuel to burn in their cores, they die - smaller stars may become white dwarfs, while larger stars explode as supernovae. The material from exploded stars then disperses to form new dust clouds, starting the next generation of star formation.
This document discusses drones or unmanned aerial vehicles (UAVs). It provides an introduction to UAVs, including a brief history starting from 1916. It describes the key sub-systems of UAVs including the unmanned aircraft, control system, control link and support equipment. The document discusses various design parameters and applications of UAVs, including disaster relief, search and rescue, sports and armed attacks. It also discusses UAV programs in India and compares Indian and U.S. drones. Finally, it outlines some disadvantages of UAVs such as civilian casualties and hacking risks.
An unmanned aerial vehicle (UAV), commonly known as a Drone, is an aircraft without a human pilot on board. UAVs can be remote controlled aircraft (e.g. flown by a pilot at a ground control station) or can fly autonomously based on pre-programmed flight plans or more complex dynamic automation systems
A UAV is defined as being capable of controlled, sustained level flight and powered by a jet or reciprocating engine. In addition, a cruise missile can be considered to be a UAV, but is treated separately on the basis that the vehicle is the weapon.
Unmanned Aerial Vehicles (UAVs) are aircrafts that fly without any humans being onboard. They are either remotely piloted, or piloted by an onboard computer. This kind of aircrafts can be used in different military missions such as surveillance, reconnaissance, battle damage assessment, communications relay, minesweeping, hazardous substances detection and radar jamming. However they can be used in other than military missions like detection of hazardous objects on train rails and investigation of infected areas. Aircrafts that are able of hovering and vertical flying can also be used for indoor missions like counter terrorist operations
To download this ppt click on this link
https://adf.ly/PdL4V
This document provides an overview of a talk given by Dirk Gorissen on UAV and robotics technology. The talk discusses the DECODE project, which aims to develop a system to help designers understand the impact of decisions made during complex aerospace system design. As a case study, the project is using UAV design for search and rescue operations. The talk outlines UAV and robot technologies, the DECODE design system, rapid manufacturing techniques like 3D printing, and future work such as a two-seas monitoring project using UAVs.
The document provides a summary of Arthur P. McGregor's work experience, including his current role as Associate Director for Kinetic Weapons Technologies at the Office of the Assistant Secretary of Defense, where he oversees $750M in annual spending on kinetic weapons science and technology programs. It details his participation in reviews of major defense acquisition programs and technology assessments. The document also outlines his prior experience in university research programs at the Department of Defense and engineering roles related to night vision and electro-optical systems.
Performance comparison of row per slave and rows set per slave method in pvm ...eSAT Journals
Abstract Parallel computing operates on the principle that large problems can often be divided into smaller ones, which are then solved concurrently to save time by taking advantage of non-local resources and overcoming memory constraints. Multiplication of larger matrices requires a lot of computation time. This paper deals with the two methods for handling Parallel Matrix Multiplication. First is, dividing the rows of one of the input matrices into set of rows based on the number of slaves and assigning one rows set for each slave for computation. Second method is, assigning one row of one of the input matrices at a time for each slave starting from first row to first slave and second row to second slave and so on and loop backs to the first slave when last slave assignment is finished and repeated until all rows are finished assigning. These two methods are implemented using Parallel Virtual Machine and the computation is performed for different sizes of matrices over the different number of nodes. The results show that the row per slave method gives the optimal computation time in PVM based parallel matrix multiplication. Keywords: Parallel Execution, Cluster Computing, MPI (Message Passing Interface), PVM (Parallel Virtual Machine) RAM (Random Access Memory).
This document compares two methods for parallel matrix multiplication using PVM (Parallel Virtual Machine): the row per slave method and the rows set per slave method. It finds that the row per slave method provides optimal computation time. The row per slave method assigns each slave a single row from the first matrix to compute, while the rows set per slave method assigns each slave a set of rows. Experimental results on matrices of varying sizes show the row per slave method takes less time, with an average 50% reduction in computation time compared to the rows set per slave method.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijics
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the
performance of an aircraft. This is required both for the success of the mission and the survivability of the
aircraft in the harsh enemy conditions.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijcisjournal
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the performance of an aircraft. This is required both for the success of the mission and the survivability of the aircraft in the harsh enemy conditions.
A system is envisioned comprising of the accurate modeling of the physical world and the accurate model of control system. An interoperable system which can work seamlessly together will provide mission planners, System integrators, aeronautical/aerospace engineers a milieu wherein the Control System designer who is found wanted as far as the physical world is concerned is given a system which can simulate the real world in lab conditions. To achieve this, we combine the two most promising environments prevalent in the industry today namely Systems tool kit for modeling the operational environment MATLAB and LabVIEW for modeling the control system environment. Using a Math script window of LabVIEW, we have designed the aircraft model and controlling the variables of an aircraft using a simulation loop of a LabVIEW. The different flight conditions were arrived using Orthogonal Array (OA) based on different Aircraft weight, Altitude, Mach number configurations. This attempts to span the aircrafts across the regimes in aircrafts flight envelope. A system comprising of both, with seamless UDP based connection between the two is developed to expedite the process of development of feasible control system design and verification which allows the aircrafts to undertake complex mission. This system we believe would answer questions of limits of the aircrafts maneuverability and survivability in terms of its limitation concerning control system design and development of commercial fighter aircrafts, UAV's and Quad copters.
This document describes a web-based worm simulator that allows users to run network worm simulations remotely through a web browser. The simulator is hosted on a server and uses a client-server architecture where the front-end interface runs as a web applet and the backend simulation runs on the server. Users input simulation parameters through the front-end, which are sent to the backend server to run the simulation. The backend generates a network topology, populates it with hosts, runs the worm simulation, and stores the results. The front-end then displays the results graphically for the user. This allows platform-independent, remote access to running worm simulations through a standard web browser interface.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
This document outlines a test plan to evaluate initial data link terminal air traffic control (ATC) services through simulations. The plan involves training new air traffic controllers on the services over 4 days, then having them participate in full-scale simulations to validate the service designs and assess the impact of implementing the services. Data will be collected from the simulations and controller ratings/feedback to analyze impacts on communications, workload, and errors. The results will help guide development of operational data link systems.
This document discusses developing a functional model for predicting friction factor in turbulent pipe flow using an artificial neural network (ANN). The objectives are to both predict friction factor values and extract a functional model from the trained ANN. The ANN will be trained on normalized input data using backpropagation. MATLAB will be used to generate data, create and train the ANN, and extract the functional model, which will then be tested on separate validation data to evaluate accuracy. The goal is to provide a simple predictive model for friction factor that does not require ANN software.
This document summarizes a research project that aims to develop an application to predict airline ticket prices using machine learning techniques. The researchers collected over 10,000 records of flight data including features like source, destination, date, time, number of stops, and price. They preprocessed the data, selected important features, and applied machine learning algorithms like linear regression, decision trees, and random forests to build predictive models. The random forest model provided the most accurate predictions according to performance metrics like MAE, MSE, and RMSE. The researchers propose deploying the best model in a web application using Flask for the backend and Bootstrap for the frontend so users can input flight details and receive predicted price outputs.
First Steps Toward Scientific Cyber-Security Experimentation in Wide-Area Cyb...DETER-Project
Abstract: Steps towards an environment for repeatable and scalable experiments on wide-area cyber-physical systems. The cyber-physical systems that underlie the world's critical infrastructure are increasingly vulnerable to attack and failure. Our work has focused on secure and resilient communication technology for the electric power grid, a subset of the general cyber-physical problem. We have demonstrated tools and methodology for experimentation with GridStat, a middleware system designed to provide enhanced communication service for the grid, within the DeterLab cyber-security testbed. Experiment design tools for DeterLab and for GridStat will ease the creation and execution of relatively large experiments, and they should make this environment accessible to users inexperienced with cluster testbeds. This abstract presents brief overviews of DeterLab and of GridStat and describes their integration. It also describes a large scale GridStat/DeterLab experiment.
For more information, visit: http://www.deter-project.org
The document summarizes the system architecture of the Global Hawk unmanned aerial vehicle. It describes Global Hawk as a high-altitude, long-endurance aircraft system used for intelligence, surveillance, and reconnaissance missions. The key components of the Global Hawk system are the unmanned air vehicle, a common ground segment for command and control, and support systems. The air vehicle carries sensor payloads and has autonomous flight and navigation capabilities. The common ground segment includes a mission control element and launch/recovery element to monitor the vehicle and payload data and control missions.
The document discusses the lifecycle of stars from their formation to death. It begins by defining what stars and constellations are. It then focuses on our sun as a medium-sized, yellow star at the center of our solar system. Stars are classified based on temperature and brightness as young dwarf stars or older, larger supergiant stars. The document outlines the typical stages a star like the sun will progress through over billions of years from birth from clouds of dust and gas, burning hydrogen through nuclear fusion as a main sequence star, and eventual death through expansion and explosion at the end of its life. Various nebulae and supernova remnants are used as examples of star birth and death.
ADS-B es un sistema que permite que las aeronaves envíen automáticamente datos como posición, altitud y velocidad a estaciones en tierra y a otras aeronaves. El protocolo ADS-B puede implementarse a través de dos tecnologías y existen posibles riesgos para la seguridad debido a que la posición depende del GPS. Se puede recibir el tráfico ADS-B usando un receptor SDR con GNU/Linux o accediendo a sitios web que recopilan datos de receptores en todo el mundo.
This document discusses ADS-B (Automatic Dependent Surveillance - Broadcast), a technology that will replace radar as the primary means for air traffic controllers to track aircraft. It operates using two modes: ADS-B Out broadcasts data from aircraft, while ADS-B In receives data in the aircraft. By 2020, all aircraft will be required to have ADS-B Out equipment installed. ADS-B uses GPS and broadcasts aircraft's location, speed, and other data to any aircraft or ground station equipped to receive it. This allows pilots and controllers to see the same information simultaneously.
There are different life cycle stages for stars depending on their original mass. Low mass stars progress through the stages of nebula, main sequence, red giant, planetary nebula, white dwarf, and black dwarf. High mass stars go through nebula, main sequence, red supergiant, supernova, and either become a neutron star or black hole. The main sequence stage can last billions of years for low mass stars but only millions for high mass stars.
The document discusses the design and development of quadcopter unmanned aerial vehicles (UAVs). It describes the prototypes created, including improvements made to reduce weight and increase lift. Sensors and controllers are discussed, including sensors for position, proximity, and navigation. The final prototype achieved stable hovering with a weight of 43 grams and incorporated an inertial measurement unit, ultrasonic sensors, GPS, and radio frequency transmission for control and data transmission.
Stars are formed from clouds of dust and gas collapsing under gravity. They spend most of their life fusing hydrogen into helium in their cores, glowing from the heat and pressure of nuclear fusion. When stars run out of fuel to burn in their cores, they die - smaller stars may become white dwarfs, while larger stars explode as supernovae. The material from exploded stars then disperses to form new dust clouds, starting the next generation of star formation.
This document discusses drones or unmanned aerial vehicles (UAVs). It provides an introduction to UAVs, including a brief history starting from 1916. It describes the key sub-systems of UAVs including the unmanned aircraft, control system, control link and support equipment. The document discusses various design parameters and applications of UAVs, including disaster relief, search and rescue, sports and armed attacks. It also discusses UAV programs in India and compares Indian and U.S. drones. Finally, it outlines some disadvantages of UAVs such as civilian casualties and hacking risks.
An unmanned aerial vehicle (UAV), commonly known as a Drone, is an aircraft without a human pilot on board. UAVs can be remote controlled aircraft (e.g. flown by a pilot at a ground control station) or can fly autonomously based on pre-programmed flight plans or more complex dynamic automation systems
A UAV is defined as being capable of controlled, sustained level flight and powered by a jet or reciprocating engine. In addition, a cruise missile can be considered to be a UAV, but is treated separately on the basis that the vehicle is the weapon.
Unmanned Aerial Vehicles (UAVs) are aircrafts that fly without any humans being onboard. They are either remotely piloted, or piloted by an onboard computer. This kind of aircrafts can be used in different military missions such as surveillance, reconnaissance, battle damage assessment, communications relay, minesweeping, hazardous substances detection and radar jamming. However they can be used in other than military missions like detection of hazardous objects on train rails and investigation of infected areas. Aircrafts that are able of hovering and vertical flying can also be used for indoor missions like counter terrorist operations
To download this ppt click on this link
https://adf.ly/PdL4V
This document provides an overview of a talk given by Dirk Gorissen on UAV and robotics technology. The talk discusses the DECODE project, which aims to develop a system to help designers understand the impact of decisions made during complex aerospace system design. As a case study, the project is using UAV design for search and rescue operations. The talk outlines UAV and robot technologies, the DECODE design system, rapid manufacturing techniques like 3D printing, and future work such as a two-seas monitoring project using UAVs.
The document provides a summary of Arthur P. McGregor's work experience, including his current role as Associate Director for Kinetic Weapons Technologies at the Office of the Assistant Secretary of Defense, where he oversees $750M in annual spending on kinetic weapons science and technology programs. It details his participation in reviews of major defense acquisition programs and technology assessments. The document also outlines his prior experience in university research programs at the Department of Defense and engineering roles related to night vision and electro-optical systems.
Performance comparison of row per slave and rows set per slave method in pvm ...eSAT Journals
Abstract Parallel computing operates on the principle that large problems can often be divided into smaller ones, which are then solved concurrently to save time by taking advantage of non-local resources and overcoming memory constraints. Multiplication of larger matrices requires a lot of computation time. This paper deals with the two methods for handling Parallel Matrix Multiplication. First is, dividing the rows of one of the input matrices into set of rows based on the number of slaves and assigning one rows set for each slave for computation. Second method is, assigning one row of one of the input matrices at a time for each slave starting from first row to first slave and second row to second slave and so on and loop backs to the first slave when last slave assignment is finished and repeated until all rows are finished assigning. These two methods are implemented using Parallel Virtual Machine and the computation is performed for different sizes of matrices over the different number of nodes. The results show that the row per slave method gives the optimal computation time in PVM based parallel matrix multiplication. Keywords: Parallel Execution, Cluster Computing, MPI (Message Passing Interface), PVM (Parallel Virtual Machine) RAM (Random Access Memory).
This document compares two methods for parallel matrix multiplication using PVM (Parallel Virtual Machine): the row per slave method and the rows set per slave method. It finds that the row per slave method provides optimal computation time. The row per slave method assigns each slave a single row from the first matrix to compute, while the rows set per slave method assigns each slave a set of rows. Experimental results on matrices of varying sizes show the row per slave method takes less time, with an average 50% reduction in computation time compared to the rows set per slave method.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijics
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the
performance of an aircraft. This is required both for the success of the mission and the survivability of the
aircraft in the harsh enemy conditions.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijcisjournal
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the performance of an aircraft. This is required both for the success of the mission and the survivability of the aircraft in the harsh enemy conditions.
A system is envisioned comprising of the accurate modeling of the physical world and the accurate model of control system. An interoperable system which can work seamlessly together will provide mission planners, System integrators, aeronautical/aerospace engineers a milieu wherein the Control System designer who is found wanted as far as the physical world is concerned is given a system which can simulate the real world in lab conditions. To achieve this, we combine the two most promising environments prevalent in the industry today namely Systems tool kit for modeling the operational environment MATLAB and LabVIEW for modeling the control system environment. Using a Math script window of LabVIEW, we have designed the aircraft model and controlling the variables of an aircraft using a simulation loop of a LabVIEW. The different flight conditions were arrived using Orthogonal Array (OA) based on different Aircraft weight, Altitude, Mach number configurations. This attempts to span the aircrafts across the regimes in aircrafts flight envelope. A system comprising of both, with seamless UDP based connection between the two is developed to expedite the process of development of feasible control system design and verification which allows the aircrafts to undertake complex mission. This system we believe would answer questions of limits of the aircrafts maneuverability and survivability in terms of its limitation concerning control system design and development of commercial fighter aircrafts, UAV's and Quad copters.
This document describes a web-based worm simulator that allows users to run network worm simulations remotely through a web browser. The simulator is hosted on a server and uses a client-server architecture where the front-end interface runs as a web applet and the backend simulation runs on the server. Users input simulation parameters through the front-end, which are sent to the backend server to run the simulation. The backend generates a network topology, populates it with hosts, runs the worm simulation, and stores the results. The front-end then displays the results graphically for the user. This allows platform-independent, remote access to running worm simulations through a standard web browser interface.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
This document outlines a test plan to evaluate initial data link terminal air traffic control (ATC) services through simulations. The plan involves training new air traffic controllers on the services over 4 days, then having them participate in full-scale simulations to validate the service designs and assess the impact of implementing the services. Data will be collected from the simulations and controller ratings/feedback to analyze impacts on communications, workload, and errors. The results will help guide development of operational data link systems.
This document discusses developing a functional model for predicting friction factor in turbulent pipe flow using an artificial neural network (ANN). The objectives are to both predict friction factor values and extract a functional model from the trained ANN. The ANN will be trained on normalized input data using backpropagation. MATLAB will be used to generate data, create and train the ANN, and extract the functional model, which will then be tested on separate validation data to evaluate accuracy. The goal is to provide a simple predictive model for friction factor that does not require ANN software.
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Similar to UsingNeuralNetworkstoTrainUnmannedAerial (20)
Neural Network Implementation Control Mobile Robot
UsingNeuralNetworkstoTrainUnmannedAerial
1. Using Neural Networks to Train Unmanned Aerial
Vehicle (UAV) Controller Data
ABSTRACT
This work proposes the use of neural networks for modeling of
a dynamical complex system. This model will be useful for the
development and utilization of the helicopter as an Unmanned Aerial
Vehicle (UAV). The treatment of the training commands, with
which the present results are achieved, and the feedforward
multilayer perceptron training network is examined through out this
work.
With the current Matlab software and neural network toolbox
support, we have been able to accomplish the creation of a specific
neural networks architecture; a feedforward multilayer perceptron
artificial neural network. Along with the ability to calculate and train
flight parameters such as yaw, pitch, and roll. In order to accomplish
this goal, we propose to a) Develop a neural network capable of
outputting x-y-z positions of an UAV during flight and b) Integrate
the output of the trained neural network into the robust UAV
controller.
BACKGROUND
There are many applications for an unmanned aerial vehicle
(UAV). Some of which are used for observations, mapping, mobile
target acquisition, and air-to-ground warfare missions for military
utility. For civil functions, purposes range from, but not limited to,
surveillance, inspections and imagery acquisition tasks. The type of
vehicle used ranges as well, but the most suitable vehicle for many
of the previously mentioned tasks is the helicopter for the reason that
is able to offer a good compromise between maneuverability,
forward-flight speed and the capacity of hovering.
For the development of a robust controller that permits for
autonomous flight, mathematical models of the helicopter’s flight
dynamics are vital. Flight dynamics have a choice to be modeled
with analytical, empirical, or mixed models. By combining the
analytical expressions and empirical approximations, the mixed
model is created that is able to reach a good compromise between
accuracy and speed. Also by using a multilayer perceptron
architecture, multiple layers of neurons with nonlinear transfer
functions allow the network to learn nonlinear and linear
relationships between input and output vectors (1), thus allowing the
network to produce output values outside -1 and 1 ranges;
comfortable qualified input data for the accurate target on UAV
controller.
This research aims at creating a successful feedforward network
with a multilayer perceptron architecture that will train empirical
flight data to be the input for the autonomous flight controller on a
UAV. By using the mathematical precision of Matlab, data is stored
as matrices to be trained and tested by a network created with the
programs command input feature.
Pablo Vazquez1, Dr. Amar Raheja, Ph. D.2, Dr. Subodh Bhandari, Ph.D.3, Dr. Fang “Daisy” Tang, Ph.D.2, Kevin Ortega2, Alexander Gutierrez2
1Citrus Community College, 2Computer Science Department, Cal Poly Pomona 3Aerospace Engineering Department, Cal Poly Pomona California State Polytechnic University, Pomona
OBJECTIVE
The goal of the project is to construct a robust nonlinear flight
controller using neural networks. With different
applications of neural networks, especially for the
networks ability to learn based on feedback data, it
is possible to implicate an artificial neural networks
into the control system of the aerial vehicle. One
type of artificial neural network that can
accommodate to the necessities of aerial maneuvers
is the multilayer perceptron, also known as MLP.
This type of artificial neural network was used to explore the
capabilities of neural networks delivering specific
commands to scheme the path of an unmanned aerial
vehicle before, during, and after flight. Real flight
data acquired from a human-piloted UAV will be
trained for a network and the same network will test
new flight data for accuracy results. Scripts with
commands for the creation, training, and testing of a
network using a feedforward algorithm will be
thoroughly crafted.
METHODS
Acquire flight data from human-piloted UAV
-Differentiate and separate flight data into specific parameters matrices; roll,
pitch, yaw
-Within each flight parameters matrix, distinguish between hovering states and
maneuver states; which row and columns from the specific parameter matrix
produced a change in movement in respect to the one of the three
parameters. These will be the input for the network to create an output that
will consequently be used for the input of the UAV controls.
Create network
-Use Matlab’s editing tool to write a script with a list of commands in order to
create a network (newff) with a feedforward algorithm that will train the row
and columns of a flight parameter matrix which produced change in
movement in respect to the one of three parameters
-The activation functions and properties for the new feedforward function
(newff) are as follows;
100 hidden layers
unbounded range of input data
“tansig” nonlinear transfer function followed by a linear “purelin” transfer
function allowing to ultimately produce unbounded range of outputs.
-Run the network on Matlab’s command window
-Train the data with the network
-Use the following row and columns within the respective parameter matrix that
produced new sets of movements to test the network on.
-Continue and repeat until all three parameter matrices have been trained and
tested
Store results
-Store the output of the network to be used for input for the UAV controls
Compare results
-Plot histograms of mean squared errors for each trained and tested inputs of the
network
-Plot regression of output targets and actual outputs from network.
-Plot expected outputs and network outputs
-Compare
RESULTS
The following results were obtained from a series of
training patterns with data collected on real flight sessions
using a human-piloted UAV. As justified earlier, a feedforward
multi-layer perceptron network is used as the architecture for
training flight data to be inputted to a robust UAV control
system. Shown below are the results of the “pitch” flight
parameters, the UAV’s ability to maneuver up or down by
changing the angle it flies toward to in respect to the z-axis.
This network has a mean square error (MSE) of about .8948,
with which the outputs are used to input on the UAV
controller.
CONCLUSIONS
To conclude, the results confirm the hypothesis that the multilayer perceptron
neural network is competent enough to understand flight dynamics of an
Unmanned Aerial Vehicle
Vision for a future project will be to incorporate the trained data into a flight
oriented controller for a UAV and perform simulations to test and improve its
precision.
Improve training results to reduce the error after each training.
Produce the development of a network particularly for understanding and training
flight dynamics
Fabricate a network architecture for a multilayer perceptron that produced precise
results that enables input for a robust control system on a UAV
RESULTS
The graphs illustrate the success of the training network. Figure 3.
represents the Error Plot for the “Pitch” data Network. It plots all the errors in the
network to calculate the mean squared error (MSE). The MSE measures the
networks performance based on the error, by calculating the networks target
minus the networks output. It is shown to have the majority of the errors
surrounding the zero error anticipation. An error closer to zero indicates good
results from the training network. The skewed results can be a cause from the
first unsuccessful attempts of the network to correctly train the flight data, until
finally reaching reasonable values close to zero error. Figure 4 depicts the
Regression Graph for the “Pitch” data Network. It plots the expected (target) data
values vs. the actual (network output) values after training. Most of the data is
clustered near the bottom left corner where the line-of-best-of-fit and expected
target line meet. This is a good sign. The outliers, however, stray off the line-of-
best-fit away from the targeted values. A possible cause for this is the skewed
errors that the network first began displaying before it reached an MSE relatively
close to 0. Figure 5 shows the “Pitch” data Network Performance Graph after
200 Epochs. It shows that the trained network minimized error (MSE) to increase
best performance and results possible. The line converges to the best MSE
possible, hence the good quality of results. These figures doesn't indicate any
major problems with the training, however there is still room to improve the
training network to the point where error is significantly close to 0 and the
training output reaches the same expected values

ACKNOWLEDGEMENTS
This work is supported by the Race to STEM Program at Citrus College
in collaboration with the Cal Poly Pomona Summer Research Program.
Thanks to Amar Raheja, Ph. D.; Fang “Daisy” Tang, Ph. D.; Sobodh
Bhandari, Ph. D.; Kevin Ortega and Alexander Gutierrez at the CPP
Science Lab for their collaboration.
Thanks to Professor Lucia Riderer and Marianne Smith, Ph.D. at Citrus
College for the opportunity to participate.
For additional information please contact: Amar Raheja, Ph.D. at
California State Polytechnic University, Pomona CA
{raheja}@csupomona.edu
Fig. 4 Regression Graph for “Pitch” data Network. Expected
(target) vs Actual (network output).
REFERENCES
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rantz.pdf. Last accessed 19th July 2012
Fig. 1 Basic Feedforward Multilayer Perceptron Neural Network
Architecture
Fig. 3 Error Plot for “Pitch” data Network. Clusters surrounding
Zero Error
Fig. 5 “Pitch” data Network Performance Graph. After 200 Epochs,
the Network minimized error (MSE).
Fig. 2 In-depth Network Architecture showing path of input
vectors through the Transfer Functions (tansig and purelin) to
produce an unbounded Output