This document describes Hsu-Yung Cheng's master's thesis on object tracking, trajectory analysis, and event detection in intelligent video systems, advised by Jenq-Neng Hwang at the University of Washington. The thesis involves developing algorithms for object tracking using background subtraction, Kalman filtering and data association, trajectory analysis using angle features and hidden Markov models, and event detection through rule-based and anomalous trajectory classification methods. Experimental results demonstrate high tracking and classification accuracy.
The document summarizes a research project on multi-resolution data fusion using agent-based sensors. The project aims to develop collaborative signal processing techniques that are energy-aware, fault-tolerant, and progressively improve accuracy. Key accomplishments include developing mobile agent-based collaborative signal processing, energy-aware task scheduling algorithms, analytical battery modeling, and sensor deployment algorithms. The project has resulted in several publications and integrated some techniques successfully, while other integration efforts faced challenges.
A Brain Computer Interface Speller for Smart DevicesMahmoud Helal
This document presents a motor imagery-based brain-computer interface speller for mobile devices. It introduces a motor imagery structure model consisting of pre-processing, feature extraction, dimensionality reduction, and classification blocks. It develops an autoencoder-based dimensionality reduction method and compares it to PCA. It also develops a Hex-O-Spell mobile application using motor imagery to spell words. Results show the autoencoder approach achieves better performance than PCA. Testing on three subjects demonstrates the utility of the Hex-O-Spell mobile application. Future work involves enhancing the methods and application.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
This document presents a methodology for detecting epileptic seizures using statistical features in the empirical mode decomposition (EMD) domain. The objective is to transform EEG signals into the EMD domain, extract statistical and chaotic features, and design an artificial neural network classifier to diagnose epilepsy and detect seizures. Statistical features like variance, skewness, and kurtosis show larger differences between healthy and epileptic EEG signals in the EMD domain compared to the original signals. Chaotic features like largest Lyapunov exponent and correlation dimension also differ more between healthy and epileptic signals for some intrinsic mode functions. The proposed method achieves 100% accuracy for seizure detection and epilepsy diagnosis using only 3 statistical features from selected IMFs. Including additional
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...Victor Asanza
This document discusses device-free indoor localization using machine learning techniques at 28 GHz. The methodology uses ray tracing to generate fingerprint data and selects features from received power values. A random forest algorithm is used for classification and regression training on global and combined classifiers. Results show that combining independent classifiers from one or two transmitters reduces positioning error by at least 16-19% compared to global classification, and by at least 36-37% when combining two transmitters with classification-regression. The size and number of partition classes impacts error, and additional small improvements are achieved through classification-regression combination.
Particle Learning in Online Tool Wear Diagnosis and PrognosisJianlei Zhang, PhD
Automated Tool condition monitoring is critical in intelligent manufacturing to improve both productivity and sustainability of manufacturing operations. Estimation of tool wear in real-time for critical machining operations can improve part quality and reduce scrap rates. This paper proposes a probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process. By applying PL, the method helps to avoid developing a complex closed form formulation for a specific tool wear model. It increases the robustness of the algorithm and reduces the time complexity of computation. The application of the PL approach is tested using experiments performed on a milling machine. We have demonstrated one-step and two-step look ahead tool wear state prediction using online indirect measurements obtained from vibration signals. Additionally, the study also estimates remaining useful life (RUL) of the cutting tool inserts.
Threshold adaptation and XOR accumulation algorithm for objects detectionIJECEIAES
Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and Gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.
The document summarizes a research project on multi-resolution data fusion using agent-based sensors. The project aims to develop collaborative signal processing techniques that are energy-aware, fault-tolerant, and progressively improve accuracy. Key accomplishments include developing mobile agent-based collaborative signal processing, energy-aware task scheduling algorithms, analytical battery modeling, and sensor deployment algorithms. The project has resulted in several publications and integrated some techniques successfully, while other integration efforts faced challenges.
A Brain Computer Interface Speller for Smart DevicesMahmoud Helal
This document presents a motor imagery-based brain-computer interface speller for mobile devices. It introduces a motor imagery structure model consisting of pre-processing, feature extraction, dimensionality reduction, and classification blocks. It develops an autoencoder-based dimensionality reduction method and compares it to PCA. It also develops a Hex-O-Spell mobile application using motor imagery to spell words. Results show the autoencoder approach achieves better performance than PCA. Testing on three subjects demonstrates the utility of the Hex-O-Spell mobile application. Future work involves enhancing the methods and application.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
This document presents a methodology for detecting epileptic seizures using statistical features in the empirical mode decomposition (EMD) domain. The objective is to transform EEG signals into the EMD domain, extract statistical and chaotic features, and design an artificial neural network classifier to diagnose epilepsy and detect seizures. Statistical features like variance, skewness, and kurtosis show larger differences between healthy and epileptic EEG signals in the EMD domain compared to the original signals. Chaotic features like largest Lyapunov exponent and correlation dimension also differ more between healthy and epileptic signals for some intrinsic mode functions. The proposed method achieves 100% accuracy for seizure detection and epilepsy diagnosis using only 3 statistical features from selected IMFs. Including additional
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...Victor Asanza
This document discusses device-free indoor localization using machine learning techniques at 28 GHz. The methodology uses ray tracing to generate fingerprint data and selects features from received power values. A random forest algorithm is used for classification and regression training on global and combined classifiers. Results show that combining independent classifiers from one or two transmitters reduces positioning error by at least 16-19% compared to global classification, and by at least 36-37% when combining two transmitters with classification-regression. The size and number of partition classes impacts error, and additional small improvements are achieved through classification-regression combination.
Particle Learning in Online Tool Wear Diagnosis and PrognosisJianlei Zhang, PhD
Automated Tool condition monitoring is critical in intelligent manufacturing to improve both productivity and sustainability of manufacturing operations. Estimation of tool wear in real-time for critical machining operations can improve part quality and reduce scrap rates. This paper proposes a probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process. By applying PL, the method helps to avoid developing a complex closed form formulation for a specific tool wear model. It increases the robustness of the algorithm and reduces the time complexity of computation. The application of the PL approach is tested using experiments performed on a milling machine. We have demonstrated one-step and two-step look ahead tool wear state prediction using online indirect measurements obtained from vibration signals. Additionally, the study also estimates remaining useful life (RUL) of the cutting tool inserts.
Threshold adaptation and XOR accumulation algorithm for objects detectionIJECEIAES
Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and Gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.
The document proposes a methodology for selectively protecting convolutional neural networks (CNNs) deployed on GPUs. The methodology involves three stages: (1) detecting faults at runtime in matrix-matrix multiplication layers, (2) cataloging diagnostics techniques, and (3) selectively applying protections based on diagnostic coverage and performance impact. An evaluation on a Tiny YOLO-v3 object detection network found higher misclassification rates in initial layers and demonstrated diagnostic coverage between 2.61-3.8x network execution time with less than 5% performance impact. The methodology aims to safely deploy CNNs for safety-critical applications.
Design of Vibration Test Fixture for Opto-Electronic EquipmentIJMERJOURNAL
ABSTRACT : In this paper an attempt is made in designing vibration test fixture for opto-electronic equipment. It can be achieved by formulating basic or preliminary 3D CAD assembled model followed by attaching material to its components. The analysis carried by defining the contact conditions of every part of it, defining the fixing conditions, applying loads virtually, good quality meshing, followed by optimization of the same by taking concurrence from the results of Linear Harmonic Structural Vibration Analysis. SolidworksSimulation premium software is used for modelling and solving the Linear Harmonic Structural vibration analysis for all Environmental Stress Screening (ESS) tests like vibration, shock for elimination of failures before physical testing. The comparison between experimental results and simulation results are presented in this paper.
The document describes a proposed patient positioning system for maskless head and neck radiotherapy using a soft robot. The system uses a Kinect camera for vision-based sensing of patient head position. A soft robot consisting of an inflatable air bladder and pneumatic valves would manipulate the patient's head to correct for any motion during treatment. Preliminary results show the system was able to control 1 degree of freedom of motion (flexion/extension) of a mannequin head using proportional valve control and Kinect vision feedback to a control system. Further work is needed to validate the system for actual use in radiotherapy treatment.
Multivariate dimensionality reduction in cross-correlation analysis ivanokitov
1. Dimensionality reduction techniques like PCA can be used to optimize master event templates for cross-correlation based seismic event detection and location. 2. The document explores using various dimensionality reduction methods such as PCA, IPCA, and SSD on both real and synthetic seismic data to minimize the number of templates needed. 3. Representing seismic data as hypercomplex numbers or tensors can allow dimensionality reduction techniques to utilize the full multidimensional information from seismic arrays for improved master event design.
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
Challenges in Protection Relay Testing for Tomorrow’s Power Grid
Very many challenges related to protection relay testing are met today in the field and in the research industry.
There are often new and more complex applications such as wind turbines, very fast switching power electronics, photovoltaic cells and the battery and electric vehicle technologies. This implies among other things new converter topologies and smart grid considerations. These systems cannot be protected the same way as what was already being done, so this increases the complexity of the algorithms used.
Real-time simulation is a novel approach to design and test protection relay algorithms.
IEEE ICIP'22:Efficient Content-Adaptive Feature-based Shot Detection for HTTP...Vignesh V Menon
Abstract: Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in Video on Demand (VoD) HTTP Adaptive Streaming (HAS) applications. Therefore, we propose a novel Discrete Cosine Transform (DCT) feature-based shot detection and successive elimination algorithm for shot detection and compare it against the default shot detection algorithm of the x265 implementation of the High Efficiency Video Coding (HEVC) standard. Our experimental results demonstrate that our proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.
This document describes an improved direct multiple shooting approach combined with collocation and parallel computing to handle path constraints in dynamic nonlinear optimization problems. It combines direct multiple shooting with collocation discretization to transform the dynamic optimization problem into a nonlinear programming problem. The approach discretizes the time horizon into finite elements and applies collocation at the nodes. It then uses parallel computing to simulate each time interval independently. Case studies on controlling a Van der Pol oscillator and continuous stirred tank reactor are presented to demonstrate the method.
Towards Functional Safety compliance of Matrix-Matrix MultiplicationJavier Fernández Muñoz
The document discusses ensuring functional safety for machine learning-based autonomous systems. It proposes using checksum algorithms to detect errors in matrix-matrix multiplication, a key computation. The solution was evaluated on sequential and AVX-based multiplication, finding that checksums can achieve 100% diagnostic coverage with minimal performance impact depending on matrix size. Future work includes evaluating multiple bit errors and accelerators.
"An adaptive modular approach to the mining of sensor network ...butest
This document summarizes an adaptive modular approach for mining sensor network data using machine learning techniques. It presents a two-layer architecture that uses an online compression algorithm (PCA) in the first layer to reduce data dimensionality and an adaptive lazy learning algorithm (KNN) in the second layer for prediction and regression tasks. Simulation results on a wave propagation dataset show the approach can handle non-stationarities like concept drift, sensor failures and network changes in an efficient and adaptive manner.
This document summarizes Deepak Agarwal's master's dissertation on time-optimal control of the Z-axis motion in a wire bonding machine. Agarwal developed models of the wire bonder's rigid body and flexible dynamics. He then generated time-optimized motion commands using these models and implemented a two-degree-of-freedom controller with shaped commands, feedforward control, and feedback control to achieve precise motion with minimal residual vibrations in under 285.6 milliseconds. Simulation results showed the controller was able to successfully drive the system using the optimized commands.
This document discusses outdoor module characterization methods used to generate power matrices and correct for angle of incidence and spectral mismatch effects. It presents three outdoor methods for generating power matrices: 1) an automated two-axis tracker method used by TUV Rheinland PTL, 2) a manual two-axis tracker with mesh screens method also used by TUV, and 3) a method using fixed tilt modules or grid-tied arrays. It also examines the effects of angle of incidence on clean and soiled modules, and how to calculate and minimize spectral mismatch error for outdoor characterization methods.
This document presents a study on using vibration sensors and machine learning methods for occupancy detection. It discusses current energy issues in buildings and the need for an occupancy detection system. It describes using vibration sensors as an alternative to other sensor types. The study uses two wireless accelerometers to collect vibration data from a hallway and classroom as people walk by. Features are extracted from the data and a neural network is used to classify the number of occupants. The neural network model achieves over 90% accuracy in detecting 1-6 occupants. The study concludes neural networks provide the best results for occupancy detection compared to other machine learning models.
Surveillance scene classification using machine learningUtkarsh Contractor
The problem of scene classification in surveillance footage is of great importance for ensuring security in public areas. With challenges such as low quality feeds, occlusion, viewpoint variations, background clutter etc. The task is both challenging and error-prone. Therefore it is important to keep the false positives low to maintain a high accuracy of detection. In this paper, we adapt high performing CNN architectures to identify abandoned luggage in a surveillance feed. We explore several CNN based approaches, from Transfer Learning on the Imagenet dataset to object classification using Faster R-CNNs on the COCO dataset. Using network visualization techniques, we gain insight into what the neural network sees and the basis of classification decision. The experiments have been conducted on real world datasets, and highlights the complexity in such classifications. Obtained results indicate that a combination of proposed techniques outperforms the individual approaches.
Modeling Uncertainty For Middleware-based Streaming Power Grid ApplicationsJenny Liu
The document describes modeling uncertainty in middleware-based streaming applications for power grids. It presents a discrete-event model built in Ptolemy II to capture uncertainty from sources like middleware latency, network delays, and number of sensor streams. Monte Carlo simulations are run over this model by varying parameters like middleware concurrency and sensor streams. Regression analysis is then used to understand the relationship between these influential parameters and the end-to-end application run time.
A walk through the intersection between machine learning and mechanistic mode...JuanPabloCarbajal3
Talk at EURECOM, France.
It overviews regression in several of its forms: regularized, constrained, and mixed. It builds the bridge between machine learning and dynamical models.
Design and implementation of a Neural Network based image compression engine as part of Final Year Project by Jesu Joseph and Shibu Menon at Nanyang Technological University. The project won the best possible grade and excellent accolades from the research center.
To identify and simulate conventional type of disturbance on the overhead transmission line by using PSCAD / EMTDC software package
To develop mathematical model for various type of disturbance on overhead transmission line.
To develop a smart algorithm for fault detection using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO).
Amplification, ROADM and Optical Networking activities at CPqDCPqD
The document summarizes the amplification, ROADM, and optical networking activities at CPqD. It discusses the development of an automated amplifier characterizer, work on transient response issues in cascaded ROADMs, and dual-optimization algorithms for adaptive EDFA gain control and global WSS equalization. It also outlines collaboration with UTD on efficient numerical modeling of EDFA output power and network-wide signal power control strategies.
Park’s Vector Approach to detect an inter turn stator fault in a doubly fed i...cscpconf
An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach , using a neural network s.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
The document proposes a methodology for selectively protecting convolutional neural networks (CNNs) deployed on GPUs. The methodology involves three stages: (1) detecting faults at runtime in matrix-matrix multiplication layers, (2) cataloging diagnostics techniques, and (3) selectively applying protections based on diagnostic coverage and performance impact. An evaluation on a Tiny YOLO-v3 object detection network found higher misclassification rates in initial layers and demonstrated diagnostic coverage between 2.61-3.8x network execution time with less than 5% performance impact. The methodology aims to safely deploy CNNs for safety-critical applications.
Design of Vibration Test Fixture for Opto-Electronic EquipmentIJMERJOURNAL
ABSTRACT : In this paper an attempt is made in designing vibration test fixture for opto-electronic equipment. It can be achieved by formulating basic or preliminary 3D CAD assembled model followed by attaching material to its components. The analysis carried by defining the contact conditions of every part of it, defining the fixing conditions, applying loads virtually, good quality meshing, followed by optimization of the same by taking concurrence from the results of Linear Harmonic Structural Vibration Analysis. SolidworksSimulation premium software is used for modelling and solving the Linear Harmonic Structural vibration analysis for all Environmental Stress Screening (ESS) tests like vibration, shock for elimination of failures before physical testing. The comparison between experimental results and simulation results are presented in this paper.
The document describes a proposed patient positioning system for maskless head and neck radiotherapy using a soft robot. The system uses a Kinect camera for vision-based sensing of patient head position. A soft robot consisting of an inflatable air bladder and pneumatic valves would manipulate the patient's head to correct for any motion during treatment. Preliminary results show the system was able to control 1 degree of freedom of motion (flexion/extension) of a mannequin head using proportional valve control and Kinect vision feedback to a control system. Further work is needed to validate the system for actual use in radiotherapy treatment.
Multivariate dimensionality reduction in cross-correlation analysis ivanokitov
1. Dimensionality reduction techniques like PCA can be used to optimize master event templates for cross-correlation based seismic event detection and location. 2. The document explores using various dimensionality reduction methods such as PCA, IPCA, and SSD on both real and synthetic seismic data to minimize the number of templates needed. 3. Representing seismic data as hypercomplex numbers or tensors can allow dimensionality reduction techniques to utilize the full multidimensional information from seismic arrays for improved master event design.
Presentation on machine learning and materials science at Computing in Engineering Forum 2018, Machine Ground Interaction Consortium (MaGIC) 2018, Wisconsin, Madison, December 4, 2018
Challenges in Protection Relay Testing for Tomorrow’s Power Grid
Very many challenges related to protection relay testing are met today in the field and in the research industry.
There are often new and more complex applications such as wind turbines, very fast switching power electronics, photovoltaic cells and the battery and electric vehicle technologies. This implies among other things new converter topologies and smart grid considerations. These systems cannot be protected the same way as what was already being done, so this increases the complexity of the algorithms used.
Real-time simulation is a novel approach to design and test protection relay algorithms.
IEEE ICIP'22:Efficient Content-Adaptive Feature-based Shot Detection for HTTP...Vignesh V Menon
Abstract: Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in Video on Demand (VoD) HTTP Adaptive Streaming (HAS) applications. Therefore, we propose a novel Discrete Cosine Transform (DCT) feature-based shot detection and successive elimination algorithm for shot detection and compare it against the default shot detection algorithm of the x265 implementation of the High Efficiency Video Coding (HEVC) standard. Our experimental results demonstrate that our proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.
This document describes an improved direct multiple shooting approach combined with collocation and parallel computing to handle path constraints in dynamic nonlinear optimization problems. It combines direct multiple shooting with collocation discretization to transform the dynamic optimization problem into a nonlinear programming problem. The approach discretizes the time horizon into finite elements and applies collocation at the nodes. It then uses parallel computing to simulate each time interval independently. Case studies on controlling a Van der Pol oscillator and continuous stirred tank reactor are presented to demonstrate the method.
Towards Functional Safety compliance of Matrix-Matrix MultiplicationJavier Fernández Muñoz
The document discusses ensuring functional safety for machine learning-based autonomous systems. It proposes using checksum algorithms to detect errors in matrix-matrix multiplication, a key computation. The solution was evaluated on sequential and AVX-based multiplication, finding that checksums can achieve 100% diagnostic coverage with minimal performance impact depending on matrix size. Future work includes evaluating multiple bit errors and accelerators.
"An adaptive modular approach to the mining of sensor network ...butest
This document summarizes an adaptive modular approach for mining sensor network data using machine learning techniques. It presents a two-layer architecture that uses an online compression algorithm (PCA) in the first layer to reduce data dimensionality and an adaptive lazy learning algorithm (KNN) in the second layer for prediction and regression tasks. Simulation results on a wave propagation dataset show the approach can handle non-stationarities like concept drift, sensor failures and network changes in an efficient and adaptive manner.
This document summarizes Deepak Agarwal's master's dissertation on time-optimal control of the Z-axis motion in a wire bonding machine. Agarwal developed models of the wire bonder's rigid body and flexible dynamics. He then generated time-optimized motion commands using these models and implemented a two-degree-of-freedom controller with shaped commands, feedforward control, and feedback control to achieve precise motion with minimal residual vibrations in under 285.6 milliseconds. Simulation results showed the controller was able to successfully drive the system using the optimized commands.
This document discusses outdoor module characterization methods used to generate power matrices and correct for angle of incidence and spectral mismatch effects. It presents three outdoor methods for generating power matrices: 1) an automated two-axis tracker method used by TUV Rheinland PTL, 2) a manual two-axis tracker with mesh screens method also used by TUV, and 3) a method using fixed tilt modules or grid-tied arrays. It also examines the effects of angle of incidence on clean and soiled modules, and how to calculate and minimize spectral mismatch error for outdoor characterization methods.
This document presents a study on using vibration sensors and machine learning methods for occupancy detection. It discusses current energy issues in buildings and the need for an occupancy detection system. It describes using vibration sensors as an alternative to other sensor types. The study uses two wireless accelerometers to collect vibration data from a hallway and classroom as people walk by. Features are extracted from the data and a neural network is used to classify the number of occupants. The neural network model achieves over 90% accuracy in detecting 1-6 occupants. The study concludes neural networks provide the best results for occupancy detection compared to other machine learning models.
Surveillance scene classification using machine learningUtkarsh Contractor
The problem of scene classification in surveillance footage is of great importance for ensuring security in public areas. With challenges such as low quality feeds, occlusion, viewpoint variations, background clutter etc. The task is both challenging and error-prone. Therefore it is important to keep the false positives low to maintain a high accuracy of detection. In this paper, we adapt high performing CNN architectures to identify abandoned luggage in a surveillance feed. We explore several CNN based approaches, from Transfer Learning on the Imagenet dataset to object classification using Faster R-CNNs on the COCO dataset. Using network visualization techniques, we gain insight into what the neural network sees and the basis of classification decision. The experiments have been conducted on real world datasets, and highlights the complexity in such classifications. Obtained results indicate that a combination of proposed techniques outperforms the individual approaches.
Modeling Uncertainty For Middleware-based Streaming Power Grid ApplicationsJenny Liu
The document describes modeling uncertainty in middleware-based streaming applications for power grids. It presents a discrete-event model built in Ptolemy II to capture uncertainty from sources like middleware latency, network delays, and number of sensor streams. Monte Carlo simulations are run over this model by varying parameters like middleware concurrency and sensor streams. Regression analysis is then used to understand the relationship between these influential parameters and the end-to-end application run time.
A walk through the intersection between machine learning and mechanistic mode...JuanPabloCarbajal3
Talk at EURECOM, France.
It overviews regression in several of its forms: regularized, constrained, and mixed. It builds the bridge between machine learning and dynamical models.
Design and implementation of a Neural Network based image compression engine as part of Final Year Project by Jesu Joseph and Shibu Menon at Nanyang Technological University. The project won the best possible grade and excellent accolades from the research center.
To identify and simulate conventional type of disturbance on the overhead transmission line by using PSCAD / EMTDC software package
To develop mathematical model for various type of disturbance on overhead transmission line.
To develop a smart algorithm for fault detection using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO).
Amplification, ROADM and Optical Networking activities at CPqDCPqD
The document summarizes the amplification, ROADM, and optical networking activities at CPqD. It discusses the development of an automated amplifier characterizer, work on transient response issues in cascaded ROADMs, and dual-optimization algorithms for adaptive EDFA gain control and global WSS equalization. It also outlines collaboration with UTD on efficient numerical modeling of EDFA output power and network-wide signal power control strategies.
Park’s Vector Approach to detect an inter turn stator fault in a doubly fed i...cscpconf
An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach , using a neural network s.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
1. Information Processing Lab
Electrical Engineering
1
Object Tracking, Trajectory Analysis
and Event Detection in Intelligent
Video Systems
Student: Hsu-Yung Cheng
Advisor: Jenq-Neng Hwang, Professor
Department of Electrical Engineering
University of Washington
2. Information Processing Lab
Electrical Engineering
2
Outlines
Motivation
Object Tracking
Trajectory Analysis
Event Detection
Conclusions and Future Work
3. Information Processing Lab
Electrical Engineering
3
Motivation
Advantage of Video-based systems
Being able to capture a large variety of information
Relatively inexpensive
Easier to install, operate, and maintain
Applications
Security surveillance
Home care surveillance
Intelligent transportation systems
There is an urgent need for intelligent video systems
to replace human operators to monitor the areas under
surveillance.
9. Information Processing Lab
Electrical Engineering
9
Proposed Tracking Mechanism
Image
Frames
Moving
Object
Segmentation
Tracking Result
for Previous Frame
Tracking
List
Construction
Data
Association
and Update
Prediction
Background
Estimation
and Updating
Background
Image
Tracking Result
for Current Frame
Measurement
Candidate List
Current
Frame
Constructing
measurement
candidate List
for Each Target
10. Information Processing Lab
Electrical Engineering
10
Background Estimation and Updating
Based on Gaussian mixture models [Stauffer 1999]
Model the recent history of each pixel by a mixture of K
Gaussian distributions.
Every pixel value is checked among the existing K Gaussian
distributions for a match.
Update the weights for the K distributions and the parameters
of the matched distribution
The kth Gaussian is ranked by ( )
The top-ranked Gaussians are selected as the background models.
Pixel values that belong to background models are accumulated
and averaged as the background image.
The background image is updated for every certain interval of
time.
k
k
w
/ I
k
k
2
11. Information Processing Lab
Electrical Engineering
11
Moving Object Segmentation
Based on background subtraction
Fourth order moment
[S. Colonnese et al. Proc. of SPIE 2003]
Thresholding
)
,
(
)
,
(
4
)
4
(
)
ˆ
)
,
(
_
(
1
)
,
(
y
x
t
s
d
d m
t
s
img
diff
N
y
x
)
,
(
,
0
)
,
(
,
1
)
,
( )
4
(
)
4
(
y
x
if
y
x
if
y
x
S
d
d
12. Information Processing Lab
Electrical Engineering
12
Kalman Filter
Kalman filters are modeled on a Markov chain
built on linear operators perturbed by Gaussian
noises.
k
k
k
k w
x
F
x
1
At time k, each target has state
k
k
k
k v
x
H
y
and observation (measurement)
k
x
k
y
)
,
0
(
~ k
k Q
w
, where
, where )
,
0
(
~ k
k R
v
Kalman, R. E. "A New Approach to Linear Filtering and Prediction Problems,“
Transactions of the ASME - Journal of Basic Engineering Vol. 82: pp. 35-45, 1960.
13. Information Processing Lab
Electrical Engineering
13
Kalman Filter Phases
Predict
Update
Predicted State
Observed
Measurements
1
ˆ
k
k
x
k
y
Updated
State
Estimate
k
k
x |
ˆ
1
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
k
H
1
0
0
0
0
1
0
0
1
0
1
0
0
1
0
1
k
F
T
k
k
k
k
k
k v
u
v
u
y
x ]
[
14. Information Processing Lab
Electrical Engineering
14
Kalman Filter Phases
Predict Phase
Update Phase
k
T
k
k
k
k
k
k
k
k
k
k
k
Q
F
P
F
P
x
F
x
1
1
1
1
1
1
ˆ
ˆ
• Predicted Estimate
Covariance
• Predicted State k
k
k
k
k
k y
K
x
x ~
ˆ
ˆ 1
|
|
• Updated State Estimate
• Updated Estimate Covariance
• Kalman Gain
• Innovation (Measurement) Residual
• Innovation Covariance
1
1
|
k
T
k
k
k
k S
H
P
K
1
|
| )
(
k
k
k
k
k
k P
H
K
I
P
k
T
k
k
k
k
k R
H
P
H
S
1
|
1
|
ˆ
~
k
k
k
k
k x
H
y
y
16. Information Processing Lab
Electrical Engineering
16
Searching for measurement
candidate representation points
• Search for q1 and q2 in the two nxn
windows centered around p1 and p2,
respectively.
)
)
(
(
max
arg
)
,
(
1
1
c
y
x
q
S
q
O
Area
q
)
)
(
(
max
arg
)
,
(
2
2
c
y
x
q
S
q
O
Area
q
• Compute the dissimilarities
between the target object and
the potential measurement
candidates.
17. Information Processing Lab
Electrical Engineering
17
Data Association
To associate measurements with targets when
performing updates
Nearest Neighbor Data Association
For all the measurement in the validation gate of a
target, select the nearest measurement.
Probabilistic Data Association (PDA)
Joint Probabilistic Data Association (JPDA)
2
1
]
[
]
[
k
k
k
k
T
k
k
k x
H
y
S
x
H
y
18. Information Processing Lab
Electrical Engineering
18
Probabilistic Data Association
x
y1
y2
y3
Y . Bar-Shalom and E. Tse, “Tracking in a cluttered environment
with probabilistic data association,” Automatica, vol. 11, pp. 451-460, Sept. 1975.
}
|
{ k
j
j Y
X
P
1
2
3
Consider a single target independently
of others
denotes the event that the j th
measurement belongs to that target.
j
m
j
k
k
k
kj
j
m
j
kj
j
k x
H
y
y
y
1
1
|
1
)
ˆ
(
~
~
Combined (Weighted) Innovation
19. Information Processing Lab
Electrical Engineering
19
1
0
Modified PDA for
Video Object Tracking
)
)
(
)(
)
(
(
)
)(
(
2
2
m n
mn
m n
mn
m n
mn
mn
R
B
B
A
A
B
B
A
A
C
m
i
i
j
m
i
i
j
a
OverlapAre
a
OverlapAre
Similarity
Similarity
1
1
)
1
(
• To handle video objects (regions), incorporate the
following factor when computing j
• Similarity measure: cross correlation function
27. Information Processing Lab
Electrical Engineering
27
Angle Feature Extraction
)
~
~
,
~
~
(
1 k
t
t
k
t
t y
y
x
x
v
)
~
~
,
~
~
(
2 t
k
t
t
k
t y
y
x
x
v
2
1
2
1
1
cos
v
v
v
v
p
2
2
1
cos
v
v
v
v
h
h
h
Relative Angle Absolute Angle
28. Information Processing Lab
Electrical Engineering
28
Hidden Markov Model
Sunny Cloudy Rainy
P(walk) = 0.5
P(bike) = 0.4
P(bus) = 0.1
P(walk) = 0.4
P(bike) = 0.3
P(bus) = 0.3
P(walk) = 0.2
P(bike) = 0.1
P(bus) = 0.7
N states Si , i=1,…, N
Transition probability aij
Initial probability pi
Observation symbol probability bj(k)
A complete model l=(A,B,P)
A={aij}
B={bjk}
P={pi}
30. Information Processing Lab
Electrical Engineering
30
Three Problems in HMM
Given l, compute the probability that O is
generated by this model
How likely did O happen at this place?
Given l, find the most likely sequence of
hidden states that could have generated O
How did the weather change day-by-day?
Given a set of O, learn the most likely l
Train the parameters of the HMM
forward-backward algorithm
Viterbi algorithm
Baum-Welch algorithm
33. Information Processing Lab
Electrical Engineering
33
Number of Training
and Test sequences
Trajectory
Class
Training
Trajectories
Testing
Objects
Testing
Trajectories
Class 1 12 64 307
Class 2 11 18 66
Class 3 13 27 27
Class 4 5 20 20
Class 5 8 26 32
Class 6 8 29 45
Video for both training and testing
Video for testing only
34. Information Processing Lab
Electrical Engineering
34
Trajectory Classification Statistics
C 1 C 2 C 3 C 4 C 5 C 6 Accuracy
Class 1 307 0 0 0 0 0 100%
Class 2 0 64 0 0 0 2 97.4%
Class 3 2 0 25 0 0 0 92.6%
Class 4 0 0 0 20 0 0 100%
Class 5 1 0 0 0 31 0 96.8%
Class 6 0 2 0 0 0 43 95.5%
36. Information Processing Lab
Electrical Engineering
36
Event Detection
Type I Events
Simple rule-based decision logic
Entering a dangerous region
Stopping in the scene
Driving on the road shoulder
Type II Events
Based on trajectory classification results via HMM using angle features
Illegal U-turns or left turns
Anomalous trajectories
Type III Events
Based on trajectory classification results via HMM using speed features
Speed change
37. Information Processing Lab
Electrical Engineering
37
Conclusions and Future Works
Tracking
Kalman filtering for prediction
Modified PDA for data association
Basic Events
Simple rule-based decision logic
HMM
Higher Level Events
Combining basic events
More flexible models
Trajectories
HMMs
Basic Events
DBNs
Angle Features,
Speeds,
Positions,
Other Features
Intermediate
Events
HMMs
Type II,
Type III
Events
Type I
Events
Rule-based
Detection Logic
High
Level
Events