Electrical and Computer Engineering Commons^™

Development Of A Model And Imbalance Detection System For The Cal Poly Wind Turbine, Ryan Miki Takatsuka

Master's Theses

This thesis develops a model of the Cal Poly Wind Turbine that is used to determine if there is an imbalance in the turbine rotor. A theoretical model is derived to estimate the expected vibrations when there is an imbalance in the rotor. Vibration and acceleration data are collected from the turbine tower during operation to confirm the model is useful and accurate for determining imbalances in the turbine.

Digital signal processing techniques for analyzing the vibration data are explored and tested with simulation data. This includes frequency shifts, lock-in amplifiers, phase-locked loops, discrete Fourier transforms, and decimation filters. The …

Smart-Detect: An Iot Based Monitoring System For Oil Leak Detection, Youssef Mohamed Baiji

Electrical Engineering Theses

In the past couple of years, the oil and gas industry is aiming to reduce it’s day-to-day costs due to reasons such as reduction in oil prices, mass overproduction and so on. This has the Oil and Gas industries aiming for innovative ways to reduce costs and minimize nonproductive time. In order to accomplish this goal, oil companies need to improve and control measurements with more reliable but relatively cheaper systems. One of the methods is using Internet-of-Things (IoT) based monitoring systems which can help in remote monitoring. IoT is offering better solutions for oil and gas companies to reduce …

Increased Cyclist Safety Using An Embedded System, Matthew Ryan Heydorn

Theses and Dissertations

In order to reduce bicycle-vehicle collisions, we design and implement a cost effectiveembedded system to warn cyclists of approaching vehicles. The system uses an Odroid C2 singleboard computer (SBC) to do vehicle and lane detection in real time using only vision. The system warns cyclist are warned of approaching cars using both a smartphone app and an LED indicator. Due to the limited performance of the Odroid C2 and other low power and low cost SBCs,we found that existing detection algorithms run either too slowly or do not have sufficient accuracy to be practical. Our solution to these limitations is …

Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas

Open Access Theses & Dissertations

Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …

Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal

Theses and Dissertations--Computer Science

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …

Estimation Of Multi-Directional Ankle Impedance As A Function Of Lower Extremity Muscle Activation, Lauren Knop

Dissertations, Master's Theses and Master's Reports

The purpose of this research is to investigate the relationship between the mechanical impedance of the human ankle and the corresponding lower extremity muscle activity. Three experimental studies were performed to measure the ankle impedance about multiple degrees of freedom (DOF), while the ankle was subjected to different loading conditions and different levels of muscle activity. The first study determined the non-loaded ankle impedance in the sagittal, frontal, and transverse anatomical planes while the ankle was suspended above the ground. The subjects actively co-contracted their agonist and antagonistic muscles to various levels, measured using electromyography (EMG). An Artificial Neural Network …

Computer-Aided Classification Of Impulse Oscillometric Measures Of Respiratory Small Airways Function In Children, Nancy Selene Avila

Open Access Theses & Dissertations

Computer-aided classification of respiratory small airways dysfunction is not an easy task. There is a need to develop more robust classifiers, specifically for children as the classification studies performed to date have the following limitations: 1) they include features derived from tests that are not suitable for children and 2) they cannot distinguish between mild and severe small airway dysfunction.

This Dissertation describes the classification algorithms with high discriminative capacity to distinguish different levels of respiratory small airways function in children (Asthma, Small Airways Impairment, Possible Small Airways Impairment, and Normal lung function). This ability came from innovative feature selection, …

Learning Models For Corrupted Multi-Dimensional Data: Fundamental Limits And Algorithms, Ishan Jindal

Wayne State University Dissertations

Developing machine learning models for unstructured multi-dimensional datasets such as datasets with unreliable labels and noisy multi-dimensional signals with or without missing information have becoming a central necessity. We are not always fortunate enough to get noise-free datasets for developing classification and representation models. Though there is a number of techniques available to deal with noisy datasets, these methods do not exploit the multi-dimensional structures of the signals, which could be used to improve the overall classification and representation performance of the model.

In this thesis, we develop a Kronecker-structure (K-S) subspace model that exploits the multi-dimensional structure of the …

Machine Learning Based Computational Electromagnetic Analysis For Electromagnetic Compatibility, L. (Lijun) J. Jiang, H. M. Yao, H. H. Zhang, Y. W. Qin

Electrical and Computer Engineering Faculty Research & Creative Works

While machine learning is becoming a demanding request in every corner of modern technology development, we are trying to see if we could make computational electromagnetic algorithms compatible to machine learning methods. In this paper, we introduce two efforts in line with this direction: solving method of moments (MoM) can be seen as a training training process. Consequently, the artificial neural network (ANN) could be used to solve MoM naturally through training. Amazon Web Service (AWS) can be used as the computations platform to utilize the existing hardware and software resources for machine learning. Another effort regarding to the nonlinear …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

An Unsupervised Cluster: Learning Water Customer Behavior Using Variation Of Information On A Reconstructed Phase Space, Michele Rae Bizub Malinowski

Dissertations (1934 -)

The unsupervised clustering algorithm described in this dissertation addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into ‘normal’ and ‘unusual’ customer behavior patterns. This research focuses upon individually metered water utility customers and includes both residential and commercial customer accounts serviced by utilities within North America. The contributions of this dissertation not only represent a novel academic work, but also solve a practical problem for …

Machine Learning Based Neural Network Solving Methods For The Fdtd Method, He Ming Yao, Li (Lijun) Jun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, two novel computational processes are proposed to solve Finite-Difference Time-Domain (FDTD) based on machine learning deep neural networks. The field and boundary conditions are employed to establish recurrent neural network FDTD (RNN-FDTD) model and convolution neural network FDTD (CNN-FDTD) model respectively. Numerical examples from scalar wave equations are provided to benchmark the performance of the proposed methods. The results demonstrate that the newly proposed methods could solve FDTD steps with satisfactory accuracy. According to our knowledge, these are unreported new approaches for machine learning based FDTD solving methods.

Learning Convolutional Neural Network For Face Verification, Elaheh Rashedi

Wayne State University Dissertations

Convolutional neural networks (ConvNet) have improved the state of the art in many applications. Face recognition tasks, for example, have seen a significantly improved performance due to ConvNets. However, less attention has been given to video-based face recognition. Here, we make three contributions along these lines.

First, we proposed a ConvNet-based system for long-term face tracking from videos. Through taking advantage of pre-trained deep learning models on big data, we developed a novel system for accurate video face tracking in the unconstrained environments depicting various people and objects moving in and out of the frame. In the proposed system, we …

Fault Classification And Location Identification On Electrical Transmission Network Based On Machine Learning Methods, Vidya Venkatesh

Theses and Dissertations

Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type …

Video Quality Prediction Under Time-Varying Loads, Obinna Izima, Ruairí De Fréin, Mark Davis

Conference papers

We are on the cusp of an era where we can responsively and adaptively predict future network performance from network device statistics in the Cloud. To make this happen, regression-based models have been applied to learn mappings between the kernel metrics of a machine in a service cluster and service quality metrics on a client machine. The path ahead requires the ability to adaptively parametrize learning algorithms for arbitrary problems and to increase computation speed. We consider methods to adaptively parametrize regularization penalties, coupled with methods for compensating for the effects of the time-varying loads present in the system, namely …

Offline And Online Density Estimation For Large High-Dimensional Data, Aref Majdara

Dissertations, Master's Theses and Master's Reports

Density estimation has wide applications in machine learning and data analysis techniques including clustering, classification, multimodality analysis, bump hunting and anomaly detection. In high-dimensional space, sparsity of data in local neighborhood makes many of parametric and nonparametric density estimation methods mostly inefficient.

This work presents development of computationally efficient algorithms for high-dimensional density estimation, based on Bayesian sequential partitioning (BSP). Copula transform is used to separate the estimation of marginal and joint densities, with the purpose of reducing the computational complexity and estimation error. Using this separation, a parallel implementation of the density estimation algorithm on a 4-core CPU is …

Data Fusion Techniques For Biomedical Informatics And Clinical Decision Support, Peng Guo

Doctoral Dissertations

"Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. …

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

USF Tampa Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …

Machine Learning Based Method Of Moments (Ml-Mom), He Ming Yao, Li (Lijun) Jun Jiang, Yu Wei Qin

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel method by rethinking the method of moments (MoM) solving process into a machine learning training process. Based on the artificial neural network (ANN), the conventional MoM matrix is treated as the training data set, based on which machine learning training process becomes conventional linear algebra MoM solving process. The trained result is the solution of MoM. The multiple linear regression (MLR) is utilized to train the model. Amazon Web Service (AWS) is used as the computations platform to utilize the existing hardware and software resources for machine learning. To verify the feasibility of the proposed …

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …

Prediction Of Graduation Delay Based On Student Characterisitics And Performance, Tushar Ojha

Electrical and Computer Engineering ETDs

A college student's success depends on many factors including pre-university characteristics and university student support services. Student graduation rates are often used as an objective metric to measure institutional effectiveness. This work studies the impact of such factors on graduation rates, with a particular focus on delay in graduation. In this work, we used feature selection methods to identify a subset of the pre-institutional features with the highest discriminative power. In particular, Forward Selection with Linear Regression, Backward Elimination with Linear Regression, and Lasso Regression were applied. The feature sets were selected in a multivariate fashion. High school GPA, ACT …

Predictive Analytics In Cardiac Healthcare And 5g Cellular Networks, Dilranjan S. Wickramasuriya

USF Tampa Graduate Theses and Dissertations

This thesis proposes the use of Machine Learning (ML) to two very distinct, yet compelling, applications – predicting cardiac arrhythmia episodes and predicting base station association in 5G networks comprising of virtual cells. In the first scenario, Support Vector Machines (SVMs) are used to classify features extracted from electrocardiogram (EKG) signals. The second problem requires a different formulation departing from traditional ML classification where the objective is to partition feature space into constituent class regions. Instead, the intention here is to identify temporal patterns in unequal-length sequences. Using Recurrent Neural Networks (RNNs), it is demonstrated that accurate predictions can be …

Machine Learning Based Mom (Ml-Mom) For Parasitic Capacitance Extractions, He Ming Yao, Yu Wei Qin, Li (Lijun) Jun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This paper is a rethinking of the conventional method of moments (MoM) using the modern machine learning (ML) technology. By repositioning the MoM matrix and unknowns in an artificial neural network (ANN), the conventional linear algebra MoM solving is changed into a machine learning training process. The trained result is the solution. As an application, the parasitic capacitance extraction broadly needed by VLSI modeling is solved through the proposed new machine learning based method of moments (ML-MoM). The multiple linear regression (MLR) is employed to train the model. The computations are done on Amazon Web Service (AWS). Benchmarks demonstrated the …

Quantitative Estimation Of Causality And Predictive Modeling For Precipitation Observation Sites And River Gage Sensors, Tri Vu Nguyen

LSU Master's Theses

This project seeks to investigate two questions: correlations from precipitation measurement sensors to river gage sensors, and predictive modeling of peak river gage heights during precipitation events. First, if correlations can be quantified, then a predictive model can be explored to predict peak water levels at river gage sensors, in response to precipitation inputs. Answering both research questions can provide early flood detection benefits and provide quantitative time assessments for flood risks. An extensive data-driven study was conducted across a geographical area of the U.S, spanning the time period 2008-2016 to identify river gage sensors that are closely correlated to …

Osem : Occupant-Specific Energy Monitoring., Anand S. Kulkarni

Electronic Theses and Dissertations

Electricity has become prevalent in modern day lives. Almost all the comforts people enjoy today, like home heating and cooling, indoor and outdoor lighting, computers, home and office appliances, depend on electricity. Moreover, the demand for electricity is increasing across the globe. The increasing demand for electricity and the increased awareness about carbon footprints have raised interest in the implementation of energy efficiency measures. A feasible remedy to conserve energy is to provide energy consumption feedback. This approach has suggested the possibility of considerable reduction in the energy consumption, which is in the range of 3.8% to 12%. Currently, research …

Global Thermospheric Response To Geomagnetic Storms, Padmashri Suresh

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The terrestrial atmospheric region between the altitudes of 90 km and 600 km is known as the thermosphere region. The thermosphere is continuously modulated by particle emissions and magnetic fields that originate from the sun. These fields and emissions are intensified during events known as geomagnetic storms which alter the state of the thermosphere by dumping gigawatts of energy. This energy is mostly deposited in the lower thermosphere regions of 150 km and below and can potentially have hazardous repercussions on the technological assets of mankind. These storms can disrupt radio communication systems, interrupt electric power systems, threaten the safety …

Accelerated Hyperspectral Unmixing With Endmember Variability Via The Sum-Product Algorithm, Charan Puladas

Browse all Theses and Dissertations

The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop health monitoring, to military surveillance and combatant identification. However, due to limited spatial resolution, multiple ground materials generally contribute, i.e. mix, to form the spectrum recorded for a single pixel. The unmixing problem considers the inverse problem of determining the underlying material spectra, called endmembers, from sensor measurements. While classical unmixing approaches were deterministic in nature and did not attempt to identify in-scene materials, recent methods use labeled training data to generate statistical models of endmember …

Study Of Machine Learning Methods In Intelligent Transportation Systems, Vishal Jha

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine learning and data mining are currently hot topics of research and are applied in database, artificial intelligence, statistics, and so on to discover valuable knowledge and the patterns in big data available to users. Data mining is predominantly about processing unstructured data and extracting meaningful information from them for end users to help take business decisions. Machine learning techniques use mathematical algorithms to find a pattern or extract meaning out from big data. The popularity of such techniques in analyzing business problems has been enhanced by the arrival of big data.

The main objective of this thesis is to …

Performance Analysis Of Hybrid Algorithms For Lossless Compression Of Climate Data, Bharath Chandra Mummadisetty

UNLV Theses, Dissertations, Professional Papers, and Capstones

Climate data is very important and at the same time, voluminous. Every minute a new entry is recorded for different climate parameters in climate databases around the world. Given the explosive growth of data that needs to be transmitted and stored, there is a necessity to focus on developing better transmission and storage technologies. Data compression is known to be a viable and effective solution to reduce bandwidth and storage requirements of bulk data. So, the goal is to develop the best compression methods for climate data.

The methodology used is based on predictive analysis. The focus is to implement …

Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas

Open Access Theses & Dissertations

Every year thousands of people are involved in traffic accidents, some of which are fatal. An important percentage of these fatalities are caused by human error, which could be prevented by increasing the awareness of drivers and the autonomy of vehicles. Since driver assistance systems have the potential to positively impact tens of millions of people, the purpose of this research is to study the micro-Doppler characteristics of vulnerable urban traffic components, i.e. pedestrians and bicyclists, based on information obtained from radar backscatter, and to develop a classification technique that allows automatic target recognition with a vehicle integrated system. For …