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Novel Methods For Permanent Magnet Demagnetization Detection In Permanent Magnet Synchronous Machines, Min Zhu
Electronic Theses and Dissertations
Monitoring and detecting PM flux linkage is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. The key problems that need to be solved at this stage are to: 1) establish a demagnetization magnetic flux model that takes into account the influence of various nonlinear and complex factors to reveal the demagnetization mechanism; 2) explore the relationship between different factors and demagnetizing magnetic field, to detect the demagnetization in the early stage; and 3) propose post-demagnetization measures. This thesis investigates permanent magnet (PM) demagnetization detection for PMSM machines to achieve high-performance and reliable machine drive for practical industrial …
The Ecology Of Fecal Indicators, Dennis A. Gilfillan
The Ecology Of Fecal Indicators, Dennis A. Gilfillan
Electronic Theses and Dissertations
Animal and human wastes introduce pathogens into rivers and streams, creating human health and economic burdens. While direct monitoring for pathogens is possible, it is impractical due to the sporadic distribution of pathogens, cost to identify, and health risks to laboratory workers. To overcome these issues, fecal indicator organisms are used to estimate the presence of pathogens. Although fecal indicators generally protect public health, they fall short in their utility because of difficulties in public health risk characterization, inconsistent correlations with pathogens, weak source identification, and their potential to persist in environments with no point sources of fecal pollution. This …
Machine Learning Based Early Fault Diagnosis Of Induction Motor For Electric Vehicle Application, Eshaan Ghosh
Machine Learning Based Early Fault Diagnosis Of Induction Motor For Electric Vehicle Application, Eshaan Ghosh
Electronic Theses and Dissertations
Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in …
Machine Learning For Omics Data Analysis., Ameni Trabelsi
Machine Learning For Omics Data Analysis., Ameni Trabelsi
Electronic Theses and Dissertations
In proteomics and metabolomics, to quantify the changes of abundance levels of biomolecules in a biological system, multiple sample analysis steps are involved. The steps include mass spectrum deconvolution and peak list alignment. Each analysis step introduces a certain degree of technical variation in the abundance levels (i.e. peak areas) of those molecules. Some analysis steps introduce technical variations that affect the peak areas of all molecules equally while others affect the peak areas of a subset of molecules with varying degrees. To correct these technical variations, some existing normalization methods simply scale the peak areas of all molecules detected …
Identification Of User Behavioural Biometrics For Authentication Using Keystroke Dynamics And Machine Learning, Sowndarya Krishnamoorthy
Identification Of User Behavioural Biometrics For Authentication Using Keystroke Dynamics And Machine Learning, Sowndarya Krishnamoorthy
Electronic Theses and Dissertations
This thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision …
Acceleration Of K-Nearest Neighbor And Srad Algorithms Using Intel Fpga Sdk For Opencl, Liyuan Liu
Acceleration Of K-Nearest Neighbor And Srad Algorithms Using Intel Fpga Sdk For Opencl, Liyuan Liu
Electronic Theses and Dissertations
Field Programmable Gate Arrays (FPGAs) have been widely used for accelerating machine learning algorithms. However, the high design cost and time for implementing FPGA-based accelerators using traditional HDL-based design methodologies has discouraged users from designing FPGA-based accelerators. In recent years, a new CAD tool called Intel FPGA SDK for OpenCL (IFSO) allowed fast and efficient design of FPGA-based hardware accelerators from high level specification such as OpenCL. Even software engineers with basic hardware design knowledge could design FPGA-based accelerators. In this thesis, IFSO has been used to explore acceleration of k-Nearest-Neighbour (kNN) algorithm and Speckle Reducing Anisotropic Diffusion (SRAD) simulation …
Machine Learning Approaches For Cancer Analysis, Alkhateeb Abedalrhman
Machine Learning Approaches For Cancer Analysis, Alkhateeb Abedalrhman
Electronic Theses and Dissertations
In addition, we propose many machine learning models that serve as contributions to solve a biological problem. First, we present Zseq, a linear time method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors, such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score …
Gaining Scientific And Engineering Insight Into Ground Motion Simulation Through Machine Learning And Approximate Modeling Approaches, Naeem Khoshnevis
Gaining Scientific And Engineering Insight Into Ground Motion Simulation Through Machine Learning And Approximate Modeling Approaches, Naeem Khoshnevis
Electronic Theses and Dissertations
This dissertation presents a series of methods for gaining scientific and engineering insight into earthquake ground motion simulation in three areas: synthetic validation, attenuation modeling, and nonlinear effects estimation. First, I present guidelines to reduce the number of metrics used to evaluate the goodness-of-fit (GOF) between ground motion synthetics and recorded data in an application independent framework. Validation of ground motion simulations is mostly done using metrics that are user- or application-biased. Comparisons between synthetics from regional scale ground motion simulations and recorded data from past earthquakes provide opportunities to approach the problems using data-driven methods. I used a combination …
Automated Artifact Removal And Detection Of Mild Cognitive Impairment From Single Channel Electroencephalography Signals For Real-Time Implementations On Wearables, Saleha Khatun
Electronic Theses and Dissertations
Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has …
Digital Forensic Tools & Cloud-Based Machine Learning For Analyzing Crime Data, Majeed Kayode Raji
Digital Forensic Tools & Cloud-Based Machine Learning For Analyzing Crime Data, Majeed Kayode Raji
Electronic Theses and Dissertations
Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis documents consists of three studies related to data analysis. The first study involves analyzing data from an android smartphone while making a comparison between …