Engineering Commons^™

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

Design And Implementation Of Anomaly Detections For User Authentication Framework, Iman Abu Sulayman

Electronic Thesis and Dissertation Repository

Anomaly detection is quickly becoming a very significant tool for a variety of applications such as intrusion detection, fraud detection, fault detection, system health monitoring, and event detection in IoT devices. An application that lacks a strong implementation for anomaly detection is user trait modeling for user authentication purposes. User trait models expose up-to-date representation of the user so that changes in their interests, their learning progress or interactions with the system are noticed and interpreted. The reason behind the lack of adoption in user trait modeling arises from the need of a continuous flow of high-volume data, that is …

Objective Estimation Of Tracheoesophageal Speech Quality, Yousef S Ettomi Ali

Electronic Thesis and Dissertation Repository

Speech quality estimation for pathological voices is becoming an increasingly important research topic. The assessment of the quality and the degree of severity of a disordered speech is important to the clinical treatment and rehabilitation of patients. In particular, patients who have undergone total laryngectomy (larynx removal) produce Tracheoesophageal (TE) speech. In this thesis, we study the problem of TE speech quality estimation using advanced signal processing approaches. Since it is not possible to have a reference (clean) signal corresponding to a given TE speech (disordered) signal, we investigate in particular the non-intrusive techniques (also called single-ended or blind approaches) …

Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo

Electronic Thesis and Dissertation Repository

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …

Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian

Electronic Thesis and Dissertation Repository

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …

A Wearable Mechatronic Device For Hand Tremor Monitoring And Suppression: Development And Evaluation, Yue Zhou

Electronic Thesis and Dissertation Repository

Tremor, one of the most disabling symptoms of Parkinson's disease (PD), significantly affects the quality of life of the individuals who suffer from it. These people live with difficulties with fine motor tasks, such as eating and writing, and suffer from social embarrassment. Traditional medicines are often ineffective, and surgery is highly invasive and risky. The emergence of wearable technology facilitates an externally worn mechatronic tremor suppression device as a potential alternative approach for tremor management. However, no device has been developed for the suppression of finger tremor that has been validated on a human.

It has been reported in …

Blockchain-Based Distributed Network Architecture For Internet Of Things, Min Li

Electronic Thesis and Dissertation Repository

IoT networks have already been widely deployed due to their convenience and low-cost advantage. However, due to the lack of strong self-protection mechanisms and the imperfect network architectures, many IoT devices are vulnerable to malicious cyber-attacks, which will further threaten the availability and security of IoT applications. Therefore, securing the network infrastructure while protecting data from malicious or unauthorized devices/users become a vital aspect of IoT network design. In the thesis, two types of IoT security mechanisms are mainly investigated, namely, IoT routing protection and smart community device authentication.

By adopting the distributed consensus mechanism, we propose a blockchain-based reputation …

Novel Control Of Pv Solar Farms As Statcom (Pv-Statcom) For Frequency Control And Power Oscillation Damping, Mohammad Akbari Kelishadi

Electronic Thesis and Dissertation Repository

Frequency stability and low-frequency power oscillations are two major concerns in modern power systems. PV-STATCOM is a patented concept which enables PV inverters to provide STATCOM functions, during day and night, as well as real power modulation during daytime. This thesis aims to utilize PV-STATCOM capability effectively for enhancing frequency stability and power oscillations damping.

A novel, simultaneous real power-based Fast Frequency Response (FFR) and reactive power-based Power Oscillation Damping (POD) control is proposed for PV-STATCOMs. This control not only significantly reduces system under- and over-frequency deviations, but also uses the unutilized capacity of PV inverters to enhance damping of …

Exploitation Of Robust Aoa Estimation And Low Overhead Beamforming In Mmwave Mimo System, Yuyan Zhao

Electronic Thesis and Dissertation Repository

The limited spectral resource for wireless communications and dramatic proliferation of new applications and services directly necessitate the exploitation of millimeter wave (mmWave) communications. One critical enabling technology for mmWave communications is multi-input multi-output (MIMO), which enables other important physical layer techniques, specifically beamforming and antenna array based angle of arrival (AoA) estimation. Deployment of beamforming and AoA estimation has many challenges. Significant training and feedback overhead is required for beamforming, while conventional AoA estimation methods are not fast or robust. Thus, in this thesis, new algorithms are designed for low overhead beamforming, and robust AoA estimation with significantly reduced …

Split Dc Bus Converters For Power Electronic And Ac-Dc Microgrid Applications, Javad Khodabakhsh

Electronic Thesis and Dissertation Repository

Power electronic converters are used extensively for electrical power conversion in applications such as renewable energy systems, utility applications, and electric vehicles. Such converters are needed as it is rare for a source voltage to fit the needs of a load or a set of loads for any particular application. They consist of active semiconductor switches and passive components that are combined in circuit structures (topologies) that are operated with a control strategy. The focus of this thesis is on AC-DC and DC-DC converters and their applications in AC-DC microgrids.

AC-DC converters are typically two-stage converters that consist of a …

Energy-Efficient Multicarrier And Multicarrier-Cdma Systems, Moftah Ali

Electronic Thesis and Dissertation Repository

Design, analysis, and performance of Multicarrier (MC) and Multicarrier-Code Division Multiple Access (MC-CDMA) systems are considered. Specifically, Multicarrier Modulation (MCM) such as Orthogonal Frequency Division Multiplexing (OFDM) is used in these systems with emphasis on energy efficiency. These systems are examined for their performances over frequency-selective and frequency-non-selective channels. Methods are proposed for reduction of energy costs of an OFDM system. They are: i) Clipping (CL) based on symbol statistics; ii) Partial Transmit Sequence (PTS) using Magic square pattern and adjacent partitioning; and iii) Selective Mapping (SLM) and Tone Reservation (TR) with reduced complexity. These methods are described and algorithms …

Novel Night And Day Control Of Pv Solar Farm As Statcom (Pv-Statcom) For Critical Induction Motor Stabilization And Fidvr Alleviation, Sibin Mohan

Electronic Thesis and Dissertation Repository

Induction motors are globally used in several critical operations such as petrochemicals, mining, process control, etc., where their shutdown during faults causes significant financial loss. System faults can also lead to Fault Induced Delayed Voltage Recovery (FIDVR) causing service disruptions. Dynamic reactive power compensators such as SVC and STATCOM are conventionally employed to mitigate these issues, however, these are very expensive.

PV solar plants are growing at unprecedented rate globally and are likely to be installed near such critical motors. This thesis presents several novel applications of a patented technology of utilizing PV solar plants, both during night and day, …

Novel Pv Solar Farm Control As Statcom (Pv-Statcom) For Ssr Mitigation In Synchronous Generators And Wind Farms, Reza Salehi Sharafdarkolaee

Electronic Thesis and Dissertation Repository

Transmission lines across the world are extensively compensated by series capacitors to increase their power transfer capacity. However, series compensation can potentially cause subsynchronous resonance (SSR) which can lead to significant damage in shafts of synchronous generators and wind farms. Dynamic reactive power compensators such as STATCOM and SVCs are typically used for mitigation of SSR, but these are quite expensive.

Large scale PV solar farms are growing at a very rapid rate across the world. It is quite likely that they may get installed near synchronous generators or wind farms which are directly or indirectly interfaced with series compensated …

An Open-Source Integration Platform For Multiple Peripheral Modules With Kuka Robots, Mahyar Abdeetedal, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

This paper presents an open-source software interface for the integration of a Kuka robot with peripheral tools and sensors, KUI: Kuka User Interface. KUI is developed based on Kuka Fast Research Interface (FRI) which enables soft real-time control of the robot. Simulink Desktop Real-Time™ or any User Datagram Protocol (UDP) client can send real-time commands to Kuka robot via KUI. In KUI, third-party tools can be added and controlled synchronously with Kuka light-weight robot (LWR). KUI can send the control commands via serial communication to the attached devices. KUI can generate low-level commands using data acquisition (DAQ) boards. This feature …

Automated Segmentation Of Temporal Bone Structures, Daniel Allen

Electronic Thesis and Dissertation Repository

Mastoidectomy is a challenging surgical procedure that is difficult to perform and practice. As supplementation to current training techniques, surgical simulators have been developed with the ability to visualize and operate on temporal bone anatomy. Medical image segmentation is done to create three-dimensional models of anatomical structures for simulation. Manual segmentation is an accurate but time-consuming process that requires an expert to label each structure on images. An automatic method for segmentation would allow for more practical model creation. The objective of this work was to create an automated segmentation algorithm for structures of the temporal bone relevant to mastoidectomy. …

Machine Learning Classification Of Interplanetary Coronal Mass Ejections Using Satellite Accelerometers, Kelsey Doerksen

Electronic Thesis and Dissertation Repository

Space weather phenomena is a complex area of research as there are many different variables and signatures that are used to identify the occurrence of solar storms and Interplanetary Coronal Mass Ejections (ICMEs), with inconsistencies between databases and solar storm catalogues. The identification of space weather events is important from a satellite operation point of view, as strong geomagnetic storms can cause orbit perturbations to satellites in low-earth orbit. The Disturbance storm time (Dst) and the Planetary K-index (Kp) are common indices used to identify the occurrence of geomagnetic storms caused by ICMEs, among several other signatures that are not …

Schrödinger Filtering: A Novel Technique For Removing Gradient Artifact From Electroencephalography Data Acquired During Functional Magnetic Resonance Imaging, Gabriel Bruno Benigno

Electronic Thesis and Dissertation Repository

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are complementary modalities commonly acquired simultaneously to study brain function with high spatial and temporal resolution. The time-varying gradient fields from fMRI induce massive-amplitude artifacts (GRAs) that overlap in time and frequency with EEG, making GRA removal a challenge for which no satisfactory solution yet exists. We present a new GRA removal method termed Schrödinger filtering (SF). SF is based on semi-classical signal analysis in which a signal is decomposed into a series of energy-based components using the discrete spectrum of the Schrödinger operator. Using a publicly available dataset, we compared our …

Similarity-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian, Ljubisa Sehovac, Katarina Grolinger

Electrical and Computer Engineering Publications

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by …

A Heterogeneous Patient-Specific Biomechanical Model Of The Lung For Tumor Motion Compensation And Effective Lung Radiation Therapy Planning, Parya Jafari

Electronic Thesis and Dissertation Repository

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation …

Height Measurement Of Basil Crops For Smart Irrigation Applications In Greenhouses Using Commercial Sensors, Leila Bahman

Electronic Thesis and Dissertation Repository

Plant height is a key phenotypic attribute that directly represents how well a plant grows. It can also be a useful parameter in computing other important features such as yield and biomass. As the number of greenhouses increase, the traditional method of measuring plant height requires more time and labor, which increases demand for developing a reliable and affordable method to perform automated height measurements of plants. This research is aimed to develop a solution to automatically measure plant height in greenhouses using low cost sensors and computer vision techniques. For this purpose, the performance of various depth sensing technologies …

Machine Learning For Performance Aware Virtual Network Function Placement, Dimitrios Michael Manias

Electronic Thesis and Dissertation Repository

With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization has been identified as a potential solution, several challenges must be addressed to ensure its feasibility. The work presented in this thesis addresses the Virtual Network Function (VNF) placement problem through the development of a machine learning-based Delay-Aware Tree (DAT) which learns from the previous placement of VNF instances forming a Service Function Chain. The DAT is able to predict VNF instance placements …

Classifying Appliances Operation Modes Using Dynamic Time Warping (Dtw) And K Nearest Neighbors (Knn), Abdelkareem M. Jaradat

Electronic Thesis and Dissertation Repository

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system approach that is applied to user's disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation to detect times of the occurrences when an appliance …

Integrity Protection Of The Dc Microgrid, Jafar Mohammadi

Electronic Thesis and Dissertation Repository

The direct current (DC) microgrid has attracted great attention in the recent years due to its significant advantages over the alternating current (AC) microgrid. These advantages include elimination of unnecessary AC/DC power converters, lower investment cost, lower losses, higher reliability, and resilience to utility-side disturbances. A practical DC microgrid requires an effective control strategy to regulate the DC bus voltages, enable power sharing between the distributed energy resources (DERs), and provide acceptable dynamic response to disturbances. Furthermore, when the power demand of the loads is higher than the power generation of the DERs in the DC microgrid, the power balance …

Parkinsonian Speech And Voice Quality: Assessment And Improvement, Amr Gaballah

Electronic Thesis and Dissertation Repository

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Statistics show that nearly 90% of people impaired with PD develop voice and speech disorders. Speech production impairments in PD subjects typically result in hypophonia and consequently, poor speech signal-to-noise ratio (SNR) in noisy environments and inferior speech intelligibility and quality. Assessment, monitoring, and improvement of the perceived quality and intelligibility of Parkinsonian voice and speech are, therefore, paramount. In the first study of this thesis, the perceived quality of sustained vowels produced by PD patients was assessed through objective predictors. Subjective quality ratings of sustained vowels were collected from …

Objective And Subjective Evaluation Of Binaural Beamformers In Hearing Aids, Scott Aker

Electronic Thesis and Dissertation Repository

Hearing aids use a variety of noise reduction techniques to enhance the experience of hearing impaired listeners. One of these techniques is beamforming, which typically aims to preserve sounds coming from the front of the user and suppresses those from the sides and back. Recently, hearing aids have begun employing a wireless connection between the left and right hearing aids in order to augment the directionality of the beamformers, called binaural beamformers. However, the effect of these binaural beamformers on perceived quality and intelligibility has not been thoroughly tested. This thesis investigated the benchmarking of hearing aids which utilize binaural …

Data Analytics And Performance Enhancement In Edge-Cloud Collaborative Internet Of Things Systems, Tianqi Yu

Electronic Thesis and Dissertation Repository

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets …

Optimal Decentralized Coordination Of Sources In Islanded And Grid-Connected Microgrids, Mithun Mallick

Electronic Thesis and Dissertation Repository

Rising climate change concerns in recent years have instigated the emergence of sustainable sources to reduce dependence on high-emission, isolated bulk generation systems. The microgrid framework relies on integrating these distributed energy resources (DERs) to achieve regional energy independence that leads to a reliable and environment-friendly power grid. In this work, highly granular and decentralized coordination schemes are proposed that will enable fast computation of source dispatch set-points, thereby appropriately accounting for frequent changes in regional load-supply configuration of a microgrid. The mathematical models utilized in the study sufficiently represent the steady-state electrical interdependencies and feasibility limits in islanded or …

Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez

Electronic Thesis and Dissertation Repository

Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …

Nonlinear Attitude And Pose Filters With Superior Convergence Properties, Hashim Abdellah Hashim Mohamed

Electronic Thesis and Dissertation Repository

In this thesis, several deterministic and stochastic attitude filtering solutions on the special orthogonal group SO(3) are proposed. Firstly, the attitude estimation problem is approached on the basis of nonlinear deterministic filters on SO(3) with guaranteed transient and steady-state measures. The second solution to the attitude estimation problem considers nonlinear stochastic filters on SO(3) with superior convergence properties with two filters being developed in the sense of Ito, and one in the sense of Stratonovich.

This thesis also presents several deterministic and stochastic pose filtering solutions developed on the special Euclidean group SE(3). The first solution includes two nonlinear deterministic …

Nonlinear Observer For Visual-Inertial Navigation Using Intermittent Landmark Measurements, Miaomiao Wang

Western Research Forum

The development of reliable orientation, position and linear velocity estimation algorithms for the 3D visual-inertial navigation system (VINS) is instrumental in many applications, such as autonomous underwater vehicles (AUVs), and unmanned aerial vehicles (UAVs). It is extremely important when the global position system (GPS) is not available in GPS-denied environments. Recently, observers design for VINS using landmark position measurements from Kinect sensors or stereo cameras has been increasingly investigated in the literature.

The aim of this work is to design a nonlinear observer for VINS under the assumption that landmark position measurements are intermittent. In practice, the landmark measurements are …