Matching Items (1,667)
Filtering by
- All Subjects: Electrical Engineering
- All Subjects: Mechanical Engineering
Description
Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management.
ContributorsChen, Ang (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Allee, David (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Polyurea is a highly versatile material used in coatings and armor systems to protect against extreme conditions such as ballistic impact, cavitation erosion, and blast loading. However, the relationships between microstructurally-dependent deformation mechanisms and the mechanical properties of polyurea are not yet fully understood, especially under extreme conditions. In this work, multi-scale coarse-grained models are developed to probe molecular dynamics across the wide range of time and length scales that these fundamental deformation mechanisms operate. In the first of these models, a high-resolution coarse-grained model of polyurea is developed, where similar to united-atom models, hydrogen atoms are modeled implicitly. This model was trained using a modified iterative Boltzmann inversion method that dramatically reduces the number of iterations required. Coarse-grained simulations using this model demonstrate that multiblock systems evolve to form a more interconnected hard phase, compared to the more interrupted hard phase composed of distinct ribbon-shaped domains found in diblock systems. Next, a reactive coarse-grained model is developed to simulate the influence of the difference in time scales for step-growth polymerization and phase segregation in polyurea. Analysis of the simulated cured polyurea systems reveals that more rapid reaction rates produce a smaller diameter ligaments in the gyroidal hard phase as well as increased covalent bonding connecting the hard domain ligaments as evidenced by a larger fraction of bridging segments and larger mean radius of gyration of the copolymer chains. The effect that these processing-induced structural variations have on the mechanical properties of the polymer was tested by simulating uniaxial compression, which revealed that the higher degree of hard domain connectivity leads to a 20% increase in the flow stress. A hierarchical multiresolution framework is proposed to fully link coarse-grained molecular simulations across a broader range of time scales, in which a family of coarse-grained models are developed. The models are connected using an incremental reverse–mapping scheme allowing for long time scale dynamics simulated at a highly coarsened resolution to be passed all the way to an atomistic representation.
ContributorsLiu, Minghao (Author) / Oswald, Jay (Thesis advisor) / Muhich, Christopher (Committee member) / Jiang, Hanqing (Committee member) / Peralta, Pedro (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2020
Description
Combining the rapid development of semiconductor technologies, miniaturization of integrated circuits (ICs), and scaling down the device size is trending towards faster, cheaper, and more reliable components for low-power integrated circuits. Most research and development relate to efficiency, structure, materials, and performance. However, the thermal problem is also created and becomes more critical with shrinking device dimensions and increased integration densities, such that it affects the device performance and leads to degradation and damage. At the nanometer scale, the self-heating effect (SHE) is one of the main factors to degrade devices. Therefore, tracking and quantifying the SHE is important for reliability and efficiency issues. In this dissertation, engineers design two identical and closely spaced 40nm gate length silicon-on-insulator (SOI) n-channel metal-oxide-semiconductor-field-effect transistors (NMOSFETs) that share a common source with the same active silicon region. One of the MOSFETs acts as a heater to heat-up the active region, while the other one is a thermometer to evaluate the SHE and local temperature changes. The thermometer provides a method to calibrate the numerical models of self-heating and track the heat flow. Moreover, it also involves a trap-rich SOI wafer technology, in which a trap-rich layer, with higher resistivity and lower thermal conductivity compared to conventional bulk silicon substrates. The trap-rich SOI substrates can reduce the cross-talk and minimize the power consumption to increase the system performance. In particular, it offers a solution to radio frequency integrated circuits (RFICs) which require fast switching and low leakage. In high power amplifier (PA) applications, Watt-level PAs operates at less than 50% efficiency because of temperature limitations. The author uses experimental measurements of the local temperature changes, combined with simulations to examine the heat flow and temperature distribution. The approach may be useful to build a self-test application, because it can quantify the temperature changes by putting one or multiple NMOSFET thermometers around a complementary metal-oxide-semiconductor (CMOS) power amplifier, while only adding minimum die area. It points to ways in which it can optimize the reliability of RFIC applications, which operate under high-temperature or high-power conditions to protect the device before it is overheated or damaged.
ContributorsZhang, Xiong (Author) / Thornton, Trevor TT (Thesis advisor) / Vasileska, Dragica DV (Committee member) / Goryll, Michael MG (Committee member) / Myhajlenko, Stefan SM (Committee member) / Arizona State University (Publisher)
Created2020
Description
The first half of this dissertation introduces a minimum cost incentive mechanism for collecting discrete distributed private data for big-data analysis. The goal of an incentive mechanism is to incentivize informative reports and make sure randomization in the reported data does not exceed a target level. It answers two fundamental questions: what is the minimum payment required to incentivize an individual to submit data with quality level $\epsilon$? and what incentive mechanisms can achieve the minimum payment? A lower bound on the minimum amount of payment required for guaranteeing quality level $\epsilon$ is derived. Inspired by the lower bound, our incentive mechanism (WINTALL) first decides a winning answer based on reported data, then pays to individuals whose reported data match the winning answer. The expected payment of WINTALL matches lower bound asymptotically. Real-world experiments on Amazon Mechanical Turk are presented to further illustrate novelty of the principle behind WINTALL.
The second half studies problem of iterative training in Federated Learning. A system with a single parameter server and $M$ client devices is considered for training a predictive learning model with distributed data. The clients communicate with the parameter server using a common wireless channel so each time, only one device can transmit. The training is an iterative process consisting of multiple rounds. Adaptive training is considered where the parameter server decides when to stop/restart a new round, so the problem is formulated as an optimal stopping problem. While this optimal stopping problem is difficult to solve, a modified optimal stopping problem is proposed. Then a low complexity algorithm is introduced to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each iteration.
The second half studies problem of iterative training in Federated Learning. A system with a single parameter server and $M$ client devices is considered for training a predictive learning model with distributed data. The clients communicate with the parameter server using a common wireless channel so each time, only one device can transmit. The training is an iterative process consisting of multiple rounds. Adaptive training is considered where the parameter server decides when to stop/restart a new round, so the problem is formulated as an optimal stopping problem. While this optimal stopping problem is difficult to solve, a modified optimal stopping problem is proposed. Then a low complexity algorithm is introduced to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each iteration.
ContributorsJiang, Pengfei (Author) / Ying, Lei (Thesis advisor) / Zhang, Junshan (Committee member) / Zhang, Yanchao (Committee member) / Wang, Weina (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the era of artificial intelligent (AI), deep neural networks (DNN) have achieved accuracy on par with humans on a variety of recognition tasks. However, the high computation and storage requirement of DNN training and inference have posed challenges to deploying or locally training the DNNs on mobile and wearable devices. Energy-efficient hardware innovation from circuit to architecture level is required.In this dissertation, a smart electrocardiogram (ECG) processor is first presented for ECG-based authentication as well as cardiac monitoring. The 65nm testchip consumes 1.06 μW at 0.55 V for real-time ECG authentication achieving equal error rate of 1.7% for authentication on an in-house 645-subject database. Next, a couple of SRAM-based in-memory computing (IMC) accelerators for deep learning algorithms are presented. Two single-array macros titled XNOR-SRAM and C3SRAM are based on resistive and capacitive networks for XNOR-ACcumulation (XAC) operations, respectively. XNOR-SRAM and C3SRAM macros in 65nm CMOS achieve energy efficiency of 403 TOPS/W and 672 TOPS/W, respectively. Built on top of these two single-array macro designs, two multi-array architectures are presented. The XNOR-SRAM based architecture titled “Vesti” is designed to support configurable multibit activations and large-scale DNNs seamlessly. Vesti employs double-buffering with two groups of in-memory computing SRAMs, effectively hiding the write latency of IMC SRAMs. The Vesti accelerator in 65nm CMOS achieves energy consumption of <20 nJ for MNIST classification and <40μJ for CIFAR-10 classification at 1.0 V supply. More recently, a programmable IMC accelerator (PIMCA) integrating 108 C3SRAM macros of a total size of 3.4 Mb is proposed. The28nm prototype chip achieves system-level energy efficiency of 437/62 TOPS/W at 40 MHz, 1 V supply for DNNs with 1b/2b precision.
In addition to the IMC works, this dissertation also presents a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with momentum based training algorithm in 16-bit fixed-point precision. The65nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for16-bit fixed-point operations, consuming 10.45 mW at 0.55 V. In summary, in this dissertation, several hardware innovations from circuit to architecture level are presented, exploiting the reduced algorithm complexity with pruning and low-precision quantization techniques. In particular, macro-level and system-level SRAM based IMC works presented in this dissertation show that SRAM based IMC is one of the promising solutions for energy-efficient intelligent systems.
In addition to the IMC works, this dissertation also presents a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with momentum based training algorithm in 16-bit fixed-point precision. The65nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for16-bit fixed-point operations, consuming 10.45 mW at 0.55 V. In summary, in this dissertation, several hardware innovations from circuit to architecture level are presented, exploiting the reduced algorithm complexity with pruning and low-precision quantization techniques. In particular, macro-level and system-level SRAM based IMC works presented in this dissertation show that SRAM based IMC is one of the promising solutions for energy-efficient intelligent systems.
ContributorsYin, Shihui (Author) / Seo, Jae-sun Seo J. S. (Thesis advisor) / Cao, Yu Y. C. (Committee member) / Vrudhula, Sarma S. V. (Committee member) / Chakrabarti, Chaitali C. C. (Committee member) / Arizona State University (Publisher)
Created2020
Description
Unmanned aerial systems (UASs) have recently enabled novel applications such as passenger transport and package delivery, but are increasingly vulnerable to cyberattack and therefore difficult to certify. Legacy systems such as GPS provide these capabilities extremely well, but are sensitive to spoofing and hijacking. An alternative intelligent transport system (ITS) was developed that provides highly secure communications, positioning, and timing synchronization services to networks of cooperative RF users, termed Communications and High-Precision Positioning (CHP2) system. This technology was implemented on consumer-off-the-shelf (COTS) hardware and it offers rapid (<100 ms) and precise (<5 cm) positioning capabilities in over-the-air experiments using flexible ground stations and UAS platforms using limited bandwidth (10 MHz). In this study, CHP2 is considered in the context of safety-critical and resource limited transport applications and urban air mobility. The two-way ranging (TWR) protocol over a joint positioning-communications waveform enables distributed coherence and time-of-flight(ToF) estimation. In a multi-antenna setup, the cross-platform ranging on participating nodes in the network translate to precise target location and orientation. In the current form, CHP2 necessitates a cooperative timing exchange at regular intervals. Dynamic resource management supports higher user densities by constantly renegotiating spectral access depending on need and opportunity. With these novel contributions to the field of integrated positioning and communications, CHP2 is a suitable candidate to provide both communications, navigation, and surveillance (CNS) and alternative positioning, navigation, and timing (APNT) services for high density safety-critical transport applications on a variety of vehicular platforms.
ContributorsSrinivas, Sharanya (Author) / Bliss, Daniel W. (Thesis advisor) / Richmond, Christ D. (Committee member) / Chakrabarti, Chaitali (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2020
Description
Wearable technology has brought in a rapid shift in the areas of healthcare and lifestyle management. The recent development and usage of wearable devices like smart watches has created significant impact in areas like fitness management, exercise tracking, sleep quality assessment and early diagnosis of diseases like asthma, sleep apnea etc. This thesis is dedicated to the development of wearable systems and algorithms to fulfill unmet needs in the area of cardiorespiratory monitoring.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
ContributorsTipparaju, Vishal Varun (Author) / Xian, Xiaojun (Thesis advisor) / Forzani, Erica (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Angadi, Siddhartha (Committee member) / Arizona State University (Publisher)
Created2020
Description
An ongoing effort in the photovoltaic (PV) industry is to reduce the major manufacturing cost components of solar cells, the great majority of which are based on crystalline silicon (c-Si). This includes the substitution of screenprinted silver (Ag) cell contacts with alternative copper (Cu)-based contacts, usually applied with plating. Plated Cu contact schemes have been under study for many years with only minor traction in industrial production. One of the more commonly-cited barriers to the adoption of Cu-based contacts for photovoltaics is long-term reliability, as Cu is a significant contaminant in c-Si, forming precipitates that degrade performance via degradation of diode character and reduction of minority carrier lifetime. Cu contamination from contacts might cause degradation during field deployment if Cu is able to ingress into c-Si. Furthermore, Cu contamination is also known to cause a form of light-induced degradation (LID) which further degrades carrier lifetime when cells are exposed to light.
Prior literature on Cu-contact reliability tended to focus on accelerated testing at the cell and wafer level that may not be entirely replicative of real-world environmental stresses in PV modules. This thesis is aimed at advancing the understanding of Cu-contact reliability from the perspective of quasi-commercial modules under more realistic stresses. In this thesis, c-Si solar cells with Cu-plated contacts are fabricated, made into PV modules, and subjected to environmental stress in an attempt to induce hypothesized failure modes and understand any new vulnerabilities that Cu contacts might introduce. In particular, damp heat stress is applied to conventional, p-type c-Si modules and high efficiency, n-type c-Si heterojunction modules. I present evidence of Cu-induced diode degradation that also depends on PV module materials, as well as degradation unrelated to Cu, and in either case suggest engineering solutions to the observed degradation. In a forensic search for degradation mechanisms, I present novel evidence of Cu outdiffusion from contact layers and encapsulant-driven contact corrosion as potential key factors. Finally, outdoor exposures to light uncover peculiarities in Cu-plated samples, but do not point to especially serious vulnerabilities.
Prior literature on Cu-contact reliability tended to focus on accelerated testing at the cell and wafer level that may not be entirely replicative of real-world environmental stresses in PV modules. This thesis is aimed at advancing the understanding of Cu-contact reliability from the perspective of quasi-commercial modules under more realistic stresses. In this thesis, c-Si solar cells with Cu-plated contacts are fabricated, made into PV modules, and subjected to environmental stress in an attempt to induce hypothesized failure modes and understand any new vulnerabilities that Cu contacts might introduce. In particular, damp heat stress is applied to conventional, p-type c-Si modules and high efficiency, n-type c-Si heterojunction modules. I present evidence of Cu-induced diode degradation that also depends on PV module materials, as well as degradation unrelated to Cu, and in either case suggest engineering solutions to the observed degradation. In a forensic search for degradation mechanisms, I present novel evidence of Cu outdiffusion from contact layers and encapsulant-driven contact corrosion as potential key factors. Finally, outdoor exposures to light uncover peculiarities in Cu-plated samples, but do not point to especially serious vulnerabilities.
ContributorsKaras, Joseph (Author) / Bowden, Stuart (Thesis advisor) / Alford, Terry (Thesis advisor) / Tamizhmani, Govindasamy (Committee member) / Michaelson, Lynne (Committee member) / Arizona State University (Publisher)
Created2020
Description
Information exists in various forms and a better utilization of the available information can benefit the system awareness and response predictions. The focus of this dissertation is on the fusion of different types of information using Bayesian-Entropy method. The Maximum Entropy method in information theory introduces a unique way of handling information in the form of constraints. The Bayesian-Entropy (BE) principle is proposed to integrate the Bayes’ theorem and Maximum Entropy method to encode extra information. The posterior distribution in Bayesian-Entropy method has a Bayesian part to handle point observation data, and an Entropy part that encodes constraints, such as statistical moment information, range information and general function between variables. The proposed method is then extended to its network format as Bayesian Entropy Network (BEN), which serves as a generalized information fusion tool for diagnostics, prognostics, and surrogate modeling.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
The proposed BEN is demonstrated and validated with extensive engineering applications. The BEN method is first demonstrated for diagnostics of gas pipelines and metal/composite plates for damage diagnostics. Both empirical knowledge and physics model are integrated with direct observations to improve the accuracy for diagnostics and to reduce the training samples. Next, the BEN is demonstrated in prognostics and safety assessment in air traffic management system. Various information types, such as human concepts, variable correlation functions, physical constraints, and tendency data, are fused in BEN to enhance the safety assessment and risk prediction in the National Airspace System (NAS). Following this, the BE principle is applied in surrogate modeling. Multiple algorithms are proposed based on different type of information encoding, such as Bayesian-Entropy Linear Regression (BELR), Bayesian-Entropy Semiparametric Gaussian Process (BESGP), and Bayesian-Entropy Gaussian Process (BEGP) are demonstrated with numerical toy problems and practical engineering analysis. The results show that the major benefits are the superior prediction/extrapolation performance and significant reduction of training samples by using additional physics/knowledge as constraints. The proposed BEN offers a systematic and rigorous way to incorporate various information sources. Several major conclusions are drawn based on the proposed study.
ContributorsWang, Yuhao (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Yan, Hao (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2020
Description
The availability of data for monitoring and controlling the electrical grid has increased exponentially over the years in both resolution and quantity leaving a large data footprint. This dissertation is motivated by the need for equivalent representations of grid data in lower-dimensional feature spaces so that machine learning algorithms can be employed for a variety of purposes. To achieve that, without sacrificing the interpretation of the results, the dissertation leverages the physics behind power systems, well-known laws that underlie this man-made infrastructure, and the nature of the underlying stochastic phenomena that define the system operating conditions as the backbone for modeling data from the grid.
The first part of the dissertation introduces a new framework of graph signal processing (GSP) for the power grid, Grid-GSP, and applies it to voltage phasor measurements that characterize the overall system state of the power grid. Concepts from GSP are used in conjunction with known power system models in order to highlight the low-dimensional structure in data and present generative models for voltage phasors measurements. Applications such as identification of graphical communities, network inference, interpolation of missing data, detection of false data injection attacks and data compression are explored wherein Grid-GSP based generative models are used.
The second part of the dissertation develops a model for a joint statistical description of solar photo-voltaic (PV) power and the outdoor temperature which can lead to better management of power generation resources so that electricity demand such as air conditioning and supply from solar power are always matched in the face of stochasticity. The low-rank structure inherent in solar PV power data is used for forecasting and to detect partial-shading type of faults in solar panels.
The first part of the dissertation introduces a new framework of graph signal processing (GSP) for the power grid, Grid-GSP, and applies it to voltage phasor measurements that characterize the overall system state of the power grid. Concepts from GSP are used in conjunction with known power system models in order to highlight the low-dimensional structure in data and present generative models for voltage phasors measurements. Applications such as identification of graphical communities, network inference, interpolation of missing data, detection of false data injection attacks and data compression are explored wherein Grid-GSP based generative models are used.
The second part of the dissertation develops a model for a joint statistical description of solar photo-voltaic (PV) power and the outdoor temperature which can lead to better management of power generation resources so that electricity demand such as air conditioning and supply from solar power are always matched in the face of stochasticity. The low-rank structure inherent in solar PV power data is used for forecasting and to detect partial-shading type of faults in solar panels.
ContributorsRamakrishna, Raksha (Author) / Scaglione, Anna (Thesis advisor) / Cochran, Douglas (Committee member) / Spanias, Andreas (Committee member) / Vittal, Vijay (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2020