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Title: Towards the Robust Situation Awareness in Distribution Management System
Creator: Yao, Yiyun
Date: 2019
Description: In distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system...
Show moreIn distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system operating conditions. This motivates the establishment of modern distribution management system (DMS) for real-time net-work monitoring, resource optimization, and demand management. Three subproblems are mainly discussed when establishing the robust situation awareness in DMS. A measurement placement problem is proposed to decide the optimal locations and types of measurements to be placed in the distribution systems that minimize the worst-case estimation errors for DSSE over different system operating conditions. Four indices of the estimation error covariance matrix are chosen as the criteria of accuracy. The proposed measurement placement problem is formulated as a mixed-integer sem-idefinite programming (MISDP) problem. To avoid the combinatorial complexity, a con-vex relaxation, followed by a local optimization method, is employed to solve the MISDP problem. The proposed problem and the effectiveness of the proposed solution method are numerically demonstrated on the 33-bus distribution system.Distribution system state estimation (DSSE) is one of the vital components in the next-generation distribution management system (DMS), which allows the operators to monitor the entire system’s operating conditions. Due to the lack of real-time measurements, DSSE has to process measurements whose quality varies significantly across different sources, which causes convergence issue to the Gauss-Newton solver. In this chapter, a semidefinite programming (SDP) framework is developed to reformulate the DSSE problem into a rank- constrained SDP problem. One challenge of this technique is the nonconvex rank-one constraint, which is generally relaxed. However, the relaxed SDP-DSSE problem cannot guarantee a rank-one solution and hence lose optimality. Therefore, we propose two solution approaches, namely the rank reduction approach and the convex iteration approach, to obtain rank-one solutions for the SDP-DSSE problem. The proposed model and the effectiveness of the proposed solution approaches are numerically demonstrated on the IEEE 13-, 34-bus, and 123-bus distribution systems.A SE algorithm based on random measurements selection, which is inspired by the concept of moving target defense (MTD), is developed to prevent and mitigate stealthy cyber-attacks. With the proposed SE, a library of selected measurements scenarios is first generated offline given the available measurements and network topology. During online operation, multiple weighted least square (WLS) based SEs are processed in parallel with randomly picked scenarios from the library. The final solution is selected based on the largest normalized residuals with regard to individual scenarios. The effectiveness of the proposed SE is examined by attack-defense experiments on IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems.
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Title: MICROGRID COMMUNICATION: HARDWARE AND SOFTWARE TECHNOLOGIES
Creator: GONG, WENLONG
Date: 2019
Description: The Keating Nanogrid at Illinois Institute of Technology (IIT) was designed to be an islandable ac/dc hybrid nanogrid. The on-site rooftop...
Show moreThe Keating Nanogrid at Illinois Institute of Technology (IIT) was designed to be an islandable ac/dc hybrid nanogrid. The on-site rooftop solar and battery system are supporting the interconnected dc and ac subsystems. The nanogrid system at the ac bus is eventually interconnected with the IIT Microgrid. The battery storage system at Keating Nanogrid was designed to support its critical loads for about 8 hours daily. A battery management system (BMS) was employed so that it can monitor and report storage system status to the Keating Nanogrid controller for optimal decision making.The dc load including 94 fixtures controllable LED lighting system was designed to replace the original 189 fixtures ac florescent lighting system. The LEDs’ dc-dc driver was designed and built to enable the dc input provided by the rooftop solar photovoltaic system. The dc system control and communication module was designed and built to make the LED lights controllable individually by the Keating Nanogrid controller or sensor network.To enhance safety at night, 4 islandable LED streetlights were deployed on the east side of the Keating Nanogrid where grid connection was not available for lighting. The east-side streetlight is self-sustained with its own wind turbine, solar panel and battery. The real-time monitoring system was designed and built for the streetlights.The Keating Nanogrid was designed for multiple purposes including the monitoring and control of all elements via pertinent communication pathways. It exchanges the real-time information with the IIT Microgrid and together they make optimal operation decisions to enhance efficiency and reliability of the entire IIT Microgrid system.
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Title: Simulation, design and applications of a table top analyzer-based phase contrast mammography system
Creator: Caudevilla Torras, Oriol
Date: 2019
Description: Analyzer-based Imaging is a promising phase contrast technology with huge potential for soft tissue imaging. Unlike absorption-contrast...
Show moreAnalyzer-based Imaging is a promising phase contrast technology with huge potential for soft tissue imaging. Unlike absorption-contrast methods, phase-contrast modalities measure refraction and scatter properties of the tissue. Such images are particularly suitable for applications such as mammography.The potential advantages of the Analyzer-Based Imaging technology are three fold. First, it shows exceptional contrast when imaging soft tissue, which produces extremely sharp images of the breast compared to absorption images. Second, it provides additional insights about the breast. In particular, the density and scatter images of breast micro-calcifications can help assessing their malignancy better than common mammograms. Third, it has shown potential to reduce the radiation dose deposited in the breast tissue by an order of magnitude compared to common mammography procedures.In the past, Analyzer-Based Imaging has been mainly developed with synchrotron light sources and focused on obtaining micro-resolution images. For such applications, quasi-monoenergetic beams are required. Nevertheless, monochromatic radiation can be easily obtained in synchrotron setups by filtering the source’s spectrum with crystal optics. Since synchrotrons are very brilliant sources, most of their radiation can be filtered out and still obtain low noise phase contrast images. Nowadays, there is a lot of interest in transitioning the technology to a table-top system using compact X-ray sources for mammography. However, compact sources are several orders of magnitude less brilliant, which causes extremely long exposure times. Additionally, the trade-off between exposure time (throughput) and resolution in compact analyzer-based imaging systems is yet to be completely understood.In this thesis, we lay down the principles to develop compact analyzer-based imaging systems capable of imaging a full-sized breast under ten seconds, while ensuring a resolution under 100 microns. This represents a major breakthrough towards obtaining a clinical analyzer-based mammography system. Additionally, we explore a unique application of the analyzer-based technology for breast diagnosis consisting on the assessment of the chemical composition of micro-calcifications. In conjunction with ABI’s unparalleled image quality, determining the chemical composition of micro- calcifications can help to mitigate the high false positive rate in common mammography.
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Title: Leakage Power Attack-Resilient Designs of A SRAM Cell in 7nm FinFET Technology
Creator: Chen, Kangqi
Date: 2019
Description: Recently, the classic metal-oxide-semiconductor field-effect-transistor (MOS- FET) has reached its limit for scaling. Another transistor...
Show moreRecently, the classic metal-oxide-semiconductor field-effect-transistor (MOS- FET) has reached its limit for scaling. Another transistor structure, FinFET, gradually has become the alternative choice for next generation of integrated circuits. Excellent features like reduced short channel effects, low threshold-voltage variability, less random dopant fluctuation, etc, offer this transistor model more stability, less leakage and faster performance. In particular, scaling trends force SRAM cells to be more vulnerable while using conventional MOSFET. The application of FinFET helps SRAM cell designs to overcome stability issues and achieve less power and faster speed. Another critical feature of an SRAM cell that needs to be considered is the correlation between data stored in cell and leakage of this cell. Side-Channel Attacks (SCA) like Leakage Power Analysis (LPA) would exploit this correlation to decrypt the secret key inside the memory. SCA has been proved to be a non-invasive but dangerous threat. Therefore, LPA would be the main focus of this thesis research.In this thesis, firstly, threshold voltage of various models are investigated using fundamental logic circuits including full-adders built with pass transistors, CLRCL and SERF. Secondly, conventional 6T SRAM cell design and single-ended 9T SRAM cell design targeting high stability and low power, are implemented and compared. Thirdly, the leakage balance method is applied to 9T cell design. Two novel solutions for LPA prevention of 9T design are proposed, implemented and compared against the original 9T design and conventional 6T design. The results confirm improved leakage balance and attack resilience while maintaining the stability and low-power features of the original 9T SRAM cell.
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Title: DAMAGE ASSESSMENT OF CIVIL STRUCTURES AFTER NATURAL DISASTERS USING DEEP LEARNING AND SATELLITE IMAGERY
Creator: Jones, Scott F
Date: 2019
Description: Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars....
Show moreSince 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster. The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.
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Title: Fast mesh based reconstruction for cardiac-gated SPECT and methodology for medical image quality assessment
Creator: Massanes Basi, Francesc
Date: 2018
Description: In this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to...
Show moreIn this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to improve the quality of single photon emission computed tomography (SPECT) imaging. Currently, SPECT images assist physicians to evaluate perfusion levels within the myocardium, aide in the diagnosis of various types of carcinomas, and measure pulmonary function. The SPECT technique relies on injecting a radioactive material into the patient's body and then detecting the emitted radiation by means of a gamma camera. However, the amount of radioactive material that can be given to a patient is limited by the negative effects that the radiation will have on the patient's health. This causes SPECT images to be highly corrupted by noise. We will focus our work on cardiac SPECT, which adds the challenge of the heart's continuous motion during the acquisition process. First, we describe the methodology used in SPECT imaging and reconstruction. Our methodology uses a content adaptive model, which uses more samples on the regions of the body that we want to be reconstructed more accurately and less in other areas. Then we describe our algorithm and our novel implementation that lets us use the content adaptive model to perform the reconstruction. In this work, we show that our implementation outperforms the reconstruction method used for clinical applications. In the second subject we are evaluating tools to measure image quality in the context of medical diagnosis. In signal processing, accuracy is typically measured as the amount of similarity between an original signal and its reconstruction. This similarity is traditionally a numeric metric that does not take into account the intended purpose of the reconstructed images. In the field of medical imaging, a reconstructed image is meant to aid a physician to perform a diagnostic task. Therefore, the quality of the reconstruction should be measured by how much it helps to perform the diagnostic task. A model observer is a computer tool that aims to mimic the performance of human observer, usually a radiologist, at a relevant diagnosis task. In this work we present our linear model observer designed to automatically select the features needed to model a human observer response. This is a novelty from the model observers currently being used in the medical imaging field, which instead usually have ad-hoc chosen features. Our model observer dependents only on the resolution of the image, not the type of imaging technique used to acquire the image.
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Title: Polarization Induced by a Terahertz Electric Field on a Core-shell Particle
Creator: Li, Yanlin
Date: 2018
Description: Interactions of an electromagnetic wave with a sphere that is smaller than the wavelength can be accounted for by studying the dipole moments,...
Show moreInteractions of an electromagnetic wave with a sphere that is smaller than the wavelength can be accounted for by studying the dipole moments, which are the valid explanation for the scattering characteristics in the frequency region known as the Rayleigh region. The semiconductor nanoparticle with a core-shell structure describes a specific geometry yielding tunable plasmon resonance of the nanostructure. This is achieved by varying the thickness of the dielectric material shell layer on a semiconductor core. The polarization of core-shell sphere induced by a dynamic field is studied both analytically and numerically. Dielectric function is used for the description of the response of bound charges to an applied field, resulting in the electric polarization, which has been employed to explicate scattering and absorption properties of particles over the years. However, this traditional model has some limitations in accounting for some aspects of polarization when mobile charges are present. By coupling the transport equations of the charge carriers to the Maxwell’s equations, we can derive the electric field, charge and the total induced dipole moment of a nano-core-shell particle. Results of calculations accomplished for elementary structures such as plates and spheres represented the screening of the internal field while dispersion and absorptions effects are revealed by the complex dipole moments. And the results in static and quasi-static field are shown. Equivalent circuits for the core-shell structures are obtained from carrier transport consideration, which can be employed to guide the synthesis of new nanoparticles with heterogeneous internal structures to achieve novel polarization properties for sensing and terahertz circuitry applications.
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Title: LOW DIMENSIONAL SIGNAL SETS FOR RADAR APPLICATIONS
Creator: Alphonse Joseph Rajkumar, Sebastian Anand
Date: 2018
Description: In this dissertation we present a view in which the radar signals as the elements of a high dimensional signal set. The dimension is equal to...
Show moreIn this dissertation we present a view in which the radar signals as the elements of a high dimensional signal set. The dimension is equal to the number of discrete samples (M) of the signal. Because the radar signals should satisfy certain conditions for good performance, most lie in much smaller subsets or subspaces. By developing appropriate lower dimensional signal spaces that approximate these areas where the radar signals live, we can realize potential advantage because of the greater parametric simplicity. In this dissertation we apply this low dimensional signal concept in radar signal processing. In particular we focus on radar signal design and radar signal estimation. Signal design comes under radar measures and signal estimation comes under radar countermeasures.In signal design problem one searches for the signal element that has smaller sidelobes and also satisfies certain constraints such as bandwidth occupancy, AC mainlobe width, etc. The sidelobe levels are quantified by Peak Sidelobe Ratio (PSLR) and Integrated Sidelobe Ratio (ISLR). We use linear combination of these two metrics as the cost function to determine the quality of the designed signal. There is a lot of effort in designing parameterized signal sets including our proposed Asymmetric Time Exponentiated Frequency Modulated (ATEFM) signal and Odd Polynomial FrequencySignal (OPFS). Our contribution is to demonstrate that the best signal elements from these low dimensional signal sets (LDSS) mostly outperform the best signal elements that are randomly chosen from the radar signal subset with dimensionality M. Since searching the best signal element from the LDSS requires less computational resources it is prudent to search for the best signal elements from the low dimensional signal sets.In signal estimation problem we try to estimate the signal transmitted by a noncooperating radar which is intercepted by multiple passive sensors. The intercepted signals often have low SNR and there could be only few intercepted signals available for signal estimation. Predominantly used method for estimating the radar signals is Principal Component Analysis (PCA). When the SNR is low (< 0 dB) we need large number of intercepted signals to get an accurate estimates from PCA method. Our contribution is to demonstrate that by limiting the search for the best signal estimate within the low dimensional signal sets one can get more accurate estimates of the unknown transmitted signal at low SNRs with smaller number of sensors compared to PCA.
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Title: A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion
Creator: Almagro Yravedra, Fernando
Date: 2020
Description: The object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony...
Show moreThe object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony Lithium-Ion cell given its current and previous states. In order to build the model, a realistic electro-thermal model of the cell under study is developed in Matlab Simulink, being used to recreate the cell's behavior under a set of real operating conditions. The data generated by the electro-thermal model is used to train a recurrent neural network, which returns the chance of future combustion of the US18650 Sony Lithium-Ion cell. Independently obtained data is used to test and validate the developed recurrent neural network using advanced metrics.
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Title: Distributed Resource Management for Wireless Networks Over Unlicensed Spectrum
Creator: Han, Mengqi
Date: 2020
Description: In the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local...
Show moreIn the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local network networks (WLANs), cloud radio access network (C-RANs), wireless metropolitan area networks (WMANs), wireless body area networks (WBANs) and etc.To meet the exponential growth of traffic demands and improve the network throughput, different enhancement in the MAC protocols have been proposed for the emerging networks. For example, U-LTE (Unlicensed LTE) is proposed for LTE users to aggregate the spacious unlicensed spectrum with the licensed spectrum to boost the network throughput. Meanwhile, Wi-Fi users are allowed to opportunistically bond available channels for high data rate transmissions to improve the spectrum efficiency and network throughput. But the performance of the emerging networks with the new techniques has not been well investigated. Thus, in this thesis, we comprehensively investigate the network performance in different network scenarios. In each scenario, we first develop mathematical models to identify the performance bottlenecks in the existing MAC protocols. We then propose an algorithm to intelligently tune the protocol parameters to maximize network performance. Finally, the proposed algorithm is compared with some existing algorithms. Specifically, in the first scenario, we evaluate the coexistence performance between the Wi-Fi users with channel bonding capability and the legacy users without channel bonding capability. Specifically, the channel bonding probability and the channel access delay of wireless users are first analyzed, considering the contentions among legacy and multi-channel users in the same channel and across multiple channels. Based on the analysis, the network capacity, i.e., the maximum number of traffic flows that can be supported with the bounded delay performance in a multi-channel Wi-Fi with and without channel bonding, is then derived. Based on the analytical results, we propose a heuristic bonding policythat can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. In addition, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints for multi-channel users with channel aggregation capability. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput.In the second scenario, we consider that U-LTE users are coexisting with the legacy users without channel bonding capability in the same unlicensed spectrum. The throughput of both Wi-Fi and U-LTE users are both derived when U-LTE users adopting two Load Based Equipment(LBE) random access protocols and Category 4 (Cat 4) algorithm agreed in 3GPP release 13.Based on the analysis, we find that the current protocols of U-LTE users are far from perfect to achieve harmony coexistence. Subject to the system fairness constraint, the aggregate throughput of U-LTE and Wi-Fi networks is maximized based on a semi branch and bound algorithm. To make the complex optimization tractable, reinforcement learning techniques are introduced to intelligently tune the contention window size for both U-LTE and Wi-Fi users. Specifically, a cooperative learning algorithm is developed assuming that the information between different systems is exchangeable. A non-cooperative version is subsequently developed to remove the previous assumption for better practicability. Extensive simulations are conducted to demonstrate the performance of the proposed learning algorithms in contrast to the analytical upper bound under various conditions. It is shown that both proposed learning algorithms can significantly improve the total throughput performance while satisfying the fairness constraints.Finally, by considering the energy constraints, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. In an IoT network with energy harvesting, an IoT device can contend for channel access only when it is ready, i.e., it has data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by adapting the transmission probability p of IoT devices. Given that the wireless environment is unknown at different IoT devices, e.g., the total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.
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Title: Application-Oriented Scheduling for Optimizing Information Freshness in Wireless Networks
Creator: Yin, Bo
Date: 2020
Description: Recent years have witnessed a significant advancement of networking technologies as well as the proliferation of mobile devices. Due to the...
Show moreRecent years have witnessed a significant advancement of networking technologies as well as the proliferation of mobile devices. Due to the convergence of pervasive connectivity and ubiquitous computing, Internet of Things (IoT) systems are becoming increasingly information-centric. For those IoT devices, wireless communication is the dominant way to exchange information. The development of IoT has spawned a plethora of real-time applications, boosting the demand for timely information updates. Age of Information (AoI) has recently been introduced to quantify the freshness of the knowledge the controller has about the remote information sources. Due to its sheer novelty in capturing the timeliness requirements of various applications, AoI has sparked tremendous interest and been studied in many communication systems. This thesis aims at an exploratory study on how to characterize the essence of wireless scheduling for effective information freshness from the decision-making perspectives through two representative application scenarios, information retrieval and information integration. For the former, request-aware proactive scheduling policies in both static and dynamic request patterns are developed, which target at minimizing time-average effective AoI (EAoI). For the latter, an experience-driven scheduling framework based on deep reinforcement learning techniques is investigated to minimize the time-average AoI in the presence of correlated information sources. Future research directions are also discussed to present possible extensions of this thesis work to a broader range of network scenarios.
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Title: GAME THEORY BASED LOCATION-AWARE CHARGING SOLUTIONS FOR NETWORKED ELECTRIC VEHICLES
Creator: Laha, Aurobinda
Date: 2020
Description: The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of...
Show moreThe recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid (V2G) network, vehicles and charging stations can respectively make better charging and pricing decisions via real-time information sharing. In this research, we study the charging problem in an intelligent transportation system (ITS), which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location while the objective of a charging station is to maximize its revenue given the charging strategy of the vehicles. We employ a multileader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that there exists a unique equilibrium for the followers’ subgame played by the vehicles, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable through the proposed charging scheme. We further evaluate the price of anarchy (PoA) of the proposed charging scheme by using a centralized optimization model, in which a modified matching algorithm is applied. In state-of-the-art research works, PHEVs tend to charge or discharge to a smart grid individually. In our extended work, we also consider the discharging scenarios for PHEVs, which is generally during the peak hours of a micro-grid system. We propose that by leveraging the cooperation between charging and discharging PHEVs, the grid will be able to properly disperse the charging load in the load valley and discharging during the load peak hours. As a consequence, the electricity load will be well balanced. In this process, the PHEVs also receive greater benefit, thus serving the PHEV charging and discharging cooperation as a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of a coalition game. Finally, simulation results confirm the uniqueness of the equilibrium in both the game strategies. A performance comparison between the proposed distributed and centralized strategy with existing solutions are presented. We also provide the results of the coalition game when both charging and discharging PHEVs are present in the network. The proper management of charging and discharging of EVs poses one of the most challenging and interesting issues in our research. We aim to provide a complete demand response management solution to PHEVs and micro-grids in a real-time scenario.
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Title: Reconfigurable High-Performance Computation and Communication Platform for Ultrasonic Applications
Creator: Wang, Boyang
Date: 2021
Description: In industrial and medical applications, ultrasonic signals are used in nondestructive testing (NDT), medical imaging, navigation, and...
Show moreIn industrial and medical applications, ultrasonic signals are used in nondestructive testing (NDT), medical imaging, navigation, and communication. This study presents the architecture of high-performance computational systems designed for ultrasonic nondestructive testing, data compression using machine learning, and a multilayer perceptron neural network for ultrasonic flaw detection and grain size characterization. We researched and developed a real-time software-defined ultrasonic communication system for transmitting information through highly reverberant and dispersive solid channels. Orthogonal frequency-division multiplexing is explored to combat the severe multipath effect in the solid channels and achieve an optimal bitrate solution. In this study, a reconfigurable, high-performance, low-cost, and real-time ultrasonic data acquisition and signal processing platform is designed based on an all-programmable system-on-chip (APSoC). We designed the unsupervised learning models using wavelet packet transformation optimized by convolutional autoencoder for massive ultrasonic data compression. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data. For ultrasonic signal analysis in NDT applications, we utilized the multilayer perceptron neural network (MLPNN) to detect flaw echoes masked by strong microstructure scattering noise (i.e., about zero dB SNR or less) with detection accuracy above 99%. It is of high interest to characterize materials using ultrasonic scattering properties for grain size estimation and classification. We successfully designed an MLPNN to classify the grain sizes of materials with an accuracy of 99%. Furthermore, a software-defined ultrasonic communication system based on the APSoC is designed for real-time data transmission through solid channels. Transducers with a center frequency of 2.5 MHz are used to transmit and receive information-bearing ultrasonic waves in solid channels where the communication bit rate can reach up to 1.5 Mbps.
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Title: AUTOMATION OF ULTRASONIC FLAW DETECTION APPLICATIONS USING DEEP LEARNING ALGORITHMS
Creator: Virupakshappa, Kushal
Date: 2021
Description: The Industrial Revolution-4.0 promises to integrate multiple technologies including but not limited to automation, cloud computing, robotics,...
Show moreThe Industrial Revolution-4.0 promises to integrate multiple technologies including but not limited to automation, cloud computing, robotics, and Artificial Intelligence. The non-Destructive Testing (NDT) industry has been shifting towards automation as well. For ultrasound-based NDT, these technological advancements facilitate smart systems hosting complex signal processing algorithms. Therefore, this thesis introduces the effective use of AI algorithms in challenging NDT scenarios. The first objective is to investigate and evaluate the performance of both supervised and unsupervised machine learning algorithms and optimize them for ultrasonic flaw detection utilizing Amplitude-scan (A-scan) data. Several inferences and optimization algorithms have been evaluated. It has been observed that proper choice of features for specific inference algorithms leads to accurate flaw detection. The second objective of this study is the hardware realization of the ultrasonic flaw detection algorithms on embedded systems. Support Vector Machine algorithm has been implemented on a Tegra K1 GPU platform and Supervised Machine Learning algorithms have been implemented on a Zynq FPGA for a comparative study. The third main objective is to introduce new deep learning architectures for more complex flaw detection applications including classification of flaw types and robust detection of multiple flaws in B-scan data. The proposed Deep Learning pipeline combines a novel grid-based localization architecture with meta-learning. This provides a generalized flaw detection solution wherein additional flaw types can be used for inference without retraining or changing the deep learning architecture. Results show that the proposed algorithm performs well in more complex scenarios with high clutter noise and the results are comparable with traditional CNN and achieve the goal of generality and robustness.
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Title: DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
Creator: Khairy, Sami
Date: 2021
Description: The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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Title: Efficient Power System Transient Simulation for Stability Studies Based on Frequency Response Optimized Approximation
Creator: Lei, Sheng
Date: 2021
Description: Power systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on...
Show morePower systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on the intrinsically unbalanced distribution side. The new features of power systems violate the fundamental assumptions and challenge the feasibility of transient stability simulation, a traditional tool for stability studies. Electromagnetic transient simulation is applicable to power systems with the new features, but its computational efficiency is too low with the typical microsecond-level step sizes.This dissertation aims at enabling millisecond-level step sizes, typically used in traditional transient stability simulation, in efficient electromagnetic transient simulation for system-level stability studies on unbalanced power systems, while assuring satisfactory accuracy. The approach taken is to introduce novel highly accurate numerical methods into electromagnetic transient simulation.Several implicit one-step frequency response optimized integrators considering second order derivative are proposed. Some existing numerical integrators in the literature of this category are reviewed. Their numerical properties are studied. Some of these numerical integrators are especially suitable to be used as numerical differentiators.A novel power system transient simulation scheme is put forward using the implicit one-step frequency response optimized integrators as the main numerical integrators and differentiators. Large step sizes are applicable with the proposed simulation scheme to achieve efficient electromagnetic transient simulation without sacrificing accuracy. Execution of the proposed simulation scheme is detailed.Several explicit and implicit multistep frequency response optimized integrators considering first or second order derivative are proposed. Some existing numerical integrators of these types are reviewed from the error frequency response viewpoint. Based on these numerical integrators, a prediction method is put forward to further accelerate the proposed simulation scheme without impacting its accuracy.Initialization process of the proposed simulation scheme is put forward. The initialization process calculates the periodic steady state solution of unbalanced power systems considering power flow conditions. The requirements of power system stability studies on the initial conditions for transient simulation runs are thus satisfied. Effectiveness and efficiency of the initialization process are demonstrated.Computational models of power system network elements in the proposed simulation scheme are detailed. The extended nodal analysis is put forward for the proposed simulation scheme to organize the computational models of most network elements in an efficient and elegant manner.Some power system devices are implemented with the proposed simulation scheme, including single-phase grid-feeding converter system, three-phase grid-feeding converter system, three-phase synchronous machine and three-phase induction machine. The proposed simulation scheme is shown to simultaneously achieve efficiency and accuracy as applied to these devices.The proposed simulation scheme is applied to different types of power systems, including transmission system, distribution system and combined transmission and distribution system. Its versatility is revealed. Its efficiency and accuracy are demonstrated with numerical case studies as applied to these systems.
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Title: Electronically Assisted Direct Current Circuit Breakers
Creator: Feng, Yanjun
Date: 2019
Description: DC power is gaining tractions recently, however, DC fault protection remains a major technical challenge. Popular and cost-effective AC...
Show moreDC power is gaining tractions recently, however, DC fault protection remains a major technical challenge. Popular and cost-effective AC mechanical circuit breakers do not offer sufficient DC interruption capability. Solid state circuit breakers have drawbacks of high cost and high conduction loss. The reported hybrid circuit breakers solutions require fast responding current sensors and mechanical actuation mechanism vastly different from and far more complex than the conventional AC circuit breakers.This thesis introduces a new DC hybrid circuit breaker concept termed Electronically Assisted Circuit Breaker (EACB). A conventional AC mechanical circuit breaker (MCB) is used to interrupt DC fault currents with the assistance of an electronic commutation circuit, which is activated for a short time period only during the late phase of the breaking process. Unlike other prior art HCB concepts, an EACB uses (1) a conventional thermal-magnetic AC baseline breaker design with minimal modification; and (2) an electronic commutation circuit which only needs to commutate a fault current already reduced from its peak for a very short duration (~100µs), both contributing to significant cost savings. While an EACB does not facilitate arc-free or ultrafast breaking, it does provide a simple and cost-effective way to enhance the DC current interruption capability of conventional thermal-magnetic AC circuit breakers currently dominating the low voltage circuit breaker market. The EACB concept has been validated both experimentally and by simulation. A 600VDC/250A (nominal) EACB prototype is designed and tested. It has experimentally demonstrated a fault current interruption capability of over 8kA at a DC voltage of 600V within 6 milliseconds.
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Title: Implementation of a multisensor wearable artificial pancreas platform: ensuring safety with communication robustness and cyber security
Creator: Lazaro Martinez, Carmen Caterina
Date: 2019
Description: Advances in IoT technologies and new sensor capabilities contributed to the rapid growth of wearable medical devices. Today, mobile sensor...
Show moreAdvances in IoT technologies and new sensor capabilities contributed to the rapid growth of wearable medical devices. Today, mobile sensor platforms can be effectively, cost efficiently integrated in healthcare applications. However, the increased risks of these devices, inherent vulnerabilities of mobile operating systems and open nature of the wireless protocols call for improved safety and security measures to prioritize patient's well-being. In the field of type 1 diabetes, blood glucose level management with insulin control algorithms are available in diabetes therapy systems, though none are fully automated and require extra announcements (such as meal and exercise) to operate. A mobile artificial pancreas (AP), based on Android smartphone, is developed: such a platform relies on off-the-shelf components and receives in real-time the physiological measurements from the wrist worn physical activity tracker and the glucose measurements, then used in a predictive control algorithm (originally developed and tested on a laptop), to compute the optimal amount of insulin to administer via an insulin pump. A dedicated remote server provides additional support for registration, authentication and data backup.The nature of the algorithm required a fast, reliable method to translate its inherent functions. Therefore, we implement a new semi-automatic conversion mechanism which ports MATLAB to Android as native C code. Validation tests of the mobile version confirm there are no deviations in the results.Moreover, in order to enhance safety guarantees for the patient, this cyber-physical system needs a robust implementation also resilient to attacks and failures. A central monitor module is introduced, wherein wireless devices and communications channels are integrated with complementary alarm and safety subsystems. The parameterization of the AP as a state machine demonstrates the efficiency to detect and react to possible errors, since any state change triggers the appropriate correcting response. The result is a protected and fail-safe environment, further expanded with security modules enforcing encryption, authenticated access and data-flow rules for intrusion detection.Overall, this research demonstrates, in the case of an AP, how challenges in diverse fields such as sensor fusion, control systems, wireless communications and cybersecurity can be addressed with a holistic approach for mobile health (mHealth).
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Title: Security analysis in device-to-device wireless networks
Creator: Liu, Kecheng
Date: 2019
Description: Device-to-device (D2D) network has now become a standardized feature in many mobile devices, by which mobile devices can communicate with each...
Show moreDevice-to-device (D2D) network has now become a standardized feature in many mobile devices, by which mobile devices can communicate with each other even when internet access is not available. Because D2D network is expected to be an intrinsic part of the Internet of Things (IoT) and mobile device is the smartest and the most advanced commercial device in everyday usage, the D2D feature and related security protocols can influence the design and implementation of many other IoT devices. While D2D network provides tangible benefits to users, it also raises the security risks of information leaking. Our work performs an in-depth systematical security analysis on 802.11 based D2D network among commercial devices, including personal mobile devices such as phones and tablet, as well as business POS and printers. In this paper, we focus on most popular apps in the Google Play Store, the best selling printers in the market and the most widely adopted commercial POS devices for small businesses. Our analysis reveals some critical vulnerabilities. The key findings are multi-fold. First, the current mobile D2D network framework established on 802.11 protocol has significant flaw of over-privileged issue. Second, we have identified that data transfer over D2D network can be eavesdropped. Furthermore, we exploit the identified framework flaws to construct multiple proof-of-concept attacks and we conclude the paper with security lessons and suggestions of possible solutions against the identified security issues.
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Title: TRANSIENT STABILITY SIMULATION OF COMBINED THREE-PHASE UNBALANCED TRANSMISSION AND DISTRIBUTION NETWORKS
Creator: Alsharief, Yagoob
Date: 2019
Description: Historically, transmission (T) system and distribution (D) system analysis has been done separately. The main reasons are 1) different...
Show moreHistorically, transmission (T) system and distribution (D) system analysis has been done separately. The main reasons are 1) different modeling frameworks, i.e., positive-sequence versus three-phase unbalanced, 2) system size, and 3) lack of dynamic two-way interaction between T&D. The typical power system usually consists of tens of thousands of transmission buses and thousands of distribution feeders with hundreds of customers per feeder. In the past, distribution networks have been largely passive with relatively little dynamic interaction with the transmission network. However, due to the new trends that the electric grid has been witnessing in the last decade with the installation of distributed energy resources (DERs) on the distribution level, such as behind-the-meter generation and energy storage units, electric vehicles, etc., dynamic simulation tools for combined T&D will become necessary in the near future. These tools will aid system operators and planning engineers in understanding the impact of these new trends on large-scale power systems. Taking advantage of the advancements in the field of high performance computing and parallel computing could enable accurate, wide-area T&D dynamics simulation. These comprehensive simulation capabilities would dramatically improve our ability to predict the complex interactions among DERs, customer loads and traditional utility control devices, thereby allowing higher penetrations of renewable energy, electric vehicles and energy storage.
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