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Title: Constellation and Detection Design for Non-orthogonal Multiple Access System
Creator: Hao, Xing
Date: 2022
Description: It is well known that the Non-Orthogonal Multiple Access (NOMA) system has the capability to achieve higher spectral efficiency and massive...
Show moreIt is well known that the Non-Orthogonal Multiple Access (NOMA) system has the capability to achieve higher spectral efficiency and massive connectivity. In this thesis, some optimized designs in both code-domain and power-domain NOMA systems are studied. Overall, the main contributions are listed as follows:Firstly, we investigate a NOMA system based on the combinatorial design with a novel constellation design for eliminating the surjective mapping from the linear adding data of multiuser and lowering the complexity of constellation design and Multiuser Detection (MUD). And for further enlarge the connectivity, we propose a Low-Density Codes structure to build a trade-off between the diversity and multiusers in resources by expurgating excessive interference on coding matrices. Therefore, our scheme can not only provide a one-to-one mapping pattern with a sparser multiple access structure but also be adjusted with more flexibility to achieve diversity and transmit a large number of users.Secondly, we proposed a constellation mapping scheme based on sub-optimized signal constellation designs by shaping the receiver’s constellation with a strategy that allows differentiated users by which resolvable points will be received allowing simpler detection and design.Thirdly, a novel NOMA system in uplink with time-delayed symbols is investigated, in which a modified Successive Interference Cancellation (SIC) scheme is used at the receiver side. In conventional SIC, when the transmission power is distributed to one user with trivial shifts to other users, the Bit Error Rate (BER) performance will be decreased significantly. Thus, we evaluated a modified SIC by adding artificial time offsets to the conventional power domain-NOMA (PD-NOMA) between users, which can provide higher degrees of freedom for power allocation of users and reduce mutual interference. And then, the added time offsets can provide additional resources to detect the superimposed signals, then the combination of users’ estimations of overall time slots will be considered to get detection improvements. Numerical results demonstrate that the BER performance of our modified SIC outperforms the PD-NOMA with other SIC-based schemes.Thirdly, we propose a new modulation scheme based on polynomial phase signals (PPS) for downlink and uplink non-orthogonal multiple-access (NOMA) transceivers in both the code and power domains. The PPS leads to outstanding spectral efficiency and bit error rate (BER) performance. We also propose a design criterion for CD-NOMA systems to enable the NOMA system to deploy a large number of users with more flexibility as well as lower design and detection complexity than traditional CD-NOMA systems, such as SCMA and PDMA.
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Title: Fault Detection and Localization in Flying Capacitor Multilevel Converters
Creator: Hekmati, Parham
Date: 2021
Description: This dissertation addresses fault detection, fault localization, and recovery in different topologies of the flying capacitor multilevel...
Show moreThis dissertation addresses fault detection, fault localization, and recovery in different topologies of the flying capacitor multilevel converters to guarantee the safe post-fault operation of the system and maintain load supply. There are multiple contributions of this dissertation, including techniques for device open-circuit fault (OCF) detection in stacked multicell converters (SMCs), a windows detector circuit to track the output terminal voltage levels and current directions, a fast and straightforward active power device OC fault detection and localization technique for the family of flying capacitor multilevel converters (FCMCs), a model-based open circuit fault detection and localization technique for the Buck-FCMC, a new estimator for tracking the voltage of flying capacitors, and fault detection and localization for interleaved converters. Each of these contributions is summarized below.The first contribution of this dissertation proposes a fast and straightforward technique for power device OCF detection in SMCs. The fault detection concept only needs to sense the converter's output terminal voltage and current. The sensed output terminal voltage is compared to a predicted one to detect and localize the OCF. A front-end routing circuit is then added to the SMC to maintain the operation of the converter post fault. The second contribution proposes a window detector circuit to track the output terminal voltage levels and current directions. The window detector circuit detects output terminal voltage level and current direction instead of requiring high sample rates and interrupt loops in the controller.The third contribution proposes a fast and straightforward active power device OCF detection and localization technique for the family of FCMCs, including DC to DC FCMCs, single or multi-phase H-bridge FCMCs, and cascaded H-bridge multilevel converters. This technique only needs to sense voltage and direction of current at the output terminals of the converters to detect and localize the fault. The method compares the measured and the expected terminal voltage while considering the commanded switch states and the terminal current direction. As switches transition to different states, healthy switches are excluded from the set of possible faulty switches until only one faulty switch remains. Coordination of the asynchronous operation of FPGA, DSP, and sensors is addressed for practical implementation. The fourth contribution is a model-based OCF detection and localization technique for the Buck-FCMC using model predictive control. In this technique, state-space equations of the system are developed. Comparison of the measured output inductor with the predicted one from the state-space model is used for the OCF detection and localization. This technique can potentially be used for other converters of the FCMC family. The fifth contribution is a new estimator for tracking the voltage of flying capacitors as the internal states of the FCMC. Using the proposed flying capacitor voltage estimator reduces the number of required sensors compared to the conventional model-based methods. At the same time, the overall technique's robustness to dynamic changes, including startup and load changes, is maintained. The last contribution is open and short circuit switch fault detection and localization for interleaved converters using the harmonic analysis of the output terminal parameters. With this method, monitoring electrical parameters of each leg of the interleaves converters is no longer required for fault detection and localization purposes.
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Title: INTELLIGENT SOLID STATE CIRCUIT BREAKERS USING WIDE BANDGAP SEMICONDUCTORS
Creator: Zhou, Yuanfeng
Date: 2021
Description: Electricity, in its predominant form of alternating current (AC), is at the heart of modern civilization. However, direct current (DC)...
Show moreElectricity, in its predominant form of alternating current (AC), is at the heart of modern civilization. However, direct current (DC) electricity is re-emerging, offering higher transmission efficiency, better system stability, better match with modern electrical loads, and easier integration of renewable and storage resources than AC. DC power is gaining tractions in HVDC or MVDC grids, DC data centers, photovoltaic farms, EV charging infrastructures, shipboard, and aircraft power systems. However, DC fault protection remains a major challenge. Interruption of DC currents is extremely difficult due to the lack of current zero crossings which are naturally available in AC power systems. Conventional mechanical breakers only offer a very limited DC current interruption capability even after significant power derating. Hybrid circuit breakers (HCBs) offers a relatively low conduction loss but a response time too slow to protect many low-impedance DC grids. Solid state circuit breakers (SSCBs) can quickly interrupt a DC fault current within tens of microseconds but suffer from high conduction losses. Furthermore, it is generally difficult for an SSCB to distinguish between a short circuit fault and a normal inrush current condition during the start-up of a capacitive load.The purpose of this thesis is to develop a tri-mode, intelligent solid-state circuit breaker technology using wide bandgap semiconductors (especially Gallium Nitride transistors), referred to as iBreaker. The iBreaker design methodology includes the use of mΩ-resistance GaN and SiC devices, new circuit topology and control techniques beyond the commonly used ON/OFF switch configuration, and integration of intelligent functions without increasing component count. The iBreaker adopts a distinct pulse width modulation (PWM) current limiting (PWM-CL) state in addition to the conventional ON and OFF states to facilitate soft startup, fault authentication, and fault location functions. Key design elements, such as use of wide bandgap (particularly GaN) switches, tri-mode operation, combined digital and analog control, the bidirectional buck topology, variable PWM frequency control and universal hardware/software architecture, are discussed in detail. Multiple iBreaker prototypes, rated at 380 V/20 A and 1000 V/10 A, respectively, are built and tested to validate the proposed SSCB design concept. 99.95% transmission efficiency, passive cooling, and μs-scale response time are demonstrated experimentally.
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Title: Intelligent Battery Switching Module for Hybrid Electric Aircraft
Creator: Kamal, Ahmad
Date: 2022
Description: The growth in world economics, tourism and international cooperation has resulted in significant growth of civil aviation industry. This...
Show moreThe growth in world economics, tourism and international cooperation has resulted in significant growth of civil aviation industry. This growing number of fossil fuel reliant aircrafts will significantly increase waste gas emissions with detrimental impact on the environment. The system efficiency of the aircraft must be substantially improved to reduce the fuel burn and thus waste gas emissions. Therefore, the aircraft industry is pushing towards higher electrification of future aircrafts to increase system efficiency, reduce fuel burn and to lower emissions as well as operational costs. The more electric aircraft (MEA) design concept, commercially realized by Boeing 787 and Airbus A380, increases system efficiency by replacing the mechanical, pneumatic, and hydraulic systems with electrical systems. However, global regulation authorities demand further reduction in waste gas emissions and fuel burn. To meet these stringent demands, the aircraft industry is exploring hybrid electric aircrafts which can significantly reduce fuel burn by electrifying the propulsion train of the aircraft. This higher penetration of electrical energy in the aircraft warrants smart short-circuit protection with ultrafast response time. However, current hybrid aircrafts still use outdated mechanical and thermal short-circuit protection which have historically proven to cause numerous tragedies. Solid-state power controller (SSPC) is an alternate solution which uses semiconductor devices to offer faster response. However, the main drawbacks of SSPCs are their need for active cooling due to higher conduction loss and the use of foldback current limiting approach to limit the inrush current of DC-link capacitor of the powertrain. The foldback current limiting approach degrades the power semiconductor devices used due to excessive heat loss by driving the device near the safe operating area (SOA) limits of the device. This thesis presents a 750V/250A intelligent Li-ion battery switching module (BSM) for hybrid electric aircraft propulsion application. The BSM uses commercially available 1200 V SiC JFET power modules with ultra-low RDSON in parallel to achieve sub-mΩ total on-resistance, comparable to the incumbent mechanical contactor solution. This allows the total nominal conduction power loss of the BSM to be less than merely 23 W, permitting maintenance-free passive cooling. In contrast to the incumbent contactor solution, the BSM has ultrafast response (µs-level) to a fault condition. Which, in conjunction with the reduced fault current stress, significantly improves the operation lifetime of the entire system. The BSM incorporates various intelligent features by implementing a tri-mode operation concept, which allows to pre-charge the DC-link capacitor with a limited charging current in PWM mode. To mitigate single-point failures, several design redundancy measures are implemented to ensure reliability and safety for the aircraft. Design considerations of the circuit and physical design of the BSM are discussed in detail including the design of the custom laminated busbar and thermal analysis. Furthermore, the inherent uncontrolled oscillation phenomenon of the JFET cascode structure is explored and addressed. Finally, the experimental results obtained from the built and tested prototype of the BSM are reported.
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Title: EMAT DESIGN CONFIGURATIONS AND SOFTWARE-DEFINED ULTRASONIC COMMUNICATIONS THROUGH METALLIC CHANNELS IN NUCLEAR FACILITIES
Creator: Huang, Xin
Date: 2022
Description: Nuclear facilities are partitioned into different blocks, and all equipment therein is well-packed for isolation purposes. The primary...
Show moreNuclear facilities are partitioned into different blocks, and all equipment therein is well-packed for isolation purposes. The primary barriers of each block include a thick, reinforced, high-strength concrete wall. The presence of physical boundaries introduces a major challenge to implementing wired or radio frequency (RF) wireless communication. Achieving data communication through the solids channel, especially considering the complex environment in nuclear power plants, is very challenging. Ultrasonic communication is a desirable method for information transfer through solid mediums such as metallic bars or pipes. This thesis is methodologically innovative in the way it seeks the best solution for ultrasonic communications through metallic channels. Therefore, we address the following research areas: 1. The advantages of using electrical-magnetic acoustic transducers (EMATs) as transmitter and receiver; 2. The fundamentals of the EMAT structure and wave generation mechanism for ultrasonic communications; 3. The channel model and behavior of ultrasonic wave propagation in a different structure of solid channels; 4. How to minimize the adverse impact of wave dispersion and reverberation; 5. How to increase the bitrate and decrease the bit error rate (BER) of an ultrasonic communication system; 6. How to utilize the software-defined system-on-chip (SoC) platform for ultrasonic communications; and 7. How to implement secure ultrasonic video transmission through solid channels. In this thesis, we have investigated the feasibility of using Periodic-permanent-magnet electromagnetic acoustic transducers (PPM-EMATs) transmitter and receiver as the information-bearing of ultrasonic waves across the plate channels (shear horizontal waves) and pipe channels (torsional waves). Methods such as time-reversal (TR), pulse shaping, and adaptive equalizer techniques are studied for improving the signal-to-noise ratio (SNR) of ultrasonic communication systems. We also investigated a novel software-defined ultrasonic communication system (SDUC) for real-time video transmission through a highly reverberant and dispersive metallic bar channel. Furthermore, we investigated the feasibility of combining orthogonal frequency-division multiplexing (OFDM) with quadrature amplitude modulations (QAM) for bitrate peak performance. Strategies and guidelines were established for the best solutions to combat intersymbol interference (ISI) caused by the severe reverberation inherent in metallic channels. A practical solution for video transmission, adhering to the Digital Video Broadcasting Terrestrial (DVB-T) standard, was also examined for video streaming transmission of 240p, 480p, and 720p resolutions at 20 frames per second (FPS) across a rectangular aluminum bar (ARB) channel. Through ultrasonic experimental studies for channel analysis, we achieved a peak video transmission rate of 1074 kbps with 3.3×10-4 BER despite reverberation, the multipath effect, and signal fading within the ARB channel.
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Title: Advances in Distribution System State Estimation
Creator: Huang, Jianqiao
Date: 2022
Description: With the increasing penetration of renewable energy in the distribution system, the system states are becoming more volatile. How to maintain...
Show moreWith the increasing penetration of renewable energy in the distribution system, the system states are becoming more volatile. How to maintain the normal operation is an urgent question to the operators. Distribution system state estimation (DSSE) is the key to the monitor and control of distribution systems.Distribution systems feature a larger number of nodes and heterogeneous measurements. Due to these features, directly employing traditional state estimation methods cannot provide fast and accurate estimation results. The existing semidefinite programming based methods show promising for the accuracy, but it is not scalable for a large system. In this thesis, we propose fast and accurate DSSE methods. First, we improve the efficiency of the state-of-art SDP-DSSE method, convex iteration (CI) method. We design a scalable convex iteration method, CDQC, by fully exploiting the radial topology of distribution system. However, the efficiency of CDQC depends on efficient feeder partition solution. It is time-consuming to get a good partition especially when the system is large. Hence, we propose a bus injection based semidefinite relaxation method (SDR-BIM) that fully exploits the radial topology of the network without the need for partitioning the networks. However, SDR-BIM has numerical issue for large scale network. This motivates us to design a branch power model based SDR-DSSE method. The proposed SDR-BPM-DSSE method improves the numerical stability and the increase in the average estimation error of voltage is less than $0.04\%$. To further improve the computational efficiency, we developed a generalized linear power flow model (GLDF) and propose an iterative method to solve the DSSE based on GLDF.Finally, the efficiency and accuracy of the proposed methods are validated on IEEE 13-bus, 37-bus, 123-bus, and 8500-node test feeders.
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Title: Active Load Control in a Synchronized and Democratized (SYNDEM) Smart Grid
Creator: Lv, Zijun
Date: 2021
Description: Smart grid is envisioned to take advantage of modern information and communication technologies in achieving a more intelligent grid in order...
Show moreSmart grid is envisioned to take advantage of modern information and communication technologies in achieving a more intelligent grid in order to facilitate: Integration of renewable resources; Integration of all types of energy storage; Two-way communication between the consumer and utility so that end users can actively participate. The Synchronized and Democratized (SYNDEM) smart grid is regarded as the next generation smart grid. The objective of the SYNDEM smart grid is for all active players in a grid, large or small, conventional or renewable, supplying or consuming, to be able to equally and laterally regulate the grid in a synchronous manner to enhance the stability, reliability, and resiliency of future power systems. In a SYNDEM smart grid, power electronic converters are controlled to behave as conventional synchronous machines. Such converters are called virtual synchronous machines (VSMs).Following the SYNDEM structure, this thesis mainly focuses on developing the VSM technology for the automatic grid regulation at the demand side. The major aim and objective is to achieve active or intelligent loads that can flexibly and automatically take part in grid regulation. Moreover, the active load is expected to have similar grid regulation behavior as other active players in the grid, for e.g., renewable generations. To achieve this, a droop-controlled rectifier is proposed that acts as a general interface for a load to grid. The rectifier is controlled as a VSM so that a load equipped with such a rectifier can take part in grid regulation continuously like a traditional synchronous machine. Such a rectifier has a built-in storage port, in addition to the normal AC and DC ports. The flexibility required by the AC port to support the grid is provided by the storage port. The DC-bus voltage of the storage port is able to fluctuate with in a wide range to exchange energy with the grid.In order to further take use of the energy in the storage port (DC-bus capacitor) of a rectifier more reasonably and increase the support time to grid, an adaptive droop mechanism is proposed. Under such a droop mechanism, the rectifier can automatically change the power consumed according to the grid voltage variations as well as its potential to provide grid support. To achieve this, a flexibility coefficient is introduced to indicate the power flexibility level of the DC-bus capacitor. Then this flexibility coefficient is embedded into the universal droop controller (UDC) to make it adaptive. Hence, the adaptive droop controller has a changing droop coefficient corresponding to the power flexibility of a rectifier, so it can take advantage of the energy stored in its DC-bus capacitor wisely to support the grid. This droop controller can also be applied into connection between two SYNDEM smart grids. To achieve this, a grid bridge (GB) that enables autonomous and equal regulation between two SYNDEM grids is proposed. The real power transferred through a GB has linear relationship with the voltage deviation between the two micro-grids connected. The micro-grid with a higher voltage will automatically provide power to the lower one. Moreover, the power direction of a GB is bidirectional and determined by the grid voltage difference, this makes the two micro-grids equal to each other. The GB is physically a back-to-back converter. In order to achieve autonomous and equal regulation, both sides of the back-to-back converter are controlled under droop controller with the same droop coefficients. The VSM control technology is also developed to control Modular multilevel converters (MMCs) for high voltage applications. Like active loads introduced above, the MMCs can take part in the grid regulation according to the droop mechanism designed. In order to eliminate the circulating current that exists in MMCs, proportional-resonant (PR) controllers are adopted to inject second-order harmonics to the MMCs to suppress the second order circulating current. The dynamics, implementation and operation of the VSM-like MMC are introduced and analyzed. Particularly, how the VSM control algorithm works with the circulating current control in MMCs is presented. An IIT SYNDEM Smart Grid Testbed is built in an aim of achieving a minimize realization of the SYNDEM system. Extensive experiments are done on the system to show the operational scenarios when the proposed active loads are integrated in the system. There are in total eight nodes in the IIT SYNDEM testbed, which contains two utility grids, one AC load, one DC load, two solar farms and two wind farms. All the nodes are connected to a local grid through VSMs, so that they can take part in the local grid regulations in similar ways. The IIT SYNDEM Smart Grid Testbed is described in details and experimental results are provided to show the dynamic and steady performance of the IIT SYNDEM smart grid.
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Title: Video Object Detection using CenterNet
Creator: Mondal, Madhusree
Date: 2021
Description: This thesis investigates the options of video object detection with key-point-based approaches. The problem of recognizing, locating, and...
Show moreThis thesis investigates the options of video object detection with key-point-based approaches. The problem of recognizing, locating, and tracking objects in videos has been a challenging task in the computer vision area. There are few applications on key-point-based object detectors like CornerNet and CenterNet. At the first stage, this work involves the use of the previously proposed CenterNet module as a baseline detector on each frame of the Imagenet Video dataset. Then we apply an RNN module to exploit the temporal information from the past frames for better results.There are challenges in video object detection compared to still image-based object detection. It is not efficient to apply a still-image-based detector on each frame independently because we cannot exploit the temporal contextual information in videos since neighboring frames in a video are highly correlated. Object detection from videos suffers from motion blur, video focus, rare poses, etc. To overcome these issues one way of improving CenterNet for video object detection is to propagate the previous reliable detection results to boost the detection performance.
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Title: DEEP LEARNING IMAGE-DENOISING FOR IMPROVING DIAGNOSTIC ACCURACY IN CARDIAC SPECT
Creator: Liu, Junchi
Date: 2022
Description: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a noninvasive imaging modality widely utilized...
Show moreMyocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a noninvasive imaging modality widely utilized for diagnosis of coronary artery diseases (CAD) in nuclear medicine. Because of the concern of potential radiation risks, the imaging dose administered to patients is limited in SPECT-MPI. Due to the low count statistics in acquired data, SPECT images can suffer from high levels of noise. In this study, we investigate the potential benefit of applying deep learning (DL) techniques for denoising in SPECT-MPI studies. Owing to the lack of ground truth in clinical studies, we adopt a noise-to-noise (N2N) training approach for denoising in full-dose studies. Afterwards, we investigate the benefit of applying N2N DL on reduced-dose studies to improve the detection accuracy of perfusion defects. To address the great variability in noise level among different subjects, we propose a scheme to account for the inter-subject variabilities in training a DL denoising network to improve its generalizability. In addition, we propose a dose-blind training approach for denoising at multiple reduced-dose levels. Moreover, we investigate several training schemes to address the issue that defect and non-defect image regions are highly unbalanced in a data set, where the overwhelming majority by non-defect regions tends to have a more pronounced contribution to the conventional loss function. We investigate whether these training schemes can effectively improve preservation of perfusion defects and yield better defect detection accuracy. In the experiments we demonstrated the proposed approaches with a set of 895 clinical acquisitions. The results show promising performance in denoising and improving the detectability of perfusion-defects with the proposed approaches.
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Title: Control and Operation of Microgrids and Networked Microgrids
Creator: Sheikholeslami, Mehrdad
Date: 2022
Description: This dissertation presents the practical operation and control of microgrids and networked microgrids, particularly, the networked IIT Campus...
Show moreThis dissertation presents the practical operation and control of microgrids and networked microgrids, particularly, the networked IIT Campus Microgrid (ICM) and Bronzeville Community Microgrid (BCM). Microgrids (MGs) provide a potential solution to accommodating renewable and distributed energy resources (DERs). MGs and the networked form of MGs, i.e., networked microgrids or NMGs, have received significant attention in the past two decades. However, several details are often neglected in the literature that need to be considered for the practical operations of MGs and NMGs. First, there is a need for a step-by-step sequence of operations (SOO) that clearly defines the procedures for changing the operation modes of MGs and NMGs for their reliable and resilient operation. Second, there is a need to develop new control strategies for the centralized and distributed control of MGs and NMGs that are resilient to extreme events and are also more sustainable than the ones available in the literature. Third, there is a need for developing the model of MGs and NMGs in a real-time simulator to safely evaluate the performance of the control and operation of MGs and NMGs. Finally, to close the engineering loop, there is a need to connect the digital and physical layers which are known as digital twins. This dissertation proposes solutions for these four requirements and presents results to evaluate the performance of the proposed solutions. First, an SOO is proposed to enable the reliable and safe transition between different microgrid operation modes. The proposed SOO is adaptable to any MG and NMG with minor modifications. Second, for the centralized control, a DER control model is proposed that allows for the regulated power exchange between networked MGs to ensure information privacy and respect the electrical boundary of each MG. For the distributed control, two control schemes are proposed that are resilient to extreme cases, allow the integration of renewable energy resources (RES), and require the minimum intervention of the operators. Third, several techniques are proposed that can be adopted for developing the real-time models of MGs and NMGs. Finally, as a proof of concept, a digital twin of a microgrid with connections between the physical and digital layers is implemented and tested. The IIT Campus Microgrid (ICM) and Bronzeville Community Microgrid (BCM), as well as their networked form (networked ICM-BCM), are selected as the practical testbeds and are modeled in Real-time Digital Simulator (RTDS). The RTDS model is interfaced with microgrid master controllers (MMC) for real-time data exchange and the performance of the MMCs and the distributed control strategies are tested to illustrate the importance of adopted methods in the real-time control of MGs and NMGs. Finally, a proof of concept for the digital twin of ICM is presented.
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Title: SOLID-STATE SMART PLUG DEVICE
Creator: Deng, Zhixi
Date: 2022
Description: Electrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation...
Show moreElectrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation degradation that may lead to a variety of electrical faults. Smart Plugs are a type of plug-in device controlling electrical loads via wireless communication for consumer market. However, there is lack of circuit protection features in existing Smart Plug products. Moreover, there is no previous product or research on Smart Plug with circuit protection features. This thesis introduces a new Smart Plug 2.0 concept which offers all-in-one protection against over-current, arc, and ground faults in addition to the smart features in Smart Plug products. It aims at preventing fire and shock hazards caused by degraded or damaged power cords and electrical connections in homes and offices. It offers microsecond-scale time resolution to detect and respond to a fault condition, and significantly reduces the electrothermal stress on household electrical wires and loads. A new arc fault detection method is developed using machine learning models based on load current di/dt events. The Smart Plug 2.0 concept has been validated experimentally. A 120V/10A solid-state Smart Plug 2.0 prototype using power MOSEFTs is designed and tested. It has experimentally demonstrated the comprehensive protection features against all types of electrical faults.
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Title: WIDE BANDGAP FRACTIONAL POWER PROCESSING
Creator: Kundu, Aritra
Date: 2022
Description: The adoption of wide bandgap (WBG) power semiconductors can improve the performance of power converters at the expense of significantly higher...
Show moreThe adoption of wide bandgap (WBG) power semiconductors can improve the performance of power converters at the expense of significantly higher cost than Si at present time. In this thesis, an innovative Wide bandgap Fractional Power Processing (WFPP) design concept is introduced where silicon devices process the base power at a low switching frequency (2kHz or lower) while WBG devices are judiciously used to process only a fraction of the total power at a much higher frequency (tens of kHz). WFPP inverter is a design concept that splits the power processing into a low frequency Si base power processor and a high-frequency WBG fractional power processor. WBG devices are therefore judiciously used to process only a fraction of the total power to achieve both high-efficiency and lower cost than a full-WBG converter design. This thesis investigates an optimization strategy to minimize the total power loss while maintaining a reasonable THD and cost for a hybrid inverter design with comprehensive power loss analysis and calculation on fundamental and harmonics currents. Optimal selection of power sharing between the Si and WBG inverters and switching frequency are discussed in the thesis. The circulating current paths in topology with hybrid switching frequencies are also analyzed and presented in this thesis. Experimental results on a 9kW SiC/Si hybrid inverter prototype with isolated and non-isolated DC power supplies are presented to validate the design concept.
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Title: ANALYTICAL APPROACH TO ESTIMATE ROTOR TEMPERATURE IN SWITCHED RELUCTANCE MOTOR
Creator: Koujalagi, Shweta Manohar
Date: 2022
Description: Motors contribute most of the loads. Motors find major applications in automobile industries, household appliances, industrial equipment, and...
Show moreMotors contribute most of the loads. Motors find major applications in automobile industries, household appliances, industrial equipment, and other areas. With the time, engineers and industries found some of the drawbacks or disadvantages of using induction motors in certain applications. They started developing other types of motors that are more efficient than existing ones. Among those, switched reluctance motor, referred as SRM is the one. SRMs are simple in construction, rugged and highly efficient motors.Even though SRM has higher efficiency, it still contribute some losses in the form of heat which will increase the temperature of SRM. If the temperature increases beyond certain limit, cable insulation fails, degrades rotor capability of aligning characteristics, damages bearings, etc. Therefore, it is important to understand the flow of heat in SRM. This thesis focuses on heat transfer analysis from stator coil to rotor of SRM using analytical method and numerical method such as finite element analysis from available coil temperature without using any kind of sensors. Analytical and FEA models are built separately to obtained rotor temperatures at various coil temperatures and rotor speeds. Finally, analytical results are validated with FEA model results. Therefore, once the rotor temperature is estimated accurately, model can be implemented in automotive and other industrial applications to continuously monitor the rotor temperature. It is important to monitor temperature to avoid damage of SRM by thermal effects.
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Title: Exploiting contextual information for deep learning based object detection
Creator: Zhang, Chen
Date: 2020
Description: Object detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great...
Show moreObject detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great progress made in recent years, object detection is still a challenging task. One of the keys to improving the performance of object detection is to utilize the contextual information from the image itself or from a video sequence. Contextual information is defined as the interrelated condition in which something exists or occurs. In object detection, such interrelated condition can be related background/surroundings, support from image segmentation task, and the existence of the object in the temporal domain for video-based object detection. In this thesis, we propose multiple methods to exploit contextual information to improve the performance of object detection from images and videos.First, we focus on exploiting spatial contextual information in still-image based object detection, where each image is treated independently. Our research focuses on extracting contextual information using different approaches, which includes recurrent convolutional layer with feature concatenation (RCL-FC), 3-D recurrent neural networks (3-D RNN), and location-aware deformable convolution. Second, we focus on exploiting pixel-level contextual information from a related computer vision task, namely image segmentation. Our research focuses on applying a weakly-supervised auxiliary multi-label segmentation network to improve the performance of object detection without increasing the inference time. Finally, we focus on video object detection, where the temporal contextual information between video frames are exploited. Our first research involves modeling short-term temporal contextual information using optical flow and modeling long-term temporal contextual information using convLSTM. Another research focuses on creating a two-path convLSTM pyramid to handle multi-scale temporal contextual information for dealing with the change in object's scale. Our last work is the event-aware convLSTM that forces convLSTM to learn about the event that causes the performance to drop in a video sequence.
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Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
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Title: Improving self-supervised monocular depth estimation from videos using forward and backward consistency
Creator: Shen, Hui
Date: 2020
Description: Recently, there has been a rapid development in monocular depth estimation based on self-supervised learning. However, these existing self...
Show moreRecently, there has been a rapid development in monocular depth estimation based on self-supervised learning. However, these existing self-supervised learning methods are insufficient for estimating motion objects, occlusions, and large static areas. Uncertainty or vanishing easily occurs during depth inferencing. To address this problem, the model proposed in this thesis further explores the consistency in video and builds a multi-frame model for depth estimation; secondly, by taking advantage of the optical flow, a motion mask is generated, with additional photometric loss applied for those masked regions. Experiments are carried out on the KITTI dataset. The proposed model performs better than the baseline model in quantitative results, and as seen from the depth map, the scale uncertainty and depth incomplete situations are improved in motion objects and occlusions explicitly.
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Title: LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Creator: Song, Chao
Date: 2019
Description: Single photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery...
Show moreSingle photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery diseases. The image quality in cardiac SPECT can be adversely affected by cardiac motion and respiratory motion, both of which can lead to motion blur and non-uniform heart wall. In this thesis, we mainly investigate imaging de-noising algorithms and motion correction methods for improving the image quality in cardiac SPECT on both standard dose and reduced dose.First, we investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in cardiac-gated SPECT images. Since in recent years low-dose studies have gained increased attention in cardiac SPECT owing to its potential radiation risk, to further improve the image quality on reduced dose, we investigate a novel de-noising method for low-dose cardiac-gated SPECT by using a three dimensional residual convolutional neural network (CNN). Furthermore, to reduce the negative effect of respiratory-binned acquisitions and assess the benefit of this approach in both standard dose and reduced dose using simulated acquisitions. Inspired by the success in respiratory correction, we investigate the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. Finally, to combine the benefit of above two types of motion correction, dual-gated data acquisitions are implemented, wherein the acquired list-mode data are further binned into a number of intervals within cardiac and respiratory cycle according to the electrocardiography (ECG) signal and amplitude of the respiratory motion.
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Title: Large-Signal Transient Stability and Control of Inverter-Based Resources
Creator: Wang, Duo
Date: 2024
Description: Renewable generation, including solar photovoltaic (PV) systems, type 3 and 4 wind turbine generation systems (WTG), battery energy storage...
Show moreRenewable generation, including solar photovoltaic (PV) systems, type 3 and 4 wind turbine generation systems (WTG), battery energy storage systems (BESS), as well as high voltage direct current (HVDC) and flexible alternating current (FACT) transmission system devices with increasing penetration level are being connected to the bulk power systems (BPS) via power electronic (PE) converters as the interface, referred to as the inverter-based resources (IBRs) on the transmission and sub-transmission levels or distributed energy resources (DERs) located on the distribution level. The IBR is almost entirely defined by the control algorithms and found to be more prone to experiencing large disturbances due to the lack of the conventional synchronous machine (SM) intrinsic synchronous characteristics and mechanical inertia, as well as being in smaller capacity sizes. Thus, these reasons motivate this dissertation to study the large-signal transient stability and control of IBRs for reliable grid integration and rapid grid transformation. For large-signal stability analysis methods, Lyapunov-based methods are the fundamental theory used to characterize the stability issues with analytical solutions, although other non-Lyapunov methods could also be very helpful. A main difficulty hindering the widespread adoption of the Lyapunov stability analysis method is the difficulty of finding the proper Lyapunov function candidate for a higher dimensional nonlinear system. The Port-Hamiltonian (PH) nonlinear control theory is explored in this dissertation as a promising theoretical framework solution addressing this challenging issue. A PH-based tracking and robust control method is proposed to facilitate the practical application of the PH framework in IBR controls. In addition, considering the typical grid-forming (GFM) IBR control with a first-order low pass filter (LPF) block is usually involved with control saturation function for protection purposes under abnormal operating conditions with anti-windup issue in practical implementation, a PH-based bounded LPF (PH-BLPF) control is proposed to incorporate this in the large-signal PH interconnection modeling framework while preserving the robust tracking Lyapunov stability with improved transient dynamic performance and stability margin.Moreover, specific real-world transient synchronization stability issues, such as the grid voltage large fault disturbance case, are studied. In addition, to meet the recent emerging IBR grid code requirements, such as the current magnitude limitation, grid support function, and fault recovery capability of GFM-VSCs, a virtual impedance-based current-limiting GFM control with enhanced transient stability and grid support is proposed.
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Title: Empowering Visually Impaired Individuals With Holistic Assistance Using Real-Time Spatial Awareness System
Creator: Yu, Xinrui
Date: 2024
Description: The integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired...
Show moreThe integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired individuals, offering them greater autonomy and quality of life. This thesis introduces a Visually Impaired Spatial Awareness (VISA) system designed to assist visually impaired individuals holistically through a structured approach. At the foundational level, the VISA system incorporates several key technologies to interpret the surroundings and assist in basic navigation tasks. It utilizes Augmented Reality (AR) markers to facilitate recognition of places and aid in navigation, employs neural network models for advanced object detection and tracking, and leverages depth information for accurate object localization. Progressing to the intermediate level, the VISA system integrates the data obtained from object detection and depth sensing to assist in more complex navigational tasks such as obstacle avoidance and pathfinding toward a desired destination. At the advanced level, the VISA system synthesizes the capabilities developed at the foundational and intermediate levels to enhance the spatial awareness of visually impaired users, allowing them to undertake complex tasks, such as navigating complex environments and locating specific items. The VISA system also emphasizes efficient human-machine interaction, incorporating text-to-speech and speech-to-text technologies to facilitate natural and intuitive communication between the user and the system. The VISA system's performance was evaluated in different environments simulating real-world scenarios. The experimental results show that the user can interact with our system intuitively with minimal effort, and affirm that the VISA system can effectively assist the visually impaired user in locating and reaching for objects, navigating indoors, identifying merchandise, and recognizing both handwritten and printed texts.
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Title: Application of Blockchain and Artificial Intelligence Methods in Power System Operation and Control
Creator: Farhoumandi, Matin
Date: 2023
Description: The proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving...
Show moreThe proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers (both consumers and producers) in a coordinated system of power distribution network (PDN) and urban transportation network (UTN). In this new paradigm, peer-to-peer (P2P) energy trading is a promising energy management strategy for dynamically balancing the supply and demand in electricity markets. In this thesis, we propose the applications of artificial intelligence technology to power system operation and control. First, blockchain (BC) is applied to electric vehicle charging station (EVCS) operations to optimally transact energy in a hierarchical P2P framework. In the proposed framework, a decentralized privacy-preserving clearing mechanism is implemented in the transactive energy market (TEM) in which BC’s smart contracts are applied in a coordinated PDN and UTN operation. The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123-bus PDN and a modified Sioux Falls UTN. Second, machine learning and deep learning methods are applied to short-term forecasting of non-conforming net load (STFNL). STFNL plays a vital role in enhancing the secure and efficient operation and control of power systems. However, power system consumption is affected by a variety of external factors and thus includes high levels of variations. These variations cause STFNL to be a challenging task as more DERs are integrated into the power grid. This thesis proposes two commonly used machine learning and deep learning methods, i.e., ensemble bagged and long short-term memory, for STFNL. The advantages, features and applications of these methods are expanded in a proposed fusion forecasting model that improves the STFNL accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed fusion model. A comparative study based on practical load data is performed to demonstrate that the proposed fusion methodology can reach a relatively higher forecasting accuracy with lower error indices. Index Terms—Blockchain, deep learning and machine learning, electric vehicle charging stations, non-conforming net load forecasting, peer-to-peer transactive energy, power distribution and transportation networks, distributed energy resources, behind-the-meter supply resources.
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