Search results

Title: TOWARD THE AUTOMATIC ORGANIZATION AND COMPREHENSION OF SOCIAL NETWORK COMMUNICATION
Creator: Platt, Alana
Date: 2013, 2013-05
Description: Social networking sites are radically transforming the way we communicate and relate to each other. They facilitate timely information...
Show moreSocial networking sites are radically transforming the way we communicate and relate to each other. They facilitate timely information exchange and give us unprecedented access to numerous sources of information on a myriad of topics. Although the information is available, there are a number of challenges that inhibit utilization of this information: Social Networks have a great volume of messages that the user must sift through to find relevant ones, messages are frequently repetitive, the information is not organized topically, and there is little context information. The information consumer (user) must take on many of the tasks traditionally performed by the information producer to get a “big picture” understanding of the topic. This thesis introduces a framework for an automated information gathering and organization system to facilitate the information consumer’s comprehension of a given topic. The framework addresses two primary components: the user interface for the system and identification of sub-topics. The framework was implemented as a research platform designed to bring these two components together and support future research in the domain.
PH.D in Computer Science, May 2013
Show less

Title: ADVANCED BASE DRIVERS FOR SILICON CARBIDE BJTs
Creator: Pozo Arribas, Alejandro
Date: 2017, 2017-07
Description: This thesis focuses on the optimization of base drivers for SiC BJTs and presents a novel driver topology that targets minimum power...
Show moreThis thesis focuses on the optimization of base drivers for SiC BJTs and presents a novel driver topology that targets minimum power consumption. SiC BJTs have been studied for over a decade, during which time, they have been proven to have superior performance than Si IGBTs and even other normally-off SiC devices such as MOSFETs. Despite this, SiC BJTs are the least popular among the family of SiC power switches. As current controlled devices, BJTs require a continuous sup- ply of current through the base during the on-time. And, even though current gains over 100 have been reported, the base current required translates into a considerable amount power consumed by its driver, compared to its competitors. This power can affect the overall efficiency of a converter if the driver circuit is not designed properly. Since, the driver represents a key system for the success of SiC BJTs as power semiconductor devices, this thesis conducts a comprehensive evaluation of previous solutions and an analysis of the driver power losses to identify the optimal driver configuration. As a conclusion of this study, a novel topology is proposed, designed and built for its latter validation through experimental tests. The proposed solution allows the replacement of a SiC MOSFET or Si IGBT and driver with a SiC BJT and driver without the need of a current sensor or a dedicated DSP/FPGA. The driver power consumption is minimized with a proportional base current design based on a MHz synchronous buck converter operating as a Class D amplifier. This switched mode power amplifier uses a reference signal to provide a voltage that causes a base current proportional to the instantaneous collector current. The reference signal is generated with a high bandwidth sensor that measures the instantaneous voltage drop across the BJT (vCE) during the on-time. Hence, current sensors are avoided. Different alternatives for a voltage sensor are discussed and analyzed through simulations and experimental results. Moreover, the use of vCE to estimate the instantaneous collector current makes the proposed driver a temperature-sensitive design. For the first time, a proportional base current driver generates a base current proportional to the instantaneous collector current taking into account the effect of temperature on the DC current gain. Moreover, all this is achieved with solely analog electronics in a standalone solution. A 1.5kW Boost converter was built to validate the proposed driver under different collector currents and operating temperatures. In order to show the performance improvement offered by the proposed solution, the same Boost converter was operated with a commercial base current driver. This exercise showed a reduction of the driver power consumption by up to a factor of 4 without affecting the efficiency of the Boost converter. The switching behavior of a SiC BJT operated with the proposed driver and some of its limitations are discussed. These have, in fact, motivated additional research to develop efficient, isolated MHz regulators for faster operating frequencies of the SiC BJT. In addition, a new over-current protection integrated into the proposed driver is suggested and tested with interrupt times of less than 500ns for a collector current of 50A.
Ph.D. in Electrical Engineering, July 2017
Show less

Title: A NOVEL HYDRO-GENERATOR BASED ENERGY STORAGE CONCEPT FOR MICROGRID APPLICATIONS
Creator: Gu, Ran
Date: 2012-04-26, 2012-05
Description: The solution to deal with the current non-renewable energy crisis, global warming, pollution and green gas emission is to reduce fossil fuels...
Show moreThe solution to deal with the current non-renewable energy crisis, global warming, pollution and green gas emission is to reduce fossil fuels usage and increase use of renewable sources of energy with minimal environmental impact. Solar energy and wind energy have gained significant popularity as natural resources across the world. However, these renewable energies bring new challenges to the control of power systems and distributed generation since they depend on natural elements that can be unpredictable and intermittent. One way to address the intermittency of these resources is to transfer energy in to an energy storage device. Historically, a battery has been viewed as one of the primary energy storage devices. Even though some battery can have high efficiency, they can be limited by the size and volume required to store a large amount of energy. In addition, they can also cause environmental pollution owing to the chemicals used and tend to have a high cost and short life. A novel hybrid energy storage system, which comprises of an integrated hydroelectric-compressed air solution have been proposed in this thesis. Three potential configurations have been outlined, where energy is provided by wind and solar energy. To extract maximum power from wind, solar and water, maximum power point tracking (MPPT) techniques for both renewable sources have been proposed. For researching the interaction between charging and discharging elements, extensive simulations have been conducted using Matlab/ Simulink.
M.S. in Electrical Engineering, May 2012
Show less

Title: IMPROVED MAXIMUM LIKELIHOOD ESTIMATION FOR GENERALIZED BASS MODEL
Creator: Razo, Martha
Date: 2017, 2017-05
Description: Today, businesses operate in an interconnected global economy, in which innovation happens on a moment to moment basis. Statistical predictive...
Show moreToday, businesses operate in an interconnected global economy, in which innovation happens on a moment to moment basis. Statistical predictive modeling in marketing and development is emerging as a crucial component to the success of small companies and large corporations alike. The goal of this paper is to analyze the Bass model as it pertains to sales of the Chevy Volt. The Bass model has been shown to be a useful tool for forecasting the sales of new products as they become available in the marketplace, but what are the model's limitations? The widely studied Bass model produces computational problems when we evaluate the model for a larger set data which extends to modified models constructed from the original Bass model. Kijek in [14] and Srinivasan and Mason in [16] alert us of the shortcomings of the Maximum Likelihood approach of solving the Bass model which extends to solving the Generalized Bass model, but these authors limit us to a vague listing without a close analysis. In this paper we present the issues of estimating the Bass model parameters when using the Maximum Likelihood approach. Furthermore, we introduce an improved generalized model which takes into account the shortcomings of the Bass model and our proposed approach of overcoming these. We will illustrate the limitations of the Bass model when using a large data set from the Chevy Volt car data published by Inside EVs[12]. Careful analysis of the Bass model and its current modifications provides a rich tool that has potential in changing the future of a company when introducing new products to the market.
M.S. in Applied Mathematics, May 2017
Show less

Title: ESTIMATION OF THERMAL STATE OF CHARGE FOR PCC BASED LITHIUM-ION BATTERY PACKS
Creator: Salameh, Mohamad
Date: 2016, 2016-07
Description: With continuing efforts to improve energy and power density of Li-ion batteries, heat generation and thermal safety remain critical barriers...
Show moreWith continuing efforts to improve energy and power density of Li-ion batteries, heat generation and thermal safety remain critical barriers to commercial success. Energy conversion in a battery is an exothermic process. Whenever the temperature of lithium-ion batteries increases, there can be direct consequences-reduced calendar and cycle life and higher risk of a battery re or explosion. Conventional approaches to prevent overheating use active thermal management systems, such as air conditioning or liquid cooling. However, these systems can be costly, bulky, and consume energy during operation. In addition they o er no overheat protection while the application or the vehicle is powered down. Phase change material composites (PCC) can be employed to rapidly absorb heat from the battery and distribute it, thereby enabling lightweight and compact packs with extended cycle-life and safety. This thesis proposes an online temperature estimation technique for a novel intelligent battery thermal management to actively monitor thermal mass of the phase change material. Such a system will not only enable avoidance of thermal issues, but will extend life of the battery pack by optimally selecting the operating point of the Energy Storage System. It can also be used to predict when active cooling should be employed just before the battery exits the phase change temperature plateau, to ensure latent heat absorption is spread across the entire drive cycle.
M.S. in Electrical Engineering, July 2016
Show less

Title: APPLICATION OF THE FEAR-AVOIDANCE MODEL OF CHRONIC PAIN TO UNDERSTAND NEUROCOGNITIVE AND BEHAVIORAL FACTORS THAT CONTRIBUTE TO FUNCTIONAL IMPAIRMENT AND DEPRESSION IN ADULTS WITH SICKLE CELL DISEASE
Creator: Piper, Lauren E.
Date: 2017, 2017-07
Description: Acute and chronic pain in sickle cell disease (SCD) are associated with functional impairment and depressive symptoms. Given the suboptimal...
Show moreAcute and chronic pain in sickle cell disease (SCD) are associated with functional impairment and depressive symptoms. Given the suboptimal management of pain in SCD and serious health risks associated with current treatment methods for pain, there is a need to identify factors associated with pain that impact functional outcomes and depression. The fear-avoidance (FA) model of chronic pain has been examined in other chronic pain populations as a means to understand how pain-related cognitive and behavioral factors contribute to functional impairment and depression, but has not been applied in individuals with SCD. The purpose of the present study was to apply the FA model of chronic pain to adults with SCD via mediation analyses. Additionally, mental flexibility was examined as a possible moderator in the FA model. Results demonstrated that pain catastrophizing mediated the relationship between pain severity and pain-related fear. No other mediators within the model were identified. Additionally, results did not demonstrate that mental flexibility moderated the relationship between pain severity and pain catastrophizing. Post-hoc exploratory analyses demonstrated that pain catastrophizing and pain-related fear significantly predicted functional impairment and depression, respectively, above and beyond pain severity. Overall, results suggest that the FA model of chronic pain does not apply to individuals with SCD and the predictive roles that pain catastrophizing and pain-related fear play in functional impairment and depression are not consistent with results in other chronic pain populations. Further studies are needed to identify factors that explain the relationship between pain, functional impairment, and depression so that these factors may be targeted for intervention as a means to improve pain, mood, and functional independence.
Ph.D. in Psychology, July 2017
Show less

Title: Deep Learning and Model Predictive Methods for the Control of Fuel-Flexible Compression Ignition Engines
Creator: Peng, Qian
Date: 2022
Description: Compression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However,...
Show moreCompression ignited diesel engines are widely used for transportation and power generation because of their high fuel efficiency. However, diesel engines can cause concerning environmental pollution because of their high nitrogen oxide (NOx) and soot emissions. In addition to meeting the stringent emission regulations, the demand to reduce greenhouse gas emissions has become urgent due to the more frequent destructive catastrophes caused by global warming in recent decades. In an effort to reduce emissions and improve fuel economy, many techniques have been developed and investigated by researchers. Air handling systems like exhaust gas recirculation and variable geometry turbochargers are the most widely used techniques on the market for modern diesel engines. Meanwhile, the concept of low temperature combustion is widely investigated by researchers. Low temperature combustion can increase the portion of pre-mixed fuel-air combustion to reduce the peak in-cylinder temperature so that the formation of NOx can be suppressed. Furthermore, the combustion characteristics and performance of bio-derived fuel blends are also studied to reduce overall greenhouse gas emissions through the reduced usage of fossil fuels. All the above mentioned systems are complicated because they involve not only chemical reactions but also complex fluid motion and mixing processes. As such, the control of these systems is always challenging and limits their commercial application. Currentlymost control methods are feed-forward control based on load condition and engine speed due to the simplicity in real-time application. With the development of faster control unit and deep learning techniques, the application of more complex control algorithms is possible to further improve the emissions and fuel economy. This work focuses on improvements to the control of engine air handling systems and combustion processes that leverage alternative fuels.Complex air handling systems, featuring technologies such as exhaust gas recirculation (EGR) and variable geometry turbochargers (VGTs), are commonly used in modern diesel engines to meet stringent emissions and fuel economy requirements. The control of diesel air handling systems with EGR and VGTs is challenging because of their nonlinearity and coupled dynamics. In this thesis, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are applied to control the low pressure (LP) EGR valve position and VGT vane position simultaneously on a light-duty multi-cylinder diesel engine. In addition, experimental examination of a low temperature combustion based on gasoline compression ignition as well as its control has also been studied in this work. This type of combustion has been explored on traditional diesel engines in order to meet increasingly stringent emission regulations without sacrificing efficiency. In this study, a six-cylinder heavy-duty diesel engine was operated in a mixing controlled gasoline compression ignition mode to investigatethe influence of fuels and injection strategies on the combustion characteristics, emissions, and thermal efficiencies. Fuels, including ethanol (E), isobutanol (IB), and diisobutylene (DIB), were blended with a gasoline fuel to form E10, E30, IB30, and DIB30 based on volumetric fraction. These four blends along with gasoline formed the five test fuels. With these fuels, three injections strategies were investigated, including late pilot injection, early pilot injection, and port fuel injection/direct injection. The impact of moderate exhaust gas recirculation on nitrogen oxides and soot emissions was examined to determine the most promising fuel/injection strategy for emissions reduction. In addition, first and second law analyses were performed to provide insights into the efficiency, loss, and exergy destruction of the various gasoline fuel blends at low and medium load conditions. Overall, the emission output, thermal efficiency, and combustion performances of the five fuels were found to be similar and their differences are modest under most test conditions.While experimental work showed that low temperature combustion with alternative fuels could be effective, control is still challenging due to not only the properties of different gasoline-type fuels but also the impacts of injection strategies on the in-cylinder reactivity. As such, a computationally efficient zero-dimension combustion model can significantly reduce the cost of control development. In this study, a previously developed zero-dimension combustion model for gasoline compression ignition was extended to multiple gasoline-type fuel blends and a port fuel injection/direct fuel injection strategy. Tests were conducted on a 12.4-liter heavy-duty engine with five fuel blends. A modification was made to the functional ignition delay model to cover the significantly different ignition delay behavior between conventional and oxygenated fuel blends. The parameters in the model were calibrated with only gasoline data at a load of 14 bar brake mean effective pressure. The results showed that this physics-based model can be applied to the other four fuel blends at three differentpilot injection strategies without recalibration. In order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control(MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional-integral (PI) control as the baseline. The simulation results show that MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.
Show less

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.
Show less

Title: TWO ESSAYS IN SUSTAINABILITY AND ASSET RETURN PREDICTABILITY
Creator: Nguyen, Lanh Vu Thuc
Date: 2021
Description: Our paper consists of two chapters in Financial Modeling for Sustainability and Asset Return Predictability. Recent developments in data...
Show moreOur paper consists of two chapters in Financial Modeling for Sustainability and Asset Return Predictability. Recent developments in data scraping and analytical methods have enhanced the possibility to construct the data and modeling required to examine the topics in each chapter. Chapter 1 proposes a simple yet strategic model involving a personal financial system to achieve a sustainable and prosperous future. The proposed model emphasizes the optimization of carbon footprints of one person at a time through the decentralization of the electricity use. While describing steps to develop a decentralized system considering electricity as a credit product, the model also underlines the importance of geographic economic dimensions and energy market prices due to their anticipated impact on the effectiveness of designing strategies for optimizing individuals’ energy use habits. Geographical conditions as well as market electricity prices can be used to signal individual energy use scores over time, therefore could also be instrumental in customizing energy use habits as the users realize variations in their energy use scores resulting from hourly electricity price changes at their locations. In other words, not only the changes in the individual’s behavior, but also the changes in the geographical conditions and community of users will affect the improvement of energy use behaviors of an individual over time using our model. We believe that the proposed model can be efficiently adopted to take on challenges threatening the future sustainability. While describing the basic characteristics of the model, we also open the possibility for future studies its capabilities to reduce carbon footprints from other societal choices, for example, using water, managing waste, or designing sustainable transportation systems. In Chapter 2, we examine asset return predictability, which is an important topic in finance with rich literature. Much of the current literature considers dividend yield as the main predictor for expected returns, and the main discussion centers around confirming or rejecting the predictive power of dividend yield with mixed evidence. However, dividend payments have been consistently declining and public firms have been increasingly using stock repurchase as the alternative to return values to shareholders. We aim to contribute to the literature by investigating a panel data of total equity payout, which takes into account not only dividend payout but also other forms of payment such as stock repurchase, as the main predictor for expected returns. In the asset return predictability literature, existing studies gather stock repurchase data from financial statements. In this paper, we manually construct our database of returns and payouts of public companies from various sources to create precise firm-level total equity payout dataset without relying on approximations from annual financial statements. This study adds to understanding of total equity payout and stock returns by analyzing a finer granularity than an annum and cross section of stock returns.
Show less

Title: A SCALABLE AND CUSTOMIZABLE SIMULATION PLATFORM FOR ACCURATE QUANTUM NETWORK DESIGN AND EVALUATION
Creator: Wu, Xiaoliang
Date: 2021
Description: Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity...
Show moreRecent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. The scale and complexity of quantum networks require cost-efficient means for testing and evaluation. Simulators allow for testing hardware, protocols, and applications cost-effectively before constructing experimental networks. This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. We have explored SeQUeNCe for quantum communication network evaluation. We use SeQUeNCe to study the performance of the quantum network with different hardware and applications. Additionally, we extend SeQUeNCe to a parallel discrete-event simulator by using the message passing interface (MPI). We comprehensively analyze the benefit and overhead of parallelization. The parallelization technique significantly increases the scalability of SeQUeNCe. In the future, we would like to improve SeQUeNCe in three aspects. First, we plan to continue reducing overhead from parallelization and increasing the scalability of SeQUeNCe. Second, we plan to investigate means to model quantum memory, entanglement protocols, and control protocols to enrich simulation models in the SeQUeNCe library. Third, we plan to integrate hardware with SeQUeNCe to enable high-fidelity analysis.
Show less

Title: CARING FOR THE CAREGIVER: INTERPERSONAL FACTORS AND DEPRESSION AS PARALLEL-SERIAL MEDIATORS BETWEEN STIGMA AND SUICIDAL IDEATION
Creator: Tsen, Jonathan Y.
Date: 2022
Description: Background/Objectives: This study applied Joiner's Interpersonal PsychologicalTheory to a caregiver population, by describing relationships...
Show moreBackground/Objectives: This study applied Joiner's Interpersonal PsychologicalTheory to a caregiver population, by describing relationships among affiliate stigma, thwarted-belongingess (TB), perceived-burdensomeness (PB), and depression, and suicidal ideation (SI). Participants/Setting: 243 adult caregivers participated in this study via Prolific Academic and caregiver-related websites. Design/Main Outcome Measures: This study used a cross-sectional, survey-based design including demographics, the Affiliate Stigma Scale (α=.93), Interpersonal Needs Questionnaire-15 (α=.95), Center of Epidemiology Studies–Depression-10 (α=.90), and Depressive Symptom Inventory— Suicide Subscale (α = .91) via Qualtrics. Analyses run on SPSSv27/Hayes’ PROCESS macro. Results: Parallel-serial mediation found after controlling for covariates that the total indirect effect of affiliate stigma on SI through both TB and PB then through depression was significant, B = .0271, SE = .0062, β = .1659, 95%CI [.0152, .0393]. Conclusions: Findings indicated that affiliate stigma indirectly affected SI through both TB and PB then through depression. Interventions to improve caregiver wellbeing should capitalize on both improving interpersonal functioning and depressive symptoms in tandem in order to reduce SI risk.
Show less

Title: SYNTHESIS AND APPLICATION OF ORGANOMETALLIC PRECURSORS FOR TUNGSTEN AND MOLYBDENUM SULFIDE
Creator: Liu, Bo
Date: 2021
Description: Transition metal chalcogenides (TMCs) have unique properties. They are promising materials for the next generation electrical devices due to...
Show moreTransition metal chalcogenides (TMCs) have unique properties. They are promising materials for the next generation electrical devices due to their suitable band gap, outstanding electron mobility, and controllable atomic thickness. In the last few decades, atomic layer deposition (ALD) has been one of the hottest research frontiers for the fabrication of TMCs films. Signification progress has been made on the varieties of material grown by ALD and the improvement of ALD equipment. However, the fast-evolving microelectronic industry set higher requirements for the ALD application. In the potential electronic fabrication process, low-temperature preparation and non-corrosive procedure are critical for the advanced device architecture. Thus the novel precursor development and the investigation of reaction mechanism are necessary. In addition, as the comprehensive research of film deposition, the prevailing crystallographic defects on the as-prepared films are another appealing thing for us to think about and try to eliminate for better film quality. Therefore, this dissertation will describe the precursor ligand design and its effect on the morphology, the development of W/Mo precursors for tungsten/molybdenum disulfide, and the defect passivation of tungsten diselenide films.In chapter 2, a series of heteroleptic tungsten precursors of tetrathiotungstates (WS42-) were prepared through the facile ligand transfer method. Ligand variation has a significant effect on the crystallinity of the resulting tetrathiotungstate products. Crystalline tetrathiotungstates with preferred orientation were prepared from the reaction of synthesized precursors with H2S at room temperature. Results indicated the morphologies and crystallinities of the tetrathiotungstates can be well controlled by their ligand behaviors which give us a better understanding of the growth mechanism. Chapters 3 and 4 focus on the development of W and Mo precursors for W/Mo disulfide and their performance in wet chemistry reactions and ALD. WS2 can be synthesized at the ambient temperature in solution by the non-redox reaction. WS2 film growth can be achieved at the exciting low temperature of 125°C by ALD. Based on the performance of the tungsten precursor, a new molybdenum dimer precursor with improved reactivity was synthesized, and MoSx can be prepared at the ambient temperature in seconds. X-ray absorption spectroscopy (XAS) was also utilized to investigate the interaction between the organometallic precursor and the SiO2 surface. Chapter 5 will focus on the defect passivation of WSe2 films for the improvement of their electrical performance. Precursors were synthesized, and the wet chemistry method was designed for oxidation removal and vacancy healing. Raman spectroscopy was used as the express characterization method to reveal the treatment results. A promising healing reagent was screened out, and the repaired films were fabricated to field-effect transistors (FETs) for electrical measurements. The final results showed the electrical performance of the WSe2 films was improved after the convenient chemical treatment.
Show less

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.
Show less

Title: Essays on Clean Energy Finance and Cryptocurrency Market
Creator: Xie, Yao
Date: 2021
Description: This dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the...
Show moreThis dissertation includes four essays with several empirical investigations in the areas of clean energy finance and cryptocurrencies.In the first essay, I investigate the heterogeneous relationship between various determinants of the clean energy market across all subsectors of the clean energy stock market. My findings reveal that VIX is the most significant predictor of all clean energy subsectors conditional volatility. During the COVID-19 stress period, economic uncertainty measures become more significant measures. The heterogeneity of clean energy market persists in the out-of-sample results. These results suggest that portfolio diversification for different clean energy subsector is necessary. In the second essay, I study the safe haven property of several volatility indexes on clean energy subsectors. I compare the current COVID-19 stress period and the time before. The results show that market volatility and commodity volatility are good safe haven assets during the COVID-19 period. But they are not safe haven assets against the clean energy subsector before the pandemic period. Among all volatility indexes, gold volatility index is the most effective safe haven assets. In the third essay, I investigate the characteristics of Bitcoin as a financial asset. A comprehensive set of information variables under five categories: macroeconomics, blockchain technology, other markets, stress level, and investor sentiment. The empirical results show that blockchain technology, stress level and investor sentiment have strong predicting power on Bitcoin returns. In the fourth essay, I aim to study how extreme sentiment measures from Google Trend and Wikipedia Pageviews affect both traditional cryptocurrency, such as Bitcoin and stablecoin, like Tether. Our results show that Tether’s return is not affected by the extreme sentiment measures during the COVID-19 stress period which suggests that stablecoin can offer price stability.
Show less

Title: Relations Between Inhibitory Control, Teacher Support, and Externalizing Behaviors in Elementary School Children
Creator: Kurian, Jennifer
Date: 2021
Description: The aim of this study was to examine the relation between child hot and cool inhibitory control (IC) at the beginning of the school year and...
Show moreThe aim of this study was to examine the relation between child hot and cool inhibitory control (IC) at the beginning of the school year and externalizing behaviors at the end of the year, and to determine if teacher support moderates this relation in early elementary school. Participants included a diverse sample of 138 children in grades 1 (n = 62) and 2 (n = 76), with a mean age of 7.2 years (SD = 10.1 months), about half of whom were male. Hot IC was assessed with the Puzzle Box Task and cool IC with the Happy-Sad Stroop Task. Teacher support was rated by independent observers using the Adapted Teaching Style Rating Scale. A composite teacher-report score based on ratings on subscales from two measures, the Strengths and Weaknesses of Attention Deficit Hyperactive Disorder Symptoms and Normal Behavior and the Strengths and Difficulties Questionnaire, was used to assess externalizing behavior at both time points. Results of hierarchical regression analyses revealed that, contrary to expectation, neither hot nor cool IC significantly predicted child externalizing behavior at the end of the school year. A moderation analysis also failed to show a significant moderating effect for teacher support. The only variable that significantly predicted externalizing behavior at the end of the year was externalizing behavior at the beginning of the year. There were significant concurrent associations between hot IC and externalizing behaviors at both the beginning and end of the school year. These findings suggest that externalizing behaviors in early elementary school are relatively stable. Thus, early and comprehensive intervention may be critical for implementing prevention strategies designed to increase self-regulation and thereby decrease externalizing behaviors after formal school entry.
Show less

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.
Show less

Title: RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Creator: Mo, Fei
Date: 2021
Description: Deep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine...
Show moreDeep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine fields, capable of complementing human operators in disease diagnosis and treatment but optimizing and streamlining medical image display. While incredibly powerful, images produced via Deep Learning or Artificial Intelligence should be analyzed critically in order to be cognizant of how the algorithms are producing the new image and what the new imagine is. One such opportunity arose in the form of a unique collaborative project: the technical development of an image assessment tool that would analyze outputs between DL-based and non DL-based Magnetic Resonance Imaging reconstruction methods.More specifically, we examine the operator input dependence of the existing reference method in terms of accuracy and precision performance, and subsequently propose a new metric approach that preserves the heuristics of the intended quantification, overcomes operator dependence, and provides a relative comparative scoring approach that may normalize for angular dependence of examined images. In chapter 2 of this thesis, we provide a background description pertaining to the two imaging science principles that yielded our proposed method description and study design. First, if treated naively, the examined linear measurement approach exhibits potential bias with respect to the coordinate lattice space of the examined image. Second, the examined DL-based image reconstruction methods used in this thesis warrants an elaborate and explicit description of the measured noise and signal present in the reconstructed images. This specific reconstruction approach employs an iterative scheme with an embedded DL-based substep or filter to which we are blinded. In chapters 3 and 4 of this thesis, the imaging and DL-based image reconstruction experiments are described. These experiments employ cardiac MRI datasets from multiple clinical centers. We first outline the clinical and technical background for this approach, and then examine the quality of DL-based reconstructed image sharpness by two alternative methods: 1) by employing the gold-standard method that addresses the lattice point irregularity using a ‘re-gridding’ method, and 2) by applying our novel proposed method inspired by radial MRI k-space sampling, which exploits the mathematical properties of uniform radial sampling to yield the target voxel counts in the ‘gridded’ polar coordinate system. This new measure of voxel counts is shown to overcome the limitation due to the operator-dependence for the conventional approach. Furthermore, we propose this metric as a relative and comparative index between two alternative reconstruction methods from the same MRI k-space.
Show less

Title: LOAD ANALYSIS BASED ON MACHINE LEARNING IN POWER SYSTEMS
Creator: Lu, Dan
Date: 2017, 2017-05
Description: The dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density...
Show moreThe dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE); second, load forecasting based on PCA and Bayesian ridge regression; third, anomalies detection based on Machine Learning methodology; fourth the long-term planning of Battery-based Energy Storage Transportation (BEST) in power system. Mathematical models are constructed to fulfill the research of the three targets, and numerical examples are used to test the models. The first three parts are based on PCA, which reduced the load dimensions. In the first part, a robust power system Unit Commitment (UC) is the aim to fulfil the possible load. In the second part, a novel short-term nodal load forecasting is raised to give better prediction of the next day load to improve the next data UC scheduling. In the third part, anomalies are detected in the reduced power flow space based on the pattern identified in the lower dimensional space. The purpose of the fourth part is to find ways of better utilizing the existing resources from integrating the frontier technology, the mobility of more compact and higher capacity batteries. Mix-integer programming (MIP) is used in the formulation.
Ph.D. in Electrical Engineering, May 2017
Show less

Title: METHODOLOGY FOR VEHICLE EMISSION IMPACTS ANALYSIS FROM SIGNAL TIMING OPTIMIZATION OF AN URBAN STREET NETWORK
Creator: Lu, Pu
Date: 2017, 2017-05
Description: The pace of urban street capacity expansion is much slower than the growth of vehicle travel, leading to several traffic congestions. To...
Show moreThe pace of urban street capacity expansion is much slower than the growth of vehicle travel, leading to several traffic congestions. To mitigate traffic congestion expanding capacity is not feasible for many cases due to the high cost and space restriction. Improving the efficient use of the available capacity becomes the solution. Traffic signal optimization is one of the most widely used ways of efficient capacity utilization. Concurrent to traffic signal optimization, more smooth traffic operations in term of reasonably higher speed and a reduced traffic delay will in turn change vehicle emissions. This research aims to quantify changes in vehicle emissions resulted from traffic signal optimization by introducing a new methodology for quantifying network wide vehicle emissions and real world application in of the Chicago urban network for validation. The proposed methodology considers undersaturation and oversaturation of traffic conditions and urban street segments with varying speeds for different types of vehicles and pollutants by hour of the day and location within the network. It begins with information collection and research through a review of existing methods for urban street network vehicle emission estimation, intersection vehicle emission evaluation, and the running vehicle emission modeling. The proposed methodology focuses on three elements: estimation of emissions from vehicles stopped at intersections and for vehicles cruising along segments, as well as analysis of network wide vehicle emissions and changes in overall network vehicle emissions by time of the day and by areas. Major steps of methodology application included the use of Chicago TRANSIMS model implementing optimized signal timing plans to obtain refined traffic volumes at intersections and on segments, increased vehicle operating speeds, changed green splits, and vehicle compositions for all intersections and segments in the urban street network, the application of an intersection vehicle emission model for stopped vehicles and a segment vehicle emission model for vehicles cruising on segments, and the network wide analysis of vehicle emission changes by vehicle type and pollutant type in a 24-hour period within an urban street network, respectively. The proposed methodology for intersection vehicle emission estimation was successfully applied to a dense urban street network in Chicago for each approach per cycle and then extended for intersections in hours of the day to analyze the impacts of traffic changes at intersections on exhaust changes. In order to develop the network vehicle emission analysis method, it is essential to evaluate the segment vehicle emissions. This is achieved by using the concept of vehicle specific power which is used to estimate emissions of cruising vehicles considered along with vehicle speeds and speed changes and hence analyzing changes in segment vehicle emissions affected by traffic volume changes derived from signal timing optimization. The decreased number of vehicles stopped at intersections by applying signal timing optimization will reduce intersection emissions, hence reducing overall network vehicle emissions. In addition to have vehicle emissions got reduced at intersections, the increasing vehicle speed for vehicles on segments could further reduce vehicle emissions on segments.
Ph.D. in Civil Engineering, May 2017
Show less

Title: NEUROPSYCHOLOGICAL PROFILES IN ADULTS WITH SICKLE CELL DISEASE
Creator: Piper, Lauren E.
Date: 2014, 2014-12
Description: Cognitive impairment is documented in individuals with sickle cell disease (SCD). Studies investigating cognitive impairment in this...
Show moreCognitive impairment is documented in individuals with sickle cell disease (SCD). Studies investigating cognitive impairment in this population have primarily examined group differences in neuropsychological performance, which may have overlooked the heterogeneity of cognitive functioning. The objectives of this study were to determine whether distinct cognitive profiles occur in individuals with SCD and, if so, to examine potential differences in demographic, clinical, and psychosocial characteristics. Participants with SCD (n = 73) and similarly matched controls (n = 82), completed a brief neuropsychological protocol (Hachinski et al., 2006) and self-report measures of pain and mood-related symptoms. Cluster analysis was used to identify groups of participants based on their cumulative scores across the domains of executive functioning, language, memory, and visuospatial ability. Multivariate analysis of variance (MANOVA) was used to compare the cluster groups across the four cognitive domains. Analysis of variance (ANOVA) and chi-square tests were used to compare cluster groups on demographic, clinical, and psychosocial characteristics. Results indicated three distinct cognitive subtypes: (1) executive and memory impaired (56% of participants); (2) globally impaired (14%); and (3) cognitively intact (30%). The three cluster groups did not differ on most demographic factors, stroke history, or pain severity, but differed on level of education and current mood-related distress. Results demonstrated the presence of distinct cognitive profiles in adults with SCD, with a proportion of cognitively intact individuals. Implications for intervention and cognitive rehabilitation are discussed.
M.S. in Psychology, December 2014
Show less

repository.iit

Search the repository

Pages

Pages