Electrical and Computer Engineering Commons^™

Suitability Of Quantized Devs-Lim Methods For Simulation Of Power Systems, Navid Gholizadeh

Theses and Dissertations

This research presents an investigation of behavioral intricacies of the Quantized DEVS Latency Insertion Method (QDL) method and proposes resolutions to the previously unsolved and unexplained discrepancies between the reference solution and the QDL method. QDL method is a combination of two other methods namely the Latency Insertion Method (LIM) and Linear Implicit Quantized State (LIQSS)method. This technique is rigorously evaluated across a diverse array of systems and scenarios, with the aim of unearthing nuanced insights into its respective functionalities.

The research seeks to discern novel attributes of this method while gauging its comparative efficacy against conventional discrete-time methodologies, both …

Tutorial - Shodhguru Labs: Optimization And Hyperparameter Tuning For Neural Networks, Kaushik Roy

Publications

Neural networks have emerged as a powerful and versatile class of machine learning models, revolutionizing various fields with their ability to learn complex patterns and make accurate predictions. The performance of neural networks depends significantly on the appropriate choice of hyperparameters, which are critical factors governing their architecture, regularization, and optimization techniques. As the demand for high-performance neural networks grows across diverse applications, the need for efficient optimization and hyperparameter tuning methods becomes paramount. This paper presents a comprehensive exploration of optimization strategies and hyperparameter tuning techniques for neural networks. Neural networks have emerged as a powerful and versatile class …

Ierl: Interpretable Ensemble Representation Learning - Combining Crowdsourced Knowledge And Distributed Semantic Representations, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth

Publications

Large Language Models (LLMs) encode meanings of words in the form of distributed semantics. Distributed semantics capture common statistical patterns among language tokens (words, phrases, and sentences) from large amounts of data. LLMs perform exceedingly well across General Language Understanding Evaluation (GLUE) tasks designed to test a model’s understanding of the meanings of the input tokens. However, recent studies have shown that LLMs tend to generate unintended, inconsistent, or wrong texts as outputs when processing inputs that were seen rarely during training, or inputs that are associated with diverse contexts (e.g., well-known hallucination phenomenon in language generation tasks). Crowdsourced and …

Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Publications

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …

A Semantic Web Approach To Fault Tolerant Autonomous Manufacturing, Fadi El Kalach, Ruwan Wickramarachchi, Ramy Harik, Amit Sheth

Publications

The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future factories are required to be agile, allowing for more customized production, and resistance to disturbances. Such production lines would be able to reallocate resources as needed and minimize downtime while keeping up with market demands. These systems must be capable of complex decision-making based on parameters such as machine status, sensory/IoT data, and inspection results. Current manufacturing lines lack this complex capability and instead focus on low-level decision-making on the machine level without utilizing the generated data to its full extent. This …

Knowledge Graph Guided Semantic Evaluation Of Language Models For User Trust, Kaushik Roy, Tarun Garg, Vedant Palit, Yuxin Zi, Vignesh Narayanan, Amit Sheth

Publications

A fundamental question in natural language processing is - what kind of language structure and semantics is the language model capturing? Graph formats such as knowledge graphs are easy to evaluate as they explicitly express language semantics and structure. This study evaluates the semantics encoded in the self-attention transformers by leveraging explicit knowledge graph structures. We propose novel metrics to measure the reconstruction error when providing graph path sequences from a knowledge graph and trying to reproduce/reconstruct the same from the outputs of the self-attention transformer models. The opacity of language models has an immense bearing on societal issues of …

Acm Web Conference 2023, Usha Lokala, Kaushik Roy, Utkarshani Jaimini, Amit Sheth

Publications

Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic …

The Troubling Emergence Of Hallucination In Large Language Models--An Extensive Definition, Quantification, And Prescriptive Remediations, Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das

Publications

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a finegrained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) …

Event-Triggered Optimal Adaptive Control Of Partially Unknown Linear Continuous-Time Systems With State Delay, Rohollah Moghadam, Vignesh Narayanan, Sarangapani Jagannathan

Publications

This paper proposes an event-triggered optimal adaptive output feedback control design approach by utilizing integral reinforcement learning (IRL) for linear time-invariant systems with state delay and uncertain internal dynamics. In the proposed approach, the general optimal control problem is formulated into the game-theoretic framework by treating the event-triggering threshold and the optimal control policy as players. A cost function is defined and a value functional, which includes the delayed system output, is considered. First, by using the value functional and applying stationarity conditions using the Hamiltonian function, the output game delay algebraic Riccati equation (OGDARE) and optimal control policy are …

Tutorial: Knowledge-Infused Learning For Autonomous Driving (Kl4ad), Ruwan Wickramarachchi, Cory Henson, Sebastian Monka, Daria Stepanova, Amit Sheth

Publications

Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Metaversekg: Knowledge Graph For Engineering And Design Application In Industrial Metaverse, Utkarshani Jaimini, Tongtao Zhang, Georgia Olympia Brikis

Publications

While the term Metaverse was first coined by the author Neal Stephenson in 1992 in his science fiction novel “Snow Crash”, today the vision of an integrated virtual world is becoming a reality across different sectors. Applications in gaming and consumer products are gaining traction, industrial metaverse applications are, still in their early stages of development with one of the challenges being interoperability across various metaverse development platforms and existing software tools. In this work we propose the use of a knowledge graph based semantic data exchange layer, the Metaverse Knowledge Graph, to enable seamless transfer of information across platforms. …

Applications Of Laser Liftoff Technique For Wide Bandgap Power And Flexible Electronics, Md Didarul Alam

Theses and Dissertations

Wide bandgap (WBG) AlGaN/GaN and ultrawide bandgap (UWBG) AlGaN/AlGaN III-nitride high electron mobility transistors (HEMTs) have come a long way since their initial demonstration and are desired for a multitude of applications in deep-scaled high-frequency, high-voltage, high-current, and high-temperature power electronics. Although AlGaN/GaN HEMTs have recently become state-of-the-art in consumer electronics chargers and amplifiers, the performance of the devices is limited by severe self-heating which significantly reduce their efficacy in demanding applications that require high-current density. One strategy to reduce this self-heating effect in GaN HEMTs is to use high thermal conductivity SiC or bulk AlN substrates. While this approach …

Mimo Antenna Systems For Wireless Handheld Devices, Ahmed H. Abdelgawwad

Theses and Dissertations

Mobile communications have seen tremendous growths in the last decades in the form of smartphones, watches, wireless enabled Personal Digital Assistants (PDAs) and so on. The current and future wireless communication systems require high data-rate capabilities to support high speed needs of users in modern applications. Multiple-input multiple-output (MIMO) antenna systems have become the most promising candidate to support the increased data demand. Therefore, they have emerged as an integral part of the new 5G wireless standard. One key challenge regarding MIMO systems for the handheld is to be able to successfully accommodate multiple miniature platform integrated antennas within a …

Closed Form Implicitly Integrated Models For Computationally Efficient Simulation Of Power Electronics, Andrew Wunderlich

Theses and Dissertations

This work describes novel closed-form, implicitly integrated (CF-implicit) models of switched-mode power converters which feature implicit integration but require no iterative numerical solving algorithm for evaluation because they are explicitly solved prior to model execution. The derived models capture the large-signal dynamic behavior of the power converters, so their use and accuracy are not limited to any one set of operating conditions. These models can be implemented in any computational environment, including directly on an existing embedded controller as a digital twin. Since no iterative solver is required, the models are highly computationally efficient and have a very predictable worst-case …

Pulse Width Modulation-Based Voltage Balancing And Circulating Current Control For Modular Multilevel Converters, Md Multan Biswas

Theses and Dissertations

In the last decade, because of some key features and advantages of Modular Multilevel Converter (MMC), it is widely applied for various Medium Voltage (MV) and High Voltage (HV) power electronic interfacing applications. The structure of MMCs varies by application but in general, they are composed of multiple Sub-Modules (SMs) which each contains a floating capacitor. For the operation of MMCs, capacitor voltages are required to remain equally set. This is referred to as the balanced condition and requires a method for SM Capacitor Voltage Balancing (CVB). Connections of SMs for most of the common MMC configurations result in additional …

Decellularization Strategies Of Naturally Derived Biomaterials For Tissue Engineering Applications, Julia Elizabeth Hohn

Theses and Dissertations

In 2017, over 3.5 million peripheral vascular surgeries were performed worldwide with over 400,000 vascular repair or replacement surgeries being performed in the United States each year alone. As the number of vascular repair surgeries, including both coronary and peripheral bypass grafting procedures, continues to increase each year, these statistics indicate an urgent need for more effective and readily available replacement materials. Regenerative medicine and tissue engineering (TE) approaches, including the design, fabrication, and validation of suitable biomaterials in vitro that direct the repair and regeneration of damaged tissues, have been proposed to alleviate this problem. While advanced biomaterials have …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

Towards Efficient Scoring Of Student-Generated Long-Form Analogies In Stem, Thilini Wijesiriwardene, Ruwan Wickramarachchi, Valerie L. Shalin, Amit P. Sheth

Publications

Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature …

Analysis Of The Temperature Characteristics Of High-Speed Train Bearings Based On A Dynamics Model And Thermal Network Method, Baosen Wang, Yongqiang Liu, Bin Zhang, Wenqing Huai

Faculty Publications

High-speed trains often use temperature sensors to monitor the motion state of bearings. However, the temperature of bearings can be affected by factors such as weather and faults. Therefore, it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors. In this study, a dynamics model of the axle box bearing of high-speed trains is established. The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring, inner-ring, and rolling-element faults. Based on the model, a thermal network method is introduced to study the temperature …

Distributed Interdigital Capacitor (Idc) Sensing For Cable Insulation Aging And Degradation Detection, Md Nazmul Al Imran

Theses and Dissertations

Nuclear power plants (NPPs) contain myriad power, control, instrumentation, and other types of cables. The polymer insulation and jacket materials of such cables degrade over time due to operation and environmental conditions e.g., heat, humidity, and radiation. Since the life span of NPPs may extend beyond 40-50 years regular monitoring of cable insulation and jacket polymers is critical to ensure safe and reliable operation. The agingrelated degradation of cables causes changes in the relative permittivity or dielectric constant of the insulation and jacket materials. Capacitor sensors, if properly designed and developed can measure this change and thus can provide an …

Automated Contingency Management For Water Recycling System, Shijie Tang

Theses and Dissertations

NASA’s exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and beyond. Communication latency and extreme limitations of power and life-supporting resources make it imperative to operate the DSH systems in a highly autonomous fashion. One such system is the Environmental Control and Life Support System (ECLSS) which needs to be monitored and optimized to support its designated missions.

Integrated System Health Management (ISHM) technologies have been developed in the past decades to provide a real-time assessment of system health and use this information to improve …

Sex Differences And Potential Non-Invasive Treatments For Calcific Aortic Valve Disease, Henry Pascal Helms

Theses and Dissertations

Calcific Aortic Valve Disease (CAVD) is a progressive heart disease that ranges from aortic valve sclerosis to aortic valve stenosis. It is characterized by intense calcification and compromised valve function. CAVD affects 25% of people older than 65 and 50% of people older than 85. These rates are expected to increase in the United States due to higher levels of obesity and diabetes, as well as an aging population. CAVD is the leading cause of valve replacement surgery. Annual healthcare costs for these valve replacements are currently estimated to be approximately 2 billion dollars. There are currently no medications approved …

Non-Intrusive Microwave Surface Wave Technique For Cable Damage And Aging Detection, Ahmed Shah Arman

Theses and Dissertations

Power plants, power distribution and transmission networks, automobiles, aircrafts, trains, industrial manufacturing plants etc. use a variety of cables and wires. The insulation and jacket polymer materials of cables can degrade over time due to operational stressors and environmental conditions. Materials may age, corrode, get chaffed and go missing which if remain undetected and unaddressed can result in major failures. Insulation degradation or damage detection from a distance normally involves the application of direct contact reflectometry techniques. This requires the cable to be disengaged and a diagnostic signal to be directly applied to a cable with a return path. While …

Networked Digital Predictive Control For Modular Dc-Dc Converters, Castulo Aaron De La O Pérez

Theses and Dissertations

The concept of power electronics building blocks (PEBB) has driven advancements in highly modularized converter systems with many identical subsystems. PEBBs are distributed subsets of converter systems and thus require communication with a control system for their coordination. For this type of system, the communication latency with hard deterministic deadlines is the driving attribute of communication system requirements. However, inherent communication requirements for PEBB-based converter systems also provide opportunities for coordination of energy flow.

Leveraging developments in Gigabit serial communication channels, a control and communication platform architecture for distributed control schemes based on the 2D-Torus communication network topology was developed …

Development Of Micro-Sized Algan Deep Ultraviolet Light Emitting Diodes And Monolithic Photonic Integrated Circuits, Richard Speight Floyd Iii

Theses and Dissertations

III-Nitride materials-based visible emission LEDs have emerged as a disruptive technology in the fields of lighting,ⁱ) communications,^ii,iii,iv) and displays.^v,vi) Shorter wavelength LEDs in the DUV spectral region (210nm – 360nm) with ultra-wide bandgap (UWBG) Al_xGa_1-xN active layers are now poised to displace toxic Mercury-based light sources.^vii) Over the past decade AlGaN LEDs operating in the deep ultra-violet (DUV) spectral region (200 nm < λ_emission < 300 nm) have been deployed in novel applications including autonomous drone-based sterilization and sanitization systems, ^viii) point-of-use water purification systems, ^ix) photo-therapeutics,x) gas sensors,xi) and non-line-of-sight (NLOS) communications.^xii) Similarly, DUV light detectors using ultra-wide bandgap (UWBG) Al …

Can Language Models Capture Graph Semantics? From Graphs To Language Model And Vice-Versa, Tarun Garg, Kaushik Roy, Amit Sheth

Publications

Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities. However, current deep learning models takes as input distributed representations or vectors. Thus, the graph is compressed in a vectorized representation. We conduct a study to examine if the deep learning model can compress a graph and then output the same graph with most of the semantics intact. Our experiments show that Transformer models are not able to express the full semantics of the input knowledge graph. We find that this is due to the disparity between the directed, relationship and …

Knowledge-Driven Drug-Use Namedentity Recognition With Distant Supervision, Goonmeet Bajaj, Ugur Kursuncu, Manas Gaur, Usha Lokala, Ayaz Hyder, Srinivasan Parthasarathy, Amit Sheth

Publications

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for …

Tropospheric Attenuation Prediction For Future Millimeter Wave Terrestrial Systems: Estimating Statistics And Extremes, Jinwen Liu, David W. Matolak, Ismail Güvenç, Hani Mehrpouyan

Faculty Publications

Tropospheric attenuations can be significant in the millimeter wave (mmWave) frequency bands; hence, accurate prediction modeling of tropospheric attenuation is important for reliable mmWave communication. Several models have been established by the International Telecommunication Union (ITU), yet estimation accuracy is limited due to the large spatial scales used for model input parameters. In this paper, we address this and apply local precipitation data to analyze tropospheric attenuation statistics and compare to results when using ITU regional input rain data. Specifically, tropospheric attenuation is predicted via simulations using the ITU method at 30, 60, and 90 GHz in four distinct geographic …

A Dual-Band Filtering Structure For Highly Selective Reconfigurable Bandpass Filter And Filtering Balun, Jinqun Ge, Guoan Wang

Faculty Publications

This article proposes a filtering structure consisting of two half-wavelength resonators and two open-stub loaded resonators, which generates two third-order passbands. Multiple transmission zeros are introduced by the newly developed coupling scheme, resulting in extremely sharp roll-off desirable for highly selective filters. The proposed structure is applied to design a PIN-diodes switch-controlled reconfigurable dual-band bandpass filter (BPF) with four-state filtering responses: both passbands ON, both passbands OFF, high-frequency passband ON, and low-frequency passband ON. Stepped-impedance open stubs and one-end-grounded coupled lines are studied and employed in the design to suppress unwanted responses. In addition, two filtering structures are placed symmetrically …