Electrical and Computer Engineering Commons^™

Advancing Drug Discovery And Disease Understanding Through Knowledge Graphs And Machine Learning Techniques, Swastika Tenkila Purushotham

Electrical and Computer Engineering ETDs

This thesis investigates knowledge graphs with particular reference to the NIH-funded Common Fund Data Ecosystem Data Distillery project. By combining data from nine Common Fund projects and other sources, this project has created a large knowledge graph using Neo4j. To find new and undiscovered drug targets, UNM’s Illuminating the Druggable Genome (IDG) Data Coordinating Center has supplied data and use cases. Condensed Knowledge Graph, a condensed version that is based on the Data Distillery Knowledge Graph, improves usability for IDG applications. Condensed Knowledge Graph research endeavors to enhance data organization through the categorization of disease terms, examination of Cerebellar Stroke …

Reducing Carbon Footprint In Ai: A Framework For Sustainable Training Of Large Language Models, Sunbal Iftikhar, Steven Davy

Conference papers

In the world of artificial intelligence (AI), large language models (LLMs) are leading the way, transforming how people understand and use language. These models have significantly impacted various domains, from natural language processing (NLP) to content generation, sparking a wave of innovation and exploration. However, this rapid progress brings to light the environmental implications of LLMs, particularly the significant energy consumption and carbon emissions during their training and operational phases. This requires a shift towards more energy-efficient practices in training and deploying LLMs, balancing AI innovation with environmental responsibility. This paper emphasizes the need for improving the energy efficiency of …

A 3d Memristor Architecture For In-Memory Computing Demonstrated With Sha3, Muayad J. Aljafar, Rasika Joshi, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid CMOS-memristors technology demonstrated with SHA3 over implementations that use only memristor technology. In the proposed technique, SHA3 is implemented in a set of perpendicular crossbar arrays structured to facilitate logic implementation and circular bit rotation (Rho operation), which is perhaps the most complex operation in SHA3 when carried out in memristor arrays. The Rho operation itself is implemented with CMOS multiplexers (MUXs). The proposed accelerator is standby power-free and circumvents …

Development Of A Non-Intrusive Load Monitoring Technique Using Phase-Space-Reconstruction And 2-D Fourier Series Current Waveform Features, Motaz Abu Sbeitan

Thesis/ Dissertation Defenses

The growing need for energy and efficient energy control has emphasized the importance of tracking appliance-level energy usage. The capability of Non-Intrusive Load Monitoring (NILM) to separate energy usage data per appliance from a single measurement provides a practical solution. This thesis explores developing a novel NILM method using Phase-Space Reconstruction (PSR) and 2-D Fourier Series to enhance feature extraction from the steady-state current waveforms. Existing NILM techniques frequently encounter accuracy challenges caused by overlapping power signatures of appliances and complex operational states. The proposed method is designed to efficiently capture the steady-state characteristics of electrical appliances. It is evaluated …

Hypoxic Incubator: Improving Robustness/Reliability And Demonstrating Physiological Efficacy, Damon Dennis Tan

Master's Theses

The Microphysiological Systems Laboratory aims to develop colorectal cancer tumor models under a hypoxic environment to assess model response to pharmaceutical compounds in vitro. To perform relevant studies, researchers have attempted to use different hypoxic inducing strategies such as a nitrogen pod and hypoxic incubator to recreate in vivo physiological responses to hypoxia. However, studies would be interrupted due to incubator functionality failure. To ensure successful and physiologically relevant studies, I improved and verified the robustness and reliability of a hypoxic incubator previously designed and manufactured in the lab. Through the testing and iterating design processes, I engineered and implemented …

Measuring Soil Salinity, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

To make sound decisions on managing saline sites, you need to know the source of salt, how salinisation is occurring, the landscape context, and most importantly, the actual salt concentration of the soil.

The most common 'measures' of salt concentration are actually estimates based on electrical conductivity of a soil and water solution. Soil salt content can be measured in a laboratory by measuring the total dissolved solids in a sample. In the field, salt concentration can also be estimated using electromagnetic induction-based soil sensors.

Smart Systems For Employing Iot Devices For Monitoring And Control Of Electric Vehicle Residential Charging, Grant M. Fischer, Steven B. Poore, Rosemary E. Alden, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

As the number of electric vehicles (EVs) on the road continues to increase, the rise in power demand may pose challenges, especially for the localized grid capacity during peak load events. This paper briefly reviews methods of power monitoring and load shedding such as smart charging and load management systems. An internet-of-things (IoT) power monitoring system is proposed for high-resolution power monitoring and control of J1772 standard level 2 EV charging systems to provide detailed data to enable future studies on EV grid integration. Additionally, a hardware test bench (HTB) including a DC battery emulator, oscilloscope, and commercial EV charger …

Refining Chlorosulfonation Methods For The Synthesis Of A Perfluoroalkyl Arylsulfonimide (Pfsi) Monomer, Ainsley P. Foster, Benjamin Varney, Hua Mei

Science University Research Symposium (SURS)

Proton-exchange membrane (PEM) fuel cells are sources of energy that are clean, quiet, and highly responsive to changes in power needs, making them promising for use in automobiles and other portable power devices. The electrolyte of a PEM cell–the layer responsible for conductivity–is a polymer membrane, commonly consisting of perfluoroalkyl sulfonic acid (PFSA) polymers. Perfluoroalkyl arylsulfonimide (PFSI) polymers are expected to improve the efficiency of PEM fuel cells through optimal stability and proton conductivity. The trifluovinylether (TFVE) aryl perfluorosulfonamide monomer is a new PFSI monomer proposed to fulfill these benefits once polymerized. A six-step synthesis route was initially designed to …

Causes And Prevention Of Thermal Runaway In Lithium-Ion Batteries — A U.S. Utility Perspective, Shaun Lavin, Kwabena Kyeremeh, Elias Nemeh, David Beyerle, Chad Alkire, Samuel Kelty, Lakshmi Srinivasan, Stephanie Shaw, Erin Minear, Haresh Kamath, Dan Ionel, Aron Patrick

Power and Energy Institute of Kentucky Faculty Publications

Lithium-ion batteries are a critically important technology for maintaining grid reliability with the integration of variable intermittent renewable energy resources. However, lithium-ion batteries also pose significant human safety and infrastructure hazards due to the inherent risk of thermal runaway, fire, and explosion, which should be understood, mitigated, and managed. While battery manufacturers have primary responsibility for the design, manufacturing, and software which are necessary to keep battery storage systems safe, electric utilities also have an important role in understanding these risks and adopting best practices to mitigate them. This paper provides background on utility-scale battery deployment, an overview of a …

Combined Machine Learning And Differential Evolution For Optimal Design Of Electric Aircraft Propulsion Motors, David R. Stewart, Matin Vatani, Rosemary E. Alden, Donovin D. Lewis, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Electric aircraft propulsion requires highly efficient and power-dense fault-tolerant electric motors optimized for specific flight profile operation. State-of-the-art design of electric motors involves substantial computational resources and combines electromagnetic finite element analysis (FEA) and optimization techniques. This paper proposes a new approach using a physics-based machine learning (ML) multi-input univariate meta-model trained on FEA and differential evolution (DE) optimization results to predict electromagnetic torque output. Hundreds of individual designs, generated through multiple generations of a DE algorithm, are analyzed by 3D FEA to create a database, which is then employed for the training and satisfactory validation of the ML model. …

Spatiotemporal Wind Energy Assessment For Transmission Network Integration Considering The Location Of Electrical Substations And Loads, Kwabena Kyeremeh, Rosemary E. Alden, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wind energy is an abundant renewable resource that can support decarbonization of energy supply. It is, therefore, essential to conduct a comprehensive assessment of wind energy potential for effective transmission network planning and integration. This work introduces a spatial-temporal assessment methodology for wind energy name plate-rated power capacity that considers the locations of electrical substations and transmission lines. This methodology applies Geographic Information System (GIS) land cover data to define siting exclusions for wind turbine installations. The correlation between generated wind power and estimates of power system load, as well as capacity factor, on a sub-regional basis for specific latest …

Analysis Of Multivariable Sensor Responses To Multi-Analyte Gas Samples In The Presence Of Interferents And Humidity, Sakin Sarwar Satter, Florian Bender, Nicholas Post, Antonio J. Ricco, Fabien Josse

Electrical and Computer Engineering Faculty Research and Publications

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the …

Combined 3d Fea And Machine Learning Design Of Inductive Polyphase Coils For Wireless Ev Charging, Lucas A. Gastineau, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wireless power transfer (WPT) technologies are currently researched and developed for charging the batteries of electric unmanned air and ground vehicles. This paper presents systems with special polyphase inductive coils, which generate rotating fields and achieve high power density and efficiency. The complex geometry is modeled and studied with 3D electromagnetic finite element analysis (FEA). In order to reduce the substantial computational effort, machine learning techniques are proposed for surrogate modeling. A deep learning algorithm is introduced to capture the physics-based relationships between geometry and electromagnetic properties in inductive coils for wireless charging. Parametric models are systematically generated and analyzed …

Effects Of Silver Nanoparticles In Pectin Polysaccharide Thin Film On Resistive Switching Characteristics, Jia Zheng Yeoh, Muhammad Awais, Feng Zhao, Kuan Yew Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

This study investigates the resistive switching characteristics of Ag nanoparticle (AgNP)-incorporated pectin (pectin-AgNP) as a memristive thin film, with varying concentrations of AgNP (0.0 wt.%, 0.5 wt.%, and 1.0 wt.%) and pectin (5.0 mg/L, 5.5 mg/L, 6.0 mg/L, 6.5 mg/L, and 7.0 mg/L), sandwiched between Au and indium tin oxide (ITO) electrodes on glass substrate. The structural, chemical, and electrical properties of these pectin-AgNP thin films were evaluated. With AgNP concentration of 0.5 wt.% in a pectin concentration of 5.5 mg/mL, the Fourier transform infrared (FTIR) spectra indicated the highest presence of C–O bonds. This suggests the incorporation of AgNP …

Verifying Models Of The Underwater Soundscape From Wind And Ships With Benchmark Scenariosa), S Bruce Martin, Martin Siderius, Michael A. Ainslie, Michele B. Halvorsen, Leila Hatch, Mark K. Prior, Daniel Brooker, James Caplinger, Christine Erbe, John Gebbie, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

Models of the underwater acoustic soundscape are important for evaluating the effects of human generated sounds on marine life. The performance of models can be validated against measurements or verified against each other for consistency. A verification workshop was held to compare models that predict the soundscape from wind and vessels and estimate detection ranges for a submerged target. Eight modeling groups participated in the workshop which predicted sound levels with observation windows of 1 min and 1 km2. Substantial differences were found in how modelers computed the propagation losses for decidecade bands and estimated the source level of wind. …

Safety-Centric Analysis Of Grounding Systems For Substations In Distribution Grids, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

The safety of grounding systems for substations in distribution grids is paramount to ensuring operational reliability, protecting personnel and equipment, and maintaining the stability of distribution grids while complying with regulatory standards. This paper explores essential safety aspects of grounding systems, including fault current handling strategies, the interdependence between protective devices and grounding systems, and maintenance practices. The integration of grounding systems design with overall substation layout and design considerations by focusing on mitigating Ground Potential Rise (GPR) and optimizing bonding techniques, is examined. Additionally, advanced techniques, such as high-frequency grounding and Transient Ground Potential Rise (TGPR) management, are presented …

3d Printed Microfluidic Fabrication Methodology, Characterization, Mechanical Design, And Applications In Electrostatic Artificial Muscles And Benthic Microbial Fuel Cells, Terak B. Hornik

Master's Theses

The fabrication of microfluidic devices often requires specialized methods. The development of these methods requires careful characterization and understanding of the processes involved. Using primarily PolyJet 3D printing technology, microfluidics offers a wide scope of applications such as microfluidic benthic microbial fuel cells (MBMFCs) and electrostatic artificial muscles. MBMFCs benefit from the confinement of the microbes resulting in close proximity between the electrode and the organisms. Using a modular design called the Sponge, assembly and upscaling is possible. Electrostatic artificial muscles benefit from a microfluidic approach due to the non-linearity of electrostatic attraction creating disproportionate benefits when miniaturized. When designed …

An Overview Of Ancillary Services Provided By Vehicle-To-Grid Systems, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

Vehicle-to-Grid (V2G) systems are emerging as a pivotal technology in modern power systems, offering a range of ancillary services that enhance the stability and reliability of power systems. This paper provides an overview of the key ancillary services provided by V2G systems, highlighting their role in grid modernization. Technical challenges, economic implications, and policy considerations associated with the deployment of V2G systems are explored to assess their potential impact on advancing a more resilient and sustainable energy infrastructure.

Visual Causal Question And Answering With Knowledge Graph Link Prediction, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The ability to answer causal questions is important for any system that requires robust scene under- standing. In this demonstration, we develop a prototype system that leverages our causal link prediction framework, CausalLP. CausalLP framework uses a visual causal knowledge graph and associated knowledge graph embedding for two visual causal question and answering tasks- (i) causal explanation and (ii) causal prediction. In the live demonstration sessions, the participants will be invited to test the efficiency and effectiveness of the system for visual causal question and answering.

Techniques For Modeling Ocean Soundscapes: Detailed Description For Wind Contributionsa, Martin Siderius, Michael A. Ainslie, John Gebbie, Alexandra Schäfke, N Ross Chapman, Bruce Martin, Kay L. Gemba

Electrical and Computer Engineering Faculty Publications and Presentations

Wind over the ocean creates breaking waves that generate air-filled bubbles, which radiate underwater sound. This wind-generated sound is a significant component of the ocean soundscape, and models are essential for understanding and predicting its impact. Models for predicting sound pressure level (SPL) from wind have been studied for many years. However, the terminology and definitions behind modeling approaches have not been unified, and ambiguity has led to differences in predicted SPL. The 2022 Ambient Sound Modeling Workshop was organized to compare ambient sound modeling approaches from different researchers. The main goal of the workshop was to quantify differences in …

Stressfit: A Hybrid Wearable Physicochemical Sensor Suite For Simultaneously Measuring Electromyogram And Sweat Cortisol, Nafize Ishtiaque Hossain, Tanzila Noushin, Shawana Tabassum

Electrical Engineering Faculty Publications and Presentations

This study introduces StressFit, a novel hybrid wearable sensor system designed to simultaneously monitor electromyogram (EMG) signals and sweat cortisol levels. Our approach involves the development of a noninvasive skin patch capable of monitoring skin temperature, sweat pH, cortisol levels, and corresponding EMG signals using a combination of physical and electrochemical sensors integrated with EMG electrodes. StressFit was optimized by enhancing sensor output and mechanical resilience for practical application on curved body surfaces, ensuring accurate acquisition of cortisol, pH, body temperature, and EMG data without sensor interference. In addition, we integrated an onboard data processing unit with Internet of Things …

Enhancement Of Search And Rescue Within The Coastal Waters Of Sierra Leone, Mohamed Osman Kamara

World Maritime University Dissertations

No abstract provided.

Causal Neuro-Symbolic Ai For Root Cause Analysis In Smart Manufacturing, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Root cause analysis is the process of investigating the cause of a failure and providing measures to prevent future failures. It is an active area of research due to the complexities in manufacturing production lines and the vast amount of data that requires manual inspection. We present a combined approach of causal neuro-symbolic AI for root cause analysis to identify failures in smart manufacturing production lines. We have used data from an industry-grade rocket assembly line and a simulation package to demonstrate the effectiveness and relevance of our approach.

Ontology Design Metapattern For Relationtype Role Composition, Utkarshani Jaimini, Ruwan Wickramarachchi, Cory Henson, Amit Sheth

Publications

RelationType is a metapattern that specifies a property in a knowledge graph that directly links the head of a triple with the type of the tail. This metapattern is useful for knowledge graph link prediction tasks, specifically when one wants to predict the type of a linked entity rather than the entity instance itself. The RelationType metapattern serves as a template for future extensions of an ontology with more fine-grained domain information.

Causal Knowledge Graph For Scene Understanding In Autonomous Driving, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The current approaches to autonomous driving focus on learning from observation or simulated data. These approaches are based on correlations rather than causation. For safety-critical applications, like autonomous driving, it’s important to represent causal dependencies among variables in addition to the domain knowledge expressed in a knowledge graph. This will allow for a better understanding of causation during scenarios that have not been observed, such as malfunctions or accidents. The causal knowledge graph, coupled with domain knowledge, demonstrates how autonomous driving scenes can be represented, learned, and explained using counterfactual and intervention reasoning to infer and understand the behavior of …

Autonomous Coalition Formation For Energy Trading In Intelligent Grid Systems, Issiac Baca

Electrical and Computer Engineering ETDs

This paper introduces an interactive model and architectural framework for a smart grid system, emphasizing the prosumers dual roles as energy buyers and sellers within localized energy trading markets, i.e., coalitions. Initially, the utilities of both buyers and sellers are formulated to capture the benefits derived from their energy-related activities. Then, the paper presents the Approximate SMARTFORM (ASMARTFORM) mechanism, based on the principles of matching theory, to enhance the prosumers engagement in coalitions by addressing externalities influencing their decisions. To further improve the coalition formation process, the Accurate SMARTFORM (AccSMARTFORM) mechanism is formulated based on the theory of coalition games, …

Addressing Uncertainty In Renewable Energy Integration For Western Australia’S Mining Cector: A Robust Optimization Approach, Mehrdad Ghahramani, Daryoush Habibi, Seyyedmorteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various …

Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch, Anthony L. Franz, Eugene V. Dao, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons

Faculty Publications

Intense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (E_s), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global E_s occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E …

The Adviser Of Journal Of Electrochemistry Professor Christian Amatore Received The Chinese Government Friendship Award, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Goodbye With Tears! Deep Condolences To Academician Zhao-Wu Tian!, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.