Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 65611 - 65640 of 1114360

Full-Text Articles in Entire DC Network

Health Care Professionals’ Perspectives On Technology Use In Urinary Care: Cross-Sectional Survey-Based Study, Nicole Zhang, Ni Zhang, Yun Wang, Rachel Wong, Kaylin Ma Jan 2025

Health Care Professionals’ Perspectives On Technology Use In Urinary Care: Cross-Sectional Survey-Based Study, Nicole Zhang, Ni Zhang, Yun Wang, Rachel Wong, Kaylin Ma

Faculty Research, Scholarly, and Creative Activity

Background: Urinary issues pose a significant burden on health care systems, necessitating innovative solutions to enhance patient care and alleviate the provider burden. Objective: The objective of this study was to explore health care professionals’ perceptions of urinary issues and assess their acceptance and readiness to adopt wearable and remote technologies for managing these conditions. The study aimed to identify the attributes and barriers influencing technology integration in clinical practice, using established theoretical frameworks, such as the Health Belief Model (HBM) and the Technology Acceptance Model (TAM). Methods: A cross-sectional survey-based study was conducted. A structured survey questionnaire was administered …

Go to article

Design And Integration Of A Multisensory Board For Real-Time Monitoring Of Metal Casting Foundry Sand Molds, Nathaniel Addo Jan 2025

Design And Integration Of A Multisensory Board For Real-Time Monitoring Of Metal Casting Foundry Sand Molds, Nathaniel Addo

Dissertations and Theses @ UNI

No abstract provided.

Go to article

Exploration Of The Effects Of Tai Chi Practice On Lower Limb Corticomuscular Coherence During Balance-Demanding Virtual Reality Conditions In Older Adults, Yang Hu, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez Jan 2025

Exploration Of The Effects Of Tai Chi Practice On Lower Limb Corticomuscular Coherence During Balance-Demanding Virtual Reality Conditions In Older Adults, Yang Hu, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez

Faculty Research, Scholarly, and Creative Activity

Tai Chi practice has been widely adopted to improve balance and prevent falls in older adults. However, the neural mechanisms underlying the benefits of Tai Chi are difficult to evaluate during traditional balance assessments. The goal of this study was to evaluate the effects of Tai Chi and healthy aging on corticomuscular coherence (CMC) while standing in virtual balance-demanding environments. We recorded neural, muscular, and behavioral data in an immersive virtual reality environment while implementing sensory and mechanical perturbations to introduce high postural threats. Through the acquisition of electroencephalography and electromyography signals, we examined β and γ CMC changes in …

Go to article

Temporal Patterns Of The Introduced Sponge Hymeniacidon Perlevis (Montagu, 1814) In The Elkhorn Slough, California, Usa, Jackson T. Hoeke, Kerstin Wasson, Amanda S. Kahn Jan 2025

Temporal Patterns Of The Introduced Sponge Hymeniacidon Perlevis (Montagu, 1814) In The Elkhorn Slough, California, Usa, Jackson T. Hoeke, Kerstin Wasson, Amanda S. Kahn

Faculty Research, Scholarly, and Creative Activity

Hymeniacidon perlevis is a cosmopolitan sponge with a seasonal life cycle. We investigated seasonal and interannual dynamics of H. perlevis in Elkhorn Slough estuary, where it is an introduced species, and explored correlations between sponge cover and environmental conditions. We used sponge cover to estimate the potential effects of H. perlevis on its environment, and how those could vary across its seasonal life cycle. We found that recruitment is currently restricted to the upper estuary and while it varies annually, the frequency and density of sponge recruits have generally increased from 2007 to 2023. A seasonal life cycle was confirmed …

Go to article

Generating Synthetic Malware Samples Using Generative Ai, Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park Jan 2025

Generating Synthetic Malware Samples Using Generative Ai, Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in malware. However, collecting a diverse set of malware samples with various obfuscation techniques is challenging and often takes years, especially for newly developed malware. This issue is further compounded by a well-known limitation of machine learning models: their poor performance when training data is scarce. In this paper, we propose a new system for generating synthetic malware samples to augment imbalanced malware dataset. Our approach decomposes …

Go to article

Learning About The Transit Passenger Experience From Microblogging Posts, Egbe Etu Etu, Asha Weinstein Agrawal, Jordan Larot, Chaitanya Tatipigari, Imokhai Theophilus Tenebe Jan 2025

Learning About The Transit Passenger Experience From Microblogging Posts, Egbe Etu Etu, Asha Weinstein Agrawal, Jordan Larot, Chaitanya Tatipigari, Imokhai Theophilus Tenebe

Faculty Research, Scholarly, and Creative Activity

Transit agencies often track delays and malfunctions but may overlook riders’ real-time social experiences. To fill this gap, we analyzed 559 tweets from 2020 to 2023 that captured personal accounts of public transport use. Using thematic analysis, we found that 74% of posts criticized other passengers’ behavior, far outweighing comments on transit ride quality, such as complaints about delays (25%). Notably, 86% of tweets expressed negative sentiment, though some highlighted exceptional service. These findings demonstrate that interpersonal dynamics are a key contributor to riders’ experiences and illustrate the nature of the interactions that riders like and dislike.

Go to article

Finding Floral And Faunal Species Richness Optima Among Active Fire Regimes, Zachary L. Steel, Alissa M. Fogg, Raphaela Floreani Buzbee, Kate Wilkin, Brandon M. Collins, Ryan Burnett, Marc D. Meyer, Amarina Wuenschel, Scott L. Stephens Jan 2025

Finding Floral And Faunal Species Richness Optima Among Active Fire Regimes, Zachary L. Steel, Alissa M. Fogg, Raphaela Floreani Buzbee, Kate Wilkin, Brandon M. Collins, Ryan Burnett, Marc D. Meyer, Amarina Wuenschel, Scott L. Stephens

Faculty Research, Scholarly, and Creative Activity

Changing fire regimes have important implications for biodiversity and challenge traditional conservation approaches that rely on historical conditions as proxies for ecological integrity. This historical-centric approach becomes increasingly tenuous under climate change, necessitating direct tests of environmental impacts on biodiversity. At the same time, widespread departures from historical fire regimes have limited the ability to sample diverse fire histories. We examined 2 areas in California's Sierra Nevada (USA) with active fire regimes to study the responses of bird, plant, and bat communities to a broad spectrum of temporal, spatial, and severity patterns of fire. Bird and plant species richness peaked …

Go to article

Reinforcement Learning Methods For Assistive And Rehabilitation Robotic Systems: A Survey, Mojtaba Sharifi, Shreesh Tripathi, Yun Chen, Qiang Zhang, Mahdi Tavakoli Jan 2025

Reinforcement Learning Methods For Assistive And Rehabilitation Robotic Systems: A Survey, Mojtaba Sharifi, Shreesh Tripathi, Yun Chen, Qiang Zhang, Mahdi Tavakoli

Faculty Research, Scholarly, and Creative Activity

Advancements in robotic systems aimed at improving mobility for individuals with disabilities have required more sophisticated control and navigation methods. Traditional control approaches often lack the complexity and adaptability needed for the high-dimensional nature of human activities. Consequently, reinforcement learning (RL) has emerged as a dynamic and effective framework for managing robotic actions in complex and unpredictable human environments. This article reviews the integration of RL in robotic systems for enhancing the mobility of individuals with disabilities, addressing the limitations of traditional control methods in complex and unpredictable environments. We critically analyze various RL algorithms, discussing their advantages and challenges …

Go to article

From Disciplinary Enthusiasm To Soulless Tasks: Norms Behind Computing Educators' Emotion Display, Virginia Grande, Päivi Kinnunen, Matthew Barr, Anne Kathrin Peters, Mihaela Sabin, Matilde Sánchez-Peña, Ethel Tshukudu Jan 2025

From Disciplinary Enthusiasm To Soulless Tasks: Norms Behind Computing Educators' Emotion Display, Virginia Grande, Päivi Kinnunen, Matthew Barr, Anne Kathrin Peters, Mihaela Sabin, Matilde Sánchez-Peña, Ethel Tshukudu

Faculty Research, Scholarly, and Creative Activity

Computing educators' emotion display is regulated by various norms and conventions. These (unwritten) rules affect which emotions educators feel comfortable expressing to different extents when students can perceive such displays. We draw on emotions and norms literature to investigate higher education computing educators' perceptions of what kinds of norms they have as criteria when considering which emotions they are (not) comfortable showing when teaching. Based on the qualitative content analysis of 22 interviews with computing educators from seven countries, we present various–sometimes connected or conflicting–norms that influence emotion display. These norms comprise moral, national, societal, professional, and affiliation norms that …

Go to article

Grand Challenges For Human Factors And Ergonomics, Waldemar Karwowski, Gavriel Salvendy, Mica Endsley, William Rouse, Paul Salmon, Kay Stanney, Andrew Thatcher, Tony Andre, Jessie Yang, Hasan Ayaz, Ahmet Cakir, Vince Duffy, Colin Drury, Qin Gao, Yinni Guo, Peter Hancock, William S. Marras, Patrick Rau Jan 2025

Grand Challenges For Human Factors And Ergonomics, Waldemar Karwowski, Gavriel Salvendy, Mica Endsley, William Rouse, Paul Salmon, Kay Stanney, Andrew Thatcher, Tony Andre, Jessie Yang, Hasan Ayaz, Ahmet Cakir, Vince Duffy, Colin Drury, Qin Gao, Yinni Guo, Peter Hancock, William S. Marras, Patrick Rau

Faculty Research, Scholarly, and Creative Activity

Contemporary society faces a growing set of complex global issues representing significant human health, well-being, and sustainability threats. Human Factors and Ergonomics (HFE) has a critical role to play in responding to these issues; however, there remain a set of grand challenges that require resolution. This paper presents and discusses six grand challenges for HFE and related key research thrust areas for each of the challenges. The grand challenges are (1) Evolution in Societal Thinking; (2) Future of Human Work in Industry 5.0; (3) Climate Change and Sustainability; (4) Future of Education and Training; (5) Future of Personalized Health, and …

Go to article

Comparing E-Mosaics Predictions Of High-Redshift Proto-Globular Clusters With Jwst Observations In Lensed Galaxies, Joel Pfeffer, Duncan A. Forbes, Aaron J. Romanowsky, Nate Bastian, Robert A. Crain, J. M.Diederik Kruijssen, Kenji Bekki, Jean P. Brodie, Mélanie Chevance, Warrick J. Couch, Jonah S. Gannon Jan 2025

Comparing E-Mosaics Predictions Of High-Redshift Proto-Globular Clusters With Jwst Observations In Lensed Galaxies, Joel Pfeffer, Duncan A. Forbes, Aaron J. Romanowsky, Nate Bastian, Robert A. Crain, J. M.Diederik Kruijssen, Kenji Bekki, Jean P. Brodie, Mélanie Chevance, Warrick J. Couch, Jonah S. Gannon

Faculty Research, Scholarly, and Creative Activity

High-resolution imaging and strong gravitational lensing of high-redshift galaxies have enabled the detection of compact sources with properties similar to nearby massive star clusters. Often found to be very young, these sources may be globular clusters detected in their earliest stages. In this work, we compare predictions of high-redshift (-10) star cluster properties from the E-MOSAICS simulation of galaxy and star cluster formation with those of the star cluster candidates in strongly lensed galaxies from JWST and Hubble Space Telescope (HST) imaging. We select galaxies in the simulation that match the luminosities of the majority of lensed galaxies with star …

Go to article

Poster: Synthetic Malware Generation Using Generative Models, Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park Jan 2025

Poster: Synthetic Malware Generation Using Generative Models, Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Malware poses significant challenges to cybersecurity, exacerbated by the scarcity of high-quality datasets. This paper explores the use of Diffusion and Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic malware samples as opcode sequences. The synthetic data, validated through classification metrics, demonstrates improved malware detection accuracy across families, particularly in addressing zero-day attacks. Results show that Diffusion achieves up to 99.6% accuracy in multi-class classification, outperforming WGAN-GP, which reaches up to 96.1%. Incorporating synthetic samples improves detection accuracy for rare malware families by up to 100%, underscoring the potential of generative models in enhancing malware detection.

Go to article

Assessing Equity, Environmental Social Justice And Intersectionality In U.S. Federal Policies: A Comparative And Temporal Analysis Of The Department Of Transportation, Department Of Energy, And Environmental Protection Agency, Sneha Basu Jan 2025

Assessing Equity, Environmental Social Justice And Intersectionality In U.S. Federal Policies: A Comparative And Temporal Analysis Of The Department Of Transportation, Department Of Energy, And Environmental Protection Agency, Sneha Basu

Dissertations and Theses @ UNI

As the climate crisis intensifies, U.S. federal policies play a critical role in addressing the disproportionate environmental burdens borne by marginalized communities. This thesis assesses how equity, environmental social justice, and intersectionality have been integrated into the policy documents of three key federal agencies: Department of Transportation (DOT), Department of Energy (DOE), and Environmental Protection Agency (EPA), over a 20-year period (2004–2024). Using a quantitative content analysis of 46 policy documents, the study evaluates five core dimensions: depth of discussion, clarity of language, inclusiveness of marginalized groups, action-oriented language, and environmental justice considerations. The research reveals significant interagency disparities, with …

Go to article

Csm 2670 & Csm 3670, Clas Jan 2025

Csm 2670 & Csm 3670, Clas

Executive Actions

No abstract provided.

Go to article

Cheshire 4-H Presents 2026 Information Packet, Diane Dugray Jan 2025

Cheshire 4-H Presents 2026 Information Packet, Diane Dugray

UNH Cooperative Extension

No abstract provided.

Go to article

Cheshire 4-H Presents 2025 Action Exhibit Info, Extension Jan 2025

Cheshire 4-H Presents 2025 Action Exhibit Info, Extension

UNH Cooperative Extension

No abstract provided.

Go to article

Cheshire 4-H Presents 2025 Demonstrtion Info, Extension Jan 2025

Cheshire 4-H Presents 2025 Demonstrtion Info, Extension

UNH Cooperative Extension

No abstract provided.

Go to article

Empirical Study Of Far-Field Radio Frequency Wireless Power Transfer Testbed For Underground Mines Based On 1.8 Ghz, Anabi Hilary Kelechi, Samuel Frimpong Jan 2025

Empirical Study Of Far-Field Radio Frequency Wireless Power Transfer Testbed For Underground Mines Based On 1.8 Ghz, Anabi Hilary Kelechi, Samuel Frimpong

Mining Engineering Faculty Research & Creative Works

A novel 1.8 GHz far-field wireless power transfer (WPT) testbed was designed to achieve long-distance energy harvesting in underground mine (UM). Prior experimental studies confirm that underground mines experience 50 dB radio frequency (RF) signal attenuation compared to a typical wireless environment. Due to this severe signal attenuation, the currently proposed radio frequency energy harvesting (RF-EH) testbed cannot be deployed in underground mines. In this study, we proposed, designed and performed an empirical study with a military-grade class A radio frequency wireless power transfer (RF-WPT) testbed. The various digital modulation schemes experimented with included: BPSK, DBPSK, QPSK, OQPSK, Pi4DQPSK, 8PSK, …

Go to article

Enhancing Safety In U.S. Coal Mines Through A Rib Support Recommendation Tool, Alper Kirmaci, Dakshith Ruvin Wijesinghe, Dogukan Guner, Kutay E. Karadeniz, Cameron Mitchell, Taghi Sherizadeh Jan 2025

Enhancing Safety In U.S. Coal Mines Through A Rib Support Recommendation Tool, Alper Kirmaci, Dakshith Ruvin Wijesinghe, Dogukan Guner, Kutay E. Karadeniz, Cameron Mitchell, Taghi Sherizadeh

Mining Engineering Faculty Research & Creative Works

Despite ongoing efforts to enhance coal rib stability, the underground coal mining sector continues to face incidents of rib failure, leading to injuries and fatalities. The development and validation of effective rib support systems are crucial for mitigating these risks. Unfortunately, a standardized design methodology that accommodates the diverse geological conditions of U.S. coal mines is missing. Current practices are often based on trial-and-error or outdated methods, yielding unreliable outcomes. This research aims to fill this gap by creating a comprehensive methodology for designing rib support systems suitable for U.S. underground mines. It encompasses in situ pull-out tests of coal …

Go to article

Validation Framework For Robust And Explainable Machine Learning Models In Autism Spectrum Disorder Triage, Ghadeer Ghazi Shayea, Mohd Hazli Mohammed Zabil, A. S. Albahri, Rula A. Hamid, O. S. Albahri, A. H. Alamoodi, Iman Mohamad Sharaf Jan 2025

Validation Framework For Robust And Explainable Machine Learning Models In Autism Spectrum Disorder Triage, Ghadeer Ghazi Shayea, Mohd Hazli Mohammed Zabil, A. S. Albahri, Rula A. Hamid, O. S. Albahri, A. H. Alamoodi, Iman Mohamad Sharaf

Mesopotamian Journal of Big Data

The development of real-time triage applications for Autism Spectrum Disorder (ASD) is a critical challenge due to the rising prevalence of ASD and the urgent need for efficient resource allocation in healthcare systems. Previous studies have applied machine learning (ML) to ASD triage; however, most approaches overlook robustness against adversarial attacks, provide limited benchmarking across multiple evaluation criteria, and lack explainability to support clinical adoption. Building on our earlier work, which introduced a fuzzy evaluation and benchmarking framework using the 2-Tuple Linguistic Fermatean Fuzzy Decision by Opinion Score Method (2TLFFDOSM), this study proposes a comprehensive five-stage validation and evaluation framework. …

Go to article

Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou Jan 2025

Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou

Mesopotamian Journal of Artificial Intelligence in Healthcare

Type 2 diabetes mellitus (T2DM) is a growing global health concern requiring early detection strategies. This study applies a Random Forest machine learning model to predict diabetes progression using a structured clinical dataset of 100 patients. The dataset includes demographic, physiological, and biochemical variables such as age, BMI, blood pressure, glucose levels, and lipid profiles. After preprocessing and training, the model achieved strong performance metrics: accuracy of 0.80, precision of 0.84, recall of 0.94, and an AUC of 0.88. Feature importance analysis revealed that systolic blood pressure, fasting glucose, and BMI are the most critical predictors. These findings are consistent …

Go to article

Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos Jan 2025

Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Palliative care seeks to improve the quality of life of patients with life-threatening illnesses by addressing their physical, emotional, and psychological needs. However, global challenges such as workforce shortages, limited access to specialized care, and inconsistent care quality demand innovative solutions. Advances in artificial intelligence (AI)-powered robotics offer transformative potential to overcome these barriers and strengthen palliative care delivery. This study explores how AI-driven robotic technologies support palliative care through applications in symptom monitoring, clinical decision-making, emotional companionship, and personalized care planning. It reviews cutting-edge robotic systems, including assistive, companion, diagnostic, nursing, procedural, service, and rehabilitation robots. Enabled by machine …

Go to article

Mapping Biomedical Ontology Terms To Ids: Effect Of Domain Prevalence On Prediction Accuracy, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi Jan 2025

Mapping Biomedical Ontology Terms To Ids: Effect Of Domain Prevalence On Prediction Accuracy, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

This study evaluates the ability of large language models (LLMs) to map biomedical ontology terms to their corresponding ontology IDs across the Human Phenotype Ontology (HPO), Gene Ontology (GO), and UniProtKB terminologies. Using counts of ontology IDs in the PubMed Central (PMC) dataset as a surrogate for their prevalence in the biomedical literature, we examined the relationship between ontology ID prevalence and mapping accuracy. Results indicate that ontology ID prevalence strongly predicts accurate mapping of HPO terms to HPO IDs, GO terms to GO IDs, and protein names to UniProtKB accession numbers. Higher prevalence of ontology IDs in the biomedical …

Go to article

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan Jan 2025

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Go to article

An Entropy-Bounded, General, Model-Based Framework For Lossy Compression Of Sensor Data, Steven Thompson, Maciej Zawodniok Jan 2025

An Entropy-Bounded, General, Model-Based Framework For Lossy Compression Of Sensor Data, Steven Thompson, Maciej Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In many industries, digital twinning has become an indispensable element of advanced technologies. However, digital twins are heavily reliant on extensive Internet of Things (IoT) sensor measurement data to function effectively. Consequently, data mining has become a lucrative endeavor, akin to gold rushes in the XIX century. However, the substantial volume of collected data often stresses the storage capacities for smaller to medium-sized enterprises, necessitating efficient compression techniques. Error-bound lossy compression offers substantial data reduction advantages, but introduces distortion that, when uncontrolled, can adversely affect analysis. This paper proposes an information optimization scheme that employs information entropy as a comprehensive …

Go to article

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan Jan 2025

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Go to article

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan Jan 2025

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

Go to article

Enhancing Measurement Accuracy In Industrial Applications: The Impact Of Sensor Data Imputation On Model Parameter Estimation, Steven Thompson, Michkath Omanda Bouraima, Maciej Jan Zawodniok Jan 2025

Enhancing Measurement Accuracy In Industrial Applications: The Impact Of Sensor Data Imputation On Model Parameter Estimation, Steven Thompson, Michkath Omanda Bouraima, Maciej Jan Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Digital twins are meant to revolutionize the manufacturing industry by enabling advanced condition monitoring and predictive maintenance processes. However, disruptions within the manufacturing process, such as sensor malfunctions or connectivity issues are inevitable and will cripple these advanced analysis methods if not properly addressed. Therefore, efficient data management and analysis practices are key to advancing this technology. This work examines the impact of missing data imputation on model parameter estimation, a crucial task in developing models for digital twins. We theoretically derive the Cramer-Rao Lower Bound (CRLB) for a DC signal with an unknown scalar parameter in the presence of …

Go to article

Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell Jan 2025

Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT) which is subsequently spatiotemporally imaged with an infrared (IR) camera. As all antennas have spatial variation in their radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT. This nonuniform heating causes uncertainty in defect detection and has the potential to lead to false positives and/or negatives. To this end, thermographic signal reconstruction (TSR), a well-established thermographic signal …

Go to article

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan Jan 2025

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without …

Go to article

First Prev Next Last