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Identifying Early Warning Signs Of Construction Labor Shortages, Ahmed Shiha, Islam H. El-Adaway Jan 2025

Identifying Early Warning Signs Of Construction Labor Shortages, Ahmed Shiha, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction labor shortages constrain project-level objectives and national development plans. The goal of this study is to utilize the lagged effects of macroeconomic conditions as early warning signs of construction labor shortages. To this end, the authors adopted a methodology, encompassing (1) retrieval of publicly available data and preprocessing of construction labor shortage as the target variable and macroeconomic measures as the explanatory variables, (2) identification of short-term associations between shortages and economic cycles using the Granger causality test, (3) examination of long-term relationships between labor shortages and economic conditions using the Johansen cointegration test, and (4) estimation of the …

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Visual Understanding Of Rock Wettability Distribution To Contact Angle Regions And Oil Displacement Patterns In The Silty Sand Reservoir Via 2d Pore-Scale Modeling, H. Al-Ajaj, W. Al-Bazzaz, Ralph E. Flori, S. Alsayegh, H. Almubarak, D. S. Ibrahim, H. Al-Saedi Jan 2025

Visual Understanding Of Rock Wettability Distribution To Contact Angle Regions And Oil Displacement Patterns In The Silty Sand Reservoir Via 2d Pore-Scale Modeling, H. Al-Ajaj, W. Al-Bazzaz, Ralph E. Flori, S. Alsayegh, H. Almubarak, D. S. Ibrahim, H. Al-Saedi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study, conducted using a silty sand reservoir rock extracted from a Kuwait-producing oilfield, provides crucial insights into fluid distribution patterns and mineralogy, as well as wettability contact angle preferences. The method used for visual identification not only captures rock physics but also suggests effective oil recovery displacement strategies. Visual identification is presented using 2D image technology and a Scanning Electron Microscope (SEM). Analytical data are presented from electron bombardments and backscattering reflections collected in the BSE detector. These analyses were used to characterize the various surface boundary morphology parameters of the silty sand mineral surfaces, including area, perimeter, mineral …

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Topology And Parameter Joint Identification In Imbalanced Low-Voltage Distribution Networks Based On Load Characteristic Propagation, Yanan Zhang, Gan Zhou, Huan Mao, Wei Gu, Yanjun Feng, Rui Bo Jan 2025

Topology And Parameter Joint Identification In Imbalanced Low-Voltage Distribution Networks Based On Load Characteristic Propagation, Yanan Zhang, Gan Zhou, Huan Mao, Wei Gu, Yanjun Feng, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Low-voltage distribution networks often suffer from incomplete or outdated network records, making it challenging to obtain the topology and line parameters under actual operating conditions. To address this issue, a joint identification method is proposed based on the propagation of load transient characteristics. First, the principle of load characteristic propagation is elaborated, and the concept of coupling impedance is introduced. Second, a set of linear regression equations is established based on the changes in current and voltage of the terminal measurements before and after load switching, and then these equations are solved using the least squares method to form the …

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Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley Jan 2025

Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of …

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Predictive Modeling For Optimal Gel Treatment Design In Brownfields Using Ensemble Machine Learning And Data Upsampling Via Generative Ai, Munqith Aldhaheri, Baojun Bai, Mingzhen Wei Jan 2025

Predictive Modeling For Optimal Gel Treatment Design In Brownfields Using Ensemble Machine Learning And Data Upsampling Via Generative Ai, Munqith Aldhaheri, Baojun Bai, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Efficiently designed gel treatments play a vital role in extending the lifespan of brownfields through rejuvenating oil production. Recently, a three-mode mathematical methodology named the VCR approach has been proposed for designing effective treatments. To optimize this approach, it is crucial to determine the appropriate design mode systematically rather than relying solely on the intuitive judgments of field operators. This study introduces an advanced methodology for predicting the optimal design type of gel treatments using 12 reservoir and production variables. The methodology integrates ensemble machine-learning (EML) models with historical data from 65 field projects across 11 countries (1985-2020). The Random …

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Characterization And Leaching Feasibility Studies Of Copper Flue Dust For The Recovery Of Main And Trace Metals, Fardis Nakhaei, Marek Locmelis, Lana Alagha, Michael S. Moats, Carlos Eyzaguirre, Cory Smith Jan 2025

Characterization And Leaching Feasibility Studies Of Copper Flue Dust For The Recovery Of Main And Trace Metals, Fardis Nakhaei, Marek Locmelis, Lana Alagha, Michael S. Moats, Carlos Eyzaguirre, Cory Smith

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Although copper smelter dust (CSD) is classified as hazardous waste, it also serves as a valuable secondary resource, offering potential for the recovery of critical elements. In this study, CSD samples were collected from the waste heat boiler (WHB) and electrostatic precipitator (ESP) units connected to the flash smelting furnace at a copper smelter. The representative samples were extensively characterized by particle size, and chemical and mineralogical analyses. Following sample characterization, leaching experiments were performed to evaluate the potential extraction of Cu, Fe, As, Zn, Pb, In, Ga, and Ge from the flue dusts. Distilled water, H2SO4, and HCl were …

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Integration Of Physics-Informed Neural Networks And Transfer Learning For Rainfall Induced Landslide Forecasting, Shian Cao, Weibing Gong Jan 2025

Integration Of Physics-Informed Neural Networks And Transfer Learning For Rainfall Induced Landslide Forecasting, Shian Cao, Weibing Gong

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Rainfall-induced landslides are a significant geological hazard, causing severe economic losses and casualties. Accurate forecasting of these events is particularly challenging due to the complex interactions of spatial and temporal factors governing slope stability. The Iverson model, which uses the Richards equation to describe water infiltration in unsaturated soils, is a widely adopted framework for analyzing rainfall-induced landslides. However, its reliance on traditional numerical methods limits its scalability and efficiency, particularly for complex boundary conditions and transient behaviors near slope failure. To address these limitations, we propose a physics-informed neural network (PINN) enhanced with transfer learning (TL-PINN) to solve the …

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Microwave Photonics-Assisted Interrogation Of Fiber-Optic Interferometric Sensors With Joint Frequency-Time Domain Analysis, Ruimin Jie, Jie Huang, Chen Zhu Jan 2025

Microwave Photonics-Assisted Interrogation Of Fiber-Optic Interferometric Sensors With Joint Frequency-Time Domain Analysis, Ruimin Jie, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber-optic interferometers are widely used in localized sensing applications due to their compact size, high sensitivity, and immunity to electromagnetic interference. In this paper, we propose and experimentally demonstrate a novel interrogation scheme for fiber-optic interferometric sensors, utilizing microwave photonics (MWP) and joint frequency-time domain analysis. As a proof of concept, a miniature fiber in-line Fabry-Perot interferometer (FPI) is integrated with a microwave photonic single-passband filter, enhanced by a dispersion compensation module to improve sensing performance. By applying an inverse Fourier transform to the system's complex frequency response, the time-domain representation of the signal is obtained, translating spectral shifts of …

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Fast Demodulation Of Ofdr-Based Distributed Sensing Based On Enhanced Buneman Frequency Estimation, Zhaopeng Zhang, Bo Liu, Xiao Liu, Caiyun Li, Osamah Alsalman, Chen Zhu Jan 2025

Fast Demodulation Of Ofdr-Based Distributed Sensing Based On Enhanced Buneman Frequency Estimation, Zhaopeng Zhang, Bo Liu, Xiao Liu, Caiyun Li, Osamah Alsalman, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process - typically required for a finer spectral sampling interval but at the cost of demodulation efficiency - can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional …

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Miniaturized Wearable Biosensors For Continuous Health Monitoring Fabricated Using The Femtosecond Laser-Induced Graphene Surface And Encapsulated Traces And Electrodes, Homayoon Soleimani Dinani, Tatianna Reinbolt, Bohong Zhang, Ganggang Zhao, Rex E. Gerald, Zheng Yan, Jie Huang Jan 2025

Miniaturized Wearable Biosensors For Continuous Health Monitoring Fabricated Using The Femtosecond Laser-Induced Graphene Surface And Encapsulated Traces And Electrodes, Homayoon Soleimani Dinani, Tatianna Reinbolt, Bohong Zhang, Ganggang Zhao, Rex E. Gerald, Zheng Yan, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Wearable sensors are increasingly being used as biosensors for health monitoring. Current wearable devices are large, heavy, invasive, skin irritants, or not continuous. Miniaturization was chosen to address these issues, using a femtosecond laser-conversion technique to fabricate miniaturized laser-induced graphene (LIG) sensor arrays on and encapsulated within a polyimide substrate. The femtosecond laser-converted conductive traces can have a size of 20 to 2 μm compared to the traditionally larger CO2 laser dimensions of around 300 to 100 μm. This marks a 93-98% decrease in trace size when using a femtosecond laser. This miniaturization allows for the ability to process temperature, …

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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 …

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An Extendable Soft-Switched Step-Up Interleaved Converter Integrated With Voltage Multiplier Cells, Amir Hasan Babanezhad, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi Jan 2025

An Extendable Soft-Switched Step-Up Interleaved Converter Integrated With Voltage Multiplier Cells, Amir Hasan Babanezhad, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a multiphase high step-up interleaved converter is introduced, which ensures low-input current ripple, low-component currents, and voltage stresses. The proposed circuit guarantees ZVS operation for power switches and ZCS operation for power diodes, which significantly reduce converter switching losses and EMI emission and improve its efficiency. Therefore, its passive components volume can be reduced by using high switching frequencies. In addition, the interleaved technique reduces input current ripple and input filter volume and provides high-power density. High-voltage gain and low-voltage stresses on the components are also achieved due to the integration of the converter structure with the …

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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 …

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Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan Jan 2025

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

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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 …

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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 …

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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 …

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Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell Jan 2025

Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

frequency selective surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters, such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This article presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature (23 ◦C–223 ◦C). The sensor integrates two-unit cells: 1) a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity (~16–18 MHz/0.1%) and 2) a loop-based cell with a temperature-sensitive …

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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 …

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Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework, Arnold A. Fernandes, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball Jan 2025

Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework, Arnold A. Fernandes, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops an analytical model of the bidirectional AC-AC Dual Active Bridge (DAB) converter. The passive components of the AC-AC DAB are subject to grid, switching, and sideband harmonics. Thus, it is impossible to model via the conventional Generalized Average Method (GAM). It has been numerically shown that Extended GAM (EGAM) can be used to model the AC-AC DAB converter. In this paper, an analytical sixteenth order EGAM-model has been developed that considers only grid harmonics at the filter components and only sideband harmonics for the transformer leakage inductor. A closed-form expression is developed for the 2D convolution product. …

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Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell Jan 2025

Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, 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). The inspection surface of the SUT is imaged with an infrared (IR) camera over the inspection time. As the thermal excitation originates from a spatially varying radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT that is directly related to this power density. This nonuniform heating causes uncertainty in defect detection and has the potential to lead …

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Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch Jan 2025

Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, …

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A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo Jan 2025

A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Although deep learning is increasingly promising in the field of Non-Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost-effective sequence-to-points NILM solution is proposed, integrating three-point labelling with non-causal convolution techniques. The approach introduces a semi-automatic labelling framework for obtaining NILM three-point data, which provides a low-cost data collection and labelling solution for large-scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence-to-points scenario …

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A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao Jan 2025

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise …

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Distributed Sapphire Fiber Bragg Grating-Based Thermal Profiling Of Submerged Entry Nozzles, Farhan Mumtaz, Hanok W. Tekle, Bohong Zhang, Xiaodong Li, Sunday Abraham, Bryant Mathis, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang Jan 2025

Distributed Sapphire Fiber Bragg Grating-Based Thermal Profiling Of Submerged Entry Nozzles, Farhan Mumtaz, Hanok W. Tekle, Bohong Zhang, Xiaodong Li, Sunday Abraham, Bryant Mathis, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This article focuses on the application of sapphire fiber Bragg gratings (FBGs) for instrumentation in submerged entry nozzles (SENs) within the steelmaking industry. The SEN is pivotal for transferring molten steel from a tundish to a mold, while preventing the infiltration of oxygen and nitrogen from the surrounding environment. Maintaining optimal flow conditions in the mold is crucial for ensuring casting process stability and maintaining high-quality steel. Sapphire FBG sensors have been instrumented in SENs to enable distributed thermal mapping for monitoring the health of the SEN. The optical sensor comprises three cascaded sapphire FBGs inscribed using femtosecond (FS) laser …

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Ibis Model Simulation Accuracy Improvement With Slew Rate Correction, Yifan Ding, Chulsoon Hwang Jan 2025

Ibis Model Simulation Accuracy Improvement With Slew Rate Correction, Yifan Ding, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The accuracy of Power-Supply-Induced Jitter (PSIJ) simulation in Input/Output Buffer Information Specification (IBIS) models is critical for ensuring robust high-speed signal integrity analysis, but it lacks accuracy in predicting the PSIJ when the pre-driver exists in the model. Previous studies have proposed methods to improve IBIS PSIJ simulation accuracy with pre-driver effect included in the IBIS switching coefficients modification process. However, these methods fail to accurately model the output waveform slew rate change with varied power noise. In this work, an improved modification method was proposed to incorporate power-aware characteristics into the modified IBIS model, thereby improving the accuracy of …

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A High Step-Up Soft-Switched Converter Based On Coupled Inductor And Current-Fed Voltage Multiplier, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi Jan 2025

A High Step-Up Soft-Switched Converter Based On Coupled Inductor And Current-Fed Voltage Multiplier, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel non-isolated DC-DC converter that combines coupled inductor (CI) and voltage multiplier (VM) techniques is proposed. The leakage energy of the CI is effectively recycled, and soft-switching conditions are achieved for all switches and diodes. Resonance between the leakage inductor of the CI and VM capacitors provides soft-switching conditions, without requiring a separate resonant tank. The use of VM stages not only lowers the voltage stress on semiconductor components but also allows for the use of low-voltage-rated devices, leading to reduced conduction losses, lower cost, and improved efficiency. High voltage gain can be achieved by appropriately …

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Balanced Benchmarking Of Zero-Shot And Rag Approaches For Biomedical Term Normalization, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi Jan 2025

Balanced Benchmarking Of Zero-Shot And Rag Approaches For Biomedical Term Normalization, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Normalization of medical concepts to an ontology is a key aspect of the natural language processing of biomedical text. It enables the mapping of medical expressions to standardized ontology terms and their identifiers, thereby enhancing the interoperability and computability of medical concepts. Although large language models (LLMs) can identify and standardize medical terms, they may struggle to accurately map ontology terms to their corresponding ontology identifiers. These challenges arise from the stochastic nature of LLMs, their limited exposure to uncommon ontology identifiers during training, and their lack of an integrated lookup mechanism. We generated test sets of synthetic terms to …

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Measurement- And Simulated Annealing (Sa) Optimization-Based Inductor Model Coupled To Chassis, Junyong Park, Reza Vahdani, Donghyun Kim Jan 2025

Measurement- And Simulated Annealing (Sa) Optimization-Based Inductor Model Coupled To Chassis, Junyong Park, Reza Vahdani, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

In automotive systems, a metal chassis protects the components against the external environment. However, the metal chassis is conductive, which results in unwanted conducted emission (CE) coupling to electric components. An inductor used for power factor correction (PFC) is one of the affected components. When the inductor is mated with the metal chassis, the impedance of the inductor changes. It is also hard to predict the CE coupling due to the structure-dependent characteristics. That is, the CE coupling is not negligible and hard to clarify. Therefore, this article proposes an efficient modeling method for the inductor which is mated with …

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Method Of Termination With Absorbers For Far-End Crosstalk Measurements, Daniel L. Commerou, Reza Asadi, Sathvika Bandi, Seyed Mostafa Mousavi, Xiaoning Ye, Donghyun Kim Jan 2025

Method Of Termination With Absorbers For Far-End Crosstalk Measurements, Daniel L. Commerou, Reza Asadi, Sathvika Bandi, Seyed Mostafa Mousavi, Xiaoning Ye, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing demand for higher data rates in modern electronic systems has heightened the challenges of maintaining signal integrity, particularly in addressing farend crosstalk (FEXT). This paper presents a novel approach using absorber-based terminations to perform signal integrity measurements in high-speed PCB designs. The performance of magnetically and electrically loaded absorber materials is evaluated against traditional 50Ω terminations with performance parameters such as S-parameters, Time-Domain reflectometry (TDR), and induced far-end crosstalk voltage. Simulations and experimental measurements demonstrate that electrically loaded absorbers can achieve performance characteristics comparable to high-quality terminations, particularly for reflections and impedance matching. The results indicate that absorbers …

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