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Articles 121 - 150 of 3488
Full-Text Articles in Entire DC Network
Design Strategies For Skew Compensation In Highspeed Pcb Strip Line Interconnects, Sathvika Bandi, Reza Asadi, Zhekun Peng, Srinivas Venkataraman, Granthana Rangaswamy, Santosh Pappu, Xu Wang, Donghyun Kim
Design Strategies For Skew Compensation In Highspeed Pcb Strip Line Interconnects, Sathvika Bandi, Reza Asadi, Zhekun Peng, Srinivas Venkataraman, Granthana Rangaswamy, Santosh Pappu, Xu Wang, Donghyun Kim
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a comprehensive analysis of the impact of intra-pair PN skew compensation in printed circuit board (PCB) strip line (SL) traces, for a high-speed 224 Gbps lane for the first time. The study investigates the effects of skew compensation placement both with and without via discontinuities. Detailed evaluations are performed in both time and frequency domains, examining critical parameters such as time-domain reflectometry (TDR), input impedance, return loss, insertion loss, and common-mode S -parameters. The findings reveal that, in a simple strip line trace without via discontinuities, the location of skew compensation has negligible influence on signal margins. …
Characterization And Full-Wave Modeling Of Corona Discharge Induced Coupling To Touchscreen Displays, Zhekun Peng, Shubhankar Marathe, Javad Meiguni, Ali Foudazi, Jianchi Zhou, Li Shen, Viswa Pilla, Cheung Wei Lam, Donghyun Kim, David Pommerenke, Daryl G. Beetner
Characterization And Full-Wave Modeling Of Corona Discharge Induced Coupling To Touchscreen Displays, Zhekun Peng, Shubhankar Marathe, Javad Meiguni, Ali Foudazi, Jianchi Zhou, Li Shen, Viswa Pilla, Cheung Wei Lam, Donghyun Kim, David Pommerenke, Daryl G. Beetner
Electrical and Computer Engineering Faculty Research & Creative Works
An electrostatic discharge (ESD) to the glass of a touchscreen display can damage capacitive touch sensor circuitry and traces. The ESD creates a sparkless corona discharge on the glass surface, which couples to sense patches on the other side of the glass. Currents induced on sense traces during the corona discharge are measured in an evaluation setup, which resembles the touchscreen design. Waveforms for current induced at different distances from the discharge position are recorded and compared. The induced currents are analyzed by their peak value and the total charge or energy delivered to the sense trace for different discharge …
Centralized And Federated Heart Disease Classification Using Uci Dataset: A Benchmark With Interpretability Analysis, Mario Padilla Rodriguez, Eyiara Oladipo, Mohamed Nafea
Centralized And Federated Heart Disease Classification Using Uci Dataset: A Benchmark With Interpretability Analysis, Mario Padilla Rodriguez, Eyiara Oladipo, Mohamed Nafea
Electrical and Computer Engineering Faculty Research & Creative Works
Cardiovascular disease (CVD) is a leading cause of global mortality, highlighting the need for accurate diagnostic methods. This study benchmarks centralized and federated learning (FL) algorithms for heart disease binary classification using the UCI dataset, which includes 920 patient records from four hospitals in the USA, Hungary, and Switzerland. Our benchmark is supported by Shapley-value as well as Local Interpretable Model-agnostic Explanations (LIME) interpretability analyses to quantify feature importance for classification. In the centralized setup, various classification algorithms are trained on pooled data, with the Naive Bayes classifier achieving the highest test accuracy of 81.1%. Further, FL algorithms with four …
Benchmarking Deep Learning Architectures For Ecg-Based Multi-Label Heart Disease Prediction Using Mimic-Iv Database, Eyiara Oladipo, Sarwar Nazrul, Mohamed Nafea
Benchmarking Deep Learning Architectures For Ecg-Based Multi-Label Heart Disease Prediction Using Mimic-Iv Database, Eyiara Oladipo, Sarwar Nazrul, Mohamed Nafea
Electrical and Computer Engineering Faculty Research & Creative Works
Cardiovascular disease (CVD) is a leading cause of global mortality, accounting for an estimated 17.9 million deaths annually. CVD is broadly defined as a group of medical conditions influenced by modifiable or non-modifiable risk factors that affect the heart's ability to function properly. Machine learning (ML) has emerged as a powerful tool for analyzing complex medical data, aiding in early detection and accurate diagnosis of CVD and improving patient outcomes. Recent studies proposed various deep learning (DL) architectures for detecting CVD, yet there is a lack of robust benchmarks for comparing their performance on large-scale databases. In this work, we …
Honey-Reram Enabled Sustainable Edge Ai System For Iot Applications, Jinhui Wang, Feng Zhao, Mohammad Rafeeq Khan, Md Mehedi Hasan Tanim, Zoe Templin, Harshvardhan Uppaluru
Honey-Reram Enabled Sustainable Edge Ai System For Iot Applications, Jinhui Wang, Feng Zhao, Mohammad Rafeeq Khan, Md Mehedi Hasan Tanim, Zoe Templin, Harshvardhan Uppaluru
Electrical and Computer Engineering Faculty Research & Creative Works
This paper is toward a promising solution to address the environmental sustainability challenge in computing by building brain-inspired and green non-Von Neumann systems with Resistive Random-Access Memory (ReRAM) made from natural organic materials, honey, for energy-efficient operation, renewable material resources, sustainable device manufacturing, and environmentally-friendly disposal. In this paper, honey-ReRAM and its arrays are firstly manufactured and tested. The resistance modulation mechanism of honey-ReRAM is analyzed and investigated. Then a Computing-in-Memory (CIM) architecture based on honey-ReRAM for edge AI and IoT applications is proposed and evaluated. The experimental results indicate that the proposed edge AI systems with the VGG8 and …
Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan
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 …
Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan
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 …
Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan
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 …
Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria
Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …
Enhancing Measurement Accuracy In Industrial Applications: The Impact Of Sensor Data Imputation On Model Parameter Estimation, Steven Thompson, Michkath Omanda Bouraima, Maciej Jan Zawodniok
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 …
Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell
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 …
Aperture-Based Fss For Dielectric Thickness Sensing, Alexander Hook, Gage Donahue, Kristen M. Donnell
Aperture-Based Fss For Dielectric Thickness Sensing, Alexander Hook, Gage Donahue, Kristen M. Donnell
Electrical and Computer Engineering Faculty Research & Creative Works
Frequency selective surfaces (FSSs) are planar arrays of patch- or aperture-based elements that have a particular transmissive or reflective response. As FSS performance is affected by changes in the local (to the FSS) environment, FSSs may be used to detect changes in strain, temperature, or nearby material (substructure) thickness, amongst other parameters. To this end, an aperture-based FSS can be considered as a sensor for substructure thickness monitoring for surface mounted sensing scenarios. An aperture-based design was selected due to its ability to operate in reflection mode (and hence a one-sided measurement) without the need for a conductive backplane. In …
Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell
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 …
Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan
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 …
Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework, Arnold A. Fernandes, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball
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. …
Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell
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 …
Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono
Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono
Electrical and Computer Engineering Faculty Research & Creative Works
As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO2 concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic …
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
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, …
Phase-Variation Microwave Resonator For Highly Sensitive Dynamic Sensing, Chen Zhu, Rex E. Gerald, Jie Huang
Phase-Variation Microwave Resonator For Highly Sensitive Dynamic Sensing, Chen Zhu, Rex E. Gerald, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
High-precision dynamic sensing is critical in fields, such as industrial automation, structural health monitoring, and environmental sensing, where real-time responses to minuscule changes can prevent system failures or optimize performance. In this work, we introduce and demonstrate a phase-variation coaxial cable resonator (CCR) as a highly sensitive sensor for dynamic sensing applications. As a proof of concept, a prototype device based on a custom-designed CCR is thoroughly investigated for dynamic displacement measurements, as displacement is a fundamental quantity essential to numerous applications. The sensor consists of two components: a static CCR device and a movable conducting plate. As the conducting …
Discrimination Of Temperature And Strain By Characterizing Two Femtosecond Laser-Written Coincident Sapphire Fiber Bragg Gratings For Harsh Environment Applications, Farhan Mumtaz, Bohong Zhang, Koustav Dey, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang
Discrimination Of Temperature And Strain By Characterizing Two Femtosecond Laser-Written Coincident Sapphire Fiber Bragg Gratings For Harsh Environment Applications, Farhan Mumtaz, Bohong Zhang, Koustav Dey, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
In this study, two co-incident sapphire fiber Bragg gratings (SFBGs) were successfully inscribed utilizing a femtosecond (fs) laser to achieve a high fringe contrast interferogram. These two SFBGs employ a unique configuration, one parallel to the center axis, called p-SFBG, and the other forming an angle from the center-axis, called a-SFBG, allowing for simultaneous strain and temperature measurements with low crosstalk. As a proof of concept, p-SFBG and a-SFBG using line-by-line method are characterized, which are shorter in length (i.e., 1.5 mm), producing reflectivity of ~3dB. This effort demonstrates the use of two coincident SFBGs forming an angle of 2.29° …
Simultaneous Measurement Of Early-Stage Corrosion And Strain Levels In Steel Rebar Based On Graphene Oxide-Coated Ncf-Fbg Fiber Optic Sensor, Fujian Tang, Baihe Qu, Hong Nan Li, Jie Huang
Simultaneous Measurement Of Early-Stage Corrosion And Strain Levels In Steel Rebar Based On Graphene Oxide-Coated Ncf-Fbg Fiber Optic Sensor, Fujian Tang, Baihe Qu, Hong Nan Li, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
Rebar corrosion significantly reduces the lifespan of reinforced concrete structures. The value of rebar strain, especially for some key structural components, indicates the safety margin of structures. In this study, a graphene oxide (GO) coated no-core fiber-fiber Bragg grating (NCF-FBG) fiber optic sensor is proposed for simultaneously measuring strain values and early-stage corrosion of steel rebar for the first time. The impact of GO coating thickness on the monitoring sensitivity is considered. A setup was manufactured to simultaneously perform tension, optical, and corrosion tests. The strain was applied up to 1200 μϵ. The rebar corrosion was assessed using electrochemical method …
Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan
Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This article presents an integral reinforcement learning-based optimal formation tracking scheme for multiple quadrotors unmanned aerial vehicles (QUAVs) experiencing nonlinear coupled dynamics and subject to constraints. We use multilayer neural networks (MNN) within an actor-critic framework where the MNN weights are tuned using singular value decomposition (SVD) of the activation function gradient to approximate optimal control policy via backstepping. Additionally, barrier Lyapunov functions (BLF) are introduced to ensure set invariance, thereby maintaining the quadrotors within a defined safety space due to constraints. A novel weight update law for each layer is derived using the HJB approximation error and control input …
An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch
An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …
Active And Reactive Power Flow Control Of The Dual Active Bridge Converter, Lauryn Morris, Thomas W. Francois, Jonathan Saelens, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball
Active And Reactive Power Flow Control Of The Dual Active Bridge Converter, Lauryn Morris, Thomas W. Francois, Jonathan Saelens, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
The Dual Active Bridge (DAB) is a reliable and efficient converter capable of providing bi-directional power transfer and galvanic isolation. An ac-ac DAB can control both active and reactive power flow. The present work introduces a combined feedback/feed-forward current control system, utilizing the calculated and measured converter currents translated into the dq reference frame, to control the output power. The system was simulated in PLECS to demonstrate the control algorithm's ability to track the dq currents and provide the necessary output power.
A Data-Driven Adaptive Control Approach For Enhancing The Dynamic Response Ff Vsgs In Varying Grid Conditions, Shah Fahad, Buxin She, Junjie Yin, Fangxing Li, Hantao Cui, Rui Bo
A Data-Driven Adaptive Control Approach For Enhancing The Dynamic Response Ff Vsgs In Varying Grid Conditions, Shah Fahad, Buxin She, Junjie Yin, Fangxing Li, Hantao Cui, Rui Bo
Electrical and Computer Engineering Faculty Research & Creative Works
Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the …
Microwave Photonic Fiber Ring Resonator For Optical Sensing Based On In-Ring And Out-Of-Ring Modulation, Shiyu Li, Chen Zhu
Microwave Photonic Fiber Ring Resonator For Optical Sensing Based On In-Ring And Out-Of-Ring Modulation, Shiyu Li, Chen Zhu
Electrical and Computer Engineering Faculty Research & Creative Works
Intensity-modulated optical fiber sensors (IM-OFSs) have garnered significant research interest due to their advantageous characteristics, including simplified fabrication procedures, cost-efficient systems, and straightforward signal demodulation, leading to their widespread application across diverse fields. Nevertheless, the multiplexing technique for IM-OFSs remains underexplored, primarily because isolating the contributions of individual sensors within the system using traditional power measurements poses a significant challenge. In this study, we introduce and experimentally validate a novel approach leveraging a simple microwave-photonic fiber ring resonator (MWP-FRR). This approach enables the concurrent interrogation of two IM-OFSs based on an in-and-out-of-ring-modulation (IORM) strategy. The transmission losses of both IM-OFSs …
Advancing Temperature Monitoring Of The Bottom Anode In A Direct Current Electric Arc Furnace Operations With Distributed Optical Fiber Sensors., Ogbole Collins Inalegwu, Rony Kumer Saha, Yeshwanth Reddy Mekala, Farhan Mumtaz, Nicholas Dionise, Zane Voss, Jeffrey D. Smith, Ronald J. O'Malley, Rex E. Gerald, Jie Huang
Advancing Temperature Monitoring Of The Bottom Anode In A Direct Current Electric Arc Furnace Operations With Distributed Optical Fiber Sensors., Ogbole Collins Inalegwu, Rony Kumer Saha, Yeshwanth Reddy Mekala, Farhan Mumtaz, Nicholas Dionise, Zane Voss, Jeffrey D. Smith, Ronald J. O'Malley, Rex E. Gerald, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
The bottom anode in the Direct Current Electric Arc Furnace (DC EAF) is critical for completing the electrical circuit necessary for sustaining the arc within the furnace. For pin-type bottom anodes, monitoring of the temperature of select pins instrumented with thermocouples is performed to track bottom wear in the EAF and inform the operator when the furnace should be removed from service. This work presents the results from a plant trial using distributed temperature monitoring of bottom anode pins in a 165-ton DC EAF over a two-month service period utilizing two optical fiber sensing techniques: fiber Bragg grating (FBG) and …
Annotated 3d Point Cloud Dataset For Traffic Management In Simulated Urban Intersections, Elham Binshaflout, Chaima Zaghouani, Nawfal Guefrachi, Charalampos Antoniadis, Hakim Ghazzai, Ahmad Alsharoa, Gianluca Setti
Annotated 3d Point Cloud Dataset For Traffic Management In Simulated Urban Intersections, Elham Binshaflout, Chaima Zaghouani, Nawfal Guefrachi, Charalampos Antoniadis, Hakim Ghazzai, Ahmad Alsharoa, Gianluca Setti
Electrical and Computer Engineering Faculty Research & Creative Works
Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle's viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D …
Design And Numerical Investigation Of Surface Plasmon Resonance–Based Refractive Index Sensor, Inamullah Sario, Ghulam Abbas Lashari, Abdul Aziz Memon, Farhan Mumtaz
Design And Numerical Investigation Of Surface Plasmon Resonance–Based Refractive Index Sensor, Inamullah Sario, Ghulam Abbas Lashari, Abdul Aziz Memon, Farhan Mumtaz
Electrical and Computer Engineering Faculty Research & Creative Works
A photonic crystal fiber–surface plasmon resonance (PCF-SPR)-based refractive index (RI) sensor with a novel design is presented in this paper. This sensor detects the anomalies in the sample analyte by detecting the change in its RI. Silver (Ag) is used as a plasmonic material with a unique terracotta structure to sense the RI variations in the surrounding medium, also called an analyte or sample. A thin layer of titanium dioxide (TiO2) measuring 10 nm is applied on top of the plasmonic material to prevent the oxidation of silver. The designed sensor detected a good range of analyte RI from 1.31 …
High-Frequency Accurate Dual-Side Equivalent Circuit Model For Transformers, Reza Vahdani, Junyong Park, Manish Kizhakkeveettil Mathew, Zhekun Peng, Chiuk Song, Hyucksu Kweon, Jiang Lijun, Donghyun Kim
High-Frequency Accurate Dual-Side Equivalent Circuit Model For Transformers, Reza Vahdani, Junyong Park, Manish Kizhakkeveettil Mathew, Zhekun Peng, Chiuk Song, Hyucksu Kweon, Jiang Lijun, Donghyun Kim
Electrical and Computer Engineering Faculty Research & Creative Works
This study delves into the modeling of a transformer in the frequency range of 100 KHz to 30 MHZ. The coupling coefficient was considered as a function of leakage and self-inductance and incorporated in the optimization process of transformer modeling in the proposed method. The equivalent circuit focused on the critical aspects of leakage inductance, parasitic capacitance, and winding effects. At first, the winding effect of an air-core inductor over wide frequency range with both single-layer and double-layer windings was shown. Then an equivalent circuit was proposed to model a transformer over this frequency range. The comparison between the measured …