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Prompt, Accurate, And Noncontact Material Identification Using A Single Microwave Sensor With Machine Learning Analysis, Chen Zhu, Osamah Alsalman, Jie Huang
Prompt, Accurate, And Noncontact Material Identification Using A Single Microwave Sensor With Machine Learning Analysis, Chen Zhu, Osamah Alsalman, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
In This Article, a Novel Approach to Microwave Sensors is Proposed and Demonstrated that Allows for Prompt, Accurate, and Noncontact Material Identification Even with Arbitrary Lift-Off Distances between the Sensor and the Material under Test (MUT). a Multilayer Perceptron (MLP) is Trained to Directly Learn the Relation of the Measured Reflection Spectra of a Homemade Open-Ended Coaxial Cable Resonator Probe with Respect to Different MUTs and Then Achieve One-To-One Mapping between a Measured Spectrum and the MUT. as a Proof-Of-Concept Demonstration, the Performance of the MLP Model is Tested using Easily Accessible Materials, Including Wood, Glass, Water, and Metal. Reflection …
Addressing Reactive Power Sharing In Parallel Inverter Islanded Microgrid Through Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Jonathan W. Kimball
Addressing Reactive Power Sharing In Parallel Inverter Islanded Microgrid Through Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
Parallel Inverter Microgrids (MGs) Present a Significant Challenge in the Form of Inverter-Based Distributed Generators (IBDGs) Connected with Varying Line Impedances, Potentially Leading to Substantial Reactive Power-Sharing Errors (RPSE). This Paper Proposes the Fusion of Data-Driven Control into the Conventional Virtual Synchronous Generator in a Bid to Minimize the Sharing Error. First, All State Variables Associated with Each IBDG in the Microgrid Are Sensed and Used as Input Data for a Deep Reinforcement Learning (DRL) Agent. Next, the DRL Agent, motivated by a Unique Reward Function, is Trained to Satisfy Two Objectives: (1) Ensure the Output Voltage of All IBDGs …
Improved Peec Modeling Of Antennas Through Time-Dependent Partial Elements, Fabrizio Loreto, Giuseppe Pettanice, Martin Stumpf, Albert E. Ruehli, Jonas Ekman, Giulio Antonini
Improved Peec Modeling Of Antennas Through Time-Dependent Partial Elements, Fabrizio Loreto, Giuseppe Pettanice, Martin Stumpf, Albert E. Ruehli, Jonas Ekman, Giulio Antonini
Electrical and Computer Engineering Faculty Research & Creative Works
Over the Last Twenty Years, the Evolution of Communications Has Extended the Systems' Operating Frequency Range to the Tens of GHz. in This Framework, Time Domain Integral Equation-Based (TDIE) Methods for Antenna Modeling Have Gained Increasing Interest. among Them, the Partial Elements Equivalent Circuit (PEEC) Method Turns Out to Be Attractive for its Capability to Provide Compact Circuit Models. Similarly to Other Integral Equation-Based Methods, Like the Method of Moments (MoM) in the Time Domain (TD), the PEEC Method Can Suffer from Late-Time Instabilities. This Work Shows that a Rigorous Computation of the Time-Dependent Partial Elements Leads to an Improved …
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This Article Introduces a Novel Optimal Trajectory Tracking Control Scheme Designed for Uncertain Linear Discrete-Time (DT) Systems. in Contrast to Traditional Tracking Control Methods, Our Approach Removes the Requirement for the Reference Trajectory to Align with the Generator Dynamics of an Autonomous Dynamical System. Moreover, It Does Not Demand the Complete Desired Trajectory to Be Known in Advance, Whether through the Generator Model or Any Other Means. Instead, Our Approach Can Dynamically Incorporate Segments (Finite Horizons) of Reference Trajectories and Autonomously Learn an Optimal Control Policy to Track Them in Real Time. to Achieve This, We Address the Tracking Problem …
Lidar Technology For Future Wireless Networks: Use Cases And Challenges, Omar Rinchi, Ahmad Alsharoa
Lidar Technology For Future Wireless Networks: Use Cases And Challenges, Omar Rinchi, Ahmad Alsharoa
Electrical and Computer Engineering Faculty Research & Creative Works
Next-generation wireless networks are beset with challenges such as line-of-sight (LoS) shadowing and multipath scattering, significantly impacting service quality and reliability. Despite technological advances aimed at mitigating these issues, achieving consistent quality of service (QoS) standards continues to be an uphill task, particularly within the ever-changing urban landscapes and the intricate process of melding new technologies with pre-existing infrastructures. This paper delves into the integration of light detection and ranging (LiDAR) sensors as a novel solution to these persistent problems. We explore two potential use cases of LiDAR integration in wireless networks, offering detailed insights into the underlying motivation, technical …
Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang
Profitability Analysis Of Time-Restricted Double-Spending Attack On Pow-Based Large Scale Blockchains With The Aid Of Multiple Attacks, Yiming Jiang, Jiangfan Zhang
Electrical and Computer Engineering Faculty Research & Creative Works
We consider the time-restricted double-spending attack (TR-DSA) on the Proof-of-Work-based blockchain, where an adversary conducts a DSA within a finite timeframe and simultaneously launches multiple types of attacks on the blockchain. To be specific, the adversary can conduct attacks to isolate some honest miners and cause block propagation delays among miners to enhance the success probability of the TR-DSA. We first develop the closed-form expression for the success probability of a TR-DSA with the aid of multiple types of attacks, which is leveraged to develop the closed-form expression for the expected profit of a TR-DSA. The numerical analysis reveals that …
A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang
A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
This article presents a novel hybrid approach for electromagnetic near-field scanning, combining model-based, i.e., Gaussian processes regression, and data-driven, i.e., dynamic mode decomposition, techniques. We first leverage the Latin hypercube sampling technique to achieve spatially sparse measurements. Subsequently, dynamic mode decomposition is applied to analyze the resulting spatiotemporal data with sparse spatial sampling, enabling the extraction of both frequency information and sparse dynamic modes. Finally, the Gaussian processes regression, also known as the Kriging method, is adopted for the full-state reconstruction. The proposed hybrid approach is benchmarked by an example of the crossed dipole antennas. The obtained results demonstrate that …
Empowering Urban Traffic Management: Elevated 3d Lidar For Data Collection And Advanced Object Detection Analysis, Nawfal Guefrachi, Hakim Ghazzai, Ahmad Alsharoa
Empowering Urban Traffic Management: Elevated 3d Lidar For Data Collection And Advanced Object Detection Analysis, Nawfal Guefrachi, Hakim Ghazzai, Ahmad Alsharoa
Electrical and Computer Engineering Faculty Research & Creative Works
The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our …
Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan
Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …
Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed
Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed
Electrical and Computer Engineering Faculty Research & Creative Works
Artificial intelligence (AI) offers significant benefits in search and rescue applications by enhancing the efficiency and effectiveness of the search. However, an over-reliance on AI can hinder the operation due to biases embedded in the underlying algorithms. This partiality, if left un-monitored, can pose a risk to the safety of those in need of disaster relief. Typically manifests into inaccuracies in the decision-making processes and if not carefully monitored can cause larger issues. This paper extends the knowledge presented in a previous work, which presents the design and modeling of search and rescue operations using unmanned aerial vehicle (UAV) swarms. …
Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria
Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or …
Frequency Division Multiplexing And Random Phasing For Improved Uniformity In Microwave Heating Applications, Logan M. Wilcox, Ali Mirala, Mohammad Tayeb Al Qaseer, Kristen M. Donnell
Frequency Division Multiplexing And Random Phasing For Improved Uniformity In Microwave Heating Applications, Logan M. Wilcox, Ali Mirala, Mohammad Tayeb Al Qaseer, Kristen M. Donnell
Electrical and Computer Engineering Faculty Research & Creative Works
Microwave heating has been recently used as an active thermal excitation method in thermography. Within the nondestructive testing and evaluation (NDT&E) realm, this method is referred to as active microwave thermography, or AMT. In AMT, unlike other thermal excitation methods utilized in thermography, microwave heating inherently provides a nonuniform heating pattern. This is a direct result of the radiating antenna used to deliver the energy. This nonuniform pattern may result in defect detection errors (i.e., false positives and/or negatives). To reduce this potential for detection error and improve the overall robustness of the technique, a new electromagnetic system, consisting of …
Additively Manufactured Aperture-Based Fss, Alexander Hook, Doyle T. Motes, Cody Morrow, Kristen M. Donnell
Additively Manufactured Aperture-Based Fss, Alexander Hook, Doyle T. Motes, Cody Morrow, Kristen M. Donnell
Electrical and Computer Engineering Faculty Research & Creative Works
Frequency selective surfaces (FSSs) are arrays of patch- or aperture-based elements with specific high frequency reflective and/or transmissive properties. The specific FSS response of a given design is dictated by the element dimensions and spacing relative to adjacent elements (collectively referred to as the unit cell), along with the substrate (and superstrate if applicable) properties. As it relates to sensing, the FSS response may be affected by environmental (e.g., temperature) or structural (e.g., strain) changes. To this end, FSS-based sensors have been considered in recent years for a myriad of sensing applications including structural health monitoring (SHM). Concurrent to this, …
Angle-Dependent Photoinduced Changes Of Near-Infrared Transmission In Amorphous Selenium Films, Kaitlin Hellier, Emily Enlow, Seyedehnajmeh Montazeri, Mina Esmaeelpour, Shiva Abbaszadeh
Angle-Dependent Photoinduced Changes Of Near-Infrared Transmission In Amorphous Selenium Films, Kaitlin Hellier, Emily Enlow, Seyedehnajmeh Montazeri, Mina Esmaeelpour, Shiva Abbaszadeh
Electrical and Computer Engineering Faculty Research & Creative Works
Amorphous selenium (a-Se) is a promising semiconductor for a variety of photoconductive applications, predominantly in X-ray detection. In addition, material properties introduce several other potential applications in nonlinear optics. Photodarkening (PD) presents an interesting area of study; when exposed to band-region light, metastable structural changes induce a shift in the band edge and increase tail absorption. In this work, we investigate these effects utilizing a near-infrared (NIR) probe to avoid generating any darkening during beam transmission. We observe an unexpected result; here, we present the effects of band region exposure in NIR transmission (1570 nm). We observe a shift from …
High-Sensitivity Fabry-Perot Interferometric Sensor Based On Microwave Photonics With Phase Demodulation, Ruimin Jie, Hongkun Zheng, Osamah Alsalman, Jie Huang, Chen Zhu
High-Sensitivity Fabry-Perot Interferometric Sensor Based On Microwave Photonics With Phase Demodulation, Ruimin Jie, Hongkun Zheng, Osamah Alsalman, Jie Huang, Chen Zhu
Electrical and Computer Engineering Faculty Research & Creative Works
Optical fiber sensors have emerged as vital tools in various applications. Among them, Fabry-Perot interferometers (FPIs), have gained prominence due to their compactness and versatility in sensor design. Microwave photonics (MWP) techniques offer enhanced performance and flexibility for developing optical sensor interrogation methods. This paper proposes and experimentally demonstrates a novel MWP interrogation technique based on phase measurement for short-cavity FPI sensors. The technique utilizes the phase response of the FPI sensor within an MWP-assisted single radio frequency bandpass filter, providing improved sensitivity and dynamic sensing capabilities compared to traditional methods. Simulation and experimental results validate the effectiveness of the …
Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan
Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
In this work, a leader-follower tracking and formation control strategy for mobile robots (MRs) with uncertain dynamics is proposed. This strategy utilizes a continual lifelong safe reinforcement learning (CLSRL) framework based on multilayer neural networks (MNNs). The proposed design employs actor-critic MNNs, incorporating a barrier function. This function is derived from the Bellman optimality principle. It addresses the state constraints throughout the control design process. A novel online continual lifelong learning (CLL) method is introduced for MR formation. This method leverages the Bellman residual error for weight significance in MNNs. It addresses catastrophic forgetting and interlayer dependence through layer-specific regularizers. …
Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan
Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This paper addresses a constrained neural network (NN)-based optimal tracking scheme for a class of uncertain nonlinear discrete-time systems in strict-feedback form by using a control barrier function (CBF). First, a modified barrier-type cost function is introduced for each subsystem, guiding the actual system trajectory toward the safe set or desired trajectory while avoiding unwanted sets. To address the tracking problem, an augmented system is employed to convert the time-varying optimal tracking to a time-invariant optimal regulation. Then, an actor-critic framework is employed with the backstepping technique to obtain both virtual and actual optimal control policies for each subsystem to …
Highly Sensitive Liquid-Level Sensing Based On Microwave Frequency Domain Reflectometry And Interferometry, Chen Zhu, Osamah Alsalman, Jie Huang
Highly Sensitive Liquid-Level Sensing Based On Microwave Frequency Domain Reflectometry And Interferometry, Chen Zhu, Osamah Alsalman, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
Frequency domain reflectometry (FDR) and interferometry are two widely used investigative techniques that have been instrumented using optical and microwave devices for a diverse array of sensing applications. In this paper, we present a highly sensitive liquid-level sensor using a custom hollow coaxial cable transmission line. Measurements of liquid levels with high sensitivity and resolution can be achieved by both FDR and interferometry-based measurements using the same sensor device. We show that FDR-based measurements are more straightforward for liquid-level sensing and are suitable for both small and large variations of liquid level, while interferometry-based measurements provide slightly higher measurement resolution …
Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson
Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson
Electrical and Computer Engineering Faculty Research & Creative Works
This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped …
Lidar From The Sky: Uav Integration And Fusion Techniques For Advanced Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa
Lidar From The Sky: Uav Integration And Fusion Techniques For Advanced Traffic Monitoring, Baya Cherif, Hakim Ghazzai, Ahmad Alsharoa
Electrical and Computer Engineering Faculty Research & Creative Works
Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct …
Survey Of Hidden Markov Models (Hmms) For Sign Language Recognition (Slr), Iwan Sandjaja, Ahmad Alsharoa, Donald Wunsch, Jian Liu
Survey Of Hidden Markov Models (Hmms) For Sign Language Recognition (Slr), Iwan Sandjaja, Ahmad Alsharoa, Donald Wunsch, Jian Liu
Electrical and Computer Engineering Faculty Research & Creative Works
This paper surveyed several significant papers on specific topics applying the Hidden Markov Models (HMMs) for Sign Language Recognition (SLR), divided into five main episodes: Classical HMMs, Extended HMMs, HMMs and Machine Learning, HMMs and Sensor Fusion, and HMMs and Big Data. This stringent survey would contribute significantly to advanced research on unification brain models such as neural networks, adaptive resonance theory, and confabulation theory. First, the HMM was introduced as one of the popular methods of performing SLR, and each episode of its development was expounded. In each episode, a main paper and several supporting papers were summarized. Next, …
Effect Of Signal Modulation On Active Microwave Thermography, Logan M. Wilcox, Mathias Bonmarin, Kristen M. Donnell
Effect Of Signal Modulation On Active Microwave Thermography, Logan M. Wilcox, Mathias Bonmarin, Kristen M. Donnell
Electrical and Computer Engineering Faculty Research & Creative Works
Active microwave thermography (AMT) is a coupled electromagnetic and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes an active electromagnetic source to induce heat on or within a specimen under test (SUT) with subsequential infrared measurements via an infrared camera. AMT has been successfully employed in multiple fields, including aerospace, space, and infrastructure, among others. Generally, for AMT, the excitation (electromagnetic source) is applied over a specified period of time (referred to as the heating time). If inspection continues for additional time after the excitation is removed (turned off), this period is referred to as the cooling time. This …
Tightening Qc Relaxations Of Ac Optimal Power Flow Through Improved Linear Convex Envelopes, Mohammad Rasoul Narimani, Daniel K. Molzahn, Katherine R. Davis, Mariesa L. Crow
Tightening Qc Relaxations Of Ac Optimal Power Flow Through Improved Linear Convex Envelopes, Mohammad Rasoul Narimani, Daniel K. Molzahn, Katherine R. Davis, Mariesa L. Crow
Electrical and Computer Engineering Faculty Research & Creative Works
AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear …
Visualization Of Noise Coupling Paths Based On The Reciprocity Theorem, Seungtaek Jeong, Jong Hwa Kwon, Deepak Pai, Jagan Rajagopalan, Chulsoon Hwang
Visualization Of Noise Coupling Paths Based On The Reciprocity Theorem, Seungtaek Jeong, Jong Hwa Kwon, Deepak Pai, Jagan Rajagopalan, Chulsoon Hwang
Electrical and Computer Engineering Faculty Research & Creative Works
In this article, visualization of noise coupling paths is proposed and verified. The coupling coefficient (CC) is derived based on the Reciprocity theorem and time-average coupled power, which represents the contribution of each spatial point to the coupled power. The physical meaning of CC is demonstrated by relating the integrations of CC in a volume space occupied by absorbers to the coupled power being suppressed by the absorbers. The proposed method is applied to a practical device and the identified dominant coupling paths are experimentally verified.
Tilted Fiber Bragg Grating Sensors Based On Time-Domain Measurements With Microwave Photonics, Shiyu Li, Ruimin Jie, Osamah Alsalman, Jie Huang, Chen Zhu
Tilted Fiber Bragg Grating Sensors Based On Time-Domain Measurements With Microwave Photonics, Shiyu Li, Ruimin Jie, Osamah Alsalman, Jie Huang, Chen Zhu
Electrical and Computer Engineering Faculty Research & Creative Works
Tilted fiber Bragg gratings (TFBGs) have garnered substantial research attention and have found widespread applications for sensing a diverse array of physical, chemical, and biological parameters based on optical spectrum measurements. The interrogation of a TFBG sensor typically requires a high-resolution bulky optical spectrum analyzer (OSA) due to the extremely narrow dips caused by the resonance of cladding modes. However, high-resolution OSAs can be costly and have limitations on measuring speed, limiting their practicality. In this paper, a new approach to interrogating TFBG sensors is proposed and experimentally demonstrated based on a microwave photonics technique. Instead of measuring the optical …
Simultaneous Frequency Regulation And Active Power Sharing In Islanded Microgrid Using Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Lauryn Morris, Praneeth Uddarraju, Jonathan W. Kimball
Simultaneous Frequency Regulation And Active Power Sharing In Islanded Microgrid Using Deep Reinforcement Learning, Oroghene Oboreh-Snapps, Sophia A. Strathman, Jonathan Saelens, Arnold Fernandes, Lauryn Morris, Praneeth Uddarraju, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a novel approach that integrates deep reinforcement learning (DRL) with the conventional virtual synchronous generator (VSG) to address dual objectives of microgrid (MG) control, frequency regulation and precise active power sharing. MGs typically consist of multiple Inverter-Based-Distributed-Generators (IBDGs) connected in parallel through different line impedances. The conventional active power loop (APL) of the VSG encounters significant steady-state frequency errors as load increases/decreases during islanded operation. To mitigate this issue, secondary-level controllers like proportional-integral (PI) control are added to the APL to regulate the frequency of IBDGs. However, PI control compromises power-sharing capabilities when the impedance values of …
Special Session: Writing For The Ieee Teaching Excellence Hub, Steve Eugene Watkins
Special Session: Writing For The Ieee Teaching Excellence Hub, Steve Eugene Watkins
Electrical and Computer Engineering Faculty Research & Creative Works
Technical educators can benefit by sharing practices, techniques, experiences, and approaches that have been successful in their classrooms. The IEEE Teaching Excellence Hub (TEH) provides curated educational materials and resources for those teaching engineering, computing, and technology at the university level. This virtual venue for professional development is intended to complement resources found through educational journals and conferences. The online site contains practice-oriented articles, event links, and other collections that are contributed by the engineering education community. This special session will provide an overview of the TEH platform and will prepare attendees for contributing as reviewers and authors. The target …
Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch
Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper explores the pivotal role of trust in the widespread application of Artificial Intelligence (AI) across various domains. We review AI applications in sectors like energy, healthcare, and autonomous vehicles and discuss the crisis of human trust they face. This paper introduces a novel framework that delineates the relationship between AI transparency and user trust, highlighting specific industry applications. Through a systematic review of recent literature, we first delve into factors such as emotional response, acceptance, transparency, accuracy, and interpretability that shape human trust in AI. We then underscore the necessity of ethical AI practices and highlight the importance …
Rapid Control Prototyping Platform For Grid Connected Electrical Energy Conversion Systems, Shruti Pandey, Noah Damron, Michael Mcintyre
Rapid Control Prototyping Platform For Grid Connected Electrical Energy Conversion Systems, Shruti Pandey, Noah Damron, Michael Mcintyre
Electrical and Computer Engineering Faculty Research & Creative Works
Electrical Energy Conversion Systems (EECSs) are extensively utilized in contemporary stand-alone systems to supply power to isolated loads. They are also integrated into distributed generation systems, micro grids, and smart grids. In each of these contexts, it is essential to conduct efficient energy management procedures to optimize the electrical energy systems output and ensure the effective and high-quality delivery of electric energy to consumers. To create the energy management system for such a system, careful consideration must be given to the configuration of the power conversion and conditioning system, as well as the specific hardware devices that make up this …
Variation-Aware Non-Linear Mapping For Honey-Memristor Based Neuromorphic System, Harshvardhan Uppaluru, Md Mehedi Hasan Tanim, Md Omar Faruque, Mohammed Rafeeq Khan, Zoe Templin, Feng Zhao, Jinhui Wang
Variation-Aware Non-Linear Mapping For Honey-Memristor Based Neuromorphic System, Harshvardhan Uppaluru, Md Mehedi Hasan Tanim, Md Omar Faruque, Mohammed Rafeeq Khan, Zoe Templin, Feng Zhao, Jinhui Wang
Electrical and Computer Engineering Faculty Research & Creative Works
Natural organic memristors are promising synapse candidates for neuromorphic systems due to their significant benefits, such as environmental sustainability, low production and disposal cost, non-volatile storage capability, and bio/CMOS-compatibility. In this paper, experimental evaluations of a neuromorphic system based on honey-memristors are reported. First, honey-memristors are manufactured and tested based on our in-house technology, and then the non-linear characteristics inherent to honey-memristors, which causes inaccurate weight updates and reduces the inference accuracy, are explored. A non-linear mapping (NMP) method is applied to mitigate the effects of non-linearity in honey-memristor, under different scenarios including multiple cycle-to-cycle variations and Analog-to-Digital Converter (ADC) …