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2023

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Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan Jan 2023

Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a novel reinforcement learning-based optimal tracking control of partially uncertain nonlinear discrete-time (DT) systems with state constraints using zero-sum game (ZSG) formulation. To address optimal tracking, a novel augmented system consisting of tracking error and its integral value, along with an uncertain desired trajectory, is constructed. A barrier function (BF) with a tradeoff factor is incorporated into the cost function to keep the state trajectories to remain within a compact set and to balance safety with optimality. Next, by using the modified value functional, the ZSG formulation is introduced wherein an actor–critic neural network (NN) framework is …

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Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-Time Systems Using Multilayer Neural Networks, Irfan Ganie, S. (Sarangapani) Jagannathan Jan 2023

Continual Optimal Adaptive Tracking Of Uncertain Nonlinear Continuous-Time Systems Using Multilayer Neural Networks, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a lifelong integral reinforcement learning (LIRL)-based optimal tracking scheme for uncertain nonlinear continuous-time (CT) systems using multilayer neural network (MNN). In this LIRL framework, the optimal control policies are generated by using both the critic neural network (NN) weights and single-layer NN identifier. The critic MNN weight tuning is accomplished using an improved singular value decomposition (SVD) of its activation function gradient. The NN identifier, on the other hand, provides the control coefficient matrix for computing the control policies. An online weight velocity attenuation (WVA)-based consolidation scheme is proposed wherein the significance of weights is derived by …

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Three-Terminal Noise Source Extraction From A Qi-Based Wireless Power Transfer System For Predicting Conducted Emissions, Srinath Penugonda, Siqi Bai, Varittha Sanphuang, Nevin Altunyurt, Hongseok Kim, Daryl G. Beetner, Jun Fan Jan 2023

Three-Terminal Noise Source Extraction From A Qi-Based Wireless Power Transfer System For Predicting Conducted Emissions, Srinath Penugonda, Siqi Bai, Varittha Sanphuang, Nevin Altunyurt, Hongseok Kim, Daryl G. Beetner, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

An equivalent method to extract a three-terminal noise source from a Qi-based wireless power transfer system is proposed in this article. This method is capable of extracting a source for both the positive (P) and negative (N) lines with respect to the third terminal (the ground chassis or reference plane). The extracted sources are independent of the setup configuration and can be used to predict currents or voltages on the P and N lines on a setup that is different from the extraction setup. In the following article, a three-terminal model independent of the setup configuration is first extracted. The …

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Optimal Tracking Of Nonlinear Discrete-Time Systems Using Zero-Sum Game Formulation And Hybrid Learning, Behzad Farzanegan, S. (Sarangapani) Jagannathan Jan 2023

Optimal Tracking Of Nonlinear Discrete-Time Systems Using Zero-Sum Game Formulation And Hybrid Learning, Behzad Farzanegan, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel hybrid learning-based optimal tracking method to address zero-sum game problems for partially uncertain nonlinear discrete-time systems. An augmented system and its associated discounted cost function are defined to address optimal tracking. Three multi-layer neural networks (NNs) are utilized to approximate the optimal control and the worst-case disturbance inputs, and the value function. The critic weights are tuned using the hybrid technique, whose weights are updated once at the sampling instants and in an iterative manner over finite times within the sampling instants. The proposed hybrid technique helps accelerate the convergence of the approximated value functional …

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Securing The Transportation Of Tomorrow: Enabling Self-Healing Intelligent Transportation, Elanor Jackson, Sahra Sedigh Sarvestani Jan 2023

Securing The Transportation Of Tomorrow: Enabling Self-Healing Intelligent Transportation, Elanor Jackson, Sahra Sedigh Sarvestani

Electrical and Computer Engineering Faculty Research & Creative Works

The safety of autonomous vehicles relies on dependable and secure infrastructure for intelligent transportation. The doctoral research described in this paper aims to enable self-healing and survivability of the intelligent transportation systems required for autonomous vehicles (AV-ITS). The proposed approach is comprised of four major elements: qualitative and quantitative modeling of the AV-ITS, stochastic analysis to capture and quantify interdependencies, mitigation of disruptions, and validation of efficacy of the self-healing process. This paper describes the overall methodology and presents preliminary results, including an agent-based model for detection of and recovery from disruptions to the AV-ITS.

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Virtual Synchronous Generator Control Using Twin Delayed Deep Deterministic Policy Gradient Method, Oroghene Oboreh-Snapps, Buxin She, Shah Fahad, Haotian Chen, Jonathan W. Kimball, Fangxing Li, Hantao Cui, Rui Bo Jan 2023

Virtual Synchronous Generator Control Using Twin Delayed Deep Deterministic Policy Gradient Method, Oroghene Oboreh-Snapps, Buxin She, Shah Fahad, Haotian Chen, Jonathan W. Kimball, Fangxing Li, Hantao Cui, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a data-driven approach that adaptively tunes the parameters of a virtual synchronous generator to achieve optimal frequency response against disturbances. In the proposed approach, the control variables, namely, the virtual moment of inertia and damping factor, are transformed into actions of a reinforcement learning agent. Different from the state-of-the-art methods, the proposed study introduces the settling time parameter as one of the observations in addition to the frequency and rate of change of frequency (RoCoF). In the reward function, preset indices are considered to simultaneously ensure bounded frequency deviation, low RoCoF, fast response, and quick settling time. …

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Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh Jan 2023

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Dobbins, Ali R. Hurson, Sahra Sedigh

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student's interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

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Optimal Adaptive Tracking Control Of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning, Behzad Farzanegan, Rohollah Moghadam, Sarangapani Jagannathan, Pappa Natarajan Jan 2023

Optimal Adaptive Tracking Control Of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning, Behzad Farzanegan, Rohollah Moghadam, Sarangapani Jagannathan, Pappa Natarajan

Electrical and Computer Engineering Faculty Research & Creative Works

This article addresses a multilayer neural network (MNN)-based optimal adaptive tracking of partially uncertain nonlinear discrete-time (DT) systems in affine form. By employing an actor–critic neural network (NN) to approximate the value function and optimal control policy, the critic NN is updated via a novel hybrid learning scheme, where its weights are adjusted once at a sampling instant and also in a finite iterative manner within the instants to enhance the convergence rate. Moreover, to deal with the persistency of excitation (PE) condition, a replay buffer is incorporated into the critic update law through concurrent learning. To address the vanishing …

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Continual Learning-Based Optimal Output Tracking Of Nonlinear Discrete-Time Systems With Constraints: Application To Safe Cargo Transfer, Behzad Farzanegan, S. (Sarangapani) Jagannathan Jan 2023

Continual Learning-Based Optimal Output Tracking Of Nonlinear Discrete-Time Systems With Constraints: Application To Safe Cargo Transfer, Behzad Farzanegan, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Addresses a Novel Lifelong Learning (LL)-Based Optimal Output Tracking Control of Uncertain Non-Linear Affine Discrete-Time Systems (DT) with State Constraints. First, to Deal with Optimal Tracking and Reduce the Steady State Error, a Novel Augmented System, Including Tracking Error and its Integral Value and Desired Trajectory, is Proposed. to Guarantee Safety, an Asymmetric Barrier Function (BF) is Incorporated into the Utility Function to Keep the Tracking Error in a Safe Region. Then, an Adaptive Neural Network (NN) Observer is Employed to Estimate the State Vector and the Control Input Matrix of the Uncertain Nonlinear System. Next, an NN-Based …

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Design And Construction Of An Arbitrary Pulse Compressive Amplifier, Cody Goins, Aaron Harmon, Victor Khilkevich, Daryl G. Beetner Jan 2023

Design And Construction Of An Arbitrary Pulse Compressive Amplifier, Cody Goins, Aaron Harmon, Victor Khilkevich, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Compressive Pulse Amplifiers Are a Class of Amplifiers that Convert Long Low Amplitude Signals into Very Broadband Pulses of High Amplitude, Yielding a Very High Instantaneous Peak Power Output Pulse. However, in the Realm of Electronic Immunity and Susceptibility Testing, Very Broadband Short Pulses Are Not Always Desired. This Work Presents a Design for a Compressive Amplifier that is Aimed at Creating Arbitrary Pulsed Signals of Varying Bandwidths. Limitations of the Achievable Gain and Methods Used Are Discussed.

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Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan Jan 2023

Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the …

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Challenges And Prospects Of Vehicle Ota Spherical Near-Field Measurement Probes, Dao Lin, Zhanghua Cai, Lidong Chi, Fuhai Li, James L. Drewniak, Gang Feng, Yihong Qi Jan 2023

Challenges And Prospects Of Vehicle Ota Spherical Near-Field Measurement Probes, Dao Lin, Zhanghua Cai, Lidong Chi, Fuhai Li, James L. Drewniak, Gang Feng, Yihong Qi

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Discusses the Issue of Measuring Probe Indicators for Large-Scale Equipment, Such as Automobiles, under Conditions of Offset Configuration. a Simulation of Spherical Near-Field Measurement based on an Offset Configuration is Presented in This Paper. the Measurement Error is Defined According to the Reference Data Calculated by Spherical Wave Expansion Theory. through Comparative Analysis of the Simulation Results, the Main Reason for the Measurement Error is the Insufficient Coverage of the Probe's Beamwidth. by Adjusting the Probe's Radiation Pattern using Simulation Software, an Optimized Probe that Satisfies Near-Field Measurement Requirements under Meter-Level Offset Conditions is Obtained. Finally, based on …

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Lifelong Learning-Based Multilayer Neural Network Control Of Nonlinear Continuous-Time Strict-Feedback Systems, Irfan Ahmad Ganie, S. (Sarangapani) Jagannathan Jan 2023

Lifelong Learning-Based Multilayer Neural Network Control Of Nonlinear Continuous-Time Strict-Feedback Systems, Irfan Ahmad Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Paper, We Investigate Lifelong Learning (LL)-Based Tracking Control for Partially Uncertain Strict Feedback Nonlinear Systems with State Constraints, employing a Singular Value Decomposition (SVD) of the Multilayer Neural Networks (MNNs) Activation Function based Weight Tuning Scheme. the Novel SVD-Based Approach Extends the MNN Weight Tuning to (Formula Presented.) Layers. a Unique Online LL Method, based on Tracking Error, is Integrated into the MNN Weight Update Laws to Counteract Catastrophic Forgetting. to Adeptly Address Constraints for Safety Assurances, Taking into Account the Effects Caused by Disturbances, We Utilize a Time-Varying Barrier Lyapunov Function (TBLF) that Ensures a Uniformly Ultimately …

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Using Catalysis To Control The Morphology And Stiffness Of Shape Memory Poly(Isocyanurate-Urethane) (Pir-Pur) Aerogels, A. B.M. Shaheen Ud Doulah, Chandana Mandal, Hojat Majedi Far, Vaibhav A. Edlabadkar, Rushi U. Soni, Stephen Y. Owusu, Nicholas Leventis, Chariklia Sotiriou-Leventis Jan 2023

Using Catalysis To Control The Morphology And Stiffness Of Shape Memory Poly(Isocyanurate-Urethane) (Pir-Pur) Aerogels, A. B.M. Shaheen Ud Doulah, Chandana Mandal, Hojat Majedi Far, Vaibhav A. Edlabadkar, Rushi U. Soni, Stephen Y. Owusu, Nicholas Leventis, Chariklia Sotiriou-Leventis

Chemistry Faculty Research & Creative Works

A large array of anhydrous metal ions were tested as catalysts in the preparation of shape memory poly(isocyanurate-urethane) (PIR-PUR) aerogels from the reaction of 1,3,5-tris(6-isocyanatohexyl)-1,3,5-triazinane-2,4,6-trione (Desmodur N3300A: a well-known isocyanurate-based aliphatic triisocyanate) and triethylene glycol (TEG) in anhydrous acetonitrile. The reaction yielded wet gels that were dried into aerogels in an autoclave with supercritical fluid CO2. The catalytic activity was mostly identified among CH3CN-soluble salts (mainly chlorides) of third-row d-block elements from iron to zinc, group 13 elements from aluminum to thallium, as well as cadmium, bismuth, and tin. Tin (119Sn) NMR indicated that the metal ion complexes with TEG, …

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Topology Driven And Soft Phonon Mode Enabled Na-Ion Diffusion In Quaternary Chalcogenides, Na3zngax4 (X = S, And Se), Sajan Kumar, Mayanak K. Gupta, Ranjan Mittal, Santhoshkumar Sundaramoorthy, Amitava Choudhury, Naresh C. Osti, Alexander I. Kolesnikov, Matthew B. Stone, Yongqiang Cheng, Samrath L. Chaplot Jan 2023

Topology Driven And Soft Phonon Mode Enabled Na-Ion Diffusion In Quaternary Chalcogenides, Na3zngax4 (X = S, And Se), Sajan Kumar, Mayanak K. Gupta, Ranjan Mittal, Santhoshkumar Sundaramoorthy, Amitava Choudhury, Naresh C. Osti, Alexander I. Kolesnikov, Matthew B. Stone, Yongqiang Cheng, Samrath L. Chaplot

Chemistry Faculty Research & Creative Works

The compounds Na3ZnGaX4 (X = S, Se) are potential solid electrolyte materials in sodium-based batteries, which have certain advantages over oxide materials and have shown significant ionic conductivity at ambient temperature. In this paper, we bring out atomic-level features of the diffusion process in these new materials using the microscopic techniques of inelastic neutron scattering (INS), Quasi elastic neutron scattering (QENS), and ab initio molecular dynamics (AIMD) simulations. The insights obtained from these techniques are unique and not available from other macroscopic experiments. Neutron scattering experiments have been performed at temperatures from 100 to 700 K. The simulations have been …

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An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi Jan 2023

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer's disease (AD). AD diagnosis and prognosis heavily rely …

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Rovibrational States Calculations Of The H2o-Hcn Heterodimer With The Multiconfiguration Time Dependent Hartree Method, Hervé Tajouo Tela, Ernesto Quintas-Sánchez, Marie Lise Dubernet, Yohann Scribano, Richard Dawes, Fabien Gatti, Steve Ndengué Jan 2023

Rovibrational States Calculations Of The H2o-Hcn Heterodimer With The Multiconfiguration Time Dependent Hartree Method, Hervé Tajouo Tela, Ernesto Quintas-Sánchez, Marie Lise Dubernet, Yohann Scribano, Richard Dawes, Fabien Gatti, Steve Ndengué

Chemistry Faculty Research & Creative Works

Water and hydrogen cyanide are two of the most common species in space and the atmosphere with the ability of binding to form dimers such as H2O-HCN. In the literature, while calculations characterizing various properties of the H2O-HCN cluster (equilibrium distance, vibrational frequencies and rotational constants) have been done in the past, extensive calculations of the rovibrational states of this system using a reliable quantum dynamical approach have yet to be reported. In this work, we intend to mend that by performing the first calculation of the rovibrational states of the H2O-HCN van der …

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Extracellular Poly(Hydroxybutyrate) Bioplastic Production Using Surface Display Techniques, Kevin Beaver, Ashwini Dantanarayana, Willisa Liou, Markus Babst, Shelley D. Minteer Jan 2023

Extracellular Poly(Hydroxybutyrate) Bioplastic Production Using Surface Display Techniques, Kevin Beaver, Ashwini Dantanarayana, Willisa Liou, Markus Babst, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Poly(hydroxybutyrate) is a biocompatible, biodegradable polyester synthesized naturally in a variety of microbial species. A greener alternative to petroleum-based plastics and sought after for biomedical applications, poly(hydroxybutyrate) has failed to break through as a leading material in the plastic industry due to its high cost of production. Specifically, the extraction of this material from within bacterial cells requires lysis of cells, which takes time, uses harsh chemicals, and starts the process again with growing new living cells. Recently, surface display of enzymes on bacterial membranes has become an emerging technique for extracellular biocatalysis. In this work, a fusion protein lpp-ompA-phaC …

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Advances In Bioelectrode Design For Developing Electrochemical Biosensors, Nabajyoti Kalita, Sudarshan Gogoi, Shelley D. Minteer, Pranab Goswami Jan 2023

Advances In Bioelectrode Design For Developing Electrochemical Biosensors, Nabajyoti Kalita, Sudarshan Gogoi, Shelley D. Minteer, Pranab Goswami

Chemistry Faculty Research & Creative Works

The critical performance factors such as selectivity, sensitivity, operational and storage stability, and response time of electrochemical biosensors are governed mainly by the function of their key component, the bioelectrode. Suitable design and fabrication strategies of the bioelectrode interface are essential for realizing the requisite performance of the biosensors for their practical utility. A multifaceted attempt to achieve this goal is visible from the vast literature exploring effective strategies for preparing, immobilizing, and stabilizing biorecognition elements on the electrode surface and efficient transduction of biochemical signals into electrical ones (i.e., current, voltage, and impedance) through the bioelectrode interface with the …

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Delivering Broadband Light Deep Into Diffusive Media, Rohin Mcintosh, Nicholas Bender, Alexey Yamilov, Arthur Goetschy, Chia Wei Hsu, Hasan Yilmaz, Hui Cao Jan 2023

Delivering Broadband Light Deep Into Diffusive Media, Rohin Mcintosh, Nicholas Bender, Alexey Yamilov, Arthur Goetschy, Chia Wei Hsu, Hasan Yilmaz, Hui Cao

Physics Faculty Research & Creative Works

Waves propagate diffusively through disordered media, such as biological tissue, clouds, and paint, due to random scattering. Recent advances in optical wavefront shaping techniques have enabled controlling coherent light propagation in multiple-scattering samples. We overcome wave diffusion to deliver optical energy into a target region of arbitrary size and shape anywhere inside a strong-scattering system. This is particularly important for applications such as photoacoustic microscopy and optogenetics, where light needs to be deposited deep into biological tissue. For monochromatic light, we previously introduced the deposition matrix (DM) Z(ω), which maps its input wavefront to the field distribution in the target …

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Inverter Pq Control With Trajectory Tracking Capability For Microgrids Based On Physics-Informed Reinforcement Learning, Buxin She, Fangxing Li, Hantao Cui, Hang Shuai, Oroghene Oboreh-Snapps, Rui Bo, Nattapat Praisuwanna, Jingxin Wang, Leon M. Tolbert Jan 2023

Inverter Pq Control With Trajectory Tracking Capability For Microgrids Based On Physics-Informed Reinforcement Learning, Buxin She, Fangxing Li, Hantao Cui, Hang Shuai, Oroghene Oboreh-Snapps, Rui Bo, Nattapat Praisuwanna, Jingxin Wang, Leon M. Tolbert

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposed an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep reinforcement learning approach …

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Wideband Inverse Matrix For Radiated Two-Stage Mimo Measurements, Jun Li, Bin Lin, Yihong Qi, James L. Drewniak, Jiang Zhu, Daryl G. Beetner Jan 2023

Wideband Inverse Matrix For Radiated Two-Stage Mimo Measurements, Jun Li, Bin Lin, Yihong Qi, James L. Drewniak, Jiang Zhu, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Multiple-input and multiple-output (MIMO) technology is one of the significant components in the growing fifth-generation (5G) communication systems. The 5G system has expanded its frequency range and widened the bandwidth to achieve higher throughput rates and more stable wireless qualities, which brings new challenges to the over-the-air (OTA) MIMO evaluations. The wide bandwidth introduces systematic uncertainties into the MIMO measurement because of the increased amplitude and phase variation issues under different frequencies in the wideband signals, and it could lead to invalid MIMO throughput measurement results when severe. The effect on antenna isolation resulting from amplitude and phase variation in …

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Multi-Layered Energy Management Framework For Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, And Forecast Uncertainties, Waqas Ur Rehman, Jonathan W. Kimball, Rui Bo Jan 2023

Multi-Layered Energy Management Framework For Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, And Forecast Uncertainties, Waqas Ur Rehman, Jonathan W. Kimball, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy …

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Usdot Tier-1 University Transportation Center For Advancing Cybersecurity Research And Education, Yunpeng Jack Zhang, Kailai Wang, Lingguang Song, Amaury Lendasse, Houbing Herbert Song, Zhu Han, Rakesh Verma, Arlei Silva, Carlos Rubio-Medrano, Zhixia Richard Li, Guohui Zhang Jan 2023

Usdot Tier-1 University Transportation Center For Advancing Cybersecurity Research And Education, Yunpeng Jack Zhang, Kailai Wang, Lingguang Song, Amaury Lendasse, Houbing Herbert Song, Zhu Han, Rakesh Verma, Arlei Silva, Carlos Rubio-Medrano, Zhixia Richard Li, Guohui Zhang

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Transportation Cybersecurity Center for Advanced Research and Education (Cyber-Care) is a Us Department of Transportation (Usdot) Tier-1 University Transportation Center (Utc) Funded in 2023. Cyber-Care Primarily Focuses on the Usdot Statutory Research Priority Area of 'reducing Transportation Cybersecurity Risks.' Cyber-Care Aims to Establish a Fundamental Knowledge Basis and Explore Advanced Theory to Mitigate the Impacts of Large-Scale Cyberattacks on Transportation Infrastructure and Connected and Automated Vehicle (Cav) Systems. the Research Projects at Cyber-Care Will Develop Conceptual Frameworks, Construct Comprehensive Datasets, Explore Novel Analytical Approaches, Support the Implementation of Public Policies and Infrastructure Investments, and Build a High-Quality Industry Workforce …

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Environmentally-Aware And Energy-Efficient Multi-Drone Coordination And Networking For Disaster Response, Chengyi Qu, Francesco Betti Sorbelli, Rounak Singh, Prasad Calyam, Sajal K. Das Jan 2023

Environmentally-Aware And Energy-Efficient Multi-Drone Coordination And Networking For Disaster Response, Chengyi Qu, Francesco Betti Sorbelli, Rounak Singh, Prasad Calyam, Sajal K. Das

Computer Science Faculty Research & Creative Works

In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction …

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Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen Jan 2023

Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen

Computer Science Faculty Research & Creative Works

As of Today, There Has Been Increasing Research on Designing Optimization Algorithms and Intelligent Network Control Methods for Visible Light Unmanned Aerial Vehicles (UAV) Networks to Provide Pervasive and Broadband Connections. for Those Theoretical Analysis based Algorithms, there is an Urgent Need to Have a Visible Light UAV Network Platform that Can Help Evaluate the Proposed Algorithms in Real-World Scenarios. However, to the Best of Our Knowledge, there is Currently No Dedicated High Data Rate and Flexible Visible Light UAV Networking Prototype. to Bridge This Gap, in This Paper, We First Design a Novel Programmable Software-Defined Architecture for Visible Light …

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Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das Jan 2023

Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to …

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Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das Jan 2023

Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das

Computer Science Faculty Research & Creative Works

With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and tanh are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called log-sigmoid, inside the LSTM cell for time series data classification, and analyze …

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Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das Jan 2023

Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the …

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Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das Jan 2023

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

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