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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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DDDAS-Based Learning for Edge Computing at 5G and Beyond 5G 4th

DDDAS-Based Learning for Edge Computing at 5G and Beyond 5G

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4th International Conference on Dynamic Data Driven Applications systems, DDDAS 2022

作者： Runsewe, Temitope Yavuz, Abdurrahman Celik, Nurcin Saad, Walid Department of Industrial and Systems Engineering University of Miami MiamiFL33146 United States Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

ISBN: (纸本)9783031526695

The emerging and foreseen advancements in 5G and Beyond 5G (B5G) networking infrastructures enhance both communications capabilities and the flexibility of executing computational tasks in a distributed manner, including those encountered in edge computing (EC). Both, 5G & B5G and EC environments present complexities and dynamicity in their multi-level and multimodal infrastructures. DDDAS-based design and adaptive and optimized management of the respective 5G, B5G, and EC infrastructures are needed, to tackle the stochasticity inherent in these complex and dynamic systems, and to provide quality solutions for respective requirements. In fact, both emerging communication and computational technologies and infrastructure systems can benefit from their symbiotic relationship in optimizing their corresponding adaptive and optimized management. EC enabled by 5G (and future B5G) allows efficient distributed execution of computational tasks. DDDAS-based methods can support the adaptivity in bandwidth and energy efficiencies in 5G and B5G communications. On the other hand, EC has become a very attractive feature for critical infrastructure such as energy grids as it allows for secure and efficient real-time data processing. In order to fully exploit the advantages of EC, the communication network should be able to tackle the changing requirements related to the task management within edge servers. Thus, leveraging the DDDAS paradigm, we jointly optimize the scheduling and offloading of computational tasks in an EC-enabled microgrid considering both physical constraints of microgrid and network requirements. The results showcase the superiority of the proposed DDDAS-based approaches in terms of network utilization and operational efficiencies achieved, with microgrids as case example. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy efficiency

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 519-535页

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Unel, Burcin Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States New York University School of Law Institute for Policy Integrity New YorkNY10012 United States StanfordCA94305 United States Johns Hopkins University Departments of Electrical and Computer Engineering and Civil and Systems Engineering BaltimoreMD21218 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. © 2024 IEEE.

关键词： Investments

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Federated Learning for Lidar Super Resolution on Automotive Scenes 24

Federated Learning for Lidar Super Resolution on Automotive ...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Gkillas, Alexandros Arvanitis, Gerasimos Lalos, Aris S. Moustakas, Konstantinos Industrial Systems Institute Athena Research Center Patras Greece Electrical and Computer Engineering University of Patras Greece

ISBN: (纸本)9798350339598

In this paper, the problem of lidar super-resolution is explored under a federated learning perspective. The high cost of high-resolution lidar sensors is a major obstacle to the widespread adoption of connected and autonomous vehicles (CAVs). To reduce the cost, this study investigates the use of low-cost sensors in conjunction with super-resolution algorithms. Unlike previous studies that approach this problem with centralized solutions, this work employs federated learning to leverage private lidar data from different autonomous vehicles under different environmental conditions, resulting in more robust and diverse deep learning model. Extensive experiments on a real-world lidar odometry dataset highlight the merits and applicability of the proposed lidar super-resolution method with federated learning. © 2023 IEEE.

关键词： Optical radar

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Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation

arXiv

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arXiv 2024年

作者： Chakraborty, Neeloy Fang, Yixiao Schreiber, Andre Ji, Tianchen Huang, Zhe Mihigo, Aganze Wall, Cassidy Almana, Abdulrahman Driggs-Campbell, Katherine Departments of Electrical and Computer Engineering Computer Science Industrial and Enterprise Systems Engineering The University of Illinois Urbana-Champaign United States

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot’s actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://***/***/comp-teleop. © 2024, CC BY.

关键词： Agricultural robots

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Implantable UWB Microstrip Patch Antenna for Biomedical Applications 5

Implantable UWB Microstrip Patch Antenna for Biomedical Appl...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ishraq, Sadman Imran, Md Al Alam, Khorshed Khan, Mohammad Monirujjaman North South University Department of Electrical and Computer Engineering Dhaka Bangladesh Lamar University Phillip M. Drayer Department of Electrical Engineering TX United States Lamar University Industrial and Systems Engineering Department TX United States

ISBN: (纸本)9798331530013

To satisfy the requirements of UWB (3.1-10.6 GHz), a novel implanted ultra-wideband (UWB) antenna with a frequency range of 3.07-13.7 GHz has been developed, targeting biomedical applications. The antenna's performance was analyzed at 4 GHz, 6 GHz, 8 GHz, and 10 GHz in both in-body and free-space environments to ensure optimal functionality. The primary focus of the design is to create a compact, cost-effective, and high-performance antenna tailored for medical and healthcare applications. The radiating element consists of a rectangular patch fabricated on an FR-4 substrate, with total dimensions of 30\times 27\times 1.6\ \textmm^3. A partial ground plane is incorporated to achieve ultra-wideband characteristics, enabling a 10.63 GHz bandwidth that spans frequencies from 3.07 GHz to 13.7 GHz in open space. To evaluate the antenna's performance under implantation conditions, three human tissue layers are incorporated into the design. Key features such as radiation characteristics, gain, and directivity validate its suitability for use within the human body. The entire design process, including simulations and performance evaluations, was carried out using the CST Studio Suite software. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Evaluating people's perceptions of an agent as a public speaking coach

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Paladyn 2024年第1期15卷

作者： Forghani, Delara Ghafurian, Moojan Rasouli, Samira Nehaniv, Chrystopher L. Dautenhahn, Kerstin Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada Departments of Systems Design Engineering and of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

The use of interactive tools, such as voice assistants and social robots, holds promise as coaching aids during public speaking rehearsals. To create a coach that is both effective and likable, it is important to understand how people perceive these agents when they observe them during actual presentation sessions. Specifically, it is important to assess people’s perceptions of the agents’ physical embodiment and nonverbal social behaviour, taking into account both listening and feedback periods. To this end, we conducted an online study with 168 participants who watched videos of agents acting as public speaking coaches. The study had three conditions: two with a humanoid social robot in either (1) active listening mode, using nonverbal backchannelling, (2) passive listening mode, and (3) a voice assistant agent. The results showed that the social robot in both conditions was perceived more positively in terms of its human-like attributes, and likability than the voice assistant agent. The active listener robot was perceived as more satisfying, more engaging, more natural, and warmer than the voice assistant agent, but this difference was not seen between the passive listener robot and the voice assistant agent. Additionally, the active listener robot was found to be more natural than the passive listening robot. However, there were no significant differences in perceived intelligence, competence, discomfort, and helpfulness between the three agents. Finally, participants’ gender and personality traits were found to affect their evaluations of the agents. The study offered insights into general attitudes towards using social robots and voice assistants as public speaking coaches, which can guide the future design and use of these agents as coaches. © 2024 the author(s), published by De Gruyter.

关键词： Social robots

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A Novel Trajectory Planning Method Based on Trust Region Policy Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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Online Mechanism Design for Differentially Private Data Acquisition

Online Mechanism Design for Differentially Private Data Acqu...

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IEEE Conference on Decision and Control

作者： Ameya Anjarlekar Rasoul Etesami R. Srikant Department of Electrical and Computer Engineering UIUC Faculty of Electrical and Computer Engineering and Industrial and Systems Engineering UIUC Faculty of Electrical and Computer Engineering UIUC

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We address a problem involving a buyer seeking to train a logistic regression model by acquiring data from privacy-sensitive sellers. Along with compensating the sellers for their data, the buyer provides differential privacy guarantees to them where the payments depend on the privacy guarantees. In addition, each seller has a different privacy sensitivity associated with their data, which is the cost per unit of loss of privacy. The buyer transacts sequentially with the sellers, wherein the seller will disclose their privacy sensitivity, and the buyer immediately provides a payment and guarantees differential privacy. After receiving the payment, the seller provides their data to the buyer. The buyer’s goal is to optimize a weighted combination of test loss and payments, i.e., achieve a tradeoff between getting a good ML model and limiting its payments. Additionally, the buyer must design the payments and differential privacy guarantees in an online fashion. Further, the online problem is historydependent, which adds to the challenge. Consequently, we design a payment mechanism that ensures incentive compatibility and individual rationality and is asymptotically optimal. Additionally, we also provide experimental results to validate our findings.

关键词： Differential privacy Privacy Logistic regression Sensitivity Limiting Costs Data acquisition Mechanism design Games Data models

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