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IEEE Instrumentation and Measurement Technology Conference

作者： Alexander Hook Doyle T. Motes Cody Morrow Kristen M. Donnell Department of Electrical and Computer Engineering Microwave Sensing (µSense) Laboratory Missouri University of Science and Technology Rolla Missouri USA Nondestructive Evaluation Division Texas Research Institute (TRI) Austin Inc. Austin Texas USA

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

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, the growth of additive manufacturing (AM) technologies has opened the door for in-situ printing of FSS-based sensors. To this end, this work studies the potential for Fused Deposition Modeling (FDM) type AM-fabricated FSS-based sensors. Of particular interest is the effect of the reduced electrical conductivity (common to currently available conductive FDM filaments) on the performance and repeatability of FDM-printed sensors. Results from this work show that aperture-based FSS designs maintain a resonant response when printed with a reduced-conductivity material, whereas patch-based designs no longer provide FSS functionality. In addition, the results of a measurement study on a set of five additive manufactured FSS (AM-FSS) sensors show good agreement between the simulations and measurements and a high level of measurement repeatability. For these reasons, aperture-based FSS sensors fabricated via the FDM-AM process using a reduced conductivity filament are a viable option for aperture-based FSS fabrication and sensing applications.

关键词： Fabrication Visualization Additives Resonant frequency Conductivity Three-dimensional printing Sensors

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State Estimation for Integrated Energy systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

arXiv

引用

arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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Robot Person Following Under Partial Occlusion

Robot Person Following Under Partial Occlusion

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hanjing Ye Jieting Zhao Yaling Pan Weinan Cherr Li He Hong Zhang Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology (SUSTech) SUSTech Biomimetic and Intelligent Robotics Lab Guangdong University of Technology

Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often as-sume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of hislher joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.

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Multi-modal Long-Short Distance Attention-based Transformer-GAN for PET Reconstruction with Auxiliary MRI

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zeng, Pinxian Zeng, Xinyi Wang, Yan Zhou, Luping Zu, Chen Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan University School of Computer Science China University of Sydney School of Electrical and Information Engineering Australia Department of Risk Controlling Research *** China Chengdu University of Information Technology School of Computer Science China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

To obtain high-quality PET scans while minimizing potential radiation hazards for patients, various GAN-based methods have been developed to reconstruct high-quality standard-count PET (SPET) images from low-count PET (LPET) ones. While recent efforts try to integrate MRI or CT to enhance reconstruction in a multi-modal way, current architectures mainly face two limitations: (1) CNN backbones or simple Transformer bottleneck layers are insufficient for robust semantic understanding, and (2) the identical strategies for multi-modal feature extraction and fusion overlook each modality's respective importance for the reconstruction task. In this work, we propose the Multi-modal Long-Short Distance Attention-based Transformer-GAN (MLSDA-GAN), a novel network combining 3D transformer and CNN architecture for PET image reconstruction. Specifically, to extract fine-grained features with a small number of parameters, our MLSDA-GAN integrates multi-scale convolution into the embedding part of the transformer. As for our multi-modal design, given the strong correlation between LPET and SPET in structural characteristics, we treat MRI as an auxiliary modality to LPET and achieve effective multi-modal extraction and fusion strategies. These strategies include (1) a PET-specific Self-attention Extraction (PSE) block for comprehensive feature extraction of the primary LPET and (2) a Multi-modality Cross-attention Fusion (MCF) block for effective multi-modal interaction and fusion, enabling us to more efficiently model both long- and short-range relationships in the corresponding feature extraction and fusion processes. Experiments demonstrate superiority of our method quantitatively and qualitatively. Code is available at https://***/Aru321/MLSDA-GAN. © 1991-2012 IEEE.

关键词： Radiation hazards

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STAR-RIS Enhanced Covert Communication with Delay Constraint

STAR-RIS Enhanced Covert Communication with Delay Constraint

引用

2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Cheng, Beixi Hu, Jinsong Chen, Youjia Shu, Feng Chen, Zhizhang David Fuzhou University College of Physics and Information Engineering Fujian Fuzhou350108 China Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou350108 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University School of Information and Communication Engineering Haikou570228 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350345384

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results. © 2023 IEEE.

关键词： Convex optimization

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Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning

arXiv

引用

arXiv 2024年

作者： Huan, Ouwen Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

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LEARNING ACTIVE SUBSPACES FOR EFFECTIVE AND SCALABLE UNCERTAINTY QUANTIFICATION IN DEEP NEURAL NETWORKS

arXiv

引用

arXiv 2023年

作者： Jantre, Sanket Urban, Nathan M. Qian, Xiaoning Yoon, Byung-Jun Computational Science Initiative Brookhaven National Laboratory UptonNY United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States

Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the main hurdle for Bayesian deep learning is its computational complexity due to the high dimensionality of the parameter space. In this work, we propose a novel scheme that addresses this limitation by constructing a low-dimensional subspace of the neural network parameters–referred to as an active subspace–by identifying the parameter directions that have the most significant influence on the output of the neural network. We demonstrate that the significantly reduced active subspace enables effective and scalable Bayesian inference via either Monte Carlo (MC) sampling methods, otherwise computationally intractable, or variational inference. Empirically, our approach provides reliable predictions with robust uncertainty estimates for various regression tasks. © 2023, CC BY.

关键词： Deep neural networks

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A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

arXiv

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arXiv 2024年

作者： Li, Gen Wei, Yuting Chi, Yuejie Chen, Yuxin Department of Statistics The Chinese University of Hong Kong Hong Kong Department of Statistics and Data Science Wharton School University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States

Diffusion models, which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this work, we develop non-asymptotic convergence theory for a popular diffusion-based sampler (i.e., the probability flow ODE sampler) in discrete time, assuming access to 2-accurate estimates of the (Stein) score functions. For distributions in Rd, we prove that d/Ε iterations — modulo some logarithmic and lower-order terms — are sufficient to approximate the target distribution to within Ε total-variation distance. This is the first result establishing nearly linear dimension-dependency (in d) for the probability flow ODE sampler. Imposing only minimal assumptions on the target data distribution (e.g., no smoothness assumption is imposed), our results also characterize how 2 score estimation errors affect the quality of the data generation processes. In contrast to prior works, our theory is developed based on an elementary yet versatile non-asymptotic approach without the need of resorting to SDE and ODE toolboxes. © 2024, CC BY.

关键词： Probability distributions

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Locational Value of Distributed Energy Resources for Mitigating High Impact Events: A Case Study Based on Midwest USA

Locational Value of Distributed Energy Resources for Mitigat...

引用

IEEE General Meeting Power& Energy Society

作者： Sarangan Rajendran Adithya Melagoda Sangar Shanthanam Nirasha Bataduvaarachchi Arun-Kaarthick Manoharan Visvakumar Aravinthan Amirthagunaraj Yogarathinam Al Tamimi Charles R. Yokley Electrical and Computer Engineering Wichita State University Wichita KS USA Interdisciplinary Science Department Brookhaven National Laboratory Upton NY USA Transmission Planning and Policy Sunflower Electric Power Corporation Wichita KS USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Distributed Energy Resources (DER) aid to an electrical grid in more implicit ways beyond just supplying of power. During High Impact (HI) events caused by weather disruptions, when the grid is in a reduced state of operation, significant presence of DERs has been found to mitigate further impacts to the system. Grid operators who benefit from having DERs in their system save on system recovery costs that can be redirected back to the prosumers who contribute through DER. In this work, a methodology is introduced to value the ancillary benefits provided by the DERs during a HI event, that can be used to compensate the prosumers. The proposed method is tested on a test system with high wind energy generation, where rooftop solar PV acts as the DER, and the validity of the results are explored. The method is further expanded to include all probable variations of HI events and quantify the value of a location acting as a DER host.

关键词： Wind energy generation Measurement Costs Voltage System recovery Distributed power generation Reliability Meteorology Load modeling Load flow

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