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IEEE Journal on Miniaturization for Air and Space systems 2025年

作者： Qiu, Chen Wang, Xianbin Shen, Weiming Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Huazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

Unmanned Aerial Vehicles (UAVs) have revolutionized various sectors, including remote sensing, surveillance, environmental monitoring, and disaster management. Rapid advancements in wireless communication technologies and situational awareness discovery capabilities present unprecedented opportunities to enhance the intelligence of UAV networks. This review defines UAV network intelligence as the convergence of situational awareness discovery, communication enhancement, and goal-driven intelligent decision-making. Guided by this definition, we explore the key enabling techniques for UAV network situational awareness discovery from UAV states to UAV network environments. To facilitate the awareness discovery, we investigate the integration of advancements in communication technologies, such as massive multiple-input multiple-output, non-orthogonal multiple access, intelligent reflecting surface, and low Earth orbit satellites. Based on the situational awareness and empowered by communication enhancement technologies, we emphasize the overall objective of UAV network intelligence: maximizing the value of goal-driven UAV network operations. This is achieved by exploring recent research efforts in three categories, including 1) iterative optimization methods, 2) learning-based methods, and 3) heuristic methods. Finally, we discuss challenges and future research directions, contributing to the development of more resilient and adaptive UAV network solutions in increasingly complex and dynamic environments. © 2019 IEEE.

关键词： Decision making

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Equity-aware Load Shedding Optimization

arXiv

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arXiv 2024年

作者： Fang, Xin Wang, Wenbo Ding, Fei Department of Electrical & Computer Engineering Mississippi State University StarkvilleMS United States Energy System Integration National Renewable Energy Laboratory GoldenCO United States

Load shedding is usually the last resort to balance generation and demand to maintain stable operation of the electric grid after major disturbances. Current load-shedding optimization practices focus mainly on the physical optimality of the network power flow. This might lead to an uneven allocation of load curtailment, disadvantaging some loads more than others. Addressing this oversight, this paper introduces an innovative equity-aware load-shedding optimization model that emphasizes a fair allocation of load curtailment across the network. By proposing a novel equity indicator for load shedding and integrating it into an ACOPF-based optimization framework, we offer grid operators a more balanced and equitable load shedding strategy. Case studies highlight the importance of equity considerations in determining optimal load curtailment between buses. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric load flow

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Novel Auto-Tuning PD-Fuzzy Control of Current Harmonics to Reduce Losses in Motor Drive systems Excited by SiC-MOSFET Inverter

Novel Auto-Tuning PD-Fuzzy Control of Current Harmonics to R...

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SICE International Symposium on Control systems (ISCS)

作者： Nguyen Gia Minh Thao Ton Duc Do Duc-Kien Ngo Keisuke Fujisaki Electromagnetic Energy System Laboratory Graduate School of Engineering Toyota Technological Institute Japan Department of Robotics School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Faculty of Electrical-Electronic Engineering University of Danang–University of Technology and Education Vietnam

This paper presents a novel auto-tuning proportional derivative (PD) controller based on fuzzy logic for significant reduction of selective current harmonics in interior permanent magnet synchronous motor (IPMSM) drive systems under silicon carbide (SiC) inverter excitation, where the ultimate goal is to mitigate the IPMSM copper loss and SiC inverter loss in the motor drive systems for electric vehicles (EVs) and mobile robots. In the proposed control scheme, a fuzzy logic controller (FLC) is newly designed to automatically adjust the coefficients of the PD controller according to the operating conditions of the motor system. With the unique design of two inputs and related membership functions based on absolute values in the FLC, the number of its fuzzy association rules is remarkably decreased in order to achieve good control performance with a less computational burden in real-time implementation. The measured results obtained with an experimental SiC-based IPMSM drive system under a high carrier frequency of 200 kHz in load condition are shown and discussed to validate the feasibility and effectiveness of the introduced PD-fuzzy control. Furthermore, this model-free PD-fuzzy control scheme can be appropriately applied for other motor drives utilized in various fields.

关键词： Fuzzy logic Motor drives Silicon carbide Harmonic analysis Permanent magnet motors Inverters Real-time systems

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Large-Range and High-Sensitivity Displacement Sensing Based on Extrinsic Fabry-Perot Interferometer Assisted Microwave Photonic Filter

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Li, Shiyu Jie, Ruimin Alsalman, Osamah Huang, Jie Zhu, Chen Wuxi University School of Integrated Circuit Science and Engineering Wuxi214105 China Wuxi University Jiangsu Province Engineering Research Center of Photonic Devices and System Integration for Communication Sensing Convergence Wuxi214105 China Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65401 United States King Saud University College of Engineering Department of Electrical Engineering Riyadh11421 Saudi Arabia Zhejiang Laboratory Hangzhou311100 China

Displacement is a pivotal physical parameter, and advancements in displacement sensor technology have enabled the creation of a diverse array of physical and mechanical sensors through seamless integration with mechanical transducers. In this study, we introduce a displacement sensing technique leveraging an extrinsic Fabry-Perot interferometer (EFPI) assisted microwave photonic filter. By translating displacement-induced variations in the EFPI’s optical reflection into peak frequency shifts within its frequency response, we achieve large-dynamic-range displacement measurements with outstanding signal quality and demodulation ease. Proof-of-concept demonstrations showcase a substantial 5 mm range with a remarkable sensitivity of 1.148 GHz/mm, achieved using a basic single-mode fiber-based EFPI with a movable external mirror. Besides, nm-scale displacement monitoring is experimentally validated through a phase-measurement approach. Notably, the sensitivity and dynamic range can be tailored by adjusting the microwave photonic filter system’s parameter, i.e., the dispersion parameter of the dispersive element in the system. Furthermore, we introduce a strategy to enhance the dynamic range further by employing a virtual frequency down-conversion technique with an additional interferometer. This proposed technique opens avenues for developing a series of high-sensitivity and broad operating-range physical and mechanical sensors based on a simplified EFPI measurement configuration. © 1963-2012 IEEE.

关键词： Fabry-Perot interferometers

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Methodology, progress and challenges of geoscience knowledge graph in International Big Science Program of Deep-Time digital Earth

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Journal of Geographical Sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCAS Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application University of Chinese Academy of Sciences School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Department of Automation Tsinghua University State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing University State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Command Center of Natural Resources Comprehensive Survey School of Computer Science China University of Geosciences

Deep-time Earth research plays a pivotal role in deciphering the rates, patterns, and mechanisms of Earth's evolutionary processes throughout geological history, providing essential scientific foundations for climate prediction, natural resource exploration, and sustainable planetary stewardship. To advance Deep-time Earth research in the era of big data and artificial intelligence, the International Union of Geological Sciences initiated the “Deeptime digital Earth International Big Science Program”(DDE) in 2019. At the core of this ambitious program lies the development of geoscience knowledge graphs, serving as a transformative knowledge infrastructure that enables the integration, sharing, mining, and analysis of heterogeneous geoscience big data. The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1) establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2) developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3) implementing a comprehensive three-tiered architecture encompassing basic, discipline-specific, and application-oriented knowledge graphs, spanning approximately 20 geoscience disciplines. Through its open knowledge framework and international collaborative network, this initiative has fostered multinational research collaborations, establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable, Accessible, Interoperable, Reusable) data practices in deep-time Earth systems research.

关键词： Deep-time digital Earth International Big Science Program deep-time Earth geoscience knowledge graph

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A Scheme for Authentication and Traceability of 5G Mobile Terminal Based on IPv6 and Blockchain

A Scheme for Authentication and Traceability of 5G Mobile Te...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xinsheng Ji Ying Zhao Xingxing Liao Jie Yang Chunxiao Wang Department of Computer Science and Technology Tsinghua University Beijing China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China Purple Mountain Laboratories Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

To satisfy the service-side requirements for network layer authentication and traceability of users, this paper proposes a method for mobile terminal authentication and identity tracing based on IPv6 and blockchain. Our method introduces a new verification function (VEF) to verify the interface identification between the terminal and the 5G core network (5GC), thereby effectively enhancing the security of terminal access. Furthermore, considering the privacy protection of user identity information to prevent attackers from deducing encrypted content, it is necessary to periodically update encryption keys. Focusing on the practical deployment of VEFs on a network-wide scale and server-proximal for authentication and traceability, a time-varying key sharing scheme based on blockchain is proposed. The experimental section analyzes the communication performance and security of the proposed scheme, showing that our scheme does not significantly degrade the original network performance while providing substantial security gains.

关键词： Wireless communication Privacy 5G mobile communication Authentication Signal processing User experience Blockchains Synchronization Noise measurement Protection

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Dynamic Graph Neural Networks for Joint Terahertz based Sensing and Communication Optimization in Vehicular Networks

Dynamic Graph Neural Networks for Joint Terahertz based Sens...

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IEEE Conference on Wireless Communications and Networking

作者： Xuefei Li Mingzhe Chen Ye Hu Zhilong Zhang Danpu Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijina China Department-of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Industrial and System Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based scheme can improve the number of successfully served vehicles by up to 17% compared to a GNN based algorithm with a fixed neural network model.

关键词： Network topology Simulation Heuristic algorithms Graph neural networks Sensors Data mining Vehicle dynamics

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Topic Tracking Algorithm Based on Topic Structure Characteristics 22

Topic Tracking Algorithm Based on Topic Structure Characteri...

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6th High Performance Computing and Cluster Technologies Conference, HPCCT 2022

作者： Li, Shengdong Lv, Xueqiang Shi, Juntao Zhang, Haichuan Wu, Jianwei Wang, Yuan Department of Computer Engineering Langfang Yanjing Vocational Technical College Langfang065200 China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing Information Science and Technology University Beijing100101 China Department of Mechanical and Electrical Engineering Langfang Yanjing Vocational Technical College Langfang065200 China

ISBN: (纸本)9781450396646

Topic tracking task is used for public opinion monitoring, and its key technology is text classification algorithm. However, existing text classification algorithms need large-scale train corpus during training, while topic tracking task only provides a small amount of train corpus, resulting in that it has poor performance. We analyze story description contents in train corpus, and find that the report description contents of the same topic have the topic structure characteristics of high similarity. We use topic information to represent highly similar topic structure characteristics to make up for the lack of train corpus in text classification algorithm, and fuse topic structure characteristics and text classification algorithm to make the topic tracking algorithm consider topic structure characteristics, and propose Topic Tracking Algorithm based on Topic Structure characteristics (TTATS). To verify its performance, we carry out quantitative and qualitative experiments. The experimental results of multiple dimensions show that it has preferable topic tracking performance. © 2022 ACM.

关键词： Classification (of information)

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pFedCal: Lightweight Personalized Federated Learning with Adaptive Calibration Strategy

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IEEE Transactions on Services Computing 2025年

作者： Deng, Dongshang Wu, Xuangou Zhang, Tao Xiang, Chaocan Zhao, Wei Xu, Minrui Kang, Jiawen Han, Zhu Niyato, Dusit Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243002 China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Provincial Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Chongqing University College of Computer Science Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology School of Automation Guangzhou510062 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate an aggregation gap caused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we propose pFedCal, a novel personalized federated learning with lightweight adaptive calibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods. © 2008-2012 IEEE.

关键词： Federated learning

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Performance Bounds for Policy-Based Reinforcement Learning Methods in Zero-Sum Markov Games with Linear Function Approximation

Performance Bounds for Policy-Based Reinforcement Learning M...

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IEEE Conference on Decision and Control

作者： Anna Winnicki R. Srikant Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign C3.ai Digital Transformation Institute

Until recently, efficient policy iteration algorithms for zero-sum Markov games that converge were unknown. Therefore, model-based RL algorithms for such problems could not use policy iteration in the planning modules of the algorithms. In an earlier paper, we showed that a convergent policy iteration algorithm can be obtained by using a commonly used technique in RL called lookahead. However, the algorithm could be applied to the function approximation setting only in the special case of linear MDPs (Markov Decision Processes). In this paper, we obtain performance bounds for policy-based RL algorithms for general settings, including one where policy evaluation is performed using noisy samples of (state, action, reward) triplets from a single sample path of a given policy.

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