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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 517-533页

作者： Chen, Jingxuan Huang, Dianrun Wang, Yijie Yu, Ziping Zhao, Zhongliang Cao, Xianbin Liu, Yang Quek, Tony Q. S. Oliver Wu, Dapeng Beihang University School of Electronic and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue 639798 Singapore Singapore University of Technology and Design Information System Technology and Design Pillar Somapah Rd 487372 Singapore Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of City University of Hong Kong Department of Computer Science Kowloon Hong Kong

Vehicular Ad-hoc Networks (VANETs) have gained significant attention as a key enabler for intelligent transportation systems, facilitating vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Despite their potential, VANETs face critical challenges in maintaining reliable end-to-end connectivity due to their highly dynamic topology and sparse node distribution, particularly in areas with limited infrastructure coverage. Addressing these limitations is crucial for advancing the reliability and scalability of VANETs. To bridge these gaps, this work introduces a heterogeneous UAV-aided VANET framework that leverages uncrewed aerial vehicles (UAVs), also known as autonomous aerial vehicles, to enhance data transmission. The key contributions of this paper include: 1) the design of a novel adaptive dual-model routing (ADMR) protocol that operates in two modes: direct vehicle clustering for intra-cluster communication and UAV/RSU-assisted routing for inter-cluster communication;2) the development of a modified density-based clustering algorithm (MDBSCAN) for dynamic vehicle node clustering;and 3) an improved UAV trajectory planning method based on a multi-agent soft actor-critic (MASAC) deep reinforcement learning algorithm, which optimizes network reachability. Simulation results reveal that the UAV trajectory optimization method achieves higher network reachability ratios compared to existing approaches. Also, the proposed ADMR protocol improves the packet delivery ratio (PDR) while maintaining low end-to-end latency. These findings demonstrate the potential to enhance VANET performance, while also providing valuable insights for the development of intelligent transportation systems and related fields. © 2025 The Authors.

关键词： Vehicular ad hoc networks

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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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ENHANCING ROBUST FAIRNESS VIA CONFUSIONAL SPECTRAL REGULARIZATION

arXiv

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arXiv 2025年

作者： Jin, Gaojie Wu, Sihao Liu, Jiaxu Huang, Tianjin Mu, Ronghui The Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Beijing China Department of Computer Science University of Exeter Exeter United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom

Recent research has highlighted a critical issue known as "robust fairness", where robust accuracy varies significantly across different classes, undermining the reliability of deep neural networks (DNNs). A common approach to address this has been to dynamically reweight classes during training, giving more weight to those with lower empirical robust performance. However, we find there is a divergence of class-wise robust performance between training set and testing set, which limits the effectiveness of these explicit reweighting methods, indicating the need for a principled alternative. In this work, we derive a robust generalization bound for the worst-class robust error within the PAC-Bayesian framework, accounting for unknown data distributions. Our analysis shows that the worst-class robust error is influenced by two main factors: the spectral norm of the empirical robust confusion matrix and the information embedded in the model and training set. While the latter has been extensively studied, we propose a novel regularization technique targeting the spectral norm of the robust confusion matrix to improve worst-class robust accuracy and enhance robust fairness. We validate our approach through comprehensive experiments on various datasets and models, demonstrating its effectiveness in enhancing robust fairness. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Low Chirp Dual-Racetrack Modulator for PAM-4 Modulation with Low Driving Voltage

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Journal of Lightwave Technology 2025年第10期43卷 4840-4847页

作者： Yu, Fuhao Zeng, Zhaobang Tang, Kaifei Yang, Shichong Ji, Xiang Xin, Yu Gu, Tingyi Jiang, Wei Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Engineering Research Center for Precision Photonics Integration & System Applications Ministry of Education Nanjing210023 China Nanjing University National Laboratory of Solid-State Microstructures College of Engineering and Applied Sciences Institute of Optical Communication Engineering Nanjing210023 China Nanjing University of Information Science & Technology School of Physics and Optoelectronic Engineering Nanjing210044 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

Micro-ring modulators with a small footprint and energy consumption have been explored for densely integrated high throughput optical interconnects. However, their strong dispersive response results in frequency chirp and pulse broadening, which limits their signal transmission distance. Here we explore the low chirp operation of a dual-racetrack modulator symmetrically coupled to a bus waveguide. With opposite index changes in the two resonators, the coherent couplings of two resonators' optical fields with the bus waveguide and their interference can cancel out the output phase variation due to a single resonator. This allows the push-pull operation to achieve a lower chirp compared to the single-drive operation. The advantage of lower chirp operation is experimentally verified through open eye diagram with 100 Gbps PAM-4 transmitted over 2 km single-mode fiber (SMF), with a peak-to-peak driving voltage of 1.6 V. © 1983-2012 IEEE.

关键词： Single mode fibers

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Variable stiffness design optimization of fiber-reinforced composite laminates with regular and irregular holes considering fiber continuity for additive manufacturing

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Chinese Journal of Aeronautics 2025年第3期38卷 334-354页

作者： Yi LIU Zunyi DUAN Chunping ZHOU Yuan SI Chenxi GUAN Yi XIONG Bin XU Jun YAN Jihong ZHU School of Mechanics Civil Engineering&ArchitectureNorthwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China Key Lab for Airborne Hi-performance Electro-magnetic Window RISACJinan 130012China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116024China State IJR Center of Aerospace Design and Additive Manufacturing MIIT Lab of Metal Additive Manufacturing and Innovative DesignNorthwestern Polytechnical UniversityXi’an 710072China

Fiber-reinforced composites are an ideal material for the lightweight design of aerospace structures. Especially in recent years, with the rapid development of composite additive manufacturing technology, the design optimization of variable stiffness of fiber-reinforced composite laminates has attracted widespread attention from scholars and industry. In these aerospace composite structures, numerous cutout panels and shells serve as access points for maintaining electrical, fuel, and hydraulic systems. The traditional fiber-reinforced composite laminate subtractive drilling manufacturing inevitably faces the problems of interlayer delamination, fiber fracture, and burr of the laminate. Continuous fiber additive manufacturing technology offers the potential for integrated design optimization and manufacturing with high structural performance. Considering the integration of design and manufacturability in continuous fiber additive manufacturing, the paper proposes linear and nonlinear filtering strategies based on the Normal Distribution Fiber Optimization (NDFO) material interpolation scheme to overcome the challenge of discrete fiber optimization results, which are difficult to apply directly to continuous fiber additive manufacturing. With minimizing structural compliance as the objective function, the proposed approach provides a strategy to achieve continuity of discrete fiber paths in the variable stiffness design optimization of composite laminates with regular and irregular holes. In the variable stiffness design optimization model, the number of candidate fiber laying angles in the NDFO material interpolation scheme is considered as design variable. The sensitivity information of structural compliance with respect to the number of candidate fiber laying angles is obtained using the analytical sensitivity analysis method. Based on the proposed variable stiffness design optimization method for complex perforated composite laminates, the numerical examples cons

关键词： Variable stiffness composite laminates Discrete material interpolation scheme Normal distribution fiber optimization Discrete fiber continuous filtering strategy Additive manufacturing of composite laminates

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Covert Communications in Active-IOS Aided Uplink NOMA systems With Full-Duplex Receiver

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8419-8424页

作者： Kang, Xueyu Qi, Nan Lv, Lu Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros A. Liu, Hongwu Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University School of Telecommunications Engineering Xi'an710071 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover signal in addition to jamming the warden. With the aim of maximizing the covert rate, the FD transmit and receive beamforming, A-IOS refraction and reflection beamforming, NOMA transmit power, and FD jamming power are jointly optimized. To tackle the non-convex covert rate maximization problem subject to the highly coupled system parameters, an alternating optimization algorithm is designed to iteratively solve the decoupled sub-problems of optimizing the system parameters. The optimal solutions for the sub-problems of the NOMA transmit power and FD jamming power optimizations are derived in closed-form. To tackle the rank-one constrained non-convex fractional programming of the A-IOS beamforming and FD beamforming, a penalized Dinkelbach transformation approach is proposed to resort to the optimal solutions via semidefinite programming. Numerical results clarify that the deployment of the A-IOS significantly improves the covert rate compared with the passive-IOS aided uplink NOMA system. It is also found that the proposed scheme provides better covert communication performance with the optimized NOMA transmit power and FD jamming power compared with the benchmark schemes. © 1967-2012 IEEE.

关键词： Jamming

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A Comprehensive Power Consumption Model for GPU Data Centers with Time-VAE

A Comprehensive Power Consumption Model for GPU Data Centers...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Wang, Senyao Wu, Jianhong Zhang, Ruozhu The School of Electrical and Computer Engineering The University of Sydney SydneyNSW2008 Australia The School of Electrical Engineering Shanghai University of Electric Power Shanghai200090 China State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819618675

In this study, we propose and evaluate a comprehensive power consumption model for GPU-based data centers that integrates the energy consumption of various task types, including language processing, speech recognition, and image generation, along with the power consumption of other components such as the CPU, memory, network, and storage. Using real GPU power consumption data, we measured the server energy consumption. To compensate for the limited experimental data and ensure accurate evaluation of average power consumption per unit time after model convergence, we utilize the TimeVAE model to generate supplementary data. The PCA and t-SNE visualizations validate the effectiveness of the TimeVAE model in preserving data distribution and structure. Our comprehensive model provides valuable insights into the energy consumption patterns in GPU-based data centers and offers practical guidance for optimizing energy usage to improve overall efficiency and sustainability. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Data centers

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An effective meta-heuristics for trajectory planning problem in UAV-assisted vessel emission detection system

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-21页

作者： Zhu, Jie Cui, Weizhi Huang, Haiping Zhu, Tao Sun, Yuzhong School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100864 China

With the development of the shipping industry, the pollution problem caused by vessel fuels is becoming increasingly severe. Due to the great mobility and on-demand service capability of Unmanned Aerial Vehicles (UAVs), UAVs have become effective tools for detecting pollution in Emission Control Areas (ECAs). How to obtain the effective trajectories of UAVs within the energy limitation and the time limitation is a difficult problem to solve. The paper investigates the trajectory planning problem in UAV-assisted vessel emission detection system. In the problem, the sailing vessels in a given area are taken as the detection tasks and multiple UAVs are ready to detect these vessels. Each vessel is assigned a different detecting weight and sails to the port at a different speed. The objective is to complete as many detection tasks as possible before the deadline and maximize the total weight of these completed tasks. A Genetic Algorithm (GA)-based trajectory planning algorithm framework is proposed for the problem under study. The framework consists of key components, such as the chromosome generation method to generate chromosomes of each generation, the feasible solution generation method to compute the feasible solution according to the sequence defined in a chromosome, and the chromosome reconstruction method to improve the quality of chromosomes based on history information. The experimental results show that the proposed algorithm outperforms the baseline algorithms in terms of effectiveness and robustness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Emission control areas Genetic algorithm Trajectory planning UAV scheduling

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Modeling Method for DFIG-Based Wind Farm in High-Efficiency Real-Time Electromagnetic Transient (EMT) Simulations

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IEEE Transactions on Power Electronics 2025年

作者： Liu, Yifan Xu, Jianzhong Zhu, Yiyang Tian, Zhaoxuan Zhao, Chengyong Li, Gen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources China Energy Technology and Computer Science Section Department of Engineering Technology and Didactics Ballerup2750 Denmark

With the increasing integration of renewable energy into power systems, electromagnetic transient (EMT) simulation has become indispensable for accurate system analysis. However, the complexity of wind turbine (WT) modeling, characterized by a large number of electrical nodes, poses significant challenges and necessitates substantial real-time simulation hardware. Existing methods for reducing circuit complexity improve simulation efficiency but are each associated with inherent limitations. Aggregation methods sacrifice considerable internal station information, while existing decoupling techniques are constrained by specific requirements. This paper proposes a real-time simulation model for a Doubly Fed Induction Generator (DFIG)-based wind farm (WF) using latency decoupling and a multi-level nested fast and simultaneous solution (M-NFSS) approach, effectively reducing node count while preserving internal details. A WF test model is implemented in RTDS for validation. Results demonstrate highly accurate impedance characteristics and time-domain waveforms of the proposed model, utilizing only 33.3% of the hardware resources compared to the traditional detailed model. © 1986-2012 IEEE.

关键词： Electromagnetic transients

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