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arXiv 2024年

作者： Zheng, Chong He, Shiwen Huang, Yongming Quek, Tony Q.S. National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Computer Science and Engineering Central South University Changsha410083 China Purple Mountain Laboratories Nanjing210096 China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. However, the technical bottlenecks of traditional methods pose significant challenges to cross-layer optimization. In this paper, joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning to maximize the weighted sum rate. Specifically, we convert the underlying problem into a joint multi-task optimization problem and then propose a centralized multi-task self-supervised learning algorithm to solve the problem so as to avoid costly manual labeling. Therein, two novel and general loss functions, i.e., negative fraction linear loss and exponential linear loss whose advantages in robustness and target domain have been proved and discussed, are designed to enable self-supervised learning. Moreover, we further design a MEC-enabled distributed multi-task self-supervised learning (DMTSSL) algorithm, with low complexity and high scalability to address the challenge of dimensional disaster. Finally, we develop the distance-aware transfer learning algorithm based on the DMTSSL algorithm to handle the dynamic scenario with negligible computation cost. Simulation results under 3rd generation partnership project 38.901 urban-macrocell scenario demonstrate the superiority of the proposed algorithms over the baseline algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Research for the heat leakage caused by gaps on barrel insulation structure of reactor pressure vessel

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International Journal of Advanced Nuclear reactor design and technology 2019年 1卷 30-42页

作者： Tian, Qiu Ying, Luo Yang, Qiu Yu-guang, Li Guo-fu, Xie Li-cai, Yang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China ChengduSichuan Province610213 China

Due to the function requirements of the Cavity Injection and Cooling system, the barrel insulation structure of HPR1000 reactor pressure vessel has been greatly improved compared with the previous projects. In the past, the empirical formula method was used to evaluate the thermal properties of the insulation, but the influences of the support structure and the excessive gap on insulation are difficult to evaluate. In this paper, the fitting formula of the equivalent thermal conductivity and the qualitative temperature of the insulation have been obtained firstly through the heat transfer test. Then, ANSYS Fluent was used to simulate the barrel insulation structure with gaps of HPR1000. A circumferential 15° model was established to obtain evaluation method for flow leakage with gaps in hot state, and then a whole model of the barrel insulation structure with gaps was established to obtain heat loss of insulation based on calculation of flow leakage. The sensitivity of gap size and ventilation parameters to heat leakage was further analyzed. Finally, it was indicated that, by minimizing joint gap length, setting of overlaps, controlling cold gap, optimizing structure of insulation support, the effect of heat leakage caused by gaps can be reduced. © 2019 Xi'an Jiaotong University

关键词： Thermal insulation

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DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Distillation for Object Detection 39

DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Dist...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Lin, Yang Guo, Hang Wang, Jinbao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

Knowledge distillation (KD) has recently gained great success in the field of object detection. By transferring the knowledge of the spatial or channel domain from the teacher model to the student model, it allows for a more compact representation with minimal performance loss. Despite this progress, existing KD methods typically treat knowledge from spatial or channel domains independently, ignoring the exploitation of the mutual relationship between these domains. In this work, we first explore the connection between spatial and channel domains and find there exists a strong correlation between them, i.e. the salient channels tend to contain significant object regions in the spatial domain. Motivated by this observation, we propose DCSF-KD, a novel Dynamic Channel-wise Spatial Feature Knowledge Distillation framework for object detection by fully exploiting both spatial and channel knowledge. Specifically, we introduce channel-wise spatial feature distillation and global channel attention distillation, using information from both domains to improve the accuracy of the student network. Experiments demonstrate that our DCSF-KD outperforms existing detection methods on both homogeneous and heterogeneous teacher-student network pairs. For example, when using the MaskRCNN-Swin detector as the teacher, and based on RetinaNet and FCOS with ResNet-50 on MS COCO, our DCSF-KD can achieve 41.9% and 44.1% mAP, respectively. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Cover Picture: Materials and Corrosion. 11/2024

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Materials and Corrosion 2024年第11期75卷 1399-1399页

作者： Wei Zhang Mingyang Zhou Huifang Yue Danmin Peng Xi Qiu Jijun Yang Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

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Symbol-Level Precoding for MU-MIMO system with RIRC Receiver

Symbol-Level Precoding for MU-MIMO System with RIRC Receiver

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IEEE Conference on Wireless Communications and Networking

作者： Xiao Tong Ang Li Fan Liu Lei Lei School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China The National Mobile Communications Research Laboratory Southeast University Nanjing China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper addresses the design of the receive combining matrix in a multiuser multiple-input multiple-output (MU-MIMO) downlink system, where the base station (BS) employs symbol-level precoding (SLP) to transmit multiple data streams to multiple users with multiple antennas. Unlike in the single-antenna user scenario, the design of the receive combining matrix becomes crucial in this context. To overcome the challenge of the receive combining matrix's dependency on the transmit signals, we propose a practical scheme utilizing the interference rejection combiner (IRC) for signal decoding. However, directly applying the IRC receiver to the considered MU-MIMO system presents challenges due to the rank-one transmit precoding matrix. To address this issue, we propose a new regularized IRC (RIRC) receiver. The problem is tackled by using the alternating optimization (AO) method, enabling the derivation of an optimal solution structure for the transmit precoding matrix. Numerical results demonstrate the substantial performance gain of the practical SLP scheme with the RIRC receiver over conventional Block Diagonalization (BD) based approach.

关键词： Precoding Receivers Interference Performance gain Downlink Decoding Iterative methods

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Position-and-Force-Sensorless Control Considering Equivalent Mass Matrix and Cross-Coupling Factors

Position-and-Force-Sensorless Control Considering Equivalent...

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Annual Conference of Industrial Electronics Society

作者： Keita Shimamoto Toshiyuki Murakami Tsukuba Research Laboratory YASKAWA Electric Corporation Ibaraki Japan Graduate School of Science and Technology Keio University Yokohama Japan Department of System Design Engineering Keio University Yokohama Japan

This paper presents a hybrid position/force control with equivalent mass matrices for position-and-force-sensorless manipulators and the evaluation of the control system. Position and force estimation methods in the controller are cascaded to estimate the position and force according to the respective characteristics of the electrical and mechanical systems. The proposed controller is designed to reduce vibration of the force control axes' motion from the cross-coupling factors of the dq-axis inductance, which cause position estimation error, and the cross-coupling effects among control axes from equivalent mass matrices, which expresses the relationship between the workspace force and acceleration. The position estimation based on the voltage equation, including the cross-coupling factors, derives compensation terms of the estimation error. The cross-coupling effects between position and force control axes in the equivalent mass matrices are designed to suppress the undesired force response vibrations. The experimental results show that the proposed system kept contact with an object during the rubbing motion by a 4-degree-of-freedom manipulator without position and force sensors. The utilized equivalent mass matrix reduced the force vibration caused by the vibration of the position estimation.

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Anti-Fatigue Nanomechanics in the Pre-Cracked Graphene-Copper Artificial Nacre Under Cyclic Tension

SSRN

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SSRN 2023年

作者： Liu, Xiaoyi Xu, Yunfei Shi, Jinchun Zhu, YinBo Zhang, Sen Wu, HengAn Mechanics+X Laboratory Chengdu Development Center of Science and Technology China Academy of Engineering Physics Chengdu610200 China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Hefei230027 China

The fatigue performance of pre-cracked graphene-copper artificial nacre (GrCu nacre) under cyclic tensile loading is investigated using theoretical analysis and molecular dynamics (MD) simulations. Mechanical models are proposed to describe the fatigue performance of GrCu nacre, which agree well with MD simulations. Compared with nanocomposites consisting of large graphene sheets, graphene fragments cannot improve the ultimate strength of GrCu nacre, but significantly improve its fatigue limit. This anti-fatigue effect is dominated by the graphene-copper interfaces with van der Waals interactions, which can be adjusted by tailoring the architectural parameters of GrCu nacre. The ultimate strength of nacre decreases as the volume fraction of graphene increases. The fatigue limit of GrCu nacre decreases as the length of graphene fragments increases, and can be approximately described by a quadratic function of interlayer repeat spacing. An anomalous weakening effect occurs when the in-plane distance between graphene fragments is less than 2 nm, while this effect can be ignored when it is higher than 2 nm. The fatigue ratio of common metals is ~0.4, whereas that of GrCu nacre is generally higher than 0.9. These underlying mechanisms and theoretical models can improve the fundamental understanding of the fatigue performance of composites with incoherent interfaces, as well as the bottom-up design of graphene-metal nanocomposites. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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Trajectory Planning for Formation Variation of UGVs in Cluttered Environment

Trajectory Planning for Formation Variation of UGVs in Clutt...

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International Conference on Intelligent Transportation

作者： Jie Cheng Ruishuang Chen Zhihui Liang Shuan Ding Zaiyue Yang Shenzhen Key Laboratory of Control The-ory and Intelligent Systems School of System Design and Intelligent Manufacturing Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

The problem of trajectory planning for formation variation of unmanned ground vehicles (UGVs) is challenging due to the coupled non-collision constraints that apply to multiple vehicles. To address this issue, an innovative frame-work based on the alternating direction method of multipliers (ADMM) is proposed in this paper. The framework decouples complex constraints into two categories: individual constraints and mutual constraints. The former category involves dynamic, formation, and static obstacle constraints, which can be solved in parallel by each vehicle. Convex feasible set (CFS) algorithm is employed in this paper to simplify the static obstacle constraints. The latter category deals with collision avoidance among vehicles during the variation process and can be solved by nonlinear solvers. The convergence of the ADMM for the optimization problem is proved in this paper. Compared with solving the entire problem directly, the proposed framework reduces computation time and improves the quality of solution.

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Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

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Correction to:Study on the resource utilization of high fluorine-containing organic waste through fluidized incineration

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Waste Disposal and Sustainable Energy 2023年第2期5卷 245-245页

作者： Wenhan Li Zengyi Ma Jianhua Yan Qianming Huang Xingjian Wen Zian Zhai Bochen Huang Shuang Wang Yongqiang Chen Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of ChinaChengdu610213China State Key Laboratory of Clean Energy Utilization Institute for Thermal Power EngineeringZhejiang UniversityHangzhou310027China Zhejiang Xianchuang Energy&Technology Co.Ltd Jinhua321000China

Correction to:Waste Disposal&Sustainable Energy(2022)4:117-129 https://***/10.1007/s42768-022-00101-7 The section Confict of interest'has been amended:'Jian-hua Yan is the Editor in-Chief of Waste Disposal&Sustain-able Energy.'The revised'Confict of interest'is as follows:Jianhua Yan is the Editor-in-Chief of Waste Disposal&Sustainable *** behalf of all authors,the corresponding author states that there is no conflict of interest.

关键词： Sustainable fluorine behalf

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