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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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design and Modeling of a Spring-Based Continuum Manipulator for Minimally Invasive Surgery

Design and Modeling of a Spring-Based Continuum Manipulator ...

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IEEE International Conference on Robotics and Biomimetics

作者： Jianyong Zhang Guotao Li Tingting Su Xu Liang Zeng-Guang Hou Department of Mechanical and Electrical Engineering North China Universily of Technology Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Science and Technology Beijing University of Technology Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Minimally invasive surgery (MIS) has gained significant attention from both patients and doctors for its less trauma, less pain, and faster recovery. Robot-assisted MIS has proven effective in reducing doctors' fatigue and improving surgical precision. MIS robots require more degrees of freedom of motion within limited dimensions to complete accurate, stable and safe surgery in a constrained working space. This paper proposes a spring-based continuum manipulator for MIS. The manipulator's length can be adjusted automatically. Its simple structure makes it easy to miniaturize. In addition, it includes a central channel for instrument replacement. The stiffness of the manipulator is analyzed, and a constant curvature kinematic model for the manipulator is developed. The effectiveness of the proposed constant curvature kinematics model was verified through experiments, and the motion performance experiments of the manipulator were conducted.

关键词： Minimally invasive surgery Accuracy Trajectory tracking Friction Kinematics Medical services Manipulators Robot sensing systems Stability analysis Springs

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Bayesian Optimization Framework for Efficient Fleet design in Autonomous Multi-Robot Exploration

arXiv

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arXiv 2024年

作者： Concha, David Molina Li, Jiping Yin, Haoran Park, Kyeonghyeon Lee, Hyun-Rok Lee, Taesik Sirohi, Dhruv Lee, Chi-Guhn The Dynamic Optimization and Reinforcement Learning Laboratory The Department of Mechanical & Industrial Engineering University of Toronto TorontoONM5S3G8 Canada The Department of Electrical & Computer Engineering University of Toronto TorontoONM5S3G8 Canada The Division of Engineering Science The Faculty of Applied Science and Engineering University of Toronto TorontoONM5S3G8 Canada The Complex System Design Laboratory The Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Department of Industrial Engineering Inha University Incheon22212 Korea Republic of

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet design (BOFD), a framework leveraging multiobjective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD’s robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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TAS-TsC: A Data-Driven Framework for Estimating Time of Arrival Using Temporal-Attribute-Spatial Tri-space Coordination of Truck Trajectories

arXiv

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arXiv 2024年

作者： Li, Mengran Chen, Junzhou Jiang, Guanying Li, Fuliang Zhang, Ronghui Gong, Siyuan Lv, Zhihan Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Guangzhou510275 China Baidu Inc Beijing100085 China Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong School of Information and Engineering Chang’an University Shanxi Xi’an710064 China Department of Game Design Faculty of Arts Uppsala University Uppsala75236 Sweden

Accurately estimating time of arrival (ETA) for trucks is crucial for optimizing transportation efficiency in logistics. GPS trajectory data offers valuable information for ETA, but challenges arise due to temporal sparsity, variable sequence lengths, and the interdependencies among multiple trucks. To address these issues, we propose the Temporal-Attribute-Spatial Tri-space Coordination (TAS-TsC) framework, which leverages three feature spaces—temporal, attribute, and spatial—to enhance ETA. Our framework consists of a Temporal Learning Module (TLM) using state space models to capture temporal dependencies, an Attribute Extraction Module (AEM) that transforms sequential features into structured attribute embeddings, and a Spatial Fusion Module (SFM) that models the interactions among multiple trajectories using graph representation learning. These modules collaboratively learn trajectory embeddings, which are then used by a Downstream Prediction Module (DPM) to estimate arrival times. We validate TAS-TsC on real truck trajectory datasets collected from Shenzhen, China, demonstrating its superior performance compared to existing methods. © 2024, CC BY.

关键词： Truck transportation

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Feedback-based Optimal Dispatch for Virtual Power Plants in Active Distribution Networks 5

Feedback-based Optimal Dispatch for Virtual Power Plants in ...

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5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

作者： Xu, Tong Zeng, Xuesong Wu, Wenchuan Energy Planning and Research Institute Southwest Electric Power Design Institute Co. Ltd. China Power Engineering Consulting Group Chengdu China Department of Electrical Engineering State Key Laboratory of Power Systems Tsinghua University Beijing China

ISBN: (纸本)9781665434256

Virtual power plants (VPPs) play an increasingly important role in power systems as they provide flexibility and efficiency for active distribution networks and serve as a great tool of energy management for the power grid penetrated with plenty of distributed energy resources (DERs) in the future. This article aims to propose a novel optimal dispatch method for VPP to realize real-time autonomous optimization. The proposed method combines the advantage of model-based method and data-driven method to provide instant optimal points trajectory based on system feedback, i.e., measurements. Firstly, the approximated mapping from control variables to state variables is established. Then a zeroth order optimization method is presented to calculate real-time command of DERs. The proposed strategy is adaptively compatible with the sudden change of the system and provide effective voltage support verified by the simulation in the unbalanced distribution networks. © 2021 IEEE

关键词： Virtual Power Plants

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Joint design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Rotation Speed Estimation for Motor Fault Diagnosis Using Bidirectional Long Short Term Memory Networks

Rotation Speed Estimation for Motor Fault Diagnosis Using Bi...

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第34届中国控制与决策会议

作者： Xiaoxian Wang Siliang Lu Shiwu Zhang CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision InstrumentationUniversity of Science and Technology of China College of Electrical Engineering and Automation Anhui University

Rotation speed estimation is a key step for motor fault diagnosis under variable-speed working *** study investigates a method for speed estimation by fusing the information from vibration and current signals using a bidirectional long short term memory(BiLSTM) deep neural ***,the instantaneous frequency(IF) curves are extracted from the signals using an improved time-frequency analysis method.A BiLSTM regression model is trained for IFs fusion and rotation speed ***,the rotation angle is computed from the estimated rotation speed curve,and the vibration signal is resampled and processed for motor fault *** performance of the proposed method is validated by the experimental data acquired from a switched reluctance motor test rig.

关键词： Rotation speed estimation Motor fault diagnosis BiLSTM Deep neural networks Information fusion

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Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study

arXiv

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arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Wang, Jiacheng Sikdar, Biplab Zhang, Ping College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Data augmentation is a powerful technique to mitigate data scarcity. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective alternative to wireless data augmentation due to its excellent data generation capability. This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of GenAI-driven data augmentation in wireless networks from the physical, network, and application layers, which provides a GenAI-driven data augmentation architecture for each application. Subsequently, we propose a general generative diffusion model-based data augmentation framework for Wi-Fi gesture recognition, which uses transformer-based diffusion models to generate high-quality channel state information data. Furthermore, we develop residual neural network models for Wi-Fi gesture recognition to evaluate the role of augmented data and conduct a case study based on a real dataset. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss research directions for generative data augmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Energy-Efficient design in STAR-RIS Assisted Communication System with Antenna Selection

Energy-Efficient Design in STAR-RIS Assisted Communication S...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyang Zhang Yang Lu Bo Ai Zhangdui Zhong Zhiguo Ding Tony Q. S. Quek School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing China Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Electrical and Electronic Engineering University of Manchester Manchester UK Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

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Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach

arXiv

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arXiv 2024年

作者： Du, Hongyang Liu, Guangyuan Lin, Yijing Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

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 LLM-enabled 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. © 2024, CC BY.

关键词： Reinforcement learning

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