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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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IEEE T-BIOM Editorial Board Changes

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

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Not so griddy: internal representations of RNNs path integrating more than one agent 24

Not so griddy: internal representations of RNNs path integra...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： William T. Redman Francisco Acosta Santiago Acosta-Mendoza Nina Miolane Intelligent Systems Center Johns Hopkins Applied Physics Laboratory Department of Physics University of California Santa Barbara Dynamical Neuroscience University of California Santa Barbara Department of Electrical & Computer Engineering University of California Santa Barbara

ISBN: (纸本)9798331314385

Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for the relative position of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

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An exact active sensing strategy for a class of bio-inspired systems

arXiv

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arXiv 2024年

作者： Biswas, Debojyoti Sontag, Eduardo D. Cowan, Noah J. Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Department of Bioengineering Northeastern University BostonMA United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD United States

We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude periodic forcing function akin to so-called "active sensing" in biology, together with nonlinear output feedback. Our analysis shows that this approach leads to the emergence of an exponentially stable limit cycle. These findings offer a provably stable active sensing strategy and may thus help to rationalize the active sensing movements made by animals as they perform certain motor behaviors. © 2024, CC BY-NC-SA.

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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Analysis of Aluminum Eddy Current Effect for Planar Switched Reluctance Motors

Analysis of Aluminum Eddy Current Effect for Planar Switched...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Jun-Di Sun Zhengyou He Guang-Zhong Cao Su-Dan Huang Jiangbiao He Qing-Quan Qian Department of Electrical Engineering Southwest Jiaotong University Chengdu China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Kentucky Lexington KY USA

In this article, influence of aluminum eddy current in the PSRM stator array on the electromagnetic properties and control performance is investigated. The eddy current density distribution model is established based on Maxwell’s equations, and the inductance model is derived considering the eddy current effect. The effect of aluminum eddy current on the PSRM position control performance is qualitatively analyzed. The aluminum eddy current distribution and ohmic loss distribution characteristics with the mover at different positions are verified by finite element analysis. The validity of the developed inductance model is experimentally verified. A material replacement method is proposed to eliminate the aluminum eddy current. The experimental results show that the position control accuracy of the PSRM prototype is improved by 6.94% on average after the elimination of eddy currents, while the motor efficiency is improved by 6.51%.

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Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

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IEEE/CAA Journal of Automatica Sinica 2021年第11期8卷 1817-1826页

作者： Gaofeng Li Dezhen Song Lei Sun Shan Xu Hongpeng Wang Jingtai Liu Nankai University Tianjin 300350China Humanoids and Human Centered Mechatronics Research Line Italian Institute of Technology16163 GenovaItaly Department of Computer Science and Engineering Texas A&M UniversityCollege StationTexas 77843 USA Institute of Robotics and Automatic Information System Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China

To balance the contradiction between higher flexibility and heavier load bearing capacity,we present a novel deformable manipulator which is composed of active rigid joints and deformable *** deformable link is composed of passive spherical joints with preload forces between socket-ball *** estimate the load bearing capacity of a deformable link,we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable *** yields an important result that the load bearing capacity of a spherical joint only depends on its radius,preload force,and static friction *** further develop a parameter estimation method to estimate the product of preload force and static friction *** experimental results validate our model.80.4%of percentage errors on the maximum payload mass prediction are below 15%.

关键词： Deformable link parameters identification spherical joint with preload force static force-based model

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SecureShare: Blockchain based Secure and Verifiable Knowledge Sharing for AI-Generated Content (AIGC) Services

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IEEE Transactions on Vehicular Technology 2025年

作者： Fan, Mochan Li, Zonghang Sun, Gang Du, Hongyang Yu, Hongfang Niyato, Dusit University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu610054 China Agile and Intelligent Computing Key Laboratory of Sichuan Province Chengdu China Department of Electrical and Electronic Engineering 999077 Hong Kong Peng Cheng Laboratory Shenzhen China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Benefiting from the rapidly expanding Internet of Things (IoT) data and powerful computing devices, AI-generated content (AIGC) trains models with vast knowledge to provide automated content generation services. Sharing knowledge through the ciphertext-policy attribute-based encryption (CP-ABE) algorithm is beneficial for training high-quality AIGC models to offer better services. However, existing CP-ABE sharing schemes often involve untrusted third parties, which can result in issues such as knowledge deletion, unverifiable access, and single points of failure. To address these challenges, some blockchain-based sharing schemes have been developed. However, they still face privacy leakage problems. In this paper, we propose SecureShare, a secure and verifiable knowledge sharing scheme based on a consortium blockchain for AIGC services. We begin by outlining a blockchain knowledge sharing architecture and optimizing the Delegated Proof of Stake (DPOS) committee node selection method to ensure that entities can achieve verifiable access control. Additionally, to achieve fine-grained access to knowledge ciphertext while preserving privacy, we propose a CP-ABE scheme with Policy Hiding, attribute privacy preservation, and Revocation, referred to as PHR-CP-ABE. PHR-CP-ABE ensures the privacy of access policies and attributes, and users whose attributes have been revoked cannot decrypt knowledge further. A case study on Dall-E clearly illustrates the operational mechanism of the proposed scheme. We provide theoretical analysis of the security of both the AIGC knowledge sharing scheme and PHR-CP-ABE. Through extensive performance analysis and comparisons with existing schemes, our approach demonstrates significant advantages in terms of computation and communication overhead. © 1967-2012 IEEE.

关键词： Ciphertext

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Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning

arXiv

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arXiv 2023年

作者： Shi, Haojie Zhu, Qingxu Han, Lei Chi, Wanchao Li, Tingguang Meng, Max Q.-H. Tencent Robotics X China The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which leads to safe and energy-efficient locomotion. Inspired by this observation, we present a novel approach to train a Deep Neural Network (DNN) policy that integrates proprioceptive and exteroceptive states with a parameterized trajectory generator for quadruped robots to traverse rough terrains. Our key idea is to use a DNN policy that can modify the parameters of the trajectory generator, such as foot height and frequency, to adapt to different terrains. To encourage the robot to step on safe regions and save energy consumption, we propose foot terrain reward and lifting foot height reward, respectively. By incorporating these rewards, our method can learn a safer and more efficient terrain-aware locomotion policy that can move a quadruped robot flexibly in any direction. To evaluate the effectiveness of our approach, we conduct simulation experiments on challenging terrains, including stairs, stepping stones, and poles. The simulation results demonstrate that our approach can successfully direct the robot to traverse such tough terrains in any direction. Furthermore, we validate our method on a real legged robot, which learns to traverse stepping stones with gaps over 25.5cm. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Combining Scene Coordinate Regression and Absolute Pose Regression for Visual Relocalization

Combining Scene Coordinate Regression and Absolute Pose Regr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jiahao Ruan Li He Yisheng Guan Hong Zhang School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering Shenzhen Key - Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China

Visual relocalization is a fundamental problem in computer vision and robotics. Recently, regression-based methods become popular and they can be categorized into two classes: absolute pose regression and scene coordinate regression. In this work, we present a combined regression network that jointly learns scene coordinate regression and absolute pose regression for single-image visual relocalization. The proposed network composes of a feature encoder and two regression branches with uncertainty modeling. In particular, we design a deep feature conditioning module, aiming at propagating the coarse pose information in absolute pose regression to inform the predictions in scene coordinate regression. The proposed network is trained in an end-to-end fashion to learn both regression tasks. Moreover, we propose an uncertainty-driven RANSAC algorithm that incorporates the predicted scene coordinates and their uncertainties to solve the camera pose during inference. To the best of our knowledge, this work is the first to combine scene coordinate regression and pose regression in a hierarchical framework for visual relocalization. Experiments on indoor and outdoor benchmarks demonstrate the effectiveness and the superiority of the proposed method over the state-of-the-art methods.

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