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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding intelligent robots

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Position control of X-Y Precision Motion Stage Based on Iterative Learning and Model Prediction

Position Control of X-Y Precision Motion Stage Based on Iter...

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Chinese control and Decision Conference, CCDC

作者： Guopeng Zhang Tao Zhang Qingxin Li Li Chen Hualiang Zhang Jingzhe Fang Yiqiang Zhang School of Artificial Intelligence Shenyang University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The X-Y precision planar motion stage is a highly advanced motion control device that has been widely utilized in various precision motion control fields. This platform achieves precise positioning and control of objects through precise motion along the X and Y axes, providing an outstanding solution for scientific research, industrial production, and other applications. The X-Y platform consists of two permanent magnet synchronous linear motors. In the three-loop control of the motor, when the current loop uses a PID controller, there is a problem in tuning the parameters. This paper replaces the original PID controller in the current loop with a model predictive controller. Simultaneously, addressing the issue of improving the tracking accuracy of the X-Y precision planar motion stage, a forgetting high-order variable gain PD-type iterative learning control is proposed. Subsequently, both of these control methods are applied to the Simulink model of the X-Y precision motion stage motor. This not only solves the complex problem of tuning the motor current loop parameters but also improves the tracking accuracy of the X-Y precision motion stage. The results indicate that when using both model predictive controllers and forgetting high-order variable gain PD-type iterative learning controllers for motor control, not only can the tuning of current loop parameters be omitted, but also tracking errors can be effectively reduced, and the system convergence speed can be accelerated.

关键词： Tracking loops Trajectory tracking Predictive models Motors Mathematical models Motion control Tuning

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Semantic Communications:Theories,Technologies and Applications

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中国通信（英文版） 2024年第7期21卷前插1-前插5页

作者： Zhang Ping Shi Guangming Cui Shuguang Zhang Zhaoyang Niu Kai Xiao Yong Qin Zhijin Dai Jincheng Shao Shuo Deniz Gündüz Eleonora Grassucci School of Information and Communication Engineering at Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology IMT-2020(5G)Experts Panel Experts Panel for China's 6G development Peng Cheng Laboratory School of Artifi-cial Intelligence Xidian University electrical and computer engineering with the University of Arizona Texas A&M University UC Davis Chinese Univer-sity of Hong Kong(Shenzhen)(CUHK-Shenzhen) Zhejiang University School of Artificial Intelli-gence BUPT School of Electron-ic Information and Communications at the Huazhong University of Science and Technology(HUST) WuhanChina Peng Cheng Laboratory Guang-zhouChina and the Pazhou Laboratory(Huangpu)Guang-zhouChina Tsinghua University BeijingChina Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications School of Electronics Informa-tion and Electrical EngineeringShanghai Jiao Tong Univer-sityChina Information Processing in the Electrical and Electronic Engineering Department at Imperial College London UK Intelligent Systems and Networks Group Department at Imperial College London UK Department of Information Engineering Electronicsand Telecommunications of the Sapienza University of Rome

The last seventy years have witnessed the transi-tion of communication from Shannon's theoretical concept to current high-efficiency practical *** respect to Shannon information theory,to fulfill the high-bandwidth utilization,any further increase in the data rate requires a significant augmentation in the received signal power unless the bandwidth is extended in proportion to the incre-mental data rate.

关键词： Special issues and sections Semantics Communication systems Data processing Information theory Data augmentation

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Yue, Xinwei Xie, Jin Ouyang, Chongjun Liu, Yuanwei Shen, Xia Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Beijing100191 China The Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering Princeton University Princeton United States

The novel active simultaneously transmitting and reflecting surface (ASTARS) has recently received a lot of attention due to its capability to conquer the multiplicative fading loss and achieve full-space smart radio environments. This paper introduces the ASTARS to assist non-orthogonal multiple access (NOMA) communications, where the stochastic geometry theory is used to model the spatial positions of pairing users. We design the independent reflection/transmission phase-shift controllers of ASTARS to align the phases of cascaded channels at pairing users. We derive new closed-form and asymptotic expressions of the outage probability and ergodic data rate for ASTARS-NOMA networks in the presence of perfect/imperfect successive interference cancellation (pSIC). The diversity orders and multiplexing gains for ASTARS-NOMA are derived to provide more insights. Furthermore, the system throughputs of ASTARS-NOMA are investigated in both delay-tolerant and delay-limited transmission modes. The numerical results are presented and show that: 1) ASTARS-NOMA with pSIC outperforms ASTARS assisted-orthogonal multiple access (ASTARS-OMA) in terms of outage probability and ergodic data rate;2) The outage probability of ASTARS-NOMA can be further reduced within a certain range by increasing the power amplification factors;3) The system throughputs of ASTARS-NOMA are superior to that of ASTARS-OMA in both delay-limited and delay-tolerant transmission modes. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Performance Analysis of OMA/NOMA-aided Satellite Communication Networks: A Stochastic Geometry Approach

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IEEE Transactions on Communications 2024年

作者： Li, Kecheng Wang, Jun Hou, Tianwei Li, Anna Yue, Xinwei Liu, Yuanwei Chen, Wei School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU Leuven Leuven3001 Belgium School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes;2) fading factors have negligible effects on OP and spectral efficiency;and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users. © 2024 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Satellite communication systems

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An adaptive federated control strategy for participant selection in multi-client collaboration 1

An adaptive federated control strategy for participant selec...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Chen, Yizhu Du, Xiaoming Wang, Xiao Zhang, Jun Jason Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9781665433372

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %, 9 respectively. © 2021 IEEE.

关键词： Stream flow

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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