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Cooperative Resource Allocation for NOMA-MEC Multi-Cell Network

IEEE Transactions on Vehicular technology 2025年第6期74卷 9027-9042页

作者： Cai, Yujin Zhang, Zaichen Huang, Yongming Yu, Wenwu Nie, Xiaokai Liu, Hongzhe Southeast University School of Mathematics Jiangsu Nanjing211189 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

Integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) enables IoT nodes to offload their complicated tasks to MEC servers. Most of the existing resource allocation schemes in NOMA-MEC networks are based on the centralized optimization framework, which may increase the communicational and computational burden on the central unit. In this paper, the NOMA-MEC multi-cell network without the central unit is considered. To minimize the task completion time and offloading cost, a joint optimization problem including sub-channel allocation, transmit power, offloaded proportions, computing frequencies of IoT nodes and MEC servers, and price of MEC resources is investigated. In order to tackle the formulated mixed integer nonlinear programming problem, a two-stage low-complexity distributed algorithm based on the cooperation of all base stations (BSs) is proposed. Specifically, in Stage 1, a distributed heuristic sub-channel allocation algorithm is designed based on the sub-channel gains. In Stage 2, with the obtained sub-channel allocation result, a cooperative distributed continuous variable optimization algorithm is devised based on the distributed optimization and alternating optimization methods. Numerical results demonstrate the convergence of the proposed two-stage algorithm for three interference cases. Besides, the impacts of system parameters and schemes on the network performance are analyzed. More importantly, the resource allocation time can be significantly shortened by employing the cooperation of BSs compared with the centralized algorithm © 1967-2012 IEEE.

关键词： Nonlinear programming

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Adaptive Link State Update Scheme for Large-Scale LEO Satellite Networks Based on Distributed Deep Reinforcement Learning

Adaptive Link State Update Scheme for Large-Scale LEO Satell...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Wang, Tao Wang, Yichen Liu, Hong Liu, Haotian Su, Zhou Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China North Automatic Control Technology Institute Taiyuan030006 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

ISBN: (纸本)9798350351255

In the upcoming sixth generation (6G) era, dynamic routing relying on link state information update is crucial for global data service in large-scale low-earth orbit (LEO) satellite networks. However, the existing dynamic routing methods use a static link state update scheme where all satellites distribute their link state information with the same fixed period, while the link state of the satellites are different and vary dynamically. This makes it difficult to achieve the balance among various network performance metrics such as link state update accuracy, signaling overhead, network throughput, and energy efficiency. To solve this issue, we propose an adaptive link state update scheme for the LEO satellite network, where each satellite can dynamically adjust its own link state distribution interval according to the observation on the inter satellite links (ISLs). Based on the proposed scheme, we define the information deviation to characterize the accuracy of the link state update and derive the signaling overhead of link state distribution. To improve further the network performance, a multi-objective optimization problem (MOP) is formulated to minimize the information deviation and the signaling overhead simultaneously. By applying the weighted sum method, we convert the formulated MOP into a single-objective optimization problem (SOP). Then, we adopt the distributed reinforcement learning approach and develop the deep Q-network (DQN) algorithm for each satellite to learn its optimal link state distribution decision strategy based on local information. Simulation results demonstrate the superiority of the proposed scheme. © 2024 IEEE.

关键词： Satellite links

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Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

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China communications 2023年第1期20卷 261-276页

作者： Runzi Liu Weihua Wu Zhongyuan Zhao Xu Ding Di Zhou Yan Zhang School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina State Key Laboratory of ISN School of Telecommunications EngineeringXidian UniversityXi’an 710071ShaanxiChina Key Laboratory of Universal Wireless Communications(Ministry of Education) Beijing University of Posts and TelecommunicationsBeijing 100876China

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite ***,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded ***,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems *** alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination *** results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

关键词： heterogeneous space network transmission task task planning coordinated scheduling

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A Novel All-phase Fast Fourier Transform-based Parameters Identification of High Frequency Oscillations 60

A Novel All-phase Fast Fourier Transform-based Parameters Id...

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60th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2024

作者： Sun, Feiyu Cai, Dongsheng Zhang, Zhenyuan Huang, Qi University of Electronic Science and Technology of China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering Chengdu611731 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering 610059 China Southwest University of Science and Technology School of Information Engineering Sichuan Mianyang China

ISBN: (纸本)9798350345308

Wind and solar energy inverter-based resources (IBRs) has been employed extensively with the aim of carbon neutrality. Power systems also make extensive use of power electronic converters, leading to interactions between IBRs and with the grid, causing high frequency oscillation (HFO) problems. It damages equipment and jeopardizes the security of the system. To identify HFO components above 100 Hz, this research use Phasor measurement Unit (PMU) instantaneous value data to determine the HFO associated characteristics. To reduce the influence of spectral leakage and the fence effect, as well as to improve the identification accuracy of the related parameters, this paper adopts the RV self-convolutional window based Discrete Fourier Transform (DFT) and the Hanning double-window based all-phase Fast Fourier Transform (apFFT) hybrid algorithm to identify the HFO modal parameters. We take into account the identification accuracy in complex cases, such as non-nominal frequencies, with noise effects and multiple HFO modes. The simulation results show that the proposed method has high identification bandwidth, accuracy and strong flexibility. © 2024 IEEE.

关键词： Discrete Fourier transforms

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Active Capsule Endoscope Robot: Current Status and Future Perspectives

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Recent Patents on engineering 2022年第4期16卷 30-49页

作者： Jiang, Jingang Sun, Yang Song, Shichang Yu, Xiaoyang Wu, Dianhao Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology No. 52 Xuefu Road Nangang District Heilongjiang Province Harbin150080 China School of International Studies Harbin Institute of Technology Harbin150001 China School of Measurement-Control Tech and Communications Engineering Harbin University of Science and Technology Harbin150080 China

Background: China is a big country with a vast territory, in which gastropathy has become a common high-incidence disease in daily life. Gastroscopy is an important means of diagnosis of gastropathy, but the use of a gastroscope causes a lot of pain to patients. A cable-free, non-invasive and painless diagnosis and treatment tool, an active capsule endoscope robot, can solve this problem very well. Capsule robot has become a new development hotspot. Objective: The study aims to provide an overview of the active endoscope capsule robot and introduce its classification, characteristics and development. Methods: This paper summarizes various scientific research achievements of the active endoscope capsule robot. The structural characteristics, advantages and disadvantages of various active endoscope capsule robots are introduced. Results: The active endoscope capsule robot has been analyzed and compared to other models. Its typical characteristics have been summarized. The main problems in its development are analyzed, its development trend is prospected, and the research status and future of active endoscope capsule robot are discussed. Conclusion: The active capsule robot is classified into two categories: bionic and non-bionic. The analysis shows that the capsule robot is an effective and safe initiative and has a very broad application prospect for various gastrointestinal tests compared to gastroscope, and helps alleviate the pain of patients. © 2022 Bentham Science Publishers.

关键词： Robots

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Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Exploiting the Direct Link in IRS Assisted NOMA Networks with Hardware Impairments

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Computer Modeling in engineering & Sciences 2023年第7期136卷 767-785页

作者： Ziwei Liu Xinwei Yue Shuo Chen Xuliang Liu Yafei Wang Wanwei Tang Key Laboratory of Modern Measurement&Control Technology Ministry of EducationSchool of Information and Communication EngineeringBeijing Information Science and Technology UniversityBeijing100101China The Party Committee Office Yellow River Conservancy Technical InstituteKaifeng475001China Tangshan University Tangshan063000China

Hardware impairments(HI)are always present in low-cost wireless *** paper investigates the outage behaviors of intelligent reflecting surface(IRS)assisted non-orthogonal multiple access(NOMA)networks by taking into account the impact of ***,we derive the approximate and asymptotic expressions of the outage probability for the IRS-NOMA-HI *** on the asymptotic results,the diversity orders under perfect self-interference cancellation and imperfect self-interference cancellation scenarios are obtained to evaluate the performance of the considered *** addition,the system throughput of IRS-NOMA-HI is discussed in delay-limited *** obtained results are provided to verify the accuracy of the theoretical analyses and reveal that:1)The outage performance and system throughput for IRS-NOMA-HI outperforms that of the IRS-assisted orthogonal multiple access-HI(IRS-OMA-HI)networks;2)The number of IRS elements,the pass loss factors,the Rician factors,and the value of HI are pivotal to enhancing the performance of IRS-NOMAHI networks;and 3)It is recommended that effective methods of reducing HI should be used to ensure system performance,in addition to self-interference cancellation techniques.

关键词： Hardware impairments imperfect SIC intelligent reflecting surface non-orthogonal multiple access outage probability

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In-Vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

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Automotive Innovation 2024年第1期7卷 138-149页

作者： Haojie Ji Liyong Wang Hongmao Qin Yinghui Wang Junjie Zhang Biao Chen Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100192China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230012China

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

关键词： Classification algorithm Anomaly detection In-vehicle network Feature extraction Injecting attack

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Failure Mode Analysis and Infrared Image Recognition of Crystalline Silicon PV Module 23

Failure Mode Analysis and Infrared Image Recognition of Crys...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Ziyi Li Lijie Wang Jianbo Sun Xinyu Lu Xianwen Zhu School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology China

ISBN: (纸本)9798400709272

In this paper, the fault analysis of crystalline silicon photovoltaic modules is studied. The Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods are used to analyze the failure mode and its causes. Based on the results of fault analysis, the infrared image analysis and recognition methods are studied for the two fault modes of infrared hot spot and component shedding of Photovoltaic(PV) modules. The YOLOv5s image recognition method based on clustering improvement and feature enhancement is discussed. The experimental results show that the YOLOv5s image recognition algorithm with clustering improvement and feature enhancement improves the training effect of the model by using the EIOU loss function to adaptively adjust the confidence loss balance coefficient, and the detection speed (Frame Per Second, FPS) can reach 42.37 FPS; by adding InRe feature enhancement modules before each detection layer, the extraction ability of target features is improved. The mean Average Precision (mAP) of hot spot and component shedding are 94.85 % and 90.67 %, respectively, which can fully meet the needs of UAV automatic inspection.

关键词： FMEA FTA Image recognition Infrared image PV module

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Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

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Fluid Dynamics & Materials Processing 2023年第6期19卷 1369-1383页

作者： Xing Deng Feipeng Da Haijian Shao School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.

关键词： Blood cell analysis deep learning models classification-detection

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