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Performance analysis of UAV-based mixed underwater PLC-RF systems

Digital Communications and Networks 2024年第5期10卷 1332-1340页

作者： Shenghai Chen Liang Yang Xianfu Lei College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua418000China School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031China

In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)*** this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC *** assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician *** this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was ***,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.

关键词： ABER Average bit-error-rate ACC Average channel capacity OP Outage probability UAV Unmanned aerial vehicle UPLC Underwater power line communication

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Constrained multi-cluster game: Distributed Nash equilibrium seeking over directed graphs 63

Constrained multi-cluster game: Distributed Nash equilibrium...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Nguyen, Duong Thuy Anh Bianchi, Mattia Dorfler, Florian Nguyen, Duong Tung Nedic, Angelia Arizona State University Tempe School of Electrical Computer and Energy Engineering AZ United States Automatic Control Laboratory Eth Zürich Switzerland

ISBN: (纸本)9798350316339

Motivated by the complex dynamics of cooperative and competitive interactions within networked agent systems, multi-cluster games provide a framework for modeling the interconnected goals of self-interested clusters of agents. For this setup, the existing literature does not provide comprehensive gradient-based solutions that simultaneously address constraint sets and directed communication networks-both of which are essential for many practical applications-due to the complexities posed by the projection and the imbalanced mixing matrix. To address this gap, this paper proposes a distributed Nash equilibrium seeking algorithm that integrates consensus-based methods and gradient-tracking techniques, utilizing row-stochastic weight matrices for inter-cluster communication and column-stochastic weight matrices for intracluster communication. To handle constraints, we introduce an averaging procedure to control errors introduced by projection methods within the gradient-tracking procedure. We establish the linear convergence of the proposed algorithm, focusing on the contraction property of the optimality gap. The efficacy of the algorithm is demonstrated through its application to microgrid energy management. © 2024 IEEE.

关键词： Stochastic systems

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Optimization of Cube Storage Warehouse Scheduling Using Genetic Algorithms 19

Optimization of Cube Storage Warehouse Scheduling Using Gene...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Ha, Won Yong Jiang, Zhong-Ping Tandon School of Engineering New York University The Control and Networks Lab Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9798350320695

In this paper, a new scheduling model is presented to speed up the logistics processing in an automatic cube storage warehouse. Automated guided vehicles (AGV) are used to move all items in the warehouse according to the computer's instructions. The tasks to be performed by the AGV are optimally distributed using Genetic Algorithms (GA). The goal of our research is to optimize order scheduling in automatic warehouses to reduce human resources and lower the cost of logistics. The proposed GA's fitness function reflects removing the stacked bin, a cube storage warehouse characteristic, and getting the designated bin. Through extensive computer simulations, it is shown that the higher the generation of the GA we design, the lower the logistics processing time. As compared with other meta-heuristic optimization algorithms, our proposed GA algorithm demonstrates a maximum of 21% reduction in delivery time. © 2023 IEEE.

关键词： Genetic algorithms

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Activity-based capability updating method for task assignment in mobile crowdsensing

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computer Networks 2025年 265卷

作者： Zhu, Xiao Ma, Wenming Wu, Xiagling Sun, Shengyang Zhu, Xiaoang School of Computer and Control Engineering Yantai University ShanDong YanTai264005 China

Task allocation is a critical process for mobile crowdsensing. In this process, the platform assigns tasks uploaded by requesters to suitable workers based on specific criteria. To ensure maximum benefit, crowdsensing platforms must select enough workers to complete these tasks with high quality. Therefore, it is essential for the platform's profitability to select reliable and punctual workers. Current approaches rely on intrinsic attributes for winner selection, which can lead to issues with unstable perceived quality. In this study, we propose a worker capability update equation that takes into account workers’ temporal and spatial activity levels to select stable and high-quality contributors. Additionally, we introduce a metric based on normal distribution to evaluate workers’ task completion rates. By utilizing historical task performance, we can select workers for each task, mitigating the impact of quality fluctuations on the platform's profits. We compared our method with four baseline methods using real datasets, and all results demonstrated its efficiency. © 2025

关键词： Mobile crowdsensing Opportunistic task assignment Profit maximization

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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An Anomaly Localization Method for Large-Scale Micro-services 4

An Anomaly Localization Method for Large-Scale Micro-service...

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4th International Symposium on computer Technology and Information Science, ISCTIS 2024

作者： Zhao, Weiting Xu, Bintai Ma, Chao Zhu, Shaosong Tai, Yu Li, Gang State Grid Shandong Information & Telecommunication Company Jinan China North China Electric Power University School of Control & Computer Engineering Beijing China

ISBN: (纸本)9798350354560

As the scale of micro-service systems on the cloud continues to expand, the dependencies between micro-services become more and more complex. Service anomalies may continue to propagate with the call relationship, leading to a decrease in the overall availability of the system. In order to cope with large-scale micro-service systems with complex dependencies, this paper designs an anomaly location method MicroALLS suitable for large-scale micro-services. This method collects micro-service operation indicator data based on Istio;uses the anomaly detection module of the variational auto-encoder for anomaly detection;once an anomaly is detected, an improved random walk algorithm is used to accurately and efficiently locate the micro-service anomaly. We use the train-ticket micro-service system as a test environment to evaluate the positioning accuracy of this method. The experimental results show that the anomaly detection and accuracy of the method are improved compared with the comparison method. © 2024 IEEE.

关键词： Anomaly detection

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State-of-Health Estimation of Lithium-Ion Battery Based on Linguistic Description Considering Capacity Regeneration

State-of-Health Estimation of Lithium-Ion Battery Based on L...

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2023 China Automation Congress, CAC 2023

作者： Cui, Fangshu Li, Hui Wu, Kangjia Fan, Sitong Shi, Yuanhao School of Computer Science and Technology North University of China Taiyuan China School of Electrical and Control Engineering North University of China Taiyuan China

ISBN: (纸本)9798350303759

Owing to the wide application of lithium-ion batteries in industry, it is of great significance for accurate prediction of battery state of health (SOH) to ensure the safety and stability of equipment. The battery capacity regeneration (CR) during capacity degradation affects the accuracy of capacity prediction. This study proposes a hybrid estimation method for state of health of lithium-ion batteries based on linguistic description (LD) considering capacity regeneration. First, the capacity degradation data is decoupled into the trend component and the capacity regeneration component by the variational mode decomposition (VMD). Second, in order to improve the accuracy and reliability of battery capacity prediction, the capacity regeneration part is described by linguistic description. Finally, the trend part and the linguistic description sequence of capacity regeneration are predicted by support vector regression (SVR) model and long short term memory network with attention mechanism (Attn-LSTM) respectively, and their forecast results are superposed as the battery capacity prediction. The experimental results in NASA battery data demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Lithium-ion batteries

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Energy Efficiency Maximization for Hybrid Access Femtocell Networks via Incentive Mechanism and Power control Strategy 13

Energy Efficiency Maximization for Hybrid Access Femtocell N...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Liu, Xuelin Wang, Weimin Liu, Zhixin School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Hebei Province Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Femtocell network has a great development space in increasing coverage and capacity of wireless network. In order to meet the quality of service (QoS) requirements of MUEs in MBS coverage, we propose a seed channel incentive mechanism in the two-tier femtocell network, where the MBS provides sub-channels to FBSs to motivate them to serve for the MUEs within the MBS coverage. To maximize the energy efficiency of femtocell layer after hybrid access, the optimization problem is modeled. The combined method of worst-case, Dinkelbach and continuous convex approximation is developed to make the optimization problem solvable. In addition, in order to realize the practical application of the proposed scheme, an optimization algorithm combining sub-channel and power allocation is developed. The numerical results show that the proposed algorithm is effective in improving the energy efficiency of the femtocell layer. © 2023 IEEE.

关键词： Femtocell

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GK-Means SOM Algorithm Used to Plan the Paths for Multiple Agents Exploring Multiple Target Points 1st

GK-Means SOM Algorithm Used to Plan the Paths for Multiple A...

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1st International Conference on Cognitive Computation and Systems, ICCCS 2022

作者： Yang, Hexing Zhao, Qingjie Wang, Lei Liu, Wangwang Chong, Ling School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9789819927883

Multi-agent technology is widely used in many fields such as intelligent manufacturing, logistics and environment exploration. In this paper, we propose a greedy K-means self-organizing map algorithm to balance the tasks and plan the paths for multiple agents exploring multiple target points, where greedy k-means adopts greedy strategy to ensure the tasks allocated for agents tending equally, and the self-organizing map networks are used for parallel path planning to speed up the problem solved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Self organizing maps

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A Hybrid Secure Two-Party Protocol for Vertical Federated Learning 2nd

A Hybrid Secure Two-Party Protocol for Vertical Federated Le...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Yang, Wenti He, Zhaoyang Li, Yalei Zhang, Haiyan Guan, Zhitao Beijing100192 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819902712

Federated learning (FL) is a promising distributed machine learning technique for solving the privacy leakage problem in machine learning training process. Multiple parties collaborate to train a machine learning model, while the data is kept locally, so as to achieve privacy-preservation of users. However, some studies have shown that an attacker can infer some sensitive information from the interacted data, even if the raw data is not available. It is necessary to adopt some privacy-preserving techniques such as secure multi-party computation and homomorphic encryption to prevent the above problem. Most existing solutions have focused on FL over horizontally partitioned data but ignore the research on FL over vertically partitioned data. In this paper, we present a vertical federated learning scheme with enhanced privacy preservation. Split learning is used to enable cooperative neural network training between multiple parties with vertically partitioned datasets, and a hybrid secure two-party computation protocol is adopted for protecting users’ privacy. The experimental results demonstrate the feasibility of our scheme. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Privacy-preserving techniques

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