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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Lingxin Luo, Haibo College of Computer and Data Science College of Software FuZhou University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

The issues of limited communication distance and task capability are rarely discussed in designing multi-robot cooperative methods. However, these are important problems that cannot be avoided in practical applications. In this paper, simulation experiments are carried out on some task maps that are larger than the team's task ability. The effect of the dispersion of members on the team's task performance is tested, and the existing methods are improved by introducing long-range communication to auxiliary data sharing. At the same time, we use the calculation of Energy-Benefit to improve the task selection method based on the minimum distance to the target. Compared with existing strategies, the improved scheme performs better on communication-constrained large-map exploration tasks. © 2022 IEEE.

关键词： Multipurpose robots

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Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes

arXiv

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arXiv 2024年

作者： Huang, Yan Li, Xiang Shen, Yipeng He, Niao Xu, Jinming College of Control Science and Engineering Zhejiang University China Department of Computer Science ETH Zurich Switzerland

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure time-scale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of O∼ (ϵ−(4+δ)) for any small δ > 0, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the first distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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computer Systems science & engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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MulDoor: A Multi-target Backdoor Attack Against Federated Learning System

MulDoor: A Multi-target Backdoor Attack Against Federated Le...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuan Wu, Longfei Guan, Zhitao Du, Xiaojiang Aitsaadi, Nadjib Guizani, Mohsen North China Electric Power University School of Control and Computer Engineering China Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Université Paris-Saclay Uvsq David Versailles France Machine Learning Department United Arab Emirates

ISBN: (纸本)9798350351255

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model's output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary's flexibility to alter the model's output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack's effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting. © 2024 IEEE.

关键词： Contrastive Learning

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A Hybrid Layout Method Based on GPU for the Logistics Facility Layout Problem 17th

A Hybrid Layout Method Based on GPU for the Logistics Fac...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Jiang, Fulin Li, Lin Zhu, Junjie Liu, Xiaoping School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9789819923847

The existing facility layout problem (FLP) only considers the layout of processing facilities. However, in the current scenario of industrial logistics, there are not only working facilities, but also a lot of transportation facilities. In order to solve the logistics facility layout problem (LFLP), this paper proposes a two-step algorithmic framework based on GPU acceleration to obtain some feasible solutions. The first step is layout of working facilities by meta-heuristic algorithm, and the second step is layout of transportation facilities by routing algorithm. Then, a GPU parallel solution is used to accelerate the optimization of material handling cost (MHC) and transportation facilities cost (TFC) during the whole process. Finally, the framework is tested on three layout instances by two different meta-heuristic algorithms. Compared to no routing step or no GPU, our method is more accurate and effective in solving the LFLP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Reinforcement Learning Scheduling Research for Edge Servers 6

Reinforcement Learning Scheduling Research for Edge Servers

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Han, Yonggui Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Monitoring and Control xi'An Technological University State and Provincial Joint Engineering Lab. of Advanced Network Xi'an China

ISBN: (纸本)9798350305388

The rapid development of the Internet of Things and big data has brought about massive amounts of data and the need for real-time responses, which traditional computing models can no longer meet. Edge computing comes into being, which pushes computing resources and services closer to the source of data generation so that data can be processed and analyzed closer to the user. The scheduling of siting strategies for edge servers is crucial for improving the performance of edge computing networks. Traditional scheduling methods for siting strategies are often based on rules or optimization algorithms, which lack adaptive and intelligent management of the network *** paper proposes a reinforcement learning DQN-based edge server siting policy scheduling method, which aims to achieve intelligent management and resource optimization of edge computing networks by learning the optimal policy. Experimental results show that the method has significant performance advantages in terms of delay, load balancing and energy consumption. The experiment adopts DQN algorithm, which is based on Q-learning and guides the agent to make decisions by optimizing Q-value function. In this paper, the reinforcement learning scheduling algorithm of edge server is studied and compared with other three algorithms. According to the experimental results, the proposed reinforcement learning scheduling algorithm reduces the delay by 7%, the energy consumption by 3%, and the load balancing by 20% compared with the other three algorithms. This shows that the algorithm has significant advantages in improving system performance. © 2023 IEEE.

关键词： Reinforcement learning

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ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-term Load Forecasting 2

ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm-based Model for Short-...

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2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Dong, Enchong Luan, Wenting School of Control and Computer Engineering North China Electric Power University Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China

Given the uncertain, random, and volatile characteristics of power load, accurate forecasting of power load becomes challenging. To address this issue, this paper proposes an optimized prediction model by using a modified Adaptive White Noise Complete Integrated Empirical Modal Decomposition Algorithm (ICEEMDAN) and the improved Whale Optimization Algorithm (IWOA) for bidirectional short-term and long-term memory networks (BiLSTM). First of all, Australian power load data is preprocessed in this study. Then, the load data is decomposed into a series of sequences by using ICEEMDAN. After that, the improved whale algorithm is employed to analyze the BiLSTM super parameter optimization. Finally, each decomposed component is input into the IWOA-BiLSTM model for load forecasting. The results demonstrate that the proposed algorithm achieves higher accuracy in power load forecasting, compared to individual benchmark models and most portfolio models, indicating its applicability and practical value. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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DV-Hop Localization Strategy Based on Beacon Node Confidence Analysis 6

DV-Hop Localization Strategy Based on Beacon Node Confidence...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Shen, Han Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an China

ISBN: (纸本)9798350305388

To address the problem that the DV-Hop localization model for wireless sensor networks can no longer meet the current localization accuracy requirements, this paper proposes the concept of beacon node trustworthiness to provide ideas and basis for improving the optimization model. Based on the principle of least squares in the localization model, weighted least squares is introduced to present the trustworthiness for beacon nodes with different characteristics. To analyze the characteristics of the trustworthiness of beacon nodes, 18 functions are designed in terms of function instrumentality and decrement able, hierarchical faultiness, etc. Finally, the conclusions obtained from the credibility test are tested with weighted matrix and average hop distance weighting, and the localization error is reduced from 32.68% to 31.78%, with a role rate of 100%. The credibility characteristics of beacon nodes discussed in this paper provide ideas for subsequent optimization of the localization model. © 2023 IEEE.

关键词： Sensor nodes

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Disentangled Feature Distillation for Light Field Super-Resolution with Degradations

Disentangled Feature Distillation for Light Field Super-Reso...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhou, Linjie Gao, Wei Li, Ge Yuan, Hui Zhao, Tiesong Yue, Guanghui School of Electronic and Computer Engineering Peking University Shenzhen China School of Control Science and Engineering Shandong University Jinan China Fuzhou University Department of Communication Engineering Fuzhou China School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350313154

Light field (LF) super-resolution has achieved remarkable results with the assumption of only downsampling. However, real-world LF scenes contain multiple degradation effects, which makes it difficult for existing methods to deal with hybrid distortions. In this paper, we propose a disentangled feature distillation framework for LF super-resolution with degradations. To reduce the learning difficulty, we propose a feature disentanglement mechanism to split the mixed reconstruction for both super-resolution and denoising into two single task learning processes. We also propose a feature enhancement strategy via knowledge distillation to transfer prior feature of each single reconstruction to our task of mixed reconstruction. Finally, the separate restored representations are fused to reconstruct a clean high-resolution LF. Experiments demonstrate the superior performance of our framework for different scale factors and noise levels. Additionally, our approach can also obtain excellent performance for joint super-resolution and deblurring, showing its gencralization for practical LF super-resolution applications. © 2023 IEEE.

关键词： Distillation

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Impact of Parametric Uncertainties on the Steady-State Performances of DFIG-Based WECS 10th

Impact of Parametric Uncertainties on the Steady-State Perfo...

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10th EAI International Conference on Advancements of science and Technology, EAI ICAST 2022

作者： Ayenew, Endalew Biru, Getachew Mulatu, Asrat Berhanu, Milkias Electrical Power and Control Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Center of Excellence for Sustainable Energy College of Electrical and Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031411724

This chapter presents the study carried out to discover uncertain parameters in a model of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) and to investigate their effects on WECS plant while harvesting energy from wind at varying speeds. This is carried out by analytically formulating the steady-state models of a DFIG-based WECS and computing the parameters of these models at different wind speeds that drive wind turbines at rotor speed from sub-synchronous to super-synchronous. The main uncertain parameters in this system are the mutual reactances between stator and rotor windings, and the reactance of rotor winding. These variations happen due to the varying wind speed. The effect of parametric uncertainties on the steady-state performances of DFIG-based WECS is examined. The results depicted that the steady-state performances of DFIG-based WECS are influenced by uncertain phenomena. Typically a 0.5% change in mutual reactances caused up to 10 kW change in the generator output power. Mainly, it causes instability in the performances of the DFIG-based WECS and slightly reduces overall efficiency at a higher value of the wind speed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DFIG-based WECS HAWT Performances of WECS Uncertain parameter

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