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arXiv 2024年

作者： Silano, Giuseppe Caballero, Alvaro Liuzza, Davide Iannelli, Luigi Bogdan, Stjepan Saska, Martin Department of Cybernetics Czech Technical University in Prague Prague Czech Republic Department of System Engineering and Automation University of Seville Seville Spain Department of Engineering University of Sannio in Benevento Benevento Italy Fusion and Technology for Nuclear Safety and Security Department ENEA Frascati Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper presents a method for task allocation and trajectory generation in cooperative inspection missions using a fleet of multirotor drones, with a focus on wind turbine inspection. The approach generates safe, feasible flight paths that adhere to time-sensitive constraints and vehicle limitations by formulating an optimization problem based on Signal Temporal Logic (STL) specifications. An event-triggered replanning mechanism addresses unexpected events and delays, while a generalized robustness scoring method incorporates user preferences and minimizes task conflicts. The approach is validated through simulations in MATLAB and Gazebo, as well as field experiments in a mock-up scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： MATLAB

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Efficient Communications for Multi-Agent Reinforcement Learning in Wireless Networks

Efficient Communications for Multi-Agent Reinforcement Learn...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zefang Lv Yousong Du Yifan Chen Liang Xiao Shuai Han Xiangyang Ji Department of Information and Communication Engineering Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University School of Electronics and Information Engineering Harbin Institute of Technology Department of Automation Tsinghua University

Multi-agent reinforcement learning (RL) utilizes the observations and learning experiences shared among the agents to accelerate learning speed under partial observations and the resulting learning efficiency depends on the cooperative agent selection and the RL task state formulation. In this paper, we propose an efficient communication scheme for multi-agent RL that enables each learning agent to optimize the cooperative agent selection and the task state formulation to improve the learning performance and the quality of service for RL-based applications in wireless networks. Based on the local observation, the radio channel states, the similarity of RL task with neighboring agents and previous communication cost, this scheme formulates a communication state, which is input to a neural network to estimate the communication policy distribution. The RL task state of the learning agent, which consists of the local observation such as channel states and previous task performance, as well as the correlation between the shared and the local observation extracted based on the attention mechanism, is formulated to enhance the agent receptive field. In addition, the shared learning information is also exploited to update the local learning parameters such as the task Q-values and neural network weights and further improve the RL task policy exploration. As a case study, the proposed communication scheme is implemented in the multi-agent deep Q-network based anti-jamming unmanned aerial vehicle swarm communications and the performance gain over the benchmark is verified via simulation results.

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Distributed Parameter Estimation with Gaussian Observation Noises in Time-varying Digraphs

arXiv

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arXiv 2023年

作者： Yan, Jiaqi Ishii, Hideaki School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Information Physics and Computing The University of Tokyo Tokyo113-8656 Japan

In this paper, we consider the problem of distributed parameter estimation in sensor networks. Each sensor makes successive observations of an unknown d-dimensional parameter, which might be subject to Gaussian random noises. The sensors aim to infer the true value of the unknown parameter by cooperating with each other. To this end, we first generalize the so-called dynamic regressor extension and mixing (DREM) algorithm to stochastic systems, with which the problem of estimating a d-dimensional vector parameter is transformed to that of d scalar ones: one for each of the unknown parameters. For each of the scalar problem, both combine-then-adapt (CTA) and adapt-then-combine (ATC) diffusion-based estimation algorithms are given, where each sensor performs a combination step to fuse the local estimates in its in-neighborhood, alongside an adaptation step to process its streaming observations. Under weak conditions on network topology and excitation of regressors, we show that the proposed estimators guarantee that each sensor infers the true parameter, even if any individual of them cannot by itself. Specifically, it is required that the union of topologies over an interval with fixed length is strongly connected. Moreover, the sensors must collectively satisfy a cooperative persistent excitation (PE) condition, which relaxes the traditional PE condition. Numerical examples are finally provided to illustrate the established results. © 2023, CC BY.

关键词： Stochastic systems

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Research Roadmap of Service Ecosystems: A Crowd Intelligence Perspective

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International Journal of Crowd Science 2022年第4期6卷 195-222页

作者： Xue, Xiao Li, Guanding Zhou, Deyu Zhang, Yepeng Zhang, Lu Zhao, Yang Feng, Zhiyong Cui, Lizhen Zhou, Zhangbing Sun, Xiao Lu, Xudong Chen, Shizhan College of Intelligence and Computing Tianjin University Tianjin300072 China School of Software Shandong University Jinan250101 China Central University of Finance and Economics Beijing100081 China Shandong University Jinan250101 China School of Information Engineering China University of Geosciences Beijing100083 China Department of Automation Tsinghua University Beijing100084 China

With the mutual interaction and dependence of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. Such a development produces an ever-increasing effect on our lives and the functioning of the whole society. These facts call for research on these phenomena with a new theory or perspective, including what a smart society looks like, how it functions and evolves, and where its boundaries and challenges are. However, the research on service ecosystems is distributed in many disciplines and fields, including computer science, artificial intelligence, complex theory, social network, biological ecosystem, and network economics, and there is still no unified research framework. The researchers always have a restricted view of the research process. Under this context, this paper summarizes the research status and future developments of service ecosystems, including their conceptual origin, evolutionary logic, research topic and scale, challenges, and opportunities. We hope to provide a roadmap for the research in this field and promote sound development. © The author(s) 2022.

关键词： Information services

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High Step-down Converters for Battery Charging in Photovoltaic Applications 35

High Step-down Converters for Battery Charging in Photovolta...

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35th International Conference on Power Electronics and Electrical Drives, EDPE 2021

作者： Malović, Zvonimir Vuger, Nikola Šunde, Viktor Ban, Željko Department of Electric Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Control and Computer Engineering University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

This paper describes non-isolated high step-down converters used to connect photovoltaic modules to the battery storage of a bicycle charging station. Due to the high voltage difference between input and output, some problems may occur with conventional non-isolated DC-DC buck converters. The characteristics of alternative topologies of high step-down converters have been explored. Ideal converter models were created in the PLECS simulation software package based on which the voltage and current stress of the semiconductor switches were determined. Based on measured voltage and current relations, commercial switches were selected. Based on catalogue data, electro-thermal models of the switches were created. A comparative analysis of converters based on conduction and switching losses was performed. Converters were also compared based on the number of components and complexity of control. ©2021 IEEE.

关键词： Batteries Conduction losses Duty cycle Photovoltaic module Step-down converters Switching losses

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UPS with PFC input stage for railway applications with improved immunity on input overvoltage and energy strikes 35

UPS with PFC input stage for railway applications with impro...

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35th International Conference on Power Electronics and Electrical Drives, EDPE 2021

作者： Šolc, Ivan Hećimović, Ante Šunde, Viktor Ban, Željko Department of Electric Machines Drives and Automation University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Control and Computer Engineering University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

The main topic of this article is uninterruptible power supply (UPS) in railway applications, focusing on the power factor correction (PFC) stage of UPS and its interaction with overvoltages and energy strikes. The PFC topology used in this article is a two-phase interleaved DC/DC boost converter with diode bridge rectifier connected to the 25 kV, 50 Hz contact line via 25 kV/0.23 kV transformer. Since overvoltages and energy strikes can occur in the contact line due to thyristor locomotive commutation or self-induction, these interferences can disturb the normal operation of the PFC module and in some cases even destroy the module. A simulation model of the PFC is created, tested with simulated overvoltages, and three modifications/improvements to the existing topology are proposed. ©2021 IEEE.

关键词： Battery charger Energy strike Interferences Overvoltage Power factor correction (PFC) Railway Uninterruptible power supply (UPS)

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View-Robust Neural Networks for Unseen Human Action Recognition in Videos

View-Robust Neural Networks for Unseen Human Action Recognit...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Yu, Jiahui Ma, Tianyu Ju, Zhaojie Chen, Hang Xu, Yingke Zhejiang University Department of Biomedical Engineering Hangzhou China Binjiang Institute of Zhejiang University Innovation Center for Smart Medical Technologies & Devices Hangzhou China Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Data-driven deep learning achieved excellent performance for human action recognition. However, unseen action recognition remains a challenge for most existing neural networks. Because the action categories, collection perspectives, and scenarios considered during data collection are limited. Compared with class-unseen action recognition, view-unseen action recognition in videos is under-explored. This paper proposes view-robust neural networks (VR-Net) to recognize unseen actions in videos. The VR-Net consists of a 3D pose estimation module, skeleton adaptive transformation neural networks, and classification modules. We first extract 3D skeleton models from the video sequence based on existing pose estimation methods. Next, we propose a skeleton representation transformation scheme and achieve it based on Convolutional Neural Networks (VR-CNN) and Graph Neural Networks (VR-GCN), resulting in the optimal skeleton representations. Futhermore, we explore an associate optimization scheme and a fused output method. We evaluate the proposed neural networks on three challenging benchmarks, i.e., NTU RGB-D dataset (NTU), Kinetics-400 dataset, and Human3.6M dataset (H3.6M). The experimental results show that view robust neural networks achieve the top performance compared to state-of-the-art RGB-based and skeleton-based works, such as 93.6% on the NTU (CV) and 94.6% on the Kinetics-400 dataset (Top-5). The proposed neural networks significantly improve the recognition performance for unseen action recognition, such as 86.8% on the H3.6M (View 2). © 2022 IEEE.

关键词： Convolutional neural networks

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Modelling and experimental verification of device with a submerged evaporator for beer cooling

Modelling and experimental verification of device with a sub...

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Proceedings of the International Convention MIPRO

作者： Nina Š are Branimir Vuletić Komljen Jadranko Matuš ko Kreš imir Osman Department of Electrical Engineering Zagreb University of Applied Sciences Croatia Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing Croatia

The paper presents a mathematical model of a submerged evaporator laboratory plant for cooling beer. The model is derived as a system with distributed parameters of the counterclockwise type. Additionally, the results of the experimental testing of the submerged evaporator laboratory plant are presented and based on such simplified a static model is identified, which is suitable for control system design.

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GSLB: The Graph Structure Learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Memristive feature and mechanism induced by laser-doping in defect-free 2D semiconductor materials

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Journal of Semiconductors 2024年第7期45卷 85-91页

作者： Xiaoshan Du Shu Wang Qiaoxuan Zhang Shengyao Chen Fengyou Yang Zhenzhou Liu Zhengwei Fan Lijun Ma Lei Wang Lena Du Zhongchang Wang Cong Wang Bing Chen Qian Liu College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao 266590China National Center for Nanoscience and Technology&University of Chinese Academy of Sciences Beijing 100190China Hebei University of Water Resources and Electric Engineering Electrical Automation Department Cangzhou 061001China The MOE Key Laboratory of Weak-Light Nonlinear Photonics and International Sino-Slovenian Join Re-search Center on Liquid Crystal Pho-tonics TEDA Institute of Applied Physics and School of PhysicsNankai UniversityTianjin 300457China College of Mathematics and Physics Shandong Advanced Optoelectronic Materials and Technologies Engineering LaboratoryQingdao Uni-versity of Science and TechnologyQingdao 266061China Department of Physics Capital Normal UniversityBeijing 100048China Department of Advanced Materials and Computing International Iberian Nanotechnology Laboratory(INL)4715-330 BragaPortugal College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China

Memristors as non-volatile memory devices have gained numerous attentions owing to their advantages in storage,in-memory computing, synaptic applications, etc. In recent years, two-dimensional(2D) materials with moderate defects have been discovered to exist memristive feature. However, it is very difficult to obtain moderate defect degree in 2D materials, and studied on modulation means and mechanism becomes urgent and essential. In this work, we realized memristive feature with a bipolar switching and a configurable on/off ratio in a two-terminal MoS_(2) device(on/off ratio ~100), for the first time, from absent to present using laser-modulation to few-layer defect-free MoS_(2)(about 10 layers), and its retention time in both high resistance state and low resistance state can reach 2×10^(4) s. The mechanism of the laser-induced memristive feature has been cleared by dynamic Monte Carlo simulations and first-principles calculations. Furthermore, we verified the universality of the laser-modulation by investigating other 2D materials of TMDs. Our work will open a route to modulate and optimize the performance of 2D semiconductor memristive devices.

关键词： 2D-material memristor laser doping laser direct writing memristive mechanism

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