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arXiv 2024年

作者： Ji, Zhoulin Lin, Chenhao Wang, Hang Shen, Chao School of Software Engineering Xi’an Jiaotong University China School of Cyber Science and Engineering Xi’an Jiaotong University China School of Automation Science and Engineering Xi’an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their utility for comprehensive research. To fill this gap, we propose the Speech-Forensics dataset by extensively covering authentic, synthetic, and partially forged speech samples that include multiple segments synthesized by different high-quality algorithms. Moreover, we propose a TEmporal Speech LocalizaTion network, called TEST, aiming at simultaneously performing authenticity detection, multiple fake segments localization, and synthesis algorithms recognition, without any complex post-processing. TEST effectively integrates LSTM and Transformer to extract more powerful temporal speech representations and utilizes dense prediction on multi-scale pyramid features to estimate the synthetic spans. Our model achieves an average mAP of 83.55% and an EER of 5.25% at the utterance level. At the segment level, it attains an EER of 1.07% and a 92.19% F1 score. These results highlight the model’s robust capability for a comprehensive analysis of synthetic speech, offering a promising avenue for future research and practical applications in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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CPT-based Active Power Filter with Repetitive Controller and Capacitor Voltage Feedforward under Non-Ideal Grid

CPT-based Active Power Filter with Repetitive Controller and...

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IEEE ANDESCON

作者： José de Arimatéia O. Filho Paulo Fernando Silva Luis De Oro Arenas Tiago Davi Curi Busarello Helmo Kelis Morales Paredes Institute of Science and Technology of Sorocaba São Paulo State University (Unesp) Sorocaba Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina (UFSC) Blumenau Brazil

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

This paper proposes the design of a repetitive controller along with capacitor voltage feedforward applied to an LCL filter active power filter (APF). The repetitive controller can operate in systems with disturbances exhibiting high harmonic content and the feedforward action can reshape the output impedance of the APF. The conservative power theory (CPT) is proposed to obtain reference signals for the purpose of total mitigation of unwanted currents. The proposed technique ensures that only the active current flows through the electrical grid, while maintaining the stability of the APF-grid system through passivity theory. Finally, the proposed control strategy is validated using the Matlab/Simulink® software under several conditions of grid imbalances and harmonic distortions.

关键词： Power filters Stability criteria Capacitors Power system stability Active filters Filtering theory Impedance Voltage control Feedforward systems Power harmonic filters

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Preface: 4^th International Conference on Robotics, Intelligent automation and Control Technologies (RIACT 2023)

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AIP Conference Proceedings 2024年第1期3216卷

作者： Doss, Arockia Selvakumar Arockia Short, Michael Schilberg, Daniel Design and Automation Research Group School of Mechanical Engineering Vellore Institute of Technology Department of Mechatronics and Mechanical Engineering University of Applied Sciences School of Computing Engineering and Digital Technologies Teesside University Department of Mechatronics and Mechanical Engineering University of Applied Sciences

Preface: 4th International Conference on Robotics, Intelligent automation and Control Technologies (RIACT 2023), AIP Conference Proceedings, Volume 3216, Issue

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Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks

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IEEE Transactions on Mobile computing 2025年

作者： Zhang, Jiale Yi, Longhao Gao, Xiaofeng Bhatti, Shahzad Sarwar Yuan, Ting Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering China Emerson University Faculty of Computing and Emerging Technologies Multan Pakistan Shanghai Jiao Tong University Department of Automation China

With the popularization of smart phones, mobile crowdsourcing emerged and gained growing attention in the recent years. Mobile users are now able to conduct complex tasks with the communication between each other. In this paper, we study the task scheduling problem in the mobile crowdsourcing systems based on the spontaneously formed mobile social networks (MSNs). We introduce two crowdsourcing task scheduling problems under this system model, with one problem aiming to minimize the total cost of some crowdsourcing tasks and the other focusing on minimizing the final completion time of the tasks belonging to the same project. We introduce a unified framework to solve the problems and propose two approximation algorithms for these two problems in the offline versions respectively and prove Their approximation ratios accordingly. Based on the two algorithms, we further design two online algorithms to deal with the workers' dynamism and also analyze their competitive ratios. Finally, we verify the effectiveness and efficiency of the proposed methods through numerical experiments on real and synthetic datasets. © 2002-2012 IEEE.

关键词： Approximation algorithms

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Zero Current Detection Circuit based on Saturable Transformer Application in GaN based DC-DC Converter with Triple-Mode Switching

Zero Current Detection Circuit based on Saturable Transforme...

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International Scientific Conference on Power and Electrical engineering of Riga Technical University (RTUCON)

作者： Kaspars Kroičs Kristiāns Gaspersons Martin Makar Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper analyzes application of saturable transformer for zero current detection in GaN based converter at a switching frequency above 100 kilohertz. The design of the transformer is discussed in a simplified way, development of experimental prototype of the transformer has been given. Zero current detection (ZCD) has been tested on experimental prototype of DC-DC converter. Delay times introduced by the transformer, detection circuit and digital control system have been analyzed. The efficiency of the converter by introducing triple-mode switching with ZCD circuit has been investigated. The results show that the summary delay is at least 250 nanoseconds and at such frequencies the core heats up significantly and lowers efficiency which limits the application of the circuit for switching frequencies above 250 kilohertz.

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Adversarial Attacks on Skeleton-Based Sign Language Recognition 16th

Adversarial Attacks on Skeleton-Based Sign Language Recognit...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Li, Yufeng Han, Meng Yu, Jiahui Lin, Changting Ju, Zhaojie School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou310058 China College of Computer Science and Technology Zhejiang University Hangzhou310058 China School of Computing University of Portsmouth PortsmouthPO13HE United Kingdom

ISBN: (纸本)9789819964826

Despite the impressive performance achieved by sign language recognition systems based on skeleton information, our research has uncovered their vulnerability to malicious attacks. In response to this challenge, we present an adversarial attack specifically designed to sign language recognition models that rely on extracted human skeleton data as features. Our attack aims to assess the robustness and sensitivity of these models, and we propose adversarial training techniques to enhance their resilience. Moreover, we conduct transfer experiments using the generated adversarial samples to demonstrate the transferability of these adversarial examples across different models. Additionally, by conducting experiments on the sensitivity of sign language recognition models, we identify the optimal experimental parameter settings for achieving the most effective attacks. This research significantly contributes to future investigations into the security of sign language recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musculoskeletal system

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Task Coordination and Trajectory Optimization for Multi-Aerial Systems via Signal Temporal Logic: A Wind Turbine Inspection Study

arXiv

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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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