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arXiv 2023年

作者： Zou, Xinying Perlaza, Samir M. Esnaola, Iñaki Altman, Eitan INRIA Centre Inria d'Université Côte d'Azur Sophia Antipolis06902 France Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States GAATI Laboratory Université de la Polynésie Française Faaa98702 French Polynesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Laboratoire d'Informatique d'Avignon Université d'Avignon France

In this paper, the worst-case probability measure over the data is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. More specifically, the worst-case probability measure is a Gibbs probability measure and the unique solution to the maximization of the expected loss under a relative entropy constraint with respect to a reference probability measure. Fundamental generalization metrics, such as the sensitivity of the expected loss, the sensitivity of the empirical risk, and the generalization gap are shown to have closed-form expressions involving the worst-case data-generating probability measure. Existing results for the Gibbs algorithm, such as characterizing the generalization gap as a sum of mutual information and lautum information, up to a constant factor, are recovered. A novel parallel is established between the worst-case data-generating probability measure and the Gibbs algorithm. Specifically, the Gibbs probability measure is identified as a fundamental commonality of the model space and the data space for machine learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

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Structural reduction of CRNs with linear sub-CRNs

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IFAC-PapersOnLine 2021年第14期54卷 149-154页

作者： Katalin M. Hangos György Lipták Gábor Szederkényi Systems and Control Laboratory Computer and Automation Research Institute P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University Budapest Hungary

A novel systems theory-based structural reduction method is proposed in this paper that can be applied to chemical reaction networks (CRNs) including subsystems consisting of linear reactions, i.e. a linear sub-CRN. The reduced model is a delayed CRN with distributed delays having less complexes (monomials) and reactions than the original model. The reduction is based on the fact that the input/output response of a linear sub-CNR can be seen as a time-delayed input to output relationship, therefore linear sub-CNRs can be interpreted as reactions with distributed delay from their input to their output complexes. The practically important example of a kinetic model describing the spread of the COVID-19 epidemic is used as a case study to illustrate the basic concepts and the computation method.

关键词： Delay Model reduction Chemical Reaction Networks COVID

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Simultaneous tactile exploration and grasp refinement for unknown objects

arXiv

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arXiv 2021年

作者： de Farias, Cristiana Marturi, Naresh Stolkin, Rustam Bekiroglu, Yasemin The Extreme Robotics Laboratory School of Metallurgy and Materials University of Birmingham Birmingham United Kingdom Chalmers University of Technology Department of Electrical Engineering Automatic Control research group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

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Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 600-605页

作者： LIU Min DENG Bin TANG Ying WU Minghu WANG Juan Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System.Hubei University of Technology Wuhan 430068.China Department of Electrical and Computer Engineering.Rowan University GlassboroNJ 08028USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Smart Education Qingdao Academy of Intelligent IndustriesQingdao 266041ShandongChina

The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring ***,there is still great challenge because of the limited wireless network *** resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image ***,an adaptively learning background model is improved to discover successive similar image frames from the inputting video ***,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element ***,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or *** evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to *** to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

关键词： wireless visual sensor network(WVSN) temporal redundancy foreground background dynamic texture

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Surface Charging Phenomena on HVDC Spacers for Compressed SF_(6) Insulation and Charge Tailoring Strategies

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CSEE Journal of Power and Energy systems 2020年第1期6卷 83-99页

作者： Chuanyang Li Yuan Xu Chuanjie Lin Geng Chen Youping Tu Yao Zhou Zhipeng Lei Tao Han Simone Vincenzo Suraci Jian Wang Weidong Liu M.Tariq Nazir Shun He Andrea Cavallini Giovanni Mazzanti Davide Fabiani Jinliang He Electrical Insulation Research Center Institute of Materials ScienceElectrical and Computer EngineeringUniversity of ConnecticutStorrsCT 06269USA. China Electric Power Research Institute Beijing 100192China Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”University of BolognaViale Risorgimento 2Bologna40136Italy State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Beijing Area Major Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijing 102206China Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control National and Provincial Joint EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Smart Grid of Education Ministry School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Mechanical and Manufacturing Engineering University of New South Wales SydneyNSW 2052Australia Electric Power Research Institute Yunnan Electric Power Grid Co.ltd.KunmingYunnan 650217China

Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover *** paper discusses the laws regarding distribution patterns of surface charges and the related *** field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous *** addition,the main surface charge control methods for epoxy resin are summarized and *** potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.

关键词： GIL HVDC insulator SF6 surface charge accumulation surface flashover

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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A survey on security and privacy issues of UAVs

arXiv

引用

arXiv 2021年

作者： Mekdad, Yassine Aris, Ahmet Babun, Leonardo Fergougui, Abdeslam E.L. Conti, Mauro Lazzeretti, Riccardo Uluagac, A. Selcuk Laboratory of Computer Networks and Systems Moulay Ismail University of Meknes Zitoune Meknes11201 Morocco Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Mathematics University of Padua Padua35121 Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome00185 Italy

In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs’ security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs. © 2021, CC BY.

关键词： Drones

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Parallel Building: A Complex System Approach for Smart Building Energy Management

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IEEE/CAA Journal of Automatica Sinica 2019年第6期6卷 1452-1461页

作者： Abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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State and parameter estimation of suspended load using quadrotor onboard sensors

State and parameter estimation of suspended load using quadr...

引用

International Conference on Unmanned Aircraft systems (ICUAS)

作者： Vicko Prkačin Ivana Palunko Ivan Petrović Laboratory for intelligent autonomous systems (LARIAT) Department of Electrical and Computer Engineering University of Dubrovnik Dubrovnik Croatia Laboratory for autonomous systems and mobile robotics (LAMOR) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In this paper, we address the problem of state and parameter estimation of a suspended load using quadrotor onboard sensors. Flying with a suspended load alters the quadrotor flight dynamics, sometimes to a large extent, making it a challenging and hazardous task. Monitoring the state of the suspended load is vital for safe flight operations while parameter estimation decouples the control design from specific parameter-dependent solutions. We take advantage of the fact that the forces and torques the suspended load exerts on the quadrotor can be detected in the aircraft IMU measurements as a low frequency harmonic. Thus, by combining the available measurements and system mass we are able to estimate the state of the suspended load. Since our approach stems from understanding the aircraft-load interaction, we start off by delineating the full system model of the quadrotor with a suspended load. To isolate the natural frequency of the suspended load and determine the length of suspension cable, we employ the Fast Fourier Transform (FFT). The proposed estimation algorithms are validated through extensive numerical simulations and experimentally.

关键词： Estimation Aircraft Load modeling Sensors Atmospheric modeling Parameter estimation Numerical models

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