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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Preface

Lecture Notes in Control and Information Sciences

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Lecture Notes in control and Information sciences 2021年 485卷 v-viii页

作者： Faulwasser, Timm Müller, Matthias A. Worthmann, Karl Department of Electrical Engineering and Information Technology Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Faculty of Electrical Engineering and Computer Science Institute of Automatic Control Leibniz University Hannover Hannover Germany Faculty of Mathematics and Natural Sciences Institute for Mathematics Optimization-based Control Technische Universität Ilmenau Ilmenau Germany

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Influences of the workpiece pose errors on gear skiving accuracy

Research Square

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Research Square 2021年

作者： Lin, Xiaochuan Hong, Rongjing Wang, Yawen Peng, Yun Ren, Xiaolei School of Mechanical and Power Engineering Nanjing Tech University Nanjing211816 China Jiangsu Key Laboratory of Digital Manufacturing for Industrial Equipment and Control Technology Nanjing Tech University Nanjing211816 China No.30 Puzhu Sothern Road Pukou District Jiangsu Nanjing China Department of Mechanical and Aerospace Engineering University of Texas at Arlington 701 S Nedderman Dr ArlingtonTX76019 United States College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics No.29 Yudao Road Qinhuai District Nanjing210016 China Department of Computer Science and Engineering University of Texas at Arlington 701 S Nedderman Dr ArlingtonTX76019 United States

This paper investigates the influence of the workpiece pose errors on gear skiving accuracy. First, based on the theory of homogeneous coordinate transformation, the mapping relationship between the tooth flank of the workpiece and the skiving tool is established according to the principle of gear skiving. Second, the workpiece pose errors are defined, and the relationship between each workpiece pose error and cutting depth is analyzed. According to the kinematic chain of the machine tool, a method for measuring the workpiece pose errors based on actual inverse kinematics is proposed. Third, the tooth deviations from individual workpiece pose errors, and the coupling workpiece pose errors are studied. The sensitive errors are obtained from the tooth deviation by numerical simulation. Finally, the numerical analysis is verified by gear skiving experiments, and the proposed method can be used to identify the possible workpiece pose errors. Furthermore, the PSO algorithm is introduced to guide the workpiece setting pose errors in the gear skiving process. © 2021, CC BY.

关键词： Inverse kinematics

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

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Reliability of statistic evaluation of microscopic pictures taken from knitted fabrics

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Journal of Physics: Conference Series 2015年第1期633卷

作者： A Ehrmann T Blachowicz H Zghidi M O Weber Faculty of Engineering Sciences and Mathematics Bielefeld University of Applied Sciences 33609 Bielefeld Germany Institute of Physics - Center for Science and Education Silesian University of Technology 44-100 Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology 44-100 Gliwice Poland Faculty of Textile and Clothing Technology Niederrhein University of Applied Sciences 41065 Mönchengladbach Germany

One of the techniques which can be used to quantitatively evaluate images statistically is the so-called random-walk approach. The resulting Hurst exponent is a measure of the complexity of the picture. Especially long, fine elements in the image, such as fibres, influence the Hurst exponent significantly. Thus, determination of the Hurst exponent has been suggested as new method to measure the hairiness of yarns or knitted fabrics, since existing hairiness measurement instruments are based on different measurement principles which are not comparable. While the principal usability of this method for hairiness detection has been shown in former projects, the absolute value of the calculated Hurst exponents depends on the technique to take the photographic image of a sample, to transfer it into a monochrome picture, and on possible image processing steps. This article gives an overview of edge detection filters, possible definitions of the threshold value between black and white for the transformation into a monochrome image, etc. It shows how these parameters should be chosen in case of typical textile samples and correlates the challenges of this novel method with well-known problems of common techniques to measure yarn and fabric hairiness.

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Roadmap on Quantum Thermodynamics

arXiv

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arXiv 2025年

作者： Campbell, Steve D’Amico, Irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, Simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.S. Busch, Thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, Salambô Deffner, Sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, Sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, Thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya S. Hernández-Gómez, Santiago Hu, Chang-Kang Iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel T. Lasek, Aleksander Lemziakov, Sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, Taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark T. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario Sánchez, Rafael Santos, Alan C. Sarthour, Roberto S. Sela, Eran Solfanelli, Andrea Souza, Alexandre M. Splettstoesser, Janine Tan, Dian Tesser, Ludovico Van Vu, Tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

The last two decades has seen quantum thermodynamics become a well established field of research in its own right. In that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. This Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, The Authors. All rights reserved.

关键词： Temperature

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String Stability control Strategy Analysis of Mixed Traffic Flow with the CIVs and NCVs

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Journal of Physics: Conference Series 2021年第1期2025卷

作者： Junjie Zhang Can Yang Haiyang Yu Jun Zhang Zixiao Wang Hefei Innovation Research Institute Beihang University Hefei China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China School of Computer Science and Technology Anhui University Hefei China

With the development of vehicle-to-infrastructure cooperation system, a mixed traffic phenomenon with non-connected vehicles (NCVs) and connected and intelligent vehicles (CIVs) will exist over a long period of time. Therefore, the mixed traffic flow stability control has become a hot topic in the future. In order to improve the string stability in the complex and changeable internet of vehicles environment, it is necessary to propose the optimal control method of string stability in the mixed traffic flow. In this paper, NCV and CIV car-following modes are employed to propose a local platoon control method of the connected vehicle, which can achieve the purpose of optimizing the mixed traffic flow stability. Two types of local mixed platoon are considered when the effective communication distance with two vehicles in the vehicle-to-vehicle (V2V) communication. Numerical simulations results show that our proposed string stability control strategy has the effectiveness in the improvement of the mixed traffic flow stability.

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High-Efficiency Dielectric Metasurfaces for Polarization-Dependent Terahertz Wavefront Manipulation

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Advanced Optical Materials 2017年第1期6卷

作者： Huifang Zhang Xueqian Zhang Quan Xu Chunxiu Tian Qiu Wang Yuehong Xu Yanfeng Li Jianqiang Gu Zhen Tian Chunmei Ouyang Xixiang Zhang Cong Hu Jiaguang Han Weili Zhang Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering and the Key Laboratory of Optoelectronics Information and Technology Tianjin University Tianjin 300072 P. R. China Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin 541004 P. R. China School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 USA

Recently, metasurfaces made up of dielectric structures have drawn enormous attentions in the optical and infrared regimes due to their high efficiency and designing freedom in manipulating light propagation. Such advantages can also be introduced to terahertz frequencies where efficient functional devices are still lacking. Here, polarization-dependent all-silicon terahertz dielectric metasurfaces are proposed and experimentally demonstrated. The metasurfaces are composed of anisotropic rectangular-shaped silicon pillars on silicon substrate. Each metasurface holds dual different functions depending on the incident polarizations. Furthermore, to suppress the reflection loss and multireflection effect in practical applications, a high-performance polarization-independent antireflection silicon pillar array is also proposed, which can be patterned at the other side of the silicon substrate. Such all-silicon dielectric metasurfaces are easy to fabricate and can be very promising in developing next-generation efficient, compact, and low-cost terahertz functional devices.

关键词： dielectric metasurfaces high efficiency polarization-dependent terahertz wavefront control

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Exact Differentiator with Lipschitz Continuous Output and Optimal Worst-Case Accuracy Under Bounded Noise

Exact Differentiator with Lipschitz Continuous Output and Op...

引用

IEEE Conference on Decision and control

作者： Rodrigo Aldana-López Richard Seeber Hernan Haimovich David Gómez-Gutiérrez Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas (CIFASIS) CONICET-UNR Rosario Argentina Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

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