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Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing

Journal of Physics: Conference Series 2021年第1期2024卷

作者： Zheng Chen Jian Wang Hualiang Zhang School of Information Engineering Shenyang University of Chemical Technology Shenyang Liaoning 110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Liaoning 110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning 110169 China

Compared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic non-destructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology.

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Development of a digital platform for the implementation of distributed control and navigation systems for underwater robotic systems performing technological operations in Arctic

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IOP Conference Series: Earth and Environmental Science 2021年第1期816卷

作者： V F Filaretov D A Yukhimets A V Zuev A S Gubankov D D Minaev Department of Automation and Robotics Far Eastern Federal University Vladivostok Russia Robotic Lab Institute of Automation and Control Processes Far Eastern Branch of Russian Academy of Science Vladivostok Russia Laboratory of intelligent information systems for marine robots Institute of Marine Technology Problems Far Eastern Branch of Russian Academy of Science Vladivostok Russia PAO 'Dalpribor' Vladivostok Russia

The paper proposes the architecture of a digital platform for the implementation of distributed control and navigation systems for underwater robotic systems that perform technological operations in Arctic. A system of command has been developed for this platform, which provides flexible assignment of various types and purposes of underwater robots missions. The concept of creating distributed control systems of the underwater robots is proposed. It provides the compatibility of existing on-board underwater robots systems with the proposed solution based on compact hydroacoustic systems of global hydroacoustic navigation developed in PAO 'Dalpribor' (Vladivostok).

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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

arXiv

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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Editorial Special issue on approximate dynamic programming and reinforcement learning

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控制理论与应用（英文版） 2011年第3期 309-309页

作者： Silvia Ferrari Jagannathan Sarangapani Frank L. Lewis Laboratory for Intelligent Systems and Control(LISC)Department of Mechanical Engineering & Materials Science Box 90300 Duke University Durham NC 27708-0005 Department of Electrical & Computer Engineering University of Missouri-Rolla MO 65401 U.S.A. Automation and Robotics Research Institute The University of Texas at Arlington 7300 Jack Newell Blvd. S Ft. Worth Texas 76118-7115 U.S.A.

We are extremely pleased to present this special issue of the Journal of control Theory and *** dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain environments over *** optimizes the sensing objectives accrued over a future time interval with respect to an adaptive control law,conditioned on prior knowledge of the system,its state,and uncertainties.A numerical search over the present value of the control minimizes a Hamilton-Jacobi-Bellman (HJB) equation providing a basis for real-time,approximate optimal control.

关键词： reinforcement learning dynamic programming optimal control effective approach prior knowledge control Theory real-time control law problems special search future ADP basis

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Application of Big Data Analysis in Flood control of Small Reservoirs -A Case study of Liaoning Province, China

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Journal of Physics: Conference Series 2020年第1期1693卷

作者： Xi Chen Tong Zhang Zeyu Zheng Tianran Wang Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang 110016 China Liaoning Provincial Department of Water Resources Shenyang 110003 China Liaoning River and Reservoir Management Service Center Shenyang 110003 China

Small reservoirs play an important role in the safety of flood control and economic development. Their operation and management are characterized by a large number of objects and a large amount of information, which requires effective data analysis means support. Based on this, big data analysis technology has been gradually applied and popularized in the operation and management of small reservoirs. In this paper, we first set up a small reservoir flood reporting system based on the data collection and analysis of various aspects such as reservoir capacity, characteristic water level, real-time data of water and rain situation, meteorological data, and presented the results in the paper. Secondly, we analyzed the under-reporting situation during the flood control period of the reservoir, and explored the under-reporting factors and the time when the under-reporting occurred. Finally, we conduct a time series analysis on the situation of water level exceeding the limit of 50 reservoirs during the flood control period, so that we can find the key reservoirs and important time points of flood control more intuitively and provide reference for the smooth development of the management of small reservoirs.

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Comparative analysis of control strategies for the reduction of tracking error in CPV systems

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AIP Conference Proceedings 2023年第1期2841卷

作者： Sergio I. Palomino-Resendiz Víctor Cadix-Martín Basil Mohammed Al-Hadithi Diego A. Flores-Hernández Instituto Politécnico Nacional – ESIME-ZAC Departamento de Control y Automatización Mexico City 07738 Mexico Escuela Técnica Superior de Ingeniería y Diseño Industrial Departamento de Ingeniería Eléctrica Automática y Física Aplicada Universidad Politécnica de Madrid Madrid 28012 Spain Intelligent Control Group Universidad Politécnica de Madrid Centre for Automation and Robotics UPM-CSIC Madrid 28006 Spain Instituto Politécnico Nacional – UPIITA Applied Dynamic Systems Laboratory Mexico City 07340 Mexico

In this work, an experimental methodology is shown that allows selecting the best controller alternative to govern a two-axis solar tracking system in trajectory tracking tasks. This, based on the comparison of the performance of different controllers during the tracking of a solar trajectory (obtained offline with the help of a numerical method) using a prototype solar tracking system in a simulation environment. The selection of the controllers is based on the performance reported in the literature and commercially for other alternatives based on parameters such as: tracking error and energy consumption, computational cost, ease of implementation, ease of tuning, among others. However, for the final validation and selection of the controller, a brief analysis of the numerical results obtained during the follow-up tests is provided.

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

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Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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