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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： J.M.C. Izquierdo Y.A. Dimitriadis J.L. Coronado Group of Vision and Robotics. Department of Automatic Electronic and Industrial Electricity School of Industrial Engineering. University of Murcia Murcia Spain Department of Signal Theory Telecommunications and Telematics Enginnering School of Telecommunications Engineering. University of Valladolid Spain Department of Systems Engineering and Control School of Industrial Engineering. University of Valladolid Spain

Many research works have been reported with respect to the relation between neural and fuzzy systems. Looking for a synergistic relation of these technologies, an important property of neural network-based systems is their learning capacity, that permits to embed self-organization in fuzzy logic systems. In this paper, a new neuro-fuzzy system, called FasBack, is proposed, that combines learning based on prediction error minimization and pattern matching. FasBack adds error-based learning to a previously proposed model, called FasArt, which extended and formalized neural networks models of the ART family, as fuzzy logic systems. Experimental results are presented in nonlinear systems identification problems, typically used in the literature.

关键词： Fuzzy logic control systems Fuzzy systems Hardware Industrial engineering Neural networks Subspace constraints System performance Service robots Robot vision systems

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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SLAM-based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization

arXiv

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arXiv 2024年

作者： Wang, Jiang He, Yuanzheng Su, Daobilige Itoyama, Katsutoshi Nakadai, Kazuhiro Wu, Junfeng Huang, Shoudong Li, Youfu Kong, He The Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology No. 1088 Xueyuan Avenue Shenzhen China The College of Engineering China Agricultural University Beijing China The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan The School of Data Science The Chinese University of Hong Kong Shenzhen China The Robotics Institute University of Technology Sydney Sydney Australia The Department of Mechanical Engineering City University of Hong Kong Hong Kong

Robot audition systems with multiple microphone arrays have many applications in practice. However, accurate calibration of multiple microphone arrays remains challenging because there are many unknown parameters to be identified, including the relative transforms (i.e., orientation, translation) and asynchronous factors (i.e., initial time offset and sampling clock difference) between microphone arrays. To tackle these challenges, in this paper, we adopt batch simultaneous localization and mapping (SLAM) for joint calibration of multiple asynchronous microphone arrays and sound source localization. Using the Fisher information matrix (FIM) approach, we first conduct the observability analysis (i.e., parameter identifiability) of the above-mentioned calibration problem and establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. We also discover several scenarios where the unknown parameters are not uniquely identifiable. Subsequently, we propose an effective framework to initialize the unknown parameters, which is used as the initial guess in batch SLAM for multiple microphone arrays calibration, aiming to further enhance optimization accuracy and convergence. Extensive numerical simulations and real experiments have been conducted to verify the performance of the proposed method. The experiment results show that the proposed pipeline achieves higher accuracy with fast convergence in comparison to methods that use the noise-corrupted ground truth of the unknown parameters as the initial guess in the optimization and other existing frameworks. © 2024, CC BY.

关键词： Mapping

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SLAM-Based Joint Calibration of Differential RSS Sensor Array and Source Localization

SLAM-Based Joint Calibration of Differential RSS Sensor Arra...

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Annual Conference of Industrial Electronics Society

作者： Linya Fu Xu Qiao Shoudong Huang Guoqiang Mao Zhiyun Lin Youfu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern Uni. of Sci. and Tech. Shenzhen China Robotics Institute Uni. of Technology Sydney Sydney Australia State Key Laboratory of Integrated Services Networks Xidian University China Department of Mechanical Engineering City University of Hong Kong China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern Uni. of Sci. and Tech. Shenzhen China

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://***/SUSTech2022/DRSS-sensor-array-calibration.

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Immersion of General Nonlinear systems Into State-Affine Ones for the Design of Generalized Parameter Estimation-Based Observers: A Simple Algebraic Procedure

arXiv

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arXiv 2024年

作者： Ortega, Romeo Bobtsov, Alexey Romero, Jose Guadalupe Fang, Leyan Departamento Académico de Ingeniería Eléctrica y Electrónica ITAM Ciudad de México Mexico Control Systems and Robotics Department ITMO St. Petersburg Russia Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China Departamento Académico de Ingeniería Eléctrica y Electrónica ITAM Río Hondo 1 Ciudad de México01080 Mexico

Generalized parameter estimation-based observers have proven very successful to deal with systems described in state-affine form. In this paper, we enlarge the domain of applicability of this method proposing an algebraic procedure to immerse an ndimensional general nonlinear system into and nz-dimensional system in state affine form, with nz > n. First, we recall the necessary and sufficient condition for the solution of the general problem, which requires the solution of a partial differential equation that, moreover, has to satisfy a restrictive injectivity condition. Given the complexity of this task we propose an alternative simple algebraic method to identify the required dynamic extension and coordinate transformation, a procedure that, as shown in the paper, is rather natural for physical systems. We illustrate the method with some academic benchmark examples from observer theory literature—that, in spite of their apparent simplicity, are difficult to solve with the existing methods—as well as several practically relevant physical examples. © 2024, CC BY-SA.

关键词： Algebra

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

arXiv

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arXiv 2024年

作者： Liu, Yu Zhang, Yuexin Li, Kunming Qiao, Yongliang Worrall, Stewart Li, You-Fu Kong, He Shenzhen518055 China Department of Mechanical Engineering City University of Hong Kong Hong Kong School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Australian Centre for Field Robotics The University of Sydney NSW2006 Australia Australian Institute for Machine Learning The University of Adelaide SA5005 Australia Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering crossdataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme. © 2024, CC BY.

关键词： Trajectories

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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TRACKING DOMAINS FOR UNSTABLE PLANTS WITH SATURATING-LIKE ACTUATORS

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Asian Journal of control 2008年第4期1卷

作者： V.A. Yakubovich S. Nakaura K. Furuta Vladimir A. Yakubovich is a corresponding member of Russian Academy of Science. Vladimir A. Yakubovich was born in Novosibirsk Russia in 1926. he entered Moscow University in 1946 and graduated in 1949. he received the Candidate of Science degree (Ph.D.) in 1953 and the Doctor of Science degree in 1959 from Leningrad University. In 1959 he joined the Leningrad University and he has remained since then. He is the author of more than 270 papers and a coauthor of seven books in different areas of applied mathematics and control theory. he has worked in parametric resonance theory in the theory of stability of nonlinear systems and in optimization theory. Dr. Yakubovich has served on many scientific committees and editorial boards. he is a member of several scientific socienties in Russia. He was awarded the Norbert Wiener Prize (1991) a prize from the international editorial company “Nauka” for best publication in its journals (1995) and the IEEE Control Systems Award (1996). Since 1991 he has been a corresponding member of the Russian Academy of Sciences. Dept. of Control Engineering Tokyo Institute of Technology Japan. Shigeki Nakaura received his B.S. and M.S. degrees in Control Engineering from the Tokyo Institute of Technology in 1995 and 1997 respectively. He is currently a PhD student of the Tokyo Institute of Technology. His research interests include robotics and intelligent machining. He is a member of SICE JSPE and IEEE. Katsuhisa Furuta was born in Tokyo Japan in 1940. He received his B.S. M.S. and Ph.D. degrees in Engineering from the Tokyo Institute of Technology in 1962 1964 and 1967 respectively. he was a post doctoral fellow at Laval University (Quebec Canada) from July 1967 to August 1969. Since then he has been a member of the teaching staff of the Tokyo Institute of Technology Department of Control Engineering where he is currently a Professor Graduate School of Information Science and Engineering. He was a Russell Severance Springer Professor University of California at

In many mechanical systems, the tracking control is one of the most important issues. Therefore for the unstable system subject to actuator saturation, the domain of the initial states corresponding to an arbitrary reference signal, within which tracking condition can be achieved, is interesting to be considered. In this paper, an approximate tracking domain is derived analytically. First in Theorem 1, for simple case, it is constructed by using the partial Lyapunov function and the absolute stability theory. In the following Theorems 2 and 3, a new idea is used to obtain larger domains. Finally in the last section, a simple numerical example is given.

关键词： Actuator constraint tracking control nonlinear analysis absolute stability

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