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International Conference on Information Science and Technology (ICIST)

作者： Dianye Huang Chenguang Yang Yongping Pan Shilu Dai Zhaojie Ju Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Biomedical Engineering National University of Singapore Singapore Singapore School of Computing University of Portsmouth Portsmouth UK

In this paper, a trajectory tracking scheme for robot manipulators with unknown dynamics is investigated, with the consideration of output constraints as well as small bounded external disturbances. Firstly, a modified backstepping control scheme is employed to control the robot manipulators where in the first step of the design a tan-type barrier Lyapunov candidate is chosen in order to tackle the constraint problem. Secondly, the philosophy of dynamic surface control is incorporated to implement the calculation of prediction errors, which can also reduce “explosion of complexity” of the backstepping scheme. In addition, composite learning is introduced for a better estimation of unknown parameters, and for canceling out the uncertainties of the robot manipulators. Stability analysis shows that the proposed control scheme guarantees a small bounded tracking error with parameter convergence in the absence of the stringent persistent excitation condition. Finally, a simulation is conducted and the results demonstrate the superiority of the proposed controller in the aspects of tracking capability and parameter estimation.

关键词： Manipulator dynamics Backstepping Uncertainty control systems Convergence

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Dominant-Set-Based Consensus For Fuzzy C-Means Clustering Ensemble

Dominant-Set-Based Consensus For Fuzzy C-Means Clustering En...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Pan Su Tianhua Chen Weifeng Xu Xuqiang Shao Hongtao Wang Yitian Zhao School of Control and Computer Engineering North China Electric Power University Baoding China Department of Computer Science School of Computing and Engineering University of Huddersfield Huddersfield UK Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9781538652152

Conventional clustering approaches partition a set of objects into a certain (some can automatically detect the number of clusters such as DBScan) number of clusters. During the partitioning process, the clusters of objects are produced where each object is assigned to one cluster. On the other hand, the dominantset-based clustering provides a formalisation of clusters by sequentially searching for individual clusters in the set of objects. The resultant clusters do not necessarily form a partition of the set. With the popularity of clustering ensemble, graph-based consensus approaches have been proposed with promising results achieved, many of which are based on the partition of the graph. In this paper, a dominant-set-based consensus method for fuzzy-c-means-based clustering ensemble is proposed. Different from traditional graph-based consensus techniques, the graph generated by the fuzzy clusters are grouped on the basis of the extracted dominant sets. The proposed approach employs a similarity relation to denote the links between component clusters from which the final clusters of ensemble are derived with the extracted dominant set. The proposed method is tested on benchmark data sets against several alternative ensemble methods for fuzzy c-means. The results of experiment show that the proposed dominant-set-based clustering ensemble method generally achieves higher accuracy than its competitors.

关键词： Clustering algorithms Benchmark testing Partitioning algorithms Fuzzy sets Symmetric matrices Power systems Computer science

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Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

arXiv

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arXiv 2019年

作者： Pretto, Alberto Aravecchia, Stéphanie Burgard, Wolfram Chebrolu, Nived Dornhege, Christian Falck, Tillmann Fleckenstein, Freya Fontenla, Alessandra Imperoli, Marco Khanna, Raghav Liebisch, Frank Lottes, Philipp Milioto, Andres Nardi, Daniele Nardi, Sandro Pfeifer, Johannes Popović, Marija Potena, Ciro Pradalier, Cédric Rothacker-Feder, Elisa Sa, Inkyu Schaefer, Alexander Siegwart, Roland Stachniss, Cyrill Walter, Achim Winterhalter, Wera Wu, Xiaolong Nieto, Juan Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Research Lab 2958 Georgia Tech-CNRS Metz France Department of Computer Science University of Freiburg Germany Toyota Research Institute Los Altos United States Photogrammetry & Robotics Lab University of Bonn Germany Robert Bosch GmbH Corporate Research Renningen Germany Osimo Italy Autonomous Systems Lab. Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland Department of Environmental Systems Science Institute of Agricultural Sciences ETH Zurich Zürich Switzerland IT+Robotics Srl Padova Italy Smart Robotics Lab. Department of Computing Imperial College London London United Kingdom CSIRO Data61 Australia

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Introduction

Advances in Industrial Control

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Advances in Industrial control 2018年第9783319610207期 1-18页

作者： Orsag, Matko Korpela, Christopher Oh, Paul Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical Engineering and Computer Science United States Military Academy West PointNY United States Department of Mechanical Engineering University of Nevada Las Vegas Las VegasNV United States

Aerial robotics has reached significant maturity in the last few years. Thus, the number of new unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS), applications, and companies producing them has increased in a very important way. The number of publications and presentations in conferences has also experienced a large increase. Moreover, almost every day we can find articles, videos, and news in the general media related to drones. © Springer International Publishing AG 2018.

关键词： Antennas

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Author Correction: Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)

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Nature cell biology 2024年第5期26卷 839页

作者： Sanjay Jain Liming Pei Jeffrey M Spraggins Michael Angelo James P Carson Nils Gehlenborg Fiona Ginty Joana P Gonçalves James S Hagood John W Hickey Neil L Kelleher Louise C Laurent Shin Lin Yiing Lin Huiping Liu Alexandra Naba Ernesto S Nakayasu Wei-Jun Qian Andrea Radtke Paul Robson Brent R Stockwell Raf Van de Plas Ioannis S Vlachos Mowei Zhou Katy Börner Michael P Snyder Department of Medicine Washington University School of Medicine St Louis MO USA. sanjayjain@wustl.edu. Department of Pediatrics Washington University School of Medicine St Louis MO USA. sanjayjain@wustl.edu. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. sanjayjain@wustl.edu. Center for Mitochondrial and Epigenomic Medicine Department of Pathology and Laboratory Medicine Children's Hospital of Philadelphia and Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. lpei@pennmedicine.upenn.edu. Department of Cell and Developmental Biology and the Mass Spectrometry Research Center Vanderbilt University School of Medicine Nashville TN USA. jeff.spraggins@vanderbilt.edu. Department of Pathology Stanford School of Medicine Stanford CA USA. Texas Advanced Computing Center University of Texas at Austin Austin TX USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. GE Research Center Niskayuna NY USA. Department of Intelligent Systems Delft University of Technology Delft Netherlands. Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Microbiology and Immunology Stanford University Stanford CA USA. Departments of Medicine Chemistry and Molecular Biosciences Northwestern University Evanston IL USA. Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA USA. Division of Cardiology University of Washington School of Medicine Seattle WA USA. Department of Surgery Washington University School of Medicine St Louis MO USA. Departments of Pharmacology Medicine (Hematology and Oncology) Lurie Cancer Center Northwestern University Feinberg School of Medicine Chicago IL USA. Department of Physiology and Biophysics University of Illinois at Chicago Chicago IL USA. Biological Sciences Division

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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

Siamese-ResNet: Implementing Loop Closure Detection based on...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Qiu, Kai Ai, Yunfeng Tian, Bin Wang, Bin Cao, Dongpu School of Aritificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China School of Software Engineering University of Science and Technology of China HeFei230026 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781538644522

Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM. We proposed Siamese-ResNet network, which combines Siamese network with ResNet to detect loop closure. To show the effectiveness of Siamese-ResNet, we evaluate Siamese-ResNet and FabMap2.0 on several open published datasets, like TUM SLAM dataset and FabMap SLAM dataset. Compared with FabMap2.0, Siamese-ResNet shows higher accuracy, better robustness and shorter time-consuming. © 2018 IEEE.

关键词： Robotics

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Development of structure and behavioral model for screw driving in-pipe inspection robot based on adaptive mechanism on legs 8

Development of structure and behavioral model for screw driv...

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8th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS, MBD 2017

作者： Osman, Krešimir Kovačić, Zdenko Sales and Technical Department Termo Servis Ltd Ul. grada Vukovara 72 Zagreb10000 Croatia Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9788001061749

This paper presents the beginning of research in connection with the development of a pipeline inspection robot. The authors aimed to describe and present a structure and behavior model of a robot designed for inspecting the interior of pipelines to be used for gas distribution. The robot structure comprises a driving section and a driven section, where each has three legs with two ankles. The driven section contains a group of wheel sets adaptable by pressing against the interior wall of the pipeline, thus maintaining balance and stability of the robot, especially when passing through brackets and T-branches in the pipeline. The driven section of the robot is powered by an electric motor and a planetary gearbox where torque is transmitted to the legs with wheels, thus moving the robot based on the screw principle through the interior of the pipeline. In addition, a robot behavior model (mathematical model) was made for the structure presented, including its kinematic and dynamic models. © 2017 National Technical University of Athens. All rights reserved.

关键词： Robots

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Lom： Discovering Logic Flaws Within MongoDB-based Web Applications

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International Journal of Automation and computing 2017年第1期14卷 106-118页

作者： Shuo Wen Yuan Xue Jing Xu Li-Ying Yuan Wen-Li Song Hong-Ji Yang Guan-Nan Si Institute of Machine Intelligence College of Computer and Control Engineering Nankai University Tianjin 300350 China Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee 37212 USA Centre for Creative Computing Bath Spa University Bath BA2 9BN UK School of Information Science and Electrical Engineering Shandong Jiaotong University Ji'nan 250357 China

Logic flaws within web applications will allow malicious operations to be triggered towards back-end database. Existing approaches to identifying logic flaws of database accesses are strongly tied to structured query language （SQL） statement construction and cannot be applied to the new generation of web applications that use not only structured query language （NoSQL） databases as the storage tier. In this paper, we present Lom, a black-box approach for discovering many categories of logic flaws within MongoDB- based web applications. Our approach introduces a MongoDB operation model to support new features of MongoDB and models the application logic as a mealy finite state machine. During the testing phase, test inputs which emulate state violation attacks are constructed for identifying logic flaws at each application state. We apply Lom to several MongoDB-based web applications and demonstrate its effectiveness.

关键词： Logic flaw web application security not only structured query language （NoSQL） database black-box MougoDB.

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Plenary Lectures

Plenary Lectures

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Mediterranean Conference on control and Automation (MED)

作者： Bart De Moor Yasamin Mostofi Maryam Kamgarpour Zdenko Kovačić Maja Cepanec Airlie Chapmar Mehran Mesbahi Department of Electrical Engineering (ESAT-STADIUS) KU Leuven Leuven Belgium Department of Electrical & Computer Engineering University of of California Santa Barbara CA USA Automatic Control Laboratory Swiss Federal Institute of Technology Zurich Zurich Switzerland University of Zagreb Faculty of Electrical Engineering and Computing University of Zagreb Faculty of Education and Rehabilitation Sciences University of Melbourne Australia University of Washington USA

The following topics are dealt with: control system synthesis; stability; nonlinear control systems; closed loop systems; robust control; feedback; Lyapunov methods; mobile robots; uncertain systems; and position cont... 详细信息

ISBN: (纸本)9781538674994;9781538678909

关键词： Robot sensing systems Electrical engineering Education Autism Robot kinematics Decision making

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A Parametric Time Frequency-Conditional Granger Causality Method Using Ultra-regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs

arXiv

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arXiv 2018年

作者： Li, Yang Lei, Mengying Cui, Weigang Guo, Yuzhu Wei, Hua-Liang Department of Automation Sciences and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Advanced Innovation Center for Big Date-based Precision Medicine Beihang University Beijing China Department of Automation Sciences and Electrical Engineering Beihang University Beijing China Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3DJ United Kingdom

— Objective: This study proposes a new parametric TF (time-frequency)-CGC (conditional Granger causality) method for high-precision connectivity analysis over time and frequency in multivariate coupling nonstationary systems, and applies it to scalp and source EEG signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. Methods: The Geweke’s spectral measure is combined with the TVARX (time-varying autoregressive with exogenous input) modelling approach, which uses multiwavelets and ultra-regularized orthogonal least squares (UROLS) algorithm aided by APRESS (adjustable prediction error sum of squares), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal data themselves but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. Results: The proposed method is validated on two simulations and then applied to multichannel motor imagery (MI)-EEG signals at scalp- and source-level, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEG data are physiologically and anatomically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. Conclusion: Experimental results confirm the effectiveness of the proposed TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. Significance: The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

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