检索结果-内蒙古大学图书馆

(S)TEM Analysis of the Strain and Morphology of InAs Quantum Dots using GaAs(Sb)(N) Capping Layers for Solar Cell Applications

引用

Microscopy and Microanalysis 2016年第S4期22卷 46-47页

作者： Reyes, D.F. Utrilla, A.D. Ben, T. Saborido, J.J. Ulloa, J.M. Bárcena-Gonzálz, G. GuerreroLebrero, M.P. Guerrero, E. Gonzalez, D. Departamento de Ciencia de Los Materiales e im y QI Universidad de Cádiz Puerto Real (Cádiz) 11510 Spain Institute for Systems Based on Optoelectronics and Microtechnology (ISOM) Universidad Politécnica de Madrid Ciudad Universitaria S/n Madrid 28040 Spain Department of Computer Science and Engineering University of Cádiz Spain

来源：评论

学校读者我要写书评

暂无评论

Unsupervised learning using the tensor voting graph 5

Unsupervised learning using the tensor voting graph

引用

5th International Conference on Scale Space and Variational Methods in computer Vision, SSVM 2015

作者： Deutsch, Shay Medioni, Gérard Computer Science Department Institute for Robotics and Intelligent Systems University of Southern California Los Angeles United States

ISBN: (纸本)9783319184609

Tensor Voting is a local, non parametric method that provides an efficient way to learn the complex geometric manifold structure under a significant amount of outlier noise. The main limitation of the Tensor Voting framework is that it is strictly a local method, thus not efficient to infer the global properties of complex manifolds. We therefore suggest constructing a unique graph which we call the Tensor Voting Graph, in which the affinity is based on the contribution of neighboring points to a point local tangent space estimated by Tensor Voting. The Tensor Voting Graph compactly and effectively represents the global structure of the underlying manifold. We experimentally demonstrate that we can accurately estimate the geodesic distance on complex manifolds, and substantially outperform all state of the art competing approaches, especially when outliers are present. We also demonstrate our method’s superior ability to segment manifolds, first on synthetic data, then on standard data sets for a motion segmentation, with graceful degradation in the presence of noise. © IFIP International Federation for Information Processing 2015.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Large bridge cable tension estimation with Cyber vibration Sensing System

Large bridge cable tension estimation with Cyber vibration S...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Shaodong Zhan Meng Chen Zhi Li Winston Sun Wen J. Li Guanglie Zhang Shenzhen Academy of Robotics Institute of Intelligent Cyber Sensing Systems China Department of Mechanical and Biomedical Engineering City University of Hong Kong Hong Kong SAR College of Computer Science & Software Engineering Shenzhen University China

ISBN: (纸本)9781509027347

We present in this paper our development of a cyber physical sensing platform for monitoring the structural tension of large bridge cables using MEMS vibration sensors and proprietary wireless technology. In order to implement the online tension analysis of the bridge cables, we developed an improved technology for the general identification of cable tension using the Gauss-Newton method to determine the eigenfrequency order and bending stiffness. The improved identification method was applied to vertical suspender cables, i.e., more commonly known as “hangers”, of the south section of the Huangpu Bridge. The result shows that the proposed method can effectively estimate the cable tension of the bridge's cables of different lengths.

关键词： Bridges Vibrations Sensors Force Bridge circuits Monitoring Acceleration

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Communication Middleware for Flex Ray-Based Distributed Embedded systems

Reconfigurable Communication Middleware for Flex Ray-Based D...

引用

IEEE International Conference on Embedded and real-time Computing systems and Applications (RTCSA)

作者： Diptesh Majumdar Licong Zhang Purandar Bhaduri Samarjit Chakraborty Department of Computer Science and Engineering IIT Guwahati India TU Institute for Real-Time Computer Systems Munich Germany

In this paper we consider the case of a network of Electronic Control Units (ECUs) connected through a Flex Ray bus in the automotive domain. Multiple distributed applications can run on this underlying architecture, each partitioned into tasks that are mapped on different ECUs. These applications can often be executed in different functional modes with different requirements on the communication resources in terms of data size and sampling period. Moreover, new applications can be deployed on to the ECUs at run-time. To efficiently utilize the communication resources and accommodate new applications, a certain flexibility in reallocation of the resource is necessary. However, the Flex Ray bus requires static configuration of schedules and data mapping in order to guarantee a more deterministic system behavior, allowing little room for flexibility. In order to address this problem, we propose a reconfigurable communication middleware that lies between the application layer and the communication controller layer, which maps messages onto Flex Ray schedules, and can be reconfigured at runtime. The configuration is synthesized and deployed online, allowing a certain reallocation of communication resources to applications. In this paper, we describe the design of such a reconfigurable communication middleware and demonstrate its function with an implementation using industry-strength Flex Ray design tools.

关键词： Schedules Middleware Indexes Protocols Vehicles Payloads Automotive engineering

来源：评论

学校读者我要写书评

暂无评论

Knowledge Engineering with Big Data

引用

IEEE Intelligent systems 2015年第5期30卷 46-55页

作者： Wu, Xindong Chen, Huanhuan Wu, Gongqing Liu, Jun Zheng, Qinghua He, Xiaofeng Zhou, Aoying Zhao, Zhong-Qiu Wei, Bifang Gao, Ming Li, Yang Zhang, Qiping Zhang, Shichao Lu, Ruqian Zheng, Nanning School of Computer Science and Information Engineering Hefei University of Technology China University of Vermont United States University of Edinburgh School of Computer Science United Kingdom School of Computer Science University of Science and Technology of China China Department of Computer Science Xi'An Jiaotong University China Institute for Data Science and Engineering East China Normal University China Pennsylvania State University United States Fudan University China Department of Computer Science Zhejiang Gongshang University China Deakin University Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China Keio University Japan

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. © 2001-2011 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Distributed state estimation for linear multi-agent systems with time-varying measurement topology

Distributed state estimation for linear multi-agent systems ...

引用

作者： Viegas, Daniel Batista, Pedro Oliveira, Paulo Silvestre, Carlos Philip Chen, C.L. Institute for Systems and Robotics Instituto Superior Técnico Av. Rovisco Pais Lisboa1049-001 Portugal Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Taipa China Department of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China

This paper addresses the problem of distributed state estimation in formations of vehicles with time-varying measurement topology. One or more vehicles have access to measurements of their own state, while the rest must rely on measurements relative to other vehicles in the vicinity and limited communication to estimate their own state, and it is assumed that the vehicles may gain or lose access to some measurements over time. The proposed solution consists in the implementation of a local state observer on-board each vehicle, and the effects of changes in the measurement topology on the estimation error dynamics are studied resorting to switched systems theory. Sufficient conditions for exponential stability of the global estimation error dynamics are presented for two different switching laws. The results are applied to the practical case of a formation of Autonomous Underwater Vehicles (AUVs), and simulation results are presented that validate the proposed solution. © 2015 Elsevier Ltd.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Powered Upper-Limb Control using Passivity-Based Nonlinear Disturbance Observer for Unknown Payload Carrying Applications

Powered Upper-Limb Control using Passivity-Based Nonlinear D...

引用

IEEE International Conference on robotics and Automation

作者： Min Jun Kim Woongyong Lee Jae Yeon Choi Yong Sik Park Sung Ho Park Goobong Chung Kyung-Lyong Han I1 Seop Choi I1 Hong Suh Youngjin Choi Wan Kyun Chung the Robotics Laboratory School of Mechanical Engineering Pohang University of Science and Technology (POSTECH) Pohang 790-784 Gyung-buk Korea the Korea Institute of Robot and Convergence (KIRO) Pohang 790-784 Gyung-buk Korea the POSCO Technical Research Laboratories Pohang 790-300 Gyung-buk Korea the Department of Electronics and Computer Engineering Hanyang University Seoul Korea the Department of Electronic Systems Engineering Hanyang University Ansan Korea

ISBN: (纸本)9781467380270

This paper proposes a passivity-based nonlinear disturbance observer (DOB) design for a powered upper-limb robot control. The proposed DOB allows for the nonlinearities of the robot dynamics, whereas the typical DOB designs cannot. Moreover, by virtue of the passivity property, human operator and environmental interactions can be embedded in the control loop. As a DOB, the proposed approach has a disturbance observation property that makes the actual robot behave like a nominal model selected by the user. Performance analysis proposes a gain tuning rule. In experimental validation, actual powered upper-limb robot is used to perform unknown payload carrying applications.

关键词： Observer Unknown DOB

来源：评论

学校读者我要写书评

暂无评论

25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

引用

BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning multiple collaborative tasks with a mixture of Interaction Primitives

Learning multiple collaborative tasks with a mixture of Inte...

引用

2015 IEEE International Conference on robotics and Automation, ICRA 2015

作者： Ewerton, Marco Neumann, Gerhard Lioutikov, Rudolf Amor, Heni Ben Peters, Jan Maeda, Guilherme Intelligent Autonomous Systems Lab Department of Computer Science Technische Universität Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Institute of Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Drive AtlantaGA30332-0280 United States Max Planck Institute for Intelligent Systems Spemannstr. 38 Tuebingen72076 Germany

ISBN: (纸本)9781479969234

Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box. © 2015 IEEE.

关键词： Demonstrations

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：