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International Joint Conference on Neural Networks

作者： S.P. Adam D. A. Karras G.D. Magoulas M.N. Vrahatis Computational Intelligence Laboratory Department of Mathematics University of Patras GR-26110 Patras Greece Department of Automation Technological Educational Institution of Sterea Hellas 34400 Psahna Evia Greece Department of Computer Science and Information Systems Birkbeck College University of London Malet Street London WC1E 7HX United Kingdom

ISBN: (纸本)9781479919611

Reliable estimation of a neural network's domain of validity is important for a number of reasons such as assessing its ability to cope with a given problem, evaluating the consistency of its generalization etc. In this paper we introduce a new approach to estimate the domain of validity of a neural network based on Set Inversion Via Interval Analysis (SIVIA), the methodology established by Jaulin and Walter [1]. This approach was originally introduced in order to solve nonlinear parameter estimation problems in a bounded error context and proved to be effective in tackling several types of problems dealing with nonlinear systems analysis. The dependence of a neural network output on the pattern data is a nonlinear function and hence derivation of the impact of the input data to the neural network function can be addressed as a nonlinear parameter estimation problem that can be tackled by SIVIA. We present concrete application examples and show how the proposed method allows to delimit the domain of validity of a trained neural network. We discuss advantages, pitfalls and potential improvements offered to neural networks.

关键词： Reliable estimation neural network's domain interval analysis based inversion Neural network Parameter estimation Domain Inversion nonlinear functions

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Intelligent algorithms based on data processing for modular robotic vehicles control

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WSEAS Transactions on systems 2014年第1期13卷 242-251页

作者： Papoutsidakis, Michail Piromalis, Dimitrios Neri, Filippo Camilleri, Michel Department of Automation Technological Institute of Piraeus P.Ralli and Thivon 250 Athens Greece Dept. of Electrical Engineering and Information Technology University of Napoli Federico II Via Claudio 21 Naples Italy Dept. of Computer Information Systems University of Malta MSD 2080 Malta

One of the numerous implementations that the Intelligent systems Lab of the Technological Institute of Piraeus, Greece, has recently accomplished is described in this paper. Within the area of hand-on autonomous robotics, the project combines computer science in modern wireless network protocol communication, with microcontroller-based intelligent motion-control tasks. The design of the presented pair of versatile robots is intended for a "predator and prey" application domain. Both robot designs are based on low cost and easy to find equipment, though modern and up-to-date technology. Two different multitasking and flexible algorithms are designed in order to investigate their performance and compare the outcomes. In particular, an intelligent algorithm based on data capturing and processing within a tolerable elapsed time is presented, with two different performance scenarios explained and compared in detail.

关键词： Intelligent systems

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Service-Oriented Distributed Control Software Design for Process automation systems

Service-Oriented Distributed Control Software Design for Pro...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Wenbin (William) Dai Jukka Peltola Valeriy Vyatkin Cheng Pang Department of Computer Science Electrical and Space Engineering Lulea University of Technology Department of Information and Computer Systems in Automation Aalto University

ISBN: (纸本)9781479938414

Improving software development efficiency and reusability of existing programs is an important topic for industrial automation. Recently, modern software paradigms have been introduced into the automation domain such as object-oriented programming using the IEC 61131-3 standard and component-based design based on the IEC 61499 standard. In this paper, another software paradigm - service-oriented architecture is applied to the software design for automation programs. The proposed SOA pattern is based on a multilayered structure with enhanced reusability and flexibility. A case study of water heating system is implemented to prove the concept of SOA paradigm.

关键词： Service-Oriented Architecture Component-based design Object-Oriented Programming IEC 61131-3 PLCs IEC 61499 Function Blocks

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Networked systems with incomplete information

Abstract and Applied Analysis

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Abstract and Applied Analysis 2015年第1期2015卷

作者： Wang, Zidong Shen, Bo Dong, Hongli He, Xiao Hu, Jun Department of Computer Science Brunel University London Uxbridge Middlesex UB8 3PH United Kingdom School of Information Science and Technology Donghua University Shanghai 200051 China College of Electrical and Information Engineering Northeast Petroleum University Daqing 163318 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 China Department of Automation Tsinghua University Beijing 100084 China Department of Applied Mathematics Harbin University of Science and Technology Harbin 150080 China

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Entity-oriented semantic search in dataspace

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ICIC Express Letters, Part B: Applications 2014年第4期5卷 1117-1123页

作者： Lu, Lihua Zhang, Hengzhen Gao, Xiao-Zhi School of Computer Science Fudan University No. 220 Handan Road Shanghai 200433 China Department of Mathematics and Information Science Zhengzhou University of Light Industry No. 5 Dongfeng Road Zhengzhou 450002 China College of Information Engineering Shanghai Maritime University No. 1550 Haigang Ave. Shanghai 201306 China Department of Automation and Systems Technology School of Electrical Engineering Aalto University PL 11000 00076 AALTO Finland

In a dataspace, heterogeneous sources contain a large amount of semantic and associated information. Accessing to these sources cannot be limited to superficial text information based on keyword search, but also includes semantic and associated information based on entity search, semantic search and Q&A systems. Therefore, it is necessary to extract semantic and association information hidden in the data, and reorganize them to maximize the value of the data. This paper presents an approach to implementing entity-oriented semantic search (EOSS) in the context of personal information management (PIM). We first extract the entities and their relationships using natural language processing (NLP) techniques, and then design a novel index structure context inverted index lists (CIILs) and the corresponding query processing algorithm. Finally, a prototype, my search engine, is implemented and experimentally validates the effectiveness and efficiency of the proposed approach. © 2014 ICIC International.

关键词： Search engines

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Erratum to: A Robust Tracking System for Low Frame Rate Video

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International Journal of computer Vision 2015年第3期115卷 305-305页

作者： Xiaoqin Zhang Weiming Hu Nianhua Xie Hujun Bao Stephen Maybank Institute of Intelligent System and Decision Wenzhou University Zhejiang China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science Zhejiang University Zhejiang China Department of Computer Science and Information Systems Birkbeck College London UK

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Situation detection based on knowledge fusion patterns 2

Situation detection based on knowledge fusion patterns

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2nd International Workshop on Ontologies and information systems, WOIS 2014

作者： Levashova, Tatiana Sandkuhl, Kurt Shilov, Nikolay Smirnov, Alexander University of Rostock Institute of Computer Science Department Business Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany St.Petersburg Institute for Informatics and Automation Russian Academy of Sciences 39 14 Line St-Petersburg199178 Russia ITMO University 49 Kronverkskiy pr. St-Petersburg197101 Russia

With the increasing use of sensors and actuators in technical systems and knowledge-intensive services the need for processing the information captured by these sensors and "making sense" out of it increases. Knowledge fusion is supposed to contribute to this field since it aims at integrating knowledge from different sources. Development of knowledge fusion solutions is a complex task which can be compared to systems and software development. As in other development areas there is a need for efficient development processes which can be supported by reusing solution parts, such as patterns or components. The paper brings together experiences from knowledge fusion subsystem development and from design of knowledge fusion patterns. The main contributions of this paper are (1) a real-world application scenario presenting typical requirements to knowledge fusion systems, (2) application of knowledge fusion patterns from context-based decision support to situation recognition, (3) recommendations from this application case.

关键词： Software design

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Adaptive cubature strong tracking information filter using variational Bayesian method 19

Adaptive cubature strong tracking information filter using v...

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Ge, Quanbo Wen, Chenglin Chen, Shaodong Sun, Ruoyu Li, Yuan Institute of Systems Science and Control Engineering School of Automation Hangzhou Dianzi University Hangzhou Zhejiang310018 China Luoyang Institute of Electro Optical Equipment of AVIC Luoyang Henan471000 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55414 United States Information Engineering School Shenyang University of Chemical Technology Shenyang Liaoning100142 China

ISBN: (纸本)9783902823625

For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality. © IFAC.

关键词： information filtering

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Seeing the Arrow of Time

Seeing the Arrow of Time

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IEEE Conference on computer Vision and Pattern Recognition

作者： Lyndsey C. Pickup Zheng Pan Donglai Wei YiChang Shih Changshui Zhang Andrew Zisserman Bernhard Scholkopf William T. Freeman Visual Geometry Group Department of Engineering Science University of Oxford Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Max Planck Institute for Intelligent Systems

ISBN: (纸本)9781479951192

We explore whether we can observe Time's Arrow in a temporal sequence-is it possible to tell whether a video is running forwards or backwards? We investigate this some-what philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time's Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards/backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.

关键词： YouTube Training Support vector machines Physics Testing Histograms Visualization

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