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World Congress on Intelligent control and automation (WCICA)

作者： Yang Ge Jingcheng Wang Yi Zhang Kang Li Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China Shanghai Urban-Rural Construction and Transportation Development Research Institute Shanghai China Electrical Engineering and Computer Science Queen's University Belfast Belfast UK

The distributed networked control systems are considered in this paper. Several sub-systems which are connected with each other through a communication network make up the whole system. Each sub-system has its own quantizer so that any information which needs be transmitted to other sub-systems will be quantized due to limited bandwidth. Meanwhile, the actuator faults, including outage, loss of effectiveness and stuck are also considered in our research. A mode-based state feedback controller is given in this paper to stable such NCSs and to meet the robust H-inf performance. A simulation example is proposed to illustrate the effectiveness of our method finally.

关键词： Quantization (signal) Networked control systems State feedback Closed loop systems Communication networks Actuators Fault tolerance

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Tumor Detection: Remarkable NIR Enhancement of Multifunctional Nanoprobes for In Vivo Trimodal Bioimaging and Upconversion Optical/T2‐Weighted MRI‐Guided Small Tumor Diagnosis (Adv. Funct. Mater. 46/2015)

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Advanced Functional Materials 2015年第46期25卷

作者： Zhigao Yi Xiaolong Li Zhenluan Xue Xiao Liang Wei Lu Hao Peng Hongrong Liu Songjun Zeng Jianhua Hao College of Physics and Information Science and Key Laboratory of Low‐Dimensional Quantum Structures and Quantum Control of the Ministry of Education Hunan Normal University Changsha 410081 China School of Materials Science and Engineering Key Laboratory of Low‐Dimensional Materials and Application Technology (Ministry of Education) Xiangtan University Xiangtan 411105 China Key Laboratory of Molecular Imaging of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Applied Physics and Materials Research Center The Hong Kong Polytechnic University Hong Kong China Research Center of Heart Development School of Life Sciences Hunan Normal University Changsha Hunan 410081 China

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Beyond bag of words: Image representation in sub-semantic space 13

Beyond bag of words: Image representation in sub-semantic sp...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Wang, Shuhui Liang, Chao Liu, Jing Huang, Qingming Li, Haojie Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Software Dalian University of Technology Liaoning China Department of Computer Sciences University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

Due to the semantic gap, the low-level features are not able to semantically represent images well. Besides, traditional semantic related image representation may not be able to cope with large inter class variations and are not very ro- bust to noise. To solve these problems, in this paper, we propose a novel image representation method in the sub- semantic space. First, examplar classifiers are trained by separating each training image from the others and serve as the weak semantic similarity measurement. Then a graph is constructed by combining the visual similarity and weak se- mantic similarity of these training images. We partition this graph into visually and semantically similar sub-sets. Each sub-set of images are then used to train classifiers in order to separate this sub-set from the others. The learned sub-set classifiers are then used to construct a sub-semantic space based representation of images. This sub-semantic space is not only more semantically meaningful but also more reli- Able and resistant to noise. Finally, we make categorization of images using this sub-semantic space based representation on several public datasets to demonstrate the e®ectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Image representation

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Undo the codebook bias by linear transformation for visual applications 13

Undo the codebook bias by linear transformation for visual a...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Zhang, Yifan Wang, Shuhui Pang, Junbiao Liang, Chao Huang, Qingming Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Computer Science and Technology Beijing University of Technology 100124 Beijing China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Department of Computer Sciences University of Texas at San Antonio San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the corresponding codebook, the elements of a codebook are viewed as visual words. The local features within each image are then encoded to get the final histogram representation. However, the codebook is dataset dependent and has to be generated for each image dataset. This costs a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by codebook linear transformation. To represent every points within the local feature space using Euclidean distance, the number of bases should be no less than the space dimensions. Hence, each codebook can be viewed as a linear transformation of these bases. In this way, we can transform the pre-learned codebooks for a new dataset. However, not all of the visual words are equally important for the new dataset, it would be more effective if we can make some selection using sparsity constraints and choose the most discriminative visual words for transformation. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. Image classification experimental results on several image datasets show the effectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Linear transformations

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Semantic runtime interface description based on engineering knowledge

Semantic runtime interface description based on engineering ...

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14th IFAC Symposium on Information control Problems in Manufacturing, INCOM'12

作者： Novák, Petr Melik-Merkumians, Martin Steinegger, Michael Moser, Thomas Šindelář, Radek Zoitl, Alois Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems Vienna University of Technology AT-1040 Vienna Austria Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague 121 35 Prague Czech Republic Automation and Control Institute Vienna University of Technology AT-1040 Vienna Austria

ISBN: (纸本)9783902661982

Design and integration of industrial automation systems require cooperation of different engineering disciplines and various engineering tools during both design and run time. The scope of this paper is to bridge the gap between design-time description and runtime integration in a semi-automated way. Furthermore, we address the problem of integrating legacy systems with no access to original engineering data. It is assumed that an interface between a real industrial system and software automation tools is OPC Unified Architecture (UA) and we propose to derive the plant knowledge from the OPC UA tag list. Since it is not possible to solve this task in general, we require the tag list to adopt the naming convention of IEC 81346, which is widespread in large-scale systems. This tag list is parsed and the engineering knowledge is derived and stored in the engineering Knowledge Base. Consequently, the plant knowledge is mapped to knowledge related to software automation tools, such as simulators. The proposed methodology is evaluated on a real-life example dealing with a laboratory tank model. © 2012 IFAC.

关键词： Knowledge based systems

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An embedded co-AdaBoost and its application in classification of software document relation

An embedded co-AdaBoost and its application in classificatio...

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2012 8th International Conference on Semantics, Knowledge and Grids, SKG 2012

作者： Liu, Jin Li, Juan Xie, Yuan Lei, Jeff Hu, Qiping State Key Lab. of Software Engineering Computer School Wuhan University China State Key Lab. of Intelligent Control and Management of Complex Systems Institute of Automation CAS China Department of Computer Science and Engineering University of Texas Arlington TX United States International School of Software Wuhan University China

ISBN: (纸本)9780769547947

To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learning into the Adaboost learning framework and at the same time leverages the advantages of Co-training algorithm for performance enhancement. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of the convergence rate, the accuracy, and the steady performance as compared to the original AdaBoost algorithm, without relying on redundant and sufficient feature sets. As a algorithm application in software engineering, the Embedded Co-AdaBoost has been applied to the classification of software document relations to improve the quality of the architecture design documents and the reusability of design knowledge. © 2012 IEEE.

关键词： Reusability

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Semantic Runtime Interface Description Based on engineering Knowledge

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IFAC Proceedings Volumes 2012年第6期45卷 805-810页

作者： Petr Novák Martin Melik-Merkumians Michael Steinegger Thomas Moser Radek Ŝindelář Alois Zoitl Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems Vienna University of Technology AT-1040 Vienna Austria Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague 121 35 Prague Czech Republic Automation and Control Institute Vienna University of Technology AT-1040 Vienna Austria

关键词： automation Integration Knowledge-based systems Data acquisition OPC UA IEC 81346 Computer Simulation software tools

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An Embedded Co-AdaBoost and Its Application in Classification of software Document Relation

An Embedded Co-AdaBoost and Its Application in Classificatio...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Jin Liu Juan Li Yuan Xie Jeff Lei Qiping Hu State Key Laboratory of Software Engineering Computer School Wuhan University China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation CAS China Department of Computer Science and Engineering University of Texas Arlington USA International School of Software Wuhan University China

关键词： Classification algorithms Training software Accuracy Partitioning algorithms software algorithms Convergence

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Towards an ontology for autonomous robots

Towards an ontology for autonomous robots

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25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012

作者： Paull, Liam Severac, Gaetan Raffo, Guilherme V. Angel, Julian Mauricio Boley, Harold Durst, Phillip J. Gray, Wendell Habib, Maki Nguyen, Bao Ragavan, S. Veera Saeedi G., Sajad Sanz, Ricardo Seto, Mae Stefanovski, Aleksandar Trentini, Michael Li, Howard COBRA Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB E3B 5A3 Canada ONERA-University of Toulouse France Department of Automation and Systems Federal University of Santa Catarina Brazil Department of Computer Engineering Pontificia Universidad Javeriana Bogot Colombia National Research Council Canada US Army ERDC 3909 Halls Ferry Road Vicksburg MS 39180 United States Mechanical Engineering Department School of Sciences and Engineering American University in Cairo New Cairo Egypt Defence Research and Development Canada-CORA Ottawa ON Canada Monash University Sunway Campus Australia Systems Engineering and Automatic Control Autonomous Systems Laboratory Research Group Universidad Politcnica de Madrid Madrid Spain Defence Research and Development Canada-Atlantic Dartmouth NS Canada Department of Computer Science George Washington University United States Defence R and D Canada-Suffield PO Box 4000 Station Main Medicine Hat AB T1A 8K6 Canada

ISBN: (纸本)9781467317375

The IEEE RAS Ontologies for Robotics and automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions. © 2012 IEEE.

关键词： Ontology

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