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International Electric Machines & Drives Conference

作者： Andre A. Silva Ali M. Bazzi Shalabh Gupta Laboratory of Intelligent Networks and Knowledge-Perception Systems (LINKS) ooth Engineering Center for Advanced Technology (BECAT) University of Connecticut Storrs CT USA Advanced Power Electronics and Electric Drives Lab (APEDL) Center for Clean Energy Engineering University of Connecticut Storrs CT USA

This paper applies machine learning techniques to fault diagnosis in electric motor drives. As faults in motor drives can cause safety hazards in applications such as electric traction, propulsion, aircraft, and others, it is desired to diagnose the fault, i.e., detect it and isolate it, in order to recover or engage safe-mode operation. Machine learning methods are divided into three main steps: 1) Feature extraction using Principal Component Analysis (PCA);2) Classification using the k-Nearest Neighbor (k-NN) or Probabilistic Neural Network (PNN) methods;and 3) Classifier performance evaluation using the Cross-Validation (CV) method. While electric machine, inverter, and sensor faults are introduced, the supervised learning algorithms are applied to four case studies where two fault modes occur in a current sensor, and two occur in the speed encoder. Classification accuracy, i.e., the ability to diagnose a fault, for all four cases is shown to exceed 98%. The paper also investigates a load profile used in automotive driving cycles which produces richer dynamic responses that are more interesting from an application perspective. The final goal is to implement these algorithms in real-time such that fault diagnosis can be used to isolate drive faults, especially sensor faults, and operate in a safe mode.

关键词： PCA PNN CV

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Image processing for three-dimensional scans generated by time-of-flight range cameras

Image processing for three-dimensional scans generated by ti...

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作者： Schöner, Holger Bauer, Frank Dorrington, Adrian Heise, Bettina Wieser, Volkmar Payne, Andrew Cree, Michael J. Moser, Bernhard Software Competence Center Hagenberg 4232 Hagenberg Austria Johannes Kepler University of Linz Department of Knowledge-based Mathematical Systems 4232 Hagenberg Austria University of Waikato Department of Engineering Hamilton 3240 New Zealand

Time-of-flight (TOF) full-field range cameras use a correlative imaging technique to generate three-dimensional measurements of the environment. Though reliable and cheap they have the disadvantage of high measurement noise and errors that limit the practical use of these cameras in industrial applications. We show how some of these limitations can be overcome with standard image processing techniques specially adapted to TOF camera data. Additional information in the multimodal images recorded in this setting, and not available in standard image processing settings, can be used to improve reduction of measurement noise. Three extensions of standard techniques, wavelet thresholding, adaptive smoothing on a clustering based image segmentation, and an extended anisotropic diffusion filtering, make use of this information and are compared on synthetic data and on data acquired from two different off-the-shelf TOF cameras. Of these methods, the adapted anisotropic diffusion technique gives best results, and is implementable to perform in real time using current graphics processing unit (GPU) hardware. Like traditional anisotropic diffusion, it requires some parameter adaptation to the scene characteristics, but allows for low visualization delay and improved visualization of moving objects by avoiding long averaging periods when compared to traditional TOF image denoising. © 2012 SPIE and IS&T.

关键词： Visualization

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Towards network invariant fault diagnosis in MANETs via statistical modeling: The global strength of local weak decisions

Towards network invariant fault diagnosis in MANETs via stat...

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IEEE Symposium on Network Operations and Management

作者： Akshay Vashist Rauf Izmailov Kyriakos Manousakis Ritu Chadha C. Jason Chiang Constantin Serban Syeed E. Ali Knowledge-Based Systems Department Applied Communication Sciences Piscataway NJ USA U.S. Army Communications Electronics Research Development and Engineering Center (CERDEC) MD USA

Due to its obvious importance, fault detection and localization is a well-studied problem in communication networks, as attested by the many techniques designed to address this problem. The inherent variability, limited component reliability, and constrained resources of MANETs (Mobile Ad hoc Networks) make the problem not just more important, but also critical. Practical development and deployment considerations imply that fault detection and localization methods must i) avoid relying on overly detailed models of network protocols and traffic assumptions and instead rely on actual cross-layer measurements/observations, and ii) be applicable across different network scales and topologies with minimum adjustments. This paper demonstrates the feasibility of such goals, and proposes an important and as yet unexplored approach to fault management in MANETs: network-invariant fault detection, localization and diagnosis with limited knowledge of the underlying network and traffic models. We show how fault management methods can be derived by observing statistical network/traffic measurements in one network, and subsequently applied to other networks with satisfactory performance. We demonstrate that a carefully designed but widely applicable set of local and weak global indicators of faults can be efficiently aggregated to produce highly sensitive and specific methods that perform well when applied to MANETs with varying sizes, topologies, and traffic matrices.

关键词： Fault detection Ad hoc networks Mobile computing Network topology Fault diagnosis Topology Accuracy

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Online object categorization using multimodal information autonomously acquired by a mobile robot

Online object categorization using multimodal information au...

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作者： Araki, Takaya Nakamura, Tomoaki Nagai, Takayuki Funakoshi, Kotaro Nakano, Mikio Iwahashi, Naoto Department of Mechanical Engineering and Intelligent Systems University of Electro-Communications 1-5-1 Chofugaoka Chofu-shi Tokyo 182-8585 Japan Honda Research Institute Japan Co. Ltd. 8-1 Honcho Wako-shi Saitama 351-0188 Japan NICT Knowledge Creating Communication Research Center 2-2-2 Hikaridai Seika-cho Souraku-gun Kyoto 619-0288 Japan

In this paper, we propose a robot that acquires multimodal information, i.e. visual, auditory, and haptic information, fully autonomously using its embodiment. We also propose batch and online algorithms for multimodal categorization based on the acquired multimodal information and partial words given by human users. To obtain multimodal information, the robot detects an object on a flat surface. Then, the robot grasps and shakes it to obtain haptic and auditory information. For obtaining visual information, the robot uses a small hand-held observation table with an XBee wireless controller to control the viewpoints for observing the object. In this paper, for multimodal concept formation, multimodal latent Dirichlet allocation using Gibbs sampling is extended to an online version. This framework makes it possible for the robot to learn object concepts naturally in everyday operation in conjunction with a small amount of linguistic information from human users. The proposed algorithms are implemented on a real robot and tested using real everyday objects to show the validity of the proposed system. © 2012 Copyright Taylor & Francis and The Robotics Society of Japan.

关键词： Robots

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Semantically enabling pharmacogenomic data for the realization of personalized medicine (vol 13, pg 201, 2012)

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PHARMACOGENOMICS 2012年第5期13卷 628-628页

作者： Samwald, M. Coulet, A. Huerga, I Section for Medical Expert & Knowledge-Based Systems Center for Medical Statistics Informatics & Intelligent Systems Medical University of Vienna Spitalgasse 23 A-1090 Vienna Austria Institute of Software Technology & Interactive Systems University of Technology Vienna Favoritenstrasse 9–11/188 A-1040 Vienna Austria LORIA – INRIA Nancy–Grand-Est Campus Scientifique-BP 239 54506 Vandoeuvre-lès-Nancy Cedex France Elsevier 1600 John F Kennedy Blvd. Suite 1800 Philadelphia PA 19103-2899 USA Predictive Medicine Inc. 37 Mansion Drive Topsfield MA 01983 USA Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street Winslow 2143 Troy NY 12180 USA Division of Biomedical Statistics & Informatics Department of Health Sciences Research Mayo Clinic 200 First Street SW Rochester MN 55901 USA Genomics Education Initiative 609 Sycamore Avenue Fullerton CA 92831 USA Department of Chemistry & Chemical Biology Northeastern University 360 Huntington Avenue Boston MA 02115 USA World Wide Web Consortium/MIT 32 Vassar Street Cambridge MA 02140 USA Department of Biology School of Computer Science Institute of Biochemistry Carleton University 1125 Colonel By Drive Ottawa ON K1T 2T4 Canada Department of Medical Statistics & Bioinformatics Leiden University Medical Center/Informatics Institute University of Amsterdam Einthovenweg 20 2333 ZC Leiden The Netherlands

Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products.

关键词： clinical decision support systems knowledge representation ontologies personalized medicine pharmacogenomics translational medicine

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Reports of the AAAI 2012 conference workshops

Reports of the AAAI 2012 conference workshops

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作者： Agrawal, Vikas Baier, Jorge Bekris, Kostas Chen, Yiling D'Avila Garcez, Artur S. Hitzler, Pascal Haslum, Patrik Jannach, Dietmar Law, Edith Lecue, Freddy Lamb, Luis C. Matuszek, Cynthia Palacios, Hector Srivastava, Biplav Shastri, Lokendra Sturtevant, Nathan Stern, Roni Tellex, Stefanie Vassos, Stavros Center for Knowledge Driven Intelligent Systems Enterprise Technology Research Labs Infosys Limited India Pontificia Universidad Católica de Chile Chile Rutgers University NJ United States Harvard University United States City University London United Kingdom Wright State University Dayton OH United States Australian National University Australia TU Dortmund Germany Machine Learning Department Carnegie Mellon University United States IBM Research - Smarter Cities Technology Centre Dublin Ireland Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science and Engineering Department University of Washington United States Universidad Carlos III de Madrid Spain IBM Research - India India University of Denver United States Ben Gurion University Negev Israel Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical systems, Human Computation, intelligent Techniques for Web Personalization and Recommendation systems, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence.

关键词： Problem solving

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Identifying treatment activities for modelling computer-interpretable clinical practice guidelines

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Lecture Notes in Computer Science

作者： Kaiser, Katharina Seyfang, Andreas Miksch, Silvia Institute of Software Technology and Interactive Systems Vienna University of Technology Favoritenstraße 9-11/188 1040 Vienna Austria Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Spitalgasse 23 1090 Vienna Austria Department of Information and Knowledge Engineering Danube University Krems Dr.-Karl-Dorrek-Straße 30 3500 Krems Austria

ISBN: (纸本)3642180493

Clinical practice guidelines are important instruments to support clinical care. In this work we analysed how activities are formulated in these documents and we tried to represent the activities using patterns based on semantic relations. For this we used the Unified Medical Language System (UMLS) and in particular its Semantic Network. Out of it we generated a collection of semantic patterns that can be used to automatically identify activities. In a study we showed that these semantic patterns can cover a large part of the control flow. Using such patterns cannot only support the modelling of computer-interpretable clinical practice guidelines, but can also improve the general comprehension which treatment procedures have to be accomplished. This can also lead to improved compliance of clinical practice guidelines. © 2011 Springer-Verlag.

关键词： Semantics

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Decision support systems in a medical environment and its limitations

Decision support systems in a medical environment and its li...

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2010 International Conference on Artificial Intelligence, ICAI 2010

作者： Schuh, Ch.J. Center for Medical Statistics Informatics and Intelligent Systems Medical Expert and Knowledge-Based Systems Medical University of Vienna Spitalgasse 23 A-1090 Vienna Austria

ISBN: (纸本)9781601321480

The envisioned role of computer programs in health care is perhaps the most important. Everything we know today in medicine might not have been possible without the valuable contribution of computers. Uncertainty is the central, critical fact about medical reasoning. Particularly in an intensive care unit (ICU), where decisions must often be made quickly, or that physicians will follow it rather than openly or surreptitiously limiting care on their own. The provisioning of Decision Support systems (DSS) in an ICU would enable the discovery of patterns in health data which might be important for the fight against incorrect diagnosis. Many of this intelligent DSS are based on the fuzzy set theory which describes complex systems mathematical model in terms of linguistic rules. Fuzzy set theory and fuzzy logic, founded in 1965 by Lotfi A. Zadeh, are a highly suitable and applicable basis for developing such systems in medicine. Over the past three decades experience has shown that the expectations were not always fulfilled in this field. This paper addresses the capabilities as well as limitations of DSS on three concrete applications. All systems established as real-time applications at an ICU and have reached the state of extensive clinical integration and testing at the Vienna general hospital.

关键词： Decision making

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Preface

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Frontiers in Artificial Intelligence and Applications 2011年 234卷 V-VI页

作者： Jain, L.C. Howlett, R.J. Knowledge-Based Intelligent Engineering Systems Centre SCT-Building University of South Australia SA5095 Australia Brighton KTP Centre Intelligent Signal Processing Labs School of Engineering University of Brighton BrightonBN2 4GJ United Kingdom

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A framework for query expansion based on viewpoint-oriented manipulation of the related concepts

A framework for query expansion based on viewpoint-oriented ...

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Asia Modelling Symposium 2010: 4th International Conference on Mathematical Modelling and Computer Simulation, AMS2010

作者： Badie, Kambiz Mahmoudi, Maryam Tayefeh Ghaderi, Mohammad Ali Knowledge Engineering and Intelligent Systems Group IT Research Faculty Iran Telecom Research Center Tehran Iran Department of Electrical and Computer Eng. Faculty of Engineering University of Tehran Iran

ISBN: (纸本)9780769540627

Although search engines are quite useful for obtaining information from the World Wide Web, users still have problems obtaining the most relevant information when processing their web queries. Different methods exist for query expansion with various levels of success. A major point in query expansion techniques is to consider the relations existing between the query terms as a means for retrieving more relevant web pages. Therefore, the more comprehensible these relations are observed, the more relevance can be expected for the retrieved web pages. Taking this point into account, in this paper, a framework is represented for query expansion based on viewpoint- oriented manipulation of the related query terms. Comparison between the relevant retrieved documents, on proposed queries with Google results, illustrate how more relevant results are obtained in the situations where the very viewpoints type relations existing between the query terms are clearly detectable and in the meantime no semantic relation may at the first look exists between the query terms. In this paper, we have made use of two approaches to retrieval issue, out of which one is based on information fusion while the other is grounded on collaboration of the results in a selective way. © 2010 IEEE.

关键词： Websites

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