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Annual Conference of the IEEE Industrial Electronics Society

作者： Petros Karvelis George Georgoulas Chsysostomos D. Stylios Ioannis P. Tsoumas Jose Alfonso Antonino-Daviu Maria Jose Picazo Rodenas Vicente Climente-Alarcon Laboratory of Knowledge and Intelligent Computing Technological Educational Institute of Epirus Department of Computer Engineering Larges Drives Products R&D Department Siemens Industry Sector - Drive Technologies Instituto de Ingenieria Energetica Universitat Politecnica de Valencia Department of Electrical Engineering and Automation Aalto University

ISBN: (纸本)9781479940318

Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

关键词： Induction motor Fault diagnosis SIFT Image segmentation Object matching

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An adaptive method for the recovery of missing samples from FHR time series

An adaptive method for the recovery of missing samples from ...

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IEEE International Symposium on Computer-Based Medical Systems

作者： Vangelis P. Oikonomou Jiri Spilka Chrysostomos Stylios Lenka Lhostka Laboratory of Knowledge and Intelligent Computing Department of Informatics and Telecommunication Technology TEI of Epirus 47100 Arta Greece

ISBN: (纸本)9781479910526

Missing data cause serious problem for automatic evaluation of the fetal heart rate(FHR) series. In this work we present an algorithm to surpress this problem. More specifically, an adaptive approach is proposed based on two steps. The first step concerns the reconstruction step where we obtain an estimate of the missing data using an empirical dictionary. The second step consists from the construction of the dictionary using the updated values from the first step. The above two steps are applied iteratively until convergence. The method adapts each time the dictionary and the reconstructed time series to the new information that we gain. Results on real and simulated experiments have shown the usefullness of our approach. More specifically, a comparison with cubic spline interpolation is performed and have shown that the proposed approach achieved 4 to 9dB better reconstruction ability.

关键词： method recovery samples

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Modeling complex logistics systems using soft computing methodology of fuzzy cognitive maps

Modeling complex logistics systems using soft computing meth...

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2011 7th IEEE International Conference on Automation Science and Engineering, CASE 2011

作者： Stylios, Chrysostomos D. Georgoulas, George Knowledge and Intelligent Computing Laboratory Dept. of Informatics and Telecommunications Technology Technological Educational Institute of Epirus 47100 Artas Greece

ISBN: (纸本)9781457717307

Fuzzy Cognitive Maps (FCMs) is an abstract soft computing modeling methodology that has been applied in many areas quite successfully. In this paper we discuss its modeling applicability to complex logistics systems involved in an intermodal container terminal and the way it could represent and handle the vast amount of information by an abstract point of view based on a decentralized approach, where the supervisor of the system is modeled as an FCM. We also investigate its applicability as a metamodel of the intermodal terminal in a simulation-optimization framework. Experts have a key role in developing the FCM as they describe a general operational and behavioral model of the system using concepts for the main aspects of the system, and weighted directed edges to represent causality. On the other hand, when data is available, data driven approaches have also been proposed for the development of FCM models. The FCM representation and implementation is discussed to develop a behavioral model of any complex system mainly based on a hierarchical structure, as well as its use as a metamodel of the system. © 2011 IEEE.

关键词： Fuzzy Cognitive Maps

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Rotor Fault Diagnosis in Asynchronous Machines via Analysis of the Start-Up Transient into Intrinsic Mode Functions

Rotor Fault Diagnosis in Asynchronous Machines via Analysis ...

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International Conference on Electrical Machines

作者： George Georgoulas Ioannis Tsoumas Epaminondas Mitronikas Chrysostomos D. Stylios Athanasios Safacas Laboratory of Knowledge and Intelligent Computing Dept. of Informatics and Communications Technology TEI of Epirus 47100 Arta Kostakioi Greece Siemens Industry Sector Automation and Drives Large Drives Nuremberg Germany Electromechanical Energy Conversion Laboratory Elec. and Comp. Eng. Dept. University of Patras Rio-Patras Greece

ISBN: (纸本)9781467301435

A novel approach for rotor fault diagnosis during the start-up process is proposed. Rotor faults create asymmetries that affect the phase currents in a subtle yet distinctive way. The approach presented here is based on the application of Empirical Mode Decomposition (EMD) to the measured start-up current for the extraction of a specific complex Intrinsic Mode Function (IMF). The characteristics of this particular IMF are quite distinct for the case of an asynchronous machine with rotor asymmetry and can be used to discriminate between faulty and healthy machines. This task of fault diagnosis is performed via the use of two Hidden Markov Models (HMMs) which are well known for their temporal pattern recognition capabilities. Simulation and experimental data demonstrate the effectiveness of the proposed methodology for the detection of rotor asymmetries.

关键词： Rotor Fault Diagnosis Asynchronous Machines Start-Up Transient

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Medical Decision Support Systems based on Soft computing techniques

Medical Decision Support Systems based on Soft Computing tec...

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作者： Stylios, Chrysostomos D. Georgopoulos, Voula C. Knowledge and Intelligent Computing Laboratory Department of Informatics and Telecommunications Technology Technological Educational Institute of Epirus 47100 Artas Greece Department of Speech and Language Therapy Technological Educational Institute of Patras Koukouli 26334 Patras Greece

ISBN: (纸本)9783902661937

Medical Decision Support Systems (MDSS) are very important constructions that are incorporated into intelligent Information E-Health Systems aiming to produce warnings or to consult and suggest clinical judgments either to inexperienced medical professionals or in their lighter versions to the general public through medical advisors websites. Soft computing (SC) techniques, especially those that are based on exploiting human knowledge and experience, are extremely useful to model complex decision making procedures and thus, they have a key role in development such MDSS. Such a modeling methodology is Fuzzy Cognitive Maps which is suitable to represent human reasoning and to infer conclusions and decisions in a human-like way. In order to develop an integrated stand alone MDSS, Fuzzy Cognitive Maps could be complemented by other Soft computing techniques such as Genetic Algorithms and/or Case Based Reasoning and so to construct more efficient advanced Medical Decision Support Systems. The synergism and complementary of these methodologies may pave the way to new sophisticated systems. © 2011 IFAC.

关键词： Fuzzy Cognitive Maps

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Modeling complex logistics systems using soft computing methodology of Fuzzy Cognitive Maps

Modeling complex logistics systems using soft computing meth...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Chrysostomos D. Stylios George Georgoulas Knowledge and Intelligent Computing Laboratory Department of Informatics and Telecommunications Technology Technological Educational Institute of Epirus Artas Greece

关键词： Logistics Adaptation models Containers Computational modeling Humans Mathematical model Neural networks

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Handling drifts and shifts in on-line data streams with evolving fuzzy systems

Handling drifts and shifts in on-line data streams with evol...

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作者： Lughofer, E. Angelov, P. Department of Knowledge-based Mathematical Systems Johannes Kepler University of Linz Altenbergerstrasse 69 A-4040 Linz Austria Intelligent Systems Research Laboratory School of Computing and Communications Lancaster University LA1 4WA United Kingdom

In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact that their structure (rule base and parameters) is not fixed and not pre-determined, but is extracted from data streams on-line and in an incremental manner. When dealing with so-called drifts and s hifts in data streams, one needs to take into account (1) automatic detection of drifts and shifts and (2) automatic reaction to the drifts and shifts. This is important to avoid interruptions in the learning process and downtrends in predictive accuracy. To address the first problem, we propose an approach based on the concept fuzzy rule age. The second problem is addressed by including gradual forgetting of (1) antecedent parts and (2) consequent parameters. The latter can be achieved by including a forgetting factor in the recursive local learning process of the parameters, whose value is automatically extracted based on the intensity of the shift/drift. For addressing the former problem, we introduce two alternative methods: one is based on the evolving density-based clustering (eClustering) used to form the antecedents in the eTS approach;the other is based on the automatic adaptation of the learning rate of the evolving vector quantization (eVQ) method used to form the antecedent in the FLEXFIS approach. The paper concludes with an empirical evaluation of the impact of the proposed approaches in (on-line) real-world data sets in which drifts and shifts occur. © 2010 Elsevier B.V. All rights reserved.

关键词： Fuzzy inference

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Medical Decision Support Systems based on Soft computing techniques

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IFAC Proceedings Volumes 2011年第1期44卷 14115-14120页

作者： Chrysostomos D. Stylios Voula C. Georgopoulos Knowledge and Intelligent Computing Laboratory Dept. of Informatics and Telecommunications Technology Technological Educational Institute of Epirus 47100 Artas Greece (+***) Department of Speech and Language Therapy Technological Educational Institute of Patras Koukouli 26334 Patras Greece

Abstract Medical Decision Support Systems (MDSS) are very important constructions that are incorporated into intelligent Information E-Health Systems aiming to produce warnings or to consult and suggest clinical judgments either to inexperienced medical professionals or in their lighter versions to the general public through medical advisors websites. Soft computing (SC) techniques, especially those that are based on exploiting human knowledge and experience, are extremely useful to model complex decision making procedures and thus, they have a key role in development such MDSS. Such a modeling methodology is Fuzzy Cognitive Maps which is suitable to represent human reasoning and to infer conclusions and decisions in a human-like way. In order to develop an integrated stand alone MDSS, Fuzzy Cognitive Maps could be complemented by other Soft computing techniques such as Genetic Algorithms and/or Case Based Reasoning and so to construct more efficient advanced Medical Decision Support Systems. The synergism and complementary of these methodologies may pave the way to new sophisticated systems.

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Socio-Natural Thought Semantic Link Network: A Method of Semantic Networking in the Cyber Physical Society

Socio-Natural Thought Semantic Link Network: A Method of Sem...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Hai Zhuge Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424466955;9780769540184

Humans have been recognizing, establishing and making use of various relations consciously and unconsciously since the formation of human society. Waving and maintaining various relations accompany everyone's life. Humans' social behaviors create or emphasize relations. Various networks evolve with the development of society. Some relational networks gradually become independent of individuals' life during evolution so that they become a part of knowledge and culture sharable cross generations. The study of social relations trace back to the age of Laozi (576-BC), Confucius (551-479 BC), Thales (624 BC-546 BC) and Socrates (469 BC-399 BC). The formal definition of general relation traces to the invention of set theory in 1874. Humans have intelligence to observe and participate in social processes, to think, and to know the effect of establishing a relation. Humans can also actively select appropriate relations and persons according to requirement, situation and social rules. Machines are obviously limited in these abilities. Various graph-based models have been used to connect resources in the cyber space. Two issues are fundamental: (1) machines know little relation in human society and the nature, data structures in machines are for machines to process not for humans to read, so it is not realistic to expect machines to discover social and natural laws and resolve relevant issues without human instruction; and, (2) machines are hard to know the effect of establishing and making use of relations, and to explain computing result according to society and nature. The cause is that machines do not have any worldview. Connecting various networks and machines with nature, society, and even human minds can create a new world where individuals have semantic images that can enhance mutual understanding. The semantic images can be constructed by a Semantic Link Network SLN consisting of nodes with rich semantics, semantic links between nodes, and rules for reasoning, influe

关键词： Humans Societies Libraries Hypertext systems Spatial databases intelligent networks Information processing Computers Set theory Machine intelligence

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Linear and non-linear features for intrapartum cardiotocography evaluation

Linear and non-linear features for intrapartum cardiotocogra...

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作者： Chudáček, Václav Spilka, Jiří Huptych, Michal Georgoulas, George Lhotská, Lenka Stylios, Chrysostomos Koucký, Michal Janků, Petr Department of Cybernetics Czech Technical University in Prague Technická 2 Praha 6 166 27 Czech Republic Laboratory of Knowledge and Intelligent Computing TEI of Epirus Artas Greece Obstetrics and Gynaecology Clinic Medical Faculty of Charles University Prague Czech Republic Obstetrics and Gynaecology Clinic University Hospital in Brno Czech Republic

ISBN: (纸本)9781424473182

Since 1960's obstetricians have been using cardiotocography (CTG) to detect possible ongoing hypoxia of the fetus. CTG consists of fetal heart rate (fHR) and uterine contraction (TOCO) monitoring. The evaluation of the fHR in clinical settings is ruled by FIGO guidelines, which are based on evaluation of macroscopic morphological features derived from the fHR, such as baseline variability. Although upgrades were proposed to the guidelines - none of them is taking into account results achieved by the adult heart rate variability research. In this work, almost complete set of features previously used for fHR description is investigated and the features are assessed based on their statistical significance in the task of distinguishing the records into three FIGO classes. Inter-correlation of the features is also discussed. We assess the features on a large data set and use expert signal evaluation instead of pH values with the aim to give an overall view of the potential usefulness of the features in the clinical settings. We conclude the paper by presenting the best uncorrelated feature subset according to the meta-analysis of three different ranking methods.

关键词： Heart

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