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Lecture Notes in computer Science

作者： Werner-Stark, Ágnes Németh, Erzsébet Hangos, Katalin M. Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary School of Chemical Engineering University of Queensland Brisbane QLD Australia Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Budapest Hungary

ISBN: (纸本)9783642238536

Earlier investigations show that the results of hazard identification (HAZID) and analysis (e.g. HAZOP or FMEA) can effectively be used for knowledge-based diagnosis of complex process systems in their steady-state operation. In order to extend this approach for transient operating conditions controlled by operating procedures, the notion of nominal input-output event sequences of qualitative signals has been introduced, and the deviations used in the procedure HAZID analysis have been defined therefrom. The diagnosis can then be performed algorithmically by matching the deviation sequences of the observed input-output event sequences and the nominal ones generated by qualitative dynamic models. The concepts and methods are illustrated using a simple case study consisting of a simple tank, controlled by an operating procedure. © 2011 Springer-Verlag.

关键词： Hazards

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Computing realizations of reaction kinetic networks with given properties

Computing realizations of reaction kinetic networks with giv...

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International Research Workshop: Coping with Complexity: Model Reduction and Data Analysis

作者： Szederkényi, Gábor Hangos, Katalin M. Csercsik, Dávid Process Control Research Group Computer and Automation Research Institute Hungarian Academy of Sciences Budapest 1518 Hungary Department of Electrical Engineering and Information Systems University of Pannonia Egyetem u. 10 Veszprém 8200 Hungary

ISBN: (纸本)9783642149405

The solution to the problem of finding the reaction kinetic realization of a given system obeying the mass action law containing the minimal/maximal number of reactions and complexes is shown in this paper. The proposed methods are based on Mixed Integer Linear Programming where the mass action kinetics is encoded into the linear constraints. Although the problems are NP-hard in the current setting, the developed algorithms give a usable answer to some of the questions first raised in [1].

关键词： Reaction kinetics

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Comparison Study between a Simple Sensorless Method and Adaptive Observer for DTC-SVM Five-Phase Induction Motor Drive

Comparison Study between a Simple Sensorless Method and Adap...

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IEEE Interantional Conference on Industrial Technology

作者： B. S. Khaldi H. Abu-Rub A. Iqbal R. Kennel M. O. Mahmoudi D. Boukhetala Department of Electrical Engineering Faculty of Science and Engineering Djelfa University Algeria Department of Electrical & Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electrical Engineering College of Engineering Qatar University Qatar Institute for Electrical Drive Systems and Power Electronics Technische Universitat Munchen Munich Germany Laboratory of Process Control National Polytechnic School Algiers Algeria

ISBN: (纸本)9781467303408;9781467303422

This paper highlights the differences between a simple sensorless method and an adaptive flux and speed observer for a five-phase induction motor control. The drive is controlled using space vector modulation direct torque control (DTC- SVM) approach. The power measurement used for speed computation is simple, with a minimum processor time and memory, and relatively robust against parameters variation when compared with the adaptive observer based approach. Simulation verification and real time implementation are presented in the paper.

关键词： direct torque control (DTC) space vector modulation (SVM) Five-phase induction machine speed sensorless power measurement adaptive observer

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Mass action realizations of reaction kinetic system models on various time scales

Mass action realizations of reaction kinetic system models o...

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作者： Hangos, K.M. Szederkényi, G. Process Control Research Group Computer and Automation Reseach Institute Kende u. 13-17 Budapest H-1111 Hungary Department of Electrical Engineering and Information Systems University of Pannonia Egyetem u. 10 Veszprém Hungary

Complex chemical reaction networks often exhibit different dynamic behaviour on different time scales. A combined approach is proposed in this work for determining physically meaningful mass action realizations of complex chemical reaction networks that describe its dynamic behaviour on different time scales. This is achieved by appropriately reducing the detailed overall mass action kinetic scheme using quasi steady state assumptions fit to the particular time scale, and then searching for an optimal realization using mixed integer linear programing. Furthermore, the relationship between the properties (reversibility, deficiency, stability) of the obtained realizations of the same system on different time scales are also investigated and related to the same properties of the detailed overall model. It is shown that the reduced models obtained by quasi steady state assumptions may show exotic nonlinear behaviour, such as oscillations, when the original detailed is globally asymptotically stable. The proposed methods are illustrated by using a simple Michaelis-Menten type reaction kinetic example. The simplified versions of the well known Brusselator model have also been investigated and presented as a case study. © Published under licence by IOP Publishing Ltd.

关键词： Reaction kinetics

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Calcium coupled bursting in an integrative model of GnRH neuronal electrophysiology

Calcium coupled bursting in an integrative model of GnRH neu...

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作者： Csercsik, D. Farkas, I. Process Control Research Group Computer and Automation Research Institute Hungarian Academy of Sciences Hungary Department of Endocrine Neurobiology Institute of Experimental Medicine Hungarian Academy of Sciences Hungary MTA SzTAKI Kende U. 13-17 Budapest Hungary

In this paper, an integrative dynamical model of GnRH electrophysiology is used to study the calcium related interactions corresponding to bursting behavior. Various mechanisms affecting calcium dynamics are blocked or modified, and simulation results are compared to literature data to verify the model. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Electrophysiology

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Feature selection and classification of breast MRI lesions based on multi classifier

Feature selection and classification of breast MRI lesions b...

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International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Farzaneh Keyvanfard Mahdi Aliyari Shoorehdeli Mohammad Teshnehlab Electrical and Computer Engineering Department Khaje Nasir Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department Advanced Process Automation and Control (APAC) Khaje Nasir Toosi University of Technology Tehran Iran

This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.

关键词： Lesions Artificial neural networks Support vector machines Magnetic resonance imaging Feature extraction Breast cancer

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NN/RISE-based asymptotic tracking control of uncertain nonlinear systems

NN/RISE-based asymptotic tracking control of uncertain nonli...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Qinmin Yang S. Jagannathan Youxian Sun State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou Zhejiang China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

This paper presents a novel control methodology for the tracking control of a high-order continuous time nonlinear systems with unknown dynamics and external disturbance. The control signal consists of the robust integral of the sign of the error (RISE) feedback signal multiplied with an adaptive gain plus neural network (NN) output. The two-layer NN learns the system dynamics in an online manner while residual reconstruction errors and the external bounded system disturbances are overcome by the RISE signal. Semi-global asymptotic tracking performance is theoretically guaranteed by using the Lyapunov standard method, while the NN weights and all other signals are shown to be bounded. Further, simulations results are present to illustrate the control performance.

关键词： Artificial neural networks Approximation methods Asymptotic stability Nonlinear systems Trajectory Stability analysis Robustness

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Learning valued relations from data

Advances in Intelligent and Soft Computing

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Advances in Intelligent and Soft Computing 2011年 107卷 257-268页

作者： Waegeman, Willem Pahikkala, Tapio Airola, Antti Salakoski, Tapio De Baets, Bernard KERMIT Department of Applied Mathematics Biometrics and Process Control Ghent University Coupure links 653 B-9000 Ghent Belgium Department of Information Technology Turku Centre for Computer Science University of Turku Joukahaisenkatu 3-5 B 20520 Turku Finland

ISBN: (纸本)9783642240003

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are in many real-world applications often expressed in a graded manner. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and valued relations are considered, and it unifies existing approaches because different types of valued relations can be modeled, including symmetric and reciprocal relations. This framework establishes in this way important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated on a case study in document retrieval. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Fuzzy set theory

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Experiment design for Pauli channel estimation

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IFAC Proceedings Volumes 2011年第1期44卷 4344-4349页

作者： Gábor Balló Katalin M. Hangos Department of Electrical Engineering and Information Systems University of Pannonia Egyetem u. 10 H-8200 Veszprém Hungary Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 H-1518 Budapest Hungary

Abstract Experiment design for quantum channel parameter estimation includes the design of the quantum input to the channel and the observables to be applied on the resulting quantum output system, called the experiment configuration. An experiment design procedure based on maximizing the Fisher information of the qubit Pauli channel parameters is presented in this paper. It can be shown that the Fisher information is a convex function in both the input and the experiment configuration parameters. This leads to an optimal setting that includes pure input states and projective measurements directed towards the channel directions. An iterative method of estimating the channel directions is also proposed.

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Training ANFIS system with DE algorithm

Training ANFIS system with DE algorithm

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Allahyar Z. Zangeneh Mohammad Mansouri Mohammad Teshnehlab Ali K. Sedigh Computer Engineering Department Science and Research Branch Islamic Azad university of Tehran Iran Intelligent System laboratory (ISLAB) Control Department K. N. Toosi University of Technology Tehran Iran Advanced Process Automation & Control laboratory (APAC) Control Department K. N. Toosi University of Technology Tehran Iran

In this study, a new type of training the adaptive network-based fuzzy inference system (ANFIS) is presented by applying different types of the Differential Evolution branches. The TSK-type consequent part is a linear model of exogenous inputs. The consequent part parameters are learned by a gradient descent algorithm. The antecedent fuzzy sets are learned by basic differential evolution (DE/rand/1/bin) and then with some modifications in it. This method is applied to identification of the nonlinear dynamic system, prediction of the chaotic signal under both noise-free and noisy conditions and simulation of the two-dimensional function. Instead of DE/rand/1/bin, this paper suggests the complex type (DE/current-to-best/1+1/bin & DE/rand/1/bin) on predicting of Mackey-glass time series and identification of a nonlinear dynamic system revealing the efficiency of proposed structure. Finally, the method is compared with pure ANFIS to show the efficiency of this method.

关键词： Vectors Adaptive systems Input variables Training Mathematical model Equations Firing

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