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8th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2008

作者： Procházka, Aleš Bartošová, Veronika Vyšata, Oldřich Department of Computing and Control Engineering Institute of Chemical Technology in Prague Technická 6 166 28 Prague 6 Czech Republic Neurocenter Caregroup Jiráskova 1389 516 01 Rychnov nad Kněžnou Czech Republic

ISBN: (纸本)9780889867598

Multi-channel sensors and multi-channel signal analysis form a specific area of general digital signal processing methods with applications in medicine, environmental signal analysis or technology. The paper is devoted to general mathematical methods related to initial signal de-noising, detection of its principal components and segmentation to find its specific parts. Feature detection includes the use of discrete wavelet transform (DWT) and discrete Fourier transform (DFT) for estimation of features invariant to signal shift to form clusters of close data segments. The selforganizing neural networks are then used for signal segments classification. Results are numerically evaluated by statistical analysis of distances of individual feature vector values from the corresponding cluster centers. Proposed methods are used for electroencephalogram (EEG) signal segmentation based upon detection of changes of signal spectral components applied to its first principal component, signal segments feature extraction and their classification. Results achieved are compared for different data sets and different mathematical methods used to detect signal segments features. Numerical results are compared with experience of experts specialized to EEG data analysis to allow further correlation with MR images. Proposed methods are accompanied by the appropriate graphical user interface (GUI) designed in the MATLAB environment.

关键词： Principal component analysis

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Multi agent systems in diagnostics and exception handling of batch processes

Multi agent systems in diagnostics and exception handling of...

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CHISA 2006 - 17th International Congress of Chemical and Process engineering

作者： Burian, P. Department of Computing and Control Engineering University of Chemical Technology in Prague Technická 5 166 28 Praha 6 Czech Republic

ISBN: (纸本)8086059456

Agent is an active, permanent computer entity which is able to perceive, to behave "reasonably" towards its environment, to actuate on them (agents) and which is able to communicate with other agents. For mutual communication, negotiation, cooperation, coordination and collaboration among agents the higher-level language ACL (Agent Communication Language) is used. Agents may also help in fault detection and diagnostics. Simple model equations rewriten on discretized residual form, compare measurement values from process with calculated simple model equations. Exception handling is a critical element for achieving long-term success in batch production. Correct handling of exceptions is a key element in process safety, consistent product quality, and production cost minimalization. In Exception handling Agents represent Agent with the equipment-state machine and Agent with the procedure-state machine. Jadex is a Java based, FIPA compliant agent environment, and allows to develop goal oriented agents following the BDI model. Jadex provides a framework and a set of development tools to simplify the creation and testing of agents. Visual Paradigm for the Unified Modeling Language (VP-UML) is a UML CASE tool. Visual Paradigm for UML is a professional but easy-to-use UML modeling tool that supports complete software life-cycle - analysis, design, implementation, testing and deployment.

关键词： Multi agent systems

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On-line recognition of metabolic state of Baker's yeast culture 9th

On-line recognition of metabolic state of Baker's yeast cult...

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9th IFAC International Symposium on Computer Applications in Biotechnology, CAB 2004

作者： Hrnčiřík, P. Vovsík, J. Náhlík, J. Department of Computing and Control Engineering Institute of Chemical Technology Prague Technická 5 Prague 6166 28 Czech Republic

This paper presents the comparison of two approaches based on artificial intelligence techniques solving the task of on-line recognition of metabolic state of baker's yeast culture in a fed-batch cultivation. The first approach is represented by a knowledge-based system containing expert knowledge in the form of production rules. The other approach is based on the application of the fuzzy neural network paradigm enabling the automatic extraction of the recognition rules from experimental data. Performance of both approaches is discussed using results obtained from tests on experimental data from a laboratory cultivation unit. Copyright © IFAC Computer Applications in Biotechnology, Nancy, France, 2004.

关键词： Metabolism

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On-Line Recognition of Metabolic State of Baker's Yeast Culture

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IFAC Proceedings Volumes 2004年第3期37卷 397-402页

作者： P. Hrnčiřik J. Vovsík J. Náhlík Department of Computing and Control Engineering Institute of Chemical Technology Prague Technická 5 166 28 Prague 6 Czech Republic fax: +420 224 355 053

This paper presents the comparison of two approaches based on artificial intelligence techniques solving the task of on-line recognition of metabolic state of baker's yeast culture in a fed-batch cultivation. The first approach iS represented by a knowledge-based system containing expert knowledge in the form of production rules. The other approach is based on the application of the fuzzy neural network paradigm enabling the automatic extraction of the recognition rules from. experimental data. Performance of both approaches is discussed using results obtamed from tests on experimental data from a laboratory cultivation unit.

关键词： Fermentation processes Fuzzy systems Neural networks Recognition Rule-based systems

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Image de-noising and restoration using wavelet transform

Image de-noising and restoration using wavelet transform

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2003 European control Conference, ECC 2003

作者： Procházka, A. Šindelářová, I. Ptáček, J. Prague Institute of Chemical Technology Department of Computing and Control Engineering Technická 1905 Prague 6166 28 Czech Republic University of Economics Department of Econometrics W. Churchill Sq. 4 Prague 3130 67 Czech Republic

ISBN: (纸本)9783952417379

Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement of biomedicai structures as well. This problem occurs also in filling-in blocks of missing or corrupted data. The paper presents the use of Wavelet transform in this area including its application for image decomposition and rejection of its components at first. The main part of the paper is devoted (i) to algorithms of restoration of missing image blocks by the search of similar structures of a given image in the Wavelet domain space and (ii) to comparison of this approach with iterated Wavelet interpolation and predictive image modelling. Proposed methods are verified for simulated data and then applied for processing of magnetic resonance images. © 2003 EUCA.

关键词： Image denoising

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Signal segmentation using time-scale signal analysis 9

Signal segmentation using time-scale signal analysis

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9th European Signal Processing Conference, EUSIPCO 1998

作者： Procházka, Aleš Kolínová, Magdaléna Stříbrsky, Jaroslav University of Chemical Technology Department of Computing and Control Engineering Technická 1905 Prague 6166 28 Czech Republic

Change-point detection is a fundamental problem in many areas of signal segmentation, feature extraction and classification. There are various statistical methods that can be applied to estimate boundaries of signal segments. The paper presents another method based upon the wavelet transform and signal decomposition used to detect signal irregularities. This approach can be very efficient if the initial wavelet function is carefully chosen. Basic methods presented in the paper are verified for simulated sequences at first and then used for biomedical signal segmentation. Resulting algorithms are written in the MATLAB environment.

关键词： Signal analysis

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Adaptive FIR filter use for signal noise cancelling

Adaptive FIR filter use for signal noise cancelling

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IEEE Workshop on Neural Networks for Signal Processing

作者： M. Kolinova A. Prochazka M. Mudrova Department of Computing and Control Engineering Technická 1905 Prague University of Chemical Technology Prague Czech Republic

Noise reduction, together with signal modelling, classification and prediction, forms a basic problem in many engineering, biomedical and econometric systems. The paper summarizes basic adaptive methods based upon the application of artificial neural networks simplified in many cases to an adaptive linear element only. Adaptive noise cancelling algorithms by such finite impulse response (FIR) filters are studied in the first part of the paper both for the case that a reference signal is known and for reference-free structures. Resulting signals are compared with those obtained by noise rejection in the wavelet transform domain either based upon signal decomposition and reconstruction using properly chosen threshold levels for signal modification or incorporating adaptive FIR filtering. Signals preprocessed in this way are then used for their segmentation and classification to verify the effect of signal denoising. Methods presented in the paper are used for simulated signals at first and then applied to the processing of real signals representing physiological signals and technological time series.

关键词： Finite impulse response filter Noise cancellation Signal processing Adaptive filters Noise reduction Predictive models Biomedical engineering Econometrics Artificial neural networks Noise level

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Signal segmentation using time-scale signal analysis

Signal segmentation using time-scale signal analysis

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European Signal Processing Conference (EUSIPCO)

作者： Aleš Procházka Magdaléna Kolinová Jaroslav Stříbrský University of Chemical Technology Department of Computing and Control Engineering Technická 1905 166 28 Prague 6 Czech Republic

关键词： Wavelet analysis Time-frequency analysis Signal resolution Discrete wavelet transforms Signal analysis

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Multichannel time-series modelling and prediction by wavelet networks

Multichannel time-series modelling and prediction by wavelet...

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European Signal Processing Conference (EUSIPCO)

作者： Aleš Procházka Jonathan Smith Prague University of Chemical Technology Department of Computing and Control Engineering Technická 1905 166 28 Prague 6 Czech Republic South Bank University School of Engineering Systems and Design 103 Borough Road London SE1 OAA England

Multichannel time-series result from observations of a given engineering, biomedicai, econometric or environmental variable taken at different locations. Processing this type of signal presents problems associated with its extrapolation in given space ranges and its possible prediction. This paper presents a comparison of seasonal AR modelling of such signals and the application of wavelet networks to the system identification and prediction of a particular signal. The choice of wavelet functions and the optimization of their coefficients is discussed as well. Each method suggested in the paper is verified for simulated signals at first and then used for the analysis of real signals, including the observation of air pollution. All algorithms are written in the MATLAB environment.

关键词： Wavelet analysis Neural networks Predictive models Atmospheric modeling Wavelet transforms Air pollution Wavelet domain

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Application of the Expert control in a Sugar Factory

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IFAC Proceedings Volumes 1994年第10期27卷 177-182页

作者： J. Michal M. Kmínek P. Kmínek Department of Computing and Control Engineering Institute of Chemical Technology Technická 5 166 28 Praha 6 The Czech Republic

This paper presents a method for using a rule-based expert system for the control of several technological processes in sugar production. The structure and principle of the expert controller are shown. Extraction, crystallisation and process co-ordination were chosen for practical implementation of the expert system as processes which are very difficult to control by classical means. Some experience from the implementation in the sugar factory in Lovosice is presented.

关键词： Expert control crystallisation control vacuum pan crystallisation sugar extraction co-ordination and supervisory control control applications expert systems non-linear control systems process control

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