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Article
Publication date: 4 July 2016

Stanislaw Osowski, Krzysztof Siwek and Tomasz Grzywacz

The paper is concerned with exploration of sensor signals in differential electronic nose. It is a special type of nose, which applies double sensor matrices and exploits only…

488

Abstract

Purpose

The paper is concerned with exploration of sensor signals in differential electronic nose. It is a special type of nose, which applies double sensor matrices and exploits only their differential signals, which are used in recognition of patterns associated with them. The purpose of this paper is to study the application of differential nose in dynamic measurement of aroma of 11 brands of cigarettes.

Design/methodology/approach

The most important task in pattern recognition using electronic nose is its resistance to the noise corrupting the measurement. The authors will analyze and compare the performance of the nose in the noisy environment by applying two classifier systems: the support vector machine (SVM) and random forest (RF) of decision trees.

Findings

On the basis of numerical experiments the authors have found that application of SVM as the classifier in the electronic nose is more advantageous than RF, especially at high level of noise and small number of measuring sensors. Its application allowed to recognize 11 brands of cigarettes with the accuracy close to 100 percent.

Practical implications

Thanks to application of two identical sensors working in a differential mode the authors avoid the baseline estimation and thus the solution is well suited for on-line dynamic measurements of the process.

Originality/value

The paper has studied the advantages and limitations of the differential electronic nose following from the existence of the noise, corrupting the measurements. It has pointed an important role of the applied classifier system in getting the electronic nose of the highest quality.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 35 no. 4
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 March 2002

Stanislaw Osowski and Robert Salat

The paper presents the application of self‐organizing neural network for the location of the fault in the transmission line and estimation of the parameter of the faulty element…

501

Abstract

The paper presents the application of self‐organizing neural network for the location of the fault in the transmission line and estimation of the parameter of the faulty element. The location of fault is done on the basis of the measurement of some node voltages of the line and appropriate preprocessing it to enhance the differences between different faults. The hybrid neural network is used to solve the problem. The self‐organizing layer of this network is used as the classifier. The output postprocessing MLP structure realizes the association of the place of the fault and its parameter with the measured set of node voltages. The results of computer experiments are given in the paper and discussed.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 21 no. 1
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 June 2005

Linh Tran Hoai and Stanislaw Osowski

This paper presents new approach to the integration of neural classifiers. Typically only the best trained network is chosen, while the rest is discarded. However, combining the…

390

Abstract

Purpose

This paper presents new approach to the integration of neural classifiers. Typically only the best trained network is chosen, while the rest is discarded. However, combining the trained networks helps to integrate the knowledge acquired by the component classifiers and in this way improves the accuracy of the final classification. The aim of the research is to develop and compare the methods of combining neural classifiers of the heart beat recognition.

Design/methodology/approach

Two methods of integration of the results of individual classifiers are proposed. One is based on the statistical reliability of post‐processing performance on the trained data and the second uses the least mean square method in adjusting the weights of the weighted voting integrating network.

Findings

The experimental results of the recognition of six types of arrhythmias and normal sinus rhythm have shown that the performance of individual classifiers could be improved significantly by the integration proposed in this paper.

Practical implications

The presented application should be regarded as the first step in the direction of automatic recognition of the heart rhythms on the basis of the registered ECG waveforms.

Originality/value

The results mean that instead of designing one high performance classifier one can build a number of classifiers, each of not superb performance. The appropriate combination of them may produce a performance of much higher quality.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 24 no. 2
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 October 1998

Dinh Nghia Do and Stanislaw Osowski

The paper presents the application of neural network to the classification of the closed contours forming different shapes. The shape is represented by ‐ samples of complex…

437

Abstract

The paper presents the application of neural network to the classification of the closed contours forming different shapes. The shape is represented by ‐ samples of complex numbers zk = xk+ jyk where xk and yk are the samples in the xy plane and j is the complex operator. The same shapes may vary in scale, be rotated and translated in arbitrary proportion and be distorted by the noise. To obtain the classification invariant to all these factors the preprocessing techniques based on the application of Fourier transformation of the samples have been applied. The Fourier coefficients form the input data to the neural classifier. Different shapes have been checked in numerical experiments and the results have proved good performance of the developed neural classifier and its relative insensitivity to the noise.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 17 no. 5
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 12 July 2011

Krzysztof Siwek, Stanislaw Osowski and Mieczyslaw Sowinski

The aim of this paper is to develop the accurate computer method of predicting the average PM10 pollution for the next day on the basis of some measured atmospheric parameters…

248

Abstract

Purpose

The aim of this paper is to develop the accurate computer method of predicting the average PM10 pollution for the next day on the basis of some measured atmospheric parameters, like temperature, humidity, wind, etc. This method should be universal and applicable for any place under consideration.

Design/methodology/approach

The paper presents the new approach to the accurate forecasting of the daily average concentration of PM10. It is based on the application of the ensemble of neural networks and wavelet transformation of the time series, representing PM10 pollution.

Findings

On the basis of numerical experiments, the paper finds that application of many neural predictors cooperating with each other can significantly improve the quality of results. The paper shows that the developed forecasting system checked on the data of PM10 pollution in Warsaw generated good overall accuracy of prediction in terms of root mean squared error, mean absolute error and mean absolute percentage error.

Originality/value

The main novelty of the proposed approach is the application of the wavelet transformation and many neural networks organized in the form of ensemble. The individual neural predictors are integrated into one forecasting system using different forms of integrations, including the blind source separation method and neural‐based integration.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 30 no. 4
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 13 November 2007

Stanislaw Osowski, Bartosz Swiderski, Andrzej Cichocki and Andrzej Rysz

The purpose of this paper is to develop the new method of estimation of the short‐term largest Lyapunov exponent of electroencephalogram (EEG) waveforms for the detection and…

1340

Abstract

Purpose

The purpose of this paper is to develop the new method of estimation of the short‐term largest Lyapunov exponent of electroencephalogram (EEG) waveforms for the detection and prediction of the epileptic seizure.

Design/methodology/approach

The paper proposed the modifications concerned with the way of selection of the segments of EEG waveforms taking part in estimation of Lyapunov exponent, as well as determination of the distances between two time series. The proposed method is based on Kolmogorov‐Smirnov test of similarity of two vectors. Through the application of this test more accurate and less parameterized approach to the estimation of the short‐term largest Lyapunov exponent of EEG waveforms has been obtained.

Findings

The results of performed experiments have shown that in most cases our modified method has outperformed the classical procedure, leading to more stable results, closer to the neurologist indications. The analysis of the data has proved that the change of the largest Lyapunov exponent provides a lot of information regarding the epileptic seizure. The minimum value of Lyapunov exponent indicates fairly well the seizure moment. The Tindex applied for few different electrode sites can provide good advanced prediction of the incoming epileptic seizure.

Practical implications

After additional experiments this method may find practical application for supporting the medical diagnosis of the epilepsy.

Originality/value

The proposed modification of the estimation of the short‐term largest Lyapunov exponent of the EEG waveforms eliminates some arbitrarily chosen parameters tuned by the user and leads to more accurate estimate. Such estimation results are better suited for the characterization of the epileptic activity.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 26 no. 5
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 2 January 2009

Artur Wiliński and Stanisław Osowski

The purpose of this paper is to discover the most important genes generated by the gene expression arrays, responsible for the recognition of particular types of cancer.

904

Abstract

Purpose

The purpose of this paper is to discover the most important genes generated by the gene expression arrays, responsible for the recognition of particular types of cancer.

Design/methodology/approach

The paper presents the analysis of different techniques of gene selection, including correlation, statistical hypothesis, clusterization and linear support vector machine (SVM).

Findings

The correctness of the gene selection is proved by mapping the distribution of selected genes on the two‐coordinate system formed by two most important principal components of the PCA transformation. Final confirmation of this approach are the classification results of recognition of several types of cancer, performed using Gaussian kernel SVM.

Originality/value

The results of selection of the most significant genes used for the SVM recognition of seven types of cancer have confirmed good accuracy of results. The presented methodology is of potential use in practical application in bioinformatics.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 28 no. 1
Type: Research Article
ISSN: 0332-1649

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