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1
Content available remote

An Application of Fractional Brownian Motion to Vessel Identification Based on its Sound Spectrum

100%
Wiciak M., Gloza I., Jóźwiak R., Wiciak P.
Acta Physica Polonica A
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2015
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vol. 128
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issue 1A
A-46-A-49
EN
The paper deals with the problem of vessel identification. The presented method is based on fractional Brownian analysis of vessel power spectrum. The measurements for three vessels were carried out with the use of a mobile measuring module in the Gulf of Gdansk; next, the information obtained from sound spectra was identified. Two classifiers connected with fractional Brownian motion were used: the first-order increments and the standard deviation. Finally, classification decision was made using the Mahalanobis distance. Numerical experiments were performed using MATLAB.
2
Content available remote

Automatic Detection of Long-Term Audible Noise Indices from Corona Phenomena on UHV AC Power Lines

63%
Wszołek T., Kłaczyński M.
|
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vol. 125
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issue 4A
A-93-A-98
EN
One of the most important tasks in outdoor acoustic monitoring stations is automatic extraction of the measured signal parameters. In case of corona discharge noise from ultra-high voltage alternating current (UHV AC) power lines it is necessary to select properly the corona audible noise (CAN) parameters to be monitored for noise indicators calculation, as the monitored signal and the background noise strongly fluctuate. A combined selection of distinctive features of CAN is necessary in order to distinguish the actual signal from the external interference. The vast amount of recorded data is difficult to store and process. Therefore, particular attention was devoted to define of the collected parameters used for automatic calculation of the CAN long-term noise indicators. In addition, several new CAN parameters were introduced, including spectral moments, spectral coefficients of tonal components contribution, and power coefficients in selected frequency bands; as it allowed more efficient selection of samples with non-zero contribution from CAN. The artificial neural network was applied for final classification of the measured samples. Selected and properly filtered samples provided the basis for calculations of long-term noise indicators. Efficiency of the said method was tested for the measurement sections with the recorded sound signal and aural qualification of the CAN intensity.
3
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Artificial Intelligence and Learning Systems Methods in Supporting Long-Term Acoustic Climate Monitoring

63%
Kłaczyński M., Wszołek T.
Acta Physica Polonica A
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2013
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vol. 123
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issue 6
1024-1028
EN
Developing effective methods for automatic identification of noise sources is currently one of the most important tasks in long-term acoustical climate monitoring of the environment. Manual verification of recorded data, when it comes to proper determination of noise levels, is time-consuming and costly. A possible solution is to use pattern recognition techniques for acoustic signal recorded by a monitoring station. This paper presents usefulness of special directed measurement techniques, acoustic signal processing, and classification methods using artificial intelligence (the Sammon mapping) and learning systems methods (Support Vector Machines) in the recognition of corona audible noise from ultra-high voltage AC transmission lines.
4
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Fractal Features of Specific Emitter Identification

51%
Dudczyk J., Kawalec A.
Acta Physica Polonica A
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2013
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vol. 124
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issue 3
406-409
EN
This article presents the issues connected with emitter sources identification with low distinctive primary features of a signal. It is a specific type of identification called specific emitter identification, which distinguishes different copies of the same type of emitter. The term of specific emitter identification was presented on the basis of fractal features received from the transformation of measurement data sets. The use of linear regression and Lagrange polynomial interpolation resulted in the estimation of measurement function. The method analysing properties of measurement function which was suggested by the authors caused the extraction of two additional distinctive features. The features above extended the vector of basic radar signals' parameters. The extended vector of radar signals' features made it possible to identify the copy of emitter source.
5
Content available remote

Detection and Classification of Selected Noise Sources in Long-Term Acoustic Climate Monitoring

51%
Kłaczyński M., Wszołek T.
Acta Physica Polonica A
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2012
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vol. 121
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issue 1A
A-179-A-182
EN
Continuous acoustical climate monitoring of the environment raises several problems related to large quantities of the recorded data, which often represents information unrelated to the studied noise source. Manual verification of such data is time-consuming and costly. Therefore, developing effective methods for automatic identification of transport noise sources becomes an important task for the proper determination of noise levels. This paper presents a concept of such method of automatic detection and classification of the noise sources from the air and railway transportation in the acoustic environmental monitoring.
6
Content available remote

Research on Pattern Recognition Applied for Volume Estimation of Blood Chamber with Matrix of Optical Sensors

51%
Marczyński P., Konieczny G., Pustelny T.
Acta Physica Polonica A
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2013
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vol. 124
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issue 3
498-501
EN
The following article describes research on possibility of using pattern recognition algorithms in the optical measurement system for estimation of the blood chamber volume in the Polish Ventricular Assist Device (POLVAD). The optical system is being developed at the Department of Optoelectronics, Silesian University of Technology, Poland. Data analysis methods include a feature subset selection algorithm involving principal components analysis and objective function as quality criterion. The analysis takes into account 17 patterns reflecting particular volumes. The k-nearest neighbours method is used as pattern classifier. The pattern recognition system was initially designed for an array of gas sensors and this paper describes its further development.
7
Content available remote

Location and Identification of Acoustic Signals Recorded in Power Oil Transformers within the Band of 20-180~kHz

45%
Olszewska A., Witos F.
Acta Physica Polonica A
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2011
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vol. 120
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issue 4
709-712
EN
Emission acoustic signals, recorded in investigated power oil transformers, have been analyzed in the time, frequency and time-frequency domain. Analysis of each signal has been started by filtration within selected frequency band and subsequently the following quantities have been calculated: spectral power density, phase-time characteristic, averaging phase-time characteristic, short-time Fourier transform spectrograms, signal amplitude distributions, descriptors with acronyms ADC and ADP and thereafter maps of descriptors on lateral walls of transformers can be carried out. Frequency bands applied in order to filtration have been chosen in such a way so that signals coming from different sources (among other things from partial discharges, Barkhausen's effect, oil circulation and outer acoustic disturbances) can be differentiated. The sources have been localized using maps of descriptors calculated for selected frequency bands. The fundamental properties of obtained signals have been determined. Such properties describe: partial discharges, Barkhausen's acoustic effect and other acoustic interferences.
8
Content available remote

Analysis of Properties Characteristic for Acoustic Emission Signals Recorded On-Line in Power Oil Transformers

45%
Witos F., Olszewska A., Szerszeń G.
Acta Physica Polonica A
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2011
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vol. 120
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issue 4
759-762
EN
Investigation results of properties characteristic for acoustic emission signals recorded in two selected power oil transformers are presented. Signals were put to the filtration, whereas components coming from partial discharges have been left. The calculations concerned: phase-time characteristics, averaging phase characteristics, averaging short time Fourier transform spectrograms, amplitude distributions of signals, values of acoustic emission descriptor with acronym ADC. On the ground of calculated basic characteristics and maps of ADC descriptor three areas have been selected on lateral walls of transformer tanks. Acoustic emission signals recorded in these areas were analyzed from the point of view how is influence of propagation path on these properties.
9
Content available remote

Location of Partial Discharge Sources and Analysis of Signals in Chosen Power Oil Transformers by Means of Acoustic Emission Method

45%
Olszewska A., Witos F.
Acta Physica Polonica A
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2012
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vol. 122
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issue 5
921-926
EN
The original system useful for analysis of signals recorded during investigations of partial discharges within power oil transformers by means of acoustic method is presented. This method includes the basic and advanced analysis of recorded data. In the frame of basic analysis of data recorded signals undergo filtration in chosen frequency bands and next the analysis is made - in domain of time, frequency, time-frequency and discrimination threshold. In the frame of advanced analysis of data the amplitude distributions of acoustic emission signals and the acoustic emission descriptors (defined by the authors) are calculated in order to outline maps of acoustic emission descriptors on lateral walls of a transformer; it is a base for location of sources of partial discharges by means of the original method consisted in determination of advance degree of a signal. Results of this analysis, for signals recorded in two chosen transformers with identical construction (partial discharge occurred only within one of them), are presented in the paper. The source of partial discharge, situated within oil near transformer tank, was localized; the revision confirmed this result. Properties of recorded emission acoustic signals at chosen measuring points situated on the tank, in function of distance between the partial discharge source and measuring points, are presented.
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