Wyniki wyszukiwania - BazTech

1

Algorithms for time-frequency imaging and analysis: introduction to mixed-model spectral decomposition

Pant Animesh, Ghosal Dibakar, Puryear Charles, Verma Shashank Narayan

Acta Geophysica

|

2024

|

Vol. 72, no. 2

637--653

EN

Time-frequency algorithms help discern and filter hidden information from signals but their growing abundance induces non-uniqueness thus, complicating selection. Classification of these algorithms into approaches can bring simplification and structure to improve our selection and estimates. This study focuses on algorithms we classify here as fixed windowbased projection approach, wavelet-based projection approach, greedy-based approach and combinational-based approach while omitting heuristic-based approach and numerical-autoregressive-based approach classes. It describes the basic theory of transforms under the classes and compares them for effective stability, effective localization and resolution capabilities of time-frequency spectra for wavelet estimation and interfering beds with results demonstrating subtle advantages for each depending on nature of signal and model behind the algorithm. The combinational-based mixed-model approach wavelet-assisted constrained least squares spectral analysis concatenates a wavelet-based approach with a fixed windowbased approach and effectively functions to reassign complex amplitude coefficients from their apparent positions to their true positions. A comparison of the results suggests that it demonstrates good scope as an effective alternative general tool for hydrocarbon detection and resolution of thin beds.

2

Dekompozycja impulsów radarowych za pomocą triangulacji zer spektogramu

Abratkiewicz Karol

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2023

|

nr 4

133--136

PL

W niniejszej pracy przedstawiono wyniki analizy i dekompozycji impulsów radarowych za pomocą metody triangulacji zer spektrogramu w dziedzinie czas-częstotliwość. Sygnał niestacjonarny zaburzony szumem białym charakteryzuje się ściśle określonym rozkładem zer na płaszczyźnie czas-częstotliwość, jeśli do przekształcenia wykorzysta się okno Gaussa oraz krótkoczasową transformację Fouriera (ang. short-time Fourier transform, STFT). Wówczas odległości pomiędzy sąsiadującymi zerami spektrogramu (rozkładu energii STFT) wykazują się inną dystrybucją dla szumu i inną w otoczeniu sygnału. Dzięki tej właściwości możliwe było pokazanie w artykule procesu ekstrakcji impulsów pochodzących z nadajników radiolokacyjnych.

EN

This paper presents the results of the analysis and decomposition of radar pulses using the method of triangulation of spectrogram zeros in the time-frequency domain. A non-stationary signal disturbed by white noise is characterized by a strictly defined distribution of zeros on the time-frequency plane if the Gaussian window and the short-time Fourier transform (STFT) are used for the analysis. Then the distances between adjacent zeros of the spectrogram (STFT energy distribution) are characterized by a different distribution for noise and in the signal vicinity. Thanks to this property, the article shows the process of extracting pulses from radar transmitters.

3

Time-frequency Representation -enhanced Transfer Learning for Tool Condition Monitoring during milling of Inconel 718

Zhou Yuqing, Sun Wei, Ye Canyang, Peng Bihui, Fang Xu, Lin Canyu, Wang Gonghai, Kumar Anil, Sun Weifang

Eksploatacja i Niezawodność

|

2023

|

Vol. 25, no. 2

art. no. 165926

EN

Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of experiments, which significantly affects the performance of ML models. A time-series dimension expansion and transfer learning (TL) method is developed to boost the performance of TCM for small samples. First, a time-frequency Markov transition field (TFMTF) is proposed to encode the cutting force signal in the cutting process to two-dimensional images. Then, a modified TL network is established to learn and classify tool conditions under small samples. The performance of the proposed TFMTF-TL method is demonstrated by the benchmark PHM 2010 TCM dataset. The results show the proposed method effectively obtains superior classification accuracies for small samples and outperforms other four benchmark methods.

4

Generative modelling of vibration signals in machine maintenance

Puchalski Andrzej Adam, Komorska Iwona

Eksploatacja i Niezawodność

|

2023

|

Vol. 25, no. 4

art. no. 173488

EN

The exponential development of technologies for the acquisition, collection, and processing of data from real-world objects is creating new perspectives in the field of machine maintenance. The Industrial Internet of Things is the source of a huge collection of measurement data. The performance of classification or regression algorithms needs to take into account the random nature of the process being modelled and any incomplete observability, especially in terms of failure states. The article highlights the practical possibilities of using generative artificial intelligence and deep machine learning systems to create synthetic measurement observations in monitoring the vibrations of rotating machinery to improve unbalanced databases. Variational Autoencoder VAE generative models with latent variables in the form of high-level input features of time-frequency spectra were studied. The mapping and generation algorithm was optimised and its effectiveness was tested in the practical solution of the task of diagnosing the three operating states of a demonstration gearbox.

5

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Karabacak Yunus Emre

Eksploatacja i Niezawodność

|

2023

|

Vol. 25, no. 3

art. no. 168082

EN

CNC milling machines are frequently used in the manufacturing of mechanical parts in the industry. One of the most important components of milling machines is the cutting tool. Monitoring the cutting tool wear is important for the reliability, continuity, and quality of production. Monitoring the tool and detecting the stage of wear are difficult processes. In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

6

Combining the synchrosqueezing generalized S-transform of variational mode decomposition with the Teager-Kaiser energy operator to calculate the attenuation gradient for identifying oil and gas reservoirs

Yan Haitao, Zhou Huailai, Wang Yuanjun, Wu Nanke, Zhou Wei, Zhou Jie

Acta Geophysica

|

2023

|

Vol. 71, no. 2

795--812

EN

Because of the limited resolution of conventional time–frequency analysis algorithms, they are also limited to calculate attenuation gradients that describe oil and gas reservoirs. We propose an advanced method for calculating the attenuation gradient that combines the synchrosqueezing generalized S-transform of variational mode decomposition with the Teager–Kaiser energy operator. SSVGST takes advantage of the synchrosqueezing generalized S-transform to focus the longitudinal resolution of the time–frequency domain and variational mode decomposition for adaptive signal segmentation in the frequency domain. Thus, SSVGST can be used to improve the time–frequency resolution of seismic signals, and the Teager–Kaiser energy operator is used to enhance the extracted attenuation gradient and highlight oil and gas regions effectively. The time–frequency focusing ability of SSVGST was verified by using a synthetic signal and theoretical model. Experimental results with the model and field data showed that the combination of SSVGST with the Teager–Kaiser energy operator suppressed the fuzzy energy caused by the low resolution of conventional time–frequency analysis algorithms and could locate reservoirs accurately and effectively.

7

EMD-based time-frequency analysis methods of non-stationary audio signals

Lewandowski Marcin, Grodzicka Salomea

Vibrations in Physical Systems

|

2022

|

Vol. 33, nr 2

art. no. 2022215

EN

To ensure that any time series data is appropriately interpreted, it should be analyzed with proper signal processing tools. The most common analysis methods are kernel-based transforms, which use base functions and their modifications to represent time series data. This work discusses an analysis of audio data and two of those transforms - the Fourier transform and the wavelet transform based on a priori assumptions about the signal's linearity and stationarity. In audio engineering, these assumptions are invalid because the statistical parameters of most audio signals change with time and cannot be treated as an output of the LTI system. That is why recent approaches involve decomposition of a signal into different modes in a data-dependent and adaptive way, which may provide advantages over kernel-based transforms. Examples of such methods include empirical mode decomposition (EMD), ensemble EMD (EEMD), variational mode decomposition (VMD), or singular spectrum analysis (SSA). Simulations were performed with speech signal for kernel-based and data-dependent decomposition methods, which revealed that evaluated decomposition methods are promising approaches to analyzing non-stationary audio data.

8

Application of the discrete linear chirp transform (DLCT) to estimate the parameters of multicomponent LFM signals

Fiołka Jerzy

Przegląd Elektrotechniczny

|

2022

|

R. 98, nr 6

45--49

EN

This paper is focused on method to estimate the parameters of multicomponent linear frequency modulation (LFM) signals. These nonstationary signals, which are often referred to as ”chirp”, are encountered in many fields such as communication, vibration analysis, radar systems. The presented method, which is based on the discrete linear chirp transform (DLCT), permits the chirp parameters to be precisely estimated. Its high performance, which was proven by the simulation results, coupled with its simplicity, makes this method useful for many applications.

PL

W artykule przedstawiono metodę estymacji parametrów wieloskładnikowych sygnałów z liniową modulacją częstotliwości. Z tego typu sygnałami mamy do czynienia w takich dziedzinach jak telekomunikacja, analiza drgań, systemy radarowe. Przedstawiona metoda, bazująca na DLCT (ang. Discrete linear chirp transform), pozwala na oszacowanie parametrów wspomnianych sygnałów. Jej wysoka skuteczność, potwierdzona wynikami symulacji, w połączeniu z prostotą, czyni metodę użyteczną w wielu zastosowaniach.

9

Inter-harmonic parameter identification method based on transform with local maximum spectrum

Sun Lin, Song Jing, Jin Yan

Archives of Electrical Engineering

|

2022

|

Vol. 71, nr 1

189--209

EN

In order to improve the detection accuracy of harmonics/inter-harmonics in power systems, a new method of harmonic/inter-harmonic detection based on synchrosqueezed transform and the Hilbert operator based on local spectrum maximum is proposed. Firstly, the spectrum of inter-harmonic signals is obtained through short-time Fourier transform, and the local maximum value of the spectrum in the frequency direction is detected. Then, based on the maximum frequency of the spectrum, a new frequency redistribution operator and synchronous extraction operator are constructed. It combines the operators with ridge detection for the decomposition of harmonic/inter-harmonic signals, so as to obtain a series of intrinsic mode function (IMF) components. Finally, the instantaneous amplitude and frequency of the IMF components is obtained by using the Hilbert operator. Meanwhile, according to the instantaneous frequency mutation point in the spectrum, the starting and ending time of transient harmonics/inter-harmonics is located accurately. Based on a low signal-to-noise ratio (SNR), the wavelet packet method (WP), Hilbert Marginal Spectrum method (HMS), synchrosqueezing wavelet transform method (SST), the Hybrid SST method (HSST), enhanced empirical wavelet transform (EEWT) and the proposed method are used to identify the harmonic/inter-harmonic parameters, respectively. The experimental results show that the proposed LMSST method can effectively separate the steady-state and transient harmonic/inter-harmonic signals, and has higher detection accuracy and better noise robustness.

10

Dual-mode control magnetically-coupled energy storage inductor boost inverter for renewable energy

Chen Yiwen, Luo Sixu, Huang Zhiliang, Jiang Jiahui

Archives of Electrical Engineering

|

2022

|

Vol. 71, nr 1

211--225

EN

A novel magnetically-coupled energy storage inductor boost inverter circuit for renewable energy and the dual-mode control strategy with instantaneous value feedback of output voltage are proposed. In-depth research and analysis on the circuit, control strategy, voltage transmission characteristics, etc., providing the parameter design method of magnetically-coupled energy storage inductors and output filter. The circuit topology is cascaded by the input source 𝑉in, the input filter 𝐶in, a single-phase inverter bridge with a magnetically-coupled energy storage inductor, and a CL filter; The control strategy serves the output voltage as a reference to achieve the switch of step-down and step-up modes smoothly. The simulation results of a 1000 VA 100–200 VDC, 220 V 50 Hz AC inverter show that the proposed inverter can realize single-stage boost power conversion, which can adapt to resistive, capacitive and inductive loads, has high power density and low output waveform distortion. It has good application prospects in small and medium-capacity single-phase inverter occasions with low input voltag

11

Noise suppression of distributed acoustic sensing vertical seismic profile data based on time-frequency analysis

Shao Dan, Li Tonglin, Han Liguo, Li Yue

Acta Geophysica

|

2022

|

Vol. 70, no 4

1539--1549

EN

Distributed acoustic sensing (DAS) technology is a novel technology applied in vertical seismic profile (VSP) exploration, which has many advantages, such as low cost, high precision, strong tolerance to harsh acquisition environment. However, the field DAS-VSP data are often disturbed by complex background noise and coupling noise with strong energy, affecting the quality of seismic data seriously. Therefore, we develop a time–frequency analysis method based on low-rank and sparse matrix decomposition (LSMD) and data position points distribution maps (DPM) to separate signals from noise. We adopt Multisynchrosqueezing Transform to construct the approximate ideal time–frequency representation of DAS data, which reduces the difficulty of signal to noise separation and avoids the loss of some effective information to a certain extent. The LSMD is performed to separate the signal component and noise component preliminarily. In addition, combined with the separated low-rank matrix and sparse matrix, we propose the DPM to improve the accuracy of signal component extraction and the recovery ability of the method for weak signals through the joint analysis of the maps in time domain and frequency domain. Both synthetic and field experiments show that the proposed method can suppress coupling noise and background noise and recover weak energy signals in DAS VSP data effectively.

12

Detekcja spalania stukowego w silnikach benzynowych oparta na metodzie HVD

Fiołka Jerzy

Przegląd Elektrotechniczny

|

2021

|

R. 97, nr 3

150--156

PL

W artykule zaprezentowano koncepcję detekcji spalania stukowego w silnikach benzynowych opartą na metodzie HVD (ang. Hilbert Vibration Decomposition). Jak pokazano, oparta na HVD dekompozycja sygnału ciśnienia w komorze spalania na poszczególne składowe częstotliwościowe pozwala na wnikliwą obserwację tego zjawiska, jak również na wyznaczenie wartości parametru, określającego intensywność spalania stukowego.

EN

The article presents the concept of using the Hilbert Vibration Decomposition (HVD) method for the detection of knocking combustion in gasoline engines. It has been shown that the HVD-based decomposition of the pressure signal in the combustion chamber into a set of frequency components allows for a precise characterization of this phenomenon, as well as for the determination of the knock intensity metric.

13

Analiza wpływu czynników zewnętrznych na wyniki pomiarów infradźwięków emitowanych przez turbiny wiatrowe

Boczar Tomasz, Zmarzły Dariusz, Kozioł Michał, Wotzka Daria

Przegląd Elektrotechniczny

|

2020

|

R. 96, nr 7

113--120

PL

W artykule przedstawiono wyniki pomiarów i analiz sygnałów niskiej częstotliwości w zakresie od 1 do 10 kHz zarejestrowanych w farmie wiatrowej na południu Polski. W skład elektrowni wchodziło 15 turbin każda o mocy 2,05 MW. W części wynikowej artykułu zostaną przedstawione zależności ilustrujące wpływ zmian prędkości wiatru, a także odległości między punktami rejestracji a turbiną wiatrową na uzyskiwane zależności częstotliwościowe i czasowo-częstotliwościowe.

EN

The The paper presents the results of measurements and analyses of low frequency signals in the range from 1 to 10 kHz recorded in a wind farm in southern Poland. The power plant consisted of 15 turbines each with a capacity of 2,05 MW. In the resulting part of the article the dependencies illustrating the influence of wind speed changes and also distances between the recording stations and the wind turbine on the obtained frequency and time-frequency relationships will be presented.

14

The fractional Fourier transform as a biomedical signal and image processing tool: A review

Gómez-Echavarría Alejandro, Ugarte Juan P., Tobón Catalina

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 3

1081--1093

EN

This work presents a literature review of the fractional Fourier transform (FrFT) investiga-tions and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has been used for signal and image processing due to its capability in capturing the nonstationary characteristics of real signals. Most biomedical signals are an example of such non-stationarity. Thus, the FrFT-based solutions can be formulated, aiming to enhance the health technology. As the literature review indicates, common applications of the FrFT involves signal detection, filtering and features extraction. Establishing adequate solutions for these tasks requires a proper fractional order estimation and implementing the suitable numeric approach for the discrete FrFT calculation. Since most of the reports barely describe the methodology on this regard, it is important that future works include detailed information about the implementation criteria of the FrFT. Although the applications in biomedical sciences are not yet among the most frequent FrFT fields of action, the growing interest of the scientific community in the FrFT, supports its practical usefulness for developing new biomedical tools.

15

Seismic time–frequency spectrum analysis based on local polynomial Fourier transform

Qi Pengfei, Wang Yanchun

Acta Geophysica

|

2020

|

Vol. 68, no. 1

1--17

EN

Time–frequency analysis technology is widely used in non-stationary seismic data analysis. The energy concentration of the spectrum depends on the consistency of the kernel function of the time–frequency analysis method and the instantaneous frequency variation of the signals. The conventional time–frequency analysis methods usually require that the local instantaneous frequency of the signals remains unchanged or linearly changed. So it is difcult to accurately characterize the instantaneous frequency nonlinear variation of the non-stationary signal. The local polynomial Fourier transform (LPFT) method can efectively describe the instantaneous frequency variation by local high-order polynomial ftting and obtain the results with high spectral and energy concentration. The numerical simulations and feld seismic data applications show that the time–frequency spectrum results obtained by LPFT can refect the instantaneous frequency variation characteristics of the seismic data, while ensuring the concentration of time–frequency energy.

16

Enhancing the resolution of time–frequency spectrum using directional multichannel matching pursuit

Xu Lu, Yin Xingyao, Li Kun

Acta Geophysica

|

2020

|

Vol. 68, no. 6

1643--1652

EN

Matching pursuit is able to decompose signals adaptively into a series of wavelets and has been widely applied in signal processing of the geophysical felds. Single-channel matching pursuit could not take into account the lateral continuity of seismic traces, and the recent multichannel matching pursuit exploits the lateral coherence as a constraint, which helps to improve the stability of decomposition results. However, atoms searched by multichannel matching pursuit currently are just shared by lateral seismic traces at the same time slicers. The lack of directionality in multichannel search strategies leads to irrationality in dealing with large dip angle seismic traces. Considering that the waveforms of refection events are relatively continuous and similar, an improved multichannel matching pursuit is proposed to realize the directional decomposition of adjacent signals. Based on the principle of seismic refection events tracking and identifcation, directional multichan nel decomposition of seismic traces is realized. The seismic channel to be decomposed is correlated with the time shift of the optimal atom determined by the previous seismic channel. The time position of the maximum correlation indicates the center time of the optimal atom. Optimal atoms identifed by one iteration of multichannel decomposition have the same frequency and phase parameters, diferent center time and amplitude parameters. The center time of the optimal atoms is consistent with seismic refection events. Tests illustrate that the algorithm can successfully reconstruct 2D seismic data without reducing accuracy. Besides, the application of feld data is of great signifcance for reservoir exploration and hydro-carbon interpretation.

17

Analysis of measurement conditions influence on the magnetic Barkhausen noise phenomenon

Maciusowicz Michał, Psuj Grzegorz

Welding Technology Review

|

2019

|

R. 91, nr 12

17--24

EN

The effectiveness of the magnetic Barkhausen noise method (MBN), used for non-destructive testing of ferromagnetic materials, depends to a large extent on a number of factors determining the measurement conditions. The use of conditions allowing the highest possible level of discrimination between the various states of the materials state is of highest importance. Therefore, this paper presents an analysis of the impact of measurement conditions on Barkhausen noise signals observed for various states of the material conditions. Taking into consideration the stochastic nature of MBN and the complex characterization of its changes, the analysis was based on the time-frequency representation of the MBN signal. The paper presents selected distributions achieved using two transformation methods. In addition, the extraction methods of features allowing the quantification of complex information were given. Finally, the discrimination ability for a number of parameters and features of MBN signals were determined and the obtained results were discussed.

PL

Skuteczność metody magnetycznego szumu Barkhausena MBN (ang. Magnetic Barkhausen Noise), wykorzystywanej do badań nieniszczących materiałów ferromagnetycznych, zależy w dużej mierze od szeregu czynników określających warunki pomiarowe. Kluczowe znaczenie ma zastosowanie warunków umożliwiających najwyższy możliwy poziom dyskryminacji między różnymi stanami badanych materiałów. W związku z tym w niniejszej pracy przedstawiono analizę wpływu warunków pomiaru na sygnały szumu Barkhausena rejestrowane dla różnych stanów badanego materiału. Mając na uwadze stochastyczną naturę szumu MBN i złożoną charakterystykę jego zmian, analizę przeprowadzono na podstawie reprezentacji czasowo-częstotliwościowej sygnału MBN. W pracy zaprezentowano wybrane rozkłady z zastosowaniem dwóch metod transformacji. Ponadto przybliżono metody ekstrakcji cech umożliwiające kwantyfikację złożonej informacji. Na koniec określono poziomy rozróżnialności dla szeregu parametrów i cech sygnałów MBN oraz omówiono uzyskane wyniki.

18

Zmodyfikowany rekursywny algorytm estymacji DFT

Knioła M.

Elektronika : konstrukcje, technologie, zastosowania

|

2018

|

Vol. 59, nr 9

16--21

PL

W artykule przedstawiono podstawy rekursywnego wyznaczania dyskretnej transformaty Fouriera (ang. DFT – Digital Fourier Transform). Następnie zaproponowano uproszczoną wersję algorytmu, która umożliwia istotne oszczędności implementacyjne. Wobec ingerencji w matematyczny opis algorytmu, przeprowadzono testy funkcjonalne proponowanego podejścia. Wyniki referencyjne otrzymano za pomocą dwóch implementacji krótkookresowej transformaty Fouriera (ang. STFT – Short Time Fourier Transform). Przedstawiono zalety i ograniczenia metody oraz kierunki dalszych badań.

EN

Fundamentals of the iterative DFT (Digital Fourier Transform) computation are presented in the first part of this paper. Next, simplification of classic algorithm is proposed, which yields savings in hardware resources. As the mathematical description of algorithm is affected, functional tests of proposed approach were needed. Reference results were obtained with two implementations of STFT (Short Time Fourier Transform). Finally, advantages and limitations of proposed method are discussed and further research directions are delineated.

19

Ocena efektu sejsmicznego strzelań grupowych przodków w warunkach polskich kopalń rud miedzi

Mertuszka P., Fuławka K., Szumny M., Stolecki L.

Cuprum : czasopismo naukowo-techniczne górnictwa rud

|

2018

|

nr 3

17--30

PL

Podstawowym sposobem prowokowania zjawisk dynamicznych w kopalniach podziemnych Legnicko-Głogowskiego Okręgu Miedziowego są grupowe strzelania przodków. Polegają one na jednoczesnym odpaleniu ładunków materiału wybuchowego w kilku lub kilkunastu przodkach, co ma na celu uwolnienie energii sprężystej skumulowanej w górotworze. Dokładność stosowanych obecnie systemów nieelektrycznej inicjacji ładunków materiałów wybuchowych jest niewystarczająca, aby w sposób kontrolowany doprowadzać lokalnie do wzmacniania fali sejsmicznej, generowanej detonacją materiału wybuchowego. Oznacza to, że jednoczesne odpalanie ładunków w większej liczbie przodków nie zawsze przekłada się na poprawę skuteczności profilaktyki tąpaniowej. W ramach niniejszego artykułu przeanalizowano przebiegi fal sejsmicznych, generowanych grupowymi robotami strzałowymi w warunkach wybranego oddziału eksploatacyjnego kopalni KGHM. Oceny efektu sejsmicznego strzelań grupowych dokonano w oparciu o zarejestrowane amplitudy prędkości drgań cząsteczek górotworu i wyniki krótkoczasowej transformaty Fouriera.

EN

The Group Blasting is one of the most often applied method of dynamic events provoking in underground mines of the Lower Silesian Copper Basin. The synchronised detonation of dozens mining faces are carried out in order to release of energy which is cumulated in surrounding rock mass. The accuracy of currently used initiation systems is insufficient to provide a controlled interference of seismic wave triggered by firing of explosives. As a result, simultaneous detonation of higher number of mining faces does not always correlate with improvement of the effectiveness of rock burst prevention. In this paper, the records of seismic waves generated by detonation of explosives in one of the mining panels within KGHM mine were analyzed. The evaluation of the seismic effect generated by group winning blasting was performed based on peak particle velocity values and using short-time Fourier transform analysis.

20

Time–frequency analysis in infant cry classification using quadratic time frequency distributions

Saraswathy J., Hariharan M., Khairunizam W., Sarojini J., Thiyagar N., Sazali Y., Nisha S.

Biocybernetics and Biomedical Engineering

|

2018

|

Vol. 38, no. 3

634--645

EN

This paper presents a new investigation of time–frequency (t–f) based signal processing approach using quadratic time–frequency distributions (QTFDs) namely spectrogram(SPEC), Wigner–Ville distribution (WVD), Smoothed–Wigner Ville distribution (SWVD), Choi–William distribution (CWD) and modified B-distribution (MBD) for classification of infant cry signals. t–f approaches have proved as an efficient approach for applications involving the non stationary signals. In feature extraction, a cluster of t–f based features were extracted by extending the time-domain and frequency-domain features to the joint t–f domain from the generated t–f representation. Conventional features such as mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs) were also extracted in order to compare the effectiveness of the t–f methods. The efficacy of the extracted feature vectors was validated using probabilistic neural network (PNN) and general regression neural network (GRNN). The proposed methodology was implemented to classify different sets of binary classification problems of infant cry signals from different native. The best empirical result of above 90% was reported and revealed the good potential of t–f methods in the context of infant cry classification.