Wyniki wyszukiwania - BazTech

1

Improving the resolution of seismic data based on S transform and modified variational mode decomposition, an application to Songliao Basin, Northeast China

Liu Siqin, Zhou Zhusheng, Peng Sanxi, Yang Yang, Zeng Weizu, Chen Keyang

Acta Geophysica

|

2022

|

Vol. 70, no 3

1103--1113

EN

The sandstone of the lithologic reservoir in Songliao Basin is characterized by thin vertical thickness and rapid lateral pinch outs, which make it difficult to identify and describe the sandstone due to insufficient resolution of existing seismic data. To solve this problem, a method based on S-transform and modified variational mode decomposition is proposed to improve the resolution in the time–frequency domain by cepstrum deconvolution. Firstly, the time–frequency spectrum of seismic records is obtained by generalized S-transform; then it is transformed into cepstrum; and the wavelet amplitude spectrum is extracted by modified variational mode decomposition with permutation entropy, to realize the time–frequency domain deconvolution of cepstrum. After improving the resolution of seismic data in the study area, the frequency band of the data is broadened by more than 20%. After frequency expansion, the seismic refection structure reflects the sedimentary characteristics of the actual sandstone in the area. The seismic refection has a good correspondence with the well point sandstone, and the thin sandstone is clearly depicted. It is fully verified that this method can effectively improve the resolution of seismic data and has the characteristics of relative amplitude preservation.

2

Dispersion calculation method based on S-transform and coordinate rotation for Love channel waves with two components

Feng L., Zhang Y.

Acta Geophysica

|

2017

|

Vol. 65, no. 4

757--764

EN

Dispersion analysis is an important part of in-seam seismic data processing, and the calculation accuracy of the dispersion curve directly influences pickup errors of channel wave travel time. To extract an accurate channel wave dispersion curve from in-seam seismic two-component signals, we proposed a time–frequency analysis method based on single-trace signal processing; in addition, we formulated a dispersion calculation equation, based on S-transform, with a freely adjusted filter window width. To unify the azimuth of seismic wave propagation received by a two-component geophone, the original in-seam seismic data undergoes coordinate rotation. The rotation angle can be calculated based on P-wave characteristics, with high energy in the wave propagation direction and weak energy in the vertical direction. With this angle acquisition, a two-component signal can be converted to horizontal and vertical directions. Because Love channel waves have a particle vibration track perpendicular to the wave propagation direction, the signal in the horizontal and vertical directions is mainly Love channel waves. More accurate dispersion characters of Love channel waves can be extracted after the coordinate rotation of two-component signals.

3

Event-based S-transform approach for nonintrusive load monitoring

Khalid K., Mohamed A., Shareef H., Sabeeh M.

Przegląd Elektrotechniczny

|

2016

|

R. 92, nr 5

194--198

EN

In this study, a nonintrusive load monitoring system is developed by analyzing the power signal obtained from a single point of power meter installation to detect ON/OFF load activities. A mathematically designed model with backpropagation neural network is utilized in load pattern recognition to decompose the load operation. Leveraging its unique load signature profile, the S-transform approach is employed to extract the features from the aggregate power signal and analyze the detection of load start-up transient from signal processing. To improve the accuracy of load identification for unknown data, the power factor is used as an additive feature with 99.32% load recognition accuracy.

PL

W artykule analizowany jest system monitorowania obciążenia sieci. Wykorzystano sieć neuronową do rozpoznawania rodzaju Transformata S jest użyta do ekstrakcji danych z sygnału mocy. Dodatkowo do identyfikacji obciążenia użyto współczynnik mocy.

4

Warped s-transform for analysing the brain waves

Borowicz A.

Advances in Computer Science Research

|

2014

|

Nr 11

5--16

EN

In this paper the warped S-transform is introduced as a tool for non-uniform time-frequency representation (TFR) of the brain electrical activity. The brain oscillations are classified as the five basic rhythms. The center frequencies and frequency ranges of these rhythms are non-uniformly distributed over frequency scale. Unlike the conventional S-transform the proposed technique is based on the warped discrete Fourier transform (WDFT), that allows for frequency scale warping. This can improves a spectral resolution of the TFR in particular oscillation band. In opposition to the time-domain filtering techniques, the brain rhythms can be analysed more precisely in the time-frequency plane as a full-band signal.

PL

W artykule wprowadzamy spaczoną transformatę S, jako narzędzie nierównomiernej reprezentacji czasowo-częstotliwościowej aktywności elektrycznej mózgu. Oscylacje mózgowe klasyfikowane są, jako pięć podstawowych rytmów. Częstotliwości środkowe oraz zakresy odpowiadające tym rytmom rozmieszczone są nierównomiernie na skali częstotliwości. Proponowana technika, w przeciwieństwie do konwencjonalnej transformaty S, opiera się na spaczonej dyskretnej transformacie Fouriera, która pozwala na deformowanie skali częstotliwości. Umożliwia to zwiększenie rozdzielczości widmowej reprezentacji czasowo-częstotliwościowej w określonym pasmie oscylacji. W odróżnieniu od klasycznych metod filtracji dziedziny czasu, rytmy mózgowe mogą być dokładniej analizowane w płaszczyźnie czasowo-częstotliwościowej, jako sygnał pełno-pasmowy.

5

Zwektoryzowane algorytmy obliczania Transformaty S dla środowiska wieloprocesorowego

Tariov A., Gliszczyński M.

Pomiary Automatyka Kontrola

|

2011

|

R. 57, nr 11

1401-1403

PL

W pracy został zaprezentowany wektoryzowany algorytm obliczania transformaty S w dwóch wariantach - w postaci sekwencyjno-równoległej pozwalającej na oszczędzenie zasobów sprzętowych oraz w postaci równoległej pozwalającej wykorzystać, nowoczesne wielordzeniowe platformy obliczeniowe. W drugim przypadku możliwa jest znaczna redukcja czasu trwania algorytmu. Obie metody mogą znaleźć zastosowanie praktyczne zależnie od oczekiwanej dokładności (rozdzielczości) i szybkości działania jak też możliwości platformy obliczeniowej.

EN

In the paper the algorithm for calculating N by N-point S Transform is presented. In a sequential, recursive option hardware resources saving is available, while on the other hand, a parallel version of the algorithm allows increasing the accuracy and reducing the time when using multi-core platforms. Two of these approaches can be implemented in practical use depending on the expected accuracy, speed and power of the hardware platform. At the beginning of the paper uses of S Transform with other similar solutions are described. Advantages and disadvantages of S Transform, which are good properties of the time-frequency analysis of non-stationary signals thanks to a movable, different sized Gaussian window, but at the same time a long computation time of the standard, sequential method, are considered. Next, the theoretical, continuous form of the transform and the discrete form with the sequential algorithm are presented. Later The main part of the work deals with synthesis of the sequential and parallel version of the algorithm in the matrix-vector form. The data flow in the algorithms in space and time is shown in Figs. 1 and 2 (for sequential and parallel approach). Finally, the computation times of two versions are compared. The advantage of the two presented approaches is simple and understandable tensor product representation which makes the implementation easy. The sequential algorithm can be used for slower platforms, where the real time analysis is not necessary, while the parallel version offers quick computation on multi-core processors.

6

Power quality diagnosis in distribution networks using support vector regression based S-transform technique

Faisal M., Mohamed A., Shareef H.

Przegląd Elektrotechniczny

|

2010

|

R. 86, nr 11a

38-42

EN

This paper presents a novel method for performing automatic power quality diagnosis to identify the causes of short duration voltage disturbances such as voltage sags and swells. Such voltage disturbances can be caused by permanent or non permanent faults. A permanent fault causes permanent damage and power interruption to the customers whereas a non permanent fault can be categorized as either transient or incipient faults. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform is used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The support vector regression which is an intelligent technique is then used identify whether the voltage disturbances are caused by permanent, non permanent, transient or incipient faults. Test results proved that the proposed power quality diagnosis method can provide accurate diagnosis on the causes of short duration voltage disturbances.

PL

W artykule przedstawiono nową metodę przeprowadzania automatycznej diagnostyki jakości energii elektrycznej do identyfikacji przyczyn krótkoczasowych zakłóceń napięcia, takich jak zapady napięcia. Zaburzenia napięcia mogą być spowodowane przez długotrwałe lub chwilowe awarie. W proponowanej metodzie diagnozowania jakości zasilania, zastosowano transformatę S do ekstrakcji charakterystyk zarejestrowanych przebiegów z systemu monitoringu. Zastosowano regresję SVR jako technike inteligentna, pozwalajaca na rozróżnienie pomiędzy typami awarii. Wyniki badań wykazały, że proponowana metoda diagnozowania jakości zasilania może zapewnić dokładną diagnozę na temat przyczyn zaburzeń napięcia o krótkim czasie trwania.