• Title/Summary/Keyword: Transform Domain Analysis

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Time-frequency domain characteristics of intact and cracked red sandstone based on acoustic emission waveforms

  • Yong Niu;Jinguo Wang;Yunjin Hu;Gang Wang;Bolong Liu
    • Geomechanics and Engineering
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    • v.34 no.1
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    • pp.1-15
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    • 2023
  • This study conducts uniaxial compression tests on intact and single crack-contained rocks to investigate the time-frequency domain characteristics of acoustic emission (AE) signals monitored during the deformation failure process. A processing approach, short-time Fourier transform (STFT), is performed to obtain the evolution characteristics of time-frequency domain of AE signals. The AE signal modes at different deformation stages of rocks are different. Five modes of AE signal are observed during the cracking process of rocks. The evolution characteristics of time-frequency domain of AE signals processed by STFT can be utilized to evaluate the damage process of rocks. The difference of time-frequency domain characteristics between intact and cracked rocks is comparatively analyzed. The distribution characteristics of frequency changing from a single band-shaped cluster to multiple band-shaped clusters can be regarded as an early warning information of damage and failure of rocks. Meanwhile, the attenuation of frequency enables the exploration of rock failure trends.

Basic ]Requirements for Spectrum Analysis of Electroencephalographic Effects of Central Acting Drugs (중추성 작용 약물의 뇌파 효과의 정량화를 위한 스펙트럼 분석에 필요한 기본적 조건의 검토)

  • 임선희;권지숙;김기민;박상진;정성훈;이만기
    • Biomolecules & Therapeutics
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    • v.8 no.1
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    • pp.63-72
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    • 2000
  • We intended to show some basic requirements for spectrum analysis of electroencephalogram (EEG) by visualizing the differences of the results according to different values of some parameters for analysis. Spectrum analysis is the most popular technique applied for the quantitative analysis of the electroen- cephalographic signals. Each step from signal acquisition through spectrum analysis to presentation of parameters was examined with providing some different values of parameters. The steps are:(1) signal acquisition; (2) spectrum analysis; (3) parameter extractions; and (4) presentation of results. In the step of signal acquisition, filtering and amplification of signal should be considered and sampling rate for analog-to-digital conversion is two-time faster than highest frequency component of signal. For the spectrum analysis, the length of signal or epoch size transformed to a function on frequency domain by courier transform is important. Win dowing method applied for the pre-processing before the analysis should be considered for reducing leakage problem. In the step of parameter extraction, data reduction has to be considered so that statistical comparison can be used in appropriate number of parameters. Generally, the log of power of all bands is derived from the spectrum. For good visualization and quantitative evaluation of time course of the parameters are presented in chronospectrogram.

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Classification of Normal/Abnormal Conditions for Small Reciprocating Compressors using Wavelet Transform and Artificial Neural Network (웨이브렛변환과 인공신경망 기법을 이용한 소형 왕복동 압축기의 상태 분류)

  • Lim, Dong-Soo;An, Jin-Long;Yang, Bo-Suk;An, Byung-Ha
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2000.11a
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    • pp.796-801
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    • 2000
  • The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

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REDUCED DIFFERENTIAL TRANSFORM FOR THERMAL STRESS ANALYSIS UNDER 2-D HYPERBOLIC HEAT CONDUCTION MODEL WITH LASER HEAT SOURCE

  • SUTAR, CHANDRASHEKHAR S.;CHAUDHARI, KAMINI K.
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.25 no.2
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    • pp.54-65
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    • 2021
  • In this study, a two-dimensional thermoelastic problem under hyperbolic heat conduction theory with an internal heat source is considered. The general solution for the temperature field, stress components and displacement field are obtained using the reduced differential transform method. The stress and displacement components are obtained using the thermal stress function in the reduced differential transform domain. All the solutions are obtained in the form of power series. The special case with a time-dependent laser heat source has been considered. The problem is considered for homogeneous material with finite rectangular cross-section heated with a non-Gaussian temporal profile. The effect of the heat source on all the characteristics of a material is discussed numerically and graphically for magnesium material taking a pulse duration of 0.2 ps. This study provides a powerful tool for finding the solution to the thermoelastic problem with less computational work as compared to other methods. The result obtained in the study may be useful for the investigation of thermal characteristics in engineering and industrial applications.

Design of Low Bits Rate Transform Excitation Wide Band Speech and Audio Coder of Analysis-by-Synthesis Structure (분석/합성 구조의 저 전송률 변환여기 광대역 음성/오디오 부호화기 설계)

  • Jang, Sunghoon;Hong, Kibong;Lee, Insung
    • The Journal of the Acoustical Society of Korea
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    • v.31 no.7
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    • pp.472-479
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    • 2012
  • This paper is aimed to design 9.2 kbps low bits late transform excitation coder that target to voice and audio signal. To set up low bit rate, we used Band-selection in frequency domain and gain-shape quantization and AbS structure. To decrease lots of calculation from ABS structure, we used each band IDFT and synthesis. And we designed non-transfer band for performance by inserting comfort noise. We propose coder that has low bit rate and similar performance comparing with original 10.4 kbps AMR-WB+ TCX mode.

Advanced Algorithm for IED of Stator Winding Protection of Generator System (발전기시스템의 고정자보호 IED를 위한 개선된 알고리즘)

  • Park, Chul-Won
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.57 no.2
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    • pp.91-95
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    • 2008
  • The large AC generator fault may lead to large impacts or perturbations in power system. The generator protection control systems in Korea have been imported and operated through a turn-key from overseas entirely. Therefore a study of the generator protection field has in urgent need for a stable operation of the imported goods. In present, the algorithm using the current ratio differential relaying based DFT for stator winding protection or a fault detection had been applied that of internal fault protection of a generator. the DFT used for the analysis of transient state signal conventionally had defects losing a time information in the course of transforming a target signal to frequency domain. In this paper, the discrete wavelet transform (DWT) was applied a fault detection of the generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a muiti-level decomposition (MLD). The proposed algorithm for a fault detection using the Daubechies WT (wavelet transform) was executed with a C language and the commend line function for the real time realization after analyzing MATLAB's graphical interface. The advanced technique had improved faster a speed of fault discrimination than a conventional DFR based on DFT.

A Coherent Algorithm for Noise Revocation of Multispectral Images by Fast HD-NLM and its Method Noise Abatement

  • Hegde, Vijayalaxmi;Jagadale, Basavaraj N.;Naragund, Mukund N.
    • International Journal of Computer Science & Network Security
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    • v.21 no.12spc
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    • pp.556-564
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    • 2021
  • Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

Estimation of Ultrasonic Attenuation Coefficients in the Frequency Domain using Compressed Sensing (압축 센싱을 이용한 주파수 영역의 초음파 감쇠 지수 예측)

  • Shim, Jaeyoon;Kim, Hyungsuk
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.6
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    • pp.167-173
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    • 2016
  • Compressed Sensing(CS) is the theory that can recover signals which are sampled below the Nyquist sampling rate to original analog signals. In this paper, we propose the estimation algorithm of ultrasonic attenuation coefficients in the frequency domain using CS. While most estimation algorithms transform the time-domain signals into the frequency-domain using the Fourier transform, the proposed method directly utilize the spectral information in the recovery process by the basis matrix without the completely recovered signals in the time domain. We apply three transform bases for sparsifying and estimate the attenuation coefficients using the Centroid Downshift method with Dual-reference diffraction compensation technique. The estimation accuracy and execution time are compared for each basis matrix. Computer simulation results show that the DCT basis matrix exhibits less than 0.35% estimation error for the compressive ratio of 50% and about 6% average error for the compressive ratio of 70%. The proposed method which directly extracts frequency information from the CS signals can be extended to estimating for other ultrasonic parameters in the Quantitative Ultrasound (QUS) Analysis.

A Wavelet based Feature Selection Method to Improve Classification of Large Signal-type Data (웨이블릿에 기반한 시그널 형태를 지닌 대형 자료의 feature 추출 방법)

  • Jang, Woosung;Chang, Woojin
    • Journal of Korean Institute of Industrial Engineers
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    • v.32 no.2
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    • pp.133-140
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    • 2006
  • Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

Forced vibration of surface foundation on multi-layered half space

  • Chen, Lin
    • Structural Engineering and Mechanics
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    • v.54 no.4
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    • pp.623-648
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    • 2015
  • A numerical approach is presented for the analysis of the forced vibration of a rigid surface foundation with arbitrary shape. In the analysis, the foundation is discretized into a number of sub squaree-lements. The dynamic response within each sub-element is described by the Green's function, which is obtained by the Fourier-Bessel transform and Precise Integration Method (PIM). Incorporating the displacement boundary condition and force equilibrium of the foundation, it obtains a system of linear algebraic equation in terms of the contact forces within each sub-element. Solving the equation leads to the desired dynamic impedance functions of the foundation. Numerical results are obtained for foundation not only with simple geometrical configurations, such as rectangular and circular foundation, but also the case of irregularly shaped foundation. Several comparisons between the proposed approach and other methods are made. Very good agreement is reached. Also, parametric studies are carried out on the dynamic response of foundation. Addressed in this study are the effects of Poisson's ratio, material damping and contact condition of soil-foundation interface. Several conclusions are drawn the significance of the factors.