• 한국공간구조학회논문집
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• 제19권2호
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• pp.101-108
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• 2019

In this paper, the characteristic of intrinsic mode function(IMF) and its orthogonalization of ensemble empirical mode decomposition(EEMD), which is often used in the analysis of the non-linear or non-stationary signal, has been studied. In the decomposition process, the orthogonal IMF of EEMD was obtained by applying the Gram-Schmidt(G-S) orthogonalization method, and was compared with the IMF of orthogonal EMD(OEMD). Two signals for comparison analysis are adopted as the analytical test function and El Centro seismic wave. These target signals were compared by calculating the index of orthogonality(IO) and the spectral energy of the IMF. As a result of the analysis, an IMF with a high IO was obtained by GSO method, and the orthogonal EEMD using white noise was decomposed into orthogonal IMF with energy closer to the original signal than conventional OEMD.

• Journal of the Optical Society of Korea
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• 제12권3호
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• pp.121-125
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• 2008

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

• Smart Structures and Systems
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• 제30권1호
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• ETRI Journal
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• 제36권1호
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• pp.167-170
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• 2014

In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.

• 전기전자학회논문지
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• 제26권3호
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• pp.475-482
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• 2022

Signal pre-processing and post-processing are some areas of study around electrical resistance tomography due to the low spatial resolution of pixel-based reconstructed images. In addition, methods that improve integrity and noise reduction are candidates for application in ERT. Lately, formulations of image processing methods provide new implementations and studies to improve the response against noise. For example, compact variational mode decomposition has recently shown good performance in image decomposition and segmentation. The results from this first approach of C-VMD to ERT show an improvement due to image segmentation, providing filtering of noise in the background and location of the target.

• 한국정보전자통신기술학회논문지
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• 제12권4호
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• pp.419-424
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• 2019

본 논문에서는 광대역 재밍 신호 환경에서 원하는 목표물을 추정하기 위한 알고리즘을 제안 한다. 재밍 신호를 억제하는 방법으로, 본 연구에서는 시공간적응 알고리즘과 QR분해를 사용하여 최적의 가중치를 획득한다. 시공간적응 알고리즘은 적응배열안테나시스템에서 탭 지연 신호에 복소 가중치를 곱하여 가중치를 생성하고, 역행렬로 인한 전력소모를 최소화하기 위해서 QR분해를 이용하여 최적의 가중치를 획득한다. 모의실험을 통하여, 본 연구에서 제안한 알고리즘과 기존 알고리즘의 성능을 비교 분석한다. [-40o,0o,+40o]의 목표물 추정에서 본 연구에서 제안 한 알고리즘이 3개의 목표물을 모두 추정하였지만 기존 알고리즘은 재밍 신호 때문에 [0o]에서만 추정하였다. 본 연구의 제안 알고리즘이 재밍 신호를 제거하고 원하는 목표물을 정확히 추정하여 성능이 향상되었음을 입증하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2208-2211
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• 2003

In this paper, a variable bandwidth filter(VBF) is realized with the purpose of the decomposition of speech signals with time-varying instantaneous of frequencies. The proposed VBF can extract AM-FM components of a speech signal whose time-frequency representations(TFRs) are not overlapped in time-frequency domain

• 한국소음진동공학회논문집
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• 제15권2호
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• pp.192-198
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• 2005

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• Bulletin of the Korean Chemical Society
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• 제24권7호
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• pp.967-970
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• 2003

A new modified singular value decomposition method, piecewise polynomial truncated SVD (PPTSVD), which was originally developed to identify discontinuity of the earth's radial density function, has been used for large solvent peak suppression and noise elimination in nuclear magnetic resonance (NMR) signal processing. PPTSVD consists of two algorithms of truncated SVD (TSVD) and L₁ problems. In TSVD, some unwanted large solvent peaks and noise are suppressed with a certain soft threshold value, whereas signal and noise in raw data are resolved and eliminated in L₁ problems. These two algorithms were systematically programmed to produce high quality of NMR spectra, including a better solvent peak suppression with good spectral line shapes and better noise suppression with a higher signal to noise ratio value up to 27% spectral enhancement, which is applicable to multidimensional NMR data processing.

• Current Optics and Photonics
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• 제5권2호
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• pp.93-100
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• 2021

Three factors that degrade the accuracy of modal decomposition are extensively studied using simulated and measured beams. These include a beam size mismatch, beam center mismatch, and signal-to-noise ratio of the images. The beam size and beam center are scanned using simulated noisy beams, and the result of the modal decomposition is compared with that of real beams. Based on the suggested procedure, error functions of approximately 1-4 × 10-3 can be acquired for real beams. This study provides important information regarding the impact of the three factors on the practical modal decomposition and tolerances of a mismatch, helping estimate the achievable values of the error function in a real beam modal decomposition.