• Smart Structures and Systems
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• v.10 no.4_5
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• pp.331-351
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• 2012

In this study an output-only system identification technique for civil structures under ambient vibrations is carried out, mainly focused on using the Stochastic Subspace Identification (SSI) based algorithms. A newly developed signal processing technique, called Singular Spectrum Analysis (SSA), capable to smooth a noisy signal, is adopted for preprocessing the measurement data. An SSA-based SSI algorithm with the aim of finding accurate and true modal parameters is developed through stabilization diagram which is constructed by plotting the identified system poles with increasing the size of data matrix. First, comparative study between different approaches, with and without using SSA to pre-process the data, on determining the model order and selecting the true system poles is examined in this study through numerical simulation. Finally, application of the proposed system identification task to the real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures, using SSA-based SSI algorithm is carried out to extract the dynamic characteristics of the tower from output-only measurements.

• Smart Structures and Systems
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• v.11 no.1
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• pp.19-34
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• 2013

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified $1^{st}$ mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of $2^{nd}$ mode slope ratio could be used as another feature to indicate imminent pier settlement.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2393-2399
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• 2013

In this paper, we proposed a method to screening of Alzheimer's disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.91-107
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• 2024

Single-channel seismic exploration has proven effective in delineating subsurface geological structures using small-scale survey systems. The seismic data acquired through zero- or near-offset methods directly capture subsurface features along the vertical axis, facilitating the construction of corresponding seismic sections. However, substantial noise in single-channel seismic data hampers precise interpretation because of the low signal-to-noise ratio. This study introduces a novel approach that integrate noise reduction and signal enhancement via matrix rank optimization to address this issue. Unlike conventional rank-reduction methods, which retain selected singular values to mitigate random noise, our method optimizes the entire singular value spectrum, thus effectively tackling both random and erratic noises commonly found in environments with low signal-to-noise ratio. Additionally, to enhance the horizontal continuity of seismic events and mitigate signal loss during noise reduction, we introduced an adaptive weighting factor computed from the eigenimage of the seismic section. To access the robustness of the proposed method, we conducted numerical experiments using single-channel Sparker seismic data from the Chukchi Plateau in the Arctic Ocean. The results demonstrated that the seismic sections had significantly improved signal-to-noise ratios and minimal signal loss. These advancements hold promise for enhancing single-channel and high-resolution seismic surveys and aiding in the identification of marine development and submarine geological hazards in domestic coastal areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1117-1120
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• 2009

최근 이상기후와 같은 기후변화로 인한 기온, 강수 등의 변화는 안정적인 수자원 확보에 큰 영향을 미칠 것으로 판단되고 수자원을 필요로 하는 사회 모든 분야에 있어 큰 영향을 끼친다. 특히 농업, 공업, 도시의 용수 공급에 있어 변화는 더욱 심해질 것으로 판단되며 기후변화로 인한 기온, 강수 등의 변화의 정확한 분석이 필요로 한다. 따라서 본 연구에서는 동아시아 해수면 온도와 우리나라 강수량에 대한 MSSA (Multi-channel Singular Spectrum Analysis)를 실시함으로 두 시계열 사이에 공통적으로 나타나는 변화, 즉 특정 상관 주기 변동을 분석함으로 두 변수 사이에 변화 상관 분석을 실시하였다. 우리나라 강수량 자료로는 현재 기상청에서 운영 중인 지상 기상관측소 76개소 중 가용관측소 61개소 자료에 대하여 1973년 1월부터 2008년 12월까지의 자료를 수집하여 월 평균값을 사용하였고 동아시아 해수면 온도 자료로는 한반도 근해 해수면 온도 변화, 남중국해 해수면 온도 변화, 인도양 해수면 온도 변화, 적도 해수면 온도 변화 등을 선택하여 관측시점부터 2008년 12월까지 자료를 수집하여 사용하였다. 분석 자료에 대해 선형 회귀분석을 통한 선형추세 제거와 정규화한 자료를 사용하여 각각의 지수에 대해 MSSA 분석을 실시하였다. 이때 window length는 Vautard 등(1992)이 제시한 N/5$^{\sim}$N/3의 값인 108의 값을 사용하였고 이때 각각의 고유치는 전체 공분산에 대한 각 요소의 비율을 설명한다. 상관분석 결과는 각 지수와 강수자료 사이에 높은 상관성을 가지는 장단주기 변화가 존재함을 보여주었다. 그럼에도 불구하고 우리나라 월강수자료의 전체 변화는 계절변화를 제외하고도 장단 주기를 가지는 시간변화가 자료 전체 변화의 절반에 해당하며 장주기 변화가 나타내는 부분이 미미하다. 이는 계절 주기를 제외한 자료들 사이의 상관변화가 설명할 수 있는 부분이 미미 하며 여러 기상지수들과 국내 강수량사이의 MSSA 분석을 통하여 제시 할 수 있는 변화의 정량적 정도가 매우 제한됨을 보여준다. 그럼에도 불구하고 이러한 접근을 통하여 강수 변화의 불확실성을 줄여나가는 노력이 필요하다고 하겠다.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1E
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• pp.31-39
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• 2004

Backscattered underwater signals from multiple scatterers contain information regarding resolvable spatial distribution of scatterers. This experimental study describes the spectral characteristics of backscattered signal from multiple scatterers, which are regularly or randomly spaced, in terms of their amplitude and phase and a proper signal analysis that will eventually provide scatterer spacing estimation. Air-filled tubes suspended in water, steel balls and plastic tubes buried in the sediment are the multiple scatterers. The cepstrum and the spectral autocorrelation (SAC) methods were used to estimate the scatterer spacing from the backscattered signals. It was found that the SAC method could be improved by employing singular value decomposition (SVD) to extract the effective rank for the spectral components. Unlike the conventional method of estimating the density of scatterers within the insonified volume of water, this type of estimation method would provide better understanding of the spatial distribution of scatterers in the ocean.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.7
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• pp.617-623
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• 2013

Accurate prediction of the radiated noise is important to reduce the noise of the washing machine. It is also necessary to predict the excitation force accurately because excitation force can induce noise. In order to predict the excitation force acting on the washing machine, this paper conducts source identification method by use of phase reference spectrum. In this method, the transfer function between the cabinet and the motor through FEM and the measured response from the surface of the cabinet is used. The analysis of the radiation noise from the identified exciting force has been investigated. The comparison between the predicted SPL and the measured SPL at 1m apart from the front side of the washing machine showed good tendency.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.9
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• pp.834-841
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• 2011

Obtaining the real exciting force is important for the analysis of structural vibration or sound radiation to represent the actual condition. But in most cases, it is so difficult to get the actual force signals by direct measurement using sensors due to complex geometry. This paper suggests advanced source identification method which can be applied to the prediction of radiated noise considering correlations between measured signals. This method was implemented to the identification of the fan force in the refrigerator. The analysis of structural vibration and radiated noise caused by the fan force was also performed. The comparison between predicted SPL and measured SPL of the radiated noise by the refrigerator fan showed good agreement.

• Journal of Welding and Joining
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• v.18 no.1
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• pp.97-103
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• 2000

Time series analysis results show the SVD is a candidate of on-line monitoring of welding penetration when the covariance matrix of a full penetration is used as a mapping function. As the reconstructed embedding vectors from the chaotic scalar time series are manipulated by the covariance matrix, the mapped tim series lie on a hyper-ellipsoid which the lengths of semi-axes are the squared eigenvalues of the covariance matrix in the case of full penetration. These visualize by two dimensional stroboscope views. The other cases like partial penetration, are different in the sense of sizes and shapes. Here we test two types of time series; the ion current and the X-ray. The ion current is better than the X-ray as an on-line monitoring signal, because the difference of the eigenvalue spectrum of the ion(between the pull penetration and partial penetration) is bigger than those of the X-ray.