• Journal of Hydrogen and New Energy
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• v.27 no.3
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• pp.318-327
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• 2016

In order to improve the performance of temperature field measurements by CT-TDLAS (Computer Tomography Tunable Diode Laser Absorption Spectroscopy), a new reconstruction algorithm, named two-ratios-of-three-peaks method is proposed in this paper. Further, two methods for selecting appropriate initial values of the iterative calculation of CT-TDLAS are proposed. One is MLOS (multiplicative line of sight) method and the other one is ALOS (additive line of sight) method. Two-ratios-of-three-peaks (2R3P) algorithm combined with MART (multiplicative algebraic reconstruction technique) is finally developed for the enhancements of reconstructive calculations. The results have been compared with those obtained by the conventional one-ratio-of-two-peaks (1R2P) algorithm. In order to evaluate the performance of this algorithm, numerical test has been performed using phantom Gaussian temperature distributions with $11{\times}11$ square mesh. The performance of the constructed algorithm has been demonstrated by comparing the results obtained in actual burner experiments with those obtained by thermocouples. It has been verified that 2R3P algorithm with MART and MLOS showed best performance than that of 1R2P algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.811-816
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• 2019

This paper first proposes a technique to separate clean speech signals and mixed noise signals by using an independent vector analysis algorithm of frequency band for 4 channel speech source signals with a noise. An improved output speech signal from the proposed independent vector analysis algorithm is obtained by using the cross-correlation between the signal outputs from the frequency domain delay-sum beamforming and the output signals separated from the proposed independent vector analysis algorithm. In the experiments, the proposed algorithm improves the maximum SNRs of 10.90dB and the segmental SNRs of 10.02dB compared with the frequency domain delay-sum beamforming algorithm for the input mixed noise speeches with 0dB and -5dB SNRs including white noise, respectively. Therefore, it can be seen from this experiment and consideration that the speech quality of this proposed algorithm is improved compared to the frequency domain delay-sum beamforming algorithm.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.837-846
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• 2017

An accurate calculation of the stochastic wind field is the foundation for analyzing wind-induced structure response and reliability. In this research, the spatial correlation of structural wind field was considered based on the time domain method. A method for calculating the stochastic wind field based on cross stochastic Fourier spectrum was proposed. A flowchart of the proposed methodology is also presented in this study to represent the algorithm and workflow. Along with the analysis of regional wind speed distribution, the wind speed time history sample was calculated, and the efficiency can therefore be verified. Results show that the proposed method and programs could provide an efficient simulation for the wind-induced structure response analysis, and help determine the related parameters easily.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.213-216
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• 2000

본 연구에서는 마이크로폰 어레이를 이용하여 화자의 음성신호로부터 화자의 위치를 추정하는 기존의 대표적인 알고리듬인 CPSP(Cross Power Spectrum Phase)로부터 보다 반향에 강인한 알고리듬인 저주파 위상 복원 알고리듬을 제안한다. CPSP 함수는 상호 상관관계(Cross Correlation)가 정규화 되어있는 형태를 갖는데, CPSP 함수의 최대 값 인덱스로부터 화자의 공간정보인 TDOA(Time Difference Of Arrival)를 추출한다. 그러나 CPSP 함수를 이용한 공간정보 추정 알고리듬은 실내환경에서 심각하게 일어나는 반향신호에 대해서 취약한 단점을 갖고 있다. 본 논문에서 제안하는 저주파 위상복원 알고리듬은 주파수 측면에서 반향신호가 CPSP 함수에 미치는 영향을 분석하여 반향으로 인하여 왜곡된 위상 성분을 복원함으로써 보다 신뢰도 있는 TDOA 추정을 가능하게 한다. 반향신호로 인한 CPSP의 위상은 저주파보다 고주파에서 심하게 왜곡되는데, 각각의 반향신호의 도달 시간을 기하학적 분포를 갖는 확률변수로 모델링하여 이를 수학적으로 증명하였다. 또한 실제 환경에서 채집한 음성신호를 이용한 모의 실험을 통해 개선된 알고리듬의 성능 개선을 확인하였다.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.7-13
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• 2020

This paper focuses on detecting abnormal patterns of respiration of humans. In this study, a contact-based device was used to acquire both normal and abnormal respiration signals. To this end, this paper reports the development of a monitoring system to investigate the respiratory status of humans in a normal environment. This work aims to classify the respiratory status, i.e., normal and abnormal status, quantitatively. The respiration signal is acquired using a contact-based medical device (BIOBPAC), and noise reduction is carried out before classifying the respiratory status. To reduce noise, a mixed filter that combines the Savitzky-Golay filter and Median filter is applied to the acquired respiration signals. The inter-class distance is maximized, and the intra-class distance is minimized. The proposed algorithm is straightforward and can be applied to a practical environment. In addition, the experimental results are provided to substantiate the proposed approach.

• Journal of Advanced Navigation Technology
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• v.21 no.3
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• pp.258-263
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• 2017

This paper analyzed GNSS signal acquisition performance of a regional navigation receiver when an interference cancellation capability is applied. Intereference between the regional navigation and GNSS signal can be mitigated by the interference cancellation technique such as the successive interference cancellation (SIC) algorithm. However signal acquisition performance will be degraded when jamming-to-signal ratio (J/S) is large due to a cross-correlation properties of residual signals. In this paper we analyzed signal acquisition performance degradation due to the interference between the Kasami and GNSS Gold code signal. Monte Carlo simulation is used for the analysis and compared results with those of GNSS Gold code only condition.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.1-11
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• 2012

Computed tomography (CT) images are widely used for the analysis of the temporal evaluation or monitoring of the progression of a disease. The follow-up examinations of CT scan images of the same patient require a 3D registration technique. In this paper, an automatic and robust registration is proposed for the rigid registration of 3D CT images. The proposed method involves two steps. Firstly, the two CT volumes are aligned based on their principal axes, and then, the alignment from the previous step is refined by the optimization of the similarity score of the image's voxel. Normalized cross correlation (NCC) is used as a similarity metric and a downhill simplex method is employed to find out the optimal score. The performance of the algorithm is evaluated on phantom images and knee synthetic CT images. By the extraction of the initial transformation parameters with principal axis of the binary volumes, the searching space to find out the parameters is reduced in the optimization step. Thus, the overall registration time is algorithmically decreased without the deterioration of the accuracy. The preliminary experimental results of the study demonstrate that the proposed method can be applied to rigid registration problems of real patient images.

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.199-209
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• 2000

This study improves the feature tracking method (FTM) in estimating the ocean current vectors from the sequential AVHRR satellite imageries by adding the objective algorithm in defining the edges and boundaries of the oceanic eddies and fronts. It was implemented by using the Sobel operator. The Sobel operator has been proved to be in effective filter in detecting the edges of any object on the image. In estimating the current vectors on the edges defined by the Sobel operator, center coordinates of the Pattern and Search tiles need to be determined by the investigator. The objective feature tracking method combined with maximum cross correlation method (MCC) is turned out to be very efficient and fast, since it uses only parts of the image containing the objects instead of searching the entire image. In the validation with the in situ ADCP measurements of currents in the East Sea, the estimated current speed values are around 35% lower than and current directions are deviated by $34^{\circ}$ from ADCP current vectors. The results are regarded as improved ones compared to the previous investigators'.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.