• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3001-3008
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• 2013

To diagnose the cardiac valve abnormalities using analysis of phonocardiogram, first of all, accurate detection of S1, S2 components is needed for heart sound segmentation. In this paper, a new method that uses the third moment characteristics of an envelope of the PCG is proposed for accurate detection of S1 and S2 components of the heart sound with cardiac murmurs. The envelope of the PCG is obtained from the short-time energy profile, and its third moment profile with slope information is used for accurate time gating of the S1, S2 components. Experimental results have shown that the proposed method is superior to the conventional second moment method for detection of S1 and S2 regions from the heart sound signals with cardiac murmurs.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.959-962
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• 2000

A heart sound algorithm, which separates the heart sound signal into four parts; the first heart sound, the systolic period, the second heart sound, and the diastolic period has been developed. The algorithm uses discrete intensity envelopes of approximations of the wavelet transform analysis method to the phonocard-iogram(PCG)signal. Heart sound a highly nonstation-ary signal, so in the analysis of heart sound, it is important to study the frequency and time information. Further more, Wavelet Transform provides more features and characteristics of the PCG signal that will help physician to obtain qualitative and quantitative measurements of the heart sound.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.3
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• pp.385-396
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• 1999

In the process of structural design for large-scale structures with several thousands of degrees of freedom, a plethora of structural calculations with large amount of data storage are required to obtain the forces and displacements of the members. However, current computational environment with single microprocessor such as a personal computer or a workstation is not capable of generating a high-level of efficiency in structural analysis and design process for large-scale structures. In this paper, a high-performance parallel computing system interconnected by a network of personal computers is proposed for an efficient structural analysis. Two distributed structural analysis algorithms are developed in the form of distributed or parallel preconditioned conjugate gradient (DPCG) method. To enhance the performance of the developed distributed structural analysis algorithms, the number of communications and the size of data to be communicated are minimized. These algorithms are applied to the structural analyses of three large space structures as well as a 144-story tube-in-tube framed structure.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.47-55
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• 2010

Although heart auscultation is an important tool for the diagnosis of heart disease, it is seen that the human ear is poorly suited for cardiac auscultation such as the paradoxical split of S2, the split of S2, and the summation of S2 and S3 gallop. In this paper, the analysis-synthesis method that is suitable for the characteristics of PCG is proposed to classify the heart sound. In analyzing the PCG according to matching pursuit method and synthesizing the PCG in each bandwidth based on the frequency characteristics, it sorts normal hearts with abnormal hearts that is difficult to classify as cardiac auscultation. The result shows that it qualifies the appearance of split in S2 and the paradoxical split of S2 and also can estimate the delay between the two components.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.75-78
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• 1997

In this paper we present new sound spectral analysis methods or prosthetic heart valve sounds. Phonocardiograms(PCG) of prosthetic heart valve were analyzed in order to derive frequency domain feature suitable or the classification of the valve state. The fast orthogonal search method and MUSIC (MUltiple SIgnal Classification) method are described or finding the significant frequencies in PCG. The fast orthogonal search method is effective with short data records and cope with noisy, missing and unequally-spaced data. MUSIC method's key to the performance is the division of the information in the autocorrelation matrix or the data matrix into two vector subspaces, one a signal subspace and the other a noise subspace.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.137-144
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• 2004

Algorithms for estimating breeding values on several categorical data by using latent variables with threshold conception were developed and showed. Thresholds on each categorical trait were estimated by Newton’s method via gradients and Hessian matrix. This algorithm was developed by way of expansion of bivariate analysis provided by Quaas(2001). Breeding values on latent variables of categorical traits and observations on linear traits were estimated by preconditioned conjugate gradient(PCG) method, which was known having a property of fast convergence. Example was shown by simulated data with two linear traits and a categorical trait with four categories(CE=calving ease) and a dichotomous trait(SB=Still Birth) in threshold animal mixed model(TAMM). Breeding value estimates in TAMM were compared to those in linear animal mixed model (LAMM). As results, correlation estimates of breeding values to parameters were 0.91${\sim}$0.92 on CE and 0.87${\sim}$0.89 on SB in TAMM and 0.72~0.84 on CE and 0.59~0.70 on SB in LAMM. As conclusion, PCG method for estimating breeding values on several categorical traits with linear traits were feasible in TAMM.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.353-361
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• 2022

Linear Elastic Fracture Mechanics (LEFM) has been developed by applying stress analysis to determine the stress intensity factor (SIF, K). The finite element method (FEM) is widely used as a standard tool for evaluating the SIF for various crack configurations. The prediction accuracy can be achieved by applying an adaptive Delaunay triangulation combined with a FEM. The solution can be solved using either direct or iterative solvers. This work adopts the element-by-element preconditioned conjugate gradient (EBE-PCG) iterative solver into an adaptive FEM to solve the solution to heal problem size constraints that exist when direct solution techniques are applied. It can avoid the formation of a global stiffness matrix of a finite element model. Several numerical experiments reveal that the present method is simple, fast, and efficient compared to conventional sparse direct solvers. The optimum convergence criterion for two-dimensional LEFM analysis is studied. In this paper, four sample problems of a two-edge cracked plate, a center cracked plate, a single-edge cracked plate, and a compact tension specimen is used to evaluate the accuracy of the prediction of the SIF values. Finally, the efficiency of the present iterative solver is summarized by comparing the computational time for all cases.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.122-129
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• 2005

On the cluster system, the parallel code of rigid-plastic FEM has been developed. The cluster system, Simforge, has 15 processors and the total memory is 4.5GBytes. In the developed parallel code, the distributed data of the column-wise partitioned stiffness are stored as the compressed row storage and the diagonal preconditioned conjugate gradient solver is applied. The analysis of block upsetting is performed with the parallel code on Simforge cluster system. In this paper, the analysis results are compared and discussed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.291-298
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• 2006

A parallelized topology design optimization method is developed on a distributed memory system. The parallelization is based on a domain decomposition method and a boundary communication scheme. For the finite element analysis of structural responses and design sensitivities, the PCG method based on a Krylov iterative scheme is employed. Also a parallelized optimization method of optimality criteria is used to solve large-scale topology optimization problems. Through several numerical examples, the developed method shows efficient and acceptable topology optimization results for the large-scale problems.