• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.439-454
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• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.735-744
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• 2007

Support vector machine(SVM) is capable of providing a more complete description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse kernel regression(SKR) to overcome a weak point of SVM, which is, the steep growth of the number of support vectors with increasing the number of training data. The iterative reweighted least squares(IRWLS) procedure is used to solve the optimal problem of SKR with a Laplacian prior. Furthermore, the generalized cross validation(GCV) function is introduced to select the hyper-parameters which affect the performance of SKR. Experimental results are then presented which illustrate the performance of the proposed procedure.

• 한국가시화정보학회:학술대회논문집
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• 2002.11a
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• pp.63-66
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• 2002

The Flow patterns around two cylinders in various arrangements were studied by a discrete vortex method. The flow for the surface of each cylinder was represented by arranging bound vortices at adequate intervals. The viscous diffusion of fluid was represented by the random walk method. The vortex distributions, streaklines, timelines and velocity vectors around two cylinders were calculated for centre-to-centre pitch rations of P/D=1.5 and 2.5, attack angles of $\alpha=0^{\circ},\;30^{\circ},\;60^{\circ},\;and\;90^{\circ}$, correspond to the photographs by flow visualization and the flow intereference between two cylinders in var ious arrangements was clearly visualized by a numerical simulation.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.655-664
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• 2022

On the motivation by an integrative study of multi-omics data, we are interested in estimating the structure of the sparse cross correlation matrix of two high-dimensional random vectors. We rewrite the problem as a multiple testing problem and propose a new method to estimate the sparse structure of the cross correlation matrix. To do so, we test the correlation coefficients simultaneously and threshold the correlation coefficients by controlling FRD at a predetermined level α. Further, we apply the proposed method and an alternative adaptive thresholding procedure by Cai and Liu (2016) to the integrative analysis of the protein expression data (X) and the mRNA expression data (Y) in TCGA breast cancer cohort. By varying the FDR level α, we show that the new procedure is consistently more efficient in estimating the sparse structure of cross correlation matrix than the alternative one.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.351-353
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• 2021

자폐 스펙트럼 장애는 이질적인 신경 발달 장애로, 뇌기능영상에 기반한 기능적 연결성 행렬을 이용해 연구가 활발하게 진행된다. 기능적 연결성 행렬을 분석하기 위해 주성분 분석방법을 이용하며, 이를 통해 뇌의 기능적 경향성 패턴을 확인할 수 있다. 이 때, 서로 다른 연결성 성분 비율과 주성분 벡터를 이용해서 다양한 기능적 경향성 패턴을 얻을 수 있다. 패턴에 따른 랜덤 포레스트 분류 모델의 성능이 달라지는데 이를 비교해본 결과, 상위 50%의 성분을 이용하여 만든 기능적 경향성 패턴 1 이 데이터의 설명 비율도 높고, 우수한 분류 성능을 보이는 것을 확인했다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.271-278
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital sign processing approach. From the 20 seconds query audio file, the audio signal is segmented into 23ms frame with non-overlapped hamming window and 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS(Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we can verify the superior performance of the proposed method that provides near 90% success rate for the genre classification which means 10%∼20% improvements over the previous methods. For the case of actual user system environment, feature vector is extracted from the random interval of the query audio and it shows overall 80% success rate except extreme cases of beginning and ending portion of the query audio file.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.43-48
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• 2008

Power consumption during test can be much higher than that during normal operation since test vectors are determined independently. In order to reduce the power consumption during test process, a new BIST(Built-In Self Test) architecture is proposed. In the proposed architecture, test vectors generated by an LFSR(Linear Feedback Shift Resister) are transformed into the new patterns with low transitions using Bit Generator and Bit Dropper. Experiments performed on ISCAS'89 benchmark circuits show that transition reduction during scan testing can be achieved by 62% without loss of fault coverage. Therefore the new architecture is a viable solution for reducing both peak and average power consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2677-2684
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• 2009

In this paper, we study a watermarking method based on the informed coding and embedding by means of trellis code and entropy masking. An image is divided as $8{\times}8$ block with no overlapping and the discrete cosine transform(DCT) is applied to each block. Then the 16 medium-frequency AC terms of each block are extracted. Next it is compared with gaussian random vectors having zero mean and unit variance. As these processing, the embedding vectors with minimum value of linear combination between linear correlation and Watson distance can be obtained by Viterbi algorithm at each stage of trellis coding. For considering the image characteristics, we apply different weight value between the linear correlation and the Watson distance using the entropy masking. To evaluate the performance of proposed method, the average bit error rate of watermark message is calculated from different several images. By the experiments the proposed method is improved in terms of the average bit error rate.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of Life Science
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• v.23 no.2
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• pp.290-298
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• 2013

The genus Mycobacterium includes crucial animal and human pathogens such as Mycobacterium tuberculosis, Mycobacterium leprae, and Mycobacterium bovis. Although it is important to understand the genetic basis for their virulence and persistence in host, genetic analysis in mycobacteria was hampered by a lack of sufficient genetic tools. Therefore, many functional vectors as molecular genetic tools have been designed for understanding mycobacterial biology, and the application of these tools to mycobacteria has accelerated the study of mechanisms involved in virulence and gene expression. To overcome the pre-existing problems in genetic manipulation of mycobacteria, this paper reports new vector systems as effective genetic tools in Mycobacterium smegmatis. Three vectors were developed; pKOTs is a suicide vector for mutagenesis containing a temperature-sensitive replication origin (TSRO) and the sacB gene encoding levansucrase as a counterselectable marker. pMV306lacZ is an integrative lacZ transcriptional fusion vector that can be inserted into chromosomal DNA by site-specific recombination. pTnMod-OKmTs is a minitransposon vector harboring the TSRO that can be used in random mutagenesis. It was demonstrated in this study that these vectors effectively worked in M. smegmatis. The vector systems reported here are expected to successfully applicable to future research of mycobacterial molecular genetics.