• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.1207-1213
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• 2006

This paper shows that case-based reasoning (CBR), an artificial intelligence technique, is a quite efficient tool in monitoring financial market against its possible collapse. For this purpose, daily financial condition indicator (DFCI) monitoring financial market is built on CBR and its performance is compared to DFCI on neural network. This study is empirically done for the Korean financial market.

• ETRI Journal
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• 제31권2호
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• 한국통신학회논문지
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• 제41권9호
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• pp.1038-1040
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• 2016

본 논문은 기준 신호 기반 미세 시간 동기 기법을 제안한다. 제안 기법은 기준 신호를 이용하여 채널의 시간 임펄스 응답을 추정하고, 추정값의 통계학적 특성이 변하는 위치를 통해 시간 오차를 추정한다. 제안 기법이 다양한 채널 환경에서 기존 기법에 비해 미세 시간 동기 성능을 크게 개선할 수 있음을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.377-390
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• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• Genomics & Informatics
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• 제16권4호
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• pp.39.1-39.3
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• 2018

The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.203-223
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• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(1)
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• pp.149-152
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• 2000

In this paper, the reverse link performance of an imperfect power controlled DS/CDMA cellular system that uses a base station array antenna which can effectively remove the multiple access interference is analyzed considering both beam forming algorithm and power control error in shadowing. The blocking probability of the DS/CDMA cellular system based on array parameters, Eb/No and statistics of interference is derived, and then the system capacity corresponding to a certain blocking probability is calculated. On the assumption that the blocking probability is set 1 ％, the system capacity using MCGM algorithm with 12 antenna arrays is increased about 59 % than that using 8 antenna arrays when power control error is 2.5 ㏈.

• 한국학교보건학회지
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• 제30권2호
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• pp.174-180
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• 2017

Purpose: This study examined depression, ego-resilience and health behaviors of high school student in order to understand the relationship among these variables. Methods: The study used raw data from the Korean Children and Youth Panel Survey conducted in 2015. Data was analyzed using SPSS 21.0 for descriptive statistics, t-test, $x^2$ test, Pearson's correlation coefficients, and multiple regression analysis. Results: Gender, school performance, economic status, subjective health status, drinking behavior and ego-resilience had significant effects on depression. Moreover, school performance, subjective health status, smoking behavior, duration of exercise during physical education classes, and depression had significant effects on ego-resilience. Conclusion: Based on these findings, we need to develop the interventions for depression and ego-resilience for adolescents, considering the related factors to health status and behaviors.

• Journal of the Korean Data and Information Science Society
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• 제27권2호
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• pp.549-557
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• 2016

In this paper we propose multiclassification method for large data sets by ensembling least squares support vector machines (LS-SVM) with principal components instead of raw input vector. We use the revised one-vs-all method for multiclassification, which is one of voting scheme based on combining several binary classifications. The revised one-vs-all method is performed by using the hat matrix of LS-SVM ensemble, which is obtained by ensembling LS-SVMs trained using each random sample from the whole large training data. The leave-one-out cross validation (CV) function is used for the optimal values of hyper-parameters which affect the performance of multiclass LS-SVM ensemble. We present the generalized cross validation function to reduce computational burden of leave-one-out CV functions. Experimental results from real data sets are then obtained to illustrate the performance of the proposed multiclass LS-SVM ensemble.

• Journal of the Korean Statistical Society
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• 제29권3호
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• pp.337-350
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• 2000

This paper suggests a new diagnostic measure and a stopping rule for detecting influential observations in multiple discriminant analysis (MDA). It is developed from a Bayesian point of view using a default Bayes factor obtained from the fractional Bayes factor methodology. The Bayes factor is taken as a discriminatory information in MDA. It is shown that the effect of an observation over the discriminatory information is fully explained by the diagnostic measure. Based on the measure, we suggest a stopping rule for detecting influential observations in a given training sample. As a tool for interpreting the measure a graphical method is sued. Performance of the method is used. Performance of the method is examined through two illustrative examples.