• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.09a
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• pp.243-250
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• 2001

Industrial machines are sometimes exposed to the danger of earthquake. In the design of a mechanical system, this factor should be accounted for from the viewpoint of reliability. A method to analyze a complex nonlinear structure system under random excitation is proposed. First, the actual random excitation, such as earthquake, is approximated to the corresponding Gaussian process far the statistical analysis. The modal equations of overall system are expanded sequentially. Then, the perturbed equations are synthesized into the overall system and solved in probabilistic way. Several statistical properties of a random process that are of interest in random vibration applications are reviewed in accordance with nonlinear stochastic problem. The obtained statistical properties of the nonlinear random vibration are evaluated in each substructure. Comparing with the results of the numerical simulation proved the efficiency of the proposed method.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.583-594
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• 2021

In this paper, we propose wavelet-based procedures to identify the difference between images, including portraits and handwriting. The proposed methods are based on a novel combination of multiscale methods with a regularization technique. The multiscale method extracts the local characteristics of an image, and the distinct features are obtained through the regularized regression of the local characteristics. The regularized regression approach copes with the high-dimensional problem to build the relation between the local characteristics. Lytle and Yang (2006) introduced the detection method of forged handwriting via wavelets and summary statistics. We expand the scope of their method to the general image and significantly improve the results. We demonstrate the promising empirical evidence of the proposed method through various experiments.

• Journal of the Korean association of regional geographers
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• v.22 no.2
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• pp.424-438
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• 2016

In an effort to prevent urban decline and hollowing-out phenomenon and to vitalize stagnant local economy, a new urban regeneration paradigm is on the rise. This study aims to analyze urban decline characteristics using the spatial statistical method and GIS on the basis of decline standards in the Urban Regeneration Special Act, and spatial autocorrelation technique. The Gwangju Metropolitan City was set as a research target, and the decline standards in the Urban Regeneration Special Act - population reduction, business declines, and outworn buildings - were applied as the indicator to secure the objectivity. In particular, this study has a distinctive feature from the other existing ones, as applying GIS and the spatial statistical technique, in a sense to make urban decline characteristics analysis by the spatial autocorrelation technique. The overall analysis procedure was carried out by applying the standards of designating urban regeneration regions, and following the spatial exploratory procedure step by step. Therefore, the spatial statistical method procedure and the urban decline characteristics analysis data being presented in this study, as the results, are expected to contribute to the urban decline diagnosis at the level of metropolitan city, as well as to provide useful information for spatial decision making in accordance with urban regeneration.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.1-9
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• 2019

Various probabilistic methods have been proposed for using interpopulation allele frequency differences to infer the ethnic group of a DNA specimen. The selection of the statistical method is critical because the accuracy of the statistical classification results vary. For the ancestry classification, we proposed a new ancestry evaluation method that estimate the combined ethnicity index as well as compared its performance with various classical classification methods using two real data sets. We selected 13 SNPs that are useful for the inference of ethnic origin. These single nucleotide polymorphisms (SNPs) were analyzed by restriction fragment mass polymorphism assay and followed by classification among ethnic groups. We genotyped 400 individuals from four ethnic groups (100 African-American, 100 Caucasian, 100 Korean, and 100 Mexican-American) for 13 SNPs and allele frequencies that differed among the four ethnic groups. Additionally, we applied our new method to HapMap SNP genotypes for 1,011 samples from 4 populations (African, European, East Asian, and Central-South Asian). Our proposed method yielded the highest accuracy among statistical classification methods. Our ethnic group classification system based on the analysis of ancestry informative SNP markers can provide a useful statistical tool to identify ethnic groups.

• Proceedings of the Korean Geotechical Society Conference
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• 1993.03a
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• pp.27-32
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• 1993

In tunnelling by NATM convergence data are most Importantly to ascertain the safety of tunnel. Therefore, a reliable method is required that can predict ultimate displacements by using earler displacement data. Displacement forecasting method is classified into statistical method and functional regression method. Convergence data measured in Seoul subway 5~45 site during '92.5 ~ '92.12 were analyzed by above said two methods. The analysis results of convergence data show that the functional regression method is more relieable in completely weathered rock, but the statistical method in slightly wearhred rock.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.97-102
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• 2010

This paper deals with the variability of short term creep rupture time based on previous creep rupture tests and the statistical methodology of the creep life prediction. The results of creep tests performed using constant uniaxial stresses at 600, 650, and $700^{\circ}C$ elevated temperatures were used for a statistical analysis of the inter-specimen variability of the short term creep rupture time. Even under carefully controlled identical testing conditions, the observed short-term creep rupture time showed obvious inter-specimen variability. The statistical aspect of the short term creep rupture time was analyzed using a Weibull statistical analysis. The effect of creep stress on the variability of the creep rupture time was decreased with an increase in the stress level. The effect of the temperature on the variability also decreased with increasing temperature. A long term creep life prediction method that considers this statistical variability is presented. The presented method is in good agreement with the Lason-Miller Parameter (LMP) life prediction method.

• Atmosphere
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• v.19 no.2
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• pp.107-125
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• 2009

Statistical downscaled surface temperature datasets by employing the cyclostationary empirical orthogonal function (CSEOF) analysis and multiple linear regression method are examined. For evaluating the efficiency of this statistical downscaling method, monthly surface temperature of the ECMWF has been downscaled into monthly temperature having a fine spatial scale of ~20km over the Korean peninsula for the 1973-2000 period. Monthly surface temperature of the ECHOG has also been downscaled into the same spatial scale data for the same period. Comparisons of temperatures between two datasets over the Korean peninsula show that annual mean temperature of the ECMWF is about $2^{\circ}C$ higher than that of the ECHOG. After applying to the statistical downscaling method, the difference of two annual mean temperatures reduces less than $1^{\circ}C$ and their spatial patterns become even close to each other. Future downscaled data shows that annual temperatures in the A1B scenario will increase by $3.5^{\circ}C$ by the late 21st century. The downscaled data are influenced by the ECHOG as well as observation data which includes effects of complicated topography and the heat island.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1120-1126
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• 2000

Recently, in order to analyze the time series problems that occur in the nature word, and analyzing method using a neural electric network is being studied more than a typical statistical analysis method. A neural electric network has a generalization performance that is possible to estimate and analyze about non-learning data through the learning of a population. In this paper, after collecting weather datum that was collected from 1987 to 1996 and learning a population established, it suggests the weather forecasting system for an estimation and analysis the future weather. The suggested weather forecasting system uses 28*30*1 neural network structure, raises the total learning numbers and accuracy letting the selecting concentration learning about the pattern, that is not collected, using the descending epsilon learning method. Also, the weather forecasting system, that is suggested through a comparative experiment of the typical time series analysis method shows more superior than the existing statistical analysis method in the part of future estimation capacity.