• Convergence Security Journal
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• v.12 no.3
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• pp.115-121
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• 2012

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offers information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, we predict the future failure time by using the curve regression analysis where the s-curve, growth, and Logistic model is used. The proposed prediction method analysis used failure time for the prediction of this model. Model selection using the coefficient of determination and the mean square error were presented for effective comparison.

• Journal of the Korean Institute of Rural Architecture
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• v.12 no.1
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• pp.1-8
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• 2010

The purpose of this study was to explore the factors affecting satisfaction of street-scape. According to this purpose, in chapter 2, by inspecting conservation of street environment and streetscape, deduce the frame for analyzing streetscape in commercial district. In chapter 3, analyzing present condition and problems of selected streets in Pohang City, derive the primary factors to induce desirable streetscape through problems and their reason between the analyzed elements of building form. Analyzed elements are composed pavement of road, street furniture, height of buildings, color and material of building and outdoor advertisements, etc. In chapter 4, by conducting a questionnaire survey of pedestrians about street images and the preference, propose the direction of improvement about streetscape in commercial district. As the study method, level of satisfaction was analyzed using the components of street-scape. The collected data was analyzed through Reliability Analysis, ANOVA, Factor Analysis, Regression Analysis. A regression analysis for deriving main factors affecting the satisfaction level of street-scape showed that signboard, sign color, width of street, paving materials, street furniture, open space were found to be the most important.

• KDI Journal of Economic Policy
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• v.24 no.1
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• pp.113-190
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• 2002

The purpose of this paper is to perform empirical studies on the impact of pollution intensity on international competitiveness using 1993 and 1998 data, and to estimate the change in environmental regulation level faced by the firms during 1993~1998. Collecting relevant data and providing them for further studies in the area are another purposes of the paper. The first method is the regression of various indices of international competitiveness on factor costs, such as labor, capital, R&D and pollution abatement costs. Goal of the regression analysis is to estimate the scarcity and comparative advantage effect of each production factor, especially environmental resource. Regression results show that those industries which employ more environmental resource have higher comparative advantage in both years, which implies that Korean firms are endowed with abundant environmental resource compared to other countries. The second method is to compute the relative scarcity indices(HOVL indices) of production factors, proposed by Leamer based on Vanek's generalized Hecksher-Ohlin Theorem. This method estimates the relative scarcity of production factors by computing factor costs embodied in import and export of commodities. This method shows similar results as the regression method; i.e., trade pattern of production factors implies that the manufacturing sector in Korea is endowed with abundant environmental resource compared to other countries. Considering population density, water resource endowment, intensity of economic activity per unit area and current air and water pollution levels, it is evident that Korea is never endowed with abundant environmental resource compared to other countries. Then the abundance of environmental resource revealed by the trade patterns of commodities and production factors implies that Korea's environmental regulation level is excessively generous compared to environmental capacity, and that this increased the environmental resource endowment supplied to firms and thus distorted the inter-industry comparative advantages. Both regression and HOVL methods, on the other hand, show that overall environmental regulation level faced by the firms has been strengthened during 1993~1998.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.655-665
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• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.259-268
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• 2016

High-dimensional survival data with large numbers of predictors has become more common. The analysis of such data can be facilitated if the dimensions of predictors are adequately reduced. Recent studies show that a method called sliced inverse regression (SIR) is an effective dimension reduction tool in high-dimensional survival regression. However, it faces incapability in implementation due to a double categorization procedure. This problem can be overcome in the right-censoring type by transforming the observed survival time and censoring status into a single variable. This provides more flexibility in the categorization, so the applicability of SIR can be enhanced. Numerical studies show that the proposed transforming approach is equally good to (or even better) than the usual SIR application in both balanced and highly-unbalanced censoring status. The real data example also confirms its practical usefulness, so the proposed approach should be an effective and valuable addition to usual statistical practitioners.

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1606-1616
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• 2011

The blood pressure measuring equipment, which is being supplied and used most widely by being recognized convenience and accuracy now generally, is oscillometric blood pressure monitor. However, a change in blood pressure is basically influenced by diverse elements such as each individual's physiological status and physical condition. Thus, the measurement of blood pressure, which used single element called oscillation in blood pressure of being conveyed to cuff, is not considered on physiological elements such as cardiovascular system status and blood vessel stiffness index, and on external elements, thereby being quite in error. Accordingly, this study detected diverse bio-signals and body informations in each individual as the measurement subject such as ECG, PPG, and Korotkoff Sound in order to enhance convenience and accuracy of measuring blood pressure in the complex measurement equipment, thereby having extracted regression method for compensation in error of oscillometric blood pressure measurement on the wrist, and having improved accuracy of measuring blood pressure. To verify a method of improving accuracy, the blood pressure value in each of SBP, DBP, MAP was acquired through 4-stage experimental procedure targeting totally 51 subjects. Prior to experiment, the subjects were divided into two groups such as the experimental group for extracting regression method and the control group for verifying regression method. Its error was analyzed by comparing the reference blood pressure value, which was obtained through the auscultatory method, and the oscillometric blood pressure value on the wrist. To reduce the detected error, the blood pressure compensation regression method was calculated through multiple linear regression analysis on elements of blood pressure, individual body information, PTT, HR, K-Sound PSD change. Verification was carried out on improving significance and accuracy by applying the regression method to the data of control group. In the experimental results, as a result of confirming error on the reference blood pressure value in SBP, DBP, and MAP, which were acquired through applying regression method, the results of $-0.47{\pm}7.45$ mmHg, $-0.23{\pm}7.13$ mmHg, $0.06{\pm}6.39$ mmHg could be obtained. This is not only the numerical value of satisfying the sphygmomanometer reference of AAMI, but also shows the lower result than the numerical value in SBP : $-2.5{\pm}12.2$ mmHg, DBP : $-7.5{\pm}8.4$ mmHg, which is the mean error in the experimental results of Brram's research for verifying accuracy of Omron RX-M, which shows relatively high accuracy among wrist sphygmomanometers. Thus, the blood pressure compensation could be confirmed to be made within significant level.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.5
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• pp.400-407
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• 2011

The suggested method of previous Son's study dichotomized subjective response data to modeling noise exposure-response. The method used maximum liklihood estimation instead of least square estimation and the noise exposure-response curve of the study was logistic regression analysis result. The method was originated to modeling community response rate such as %HA or %A. It can be useful when the subjective response was investigated based on predicted noise level. It is difficult to measure the single source emitting noise such as railway because various traffic noise sources combined in our life. The suggested method was adopted to model in this study and railway noise-exposure response curves were modeled because the noise level of this area was predicted data. The data of this study was used by previous Ko's paper but he dealt the area as combined noise area and divided the data by dominant noise source. But this study used all data of this area because the annoyance response to railway noise was higher than other noise according to the result of correlation analysis. The trend of the %HA and %A prediction model to train noise of this study is almost same as the model based on measured noise of previous Lim's study although the investigated areas and methods were different.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.11
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• pp.1271-1284
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• 2011

This study improves the fitness of Air Force winter service uniforms through the development of a shirt pattern drafting method and automatic pattern drafting program for MTM production. A calculation formula is formed through a correlation analysis and regression analysis using Size Korea 2004 3D measurement data after analyzing 4 kinds of existing shirt pattern drafting methods and 3 types of shirt patterns currently used for the Air Force service uniform. The results of this study are as follows: The developed pattern drafting method has 4 parts that use calculated dimensions: neck base width, front interscye, back interscye and scye depth. Other body measuring parts that have a high correlation with calculation parts are inserted into regression analysis as independent variables to create dimension calculation formulas. The result of the final study patterns were better than existing winter service uniforms in nearly all items for the appearance evaluation and motion adaptability evaluations. The method was converted into an automatic pattern drafting program using C++ after the completion of pattern drafting method development.