• Archives of Pharmacal Research
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• v.11 no.2
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• pp.99-113
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• 1988

Quantitation of ethenzamide, isopropylantipyrine and caffeine takes about 41 hrs by conventional GC method. Quantitation of allylisoprorylacetylurea takes about 40 hrs by conventional UV method. But quantitation of them takes about 6 hrs by DRIFT developing method. Each standard and sample sieved, powdered and acquired DRIFT spectrum. Out of them peak of each component was selected and ratio of each peak to standard peak was acquired, and then linear stepwise multiple regression was performed with these data and concentration. Reflectance value, Kubelka-Munk equation and Inverse-Kubelka-Munk equation were modified by us. Inverse-Kubelka-Munk equation completed the deficit of Kubelka-Munk equation. Correlation coefficients acquired by conventioanl GC and UV against DRIFT were more than 0.95.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1513-1514
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• 2013

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Microbiology and Biotechnology Letters
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• v.13 no.3
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• pp.285-287
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• 1985

To investigate effective constituents of the many taste components in ordinary Korean soy sauce, we analyzed free amino acids, organic acids, free sugars and saline as taste components in ordinary Korean soy sauce, and determined sensory score of the ordinary Korean soy sauce taste with 45 persons of the trained pannels. The relationships between original data transformed with variables and sensory score of the ordinary Korean soy sauce were analyzed by stepwise multiple regression analysis. Eighty five percents of the ordinary Korean soy sauce taste is affected by twenty one kinds (Isoleucine, Leucine, Valine, NaCl, Lactic acid, Alanine, Phenylalanine, Tartaric acid, Sugar(\ulcorner), Proline, Malic acid, Glycine, Tryptophan, Arginine, Glutaric acid, Maltose, Histidine, Glucose, Fructose and Serine) of the taste components by stepwise multiple regression analysis of original data. Eighty one percents of the ordinary Korean soy sance taste is affected by sixteen kinds (Lactic acid, NaCl, Fumaric.Succinic acid, Tyrosine, Tartaric acid, Glycine, Malonic acid, Malic acid, Tryptophan, Glutaric acid, Methionine, Histidine, Cysteine, Maltose, Fructose and (Glutamic acid) of the taste components by stepwise multiple frgression analysis of original data transformed with square root. Eighty five percents of the ordinary Korean soy sauce taste is affected by nineteen kinds (Fumaric.Succinic acid, Lactic acid, Phenylalanine, NaCl, Tyrosine, Sugar(\ulcorner), Tartaric acid, Leucine, Glutaric acid, Methionine, Glycine, Tryptophan, Histidine, Proline, Cysteine, Glutamic acid, Maltose, Threonine and Oxalic acid) of the taste components by stepwise multiple regression analysis of original data transformed with logarithm.

• Food Science and Biotechnology
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• v.14 no.2
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• pp.249-254
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• 2005

The relationship between Sigumjang gas chromatographic patterns precisely analyzed with capillary column and ranked order in sensory analysis was investigated by stepwise multiple regression analysis. Highly predictable multiple regression models were obtained in the analysis. Ninety percent of the Sigumjang aroma was explained by the regression models at step 15 in four transformation except for absolute value transformed with root square and relative value transformed with logarithm. The aroma of Sigumjang was most affected by 2,3-dimethylpyrazine at absolute value and absolute value transformed with logarithm and by 2-furancarboxaldehyde in other transformation. The quality of sigumjang was highly affected by ${\beta}$-phallendrenal, methylpyrazine, tetramethylpyrazine, 5-methyl-2-furancarboxaldehyde, unknown 2, octanoic acid, 4-ethylphenol, methyl 10,13-octadecanoate and ethyl linoleate.

• Journal of Microbiology and Biotechnology
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• v.10 no.4
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• pp.476-481
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• 2000

A linear correlation, by the stepwise multiple regression analysis, was found between the sensory test of Sigumjang aroma and the gas chromatographic data which were transformed with logarithm. GC data is the most objective method to evaluate Sigumjang aroma. A multiple correlation coefficient and a determination coefficient of more than 0.9 were obtained at the 9th and 13th steps, respectively. At step 31, the coefficient of determination level of 0.95 was attained. The accuracy of its estimation became higher as the number of the variables entered into the regression model increased. Over 90% of the Sigumjang aroma was explained by 13 compounds indentified on GC. The contributing proportion of the peak 26 was the highest followed by peaks 57 (9.27%), 29 (7.51%), 54 (6.01%), 8 (5.99%), 49 (4.97%), and 13 (4.11%).

• Korean Journal of Applied Biomechanics
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• v.26 no.4
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• pp.353-360
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• 2016

Objective: The aim of this study was to investigate the relationship between aiming patterns and scores in archery shooting. Method: Four (N = 4) elementary-level archers from middle school participated in this study. Aiming pattern was defined by averaged acceleration data measured from accelerometers attached on the body during the aiming phase in archery shooting. Stepwise multiple regression analysis was used to test whether a model incorporating aiming patterns from all nine accelerometers could predict the scores. In order to extract period of interest (POI) data from raw data, a Dynamic Time Warping (DTW)-based extraction method was presented. Results: Regression models for all four subjects are conducted with different significance levels and variables. The significance levels of the regression models are 0.12%, 1.61%, 0.55%, and 0.4% respectively; the $R^2$ of the regression models is 64.04%, 27.93%, 72.02%, and 45.62% respectively; and the maximum significance levels of parameters in the regression models are 1.26%, 4.58%, 5.1%, and 4.98% respectively. Conclusion: Our results indicated that the relationship between aiming patterns and scores was described by a regression model. Analysis of the significance levels, variables, and parameters of the regression model showed that our approach - regression analysis with DTW - is an effective way to raise scores in archery shooting.

• Proceedings of the KSR Conference
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• 1999.11a
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• pp.566-573
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• 1999

This paper is the result of sensitivity analysis of derailment with respect to the selected suspension elements for the rail vehicle. Derailment phenominon has been explained by the derailment quotient. Thus, the sensitivity of derailment is suggested by a response surface model(RSM) which is a functional relationship between derailment quotient and characteristics of suspension elements. To summarize generation of RSM, we can introduce the procedure of sensitivity analysis as follows. First, to form a RSM, a experiment is performed by a dynamic analysis code, VAMPIRE according to a kind of the design of experiments(DOE). Second, RSM is constructed to a 1$\^$st/ order polynomial and then main effect fators are screened through the stepwise regression. Finally, we can see the sensitivity level through the RSM which only consists of the main effect factors and is expressed by the liner, interaction and quadratic effect terms.

• Journal of Environmental Science International
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• v.16 no.11
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.