• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.481-482
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• 2014

• Journal of the Korean Data and Information Science Society
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• v.2
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• pp.1-9
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• 1991

Shin, Tarumi and Tanaka(1989) discussed a method of sensitivity analysis in principal component regression(PCR) based on an influence function derived by Tanaka(1988). The present paper is its continuation. In this paper we first consider two new influence measures, then apply the proposed method to various data sets and discuss some properties of sensitivity analysis in PCR.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.107-114
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• 2009

Human walking is essential and important mean of transportation. Pedestrian safety is recently important because accidents often happen while walking. This research is showing that Walking-environmental factors have effect on safety while walking. At first, exact 15 factors and conduct survey in the preceding research. After that, exact 4 important factors through factor analysis. At result of Multiple regression analysis, null hypothesis has proved to be true by satisfying therms which is F-value 9.211 and P-value 0.000. and come to the conclusion that walking-environmental factors influence pedestrian safety. 4 important factors can be listed by below. Pedestrian-road characteristic, landscape characteristic, commercial characteristic, walking characteristics by following influence. Especially, landscape characteristic and pedestrian-road characteristic can be vital factors.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.59-67
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• 2020

Growth tests on the Wando and Baengnyeongdo cultivars of Saccharina japonica were performed at the Myeongcheon and Gyedo aquafarms, Goheung in Jeollanamdo, from February to July in 2003. Five environmental conditions and 2 traits were measured monthly. The data were used to analyze the growth patterns, relationships between traits and principal component. Box plots were used to display the growth patterns. Scatter plots and regression and correlation coefficients were used to determine the strength of relationships between the traits. A principal component analysis revealed that the first principal component explained more than 91.4% and 90.5% of the total sample variance in the Myeongcheon and Gyedo aquafarms. From the viewpoint of the economic traits (blade length, blade weight), the growth of populations from the Gyedo aquafarm was stronger than that of those from the Myeongcheon aquafarm, and the growth of the Baengnyeongdo cultivar was superior to that of the Wando one.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.11
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• pp.1623-1630
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• 2011

The effects of addition of citrus peel powders (C 0, 0.5, 1 & 2%), dried Lentinus edodes powders (L 0, 0.5, 1 & 2%), and their combination (C-L) on the chemical, sensory and textural properties of pork sausages were studied. Addition of 0.5, 1 or 2% C, L, and C-L all significantly decreased moisture content, pH, and color a-values of sausage samples, whereas ash content and color b-value were increased (p<0.05). C, L, and C-L did not affect protein, fat, carbohydrates contents or texture characteristics. Sensory evaluation was performed by multivariate data analysis, namely principal component analysis (PCA). Eighty-two percent total variation was observed in the main structured information among the test groups: the first (PC1) and second (PC2) components of variation were 59 and 23%, respectively. Eight parameters (sweet flavor, pork aroma, bitterness, rancidity, salty flavor, color, sour flavor and citrus aroma) were utilized to describe the main sensory characteristic of the sausages. Addition of 0.5, 1 & 2% citrus peel was obviously correlated with PC1 (salty flavor, sour flavor and citrus aroma, pork aroma, and sweet flavor and rancidity), whereas addition of 0.5 & 1% Lentinus edodes was related with PC2 (aroma and rancidity).

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.381-393
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• 2005

As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.

• Horticultural Science & Technology
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• v.35 no.4
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• pp.446-455
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• 2017

We investigated the physicochemical and sensory characteristics of oriental melon (Cucumis melo L.) to provide a consumer-oriented quality index. Oriental melon fruits were harvested at 20, 25, or 30 days after fruit set (DAFS), and each group was sorted by size (small, medium, and large). Fruits harvested at 25 and 30 DAFS had higher CIE $a^*$ and $b^*$ values, higher soluble solids content (SSC), and lower CIE $L^*$, firmness, and titratable acidity (TA) values than fruits harvested at 20 DAFS. Fruits harvested at 25 and 30 DAFS scored more highly for overall acceptance. A significant correlation was found between physicochemical characteristics and overall acceptance. In the delayed-harvest sample, increased sweetness and yellowness, and decreased sensorial texture were associated with an increase in overall acceptance. In principal component analysis, F1 and F2 explained 62.16% and 17.91% of the total variance (80.07%), respectively. Regression analysis of overall acceptance and F1 gave a coefficient of determination ($r^2$) of 0.87. Our results show that consideration of the physicochemical characteristics (CIE value, SSC, pH, SSC/TA ratio, and firmness) and sensory characteristics (yellowness, placenta area condition, oriental melon odor, sweetness, oriental melon flavor, texture, and off odor) of oriental melon in this way can be used as quality indices to predict consumer acceptance.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.1-9
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• 2016

A correlation analysis between raw water turbidity at two wide-area water treatment plants and hydrological data was conducted for efficient water supply, design and management of water treatment plant. Both correlation analysis and principal component analysis were conducted using hydrological time series data such as inflow discharge, outflow discharge, and rainfall at dam basin of intake station of wide-area water treatment plants. And, forecasting of change in turbidity was conducted using regression equation for turbidity prediction. The raw water turbidity of two water treatment plants was strongly related to time series of discharge. The raw water turbidity of Chungju water treatment plant is strongly related to outflow discharge at Chungju dam (0.708). Whereas, the raw water turbidity of Wabu water treatment plant is strongly related to inflow discharge at Paldang dam (0.805). Similar trends between turbidity forecasting result using regression equation and calculation result using estimation equation on Korea water supply facilities standard were obtained. The result of this study can provide basic data for construction and management of water treatment plant.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.