• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.5
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• pp.394-406
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• 2004

A split-plot designed experiment including four rice varieties and 10 nitrogen levels was conducted in 2003 at the Experimental Farm of Seoul National University, Suwon, Korea. Before heading, hyperspectral canopy reflectance (300-1100nm with 1.55nm step) and nine crop variables such as shoot fresh weight (SFW), leaf area index, leaf dry weight, shoot dry weight, leaf N concentration, shoot N concentration, leaf N density, shoot N density and N nutrition index were measured at 54 and 72 days after transplanting. Grain yield, total number of spikelets, number of filled spikelets and 1000-grain weight were measured at harvest. 14,635 narrow-band NDVIs as combinations of reflectances at wavelength ${\lambda}l\;and\;{\lambda}2$ were correlated to the nine crop variables. One NDVI with the highest correlation coefficient with a given crop variable was selected as the NDVI of the best fit for this crop variable. As expected, models to predict crop variables before heading using the NDVI of the best fit had higher $r^2$ (>10\%)$ than those using common broad- band NDVI red or NDVI green. The models with the narrow-band NDVI of the best fit overcame broad- band NDVI saturation at high LAI values as frequently reported. Models using NDVIs of the best fit at booting showed higher predictive capacity for yield and yield component than models using crop variables.

• Physical Therapy Rehabilitation Science
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• v.11 no.4
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• pp.535-545
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• 2022

Objective: This study was conducted to identify predictive factors of physical activity in total hip arthroplasty patients, and to provide basic data for the developing physical activity promotion program for total hip arthroplasty patients. Design: Descriptive correlational research. Methods: Data were collected from August 2017 to May 2018. Surveys were distributed to 60 patients in a G university hospital located at J city, Gyeongsangnam-do. Data were analyzed by frequency, mean, standard deviation, t-test, ANOVA, Pearson's correlation coefficient, multiple regression analysis using SPSS 24 Win program. Results: The variables affecting the 4-week physical activity after discharge were age (β=.07), residence after discharge (β=-.22), cerebrovascular disease (β=-.13), mental and behavioural disease (β=-.11), taking antibiotic (β=-.26), walking ability (β=.41), nutritional status (β=.25), depression (β=.05). The eight variables accounted for 39.4% in the 4-week physical activity (F=4.49 p=.001). The variables affecting the 8-week physical activity after discharge were age (β=.06), waking ability (β=.34), nutritional status (β=.20), exercise self-efficacy (β=.05), depression (β=-.05). The six variables accounted for 28.0% in the 8-week physical activity (F=4.58, p<.001). Conclusions: The walking ability in discharge important to improve the physical activity, there is a need to develop an program to improve walking ability before discharge, in total hip arthroplasty. There is a need to develop a physical activity program to consistently participate in a community.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.323-325
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• 2009

In this paper, we present simulation results of Dynamic Matrix Control(DMC) to a boiler steam temperature. In order to control of steam temperature, we choose the input-output variables and generate the step response model by each input variable's step test. After that, the control structure executes on-line control with optimization using step response model. Proposed controller is applied to the APESS(Doosan company's boiler model simulator) and it is observed that the simulation results show satisfactory performance of proposed control.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.209-223
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• 2024

Purpose - This article analyzes the impact of macroeconomic variables of the United States, China, and Korea on KOSPI and VKOSPI, in that United States and China have a great influence on Korea, having an export-driven economy. Design/methodology/approach - The influence of US, China, and Korea interest rates, industrial production index, consumer price index, US employment index, Chinese real estate index, and Korea's foreign exchange reserves on KOSPI and VKOSPI is analyzed on monthly basis from Jan 2012 to Aug 2023, using multifactor model. Findings - The KOSPI showed a positive relationship with the U.S. industrial production index and Korea's foreign exchange reserves, and a negative relationship with the U.S. employment index and Chinese real estate index. The VKOSPI showed a positive relationship with the Chinese consumer price index, and a negative relationship with the U.S. interest rates, and Korean foreign exchange reserves. Next, dividing the analysis into two periods with the Covid crisis and the analysis by country, the impact of US macroeconomic variables on KOSPI was greater than Chinese ones and the impact of Chinese macroeconomic variables on VKOSPI was greater than US ones. The result of the forward predictive failure test confirmed that it was appropriate to divide the period into two periods with economic event, the Covid Crisis. After the Covid crisis, the impact of macroeconomic variables on KOSPI and VKOSPI increased. This reflects the financial market co-movements due to governments' policy coordination and central bank liquidity supply to overcome the crisis in the pandemic situation. Research implications or Originality - This study is meaningful in that it analyzed the effects of macroeconomic variables on KOSPI and VKOSPI simultaneously. In addition, the leverage effect can also be confirmed through the relationship between macroeconomic variables and KOSPI and VKOSPI. This article examined the fundamental changes in the Korean and global financial markets following the shock of Corona by applying this research model before and after Covid crisis.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• Korean Journal of Child Studies
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• v.22 no.3
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• pp.63-73
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• 2001

The present study examined variables relevant to problem solving in childhood to determine predictive contributions of such variables as parenting style, child's temperament, self-esteem, depression, and self-efficacy. Subjects were 545 2nd, 4th, 6th grade elementary school children and their parents. Data were analyzed with bivariate correlation, multiple regression, and step-wise multiple regression. Results indicated that child's temperament and self-efficacy were significantly correlated with problem-solving, and self-efficacy was the most critical predictor of problem solving.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.1
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• pp.5-14
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• 2004

A feasibility of using Particle Entrainment Simulator (PES) to evaluate model variables describing sediment entrainment in a river system was investigated. PES in a laboratory was utilized to simulate the sediment resuspension phenomenon in the river and the subsequent relationship between shear rate and sediment entrainment was developed. The total suspended solids (TSS) data from PES was incorporated into statistical models in an effort to describe behaviors of net particle movement in the river. PES was found to be adequate for simulating particle entrainment phenomenon in a river system. Statistical analysis was used to assess propriety of PES data for predictive purposes. The results showed good relationships between PES results and system variables, such as average stream velocity and net particle movement.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1219-1235
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• 2006

This study aimed to inquire the relationship among wellness, quality of life and socio-demographic variables in Korean middle school students. 'Korean Wellness Scale for Middle school students(K-WSM), Quality of Life Scale, and other socio-demographic data were surveyed to 1,200 students in national wide area. For the results, ANOVA, Pearson's Correlation, and Multiple regression were conducted. With demographic variables, sex, religion, & academic achievement of students, social economic status(SES) and living with both of parents were positively related to wellness and quality of life. But grade and physical disease of student didn't show significant relationships. The relationship between wellness and quality of life showed high correlation (r=.66, p < .05). Among the wellness subscales, spiritual and social wellness showed significant predictive power. In the end, the contributions and limitations of this study were discussed.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.