• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.242-244
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• 1999

This paper proposes a novel wavelet transform based algorithm for the seasonal load forecasting. In this paper, Daubechies DB2, DB4 and DB10 wavelet transforms are adopted to predict the seasonal loads and the numerical results reveal that certain wavelet components can effectively be used to identify the load characteristics in electric power systems. The wavelet coefficients associated with certain frequency and time localization are adjusted using the conventional multiple regression method and then reconstructed. In order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the wavelet transform approach can be used as an attractive and effective means of the seasonal load forecasting.

• Journal of Korea Foundry Society
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• v.24 no.2
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• pp.63-70
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• 2004

Foundry techniquews for large steel casting depends on the skills of foundrymen considerably. Especially, the problem of reducing casring surface defects is difficult to clear numerically. Statistical analysis by using wuantification theory for hot tear and sand inclusion, and multiple regression analysis for dimensional defects have been shown to be examples of solving this difficulty. Many causes of surface defects can be evaluated by these analyses. These evaluations serve as the base data of defect reduction and contribute to the constant improvement of casting quality and quality enhancement activity. The system to perform quality enhancement activity was developed and it proved very useful for transfering foundry techniques and skills from the old to young generations.

• Korean Journal of Hydrosciences
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• v.2
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• pp.1-12
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• 1991

To estimate the peak discharge from the small rural watersheds, 53 storm events of seven small watersheds from 1972 to 1989 were selected and were analyzed by the multiple regression technique. The peak values by the new prediction method developed in this study were also compared to the real data of Banwol Basins and the estimated peaks of the several watersheds which were analyzed by the Korean engineering companies. These values were also compared to the results from the other method, i.e. the Rational Method, the Kajiyama Method, the Nakayasu Unit Hydrograph companies. Through Method, the Area Routing Method, etc., which are favored by the Korean engineering companies. Through these comparisons, it is proved that the proposed method may be used for day-to-day use without any problem. However, there should be some modifications and improvements as more data are available in the future.

• Journal of Global Scholars of Marketing Science
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• v.16 no.4
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• pp.75-95
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• 2006

This study attempted to determine the influence of organizational structure on the novelty preference for corporate meeting destination choice. The three dimensions of structure incorporated were formalization, centralization and complexity. A total of 75 corporate meeting planners drawn from public listed services organizations were involved. The main method of data collection was questionnaire survey and multiple regression analysis was employed as the main statistical technique. The results revealed that both formalization and centralization were negatively correlated with novelty preference while complexity was positively correlated. However, only complexity contributed significantly to the prediction of novelty preference for corporate meeting destination choice. The main implication of this study is pertaining to the segmentation and targeting of the corporate meeting market. This study helped in bridging the gap between tourism marketing and organizational research. It also contributed by developing the measurement for novelty preference from the context of experiential marketing.

• Journal of the Korean housing association
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• v.2 no.1
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• pp.1-12
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• 1991

The purpose of this study was to classify the types of housing life style. Housing life style was measured using four variables : furniture usage pattern, space usage pattern, family living pattern and heating system. A final Instrument was developed through the two stage pilot surveys. The respondents were 1,292 home-makers of the middle and high economic classes In Seoul and Daejeon, selected through stratified random sampling technique. Data were analyzed using SAS computer packages. The statistics used were frequency, percentage, Pear-3on`s correlation coefficient, Multiple Linear Regression, X2, and cluster analysis.The major findings were as follows : Five representative types of housing life style were found through cluster analysis. They were conventional minimum level life style, conventional optimum famiIy-centered life style, eclectic family-centered life style, contemporary optimum family - centered and contemporary so-cial, leasure-oriented life style.

• Journal of Families and Better Life
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• v.15 no.4
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• pp.149-164
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• 1997

This research examined the relationships of individual and intergenerational variables with psychological well-being(PWB) of elderly females and the relative significance of the effect of these variables on PWB. Structured interview technique was used to collect data from 202 elderly females in Seoul who were recruited through quota sampling method. Two-way analysis of variance result indicated that interaction effect between aged and the presence of spouse on PWB was existed. Though most variables were significantly correlated with PWB of elderly females results of multiple regression analyses indicated that three variables-self-efficacy self-esteem affectional solidarity with adult children- significantly influenced PWB and three variabels-coresiding with the first son's family satisfaction with pocket money and contact solidarity-approached the significance level to the effect on PWB.

• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.1
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• pp.1-4
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• 2003

This paper presents an attempt to develop probabilistic multistress life models to evaluate the lifetime characteristics of epoxy-encapsulated magnet wire with heavy build polyurethane enamel. A set of accelerated life tests were conducted over a wide range of pulsating voltages, temperatures, and frequencies. Samples of fine gauge twisted pairs of the encapsulated magnet wire were tested us-ing a pulse endurance dielectric test system. An electrical-thermal lifetime function was combined with the Weibull distribution of lifetimes. The parameters of the combined Weibull-electrical-thermal model were estimated using maximum likelihood estimation. Likewise, a generalized electrical-thermal-frequency life model was also developed. The parameters of this new model were estimated using multiple linear regression technique. It was found in this paper that lifetime estimates of the two proposed probabilistic multistress life models are good enough. This suggests the suitability of using the general electrical-thermal-frequency model to estimate the lifetime of the encapsulated magnet wire over a wide range of voltages, temperatures and pulsating frequencies.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.99-106
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• 2008

Digital Terrain Elevation Data is a set of numerical values that represent the heights of the terrain surface. Terrain has several properties. Among them, roughness is the most widely used one because it gives very useful information to land-based or land-reference platform. There are several ways to measure roughness of terrain. But each of them has own flaws. Moreover, it is not enough to represent the uniqueness of the terrain only by the roughness. We need another metric to measure the actual uniqueness. In this paper, we propose an improved method to measure essential characteristics, uniqueness, of terrain. It gives not only the roughness but also the unevenness. The combination of them makes up the uniqueness. And it can be applied even if there is no pre-planned path on the terrain.

• Journal of the Korean Society of School Health
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• v.13 no.1
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• pp.109-115
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• 2000

The Purpose of this study is to analyze the health behavior of high school students and to find factors influencing it. A questionnaire survey was used for collecting data from 201 male students between May 1 and June 30, 1993. The data were analyzed by the multiple regression technique in an SPSS package. The major results are as follows: (1) 84.5% of the students responded to the survey are regularly exercising to promote their health and 44.8% of them are doing something to manage stress. (2) 28.9% of the students are smoking, 43.3% of them are drinking, 5% of them are taking adhesives, 6.5% are taking stimulants, and 4.5% of them are taking analgesics. (3) while self-efficacy of the students and health interests of their parents are positively related to health promoting behaviors, they are negatively related to health risk behaviors.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.159-168
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• 2022

No one denies the importance of online courses, which provide a very important alternative, especially for students who have jobs that prevent them from attending face-to-face in traditional classes; Engagement is one of the most important fundamental variables that indicate the course's success in achieving its objectives. Therefore, the current study aims to build a model using machine learning to predict student engagement in online courses. An online questionnaire was prepared and applied to the students of Jouf University in the Kingdom of Saudi Arabia, and data was obtained from the input variables in the questionnaire, which are: specialization, gender, academic year, skills, emotional aspects, participation, performance, and engagement in the online course as a dependent variable. Multiple regression was used to analyze the data using SPSS. Kegel was used to build the model as a machine learning technique. The results indicated that there is a positive correlation between the four variables (skills, emotional aspects, participation, and performance) and engagement in online courses. The model accuracy was very high 99.99%, This shows the model's ability to predict engagement in the light of the input variables.