• 한국의류학회지
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• 제19권4호
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• pp.637-650
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• 1995

The purpose of the study was to develop a model to explain how a fashion style is determined within a society and how the style diffuses. The research was carried out in two stages, theoretical study followed by empirical study. In the theoretical study, explanatory model about decision of fashion style and diffusion was developed and then fashion diffusion theories and fashion phenomenon of postholder society were explained by the model developed. The theoretical framework of the explanatory model was constructed in that fashion changes by ambivalence of pursuit values within an individual as well as within a society. The empirical study was carried out to validate the model by looking into fashion phenomenon in the postmodern society A questionnaire was developed including style image, pursuit value, preference style and administered to 19 to 30 year-old women living in Seoul area. Frequency distribution, discriminant analysis, one-way ANOVA. were used for the statistical analysis. As pursuit values differed in each style preference stoup, and pursuit value coincided with image of preference style it was confirmed that clothing selection behavior was determined by pursuit value. In a postmodern society where variety of values are pursued, appearance of various products and preference of all styles altogether considered, it could assume that subcultural collective selection phenomenon appeared.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1293-1300
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• 2014

기온의 변화는 국가 정책에 여러 가지 영향을 준다. 본 연구에서는 충청남도 서산시 2003년 ~ 2012년 기온을 주위에서 쉽게 구할 수 있는 기상자료, 온실가스자료, 대기자료를 이용하여 자기회귀오차 (autoregressive error)모형으로 월별과 계절별로 분석하였다. 기온을 위한 기상자료로는, 풍속, 강수량, 일사량, 운량, 습도를 사용했고, 온실가스자료는 이산화탄소 ($CO_2$), 메탄 ($CH_4$), 아산화질소 ($N_2O$), 염화불화탄소 ($CFC_{11}$), 대기자료는 미세먼지 ($PM_{10}$), 이산화황 ($SO_2$), 이산화질소 ($NO_2$), 오존 ($O_3$), 일산화탄소 (CO)를 사용하였다. 분석 결과, 자기회귀오차모형으로 월별 기온을 39%-63% 정도 설명할 수 있다.

• 한국항만학회지
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• 제14권2호
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• pp.175-185
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• 2000

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the purpose of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the rap junctions of freeway with 70mph speed limit. From the traffic analyses, and model construction and verification for delay prediction on the ramp junctions of freeway, the following results were obtained : ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junctions of freeway. ⅲ) The delay-occupancy curve showed a remarkable shift based on the occupancies observed : O$\_$d/〈9% and O$\_$d/$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under O$\_$d/$\geq$9%, but lowly for delay prediction on the ramp junctions of freeway under O$\_$d/〈9%. Rather, the driver characteristics or transportation conditions around the freeway were thought to be a little higher explanatory for the delay prediction under O$\_$d/〈9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.131-140
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• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• 품질경영학회지
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• 제25권1호
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• pp.193-203
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• 1997

In this paper, we obtain some a, pp.oximate confidence intervals for the reliability of the stress-strength model when the stress and strength each depend on some explanatory variables, respectively. Also we compare the confidence intervals via Monte Carlo simulation.

• ETRI Journal
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• 제18권2호
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• pp.87-106
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• 1996

This paper proposes a new model as a framework for forecasting demand and technological substitution, which can accommodate different patterns of technological change. This model, which we named, "Adaptive Diffusion Model", is formalized from a conceptual framework that incorporates several underlying factors determining the market demand for technological products. The formulation of this model is given in terms of a period analysis to improve its explanatory power for dynamic processes in the real world, and is described as a continuous form which approximates a discrete derivation of the model. In order to illustrate the applicability and generality of this model, time-series data of the diffusion rates for some typical products in electronics and telecommunications market have been empirically tested. The results show that the model has higher explanatory power than any other existing model for all the products tested in our study. It has been found that this model can provide a framework which is sufficiently robust in forecasting demand and innovation diffusion for various technological products.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.301-309
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• 2013

This paper predicts gross box office revenue for domestic films using the Korean film data from 2008-2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory variables from the data sources available to the public. Some variables must be categorized to conduct more effective analysis and clustering methods are applied to achieve this task. We choose the best model based on performance in the test set and important explanatory variables are discussed.

• 디지털융복합연구
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• 제7권3호
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• pp.13-24
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• 2009

Technology Acceptance Model (TAM) has been a basis model for testing technology use. Post researches of TAM have been conducted with the updating the TAM by adding new independent variables in order to increase the explanatory power of the model. However, the problem is that different independent variables have to be required to keep the explanatory power whenever adopting particular technology. This might reduce the generality of the research model. Thus in order to increase the generality of the model, this study reviewed the previous researches and collected the independent variables used, and regrouped them into three basic independent constructs. New research model was designed with three basic independent constructs with three constructs selected for the involuntary information technology usage environment. Finally, this study concluded that new technology acceptance model could be used to explain the use of new technology without any adding new particular independent variables.

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.593-601
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• 2013

회귀모형에서 범주형 변수를 독립변수로 포함시켜야 할 경우가 발생한다. 회귀모형의 범주형 변수는 가변수를 통해 수량화된다. 이 연구에서는 하나의 양적 독립변수와 하나 혹은 두 개의 범주형 독립변수를 가지는 회귀모형에 대해 가설검정 결과와 함께 회귀직선을 보여주는 교육용 도구를 엑셀 VBA (Visual Basic for application)를 통해서 구현한다. 가설검정 결과와 회귀직선은 교호작용이 포함된 모형, 교호작용이 없는 모형 및 가변수가 없는 모형에 대해 단계별로 제공된다. 이 교육도구를 통해 가변수와 교호작용의 의미를 더 쉽게 이해할 수 있으며, 나아가 어떤 모형이 주어진 자료에 가장 적합한지 그림을 통해 판단할 수 있게 된다.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1153-1164
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• 2008

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.