• 한국생활과학회지
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• 제16권2호
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• pp.367-380
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• 2007

This study investigates the perception of price and the propensity to conspicuous consumption in the purchase of clothing among college students and the relation between the perception of price and the propensity to conspicuous consumption. The data for this study were collected from 1,015 Korean college students. In order to analyze the data, statistical methods such as frequency, percentage, mean, standard deviation, factor analysis, ANOVA, scheffe-test, Pearson's correlation, and Cronbach's a were used with the SPSS statistical package program. The major study findings were as follows. 1. The perception of clothing price among college students was classified into five factors: high price-oriented, discount-oriented, high quality-price rate-oriented, low price-oriented, and effectiveness-oriented. 2. The propensity to conspicuous consumption of clothing among college students was classified into four factors: brand name conspicuousness, conspicuous pursuit of vogue, symbol of their social position, and imported goods conspicuousness. 3. High price-oriented and discount-oriented were different significantly according to sex, age, father's educational level, monthly income, monthly allowance amount. High quality-price rate-oriented and low price-oriented were different significantly according to sex, father's educational level, monthly income, monthly allowance amount. Effectiveness-oriented was different significantly according to sex, age. 4. Brand name conspicuousness, conspicuous pursuit of vogue and imported goods conspicuousness were different significantly according to sex, age, father's educational level, monthly income, monthly allowance amount. Symbol of their social position was different significantly according to sex, age, father's educational level, monthly income. 5. High price-oriented, discount-oriented and high quality-price rate-oriented have the highly positive correlation with four factor of propensity to conspicuous consumption of clothing. Low price-oriented has the negative correlation with four factor of propensity to conspicuous consumption of clothing. Effectiveness-oriented has the lowly positive correlation with four factor of propensity to conspicuous consumption of clothing.

• 한국농공학회지
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• 제31권4호
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• pp.31-49
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• 1989

It is of the most urgent necessity to get hydrological time series of long duration for the establishment of rational design and operation criterion for the Agricultural hydraulic structures. This study was conducted to select best fitted frequency distribution for the monthly runoff and to simulate long series of generated flows by multi-season first order Markov model with comparison of statistical parameters which are derivated from observed and sy- nthetic flows in the five watersheds along Geum river basin. The results summarized through this study are as follows. 1. Both two parameter gamma and two parameter lognormal distribution were judged to be as good fitted distributions for monthly discharge by Kolmogorov-Smirnov method for goodness of fit test in all watersheds. 2. Statistical parameters were obtained from synthetic flows simulated by two parameter gamma distribution were closer to the results from observed flows than those of two para- meter lognormal distribution in all watersheds. 3. In general, fluctuation for the coefficient of variation based on two parameter gamma distribution was shown as more good agreement with the observed flow than that of two parameter lognormal distribution. Especially, coefficient of variation based on two parameter lognormal distribution was quite closer to that of observed flow during June and August in all years. 4. Monthly synthetic flows based on two parameter gamma distribution are considered to give more reasonably good results than those of two parameter lognormal distribution in the multi-season first order Markov model in all watersheds. 5. Synthetic monthly flows with 100 years for eack watershed were sjmulated by multi- season first order Markov model based on two parameter gamma distribution which is ack- nowledged to fit the actual distribution of monthly discharges of watersheds. Simulated sy- nthetic monthly flows may be considered to be contributed to the long series of discharges as an input data for the development of water resources. 6. It is to be desired that generation technique of synthetic flow in this study would be compared with other simulation techniques for the objective time series.

• 한국의상디자인학회지
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• 제5권3호
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• pp.111-123
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• 2003

This study is designed to understand evaluation criteria for garment depending on demographic factors among college students in Yanbian, China. Questionnaire was used for measurement tools to study the subject of the thesis. The main study was conducted against 450 college students from May 17 to June 5, 2001. The data for the study were analyzed using SAS PC program for frequency distribution, percentage, t-test, and one way ANOVA. The evaluation criteria for garment are affected by demographic factors such as gender, average monthly household income, monthly expense for clothing. The result was showed as follows: The evaluation criteria for garment based on gender showed almost no meaningful different between male and female college students. Means on factors considered highly in selecting clothes was studied. The result shows that 'fit to the body, 'quality', 'color' and 'pattern' are considered most highly and 'harmony with other clothes', 'after service', design' 'easy to manage' and 'price' are considered relatively highly, but 'brand' and 'trendy fashion' were not considered highly. A meaningful difference was showed only in one area-trendy fashion-among three different income level groups. Those with an average monthly household income between 500 and 2,000yuan showed a highest tendency compared to those with above 2,000yuan and those with 500yuan. In terms of evaluation criteria for garment based on monthly expense for clothing, 'brand' is the only area which showed a meaningful difference. Respondents with monthly clothing expense of above 100yuan showed a higher means than those with below 100yuan.

• 물과 미래
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• 제17권4호
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• pp.281-291
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• 1984

한국하천유역의 강우량관측자료는 풍부하나 하천유량측정자료가 많고 섬진강 유역내의 압록과 송정의 유량관측기록이 비교적장기간에 것이 있고, 유속측정을 많이 하고 있으므로 본유역자료를 가지고 월유출량계열의 모형식을 유도하였다. 본모형식은 월강우량기록으로서 월유출량 산출식을 Box & Jenkins의 대체함수모형식에다 ARIMA의 잔차모형식을 가하여 유도한 것이다. 또 기 강우량과 유출량 자료간에는 잔차시계열이 정상공분산을 갖는다는 가정하에 모형식을 작성하였다. 자기상관 함수의 특성으로부터 ARIMA모형을 유도함에도 먼저 계산식으로 각변수를 산출하고, 이 변수를 다소조정반복시켜 가장 정확한 융통성있는 Box & Jenkins 방식의 모형식을 작성하였다. 섬진강에서 가장 적정모형식을 다음과 같은 일반식으로 주어졌다. 여기서 $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ 월유출량, $X_t$: 월 강우량, $C_i$: 월유출률, $$\omega$o-$\omega$_1$ : 대체변수 $$\varepsilon$_t$ : 잔차(임의오차성분) 섬진강수위관측소의 기 월유출량 기록자료로서 월유출량게열의 만족할만한 모형을 비교검토 연구작성하였다.

• 한국농공학회논문집
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• 제60권5호
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• pp.81-93
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• 2018

The Kajiyama formula, which is an empirical formula based on the maximum flood data at Korean watersheds, has been widely used for the design of hydraulic structures and management of watersheds. However, this formula was developed based on meteorological data and flow measured during early 1900s so that it could not consider the recently changed rainfall pattern due to climate changes. Moreover, the formula does not provide the monthly coefficients for 5 months including July and August (flood season), which causes the uncertainty to accurately interpret runoff characteristics at a watershed. Thus, the objective of this study is to enhance the monthly coefficients based on the recent meteorological data and flow data expanding the range of rainfall classification. The simulated runoff using the enhanced monthly coefficients showed better performance compared to that using the original coefficients. In addition, we evaluated the applicability of the enhanced monthly coefficient for future runoff prediction. Based on the results of this study, we found that the Kajiyame formula with the enhanced coefficients could be applied for the future prediction. Hence, the Kajiyama formula with enhanced monthly coefficient can be useful to support the policy and plan related to management of watersheds in Korea.

• Asian-Australasian Journal of Animal Sciences
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• 제23권6호
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• pp.814-824
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• 2010

The objective of this research was to assess the effect of experience, education, record keeping, labor, and decision making on monthly milk yield per farm (MYF), monthly milk yield per cow (MYC), monthly milk revenue per farm (MRF), and monthly revenue per cow (MRC) of dairy farms supported by a private organization in Central Thailand. The dataset contained 34,082 monthly milk yield and revenue records collected from January 2004 to December 2008 on 497 farms, and information on individual farmer experience and education, record keeping, and decision making obtained with a questionnaire. Farmer experience categories were i) no experience, ii) one year, iii) two to five years, iv) six to ten years, v) eleven to fifteen years, vi) sixteen to twenty years, and vii) more than twenty years. Farmer education categories were i) no education or primary school, ii) high school, and iii) bachelor or higher degree. Record keeping categories were: i) no records and ii) kept records. Labor categories were: i) family, ii) hired people, and iii) family and hired people. Decision making categories were: i) decisions made by farmers themselves, ii) decisions made with help from government officials, and iii) decisions made with help from organization staff. The mixed linear model contained the fixed effects of year-season, farm location-farm size subclass, experience, education, record keeping, labor, and decision making on sire selection, and the random effects of farm and residual. Results showed that longer experience increased (p<0.05) monthly milk yield (MYF and MYC) and revenue (MRF and MRC). Farms that hired people produced the highest (p<0.05) monthly milk yield (MYF and MYC) and revenue (MRF and MRC), followed by farms that used family, and the lowest values were for farms that used both family and hired people. Better educated farmers produced more MYC and MRC (p<0.05) than lower educated farmers. Farms that kept records had higher MYF and MRF (p<0.05) than those without records. Although differences among farms were non-significant, farms that received help from the organization staff had higher monthly milk yield (MYF and MYC) and revenue (MRF and MRC) than those that decided by themselves or with help from government officials. These findings suggested that dairy farmers needed systematic training and continuous support to improve farm milk production and revenues in a sustainable manner.

• 한국콘텐츠학회논문지
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• 제20권3호
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• pp.245-253
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• 2020

전월세 전환율이 시장이자율 또는 임대인의 기대수익률이라면 전국의 전월세 전환율은 동일하여야 한다. 그러나 전월세 전환율은 항상 시장이자율보다 높은 수준을 유지하고 있다. 본 연구는 임대주택의 공급원가 구성요소를 현재의 주택가격, 시장이자율, 감가상각비, 보유세, 그리고 임대차에 따른 위험이 존재할 경우 위험프리미엄으로 파악하고 현재의 주택가격과 각 요소와의 관계를 파악함으로써 주택임차료를 현재 가격으로 표현하였다. 이를 통해 주택가격의 변동 폭을 암묵적으로 가정하거나 전월세 전환율에 현재의 주택가격이 반영되지 못한 단점을 극복하였다. 본 연구는 임대주택의 공급 원가를 구성 요소 간의 조합으로 표현함으로써 전월세 전환율이 시장이자율이 아니라 임대인의 필수수익률 또는 요구수익률임을 밝혔다. 이는 전월세 전환율이 항상 시장이자율보다 높은 현상도 설명할 수 있을 뿐만 아니라 전월세 전환율의 지역별 차이 및 주택 유형별 차이가 발생하는 원인도 설명할 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1330-1333
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• 2004

The methodology for modeling building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. Mostly, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents the application of neural networks (NN) to model landlord energy consumption of commercial buildings in Singapore. Firstly, a brief background information on NN and its application on the building energy research is provided. Secondly, five commercial buildings with various characteristics were selected for case studies. Monthly mean outdoor dry-bulb temperature ($T_0$), Relative Humidity (RH) and Global Solar Radiation (GSR) are used as network inputs and the landlord monthly energy consumption of the same period is the output. Up to three years monthly data are taken as training data. A forecast has been made for another year for all the five buildings. The performance of the NN analysis was evaluated using coefficient of variance (CV). The results show that NNs is powerful at predicting annual landlord energy consumption with high accuracy.

• 대한가정학회지
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• 제42권11호
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• pp.205-222
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• 2004

The purposes of this study were to examine urban household's monthly expenses for public pension and to analyze the contributing factors. Data for this study were from the 2002 Urban Household Survey and consisted of a sample of 21,093 urban households. Statistics used for the analysis were frequencies, means, ANOVA and multiple regression analysis. The major findings were as follows ; First, the average urban household monthly payment for the public pension was 104,036 won, consisting of 102,757 won for single earner households and 106,014 won for dual earner households. Second, the highest expenses for monthly public pension was urban households, followed by male household head(HH), HH's age from 41-50 years, HH's educational level was college, HH's job was public servant, family didn't live in Seoul, family w3s an extended family and family owned the house. Third, the significant factors affecting the urban household's monthly public pension were HH's gender, age, educational level, type of job, region, type of family, number of children, type of earner, monthly total income, increase of asset in a month and house ownership.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.