• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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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.

• Journal of the Korea Fashion and Costume Design Association
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• v.5 no.3
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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.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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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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• v.23 no.6
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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.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.245-253
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• 2020

If the conversion rate of jeonse price to monthly rent is the market interest rate or the landlord's expected return, then the conversion rate of jeonse price to monthly rent in the country should be the same. However, the conversion rate of jeonse price to monthly rent has always been higher than the market interest rate. This study identifies the supply cost components of rental housing as a risk premium in the presence of current housing prices, market interest rates, depreciation costs, holding taxes, and leases, and identifies the relationship between the current housing prices and each factor. Housing rent is expressed as the current price. This overcomes the shortcomings that implicitly assume fluctuations in housing prices or do not include current housing prices in the conversion rate of jeonse price to monthly rent. This study found that the conversion rate of jeonse price to monthly rent is the required rate of return or required rate of renter, not market interest rate, by expressing the supply cost of rental housing as a combination of components. This not only explained the fact that the conversion rate of jeonse price to monthly rent was always higher than the market interest rate, but also explained the regional differences. It also explained why the conversion rate of jeonse price to monthly rent varies by type of housing.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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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.

• Journal of the Korean Home Economics Association
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• v.42 no.11
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1260-1264
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• 2009

Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.