• Journal of the Korean Statistical Society
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• v.2 no.1
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• pp.9-15
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• 1973

In the first-order moving average model, we present the exact likelihood equations as function of variance, correlation and parameters of coefficients in the orthogonally transformed model. Existence of maximum likelihood estimates for these unknowns are studied and a computational method is provided. (Because of the limited space Ive do not present the computer program which is written in FORTRAN.) 40 sets of generated data and economic data are used to demonstrate, and few of them are presented in the Appendix. A numerical comparison of MLE with the efficient estimate proposed by Durbin is presented in the particular case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.437-444
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• 2014

In this paper, we propose a path loss model with the multiple antennas and doppler shift for high speed railroad communication. Path loss model is very important in order to design consider diverse characteristic in high-speed train communication. Currently wireless communication systems use the multiple antennas in order to improve the channel capacity or diversity gain. However, until recently, many researches on path loss model only consider geographical environment between the transmitter and the receiver. There is no study about path loss model considering diversity effect and doppler shift. In order to make average residuals considering doppler shift we use tuned free space path loss model which is utilized for measurement results at high speed railroad. The environment of high speed rail is mostly at viaduct and flatland over than 50 percent. And in order to make average residuals considering multiple antenna we use theoretical estimation of diversity gain with MRC scheme. proposed model predict loss of received signal by estimating average residuals between diversity effect and doppler shift.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.239-248
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• 2022

Risks in construction projects are increasing remarkably due to recent changes in the construction environment. Active risk management is required to recognize risks as opportunities. The purpose of this study is to propose a risk management model of the importance determination method through comparative analysis using Kano model, Timko CSC (Customer Satisfaction Coefficient), and ASC (Average Satisfaction Coefficient). Based on previous studies, the validity of risk management factor determination is reviewed through a questionnaire modified Kano model through interviews with working-level workers using the Delphi technique. Through this, a suitable risk management model is presented by selecting key risk management factors recognized by domestic construction project practitioners. As a result of the study, the Kano model developed to verify risk management of construction projects was evaluated to be effective in verifying the risk management of practitioners. It is expected that the Kano model presented in this study will be actively used to verify the importance of risk management for construction projects.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1193-1201
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• 2020

This study aims to develop a regression model that estimates a low-flow index that can be applied to ungauged basins. A total of 30 midsized basins in South Korea use long-term runoff data provided by the National Integrated Water Management System (NIWMS) to calculate average low-flow, average minimum streamflow, and low-flow index duration and frequency. This information is used in the correlation analysis with 18 basin factors and 3 climate change factors to identify the basin area, average basin altitude, average basin slope, water system density, runoff curve number, annual evapotranspiration, and annual precipitation in the low-flow index regression model. This study evaluates the model's accuracy by using the root-mean-square error (RMSE) and the mean absolute error (MAE) for 10 ungauged, verified basins and compares them with the previous model's low-flow calculations to determine the effectiveness of the newly developed model. Comparative analysis indicates that the new regression model produces average low-flow, attributed to the consideration of varied basin and hydrologic factors during the new model's development.

• Journal of Environmental Science International
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• v.18 no.1
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

• The Journal of Fisheries Business Administration
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• v.42 no.2
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• pp.15-30
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• 2011

This study is designed to measure the relative efficiency of regional fishery cooperatives based on Data Envelopment Analysis(DEA) methods. Selecting 40 regional fishery cooperatives in Busan as Decision Making Units (DMUs), the study uses their panel data from 2007 to 2008 to rank the relative efficiency of the DMUs. First, the efficiency score of the DMUs are calculated using CCR, SBM, and super-SMB model. Within the model, input variables are the number of employees and area of fishery cooperatives. Output variables are the amount of deposit money, loan and profit. Based on the efficiency scores calculated from super-SMB model, the efficiency ranking of the DMUs is determined. Second, the differences in average efficiency calculated from the three DEA models are tested using a pair-wise mean comparison test. The results based on the efficiency scores evaluated from super-SMB model show that seven out of the forty DMUs are efficient; among the efficient DMUs, the DMUs that can be benchmarked for inefficient DMUs through the frequency analysis of reference set being identified. Third, the differences in average efficiency of the three DEA models between 2007 and 2008 are tested using pair-wise mean comparison test and the study estimates the efficiency change of the DMUs between 2007 and 2008 using Malmquist productivity index(MPI). Finally, the paper suggests an improved composite DMU superior to the inefficient DMUs evaluated by Super-SBM model.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• Journal of Korean Academy of Nursing
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• v.17 no.1
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• pp.24-32
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• 1987

This study was designed to investigate the changes of nurses' role model, perceptions toward occupation, and self actualization value in terms of the phases of socialization process. Two hundred and sixty nine nurses working in clinical settings were randomly selected from 15 general hospitals despersed over Seoul and Kyungki province. Data were gathered by the standardized Perceptual Orientation Test, the Self-actualization Test, and Questionnaires on role models and phases of socialization process developed by the investigators from October 1985 to March 1986. The data were analysed by ANOVA and Pearson's Correlation Coefficient. The results were as follows: 1. The average time period required for the shift of phases of socialization process were; phase Ⅰ, role adjustment, took average 10 months of employment: Phase Ⅱ, interpersonal adjustment, 12 months: and Phase Ⅲ, role conflict, 15 months respectively. Conflict resolution, phase Ⅳ, began to take place 18 months of employment; and shifted to phase V, internalization and self-actualization at 25 months of employment. 2. Throughout 5 consecutive phase, the number of immediate superior nurse model was dominantly the highest among the role models. The number of head nurse role model increased at phase Ⅱ, phase Ⅲ, and phase Ⅳ. Respondents with school model in phase I tended to transfer to work model at phase Ⅱ. 3. The perceptions toward occupation were not significantly influenced by the Phases of socialization process. 4. The score of self-actualization value was not significantly influenced by the phases of socialization process. 5. In regard to perceptions toward occupation, nursing director model group showed significantly lower score in phase I (p<.01). 6. The comparison of self-actualization value between the 5 phases revealed significant difference in phase I: in particular among respondents with school model at p<.05. To conclude: 1. The phase Ⅲ of socialization process is the period of role conflict which occur at 15 months of employment, an6 conflict resolution, phase Ⅳ, begins at 18 months of employment on the average in clinical settings. 2. The immediate superior nurse and the head nurse are important role models for nurses all through their socialization process.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1226-1234
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• 2015

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.537-550
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• 2000

In this study, Neural Networks models were used to forecast daily streamflow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily streamflow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily streamflow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Mode(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more sysmatic approach, reduce model parameters, and shorten the time spent in the model development.