• Asian Journal for Public Opinion Research
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• v.5 no.2
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• pp.63-83
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• 2018

This exploratory research investigated the factors that influence foreign tourists' decision to use dental services in Thailand. The questionnaire was used to collect data from foreign tourists who used dental services in a hospital or dental clinic in Thailand. Data from 233 respondents completed questionnaires were analyzed using frequency distribution, percentages, mean, and standard deviation. The findings revealed that respondents were most likely to be Asian (54.5%) followed by European (33.9%), females (64.8%) aged between 26-35 years (39.1%), business owners (45.9%), and came to Thailand with friends (47.6%). Regarding travel purposes and contact with dental services, most of them travel for pleasure (52.89%) and contact the clinic directly through the Internet(63.95%) The health/medical services used included health check-up (34.48%), dental services (29.22%), and day spa/massage (23.23%). The dental treatment that respondents had completed or planned to do included dental check-up (29.87%), tooth cleaning (26.68%), and tooth whitening (11.41%). Based on the influence of $_7P$ factors towards the respondent's decision to use the dental service, it was found that the average mean score range from 4.33-4.15 which were at the high to the very high level of influence to their decisions. The people factor had the highest average mean score (mean 4.33, SD= 0.604)followed by the price factor (mean 4.32, SD= 0.651), which both had the highest level of influence. The physical evidence factor had the lowest mean score (mean 4.15, SD+0.752) which was at the high level of importance. It should be noted that the factor concerning the modern technology and dental equipment (mean 4.33, SD=0.606) and factor on the quality of service (mean 4.43, SD= 0.598) had the highest mean score followed by the total cost of the trip including services being reasonable (mean 4.37, SD=0.582). Regarding the country factor, it was found that this factor also had highest influence level (mean 4.30, SD=0.67) and affordability had the highest mean score (mean 4.39, SD=0.614).

• The Korean Journal of Pesticide Science
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• v.13 no.2
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• pp.70-78
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• 2009

Processing factors of pesticides in milling process of wheat grain, which are consumed and imported at large quantity were examined to establish reasonable MRL of the processed food. Azinphos-methyl, chlorpyrifos, chlorpyrifos-methyl, fenitrothion, malathion, and trichlorfon were selected for the study according to annual usage and the previous detection record in wheat grain. Dipping process for pesticide application was performed in laboratory, while milling process was conducted under pilot plant system. Processing factors were calculated by analyzing residual pesticides on wheat grain and processing products as wheat flour, bran and red dog. Processing factors were 0.05 for azinphos-methyl, 0.06 for chlorpyrifos, 0.05 for chlorpyrifos-methyl, 0.07 for fenitrothion, 0.07 for malathion, 0.06 for trichlorfon, respectively. Recovery test was also performed to establish extraction efficiency of analytical procedure. The recovery value ranged from 93.2% to 98.6% with standard deviation of 0.1-0.9%.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of Korea Water Resources Association
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• v.30 no.2
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• pp.177-191
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• 1997

The influences of the space allocation of design rainfall and partition of the subbasin on the characteristics of urban storm runoff was investigated for the 6 drainage basins by applying SWMM model. It show the deviation of -54.68∼18.77% in the peak discharge when we applied the composed JUFF quantiles to the two zones which are divided by upper and lower region of the basin. Then it is compared with the value for the case of using uniform rainfall distribution all over the drainage. Therefore, it would be helpful to decrease the flood risk when we adopt the space distribution of the design rainfall. The effects of the partitioning the drainage on the computing result shows various responses because of the surface characteristics of the each basin such as slope, imperviousness ratio, buy we can get closer result to the measured value as we make the subbasin detailed. If we use the concept of the skewness and area ratio when we determine the width of subbasin, we can improve the computed result even with fewer number of subbasins. We expect reasonable results which close into the measured results in the range of relative error, 25%, when we divide the basin into more than 3 subbasins and the total urban drainage area is less than 10$\textrm{km}^2$.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.144-158
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• 2012

A higher spatial resolution is preferable to support the accuracy of detailed climate analysis in urban areas. Airborne LiDAR (Light Detection And Ranging) and satellite (KOMPSAT-2, Korea Multi-Purpose Satellite-2) images at 1 to 4 m resolution were utilized to produce digital elevation and building surface models as well as land cover maps at very high(5m) resolution. The Climate Analysis Seoul(CAS) was used to calculate the fractional coverage of land cover classes in built-up areas and thermal capacity of the buildings from their areal volumes. It then produced analyzed maps of local-scale temperature based on the old and new input data. For the verification of the accuracy improvement by the precise input data, the analyzed maps were compared to the surface temperature derived from the ASTER satellite image and to the ground observation at our detailed study region. After the enhancement, the ASTER temperature was highly correlated with the analyzed temperature at building (BS) areas (R=0.76) whereas there observed no correlation with the old input data. The difference of the air temperature deviation was reduced from 1.27 to 0.70K by the enhancement. The enhanced precision of the input data yielded reasonable and more accurate local-scale temperature analysis based on realistic surface models in built-up areas. The improved analysis tools can help urban planners evaluating their design scenarios to be prepared for the urban climate.

• Management & Information Systems Review
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• v.35 no.2
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• pp.273-284
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• 2016

The objective of this study is to analyze the market efficiency in the futures markets. Although many previous studies have investigated market efficiency between spot and futures prices, that with different maturities has not been studied in the futures markets extensively. For our objective, this paper examines KOSPI200 stock index future market with different maturities. We analyze the dynamic serial relationship of the difference of basis between nearest-month contract and next nearest-month contract using dynamic regression analysis suggested by Kawamoto and Hamori(2011) Using the data from 2000. 1 to 2013. 12, the major empirical findings are as follows: First. the mean and standard deviation of basis of next nearest-month contract is bigger than those of nearest-month contract. Second, the t-period basis of nearest-month contract can be explained by (t-1)period basis of that. Third, the basis spread of t-period and (t-1)period have negative affect on the return of underlying assets. This result is very reasonable because two basis spreads are derived from same underlying assets. Finally, basis information of next nearest-month contract can be used for the prediction of nearest-month contract and spot market return.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.774-778
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• 2010

Upland erosion pollutes surface waters and often causes serious problems when deposition occurs. This study builds a sediment rating curve using the measured sediment yield and the simulated soil erosion by a GIS-embedded empirical model. The coefficient of determination ($R^2$) between the simulated soil erosion and the measurement sediment yields with rainfall amount are 0.427 for Donghyang and 0.667 for Cheonchen, but the values with rainfall intensity are 0.873 and 0.927 respectively. The data are divided into two groups: one for calibration during 2002-2005 (48 months) and the other for estimation during 2006-2008 (36 months). The first data group (2002-2005) was used to derive the SDR with an aid of soil erosion calculated by the USLE and the measured sediment yield. The mean SDR with rainfall amount is 6.273 and 3.353, respectively, while 4.799 and 2.874 for rainfall intensity. But the standard deviation (STD) with rainfall intensity is 0.930 and 0.407, which is much less than that with rainfall amount (3.746 and 2.090) for both sites. The results show the derived SDR provides reasonable accuracy and rainfall intensity gives better performance in calculating soil erosion than rainfall amount.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.328-335
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• 2013

The purpose of this study was to evaluate the height of workbench in dental lab for ergonomic body type which dental technicians are suitable and suggest to effective improvement of workspace. For this purpose, we measured the height, weight, body sizes of total 77 junior majoring in dental laboratory science where they study school located in Gyeonggi and Chungcheong area and we research uncomfortable body type when they had a clinical training. For comparative analysis we had visited 15 dental lab and 2 dental lab of general hospital and we had measured the height of workbench and desk including chair and then We had statistical analysis measured value. Especially we analyzed the important factor causing inconvenience for work environment. As a result, we had examined, we found following results The sand table was the most uncomfortable equipment, when they have clinical training. There is no correlation between sexual characteristics of junior in college and selection of uncomfortable equipment. It is reasonable to assume that sand table which is lower than recommended height brings out inconvenience. It is analyzed that the optimum height of sand table in dental lab for junior in college is lower than the recommended height. The optimum height of sand table in dental lab has deviation according to height of junior in college. The height of worktable in dental lab was lower than recommended height. The recommended range could not apply to posture of dental technician with different height uniformly.

• Korean Journal of Construction Engineering and Management
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• v.19 no.5
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• pp.3-9
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• 2018

Although Life Cycle Cost (LCC) must be evaluated by experts, sometimes it may not allow a sufficient time for even the experienced LCC expert to make rational decisions. Therefore, it often ends with relatively comparing the final numbers. We have broken down 110 technical proposals that are actually bade and accepted for large construction projects, and then have analyzed the uncertainty of Maintenance and Energy (M&E) cost during building life cycle, which turns out be the most volatile factor in uncertainty of LCC. Also we suggest "Value Engineering Index (VEI)" - the reduced M&E cost that is normalized by the reduced first cost. It is analyzed that the most uncertain factors of the M&E cost include repair and replacement term differing from each project, duplicated repair and replacement, non-standard repair items, and site-specific energy cost. Eventually we propose a VEI population with a mean of 1.38 and a standard deviation of 1.19, which is obtained by individually and exclusively applying the uncertain factors of the M&E cost to the 35 standard sample of technical proposals. The LCC evaluators may be able to use the VEI population as the benchmark to select the technical proposal with the most reasonable LCC among many others in two suggested manners; the one is to deterministically calculate the probability of single VEIs, and the other is to stochastically calculate the probability of the VEIs where uncertainty is quantified.