• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.1
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• pp.53-60
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• 2018

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

• The Journal of Korean Academy of Prosthodontics
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• v.48 no.1
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• pp.48-54
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• 2010

Purpose: The objective of this study was to evaluate and compare the accuracy of impression body taking by the closed and the open tray impression technique with 3 types of impression tray. Individual tray, metal stock tray and polycarbonate tray were used. Materials and methods: Nine closed tray impressions were taken by individual tray, metal stock tray and polycarbonate stock tray, respectively with polyether impression material. 9 open tray impressions were also acquired by same manner. Precision analysis on the master models was performed by attaching the reference frameworks with alternate single screws and measuring the vertical fit discrepancy of respective analogues in working cast with a stereo microscope. Data were analyzed by 1 way ANOVA and independent t-test. Results: The average fit accuracy of impression bodies was calculated. With the closed tray impression technique, there were significant statistical differences in vertical fit discrepancy according to the types of tray. The individual tray group showed the lowest value and the polycarbonate stock tray group represented the highest. With the open tray impression technique, there was no significant difference in vertical fit discrepancy. Significant statistical difference in vertical fit discrepancy was found between the open and the closed impression technique with the polycarbonate stock tray. Conclusion: From the results above, more precise impressions could be acquired by the rigid individual tray compared with the polycarbonate stock tray. It was hard to get consistent accuracy impressions by the closed tray impression technique with polycarbonate stock trays.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Health Policy and Management
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• v.34 no.3
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• pp.261-270
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• 2024

Background: Various researchers are calculating the health adjusted life expectancy (HALE) at the regional level in South Korea using several methods, most studies merely enumerate the differences in healthy life expectancy based on social characteristics. This study aims to analyze the association between various sociodemographic factors and HALE at the regional level. Methods: To calculate HALE, we utilized the various data sources, including National Health Insurance claims data, and applied the Sullivan's method. We conducted multiple linear regression with regional socioeconomic variables from Korean Statistical Information Service. For the multiple linear regression analysis, we designed three regression models. Model 1 comprised solely socioeconomic variables, model 2 involved both socioeconomic variables and individual health behaviors, and model 3 integrated model 2 with healthcare utilization. Results: The analysis shows that an increase in financial independence (p<0.05), population density (p<0.1), and the number of doctors (p<0.05) associated with an increase in HALE, whereas an increase in the number of beds (p<0.01) was associated with a decrease in HALE. In case of the obesity rate, in model 2 (p<0.1) and model 3 (p<0.05), there was a negative association between HALE and obesity rate. Conclusion: Amidst various variables, it was observed that increased financial independence in specific regions had association with an increase in HALE, highlighting the need for stronger local governance in South Korea. Additionally, the inverse association between hospital beds and HALE suggests several implications, such as the appropriate deployment of healthcare resources. To gain a deeper understanding of the relationship between hospital beds and HALE, further analysis distinguishing different types of hospital beds across healthcare institutions seems necessary.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.269-282
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• 2017

In this study, the surface broadband emissivity ($3.0-14.0{\mu}m$) was calculated using the multiple linear regression model with narrow bands (channels 29, 30, and 31) emissivity data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System Terra satellite. The 307 types of spectral emissivity data (123 soil types, 32 vegetation types, 19 types of water bodies, 43 manmade materials, and 90 rock) with MODIS University of California Santa Barbara emissivity library and Advanced Spaceborne Thermal Emission & Reflection Radiometer spectral library were used as the spectral emissivity data for the derivation and verification of the multiple linear regression model. The derived determination coefficient ($R^2$) of multiple linear regression model had a high value of 0.95 (p<0.001) and the root mean square error between these model calculated and theoretical broadband emissivities was 0.0070. The surface broadband emissivity from our multiple linear regression model was comparable with that by Wang et al. (2005). The root mean square error between surface broadband emissivities calculated by models in this study and by Wang et al. (2005) during January was 0.0054 in Asia, Africa, and Oceania regions. The minimum and maximum differences of surface broadband emissivities between two model results were 0.0027 and 0.0067 respectively. The similar statistical results were also derived for August. The surface broadband emissivities by our multiple linear regression model could thus be acceptable. However, the various regression models according to different land covers need be applied for the more accurate calculation of the surface broadband emissivities.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.705-715
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• 2010

The adoption of carbon foot print system is being activated mostly in the developed countries as one of the long-term response towards tightened up regulations and standards on carbon emission in the agricultural sector. The Korean Ministry of Environment excluded the primary agricultural products from the carbon foot print system due to lack of LCI (life cycle inventory) database in agriculture. Therefore, the research on and establishment of LCI database in the agriculture for adoption of carbon foot print system is urgent. Development of LCA (life cycle assessment) methodology for application of LCA to agricultural environment in Korea is also very important. Application of LCA methodology to agricultural environment in Korea is an early stage. Therefore, this study was carried out to find out the effect of lettuce cultivation on agricultural environment by establishing LCA methodology. Data collection of agricultural input and output for establishing LCI was carried out by collecting statistical data and documents on income from agro and livestock products prepared by RDA. LCA methodology for agriculture was reviewed by investigating LCA methodology and LCA applications of foreign countries. Results based on 1 kg of lettuce production showed that inputs including N, P, organic fertilizers, compound fertilizers and crop protectants were the main sources of major emission factor during lettuce cropping process. The amount of inputs considering the amount of active ingredients was required to estimate the actual quantity of the inputs used. Major emissions due to agricultural activities were $N_2O$ (emission to air) and ${NO_3}^-$/${PO_4}^-$ (emission to water) from fertilizers, organic compounds from pesticides and air pollutants from fossil fuel combustion in using agricultural machines. The softwares for LCIA (life cycle impact assessment) and LCA used in Korea are 'PASS' and 'TOTAL' which have been developed by the Ministry of Knowledge Economy and the Ministry of Environment. However, the models used for the softwares are the ones developed in foreign countries. In the future, development of models and optimization of factors for characterization, normalization and weighting suitable to Korean agricultural environment need to be done for more precise LCA analysis in the agricultural area.

• The Journal of Korean Academy of Prosthodontics
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• v.49 no.3
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• pp.236-244
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• 2011

Purpose: This study was aimed to compare the margin and internal fitness of 3-unit zirconia bridge cores fabricated by several CAD/CAM systems using replica technique. Materials and methods: Three unit-bridge models in which upper canine and upper second premolar were used as abutments and upper first premolar was missed, were fabricated. Fourty models were classified into 4 groups (Cerasys$^{(R)}$ (Group C), Dentaim$^{(R)}$ (Group D), KaVo Everest$^{(R)}$ (Group K), $Lava^{TM}$ (Group L)), and zirconia cores were fabricated by each company. Sixteen points were measured on each abutment by replica technique. Statistical analysis was accomplished with two way ANOVA and Dunnett T3 (${\alpha}$=.05). Results: In most systems, there was a larger gap on inter margin than outer margin. In the Group K, overall fitness was excellent, but the incisal gap was very large. In the Group C, marginal gap was significantly larger than Group K, but overall internal gap was uniform (P<.05). The axial gap was under $100\;{\mu}m$ in all system. The difference between internal and external gap was small on Group L and C. However, internal gap was significantly larger than external gap in Group D (P<.05). The fitness of canine was better than second premolar among abutments (P<.05). Conclusion: The marginal and internal gap was within the clinically allowed range in all of the three systems. There was a larger gap on second premolar than canine on internal and marginal surface. In most systems, there was a larger gap on occlusal surface than axial surface.

• Clinical and Experimental Pediatrics
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• v.48 no.1
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• pp.75-80
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• 2005

Purpose : Internal tibial torsion is prevalent in East Asian countries such as Korea and Japan, where sitting on the floor is common behavior. Internal tibial torsion or excessive lateral tibial torsion may cause esthetical, functional, or psychological problems and also may induce degenerative arthritis in older age. The purpose of this study is to measure the tibial torsion in children of the Jeju area. Methods : Tibial torsion was measured in 1,042 lower extremities of 521 children from one to 12 years of age. The values of transmalleolar angles were analyzed for each age group divided by 6 months. Quadratic and linear regression models were used to fit patterns of changes in mean values of transmalleolar angles. The age at seven, which provides the highest coefficient of determination for quadratic regression analysis, was used as a cut-off point to fit different statistical models. Results : The mean transmalleolar angle was $0.10{\pm}5.79^{\circ}$ in all children,$ 0.90{\pm}5.49^{\circ}$ in males, and $-0.80{\pm}5.97^{\circ}$ in females. The value was $4.25{\pm}4.04$ in 1 year of age, gradually decreased to the lowest level of $-1.98^{\circ}$ in four years and seven months of age, increased again with age until it reached $0.67{\pm}1.10^{\circ}$ at seven years of age, and stayed at that level thereafter. Conclusion : Internal tibial torsion in infancy is known to correct spontaneously in the normal developing process. But in this study, the mean transmalleolar angle in children of Jeju area annually decreased after one year of age; to the lowest angle at four years and seven months of age; increased again gradually to the age of seven; and persisted in that level, about $10^{\circ}$ less than western children, not correcting further thereafter. These findings suggest tibial torsion might be caused by lifestyle, especially sitting on feet. To prevent abnormalities of joints and gaits, early diagnosis of tibial torsion in childhood and posture correction or early treatment when needed, seems to be necessary.