• Journal of the Korean Society of Radiology
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• v.10 no.6
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• pp.443-450
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• 2016

In this study, we are using a computer tomography image of the abdomen, as an experimental linear research for the image of the fatty liver patients texture features analysis and computer-aided diagnosis system of implementation using the ROC curve analysis, from the computer tomography image. We tried to provide an objective and reliable diagnostic information of fatty liver to the doctor. Experiments are usually a fatty liver, via the wavelet transform of the abdominal computed tomography images are configured with the experimental image section, shows the results of statistical analysis on six parameters indicating a feature value of the texture. As a result, the entropy, average luminance, strain rate is shown a relatively high recognition rate of 90% or more, the control also, flatness, uniformity showed relatively low recognition rate of about 70%. ROC curve analysis of six parameters are all shown to 0.900 (p = 0.0001) or more, showed meaningful results in the recognition of the disease. Also, to determine the cut-off value for the prediction of disease six parameters. These results are applicable from future abdominal computed tomography images as a preliminary diagnostic article of diseases automatic detection and eventual diagnosis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.295-303
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• 2016

The purposes of this study are to describe the process for the instrument construction and the development of scales on rating college students' adaptability and to analyze the technical qualities of the test. The primary goal of this study is to inform students and institutions what is needed to college student's adjustment process into university and college life. The scales are tested by specialty group and statistical methods, and finally composed of 142 items, which measures 8 scales, the academic integration, the social integration into college, career identity, emotional stability, learning condition's stability, relationship with professors, satisfaction degree of educational service, satisfaction degree of college education. This study analyzed 1,959 students' responses from 4 colleges and universities. This study confirms that the scales which this study developed show high concurrent evidence with the college student's adaptability inventory for Korean university and college students based on various development process, specially rapid great change of college. The result of factor analysis shows the evidence based on internal structures of the scales. The Cronbach's ${\alpha}$ of the subscales is .965, from 742 to .937. The prediction model to determine the possibility of dropout by 7 scales is statistically significant in .05, except learning condition's stability. According to CFA Model, RMSEA= .08~.09. dependence factor variance are explained by this study's CFA model. In conclusion, this study confirms that the scales which this study developed are valid and reliable instrument for Korean university and college students to predict their adaptability to college.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• The Korean Journal of Applied Statistics
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• v.28 no.4
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• pp.685-701
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• 2015

The low fertility rate and the unprecedented rapid pace of population aging is a significant factor degrading the national competitiveness and the social security system of Korea. The government has implemented various maternity incentives to alleviate the low birth problem; however, the policy seems in effective to solve the problem of low fertility. This study proposes a conditional birth-order specific fertility rate and investigates the policy effects of fertility transition in Korea to provide a basis for more effective policy development. The use of a conditional birth-order specific fertility rate allows for an effective calculation of the change and the effect in total fertility rate than a birth-order specific fertility rate. We compare the effects of the total fertility rate according to various scenarios that enables us to calculate how the total fertility rate can achieve the current multi-child childbirth support policy of the government and estimate how the total fertility rate can be achieved when focusing on the first or second childbirth support policy. We also summarize the research results on policy development for a practical increase in the childbirth that considers the rapid decrease in women of childbearing age (15-49 years) due to continued low fertility and present the number of childbirths in accordance with the total fertility rate.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.81-94
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• 2010

Input variable selection is one of the various techniques for improving the performance of artificial neural network. In this study, mutual information is applied for input variable selection technique instead of correlation coefficient that is widely used. Among 152 variables of RDAPS (Regional Data Assimilation and Prediction System) output results, input variables for artificial neural network are chosen by computing mutual information between rainfall records and RDAPS' variables. At first the rainfall forecast variable of RDAPS result, namely APCP, is included as input variable and the other input variables are selected according to the rank of mutual information and correlation coefficient. The input variables using mutual information are usually those variables about wind velocity such as D300, U925, etc. Several statistical error estimates show that the result from mutual information is generally more accurate than those from the previous research and correlation coefficient. In addition, the artificial neural network using input variables computed by mutual information can effectively reduce the relative errors corresponding to the high rainfall events.

• Applied Biological Chemistry
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• v.50 no.3
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• pp.192-197
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• 2007

3D-QSARs on the fungicidal activity of N-phenylbenzenesulfonamide and N-phenyl-2-thienylsulfonamide analogues (1-37) against Phytophthora blight (Phytophthora capsici) were studied quantitatively using CoMFA and CoMSIA methods. The statistical results of the optimized CoMFA (2) model ($r^2_{cv.}(q^2)$ = 0.692 & $r^2_{ncv.}$= 0.965) show better predictability and fitness than CoMSIA (2) model ($r^2_{cv.}(q^2)$ = 0.796 & $r^2_{ncv.}$= 0.958). The fungicidal activities according to the information of the optimized CoMFA (2) model were dependent upon the steric and electrostatic fields of the molecules. Therefore, from the contribution contour maps of CoMFA (2) model, it is expected that 63% contribution was caused by the steric bulk of meta-substituent ($R_1$) on the S-phenyl ring. Also, the other contribution level of 32.9% was represented by the positive charged $R_4-group$ ($R_1$) on the N-phenyl ring and para-substituent ($R_1$) on the S-phenyl ring. A series of higher active compounds, $R_1$= 3-decyl substituent ($pred.pI_50$= 5.88) etc. were predicted based on the findings.

• The korean journal of orthodontics
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• v.27 no.6 s.65
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• pp.943-954
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• 1997

Forces needed for orthodontic tooth movement are obtained from various appliances such as orthodontic wires or elastic rubber. Orthodontic elastic rubber is widely used clinically, but permanent deformation and force decay may occur from the environmental changes, time of clinical use and the extent of the stretch, making the Prediction of force being applied difficult. The Present study examined and compared the changes in residual force between three brands of elastomeric chains (Ormco Generation II Power Chains ; brand A, RMO : Energy-Chain ; brand B, Unitek : AlastiK ; brand C) under various environmental conditions, amount of initial force, types of elastomer and the rate of extension. The characteristic physical properies of the elastomeric chains were as follows. 1. In all three brands, the residual force ratio was largest when the chains were stored in air, with no difference between water and saliva. 2. In all three brands, after 24 hours, there was no statistical difference in residual force ratio according to the initial force level. 3. In Brand A and B, the presence of filament had no correlation with the residual force ratio. In Brand C force decay was more severe when the chain contained filament. 4. In each brand, rate of extension had no effect on residual force ratio. 5. Brand B showed relatively higher residual force ratio compared to other brands.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.243-255
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• 2003

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and ${\gamma}$ operator shows the better prediction power than the results using max and min operators from the validation procedure.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.461-476
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• 2013

Carnegie-Ames-Stanford Approach (CASA) model is one of the most quick, convenient and accurate models to estimate the NPP (Net Primary Productivity) of vegetation. The purposes of this study are (1) to examine the spatial and temporal patterns of vegetation NPP of the paddy field area in Korea from 2002 to 2012, and (2) to investigate how the rice productivity responded to inter-annual NPP variability, and (3) to estimate rice yield in Korea using CASA model applied to MOderate Resolution Imaging Spectroradiometer (MODIS) products and solar radiation. MODIS products; MYD09 for NIR and SWIR bands, MYD11 for LST, MYD15 for FPAR, respectively from a NASA web site were used. Finally, (4) its applicability is to be reviewed. For those purposes, correlation coefficients (linear regression for monthly NPP and accumulated NPP with rice yield) were examined to evaluate the spatial and temporal patterns of the relations. As a result, the total accumulated NPP and Sep. NPP tend to have high correlation with rice yield. The rice yield in 2012 was estimated to be 526.93kg/10a by accumulated NPP and 520.32 kg/10a by Sep. NPP. RMSE were 9.46kg/10a and 12.93kg/10a, respectively, compared with the yield forecast of the National Statistical Office. This leads to the conclusion that NPP changes in the paddy field were well reflected rice yield in this study.