• Journal of Ecology and Environment
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• v.38 no.4
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• pp.485-491
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• 2015

This study was conducted to develop stem volume models for the volume estimation of Quercus glauca Thunb. in Jeju Island, Republic of Korea. Furthermore, this study validated the developed stem volume models using an independent dataset. A total of 167 trees were measured for their diameter at breast height (DBH), total height and stem volume using non-destructive sampling methods. Eighty percent of the dataset was used for the initial model development while the remaining 20% was used for model validation. The performance of the different models was evaluated using the following fit statistics: standard error of estimate (SEE), mean bias absolute mean deviation (AMD), coefficient of determination (R²), and root mean square error (RMSE). The AMD of the five models from the different DBH classes were determined using the validation dataset. Model 5 (V = aD^bH^c), which estimates volume using DBH and total height as predicting variables, had the best SEE (0.02745), AMD (0.01538), R² (0.97603) and RMSE (0.02746). Overall, volume models with two independent variables (DBH and total height) performed better than those with only one (DBH) based on the model evaluation and validation. The models developed in this study can provide forest managers with accurate estimations for the stem volumes of Quercus glauca in the subtropical forests of Jeju Island, Korea.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.81-92
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• 2020

Intelligent systems(i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5665-5669
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• 2012

Background: The vascular endothelial growth factor (VEGF) mediates vasculogenesis and angiogenesis through promoting endothelial cell growth, migration and mitosis, and has involvement in cancer pathogenesis, progression and metastasis. However, the prognostic value of VEGF in patients with prostate cancer remains controversial. Objectives: The aim of our study was to evaluate the prognostic value of VEGF in prostate cancer, and summarise the results of related research on VEGF. Methods: In accordance with an established search strategy, 11 studies with 1,529 patients were included in our meta-analysis. The correlation of VEGF-expression with overall survival and progression-free survival was evaluated by hazard ratio, either given or calculated. Results: The studies were categorized by introduction of the author, demographic data in each study, prostate cancer-relatived information, VEGF cut-off value, VEGF subtype, methods of hazard ratio (HR) estimation and its 95% confidence interval (CI). High VEGF-expression in prostate cancer is a poor prognostic factor with statistical significance for OS (HR=2.32, 95%CI: 1.40-3.24). However, high VEGF-expression showed no effect on poor PFS (HR=1.30, 95%CI: 0.88-1.72). Using Begg's, Egger's test and funnel plots, we confirmed lack of publication bias in our analysis. Conclusion: VEGF might be regarded as a prognostic maker for prostate cancer, as supported by our meta-analysis. To achieve a more definitive conclusion enabling the clinical use of VEGF in prostate cancer, we need more high-quality interventional original studies following agreed research approaches or standards.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1981-1986
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• 2015

Background: Knowledge of cancer incidences is essential for cancer prevention and control programs. Capture-recapture methods have been recommended for reducing bias and increasing the accuracy of cancer incidence estimations. This study aimed to estimate the completeness of gastric cancer registration by the capture-recapture method based on Ardabil population-based cancer registry data. Materials and Methods: All new cases of gastric cancer reported by three sources, pathology reports, death certificates and medical records that reported to Ardabil population-based cancer registry in 2006 and 2008 were enrolled in the study. The duplicate cases based on the similarity of first name, surname and fathers names were identified between sources. The estimated number of gastric cancers was calculated by the log-linear method using Stata 12 software. Results: A total of 857 new cases of gastric cancer were reported from three sources. After removing duplicates, the reported incidence rates for the years 2006 and 2008 were 35.3 and 32.5 per 100,000 population, respectively. The estimated completeness calculated by log-linear method for these years was 36.7 and 36.0, respectively. Conclusions: These results indicate that none of the sources of pathology reports, death certificates and medical records individually or collectively fully cover the incident cases of gastric cancer. We can obtain more accurate estimates of incidence rates using the capture-recapture method.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.275-291
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• 2013

Monitoring the global Gross Primary Pproduction (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance ($R^2=0.8164$, $RMSE=0.6126g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$, $bias=-0.0271g{\cdot}C{\cdot}m^{-2}{\cdot}d^{-1}$). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1566-1576
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• 2007

Camera pose information from 2D face image is very important for making virtual 3D face model synchronize with the real face. It is also very important for any other uses such as: human computer interface, 3D object estimation, automatic camera control etc. In this paper, we have presented a camera position determination algorithm from a single 2D face image using the relationship between mouth position information and face region boundary information. Our algorithm first corrects the color bias by a lighting compensation algorithm, then we nonlinearly transformed the image into $YC_bC_r$ color space and use the visible chrominance feature of face in this color space to detect human face region. And then for face candidate, use the nearly reversed relationship information between $C_b\;and\;C_r$ cluster of face feature to detect mouth position. And then we use the geometrical relationship between mouth position information and face region boundary information to determine rotation angles in both x-axis and y-axis of camera position and use the relationship between face region size information and Camera-Face distance information to determine the camera-face distance. Experimental results demonstrate the validity of our algorithm and the correct determination rate is accredited for applying it into practice.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.41-61
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• 2004

The purpose of this paper is to construct $\ulcorner$CIDEAR(Cross Impact-DEA-AR)$\lrcorner$ model which evaluates proposed R&D projects considering cross impact among them and selects proper projects to utilize resources efficiently as well as to maximize efficacy of investments. For this purpose, $\ulcorner$CIDEAR$\lrcorner$ model is designed as the following six steps. $\ulcorner$Decision Theory Evaluation Model$\lrcorner$ is for setting and selecting the evaluation items according to the structured procedure of evaluation system. The priority of items is decided at $\ulcorner$AR Decision Model$\lrcorner$$\ulcorner$Cross Impact Estimation Model$\lrcorner$ is for computing the final probability of success and the result is used to revise the evaluation results of $\ulcorner$Decision Theory Evaluation Model$\lrcorner$. $\ulcorner$Resource Performance Analysis Model$\lrcorner$ classifies the proposed R&D projects on the basis of required resources and expected performance. Consequently, the possibility of bias of project selection can be prevented. $\ulcorner$Priority Oder Decision Model$\lrcorner$ is for computing the efficacy of proposed projects. Finally, $\ulcorner$Efficacy-Efficiency Cause Analysis Model$\lrcorner$ analyzes the structure of efficacy and efficiency of the projects. The major findings and significances of this study are summarized as follows: (1) $\ulcorner$CIDEAR$\lrcorner$ model can deal with the affairs of R&D projects having the characteristics of mutual independence as well as mutual dependence in the point of efficacy and efficiency. Hence, it is possible to evaluate and select R&D projects more accurately. (2) It can be possible to raise the possibility of projects success. R&D manager can use the information for project management because the efficacy-efficiency structure of selected projects can be analyzed. (3) We proved the usefulness of the constructed $\ulcorner$CIDEAR$\lrcorner$ model using an case about twenty-one R&D projects of a leading company of electronic industry in Korea.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.601-610
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• 2013

As a standard water clarity variable, the vertical underwater visibility, called Secchi depth, is estimated with ocean color satellite data. In the present study, Moderate Resolvtion Imaging Spectradiometer (MODIS) data are used to measure the Secchi depth which is a useful indicator of ocean transparency for estimating the water quality and productivity. To estimate the Secchi depth $Z_v$, the empirical regression model is developed based on the satellite optical data and in-situ data. In the previous study, a semi-analytical algorithm for estimating $Z_v$ was developed and validated for Case 1 and 2 waters in both coastal and oceanic waters using extensive sets of satellite and in-situ data. The algorithm uses the vertical diffuse attenuation coefficient, $K_d$($m^{-1}$) and the beam attenuation coefficient, c($m^{-1}$) obtained from satellite ocean color data to estimate $Z_v$. In this study, the semi-analytical algorithm is validated using temporal MODIS data and in-situ data over the Yellow, Southern and East Seas including Case 1 and 2 waters. Using total 156 matching data, MODIS $Z_v$ data showed about 3.6m RMSE value and 1.7m bias value. The $Z_v$ values of the East Sea and Southern Sea showed higher RMSE than the Yellow Sea. Although the semi-analytical algorithm used the fixed coupling constant (= 6.0) transformed from Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) to Secchi depth, various coupling constants are needed for different sea types and water depth for the optimum estimation of $Z_v$.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.83-92
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• 2002

Freight mode choice models are essential to the analysis of many areas of transport research. However, observations of actual market choices have only been made in a limited number of situations. Therefore, stated preference(SP) techniques have emerged as an alternative source of actual market choices to be used for estimating freight mode choice models. Considerable confidence exists about SP data, but little consideration has been given to the potential for estimation bias. This paper has been motivated by the theoretical side of estimating SP discrete choice models, focusing on a case study of freight mode choice. Recently developed simulation methods are used to construct inherent random heterogeneity legit models, which consider individual heterogeneity, its inheritance to the next choices and overcome the independence from irrelevant alternatives (IIA) property. This Paper contributes to the development of models dealing with heterogeneity and its inheritance, and sheds light on the heterogeneity of freight transport.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.97-109
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• 2003

This study is mainly conducted to derive the design drought rainfall by the consecutive duration using probability weighted moments with rainfall in the regional drought frequency analysis. It is anticipated to suggest optimal design drought rainfall of hydraulic structures for the water requirement and drought frequency of occurrence for the safety of water utilization through this study. Preferentially, this study was conducted to derive the optimal regionalization of the precipitation data that can be classified by the climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. Five homogeneous regions in view of topographical and climatological aspects were accomplished by K-means clustering method. Using the L-moment ratio diagram and Kolmogorov-Smirnov test, generalized extreme value distribution was confirmed as the best fitting one among applied distributions. At-site and regional parameters of the generalized extreme value distribution were estimated by the method of L-moments. Design drought rainfalls using L-moments following the consecutive duration were derived by the at-site and regional analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design drought rainfall derived by at-site and regional analysis in the observed an simulated data were computed and compared. In has shown that the regional frequency analysis procedure can substantially more reduce the RRMSE. RBIAS and RR in RRMSE than those of at-site analysis in the prediction of design drought rainfall. Consequently, optimal design drought rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.