• Atmosphere
• /
• v.28 no.2
• /
• pp.211-222
• /
• 2018

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

• Journal of Korean Medicine for Obesity Research
• /
• v.14 no.1
• /
• pp.29-35
• /
• 2014

Objectives: This study was to investigate how body-shape perception could influence to weight control practice both in normal and obese group. Methods: We used 2012 Korea National Health and Nutrition Examination Survey to analysis 1) weight control practices of population; 2) consistency between body-shape perception and body mass index; 3) comparison weight control practices between normal group and body mass index (BMI) obese group in perceptional obese group; 4) odds ratio of BMI obese group using herbal drugs for weight control practice in perceptional obese group. Results: We found that study population tends to choose exercise, dietary restriction, meal skip, health functional food, one-food, drug, herbal drug, fasting and self-medication in order of frequency to control weight. The agreement between body-shape perception and BMI within obese group was approximately 64% with 0.40 of Cohen's Kappa coefficient, ranging from 0.384 to 0.423. Within perceptional obese group, choosing each weight control practice methods ratios between normal BMI group and obese BMI group were not significantly different. Within perceptional obese group, obese BMI group showed significant odds ratio (2.58, 95% confidence intervals, 1.38~4.85) than normal BMI group in choosing herbal medication for weight loss when adjusting other variables. Conclusions: We concluded that body-shape perception might be an important factor for choosing weight control program, and roles of Korean medical doctors thought to be enhanced for using herbal medication for weight loss.

• Imaging Science in Dentistry
• /
• v.48 no.2
• /
• pp.87-96
• /
• 2018

Purpose: The present study aimed to evaluate which of the following imaging methods best assessed misfit at the tooth-restoration interface: (1) bitewing radiographs, both conventional and digital, performed using a photostimulable phosphor plate (PSP) and a charge-coupled device (CCD) system; (2) panoramic radiographs, both conventional and digital; and (3) cone-beam computed tomography (CBCT). Materials and Methods: Forty healthy human molars with class I cavities were selected and divided into 4 groups according to the restoration that was applied: composite resin, composite resin with liner material to simulate misfit, dental amalgam, and dental amalgam with liner material to simulate misfit. Radiography and tomography were performed using the various imaging methods, and the resulting images were analyzed by 2 calibrated radiologists. The true presence or absence of misfit corresponding to an area of radiolucency in regions subjacent to the esthetic and metal restorations was validated with microscopy. The data were analyzed using a receiver operating characteristic (ROC) curve, and the scores were compared using the Cohen kappa coefficient. Results: For bitewing images, the digital systems (CCD and PSP) showed a higher area under the ROC curve (AUROC) for the evaluation of resin restorations, while the conventional images exhibited a larger AUROC for the evaluation of amalgam restorations. Conventional and digital panoramic radiographs did not yield good results for the evaluation of resin and amalgam restorations (P<.05). CBCT images exhibited good results for resin restorations(P>.05), but showed no discriminatory ability for amalgam restorations(P<.05). Conclusion: Bitewing radiographs (conventional or digital) should be the method of choice when assessing dental restoration misfit.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.8
• /
• pp.1055-1060
• /
• 2018

In the wake of the 4th Industrial Revolution, the problem of career education in schools has become a big issue. While various studies are being conducted on services or technologies to effectively handle artificial intelligence and big data, in the field of education, data on students is simply processed. Therefore, in this paper, we are going to design and present career prediction programs for students using artificial intelligence and big data. Using observational data from students at the institute, the decision tree is constructed with the C4.5 algorithm known to be most intelligent and effective in the decision tree and is used to predict students' path of hope. As a result, the coefficient of kappa exceeded 0.7 and showed a fairly low average error of 0.1 degrees. As shown in this study, a number of studies and data will be deployed to help guide students in their consultation and to provide them with classroom attitudes and directions.

• Journal of Korean Clinical Nursing Research
• /
• v.14 no.1
• /
• pp.161-172
• /
• 2008

Purpose: This study was to classify patient severity score for hemodialysis patients. Method: The subject of this study was 1,575 patients. To study the severity of the patients, we used t-test and ANOVA. The congruity was measured by Kappa coefficient and the severity in each medical facility was analyzed by ANOVA. Result: The results showed that there was a significant difference according to the levels of medical center (F=171.187, p<.0001). Categorizing the severity of the patients in each medical facility, group II and III of the secondary medical institution had higher ratio than the primary medical institution. There was not a single patient coming under group IV in both of the primary or secondary medical institutions. However, the tertiary medical institutions had more subjects in group II and III than the primary and secondary medical institutions. The group IV with the highest severity had 11 patients(1.5%), demonstrating that the tertiary medical institution had higher severity patients than the primary or secondary medical institutions. Conclusion: The results of this study appropriately reflects the repayment system of medical expenses by the government. Also, it provides the fundamental information to develop nursing fee system taken into account of the systemic differences among the primary, secondary and tertiary medical institutions.

• Korean Journal of Community Nutrition
• /
• v.19 no.3
• /
• pp.258-273
• /
• 2014

Objectives: The purposes of the study were to develop a questionnaire for dietary habit survey for Korean adults, and unify the terms related to dietary habits. Methods: The Delphi method by 43 professionals on dietary habit research was applied to unify the terms. Results: With regard to results on terms related to dietary habit, the respondents recorded the highest percentage (90.7%) of selecting the term "dietary habits" and also the highest percentage (76.7%) of choosing "dietary habits" for English. The biggest percentage of the respondents chose "individual dietary behaviors repeatedly formed and habitualized under the social, cultural, and psychological influence in the group" as the concept of dietary habits. The Delphi survey for the development of a questionnaire resulted in the first questionnaire of 31 items, the second one of 27 items, and the third one of 25 items. The validity of questionnaire items was tested with content validity ratios (CVR). The items whose CVR value was 0.29 or lower were eliminated or revised, because the minimum CVR value needed to test validity was 0.29. To test the reliability of questionnaire items, test-retest method was performed in 163 adults. According to the Kappa coefficient in the range of 0.314-0.716, all of the 25 items were in the reliability scope. A survey was taken with 702 adults to finally revise and supplement the third questionnaire whose validity and reliability were tested. Conclusions: Through those processes, a questionnaire for adults' dietary habit survey was finally completed. The significance of the study lies in the development of the first questionnaire on dietary habits equipped with both validity and reliability in South Korea.

• Korean Journal of Agricultural Science
• /
• v.47 no.3
• /
• pp.633-644
• /
• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Geophysics and Geophysical Exploration
• /
• v.15 no.3
• /
• pp.121-128
• /
• 2012

Absorbing boundary conditions are used to mitigate undesired reflections that can arise at the model's truncation boundaries. We apply a complex frequency shifted perfectly matched layer (CFS-PML) to elastic wave modeling in the frequency domain. Modeling results show that the performance of our implementation is superior to other absorbing boundaries. We consider the coefficients of CFS-PML to be optimal when the kinetic energy becomes to the minimum, and propose the modified CFS-PML that has the CFS-PML coefficient ${\alpha}_{max}$ defined as a function of frequency. Results with CFS-PML and modified CFS-PML are significantly improved compared with those of the classical PML technique suffering from large spurious reflections at grazing incidence.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.535-544
• /
• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.1109-1122
• /
• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.