• Journal of the korean academy of Pediatric Dentistry
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• v.27 no.1
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• pp.62-69
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• 2000

Part of the locally applied, as well as the systemic applied, fluoride is absorbed into the body to aid in the prevention of caries. However, beyond a certain level, systemic distribution of fluoride can cause chronic fluorosis with attending systemic symptoms and dental fluorosis. Thus it is vital to determine the level of fluoride with minimal side effects which will provide optimal caries prevention. A commonly utilized method of regressively determining fluoride intade is to measure the fluoride concentration of excreted urine. Thus, the aim of this study was to determine the clearance time and concentration of fluoride in urine after administration of various doses of fluoride using HMDS-diffusion technique and fluoride ion electrode(Orion, 96-09, U.S.A.). Urine samples were collected in 7 adult subjects every morning after administration of fluoride supplements such as no fluoride(control group), 1mg fluoride(group 1), 2mg fluoride(group 2), 3mg fluoride(group 3), 4mg fluoride(group 4). The obtained results were as follows 1. Mean urinary fluoride concentration of control group was $0.707{\pm}0.362ppm$. 2. Fluoride levels followed as group 4(4.076ppm). group 3(2.400ppm), group 2(1.494ppm), group 1(1.051ppm) at day 1 after fluoride administration. There were no statistical differences between the urinary fluoride concentration of group 1, 2, 3 and control group after day 2, but there was statistical difference between group 4 and control group at day 2(p<0.05). 3. Urinary fluoride concentration increased and plateaued according to increasing fluoride dosage. The increased concentration remained significantly higher till day 2, but after day 3, there was no significant difference compared to the control.

• Research in Community and Public Health Nursing
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• v.11 no.1
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• pp.127-145
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• 2000

The objectives of this study are to identify health behaviors and health problems. and the relations between health behaviors and health problems of female workers in the retail business. The number of subjects were 200 female workers of 6 department stores in Seoul and Kyonggi area, whose jobs last more than 6 months as retail employees. The data was collected during 2 months from July 1 to August 30, 1998. The Cornell Medical Index Health Questionnaire(CMI) was used to measure their health problems, while health behaViorn were investigated in terms of 'smoking', 'alcohol', exercise', 'diet', and 'sleeping'. The data were analyzed with frequency. percentage, t-test, ANOVA test, and $X^2-test$ by SPSS PC+ program. The results of this study are as follows: 1. Out of health problems. Digestive symptoms occupied the highest percent number. nervous ones the second and cardiovascular ones the third among physical health problems of retail female workers. The most frequent mental health problem was 'adequacy' and the next, 'tension' and 'anger', 2, Health problems according to general characteristics of subjects were shown that the younger or the unmarried complained more than the older or the married, especially in the items of 'eye and ear', 'respiratory system', 'cardiovascular system', 'digestive tract', 'nervous system', 'adequacy', and 'depression'. The longer working duration they have had, the more they complained of 'respiratory system' and 'adequacy'. The lower academic careers complained of 'nervous' than the higher ones with statistical significance. 3. The analysis of daily health clinic notes showed that respiratory complaints were the highest percent, successively followed by digestive tract, nervous one, external injury, musculoskeletal system, urinary-reproductive system and others. 4. The level of their health practice was generally low in smoking, diet habit and alcohol intake, exercise, sleeping, very low especially in smoking, diet, alcohol intake, and exercise among them all. 5. Present smokers and ones with past experience complained of physical and mental health problems of 'respiratory system', 'digestive tract', 'skin', 'nervous', 'urinary-reproductive system', 'fatigability', adequacy', 'depression', 'anxiety', 'anger' and 'tension', than non smokers, with statistical difference. Workers without having breakfast and with irregular diet had more complaints on 'digestive tract', 'adequacy' and 'tension', than those who had regular dietary habit. The less the subjects slept, the more they complained of eye and ear, cardiovascular system. The subjects who drank alcohol complained more digestive problem. However, whether they exercise or not did not affect physical and mental health problems in a significant manner. 6. The subjects' age and marital status were statistically significant relating to health behaviors, as the younger or unmarried recorded a low level of health practice in smoking, drinking, dietary habit. Based on the results, the suggestions are made as follows: 1. Health education program on smoking, alcohol intake, diet habit is needed to improve health problems and health behavior of female retail workers. 2. The unmarried workers of late teen and twenties, who are transitional period from teenagers to adulthood are important targets for health promotion program especially for maternal health. 3. Better working environment with sufficient time and facilities for workers to relax is required to promote female sales workers' health. 4. Further research is required to identify the relation between workers' visual fatigue and intense lights for the display of goods.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Restorative Dentistry and Endodontics
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• v.31 no.1
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• pp.58-65
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• 2006

The possibility of applying a hi-axial flexure strength test on composite resin was examined using three point and hi-axial flexure strength tests to measure the strength of the light-cured resin and to compare the relative reliability using the Weibull modulus. The materials used in this study were light-curing restorative materials, $MICRONEW^{TM},\;RENEW^{(R)}$ (Bisco, Schaumburg, USA). The hi-axial flexure strength measurements used the piston-on-3-ball test according to the regulations of the International Organization for Standardization (ISO) 6872 and were divided into 6 groups, where the radius of the specimens were 12mm (radius connecting the 3-balls: 3.75mm), 16 mm(radius connecting the 3-balls: 5mm), and the thickness were 0.5mm, 1mm, 2mn for each radius. The hi-axial flexure strength of the $MICRONEW^{TM}\;and\;RENEW^{(R)}$ were higher than the three point flexure strength and the Weibull modulus value were also higher in all of the bi-axial flexure strength groups, indicating that the hi-axial strength test is relatively less affected by experimental error. In addition, the 2 mm thick specimens had the highest Weibull modulus values in the hi-axial flexure strength test, and the $MICRONEW^{TM}$ group showed no significant statistical difference (p>0.05). Besides the 2mm $MICRONEW^{TM}$ group, each group showed significant statistical differences (p<0.05) according to the thickness of the specimen and the radius connecting the 3-balls. The results indicate that for the 2mm group, the hi-axial flexure strength test is a more reliable testing method than the three point flexure strength test.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of Korea Foundry Society
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• v.41 no.2
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• pp.144-152
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• 2021

Based on the analysis of the current state of the world's foundry industry, we looked at the international competitiveness of Korea's foundry industry for the past 20 years. Korea's total foundry production is 2.52 million tons, and the production per company (so-called productivity) is 2,831 tons, which is the eighth largest in the world and down one position for the case of total foundry production, while productivity remains its position compared to three years ago. Korea is the only one of the top 10 foundry to see a decline in production. Similar to the global situation, Korean products consist of 38% of grey csat iron, 31% of ductile cast iron, 15% of aluminum, and 9% of cast steel. In order to obtain statistics on Korea's foundry industry, the survey conducted a service project for approximately nine months from April 2020. Various statistical surveys and sample in-depth surveys by the Korean standard industry class were evaluated for various contents of the domestic casting industry. We also looked at the number of companies, the distribution by region, the number of workers and the percentage of foreigners, and the distribution of each job, as well as the R&D investment status according to the size of the enterprise. Together, sales, exports, sales and various profit ratios were analyzed to measure the earning power of foundry industry. In addition, the classification by grouping the foundry industry according to the process utilized by focusing on each company, and to determine the sales, exports, and yield status for each process was also investigated on the basis. Based on these data, the domestic foundry industry has presented a variety of offers for the following issues for sustainable growth; global ranking, marginal corporate restructuring, training of domestic technical people, differentiated support policies by company size and process.

• Korean Journal of Poultry Science
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• v.18 no.2
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• pp.97-119
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• 1991

Effect of Streptococcus faecium(SF) and an antibiotic, Colistin(Col), supplemented to diets singly or in combination, on the performances and changes of intestinal population of microflora of broiler chicks studied. A total of 252, day-old chicks(Arbor Acre) of mixed sex(M：F＝1：1) were alloted into six groups. A diet with no Col and SF was referred as a control diet. The basal diets were added with two levels of SF, 0.04 and 0.08%, singly or in combination with Col 10ppm Another diet was prepared by adding only Col 10 ppm. Numbers of the microorganism in diets added with SF 0.04% and 0.08% were 7$\times$10$^{4}$ and 1.4$\times$10$^{5}$ /g diet respectively The diets consisting of corn and soybean meal as major ingredients were fed for a period of seven weeks . During the feeding trial, fresh excreta were sampled at the end of every week in a sterilized condition to count microbial changes from each dietary group. Microbial changes of large intestine were also measured from nine birds sacrificed at the end of the 4th and 7th weeks each time per dietary group. Excreta from all the groups were also collected quantitatively at the end of 3rd and 6th weeks to measure digestibility of the diets, At the end of 7th week, nine birds from each group were also sacrificed to measure weight changes of gastrointestinal tracts . Average body weight gains of broilers fed the diets added with SF 0.08% (2.37kg) or SF 0. 08%+col 10ppm(2.34kg) were significantly larger than that of the control(2.18kg). The weight gains of the other groups were not statistically different from that of the control Feed/gain ratios of the supplemental groups were better than that of control (P＜0.05) except that of birds fed the diet added only with SF 0.04%. Digestibilities of nutrients such as dry matter, crude protein, crude fat and total carbohydrates were not altered by the consumption of the diets added with SF and/or Col throughout the whole feeding period. As expected, the numbers of Streptococci in the excreta from birds fed diets added with SF increased significantly with a statistical difference between groups with SF 0.04% and SF 0.08% most of the time. However. addition of Colistin to the diets supplemented with SF did not give any effects on the number of the microorganism. Numbers of coliforms in the excreta were apparently reduced by feeding the diets added with SF and/or Col(P＜0.05). There were, however, no additive effects observed between the two feed additives in this regard when supplementing Col to the SF diets. Distributions of intestinal microflora exhibited exactly the same pattern as those of the excreta. Length of small intestine of the birds fed diets added with SF 0.08% with or without Col 10 ppm became significantly longer with a range of about 10% than those of the birds fed diets without SF. However, the empty weight of the small inestine of the former group was lighter than that of control These changes resulted in a significant reduction in weight/unit length of the intestine of the birds fed diets supplemented with Col and SF singly or in combination. In overall conclusion, diet added with SF 0.08% appeared most effective in improving broiler performances. Colistin added at a level of 10ppm was not beneficial at all in itself or in combination with SF in terms of broiler performances or changes of intestinal microflora population. The efficacy of SF and Col could be attributed to the changes of wall thickness of the small intestine.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Pediatric Infection and Vaccine
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• v.5 no.1
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• pp.128-135
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• 1998

Purpose : This study was intended to measure seropositivities and the level of mumps- and rubella-specific IgG of MMR vaccinees from 12 to 17 years of age in Korea. Materials and Methods : From May 1996 to July 1996 we obtained sera from students of 1 middle and 2 high schools in Seoul, who were MMR vaccinees from 12 to 17 years of age and had no evidence of immunodeficiency. These 216 study population include 110 males and 106 females. Mumps- and rubella-specific IgG antibody levels were measured by ELISA. Cut-off values for seropositivity were 20 U(Gamma Unit) in mumps and over 0.17 in rubella. Results : 1) As age increased, seropositivities to mumps increased, being 68.4% in 12 year, 79.3% in 13 year, 72.2% in 14 year, 82.0% in 15 year, 87.5% in 16 year, 87.0% in 17 year, which however has no statistical significance. 2) As age increased, the level of mumps-specific IgG antibody(mean+standard deviation, GU) increased, being $52.0{\pm}49.2$ in 12 year, $65.9{\pm}51.4$ in 13 year, $71.1{\pm}66.0$ in 14 year, $67.8{\pm}53.6$ in 15 year, $82.8{\pm}67.8$ in 16 year, $92.0{\pm}68.9$ in 17 year, which however has no statistical significance. 3) As age increased, seropositivities of rubella-specific IgG increased significantly, being 26.3% in 12 year, 20.7% in 13 year, 50.0% in 14 year, 67.2% in 15 year, 66.7% in 16 year, 65.2% in 17 year(P<0.001). 4) As age increased, rubella-specific IgG increased significantly, being $0.13{\pm}0.145$ in 12 year, $0.087{\pm}0.101$ in 13 year, $0.194{\pm}0.168$ in 14 year, $0.260{\pm}0.187$ in 15 year, $0.305{\pm}0.213$ in 16 year, $0.325{\pm}0.221$ in 17 year(P<0.001). There was positive correlation between age and rubella-specific IgG titer(rubella-specific $IgG=0.0517{\times}age-0.5586$, r=0.3752, P<0.001). Conclusion : In adolescent, seropositivities and the level of mumps-specific IgG remained relatively high, but approximately 20% of study population showed seronegativity. Seropositivities and the level of rubella-specific IgG showed the lowest level at 13 years of age and were increased with age after 14 years of age. Further evaluation may be needed to elucidate the cause of these changes of rubella-specific IgG.