• Journal of Nutrition and Health
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• v.51 no.3
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• pp.228-241
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• 2018

Purpose: The health risk of women increases after menopause. This study evaluated the effectiveness of a public health center-based nutrition education program for hypertension in women older than 50 years of age. Methods: The program included 8-week nutrition education and 8-week follow-up with keeping a health diary and nutrition counseling. The program was evaluated three times: before and after the nutrition education, and after the follow-up. The subjects were classified into hypertensives (n = 44) or normotensives (n = 71). Results: The rate of taking antihypertensive drugs in the hypertensive group was 86.4%. The systolic blood pressure decreased in the hypertensive and normotensive groups after nutrition education (p < 0.05). The body weight (p < 0.001), BMI (p < 0.001), waist circumference (p < 0.001), and percent body fat (p < 0.01) were also decreased after nutrition education in both groups. The hypertensive group showed an increase in HDL-cholesterol level (p < 0.001) and decreases in triglycerides (p < 0.01) and LDL-cholesterol (p < 0.05) levels after completion of the program. The normotensive group also displayed significant changes in HDL-cholesterol (p < 0.001) and triglycerides (p < 0.01). The dietary habits and nutrition knowledge on sodium and hypertension were improved in both groups (p < 0.001). The total score of dietary behavior related to the sodium intake was improved in the normotensive group (p < 0.001). The total score of the high sodium dish frequency questionnaire decreased in both groups after nutrition education and completion of the program compared to that before the program. Decreases in the consumption frequencies of noodles, pot stews and stews, Kimchi, and beverages were significant. The total self-efficacy score was increased in both groups by the program (p < 0.001). In particular, the hypertensive group showed improvement in all items. Conclusion: This public health center-based nutrition education program may contribute to the prevention and management of hypertension and chronic diseases in women over 50 years of age.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.183-189
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• 2000

This study was carried out to analyze ecophysiological responses for seedling of Quercus serrata, Quercus mongolica and Quercus variabilis that are the typical species of deciduous broadleaved forests in Korea. And executed experiments in the climatic control room to provide necessary information to ecological forest management and methods of natural regeneration through the analysis of seedling responses. The details of experimental analysis were growth processes of 4 months after seeding that vary with the condition of three light intensity[relative light intensity(RLI) 8%, 20%, 52%] and three soil moisture[water suction(WS) Ψ=100 hPa, Ψ=280 hPa, Ψ=330 hPa] gradient, growth factors after harvesting and the nutrition condition of leaves. The results of this study are followings: 1) Early growth was prosperous after germination for the species which have more weight of acorn. 2) The formation of lammas shoot was favourable with Q. variabilis and Q. mongolica. And the rate of the occurrence was the highest in the RLI 20%, and it was remarkably reduced in the RLI 8%. 3) As the height growth of seedling of all 3 species were greater in the RLI 20% and 8% than that of the RLI 52%, they showed strong shade tolerance. 4) The increase of light intensity promoted the diameter at root collar growth, and development of main and lateral roots with all 3 species. 5) It showed that the increase of light intensity in the experimental radiation condition raised special leaf area weight(mg/cm$^2$) and leaf area productivity(mg/cm$^2$). Consequently, these resulted in the increase of leaf thickness and total dry biomass per the unit area of leaf. 6) As the increase of light intensity, the minerals contents of leaves such as N, P and K were lowered, and the increase of soil moisture resulted in the increase of P, K, Ca and Mg.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.45-53
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• 2006

The objectives of this study are to determine the primary yield components responsible for yield differences in a subtropical environment of the Hunan province China and in a temperature environment of Honam province Korea. Field experiments were conducted in a subtropical environment in Hunan province China during 2002 and in a temperate environment in Honam province Korea during 2003. Seven rice cultivars were grown under optimum crop management in each experiment field. Yield, yield components and plant dry matter were determined at maturation. The highest yield (567 kg/10a) was produced at Honam province by Jinyou 207, a Chinese cultivar, The maximum yield at Hunan province was 453 kg/10a by Sanyou 63. On the average across cultivars, Honam produced 23% greater yields than Hunan. Sink size (spikelets per $m^2$) was responsible far these yield differences. Panicle number per $m^2$ was much greater at Honam.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.3
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• pp.111-117
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• 2009

Food and Agricultural Organization (FAO) Penman-Monteith (PM) equation is one of the most widely used equations for predicting evapotranspiration (ET) of crops. The ET rate and the base crop coefficients ($K_{cb}$) of the two different grape vines (i.e., Campbell Early and Kyoho) cultivated in Suwon were calculated by using the FAO PM equation. The ET rate of Campbell Early was $2.41\;mm\;day^{-1}$ and that of Kyoho was $2.22\;mm\;day^{-1}$ in August when the leaf area index was 2.2. During this period, the $K_{cb}$ of Campbell Early based on the FAO PM equation was on average 0.49 with the maximum value of 0.72. On the other hand, the $K_{cb}$ of Kyoho was averaged to be 0.45 with the maximum value of 0.64. The seasonal leaf area index for two grape cultivars was measured as 0.15 in April, 0.5 in May, 1.4 in June, 2.2 in July-September, and 1.5 in October. The $K_{cb}$ of Campbell Early showed a seasonal variation, changing from 0.03 in April to 0.11 in May, 0.31 in June, 0.49 in July-September, and 0.33 in October. The magnitudes and the seasonality of $K_{cb}$ of Kyoho were similar to those of Campbell Early.

• 대한공업교육학회지
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• v.33 no.1
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• pp.67-91
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• 2008

The purpose of this research is to find the identity through the inductive analysis on the curriculum of the industrial high schools in Gyeonggi province. The departments of electricity, electron, and correspondence of the 22 industrial high schools in Gyeonggi province are selected as the subject school of this research. The result of this research is as follows. First, 11 out of 18 industrial schools currently arrange larger completion unit of special curriculum than common one, and 2 out of 4 general high schools do. Second, industrial schools reduced 2-4 units of required subjects of the departments, general high schools did 2 units. 11 out of 18 industrial schools arranged larger student's elective subjects than school's in the completion unit of common curriculum, and 3 out of 4 general high schools did. 31 out of 32 departments of 18 industrial schools arranged larger student's elective subjects than school's in the completion unit of common curriculum. Third, 4 out of 18 industrial schools used the certified textbooks for newly organized subjects. But 25 departments of 15 schools out of 36 departments of 22 schools changed the industrial departments into high-tech ones. Fourth, in the classification of the school curriculum by 3-type curriculum, 12 out of 18 industrial schools adopted employment-centered curriculum, 7 out of 18 schools did foundation-centered curriculum. 2 out of 4 general schools adopted employment-centered curriculum, 2 out of 4 schools did college preparation-centered curriculum. Therefore, Schools are estimated to have much effort to find their identities through the arrangement of school curriculum.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.543-559
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• 2003

The information drive of the businesses requires new alternatives in that the promotion of business efficiency through information process technologies ends up conflicting with the protection of information human rights on laborers’side. Nevertheless, apathy on information protection has a tendency to be distorted by the efficiency of the businesses. Should the capital and mass media warn economic red lights, political circles with uneasiness would ignore the significance of information protection on the behalf of business efficiency. Therefore, the importance of information protection is considered a smaller interest than that of business efficiency with the infringements of human rights on laborers’side arising. Informatization of the businesses along with the developments of information process technologies has enabled the management to monitor and control the behaviors of laborers. This new problem needs to establish both information protection mechanism and institutional devices to regulate those labor controls. The security of business activity without human rights infringement warrants both basic rights of the public and spirit of the Constitution. The study suggests the establishment and revision of laws suitable to the period of information human rights. On top of that, the establishment of the basic law for information protection of individuals’with the common principle that integrates the related laws and rules on-off line is needed. This will warrant the active participation of labor unions and create specific alternatives for information protection.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.59-69
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• 2012

As exhibition industry, which is a part of 17 new growth engines of the government, is related to other industries such as tourism, transportation and financial industries. So it has a significant ripple effect on other industries. Exhibition is a knowledge-intensive, eco-friendly and high value-added Industry. Over 13,000 exhibitions are held every year around the world which contributes to getting foreign currency. Exhibition industry is closely related with culture and tourism and could be utilized as local and national development strategies and improve national brand image as well. Many countries try various efforts to invigorate exhibition industry by arranging related laws and support system. In Korea, more than 200 exhibitions are being held every year, but only 2~3 exhibitions are hosted with over 400 exhibitors and except these exhibitions most exhibitions have few foreign exhibitors. The main reason of weakness of domestic trade show is that there are no agencies managing exhibitionrelated statistics and there is no specific and reliable evaluation. This might cause impossibility of providing buyer or seller with reliable data, poor growth of exhibitions in terms of quality and thus service quality of trade shows cannot be improved. Hosting a lot of visitors (Public/Buyer/Exhibitor) is very crucial to the development of domestic exhibition industry. In order to attract many visitors, service quality of exhibition and visitor's satisfaction should be enhanced. For this purpose, a variety of real-time customized services through digital media and the services for creating new customers and retaining existing customers should be provided. In addition, by providing visitors with personalized information services they could manage their time and space efficiently avoiding the complexity of exhibition space. Exhibition industry can have competitiveness and industrial foundation through building up exhibition-related statistics, creating new information and enhancing research ability. Therefore, this paper deals with customized service with visitor's smart-phone at the exhibition space and designing mobile framework which enables exhibition devices to interact with other devices. Mobile server framework is composed of three different systems; multi-server interaction, server, client, display device. By making knowledge pool of exhibition environment, the accumulated data for each visitor can be provided as personalized service. In addition, based on the reaction of visitors each of all information is utilized as customized information and so the cyclic chain structure is designed. Multiple interaction server is designed to have functions of event handling, interaction process between exhibition device and visitor's smart-phone and data management. Client is an application processed by visitor's smart-phone and could be driven on a variety of platforms. Client functions as interface representing customized service for individual visitors and event input and output for simultaneous participation. Exhibition device consists of display system to show visitors contents and information, interaction input-output system to receive event from visitors and input toward action and finally the control system to connect above two systems. The proposed mobile framework in this paper provides individual visitors with customized and active services using their information profile and advanced Knowledge. In addition, user participation service is suggested as well by using interaction connection system between server, client, and exhibition devices. Suggested mobile framework is a technology which could be applied to culture industry such as performance, show and exhibition. Thus, this builds up the foundation to improve visitor's participation in exhibition and bring about development of exhibition industry by raising visitor's interest.