• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.3
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• pp.31-39
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• 2021

In this study, the fiber blending ratio and strain rate effect on the tensile properties synergy effect of hybrid fiber reinforced cement composite was evaluated. Hooked steel fiber(HSF) and smooth steel fiber(SSF) were used for reinforcing fiber. The fiber blending ratio of HSF+SSF were 1.5+0.5, 1.0+1.0 and 0.5+1.5vol.%. As a results, in the cement composite(HSF2.0) reinforced with HSF, as the strain rate increases, the tensile stress sharply decreased after the peak stress because of the decrease in the number of straightened pull-out fibers by increase of micro cracks in the matrix around HSF. When 0.5 vol.% of SSF was mixed, the micro cracks was effectively controlled at the static rate, but it was not effective in controlling micro cracks and improving the pull-out resistance of HSF at the high rate. On the other hand, the specimen(HSF1.0SSF1.0) in which 1.0vol.% HSF and 1.0vol.% SSF were mixed, each fibers controls against micro and macro cracks, and SSF improves the pull-out resistance of HSF effectively. Thus, the fiber blending effect of the strain capacity and energy absorption capacity was significantly increased at the high rate, and it showed the highest dynamic increase factor of the tensile strength, strain capacity and peak toughness. On the other hand, the incorporation of 1.5 vol.% SSF increases the number of fibers in the matrix and improves the pull-out resistance of HSF, resulting in the highest fiber blending effect of tensile strength and softening toughness. But as a low volume fraction of HSF which controlling macro crack, it was not effective for synergy of strain capacity and peak toughness.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.139-150
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• 2020

The data processing of this study focuses on the textom and social media words about three areas: 'Corona 19 and professional baseball', 'Corona 19 and professional baseball', and 'Corona 19 and professional sports' The data was collected and refined in a web environment and then processed in batch, and the Ucinet6 program was used to visualize it. Specifically, the web environment was collected using Naver, Daum, and Google's channels, and was summarized into 30 words through expert meetings among the extracted words and used in the final study. 30 extracted words were visualized through a matrix, and a CONCOR analysis was performed to identify clusters of similarity and commonality of words. As a result of analysis, the clusters related to Corona 19 and Pro Baseball were composed of one central cluster and five peripheral clusters, and it was found that the contents related to the opening of professional baseball according to the corona 19 wave were mainly searched. The cluster related to Corona 19 and unrelated to professional baseball consisted of one central cluster and five peripheral clusters, and it was found that the keyword of the position of professional baseball related to the professional baseball game according to Corona 19 was mainly searched. Corona 19 and the cluster related to professional sports consisted of one central cluster and five peripheral clusters, and it was found that the keywords related to the start of professional sports according to the aftermath of Corona 19 were mainly searched.

• Korean Association of Arts Management
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• no.56
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• pp.39-87
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• 2020

This study empirically analyzed the value conflicts of cultural administration based on the needs of axiological discussions and the differences in intersubjectivity among the cultural administration groups and the contradicting attributes of culture and administration. The study classified the stakeholders into administrative staff, planners, and artists to compare their value priorities of publicness in cultural administration. A classification analysis was also conducted based on the normative by each group and the value distribution on a 2×2 value matrix between autonomy and accountability and fairness and efficiency. Based on the results of the quantitative study, the awareness of the relationships among the groups and cause and effects of value conflicts was analyzed through in-depth interviews. Thus, the study aimed to identify the directions for value distribution wherein the values of administration and culture can coexist and determine the implications of expanding this mutual understanding. The results revealed that in the conflict between autonomy and accountability, all groups had a greater awareness of accountability. In terms of normative aspects, it was possible to see a normative value line with an emphasis on autonomy, rather than on accountability from the lower stages on the budget hierarchy (administrators at the top, followed by planners and artists). In the conflict between autonomy and accountability, the size of dissonance between appropriateness and reality was the largest among the groups in the lower stages of the budget hierarchy, and became larger along the order of administrators, planners, and artists. In the conflict between efficiency and fairness, all groups had a greater awareness of efficiency. In terms of fairness in normative aspects, emphasis was placed on was artists, administrators, and planners, in that order. The size of dissonance between efficiency and fairness by groups became larger along the order of budget hierarchy-administrators, planners, and artists. Based on the results, the study compared and analyzed the 2×2 value matrix between the normative and actualities by groups. The normative value distribution emphasized Type 1 (accountability x fairness) as seeking communitarianism values through culture and Type 2 (autonomy x fairness) as seeking balanced values of cultural freedom of individualsonabalance. However, in actualities, although the communitarianism values of Type 1 were considered important, there were no distributions to the liberal values of Type 2, rather to the economic values of culture from Type 4 (accountability x efficiency). In summary, the Korean cultural administration isunderapressureof value distribution to emphasize the communitarianism and economic rather than liberal values, through bureaucratic control in actualities compared with the normative. This study will have significant implications on value distribution decision-making by groups and political implementations within the purview of cultural administration.

• Korean Journal of Poultry Science
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• v.50 no.1
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• pp.41-50
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• 2023

Over the last decade, avian influenza (AI) has been considered an emerging disease that would become the next pandemic, particularly in countries like South Korea, with continuous animal outbreaks. In this situation, risk assessment is highly needed to prevent and prepare for human infection with AI. Thus, we developed the risk assessment matrix for a high-risk area of human infection with AI in South Korea based on the notion that risk is the multiplication of hazards with vulnerability. This matrix consisted of highly pathogenic avian influenza (HPAI) in poultry farms and the number of poultry-associated production facilities assumed as hazards of avian influenza and vulnerability, respectively. The average number of HPAI in poultry farms at the 229-municipal level as the hazard axis of the matrix was predicted using a negative binomial regression with nationwide outbreaks data from 2003 to 2018. The two components of the matrix were classified into five groups using the K-means clustering algorithm and multiplied, consequently producing the area-specific risk level of human infection. As a result, Naju-si, Jeongeup-si, and Namwon-si were categorized as high-risk areas for human infection with AI. These findings would contribute to designing the policies for human infection to minimize socio-economic damages.

• Composites Research
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• v.36 no.1
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• pp.1-15
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• 2023

Demand for energy consumption reduction is increasing according to the development expectations of future mobility. Lightweight structural materials are known as a method to reduce greenhouse gas emissions and improve energy efficiency. In particular, fiber reinforced polymer composite (FRP) is attracting attention as a material that can replace existing metal alloys due to its excellent mechanical properties and light weight. In this paper, industrial applications and research trends of carbon fiber reinforced composites (CFRP, carbon FRP) and self-reinforced composites (SRC) were reviewed based on the reinforcement, polymer matrix, and manufacturing process. In order to overcome the expensive process cost and long manufacturing time of the epoxy resin-based autoclave method, which is mainly used in the aircraft field, mass production of CFRP-applied electric vehicles has been reported using a high-pressure resin transfer molding process including fast-curing epoxy. In addition, thermoplastic resin-based CFRP and interface enhancement methods to solve the recycling issue of carbon fiber composites were reviewed in terms of materials and processes. To form a perfect matrix-reinforcement interface, which is known as the major factor inducing the excellent mechanical properties of FRP, studies on SRC impregnated with the same matrix in polymer fibers have been reported. The physical and mechanical properties of SRC based on various thermoplastic polymers were reviewed in terms of polymer orientation and composite structure. In addition, a copolymer matrix strategy for extending the processing window of highly drawn polypropylene fiber-based SRC was discussed. The application of CFRP and SRC as lightweight structural materials can provide potential options for improving the energy efficiency of future mobility.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.730-741
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• 2023

Purpose: The 2020 Dietary Reference Intakes for Koreans (KDRIs) serves as a foundation for daily nutrient and energy recommendations aiming to enhance public health and prevent chronic diseases. They act as guidelines for maintaining proper nutrition and overall health. Using KDRIs is crucial for promoting healthier lifestyles and making informed dietary choices. Thus, this study explores the influence of a nutrition education program, based on the 2020 KDRIs, on the nutrition knowledge and dietary habits of undergraduates in Gyeongsangnam-do and Gyeonggi-do. Methods: The nutrition education program, designed with diverse instructional materials, was executed across a wide range of universities. The education group (n = 75) engaged in the program for a 6-week instructional period, while the control group (n = 53) underwent the survey without participating in the education program. Nutrition Quotient (NQ) and knowledge assessments were administered to both groups immediately before and after the instructional period. Results: Within the education group, the nutrition education program positively impacted responses to NQ practice items, including knowledge of nutrition, daily intake, and portion sizes (p < 0.05). In contrast, there were no significant differences between the before and after responses of the control group for most survey items. Post-program evaluations showed significantly higher self-assessment scores and increased satisfaction levels (p < 0.05), with the satisfaction rate for the education program using the 2020 KDRIs reaching 99.2%. Conclusion: This study has demonstrated the positive impact of an effective nutrition education program. However, there is a need for the continuous development and implementation of nutrition education programs to sustain these outcomes and further enhance the nutritional education experience.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.25-37
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• 2022

The purpose of this study is to survey startup founders within 7 years of founding the importance and satisfaction of the components of the scale-up entrepreneurial ecosystem at the national level in Korea and analyze the direction of scale-up policy by component using IPA (importance-performance analysis). Since the perception of founders, who are the subjects of the entrepreneurial ecosystem, affects the quantity and quality of start-ups, research is needed to analyze and diagnose the perception of scale-up components. For the development of the national economy and entrepreneurial ecosystem, companies that emerge from startups to scale-up and unicorns must be produced, and for this, elements for the scale-up entrepreneurial ecosystem are needed. The results of this study are as follows. First, the importance ranking of the components of the scale-up entrepreneurial ecosystem recognized by founders was in the order of "Financial support by growth stage," "Support for customized scale-up for enterprises," "Improvement of regulations," "Funds dedicated to scale-up," "large-scale investment," and "nurturing technical talents." Second, the factors that should be intensively improved in the importance-satisfaction matrix in the future were 'Pan-Government Integration Promotion Plan', 'Scale-Up Specialized Organization Operation', 'Company Customized Scale-Up Support', 'Regulatory Improvement', and 'Building a Korean Scale-Up Model'. As a result, various and large financial capital for the scale-up entrepreneurial ecosystem, diversification of scale-up programs by business sector, linkage of start-ups and scale-up support, deregulation of new technologies and new industries, strengthening corporate-tailored scale-up growth capabilities, and providing overseas networking opportunities can be derived. In addition, it is expected to contribute to policy practice and academic work with research that derives the components of the domestic scale-up entrepreneurial ecosystem and diagnoses its perception.

• Information Systems Review
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• v.21 no.4
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• pp.1-29
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• 2019

The knowledge resources take a role in promoting the sustainable growth of organization. Therefore, it is important for the members of organization to acquire knowledge consistently so that the company can continue to grow. Knowledge service is the field that provides information and infrastructure which enable the members of organization to acquire new knowledge. As we recognized the importance of knowledge services, we analyzed the level of knowledge service management and development through the impact of knowledge quality on user capabilities. First, the matrix of knowledge patterns was presented based on the type of information and the level of customer interaction. According to patterns, the knowledge service was classified into three types of information providing, information analysis, and infrastructure, and then the results of structural model analysis were presented for each type. It found that the impact of knowledge service quality on user competence was different according to the type of service. The results suggested new indicators for measuring the performance of knowledge services, and provided information for reconstructing services based on the user considering the integrated operation of knowledge service and organizational designing knowledge service.