• Korean Journal of Legislative Studies
• /
• v.23 no.1
• /
• pp.113-149
• /
• 2017

This study aims to identify the micro-level, social psychological foundation of regionalism and analyze its change and continuity by comparing 1988, 2003, and 2016 survey results. Drawing on the theory of prejudice and social identity, it clarifies the concept of regionalism and examines its affective, behavioral, cognitive implications. In the empirical analysis, where it takes advantage of relevant questions of the same or similar wording in three nationally representative surveys, the study identifies the changes in regionalism at the individual level focusing on anti-Honam prejudice and discrimination and attribution of regional conflict. First, anti-Honam prejudice has been in decline nationally as well as regardless of where one has grown up, except for Daegu/Kyungpook area. Second, anti-Honam prejudice has been weakened among younger generations while regional party identification now affects the sentiment in the direction of regional cleavage overlapped with ideological leanings. Third, while most respondents do not experience explicit discrimination, Honam natives are still more likely to experience discrimination, especially identity and self-esteem related, due to his or her home town. Fourth, Honam natives have been more likely to attribute regional conflict to an external, structural factor like government economic policy and less likely to a subjective one like regional sentiment, which seems to be consistent with attributional attitudes of the victims of prejudice. The study ends with the discussion of how to reduce further anti-Honam prejudice, which includes contact hypothesis, recategorization, cross-categorization, and de-categorization.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.2
• /
• pp.7-13
• /
• 2019

This study aims to develop a FE Model to simulate dissimilar friction stir welding and to address its potential for fundamental analysis and practical applications. The FE model is based on Coupled Eulerian-Lagrangian approach. Multiphysics systems are calculated using explicit time integration algorithm, and heat generations by friction and inelastic heat conversion as well as heat transfer through the bottom surface are included. Using the developed model, friction stir welding between an Al6061T6 plate and an AZ61 plate were simulated. Three simulations are carried out varying the welding parameters. The model is capable of predicting the temperature and plastic strain fields and the distribution of void. The simulation results showed that temperature was generally greater in Mg plates and that, as a rotation speed increase, not the maximum temperature of Mg plate increased, but did the temperature of Al plate. In addition, the model could predict flash defects, however, the prediction of void near the welding tool was not satisfactory. Since the model includes the complex physics closely occurring during FSW, the model possibly analyze a lot of phenomena hard to discovered by experiments. However, practical applications may be limited due to huge simulation time.

• The Korean Journal of Archival Studies
• /
• no.32
• /
• pp.45-78
• /
• 2012

The significance of Presidential Impeachment in 2004 is subject to interpretations in many different contexts, but its nature as its justice was the constitutional trial by the nation's impeachment system. This study set out to compare and analyze the understanding of the event centered around its nature as "an impeachment event as a public activity" and the records related to it. For that purpose, the study attempted to analyze the impeachment event to understand it as a public activity and examined and analyzed the records of the impeachment event in the public domain through personal visit, phone interview, and request of information disclosure based on the analysis results. An impeachment event as a public activity can be understood as an activity carried out by the National Assembly, which is to issue a motion for impeachment under the norms of the nation's impeachment system, and Constitutional Court, which is responsible for impeachment trial, through their unique rights prescribed in the Constitution. The important subjects of such a public activity included the accused president, the acting presidential system created by the motion for impeachment, and the National Election Commission that provided a decisive ground for impeachment. It was confirmed that the records, which are legal requirements, were well created and have been preserved and managed in the public domain. However, it was difficult to conclude that the records of the impeachment event were thoroughly created in terms of content in relation to affairs as they mainly covered the superficial treatment processes and the results of explicit activities. There was, in particular, the absence of records showing the context of activity.

• Journal of the Korean Chemical Society
• /
• v.66 no.2
• /
• pp.150-162
• /
• 2022

In this study, we investigated pre-service science teachers' NOS-PCK by analyzing their NOS lesson planning and demonstration. Four pre-service science teachers participated in the study. They planned and demonstrated NOS lessons in the context of 'Science Inquiry Experiment' developed under the 2015 Revised National Curriculum. Their lessons were observed. All of the teaching-learning materials were collected, and semi-structured interviews were also conducted. The analyses of the result revealed that pre-service teachers mainly referred to the curriculum and textbooks when selecting the NOS learning objectives. However, they felt difficulty because the curriculum and textbooks did not clearly present the NOS to be dealt. Although all of them took explicit approaches, there were not many open and divergent reflective approaches. In addition, they expected that high school students would consider scientific knowledge absolute and would have negative perceptions of NOS lessons. They rarely assessed students' NOS learning, and were reluctant to assess. Finally, most of them had a negative perception that learning NOS is not necessary for all students. On the bases of the results, educational implications for improving the expertise of pre-service science teachers in NOS lessons were discussed.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.33 no.1
• /
• pp.47-70
• /
• 2022

The purpose of this study is to empirically verify the factors(organization managerial factors, relational factors and personal factors) that affect the knowledge sharing intention of records management specialists. A total of 126 responses were used for the final analysis and the findings of this study are as follows: First, managers' interests and supports had a significant positive effect on tacit knowledge sharing intention, but evaluation and compensation system had a negative effect on tacit knowledge sharing intention. Second, job stress was found to have a significant positive effect on the intention of knowledge sharing(tacit, explicit). It is required to form an organizational culture where records management specialists can freely participate in knowledge sharing activities with the interest and support of managers. In addition, the process of continuously improving the causes of one of the causes of job stress, such as awareness improvement and deprivation, should be carried out through records management training for all employees. This study is meaningful in that it provides policy implications for promoting knowledge sharing as a solution to the managerial problems faced by records management specialists by utilizing the current staffing structure.

• Korean Journal of Remote Sensing
• /
• v.40 no.2
• /
• pp.203-217
• /
• 2024

The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.113-127
• /
• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.19-38
• /
• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• The Journal of Small Business Innovation
• /
• v.19 no.2
• /
• pp.53-68
• /
• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.

• KDI Journal of Economic Policy
• /
• v.12 no.3
• /
• pp.153-172
• /
• 1990

The present paper investigates the potential value of strategic antitrust policy in an oligopolistic international market. The market is characterized by a non-cooperative Cournot-Nash equilibrium and by asymmetry in costs among firms in the world market. The model is useful for two reasons. First, it is important in the context of policy-making to examine the conditions under which it may be beneficial to relax antitrust law to enhance competitiveness. Second, the explicit derivation of the level of cost-saving required for a gain in total domestic surplus provides an empirical rule for excluding industries that do not satisfy the requirements for a socially beneficial antitrust exemption. Results of the analysis include a criterion that tells how the cost-saving and concentration effects of a merger offset each other. The criterion is derived from fairly general assumptions on demand functions and is simple enough to be applied as a part of the merger guidelines. Another interesting policy implication of our analysis is that promoting mergers would not be a beneficial strategy in a net importing industry where cost-saving opportunities are thin. Cost-saving domestic mergers are more likely to increase national welfare in exporting industries. The best candidate industries for application of strategic antitrust policy are those with the following characteristics: (i) a large potential for efficiency enhancement; (ii) high market concentration at the world but not the domestic level; (iii) a high ratio of exports to imports. Recently, many policymakers and economists in Korea have also come to believe that the appropriate antitrust policy in an era of increased foreign competition may actually be to encourage rather than to prohibit domestic mergers. The Industry Development Act of 1986 and the proposed bill for Mergers and Conversions in the Financial Industry of 1990 reflect this changing perspective on antitrust policy. Antitrust laws may burden domestic firms in the sense that they have a more constrained strategy set. Expenditures to avoid antitrust attacks could also increase costs for domestic firms. But there is no clear evidence that the impact of antitrust policy is significant enough to harm the competitiveness of domestic firms. As a matter of fact, it is necessary for domestic financial institutions to become large in scale in this era of globalization. However, the absence of empirical evidence for efficiency enhancement from mergers suggests caution in the relaxation of antitrust standards.