• Korean Journal of Culture and Social Issue
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• v.9 no.spc
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• pp.35-55
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• 2003

The purpose of this study is to review the previous studies on the safety problems in Korea and to propose a psychological total safety system model. The model consisted of four agents; the government as the safety management agent, the suppliers of safety goods and services, consumer of safety goods and services, and civil movement institutions for safety. It was emphasized that the culture specific social representations of safety and accident have emerged in the course of rapid industrialization process in Korea during last 30 years. We delineated the social representations of the Korean people on safety and accident according to the model. A psychological analysis of drinking and driving behavior was performed as an application of the model. It was emphasized that safety psychologists have to develope and to apply the knowledge and the information from human engineering psychology and applied social psychology on safety and accidents.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.539-549
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• 2024

This study employed text mining techniques like frequency analysis, word clouds, and LDA topic modeling to assess consumer satisfaction and dissatisfaction with Hyundai Motor Company in the Chinese market, compared to brands such as Toyota, Volkswagen, Buick, and Geely. Focusing on compact vehicles from these brands between 2021 and 2023, this study analyzed customer reviews. The results indicated Hyundai Avante's positive factors, including a long wheelbase. However, it also highlighted dissatisfaction aspects like Manipulate, engine performance, trunk space, chassis and suspension, safety features, quantity and brand of audio speakers, music membership service, separation band, screen reflection, CarLife, and map services. Addressing these issues could significantly enhance Hyundai's competitiveness in the Chinese market. Previous studies mainly focused on literature research and surveys, which only revealed consumer perceptions limited to the variables set by the researchers. This study, through text mining and comparing various car brands, aims to gain a deeper understanding of market trends and consumer preferences, providing useful information for marketing strategies of Hyundai and other brands in the Chinese market.

• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.22-36
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• 2024

In 1993, a large number of relics was found in Cheongju Sanesa Buddhist Temple. They show superiority as bronze-based products, and are especially important in since they provide much information about the time of their creation. However, there are many opinions about the time they were hoarded: in the middle of the 13^th century, late in the 13^th century, early in the 14^th century, etc. This study estimates the time they were hoarded to be some time in April 1291 during the invasion of Kădīn (哈丹) in the Yuan Dynasty. Kădīn's troops invaded the Goryeo Dynasty, then went through Yangpyeong and Wonju in January 1291 and appeared in Yeongi-hyeon on May 1 of that year. Based on records, this study verified that the troops passed through Cheongju on their way from Chungju to Yeongi-hyeon (currently Sejong-si) and pointed out that the invasion route of the troops was the background for the hoarding of Sanesa relics. The estimation that the Sanesa relics were hoarded in 1291 when Kădīn's invasion was going on makes it possible to reasonably clear up the era of the relics in which the Heavenly Stems called gānzhī (干支) Muo (戊午), Gyeongshin (庚申), Giyu(己酉) etc. were written. That is, Giyu Geumgo is presumed to be the year 1249, Muo Hyangro 1258, Gyeongshin Hyangwan 1260, etc.

• Informatization Policy
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• v.31 no.1
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• pp.83-99
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• 2024

This study seeks to shed light on the importance of "advancing administrative computer systems" for research administration efficiency, building upon prior literature, and aims at extending the scholarly discussion on the efficiency of research administration itself. To this end, two research questions were addressed. First, this research explores how research administrators perceive the advocated "advancement of administrative computer systems" in achieving research administration efficiency. Second, it investigates how external bureaucratic control affects burnout among administrative personnel engaged in research administration, and how burnout impacts the research-administration relationship, trust, and ultimately performance. The analysis of interviews and surveys yielded several results. For one, through the analysis of interviews conducted in the field of government-funded research institutions, it was found that "advancing computer systems" is met with practical concerns and skepticism, while also recognized as having the potential to contribute to the efficiency of research administration. Furthermore, it became evident that complex issues are intertwined. From a contrasting standpoint opposing computer advancement, the view that institutional regulations and cultural efficiency should take precedence over technology appears valid and raises a crucial point for consideration. On the other hand, regression analysis related to burnout shows empirical evidence that increased control by central government bureaucrats over administrative staff in government-funded research institutions leads to higher levels of burnout. Such elevated burnout is shown to have detrimental effects on trust between researchers and administrative personnel, as well as on overall performance. Through these discussions, we aim to stimulate academic and government interest in research administration efficiency.

• Journal of Korean Home Economics Education Association
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• v.20 no.4
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• pp.19-42
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• 2008

The purpose of this study was to develop practical problem-based home economics teaching.learning process plans about a unit 'the youth and consumer life' of middle school eighth-grade Technology and Home Economics by applying blended learning(BL) strategy. According to ADDIE instructional design model, this study was conducted in the following procedure: analysis, design/development, implementation, and evaluation. In the stage of design and development, the selected unit was converted into a practical problem-based unit, and practical problem-based teaching. learning process plans were designed in detail by using BL strategy. An online study room for practical problem-based home economics instruction grounded in BL strategy was prepared by using Edunet(http://community.edunet4u.net/${\sim}$consumer2). Eight-session lesson plans were mapped out, and study aids for students and materials for teachers were prepared. In the implementation stage, the first-session teaching plans that dealt with a minor question 'what preparations should be made to become a wise consumer' were utilized when instruction was provided to 115 eighth graders who were in three different province, and the other one was in a middle school in the city of Daejeon. The experimental teaching was implemented for two weeks in the following procedure: preliminary program, pre-online learning, main instruction and post- online learning. The preliminary program was carried out in a session in the classroom, and pre-online learning was provided before the main instruction was given in a session in the classroom. After the main instruction was completed, post-online learning was offered. In the evaluation stage, a survey was conducted on all the learners and teachers to find out their opinions and suggestions.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• Information Systems Review
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• v.15 no.3
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• pp.39-69
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• 2013

Business value of information technology has been the biggest interest of all such as practitioners and scholars for decades. Information technology is considered as the driving force or success factor of firm agility. The general assumption is that organizations making considerable efforts in IT investment are more agile than the organizations that are not. However, IT that should help the strategies of the firm that can hinder business or impede agility of the firm occasionally. In other words, it is still unknown if IT helps the agility of the firm or bothers it. Therefore, we note that contrary aspects of IT such as promotion and hindrance of firm agility have been observed frequently and theorize the relationships between them. In other words, we propose a rationale that firms should need to develop superior firm-wide IT capability to manage IT resources successfully in order to realize agility. Thus, this paper theorizes two IT capabilities, including technical IT capability and managerial IT capability as key factors impacting firm agility and firm performance. Further, we operationalize firm agility into two sub-types, including operational adjustment agility and market capitalizing agility. The data from 171 firms was analyzed using PLS approach. The results showed that technical IT capability has positive impact on firm agility and managerial IT capability had positive moderating effects between technical IT capability and firm agility. In addition, it was identified that top management championship positively moderates between agility and firm performance. Finally, it was indicated that firm agility was a very important causal variable of firm performance. Our study provides more exquisite and practical empirical evidences in the relationship between IT capability and firm agility by proposing applicable solution although IT has some contradicting effects on firm agility. Our findings suggest many useful implications to agility related researches in relatively primitive stage and working level officers in organizations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.466-476
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• 2006

The objective of this study was to compare and analyze the acceptability and consumption attitude for soy foods between Korean and Canadian university students as young consumers. This survey was carried out by questionnaire and the subjects were n=516 in Korea and n=502 in Canada. Opinions for soy foods in terms of general knowledge were that soy foods are healthy (86.5% in Korean and 53.4% in Canadian) or neutral (11.6% in Korean and 42.8% in Canadian), dairy foods can be substituted by soy foods (51.9% in Korean and 41.8% in Canadian), and soy foods are not only for vegetarians and milk allergy Patients but also for ordinary People (94.2% in Korean and 87.6% in Canadian). In main sources of information about soy foods, the rate by commercials on TV, radio or magazine was the highest (58.0%) for Korean students and the rate by family or friend was the highest(35.7%) for Canadian students. In consumption attitude, all of Korean students have purchased soy foods but only 55.4% of Canadian students have purchased soy foods, and soymilk was remarkably recognized and consumed then soy beverage and margarine in order. 76.4% of Korean students and 65.1% of Canadian students think soy foods are general and popular and can purchase easily, otherwise, in terms of price, soy foods were expensively recognized as 'more expensive than dairy foods' was 59.1% (Korean) and 54.7% (Canadian), and 'similar to dairy foods' was 36.8% (Korean) and 39.9% (Canadian). Major reasons for the rare consumption were 'I am not interested in soy foods' in Korean students (27.3%) and 'I prefer dairy foods to soy foods' in Canadian students (51.7%). However, consumption of soy foods in both countries are very positive and it will be increased.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.