• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.1
• /
• pp.276-290
• /
• 2023

Several efforts in Korea are currently underway with the goal of commercializing autonomous vehicles. Hence, various studies are emerging on autonomous vehicles that drive safely and quickly according to operating guidelines. The current study examines the path search of an autonomous vehicle from a microscopic viewpoint and tries to prove the efficiency required by learning the lane change of an autonomous vehicle through Deep Q-Learning. A SUMO was used to achieve this purpose. The scenario was set to start with a random lane at the starting point and make a right turn through a lane change to the third lane at the destination. As a result of the study, the analysis was divided into simulation-based lane change and simulation-based lane change applied with Deep Q-Learning. The average traffic speed was improved by about 40% in the case of simulation with Deep Q-Learning applied, compared to the case without application, and the average waiting time was reduced by about 2 seconds and the average queue length by about 2.3 vehicles.

• Journal of Christian Education in Korea
• /
• v.64
• /
• pp.203-235
• /
• 2020

The purpose of this study is to identify the degree of social presence perceived by students and to explore the factors that have affected it after practicing Christian Education classes as synchronous distance course due to Covid-19. It is also to suggest effective ways in the aspects of the design and operation to improve social presence. In order to measure social presence and derive influencing factors, research related to synchronous distance class and social presence is summarized through literature review. The researchers also surveyed 58 students in three courses of Christian education major at a University in Gyeonggi-do and conducted in-depth interviews with 6 students. The main findings are as follows: First, the sense of social presence was moderate, the emotional bond was the lowest by sub-factor, the open communication, the sense of community was moderate, and the mutual support and concentration were the highest. Second, factors that had a positive impact on the sense of social reality were group activities, selfintroduction activities, active participation in classes, mutual communication such as Q & A or response to peer learners' opinions during lectures by professors, questions, feedback, etc, and having a smaller number of students. Factors that had a negative impact on the perception of social presence were lack of private conversations, poor participation in classes, lack of communication with each other, and difficulty concentrating. The causes of these negative factors were technical problems and limitations arising from zoom, inconvenience and distracting surroundings, lack of time, and psychological awkwardness. Reflecting the results of the study, orientation to effective synchronous distance course, guidance on smooth communication methods, strengthening the role of professors to promote learning, strengthening group activities and learner-centered activities, and proposing a smaller scale of students were ways that are offered to improve the sense of social presence in synchronous distance courses.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.83-102
• /
• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• The Journal of the Korea Contents Association
• /
• v.14 no.1
• /
• pp.30-39
• /
• 2014

The purpose of this study is to suggest the feasible strategies to vitalize e-learning through investigating the characteristics of e-learning and the evaluations of distance learners on online courses in the domestic universities in Korea. First, in order to accomplish this, 10 Universities and 17 Cyber Universities were selected to explore their characteristics and main projects of e-learning for the administration level investigation. Secondly, content analysis of the bulletin board systems(BBS) and in-depth interviews on distance learners in Cyber Universities were conducted for the user level investigation. The results revealed that Universities in Korea were focused on establishing mobile or smart campuses, diversifying online educational contents, enhancing online interactive systems, and educating e-learning system and smart device utilization. However, distance learners reported that mobile e-learning lacked stability when taking online courses despite its convenience for purpose of academic administration. In addition, distance learners requested the social application workshops to improve on their learning experience as well as the interactions among peers. Therefore, it is important to focus more on how to establish the education-oriented e-learning environment rather than how to implement the administrative projects to animate e-learning in the domestic universities in Korea.

• Journal of Music and Human Behavior
• /
• v.10 no.2
• /
• pp.35-54
• /
• 2013

The purpose of this study was to examine how Korean music therapy students experience growth under clinical supervision. The investigator conducted in-depth qualitative interviews with 9 students from 3 different universities in Seoul who had at least three semesters of clinical supervision. Data was analyzed using a modified grounded theory approach to construct the growth experience of music therapy supervisees. Results suggest that growth can be understood in terms of both personal and professional domains and includes four types of experiences: growth hindering, fostering, mediating, and revealing. In the personal domain, hindering factors are defensiveness, narcissistic trauma, avoidance and anxiety whereas growth fostering and mediating factors include reflection on self, musical self, unconscious drives and conflicting issues as well as self-driven problem solving skills. As a result, growth in the personal domain is associated with increased self-acceptance and self-awareness. Growth in the professional domain is hindered by having trust issues, performance anxiety, identity crisis, and being hypersensitive to the judgment of others. On the other hand, growth is fostered and mediated by opening the self and interacting more with others, building trusting relationships with peers and supervisors, and establishing a new relationship with music, which leads to improved attitude, increased motivation, and more efficient and effective training.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.1
• /
• pp.207-220
• /
• 2021

The purpose of the study is to investigate the application and utility of emergency rescue education and derive limitations, improvements and development directions of university education based on the field experience of 119 emergency medical technician(EMT)s. The research subjects were six new 119 emergency medical technician(EMT)s within three years of starting their first-aid service in the field. After conducting in-depth narrative interviews, the analysis was performed using Colaizzi method. The 82 formulated meanings were derived from significant statements. From formulated meanings, 23 themes, 4 theme clusters, 2 categories were identified. The four theme clusters were 'The effectiveness of university education', 'The limitations of university education', 'The direction of improvement in educational methodology' and 'The direction of improvement in educational contents. University education has been helpful overall, but limitations are observed at the same time, suggesting that it should be developed through the improvement of educational methodologies (i.e. problem-based learning, field case review, education through role-playing, simulation education, strengthening skill ect.) and educational content (i.e. training tailored to the field, education focused on trauma or cardiac arrest, expansion of triage education in disaster management, reinforcement of education on-site safety, education on special patients, diverse guidance and faculty for different perspectives).

• Korean Security Journal
• /
• no.52
• /
• pp.73-91
• /
• 2017

This study is based on the political and economic standpoint of each country, Use advanced equipment to prevent new terrorism from causing widespread damage, In order to establish a countermeasures against terrorism in accordance with the reality of Korea, which is effective in responding to terrorist attacks, Korea conducted a SWOT analysis of the terrorist environment and terrorist environment through specialists. First, internal strengths of Korea 's terrorist environment include stable security situation, weakness of religious and ethnic conflicts, strong regulation and control of firearms, and counter terrorism capabilities and know - how accumulated during major international events. Second, the internal weaknesses of the terrorist environment in Korea include the insecurity of the people, the instability caused by the military confrontation with North Korea, the absence of anti-terrorism law system, the difficulty of terrorism control and management by the development of the Internet and IT technology. Third, the external opportunities for Korea 's terrorist environment are as follows: ease of supplementation and learning through cases of foreign terrorism failure, ease of increase of terrorist budget and support with higher terrorism issues, strengthening of counterterrorism through military cooperation with allied nationsRespectively. Fourth, the external threats to the terrorist environment in Korea are the increase of social dissatisfaction due to the continuous influx of defectors and foreign workers, the goal of terrorism from international terrorist organizations through alliance with the United States,Increased frequency of incidents, and increased IS coverage of terrorism around the world. In addition, the SWOT in - depth interviews on the terrorist environment of the expert group were conducted to diagnose and analyze the problems, terrorism awareness and legal system in the Korean terror environment. The results of the study are summarized as follows.First, the basic law on terrorism should be enacted.Second, the establishment of an integrated anti-terrorism organization.Third, securing and nurturing specialized personnel in response to terrorism.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.1
• /
• pp.203-223
• /
• 2023

Recently, the platform industry is rapidly growing in the global market, and competition is intensifying at the same time. Therefore, in order for domestic platform companies to have global competitiveness in the platform market, it is necessary to study the platform business ecosystem and success factors. However, most of the recent platform-related studies have been theoretical studies on the characteristics of platform business status analysis, platform economy, and indirect network externalities of platforms. Therefore, this study comprehensively analyzed the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzed the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. And based on these factors, an IPA analysis was conducted as a way to propose a success plan to stakeholders in the platform business ecosystem. As a result of the analysis, among the success factors collected through previous studies, manpower, capital, and challenge culture were identified as factors that are relatively well maintained in both importance and satisfaction in Silicon Valley. In the end, it can be seen that the creation of an environment and culture in which Silicon Valley can use it to challenge itself based on excellent human resources and abundant capital contributes the most to the success of Silicon Valley's platform business. On the other hand, although it is of high importance to Silicon Valley's platform corporate ecosystem, the factors that show relatively low satisfaction among stakeholders are 'learning and benchmarking among active companies' and 'strong ties and cooperation between members', and it is analyzed that interest and effort are needed to strengthen these factors in the future. Finally, the systems and policies necessary for market autonomous competition, 'business support service industry', 'name value', and 'spin-off start-up' were important factors in literature research, but the importance and satisfaction of these factors were lowered due to changes in the times and environment. This study has academic implications in that it comprehensively analyzes the success factors of Silicon Valley's business ecosystem proposed in previous studies, and at the same time analyzes the success factors extracted from stakeholders in the actual Silicon Valley platform business ecosystem. In addition, there is another academic implications that importance and satisfaction were simultaneously examined through IPA analysis based on these various extracted factors. As for academic implications, it is meaningful in that it contributed to the formation of the domestic platform ecosystem by providing the government and companies with concrete information on the success factors of the platform business ecosystem and the theoretical grounds for the growth of domestic platform businesses.