• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.225-266
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• 2018

In 2017, 1,252 cases of damages relief related to air passenger transport service were received by the Korea Consumer Agency, a 0.8% drop from 1,262 cases in 2016, the first decline since 2013. In 2017, 444 cases (35.4%) out of received cases of damages relief in the field of air passenger service received by the Korea Consumer Agency were agreed on, and out of cases that were not agreed on, the most number of 588 cases (47.0%) were concluded due to information provision and counseling, and 186 cases (14.9%) were applied to the mediation of the Consumer Dispute Mediation Committee. Major legislations that contain regulations for the damages relief and disputes resolution of air service consumers include the Aviation Business Act and the Consumer Fundamental Act, etc. The Aviation Business Act provides the establishment and implementation of damage relief procedure and handling plan, and the receiving and handling of request of damage relief by air transport businessman, and the notice of protection standard for air traffic users. The Consumer Fundamental Act provides the establishment and management of the consumer counseling organization, the damage relief by the Korea Consumer Agency, the consumer dispute mediation, and the enactment of the criteria for resolving consumer disputes. The procedures for damages relief of air service consumers include the receiving and handling of damages relief by air transport businessman, the counseling, and receiving and handling of damages relief by the Consumer Counseling Center, the advice of mutual agreement by the Korea Consumer Agency, and the dispute mediation system by the Consumer Dispute Mediation Committee. The current system of damage relief and dispute mediation for air service consumer have the problem in the exemption from obligation of establishment and implementation of damage relief plan by air transport businessman under the Aviation Business Act, the problem in the exemption from liability in case of nonfulfillment and delay of transport by aviation businessman under the criteria for resolving consumer disputes in the aviation sector, and the uppermost limit in procedure progress and completion of consumer dispute mediation under the Consumer Fundamental Act. Therefore, the improvement measurements of the relevant system for proper damage relief and smooth dispute mediation for air service consumer are to be suggested as follows: First is the maintenance of the relevant laws for damage relief of air service consumer. The exemption regulation from obligation of establishment and implementation of damage relief plan by air transport businessman under the Aviation Business Act shall be revised. To enhance the structualization and expertise of the relevant regulation for protection and damage relief of air service consumer, it will be necessary to prepare the separate legislation similar to the US Federal Regulation 14 CFR and EU Regulation EC Regulation 261/2004. Second is the improvement of criteria for resolving air service consumer disputes. For this, it will be necessary to investigate whether the cause of occurrence of exemption reason was force majeure, and distinguish the exemption from liability in case of nonfulfillment and delay of transport by aviation businessman under the criteria for resolving consumer disputes in the aviation sector, and revise the same as exemption reasons regulated under the air transport chapter of the Commercial Act and Montreal Convention 1999, and unify the compensation criteria for the nonfulfillment of transport that the substitute flight was provided and the delay of transport. Third is the reinforcement of information provision for damage relief of air service consumer. Aviation-related government agencies and concerned agencies should cooperate with airlines and airports to provide rapidly and clearly diverse information to the air traffic users, including laws and policies for damages relief of air service consumers. Fourth is the supplement to the effectiveness, etc. of consumer dispute mediation. If there is no sign of acceptance for dispute mediation, it is not fair to regard it as acceptance, therefore it will be necessary to add objection system. And if a dispute resolution is requested to another dispute settlement agency in addition to the Consumer Dispute Mediation Committee, it is excluded from the damage relief package, but it should be allowed for the party to choose a mediation agency. It will be necessary to devise the institutional measures to increase the completion rate of mediation so that the consumer dispute can be resolved efficiently through the mediation. Fifth is the introduction of the air service consumer arbitration system. A measure to supplement the limitations of the consumer dispute mediation system is to introduce the consumer arbitration system, but there are two measurements which are the introduction of the consumer arbitration under the Consumer Fundamental Act and the introduction of the consumer arbitration under the Arbitration Act. The latter measurement is considered to be appropriate. In conclusion, as a policy task, the government should prepare laws and system to enhance the prevention and relief of damages and protection of the rights and interests of air service consumers, and establish and implement the consumer-centric policy for the advancement of air service.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Journal of agricultural medicine and community health
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• v.26 no.2
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• pp.15-41
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• 2001

Since medical clients and the community they live in are expected to be center of future public health and medical care system, new service programs must be developed with patients focused on in line with widening public access of information and social participation. Patients- focused service shall mean the area- oriented provision of public health service. In this study, health officers working at public health centers, public health sub- centers and medical offices in Jeonbuk- do area were taken for population in order to investigate their attitudes toward and knowledge about the service area assigning system under the public health programs. Findings from the survey to 260 health officers, divided by general category, are as follows : Government officers at public health organizations appeared to have high grade of understanding to the service area assigning system and also great appreciation for the necessity of it. Regarding the timing for the system to be introduced, they support the gradual implementation and, as for the type of service to be provided, they preferred home nursing and treatment of chronic diseases. Highly positive responses were centered on the health classes under the health promotion projects, and as far as health projects for the old are concerned, services for home nursing, for the disabled and for home- alone people are favored most. On the other hand, budgeting, manpower and reorganization are rated as prerequisite to establishment of the service area assigning system. From the viewpoint of system side, the improvement of working conditions is rendered as most urgent, while the information system for establishing the service area assigning system is conceived far from satisfactory. Proper assignment of specialists was noted as mostly important to establish the delivery system for medical service through the service area assigning system by team. As merits of the service area assigning system, it is pointed out that, through the system, health clients can better be managed and the nursing quality will be improved thank to the enhanced specialization. It is also perceived that the district health service is not well prepared to respond to the increased and diversified needs of community people and, furthermore, service programs of health centers have not been fully developed. The most serious problem standing in the way to expansion of health projects is, it is noted, uniformity (formality) of the project. Based on the results of the survey which suggest time has ripen to introduce the service area assigning system, following strategies are proposed to anchor down the system as soon as possible: First, we should introduce the system gradually, starting from the area selected, and in consideration of area specialities, refraining from the hitherto stereotyped way of providing health service. Second, we should seek to properly assign the specialists and improve the working conditions of the assigned officers by securing sufficient budget, since it is a most urgent step to lay foundation for the service area assigning system. Third, best service program should be developed to meet the satisfaction of community people by responding to their needs and solidifying the management of medical clients. Fourth, wide scope of study should further be conducted in order to help this system take roots in the central living of community residents since pilot project on the experimental base attended by specialists only can not win popularity among the masses.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Genetic Medicine
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• v.8 no.2
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• pp.89-99
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• 2011

Over the years Korean health care system has improved in delivery of quality care to the general population for many areas of the health problems. The system is now being recognized in the world as the most cost effective one. It is covered by the uniform national health insurance policy for which most people in Korea are mandatory policy holders. Genetic counseling service, however, which is well recognized as an integral part of clinical genetics service deals with diagnosis and management of genetic condition as well as genetic information presentation and family support, is yet to be delivered in comprehensive way for the patients and families in need. Two major obstacles in providing genetic counseling service in korean health care system are identified; One is the lack of recognition for the need for genetic counseling service as necessary service by the national health insurance. Genetic counseling consumes a significant time in delivery and the current very low-fee schedule for physician service makes it very difficult to provide meaningful service. Second is the critical shortage of qualified professionals in the field of medical genetics and genetic counseling who can provide the service of genetic counseling in clinical setting. However, recognition and understanding of the fact that the scope and role of genetic counseling is expanding in post genomic era of personalized medicine for delivery of quality health care, will lead to the efforts to overcome obstacles in providing genetic counseling service in korean health care system. Only concerted efforts from health care policy makers of government on clinical genetics service and genetic counseling for establishing adequate reimbursement coverage and professional communities for developing educational program and certification process for professional genetic counselors, are necessary for the delivery of much needed clinical genetic counseling service in Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.2
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• pp.74-88
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• 2019

This study developed the Assessment Indicator evaluating eco-cultural value of temple forest in Korea and applied the developed Assessment Indicator to Songgwang-sa(also known as Seungbo-sachal), one of the Three Jewels Temple. Literature reviews and the draft of Assessment Indicator were drawn from brainstorming(including 2 forest therapy experts, 1 Buddhist monk expert, 1 landscape architect, 1 forest expert, and 6 researchers). After that, the Assessment Indicator drawn from the group of experts(the 1st in-depth interview: 32 people, the 2nd in-depth interview: 30 people) was verified and revised. The final Assessment Indicator, which was composed of 4 parts and 20 items, was developed. The results are as follows. The eco-cultural Assessment Indicator of temple forest was composed of 4 parts, which were Historical Cultural value, Ecological value, Recreatory Visitational value, and Educational Useful value, and 20 items and each item had 5 points. Historical Cultural value had 5 items and its total points were 25. Ecological value had 5 items and had total 25 points. Recreatory Visitational value had 6 items, 30 total points. Educational Useful value had 4 items, 20 total points. The total points of the eco-cultural Assessment Indicator were 100 points. As a result of applying the developed Assessment Indicator to the target place, Songgwang-sa in Mt. Jogye, Historical Cultural value of temple forest was calculated as 23 points(out of 25). Ecological value was 21 point(out of 25), Recreatory Visitational value, 22 points(out of 30), and Educational Useful value, 16 points(out of 20). The total points were 82(out of 100). Consequently, this study is meaningful based on the following 5 aspects. Firstly, this study challenged the development of the eco-cultural Assessment Indicator of temple forest for the first time. It is significant because the developed Assessment Indicator can be a useful resource for the eco-cultural value of temple forest. Secondly, the result showed that Educational Useful value and Recreatory Visitational value of forest temple were very low. Therefore, the supports for leisure, tour, education, and use of temple forest are needed from Korea Forest Service, Ministry of Environment, Cultural Heritage Administration and other government agencies since they acknowledge the temple forest as the best customers in Korea. Thirdly, the excellence or for eco-cultural value of temple forest needs to be extended in a national level. It is possible to make a Korean National Bran(e.g., the Therapy at the Temple) by blending temple stay, which is only in temples, and therapy, and is also possible to be a global tour industry. Fourthly, this study suggested legal definition about the necessary of legal definition for temple forest because there is no legal definition on temple forest in the current situation. When the definition of temple forest is legally arranaged, it would be a foundation for conserving eco-cultural value of temple forest, for organizing exclusively responsible departments in governmental institutions, and further for registering temple forest as World Natural Heritage. Lastly, the developed eco-cultural Assessment Indicators of temple forest from this study would be applied to "the 7 Sansa, Buddhist Mountain Monasteries in Korea(Sansa)" and the characteristics of each 7 temple are drawn. This study would be a basic data for temples' management and use with the eco-cultural Assessment Indicator of temple forest.

• MISULJARYO - National Museum of Korea Art Journal
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• v.98
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• pp.104-129
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• 2020

Designated as a national treasure of Korea and inscribed on the UNESCO World Heritage List, the Tripitaka Koreana at Haeinsa Temple is the world's oldest and most comprehensive extant version of the Tripitaka in Hanja script (i.e., Chinese characters). The set consists of 81,352 carved woodblocks, some of which have two or more copies, which are known as "duplicate woodblocks." These duplicates are supplementary woodblocks (bogakpan) that were carved some time after the original production, likely to replace blocks that had been eroded or damaged by repeated printings. According to the most recent survey, the number of supplementary woodblocks is 118, or approximately 0.14% of the total set, which attests to the outstanding preservation of the original woodblocks. Research on the supplementary woodblocks can reveal important details about the preservation and management of the Tripitaka Koreana woodblocks. Most of the supplementary woodblocks were carved during the Joseon period (1392-1910) or Japanese colonial period (1910-1945). Although the details of the woodblocks from the Japanese colonial period have been recorded and organized to a certain extent, no such efforts have been made with regards to the woodblocks from the Joseon period. This paper analyzes the characteristics and production date of the supplementary woodblocks of the Tripitaka Koreana. The sutra with the most supplementary woodblocks is the Maha Prajnaparamita Sutra (Perfection of Transcendental Wisdom), often known as the Heart Sutra. In fact, 76 of the total 118 supplementary woodblocks (64.4%) are for this sutra. Hence, analyses of printed versions of the Maha Prajnaparamita Sutra should illuminate trends in the carving of supplementary woodblocks for the Tripitaka Koreana, including the representative characteristics of different periods. According to analysis of the 76 supplementary woodblocks of the Maha Prajnaparamita Sutra, 23 were carved during the Japanese colonial period: 12 in 1915 and 11 in 1937. The remaining 53 were carved during the Joseon period at three separate times. First, 14 of the woodblocks bear the inscription "carved in the mujin year by Haeji" ("戊辰年更刻海志"). Here, the "mujin year" is estimated to correspond to 1448, or the thirtieth year of the reign of King Sejong. On many of these 14 woodblocks, the name of the person who did the carving is engraved outside the border. One of these names is Seonggyeong, an artisan who is known to have been active in 1446, thus supporting the conclusion that the mujin year corresponds to 1448. The vertical length of these woodblocks (inside the border) is 21 cm, which is about 1 cm shorter than the original woodblocks. Some of these blocks were carved in the Zhao Mengfu script. Distinguishing features include the appearance of faint lines on some plates, and the rough finish of the bottoms. The second group of supplementary woodblocks was carved shortly after 1865, when the monks Namho Yeonggi and Haemyeong Jangung had two copies of the Tripitaka Koreana printed. At the time, some of the pages could not be printed because the original woodblocks were damaged. This is confirmed by the missing pages of the extant copy that is now preserved at Woljeongsa Temple. As a result, the supplementary woodblocks are estimated to have been produced immediately after the printing. Evidently, however, not all of the damaged woodblocks could be replaced at this time, as only six woodblocks (comprising eight pages) were carved. On the 1865 woodblocks, lines can be seen between the columns, no red paint was applied, and the prayers of patrons were also carved into the plates. The third carving of supplementary woodblocks occurred just before 1899, when the imperial court of the Korean Empire sponsored a new printing of the Tripitaka Koreana. Government officials who were dispatched to supervise the printing likely inspected the existing blocks and ordered supplementary woodblocks to be carved to replace those that were damaged. A total of 33 supplementary woodblocks (comprising 56 pages) were carved at this time, accounting for the largest number of supplementary woodblocks for the Maha Prajnaparamita Sutra. On the 1899 supplementary woodblocks, red paint was applied to each plate and one line was left blank at both ends.

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