• The Korean Society of Law and Medicine
• /
• v.24 no.3
• /
• pp.155-206
• /
• 2023

When a person with diminished mental capacity refuses necessary medical care, normative judgments about when paternalistic intervention can be justified come into question. A typical example is involuntary hospitalization for people with mental disabilities, traditionally governed by mental health law. However, Korean civil law reform in 2011 introduced a new form of involuntary hospitalization through guardianship legislation, leading to a dualized system to involuntary hospitalization. Consequently, a conflict has arisen between the 'best interest and surrogate decision-making' paradigm of civil law and the 'social defense and preventive detention' paradigm of mental health law. Many countries have criticized this dualized system as not only inefficient but also unfair. Moreover, the requirement for the presence of 'mental illness' for involuntary hospitalization under mental health law has faced criticism for unfairly discriminating against people with mental disabilities. In response, attempts have been made to integrate guardianship legislation and mental health law based on mental capacity. This study examines the legislative process and framework of the Mental Capacity Act (Northern Ireland) 2016, which reorganized the mental health care system by fusing guardianship legislation with mental health law based on mental capacity. By analyzing the case of Northern Ireland, which has grappled with conflicts between guardianship legislation and mental health law since the 1990s and recently proposed mental capacity as a single, non-discriminatory standard, we aimed to offer insights for the Korean guardianship and mental health systems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.3
• /
• pp.155-172
• /
• 2024

The COVID-19 pandemic not only posed an enormous human crisis, but also had a profound impact on firms' survival. Social distancing and global lockdown measures designed to protect human lives have paradoxically impaired the business environment. As a result, firms that sought to gain competitive advantage by leveraging external resources were cut off from the external world and faced unexpected challenges. Under these circumstances, researches were conducted in the early stage of the pandemic to explore how certain firms survived while others fell, but they were limited to re-examining business performance using traditional financial factors. However, this study aims to investigate the role of entrepreneurs' competency in crisis situations from the Resource-Based View (RBV), as such competency plays an important role in improving business performance and subsequently the probability of startups' survival. Specifically, we evaluated the performance as of end of 2019 of 1,127 startups evaluated by the Korea Technology Finance Corporation (KOTEC), which provides policy financing based on technology assessment, in 2016. We then conducted an empirical study to determine the mediating role of business performance in the relationship between entrepreneurial competencies and firm survival by verifying how many of the sample firms were still in operation at the end of June 2023, when the Korean government declared COVID-19 as an endemic. For this purpose, we defined technological, financial, and marketing competencies as the sub-factors of entrepreneurial competency, and sales growth rate and employment growth rate as the sub-factors of business performance. The results of the empirical analysis showed that technological and financial competencies of the entrepreneur had a positive impact on both business performance and firm survival, and that sales growth rate and employment growth rate mediated the relationship between technological competence and firm survival. However, the positive influence of entrepreneurs' financial competence of the survival of startups was only evident through the growth of employment. This study is the first study in South Korea to define the survival factors of startups in the context of the COVID-19 pandemic, and is expected to contribute to the theoretical and practical discussions on the importance of entrepreneurs' competency as a firms' survival factor based on RVB.

• Korean Journal of Heritage: History & Science
• /
• v.57 no.3
• /
• pp.24-35
• /
• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.1-20
• /
• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• (The)Study of the Eastern Classic
• /
• no.70
• /
• pp.435-466
• /
• 2018

"Hajaeilgi (荷齋日記)" was written by Ji Gyu-sik, a gongin of Saongwon (司饔院)'s branch, almost everyday for 20 years and 7 months from January 1st, 1891 until the leap month of June 29th, 1911. It deals with many different areas including domestic and foreign circumstances, custom, rituals, all the affairs related to the branch, and also everyday life. Particularly, Ji Gyu-sik did not belong to the yangban class, and we can hardly find diaries written by such class' people. Here, what this author pays attention to among the things written in "Hajaeilgi" is the contents about rituals, especially coming of age, wedding, funeral and ancestral rites. Ji Gyu-sik did write in his "Hajaeilgi" about coming of age, wedding, funeral and ancestral rites that were actually performed then as a person not belonging to the yangban class. Such diaries are very rare, and its value is highly appreciated as a material. Particularly, from the late 19th to the early 20th century of this author focuses on the a study of coming of age, wedding, funeral and ancestral rites as we can see some aspects about it from his diary. Coming-of-age rites were carried out in the first month of the year generally, and in this period, we can see the transformation of their performing period as it was diversified then. This was not exceptional in yangban families. About wedding, while it was discussed, it came to be canceled more often than before maybe because they were going through the process of enlightenment then. It seems that choosing the day was not done in the bride's family always. Jungin or commoners had a weeding in the bride's house, but when it was needed, it was also performed in the groom's house. Ji Gyu-sik followed the traditional wedding procedure for his children rather faithfully, but it was applied flexibly according to the two families' situations or conditions. Ignoring the traditional manners, they had a wedding in the period of mourning or performed a wedding in the groom's house bringing the bride there. It seems that this was related to the decline of Confucian order in the society in the process of modernization. Also, the form of donations changed, too. Gradually, it was altered to the form of money gifts. Moreover, unlike before, divorcing seems to have been allowed then. Remarriage or divorce was the custom transformed from before. Funeral rites had different durations from death up to balin (carrying out a bier for burial) and hagwan (lowering a coffin into the grave), and so it means that they also went through transformation. Sa-daebu used usually 3 months but here was 7 days from death to balin normally, but it seems that there were yangban families not following it. The traces of 3-iljang (burial on the third day after death) most commonly found these days and chowoo jaewoo samwooje can be also found in "Hajaeilgi". Such materials are, in fact, very highly evaluated nowadays. Meanwhile, donations also changed gradually to the form of money. Regarding ancestral rites, time for memorial service was not fixed. Ji Gyu-sik did not follow jaegye (齋戒) before carrying out gijesa, and in some worse case, he went to pub the day before the memorial service to meet his lover or drink. This is somewhat different from the practice of yangban sadaebu then. Even after entering Christianity, Ji Gyu-sik performed memorial service, and after joining Cheondogyo, he did it, too. Meanwhile, there were some exceptions, but in Hansik or Chuseok, Ji Gyu-sik performed charye (myoje) before the tomb in person or sent his little brother or son to do it. But we cannot find the contents that tell us Ji Gyu-sik carried out myoje in October. Ji Gyu-sik performed saengiljesa calling it saengsincharye almost every year for his late father. But it is noticeable that he performed saengsincharye and memorial service separately, too, occasionally. The gijesa, charye, myoje, and saengsincharye carried out by jungin family from Gyeonggi Gwangju around the time that the status system was abolished and the Japanese Empire took power may have been rather different and less strict than yangban family's practice of ancestral rites; however, it is significant that we can see with it the aspects of ancestral rites performed in family not yangban. As described above, the contents about the a study of coming of age, wedding, funeral and ancestral rites found in "Hajaeilgi" are equipped with great value as material and meaningful in the perspective of forklore.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.155-175
• /
• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of the East Asian Society of Dietary Life
• /
• v.14 no.1
• /
• pp.1-10
• /
• 2004

This study was conducted to investigate the dietary behavior and image and preference of Japanese foods. The Subjects were consisted of 570 university students(243 males and 327 females) in Daegu and Kyungbuk area, Korea. The students responses to the 10 questions about image of Japanese foods were also measured on 5 point Likert scale. Data were presented by using frequency, percentage, chi-square test and T-test. The results of this study were as follows: (1) On the eating habits, 'the whole family has breakfast together with same foods everyday'scored high as 42.3％ and 'foods put in a big platter by gathering everyday'as 35.8％. (2) About the eating customs, 53.5％ of the subjects responded that the seat was fixed at meal time, 56.4％ didn't start to eat before the patriarch started a meal and 30.9％ responded that the head of a family had more foods in number and quantity. (3) On the table manners, 13.4％ of the subjects were scolded about 'watching TV on eating', 11.5％ about 'making left-over foods', 8.0％ about 'misuse of spoon and chopsticks'. (4) The preferred ethnic foods by University students was in other of Korean, Chinese, Italian, Japanese and French foods. (5) Among subjects, 93.8％ had no experience of visiting Japan and 92.6％ wanted to visit Japan. Images on the Japanese foods were 'the price is too expensive' (mean 4.15) and 'the decoration is wonderful'(mean 4.05). But the subjects did not think Japanese foods as 'hot'(mean 2.21) and 'greasy'(mean 2.51). (6) The favorite Japanese food of subjects was Udon(mean 3.98), Sushi(mean 3.85) and Tempura(mean 3.69). So Udon turned out to be the most popular Japanese foods by university students in Daegu and Kyungbuk area, Korea. But they did not prefer Natto(mean 2.68), Ochazuke(mean 2.76), Okonomiyaki(mean 2.87) and Misosiru and did not eat. From the above results, Korean university students preferred Udon to Natto among Japanese traditional foods, and they estimated Japanese foods as 'too expensive'. Therefore, lowering the price and developing the cooking method for Korean taste were needed to increase the intake of Japanese traditional foods by Korean university students and.

• Journal of Nutrition and Health
• /
• v.45 no.6
• /
• pp.562-576
• /
• 2012

Han-sik is a term in Korean that may indicate any Korean dish or food. At present, there is no general consensus on the definition of Han-sik among scholars or professionals in related fields. The aim of this study was to investigate perceptions of Han-sik by professionals in the fields of food, nutrition, and culinary arts using 512 dishes and foods commonly consumed by Koreans using the 4th Korean National Health and Nutrition Survey. A total of 117 professionals out of 185 initially contacted professionals participated in this online survey. We calculated the rate of respondents with a positive answer, that is "It is Han-sik', on each dish and food from the 512 items in 28 dish groups. Items were categorized into five groups according to their Han-sik perception rate: over 90%, 75-89%, 50-74%, 25-49%, and below 25%. Most items in the three dish groups 'Seasoned vegetables, cooked (Namul Suk-chae)', 'Kimchis', and 'Salt-fermented foods (Jeotgal)' showed high perception rates of Han-sik, with a higher than 90% positive response. Items in 'Soups', 'Stews', and 'Steamed foods' dish groups also showed high perception rates of Han-sik. However, no item showed a greater than 90% Han-sik perception rate in 'Fried foods (Twigim)', 'Meat, poultry and fishes', 'Legumes, nuts, and seeds', 'Milk and milk products', 'Sugars and confectioneries', and 'Soup'. Most items in the 'Milk and milk products', 'Sugars and confectioneries', and 'Soup' groups belonged to the lowest perception rate of below 25%. There was a significant difference in the proportion of items perceived as Han-sik by the length of living abroad to (p < 0.05). In summary, the perception rate of Han-sik seemed to be affected by the cooking method, ingredients, and length of time living abroad by the professionals. Further studies targeting subjects with different characteristics and socioeconomic status are warranted to define the concept of Han-sik.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.117-127
• /
• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.