• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.415-426
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• 2021

Purpose: The purpose of this study is to create the deveopment plan for promotion of advanced disaster-safety awareness, which is noted as a major factor in the large disaster. Method: This study is to conduct theoretical review with regard to disaster management and safety awareness. Consciousness surveys on safety awareness and previous disaster case was analyzed to derive the cause of the disaster, and the development plan for promotion of advanced disaster-safety awareness was suggested. Result: In the survey on the public's sense of safety on the disaster management evaluation, 'Response' stage was well performed, but the 'Recovery' stage was not. Especially, it was found that disaster safety education at the 'Prevention' stage was very lacking. In the survey on the public's safety awareness, the awareness level of the evacuation facility was very low, information on infectious diseases and collapse accident was insufficient. Especially, it has been found that the awareness on safety regulation in daily life is very insufficient. Through the case study on previous disaster(COVID-19, Fire in Miryang Sejong Hospital, Forest fire in the east coas at 2004'), it was derived that the lack of safety awareness(such as safety insensitivity) was the main factor of the expansion of the damage scale. Conclusion: The development plan for promotion of advanced disaster-safety awareness are as follow. First, it is necessary to spread the safety culture movement through the expansion of safety education and safety promotion. Second, disaster confrontation training for the public should be implemented to improve the effectiveness of disaster response. Finally, it is necessary to change the individual awareness on safety. When these factors are implemented systematically, advanced disaster-safety awareness can be promoted. Ultimately, disaster accidents in our society can be reduced.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.6
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• pp.25-38
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• 2020

The objective of this study is to propose a direction for creating a memorial park for the 250 students victims of the Sewol ferry disaster. To this end, this study first attempted to understand the matters discussed at various levels to create a memorial park and find a way that the park can be built by gathering opinions from the bereaved families and the victims themselves, as well as local residents, and experts. Workshops, competitions, special lectures, and websites, etc, were analyzed. A social discourse analysis methodology was used for systematic analysis, and the analyzed discourse was categorized into 4 types for assessment, and the functions and roles were subdivided into 15 types. To assess the priorities and the adequacy of the discourse, an analytic hierarchy process (AHP) was used among 30 activists, public servants, and experts. Then, a survey was conducted to analyze the perception of the residents (467 participants including the bereaved families) about the memorial park. Based on the results of the analysis, two directions were set for the memorial park. First, is a memorial park to remember the victims in everyday life. It must be a park with various cultural contents instead of a conventional memorial park that is solemn and grave sharing anguish and sorrow. The memorial park for the Sewol ferry disaster must become a space where visitors can naturally encounter and remember the victims. Second, is a park that serves as a catalyst that brings change and innovation to the community. It must be able to bring change to the community with direct and indirect influence. It must serve as an impetus to bring change and innovation to the community in the mid-to-long-term. Having many visitors may also lead to an economic effect. These visitors may not just stay in the park, but even contribute to revitalizing the local businesses. The purpose of this study is to apply the research findings to guide the International Design Competition scheduled for 2020 and serve to establish guidelines for a continuous park management system.

• (The)Study of the Eastern Classic
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• no.66
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• pp.129-162
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• 2017

This article examines the form of hell and the nature of literary works in the late Joseon period. 'Hoeshimgok(回心曲)' divides a sinner into a man and a woman, and presents a virtue of goodness to a man and an item of evil to a woman. The elements of virtue and malice are both Buddhist ethical norms and Confucian ethical norms. Hell-related novels have common features that emphasize the ethical norms that should be kept in daily life through the causes of hell, though the patterns of punishment and their reasons are slightly different depending on the works. And 'Hoeshimgok(回心曲)' and these works are generally shown by reducing the punishment pixel of hell compared to the cause of hell. This characteristic shows that the literary works of the late Joseon literature related to hell were mainly aimed at providing or educating ethical virtues centered on 'Samgangwol(三綱五倫)' through sanctions of 'Hell' widely known to the general public. The emphasis on Confucian ethics is not limited to works of literature related to hell. In the nineteenth century, when these works were created and circulated, there is a surge in the number of chapters and publications of books for Confucian Indoctrination, Didactic Gasa, and Goodness Books, which emphasize Confucian ethics. Such a strengthening of the Confucian ethical consciousness can be attributed to the crisis of the 19th century Joseon society about the social confusion that threatens the existing system. In particular, the creation and circulation of literary works related to hell in the late Joseon period is related to the dissemination and spread of Catholicism. In the end, the hell shape of the late Joseon literature reflects the crisis of social confusion faced by Joseon society in the nineteenth century. Therefore, it can be said that it has the character of literary response to the prevalent diffusion of Catholicism.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.1
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• pp.10-20
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• 2023

Zhū Xī, the representative of Confucianism, and Báiyùchán, the representative of Taoism in the South Song Dynasty, showed different sense of appreciation and enjoyment on the same space that was Mountain Wǔyí in their ideologically cultural ways. Based on the temples Wŭyíjīngshè(武夷精舍) where Zhū Xī stayed and Zhĭzhĭān(止止庵) where Báiyùchán resided, this study revealed their lives in such temples to look into their appreciation on ideology and space. Then, based on the words 'YiBoEumYeong [移步吟詠]' shown on the poetry they chanted in relation with Wǔyíjiǔqū from its 1^st valley to its 9^th valley, this study examines their understanding of scenery and system of appreciation that appeared in dynamic ways to conclude: First, even same scenery shows different understanding of scenery and appreciation of space in accordance with the viewers' thinking ways of culture. Second, as the Confucianism and Taoism influenced in ideologically cultural ways to develop each other in the Song dynasty, they absorbed their merits each other to supplement shortcomings in their own. In this process, they made it clear that their own propositions were different between them in their essential meanings although they used common terms for such propositions. Third, as the Confucian master who compiled the Neo-Confucianism of the South Song dynasty, Zhū Xī regarded Wŭyíjīngshè and Wǔyíjiǔqū as a place of learning and a place of seeking the truth to go for 'being unified with nature' so that everyday life can be united with Tao of Li [理] everywhere beyond the limited appreciation of the scenery. That is, this thought works for 'recovery of nature of our own [復其性]', the learning goal of Confucianism, and is aimed to 'cultivate the essential nature of our own(性情涵養)' through such beautiful nature. Fourth, as the master of Keumdan family of the South Song Taoism, Báiyùchán regarded Zhĭzhĭān and Wǔyíjiǔqū as a Taoist temple that has a long history rooting from Taesangwon temple, a clean place of discipline to become a Taoist hermit through hard training. He, therefore, directly referred to Zhĭzhĭān and Wǔyíjiǔqū in relation with the Taoist legends remaining in Wǔyíjiǔqū such as hermits' dinners, female hermits, leaving the human world as a hermit and so on as ways for becoming a hermit so that he went for the level of perfectly going out of human world and becoming a hermit. He, therefore, defined Mountain Wǔyí as a world and universe of hermits where he himself too hovered between outside and inside of poetry literature as a hermit through the mood and attitude of keeping himself enjoying the scenery as a hermit.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Chest Surgery
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• v.41 no.5
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• pp.598-604
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• 2008

Background: The improvements in endoscopic equipment and surgical robots has encouraged the performance of minimally invasive cardiac operations. Yet only a few Korean studies have compared this procedure with the sternotomy approach. Material and Method: Between December 2005 and July 2007, 48 patients (group A) underwent minimally invasive cardiac surgery with AESOP through a small right thoracotomy. During the same period, 50 patients (group B) underwent conventional surgery. We compared the operative time, the operative results, the post-operative pain and the recovery of both groups. Result: There was no hospital mortality and there were no significant differences in the incidence of operative complications between the two groups. The operative $(292.7{\pm}61.7\;and\;264.0{\pm}47.9min$, respectively; p=0.01) and CPB times ($128.4{\pm}37.6\;and\;101.7{\pm}32.5min$, respectively; <0.01) were longer for group A, whereas there was no difference between the aortic cross clamp times ($82.1{\pm}35.0\;and\;87.8{\pm}113.5min$, respectively; p=0.74) and ventilator times ($18.0{\pm}18.4\;and\;19.7{\pm}9.7$ hr, respectively; p=0.57) between the groups. The stay on the ICU $(53.2{\pm}40.2\;and\;72.8{\pm}42.1hr$, respectively; p=0.02) and the hospitalization time ($9.7{\pm}7.2\;and\;14.8{\pm}11.9days$, respectively; p=0.01) were shorter for group A. The Patients in group B had more transfusions, but the difference was not significant. For the overall operative intervals, which ranged from one to four weeks, the pair score was significantly lower for the patients of group A than for the patients of group B. In terms of the postoperative activities, which were measured by the Duke Activity Scale questionnaire, the functional status score was clearly higher for group A compared to group B. The analysis showed no difference in the severity of either post-repair of mitral ($0.7{\pm}1.0\;and\;0.9{\pm}0.9$, respectively; p=0.60) and tricuspid regurgitation ($1.0{\pm}0.9\;and\;1.1{\pm}1.0$, respectively; p=0.89). In both groups, there were no valve related complications, except for one patient with paravalvular leakage in each group. Conclusion: These results show that compared with the median sternotomy patients, the patients who underwent minimally invasive surgery enjoyed significant postoperative advantages such as less pain, a more rapid return to full activity, improved cosmetics and a reduced hospital stay. The minimally invasive surgery can be done with similar clinical safety compared to the conventional surgery that's done through a median sternotomy.

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