• The Journal of Korean Medicine
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• v.45 no.3
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• pp.54-64
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• 2024

Objectives: Tonifying herbal medicines are used to nourish and balance the body's qi, blood, yin, and yang, targeting deficiencies. They are the second most frequently used category by consumers visiting Korean medicine clinics, following prescriptions for back pain. This study aimed to analyze the adverse events associated with herbal medicine products classified as tonics through a national pharmacovigilance database. Methods: We investigated 11 types of tonifying herbal medicine products (466 product codes) in the Korea Adverse Event Reporting System (KAERS) database from 2012 to 2021. Extracted adverse event reports were analyzed based on information of reports, patient demographics, classification of adverse events, reported herbal medicine products, and causality assessment results. Results: A total of 31 individual case safety reports were identified, covering 33 adverse events. The annual number of reports has increased over the study period. Most reports were filed by physicians and pharmacists, with the majority of patients being adults or elderly. Gastrointestinal disorders were the most frequently reported adverse events, accounting for 48.5% of cases. Of the 33 adverse events, 93.9% were classified as non-serious, while 6.1% were classified as serious. The most frequently reported herbal medicine products were Bojungikgi-tang, Yukmijihwang-tang, and Palmijihwang-tang. Conclusions: Although the study found that adverse events associated with tonifying herbal medicine products are generally not serious, it highlights the importance of systematic monitoring and reporting. The findings underscore the need for improved adverse event reporting systems within traditional Korean medicine to ensure patient safety and guide future research.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.3
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• pp.33-41
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• 2021

This study identified the scale that traditional landscape design has taken up by analyzing a total of 1037 services for design of cultural heritage that had been ordered by the government agencies from 2018 to 2020, and has drawn characteristics of traditional landscape design focusing on major cases. The results are as follows. First, the number of order cases for traditional landscape design has shown differences annually in the services of design of cultural heritage, but the design amount has been found to have the similar average annually, which confirmed that the same level has been maintained each year. It was found that the number of cases of traditional landscape design requiring responsibilities or participations of landscape engineers for 3 years in the entire design had a high proportion of approximately 26%. Second, the traditional landscape design has required professional knowledge and experiences of landscape engineers that could not be replaced by the business operator for design of cultural heritage consisting of architects. The expertise has been shown differently depending on types of construction. First, the topographical design for the work to build a foundation has required understanding of ground shapes and its elevations and professional knowledge on calculation of the amount of the earth work and the remains maintenance technique etc. The plantation design has required basic knowledge on growth characteristics of trees and the environment for growth and understanding of the vegetation landscape of the past. Meanwhile, the design for traditional pavement and traditional landscape structures and facilities has required the expertise on traditional materials that are different from the modern ones and their processing and construction methods. The understanding of changes to water paths and ecosystem, the principles of fluids, and characteristics of each type of fluid was essential for the design for the ecological landscape work including the maintenance of a water system such as rivers etc. As such, the traditional landscape design has a scale accounting for approximately one fourth of the entire cultural heritage design and requires the expertise differentiated from other fields. This improves the provisions of the current law on limiting the actual design, suggesting the need for the establishment of a traditional landscape design company so that all traditional landscape designs can be carried out by landscape engineers.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.665-676
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• 2017

Over the past several years, sea trade have increased traffic by ships which highlighted a problem of unwanted species invading the surrounding seas through ship's ballast water discharge. Maritime trade volume has continuously increased worldwide and the problem still exists. The respective countries are spending billions of dollars in an effort to clean up the contamination and prevent pollution. As part of an effort to solve marine environmental problem, BWM(Ballast Water Management) convention was adopted at a diplomatic conference on Feb. 13 2004. In order to comply harmoniously this convention by each country. This convention will be effective after 12 months from the date which 30 countries ratified accounting for more than 35% of the world merchant shipping volume. On Sep. 8 2016, Finland ratified this convention and effective condition was satisfied as 52 states and world merchant vessel fleet 35.1441%. Thus, after Sep. 8 2017, all existing vessels shall be equipped with BWTS(Ballast Water Treatment System) in accordance with D-2 Regulation, which physically handles ballast water from ballast water exchange system(D-1 Regulation). In this study, we analyzed in detail the optimal design method using the Risk Analysis and Evaluation technique which is mainly used in the manufacturing factory or the risky work site comparing with the traditional design concept method applying various criteria. The Risk Assessment Method is a series of processes for finding the Risk Factors in the design process, analyzing a probility of the accident and size of the accident and then quantifying the Risk Incidence and finally taking measures. In this study, this method was carried out for Electrolysis treatment type on DWT 180K Bulk Carrier using "HAZOP Study" method among various methods. In the Electrolysis type, 63 hazardous elements were identified.

• Asia pacific journal of information systems
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• v.9 no.4
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• pp.23-40
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• 1999

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining neural network and rough set approach, We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables and objects (i.e., firms) is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. Through the reduction of information table, it is expected that the performance of the neural network improve. The rules developed by rough sets show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2,400 Korean firms during the period 1994-1996 were selected, and for the validation, k-fold validation was used.

• Knowledge Management Research
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• v.24 no.3
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• pp.127-148
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• 2023

Traditional Business Intelligence environment is limited to support the rapidly changing businesses and the exponential growth of data in both volume and complexity of data. Companies should shift their business intelligence environment into Self-Service Business Intelligence (SSBI) environment in order to make smarter and faster decisions. However, firms seem to face various challenges in implementing and leveraging the effective business intelligence system, and academics do not provide sufficient studies related including the success factors of SSBI. This study analyzes the three cases of Korean companies in depth, their development process and the assessment of business intelligence, based on the theoretical model on the key success factors of business intelligence systems. The comparative analysis of the three cases including project managers' interviews and performance evaluations provide rich implications for the successful adoption and the use of business intelligence systems of firms. The study is expected to provide useful references for firms to fully leverage the effects of business intelligence systems and upgrade towards self-service business intelligence systems.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Korean society of Dental Hygiene
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• v.24 no.5
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• pp.489-501
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• 2024

Objectives: This study used Time-Driven Activity-Based Costing (TDABC) to measure the value of preventive dental hygiene care and offering a more practical and adaptable cost management method than compared to traditional cost accounting approaches.. Methods: A descriptive survey was conducted using 164 questionnaires collected from dental hygienists actively involved in preventive dental care. The cost data related to labor, materials, and overheads, were anlyzed using detailed classifications of procedures such as scaling, fluoride application, and sealant application. Results: The total allocated cost of 10,163,355 South Korean Won (KRW) was divided by the total working time of the dental clinic (23,520 min), providing a unit cost of 432 KRW/min. Among the three preventive dental care procedures, scaling had the highest total activity cost of 5,867,243 KRW. Conclusions: This study provides valuable evidence for the implementation of an appropriate fee system for preventive dental care performed by dental hygienists. Therefore, this study contributes to the establishment of a fair and rational reimbursement structure.

• Korean Journal of Heritage: History & Science
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• v.55 no.3
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• pp.74-100
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• 2022

The purpose of this study was to investigate the characteristics of Chinese seowons and to obtain data based on the characteristics of waterscapes unique to Korean seowons. The conclusion of this study from the results of investigation and analysis of the location, water system, and design characteristics of 10 representative traditional seowons in China including Yuelu Seowon(嶽麓書院) conducted based on literature research and field observation is as follows. The water system of Chinese seowons is dualized into an inner and an outer water system, and in general, two and a maximum of three water bodies are superimposed on the outside. The locations of seowons are classified into five types: Four double-sided round water type sites, three converted face water type sites, one three-sided round water type site, a four-sided round water type, and a waterproofing type(依山傍水型). Therefore, compared to the typical Korean seowon facing water in the front and a mountain in the back(背山面水型), the Chinese seowons showed a highly hydrophilic property. The water shapes of the external water system were meandering(46.0%), mooring(36.0%), and broad and irregular(9.0%). In addition, water conception(水態) were streams(31.8%), rivers(27.3%), springs(13.6%), falls(9.1%), lakes(4.5%) and ponds(4.5%), in that order. As for waterscapes of the water system inside the seowon, there were seven in Akrok Seowon and four in Mansong Seowon, indicating a comparatively higher number of waterscapes. Since the 27 detailed waterscapes in 10 seowons that were the subject of the study were classified into six types including ponds and half-moon ponds, they appeared to be more diverse than the Korean seowon. It is noteworthy that in the interior waterscape of the traditional Chinese seowon, the ritualistic order, where at least one half-moon pond or square pond(方池) was arranged, is well displayed. In particular, the half-moon pond(伴池), which is difficult to find in Korean seowon, was found to be a representative waterscape element, accounting for 42.8%. If the square pond of Nanxi Seowon based on Zhu Xi's poem 「Gwanseoyugam(觀書有感)」 is also treated as a square-shaped half-moon pond, the proportion of half-moon ponds in the waterscape will be as high as 50%. The pond shapes consisted of 28% square, 24% each for free curve and round shape, 20% for semi-moon shape, and 3.8% for mountain stream type. This seems to differ greatly from the square-shaped Korean seowon. On the other hand, there were a total of 10 types of structures related to the waterscape inside the Chinese seowon: 11(26.8%) pavilion and bridge sites, five gate room sites(牌坊: 16.5%), four gate and tower sites(樓, 1.4%), two Jae sites(齋, 6.2%), and one site each(3.1%) of Heon(軒), Sa(祠), Dae(臺), and Gak(閣). In particular, the pavilions inside seowon were classified into three types: landscape pavilion(景觀亭 27.2%), tombstone pavilion(碑亭, 18.2%), and banquet pavilion(宴集亭, 54.5%). In general, it was confirmed that the half-moon pond with a pedestal bridge, and the pavilion were the major components with a high degree of connection that dominate the waterscape inside the Chinese seowon.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.