• Restorative Dentistry and Endodontics
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• v.18 no.1
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• pp.122-132
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• 1993

The aim of this study was to investigate the level of radiopacity of glass ionomer cements and to determine the optimum level of radiopacity that is the most compatible with the radiographic diagnosis of secondary caries. The experiments were performed in two parts. In the first part, the radiopacities of 9 glass ionomer cements (FI, FII, FI-LC, FII-LC, SI, SII, Vit, B-VLC, AC) and base materials(Ultra-Blend, Zinc phoaphate cements, Cavitec, Dycal) were measured by densitometer. Then all experimental materials were divided into 5 groups based on the level of radiopacity of enamel and dentin. In the second part, class III cavities with or without secondary caries were prepared in extracted anterior teeth. The representative materials of each group with different radiopacities were inserted into each cavity. The radiographs were interpreted by 15 dentists and seconsary caries were diagnosed according to a five-point confidence rating. Sensitivity and ROC analysis were used to compare observer performance. The following results were obtained : 1. The radipacity of glass ionomer cements varied between 1.111mm Al and 6.011mm Al equivalent. 2. Among experimental materials, three materials in group I had lower radiopacity than that of dentin. The radiopacity of two materials in group II slightly exeeded that of dentin. Three materials in group III had slightly lower radiopacity than that on enamel. The radiopacity of one material in group W was slightly higher than that of enamel. Four materals in group V had the radiopacity that exeeded over 2.0mm AI equivalent to that of enamel. 3. The group IV was the highest for sensitivity and the group V was the highest for ROC area. However, no significant differences were obtained among group II, III, IV and V (P<0.05) but only group I was significantly lower(P<0.01). 4. In comparison with the observer performance for the radiographic diagnosis of secondary caries, the group II, III, IV, and V were superior to the group I (P<0.01). And so the optimum level of radiopacity to detect the secondary caries was the radiopacity that is higher than that of dentin.

• Water for future
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• v.8 no.1
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• pp.61-79
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• 1975

An attempt is made to develope a scheme for synthesizing unit hydrograph for any arbitrary small watershed in the Han or Geum River basin, which can be applied in determining various sizes of design flood for flood control prijects. Stage gauging stations, seven in the Han and five in the Geun River basin with rating curves, were selected as subbasins for the analysis. Unit hydrographs of 2-hour duration were derived for several heavy storm events using the storm and the corresponding flood runoff data for each subbasin. The Clark method programmed by the Hydrologic Engineering Center, U.S. Corps of Engineers, was utilized for derivation of instantaneous unit hydrographs which were, in turn, converted into 2-hour unit hydrograph. By averaging the 2-hour unit hydrographs from several storm events a representative 2-hour unit hydrograph was determined for each subbasin and hence a separate derivation of dimensionless unit hedrograph was also possible for the Han and Geum River basins. The physiographic characteristics such as stream length, distance to the centroid of each watershed were correlated with the characteristic parameters of the derived unit hydrograph for the subbasins within two large basins. correlation analyses between the characteristic parameters were also made. These correlation analyses resulted a series of four equations and a dimensionless unit hydrograph for the two large basins, which made it possible to draw a synthetic 2-hour unitgraph for any small watershed within the Han or Geum River basin. A detailed procedure for aplying the derived method for an arbitrary basin is summarized with one sample computation for each of the two basins. A comparison of the actually derived 2-hour unit hydrogrograph and the synthesized one showed a fair agreement. A recommendation is made for the further study.

• Journal of the Korean Geotechnical Society
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• v.35 no.4
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• pp.5-14
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• 2019

The typical four conditions related to internal erosion were set from the results of the regular dam safety inspection for 17,500 dams, and a questionnaire survey was conducted for dam safety experts to quantify these four typical current dam conditions with scores between 0 and 10, respectively. In addition, we proposed 'possible score range' for each condition to minimize the decision limits for dam managers to quantify dam conditions while helping to quantify various dam conditions except 4 representative conditions. A case study based on 'quantified score' and 'possible score range' for each condition showed that this method consistently reflects the dangerousness of the dam and provides a reasonable probability of failure. This helps to overcome limitations of dam rating determination by weighted average, and it will help to evaluate dangerous dams as dangerous dams.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.203-210
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• 2020

Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users' ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.111-122
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• 2021

This study analyzed user reviews of Dongbaekjeon and Incheoneum app, which are representative local currencies in Korea, to identify the positive/negative factors of local currency users, and established a marketing strategy based on this. App user reviews were classified into positive and negative based on the star rating, and word cloud, topic modeling, and social network analysis were performed, respectively. As a result, in the negative reviews of Dongbaekjeon and Incheoneum, dissatisfaction with app use and card issuance appeared in common. In positive reviews, keywords such as 'local economy' and 'small business owners' along with satisfaction with 'cashback' appeared. It means that local currency users perceived that their consumption support local economy, and they felt satisfaction in using local currency. Based on the satisfaction/dissatisfaction factors identified as a result of the analysis of this study, we identified what needs to be improved and to be strengthened, and appropriate marketing strategies were established. The text mining method used in this study and research results can provide meaningful information about local currencies to public officials and marketers in charge of local currencies.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.314-314
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• 2023

Extreme climate events can have a large impact on human life by hampering social, environmental, and economic development. Global circulation models (GCMs) are the widely used numerical models to understand the anticipated future climate change. However, different GCMs can project different future climates due to structural differences, varying initial boundary conditions and assumptions about the physical phenomena. The multi-model ensemble (MME) approach can improve the uncertainties associated with the different GCM outcomes. In this study, a comprehensive rating metric was used to select the best-performing GCMs out of 11 CMIP5 and 13 CMIP6 GCMs, according to their skills in terms of four temporal and five spatial performance indices, in replicating the 21 extreme climate indices during the baseline (1975-2017) in South Korea. The MME data were derived by averaging the simulations from all selected GCMs and three top-ranked GCMs. The random forest (RF) algorithm was also used to derive the MME data from the three top-ranked GCMs. The RF-derived MME data of the three top-ranked GCMs showed the highest performance in simulating the baseline extreme climate which was subsequently used to project the future extreme climate indices under both the representative concentration pathway (RCP) and the socioeconomic concentration pathway scenarios (SSP). The extreme cold and warming indices had declining and increasing trends, respectively, and most extreme precipitation indices had increasing trends over the period 2031-2100. Compared to all scenarios, RCP8.5 showed drastic changes in future extreme climate indices. The coasts in the east, south and west had stronger warming than the rest of the country, while mountain areas in the north experienced more extreme cold. While extreme cold climatology gradually declined from north to south, extreme warming climatology continuously grew from coastal to inland and northern mountainous regions. The results showed that the socially, environmentally and agriculturally important regions of South Korea were at increased risk of facing the detrimental impacts of extreme climatology.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.171-184
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• 2023

FinTech, which has brought innovation to the financial industry thanks to the advancements in ICT since 2010, has contributed to the growth of the financial ecosystem and increased consumer benefits. Furthermore, there has been a growing demand for social responsibility and sustainability in financial institutions, which have a significant impact on governments, businesses, and people's lives. Despite this, many FinTech companies and traditional financial institutions are still in the early stages of establishing ESG (Environmental, Social, and Governance) management philosophy or lack long-term plans. In this study, we aim to examine the impact of ESG management on the FinTech industry, focusing on representative domestic cases, and derive policy and institutional measures to spread it in the financial industry. Specifically, we will adopt MSCI rating indicators, which are internationally accepted by various industries such as manufacturing, healthcare, and transportation, to evaluate the 35 ESG management subcategories of FinTech companies. As a result, a total of 22 compliance items were disclosed in the ESG report, and it was possible to confirm the detailed management. Through this, we intend to propose effective management strategies for the organizational structure, operations, programs, and performance evaluation of FinTech companies, which are positioning themselves as sustainable growth drivers in the domestic industry.

• Journal of Oriental Neuropsychiatry
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• v.33 no.1
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• pp.79-111
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• 2022

Major depressive disorder (MDD) causes a persistent feeling of sadness and loss of interest. It can lead to emotional and physical problems. Treatments such as antidepressant and cognitive behavioral therapy for MDD have many limitations. Traditional East Asian Herbal Medicine (TEAM) is a representative modality of Complementary and Integrative Medicine (CIM) which can be used for MDD. However, no study has systematically reviewed the efficacy or safety of TEAM for MDD so far. Therefore, we performed a systematic review and meta-analysis to evaluate effectiveness and safety of TEAM as a monotherapy for MDD. We only included TEAM that could be used in context of clinical setting in Korean Medicine. Outcomes were the Hamilton Depression Rating Scale (HAMD) and total effective rate (TER). After comprehensive electronic search of 11 databases, we included 28 randomized controlled trials (RCTs) that compared HM as monotherapy with antidepressant for MDD. Meta-analysis showed that TEAM had significant benefits in reducing HAMD (MD=-0.40, 95% CI: -0.67 to -0.13, p=0.003, I²=85%) and improving TER (RR=1.06, 95% CI: 1.02 to 1.10, p=0.003, I²=0%). It also appeared to be safer than antidepressant in terms of adverse effects. Methods used for RCTs were poor and the quality of evidence was graded 'low' or 'moderate'. These findings indicate that the use of HM as a monotherapy might have potential benefits in MDD treatment as an alternative to antidepressant. However, considering the methodological quality of included RCTs, the clinical evidence is uncertain. Further well-designed RCTs are required to confirm these findings.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.19-36
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• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.