• Geophysics and Geophysical Exploration
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• v.10 no.3
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• pp.173-182
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• 2007

It is well known that manganese nodules enriched with valuable metals are abundantly distributed in the abyssal plain area in the Clarion-Clipperton (C-C) fracture zone of the northeast Pacific. Previous studies using deep-sea camera (DSC) system reported different observations about the relation of seafloor topographic change and nodule abundance, and they were sometimes contradictory. Moreover, proper foundation on the estimation of DSC underwater position, was not introduced clearly. The variability of the mining condition of manganese nodule according to seafloor topography was examined in the Korea Deep Ocean Study (KODOS) area, located in the C-C zone. In this paper, it is suggested that the utilization of deep towing system such as DSC is very useful approach to whom are interested in analysing the distributional characteristics of manganese nodule filed and in selecting promising minable area. To this purpose, nodule abundance and detailed bathymetry were acquired using deep-sea camera system and multi-beam echo sounder, respectively on the seamount free abyssal hill area of southern part ($132^{\circ}10'W$, $9^{\circ}45'N$) in KODOS regime. Some reasonable assumptions were introduced to enhance the accuracy of estimated DSC sampling position. The accuracy in the result of estimated underwater position was verified indirectly through the comparison of measured abundances on the crossing point of neighboring DSC tracks. From the recorded seafloor images, not only nodules and sediments but cracks and cliffs could be also found frequently. The positions of these probable unminable area were calculated by use of the recorded time being encountered with them from the seafloor images of DSC. The results suggest that the unminable areas are mostly distributed on the slope sides and hill tops, where nodule collector can not travel over.

• Journal of the Society of Naval Architects of Korea
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• v.28 no.2
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• pp.52-68
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• 1991

A new propeller series is developed using the newly developed blade section(KH18 section) which behaves better cavitation characteristics and higher lift-drag ratio at wide range of angle-of-attack. The pitch and camber distributions are disigned in order to have the same radial and chordwise loading distribution with the selected circumferentially averaged wake input. Since the geometries of the series propeller, such as chord length, thickness, skew and rate distribations, are selected by regression of the recent full scale propeller geometric data, the performance prediction of a propeller at preliminary design stage can be mure realistic. Number of blades of the series propellers is 4 and the expanded blade area ratios are 0.3, 0.45, 0.6 and 0.75. Mean pitch ratios are selected as 0.5, 0.65, 0.8, 0.75 and 1.1 for each expanded area ratio. The new propeller series is composed of 20 propellers and is named as KD(KRISO-DAEWOO) propeller series. Propeller open water tests are performed at the experimental towing tank, and the cavitation observation tests and fluctuating pressure measurements are carried out at the cavitation tunnel of KRISO. $B_{P}-\delta$ curves, which can be used to select the optimum propeller diameter at the preliminary design stage, are derived from a regression analysis of the propeller often water test results. The KD-cavitation chart is derived from the cavitation observation test results by choosing the local maximum lift coefficient and the local cavitation number as parameters. The caviy extent of a propeller can be predicted more accurately by using the KD-cavitation chart at a preliminary design stage, since it is derived from the results of the cavitation observation tests in the selected ship's wake, whereas the existing cavitation charts, such as the Burrill's cavitation chart, are derived from the test results in uniform flow.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Korean Journal of Environment and Ecology
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• v.34 no.6
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• pp.589-600
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• 2020

Effective conservation and management of protected areas require monitoring the settlement of invasive alien species and reducing their dispersion capacity. We simulated the potential distribution of invasive alien plant species (IAPS) using three representative species distribution models (Bioclim, GLM, and MaxEnt) based on the IAPS distribution in the forest genetic resource reserve (2,274ha) in Uljin-gun, Korea. We then selected the realistic and suitable species distribution model that reflects the local region and ecological management characteristics based on the simulation results. The simulation predicted the tendency of the IAPS distributed along the linear landscape elements, such as roads, and including some forest harvested area. The statistical comparison of the prediction and accuracy of each model tested in this study showed that the GLM and MaxEnt models generally had high performance and accuracy compared to the Bioclim model. The Bioclim model calculated the largest potential distribution area, followed by GLM and MaxEnt in that order. The Phenomenological review of the simulation results showed that the sample size more significantly affected the GLM and Bioclim models, while the MaxEnt model was the most consistent regardless of the sample size. The optimal model overall for predicting the distribution of IAPS among the three models was the MaxEnt model. The model selection approach based on detailed flora distribution data presented in this study is expected to be useful for efficiently managing the conservation areas and identifying the realistic and precise species distribution model reflecting local characteristics.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.

• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.173-199
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• 2011

A franchisee has to depend on the overall system, such as knowhow and management support, from a franchisor in the franchise system and the two parties do not start with the same position in economic or information power because the franchisor controls or supports through selling or management styles. For this, unfair trades the franchisor's over controlling and limiting the franchisee might occur and other side effects by the people who give the franchisee scam trades has negatively influenced on the development of franchise industry and national economy. So, the purpose of this study is preventing unfair trade for the franchisee from understanding the causes and problems of dispute between the franchisor and the franchisee focused on the dispute cases submitted the Korea Fair Trade Mediation Agency and seeking ways to secure the transparency of recruitment process and justice of franchise management process. The results of the case analysis are followed; first, affiliation contracts should run on the franchisor's exact public information statement and the surely understanding of the franchisee. Secondly, the franchisor needs to use their past experiences and investigated data for recruiting franchisees. Thirdly, in the case of making a contract with the franchisee, the franchisor has to make sure the business area by checking it with franchisee in person. Fourthly, the contracts are important in affiliation contracts, so enacting the possibility of disputes makes the disputes decreased. Fifthly, lots of investigation and interests are needed for protecting rights and interests between the franchisor and franchisee and preventing the disputes by catching the cause and more practical solutions of the disputes from the government.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Advances in pediatric surgery
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• v.5 no.1
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• pp.75-81
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• 1999

A survey on the intestinal atresias was made among 34 members of Korean Association of Pediatric Surgeons. The response rate was 82.4 %. Two hundred and fifteen patients from the January 1, 1994 to December 31, 1996 were analyzed. The lesions were 73 cases of duodenum(DA), 72 cases of jejunum(JA), 71 cases ileum(IA) and 2 cases cecum and sigmoid colon respectively. There were 2 cases of combined anomalies (DA + JA + IA and DA + JA). Male to female ratio was 1:1 in DA, and 1.8:1 in JA. Seventy four cases(34.3 %) were premature babies(DA 35.2 %, JA:48.6 %, IA:19.2 %), and 62 cases(28.7 %) had low birth weight (DA:39.4 %, JA 33.0 %, IA:13.7 %). Antenatal diagnosis was made in 92 cases(43.6 %). However 22 cases (23.9 %) of them were transferred to pediatric surgeon after delivery. Maternal polyhydramnios was observed in 63 cases(28.9 %). Seventy· five cases(34.4 %) were taken only simple abdominal film for diagnostic studies. The associated malformations were observed in 54 aresia and were observed more frequently in DA(35 cases, 47.9 %). Meconium peritonitis due to intrauterine bowel perforation was more frequently associated with IA compared to DA and JA. The overall mortality rate was 30 %. (Abbreuations: $P_{T}$;p-value in total, $P_{DJ,DI,JI}$;p value between two groups among duodenal, jejunal and ileal groups).

• Journal of Soil and Groundwater Environment
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• v.12 no.4
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• pp.25-31
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• 2007

Characterizing the risk posed by a mixture of chemicals is a challenging task due to the chemical interactions of individual components that may affect their physical behavior and hence alter their exposure to receptors. In this study, cell tests that represent subsurface environment were carried out using benz[a]anthracene (BaA) and p-xylene focusing on phasetransforming interaction to verify increased mobility and risk of highly sorbed pollutants in the presence of less sorbed, mobile liquid pollutants. A transport model was also developed to interpret results and to simulate the same process on a field scale. The experimental results showed that BaA had far greater mobility in the presence of p-xylene than in the absence of that. The main transport mechanisms in the vadose zone were by dissolution to p-xylene or water. The transport model utilizing Defined Time Steps (DTS) was developed and tested with the experimental results. The predicted and observed values showed similar tendency, but the more work is needed in the future study for more precise modeling. The field-scale simulation results showed that transport of BaA to groundwater table was significantly faster in the presence of NAPL, and the oral carcinogenic risk of BaA calculated with the concentration in groundwater was 15${\sim}$87 times larger when mixed with NAPL than when solely contaminated. Since transport rate of PAHs is very slow in the subsurface without NAPL and no degradation of PAHs was considered in this simulation during the transport, the increase of risk in the presence of NAPL is expected to be greater for the actual contaminated site.