• Journal of the Korean Geophysical Society
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• v.2 no.1
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• pp.9-26
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• 1999

The Ulleung Basin (Tsushima Basin) in the southwestern East Sea (Japan Sea) is floored by a crust whose affinity is not known whether oceanic or thinned continental. This ambiguity resulted in unconstrained mechanisms of basin evolution. The present work attempts to define the nature of the crust of the Ulleung Basin and its tectonic evolution using seismic wide-angle reflection and refraction data recorded on ocean bottom seismometers (OBSs). Although the thickness of (10 km) of the crust is greater than typical oceanic crust, tau-p analysis of OBS data and forward modeling by 2-D ray tracing suggest that it is oceanic in character: (1) the crust consists of laterally consistent upper and lower layers that are typical of oceanic layers 2 and 3 in seismic velocity and gradient distribution and (2) layer 2C, the transition between layer 2 and layer 3 in oceanic crust, is manifested by a continuous velocity increase from 5.7 to 6.3 km/s over the thickness interval of about 1 km between the upper and lower layers. Therefore it is not likely that the Ulleung Basin was formed by the crustal extension of the southwestern Japan Arc where crustal structure is typically continental. Instead, the thickness of the crust and its velocity structure suggest that the Ulleung Basin was formed by seafloor spreading in a region of hotter than normal mantle surrounding a distant mantle plume, not directly above the core of the plume. It seems that the mantle plume was located in northeast China. This suggestion is consistent with geochemical data that indicate the influence of a mantle plume on the production of volcanic rocks in and around the Ulleung Basin. Thus we propose that the opening models of the southwestern East Sea should incorporate seafloor spreading and the influence of a mantle plume rather than the extension of the crust of the Japan Arc.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.45-73
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• 1995

The current paper presents a system dynamics model which can generate the land use anq transportation system performance simultaneously is proposed. The model system consists of 7 submodels (population, migration of population, household, job growth-employment-land availability, housing development, travel demand, and traffic congestion level), and each of them is designed based on the causality functions and feedback loop structure between a large number of physical, socio-economic, and policy variables. The important advantages of the system dynamics model are as follows. First, the model can address the complex interactions between land use and transportation system performance dynamically. Therefore, it can be an effective tool for evaluating the time-by-time effect of a policy over time horizons. Secondly, the system dynamics model is not relied on the assumption of equilibrium state of urban systems as in conventional models since it determines the state of model components directly through dynamic system simulation. Thirdly, the system dynamics model is very flexible in reflecting new features, such as a policy, a new phenomenon which has not existed in the past, a special event, or a useful concept from other methodology, since it consists of a lots of separated equations. In Chapter I, II, and III, overall approach and structure of the model system are discussed with causal-loop diagrams and major equations. In Chapter V _, the performance of the developed model is applied to the analysis of the impact of highway capacity expansion on land use for the area of Montgomery County, MD. The year-by-year impacts of highway capacity expansion on congestion level and land use are analyzed with some possible scenarios for the highway capacity expansion. This is a first comprehensive attempt to use dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions. The model structure is not very elaborate mainly due to the problem of the availability of behavioral data, but the model performance results indicate that the proposed approach can be a promising one in dealing comprehensively with complicated urban land use/transportation system.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.89-108
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• 2006

Korea has been entering the ageing society as the population of age over 65 shared over 7% since the year 2000. The ageing society needs to have transportation facility considering elderly people's travel behavior. This study aims to understand the elderly people's travel behavior using recent data in Korea. The activity schedule approach begins with travel outcomes are part of an activitv scheduling decision. For tho?e approach. used discrete choice models (especially. Nested Logit Model) to address the basic modeling problem capturing decision interaction among the many choice dimensions of the immense activity schedule choice set The day activity schedule is viewed as a sot of tours and at-home activity episodes tied togather with overarching day activity pattern using the Seoul Metropolitan Area Transportation Survey data, which was conducted in June, 2002. Decisions about a specific tour in the schedule are conditioned by the choice of day activity pattern. The day activity scheduling model estimated in this study consists of tours interrelated in a day activity pattern. The day activity pattern model represents the basic decision of activity participation and priorities and places each activity in a configuration of tours and at-home episodes. Each pattern alternative is defined by the primary activity of the day, whether the primary activity occurs at home or away, and the type of tour for the primary activity. In travel mode choice of the elderly and non-workers, especially, travel cost was found to be important in understanding interpersonal variations in mode choice behavior though, travel time was found to be less important factor in choosing travel mode. In addition, although, generally, the elderly was likely to choose transit mode, private mode was preferred for the elderly over 75 years old owing to weakened physical health for such things as going up and down of stairs. Therefore. as entering the ageing society, transit mode should be invested heavily in transportation facility Planning tor improving elderly transportation service. Although the model has not yet been validated in before-and-after prediction studies. this study gives strong evidence of its behavioral soundness, current practicality. and potential for improving reliability of transportation Projects superior to those of the best existing systems in Korea.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.401-409
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• 2021

This study aimed to estimate the photosynthetic capacity of tomato plants grown in a semi-closed greenhouse using temperature response models of plant photosynthesis by calculating the ribulose 1,5-bisphosphate carboxylase/oxygenase maximum carboxylation rate (V_cmax), maximum electron transport rate (J_max), thermal breakdown (high-temperature inhibition), and leaf respiration to predict the optimal conditions of the CO₂-controlled greenhouse, for maximizing the photosynthetic rate. Gas exchange measurements for the A-C_i curve response to CO₂ level with different light intensities {PAR (Photosynthetically Active Radiation) 200µmol·m^-2·s^-1 to 1500µmol·m^-2·s^-1} and leaf temperatures (20℃ to 35℃) were conducted with a portable infrared gas analyzer system. Arrhenius function, net CO₂ assimilation (A_n), thermal breakdown, and daylight leaf respiration (R_d) were also calculated using the modeling equation. Estimated J_max, A_n, Arrhenius function value, and thermal breakdown decreased in response to increased leaf temperature (> 30℃), and the optimum leaf temperature for the estimated J_max was 30℃. The CO₂ saturation point of the fifth leaf from the apical region was reached at 600ppm for 200 and 400µmol·m^-2·s^-1 of PAR, at 800ppm for 600 and 800µmol·m^-2·s^-1 of PAR, at 1000ppm for 1000µmol of PAR, and at 1500ppm for 1200 and 1500µmol·m^-2·s^-1 of PAR levels. The results suggest that the optimal conditions of CO₂ concentration can be determined, using the photosynthetic model equation, to improve the photosynthetic rates of fruit vegetables grown in greenhouses.

• Korean Journal of Community Nutrition
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• v.26 no.5
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• pp.327-336
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• 2021

Objectives: This study was conducted to create a 3D printable snack dish model for the elderly with low food or fluid intake along with barriers towards eating. Methods: The decision was made by the hybrid-brainstorming method for creating the 3D model. Experts were assigned based on their professional areas such as clinical nutrition, food hygiene and chemical safety for the creation process. After serial feedback processes, the grape shape was suggested as the final model. After various concept sketching and making clay models, 3D-printing technology was applied to produce a prototype. Results: 3D design modeling process was conducted by SolidWorks program. After considering Dietary reference intakes for Koreans (KDRIs) and other survey data, appropriate supplementary water serving volume was decided as 285 mL which meets 30% of Adequate intake. To consider printing output conditions, this model has six grapes in one bunch with a safety lid. The FDM printer and PLA filaments were used for food hygiene and safety. To stimulate cognitive functions and interests of eating, numbers one to six was engraved on the lid of the final 3D model. Conclusions: The newly-developed 3D model was designed to increase intakes of nutrients and water in the elderly with dementia during snack time. Since dementia patients often forget to eat, engraving numbers on the grapes was conducted to stimulate cognitive function related to the swallowing and chewing process. We suggest that investigations on the types of foods or fluids are needed in the developed 3D model snack dish for future studies.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.480-489
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• 2021

Climate change poses great threats to wildlife populations by decreasing their number and destroying their habitats, jeopardizing biodiversity conservation. Asiatic salamander (Hynobiidae) species are particularly vulnerable to climate change due to their small home range and limited dispersal ability. Thus, this study used one salamander species, the Korean clawed salamander (Onychodactylus koreanus), as a model species and examined their habitat characteristics and current distribution in South Korea to predict its spatial distribution under climate change. As a result, we found that altitude was the most important environmental factor for their spatial distribution and that they showed a dense distribution in high-altitude forest regions such as Gangwon and Gyeongsanbuk provinces. The spatial distribution range and habitat characteristics predicted in the species distribution models were sufficiently in accordance with previous studies on the species. By modeling their distribution changes under two different climate change scenarios, we predicted that the distribution range of the Korean clawed salamander population would decrease by 62.96% under the RCP4.5 scenario and by 98.52% under the RCP8.5 scenario, indicating a sharp reduction due to climate change. The model's AUC value was the highest in the present (0.837), followed by RCP4.5 (0.832) and RCP8.5 (0.807). Our study provides a basic reference for implementing conservation plans for amphibians under climate change. Additional research using various analysis techniques reflecting habitat characteristics and minute habitat factors for the whole life cycle of Korean-tailed salamanders help identify major environmental factors that affect species reduction.

• Korean Journal of Environmental Agriculture
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• v.37 no.3
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• pp.189-196
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• 2018

BACKGROUND: Water quality management of river requires quantification of pollutant loads and implementation of measures through monitoring study, but it requires labour and costs. Therefore, many researchers are performing nonpoint source pollution analysis using computer models. However, calibration of model parameters needs observed data. Nitrogen concentration in rainfall is one of the factors to be considered when estimating the pollutant loads through application of the nonpoint source pollution model, but the default value provided by the model is used when there are no observed data. Therefore, this study aims to provide the representative nitrogen concentration of the rainfall for the administrative district ensuring rational modeling and reliable results. METHODS AND RESULTS: In this study, rainfall monitoring data from June 2015 to December 2017 were used to determine the nitrogen concentration in rainfall for each administrative district. Range of the $NO_3{^-}$ and $NH_4{^+}$ concentrations were 0.41~6.05 mg/L, 0.39~2.27 mg/L, respectively, and T-N concentration was 0.80~7.71 mg/L. Furthermore, the national average of T-N concentration in this study was $2.84{\pm}1.42mg/L$, which was similar to the national average of T-N 3.03 mg/L presented by the Ministry of Environment in 2015. Therefore, the nitrogen concentrations suggested in this study can be considered to be resonable values. CONCLUSION: The nitrogen concentrations estimated in this study showed regional differences. Therefore, when estimating the pollutant loads through application of the nonpoint source pollution model, resonable parameter estimation of nitrogen concentration in rainfall is possible by reflecting the regional characteristics.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• Knowledge Management Research
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• v.22 no.2
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• pp.119-144
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• 2021

Recently, as competition in the market evolves from the competition among companies to the competition among their supply chains, companies are struggling to enhance their supply chain management (hereinafter SCM). In particular, as blockchain technology with various technical advantages is combined with SCM, a lot of domestic manufacturing and distribution companies are considering the adoption of blockchain-oriented SCM (BOSCM) services today. Thus, it is an important academic topic to examine the factors affecting the use of blockchain-oriented SCM. However, most prior studies on blockchain and SCMs have designed their research models based on Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT), which are suitable for explaining individual's acceptance of information technology rather than companies'. Under this background, this study presents a novel model of blockchain-oriented SCM acceptance model based on the Technology-Organization-Environment (TOE) framework to consider companies as the unit of analysis. In addition, Value-based Adoption Model (VAM) is applied to the research model in order to consider the benefits and the sacrifices caused by a new information system comprehensively. To validate the proposed research model, a survey of 126 companies were collected. Among them, by applying PLS-SEM (Partial Least Squares Structural Equation Modeling) with data of 122 companies, the research model was verified. As a result, 'business innovation', 'tracking and tracing', 'security enhancement' and 'cost' from technology viewpoint are found to significantly affect 'perceived value', which in turn affects 'intention to use blockchain-oriented SCM'. Also, 'organization readiness' is found to affect 'intention to use' with statistical significance. However, it is found that 'complexity' and 'regulation environment' have little impact on 'perceived value' and 'intention to use', respectively. It is expected that the findings of this study contribute to preparing practical and policy alternatives for facilitating blockchain-oriented SCM adoption in Korean firms.