• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.238-238
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• 2015

There are demands for water environmental analysis of discharge processes in paddy fields, however, it is not fully understood in nutrients discharge process for watershed modeling. As hydrological processes both surface and ground water and agricultural water managements are so complex in paddy fields, the development of lowland paddy fields watershed model is more difficult than upland watershed model. In this research, the improvement of SWAT (Soil and Water Assessment Tool) model for a paddy watershed was conducted. First, modification of surface inundated process was developed in improved pot hole option. Those modification was evaluated by monitoring data. Second, the monitoring data in river and drainage channel in lowland paddy fields from 2012 to 2014 were analyzed to understand discharge characteristics. As a case study, Imbanuma basin, Japan, was chosen as typical land and water use in Asian countries. In this basin, lowland paddy fields are irrigated from river water using small pumps that were located in distribution within the watershed. Daily hydrological fluctuation was too complex to estimate. Then, to understand surface and ground water discharge characteristics in irrigation (Apr-Aug) and non-irrigation (Sep-Mar) period, the water and material balance analysis was conducted. The analysis was composed two parts, watershed and river channel blocks. As results of model simulation, output was satisfactory in NSE, but uncertainty was large. It would be coming from discharge process in return water. The river water and ground water in paddy fields were exchanged each other in 5.7% and 10.8% to river discharge in irrigation and non-irrigation periods, respectively. Through this exchange, nutrient loads were exchanged between river and paddy fields components. It suggested that discharge from paddy fields was not only responded to rainfall but dynamically related with river water table. In general, hydrological models is assumed that a discharge process is one way from watershed to river. However, in lowland paddy fields, discharge process is dynamically changed. This function of paddy fields showed that flood was mitigated and temporally held as storage in ground water. Then, it showed that water quality was changed in mitigated function in the water exchange process in lowland paddy fields. In future, it was expected that hydrological models for lowland paddy fields would be developed with this mitigation function.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.42-50
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• 2019

As the industry develops in Korea, the use of hazardous chemicals is increasing rapidly and chemical accidents are increasing accordingly. Most of the chemical accidents are caused by leaks of hazardous chemicals, but there are also accidents in which all the substances are released instantaneously due to sudden high temperature/pressure or defection of the storage tanks. This is called catastrophic failure and its frequency is very low, but consequence is very huge when it occurs. In Korea, there were 15 casualties including three deaths due to catastrophic rupture of water tank in 2013, and 64 instances of failures from 1919 to 2004 worldwide. In case of catastrophic failure, it would be able to overflow outside the bund that reduces the evaporation rate and following consequence. This incident is called overtopping. Overseas, some researchers have been studying the amount of external overflow depending on bund conditions in the event of such an accident. Based on the previous research, this study identified overtopping fraction by condition of bund in accordance with Korea Chemicals Controls Act Using CFD simulation. As a result, as the height increases and the distance to the facility decreases while meeting the minimum standard of the bund capacity, the overtopping effect has decreased. In addition, by identifying the effects of overtopping according to atmospheric conditions, types of materials and shapes of bunds, this study proposes the design of the bund considering the effect of overtopping caused by catastrophic failure with different bund conditions.

• The Korean Journal of Franchise Management
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• v.12 no.1
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• pp.21-34
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• 2021

Purpose: This study examines the structural relationship between perceived quality, relationship quality, and revisit intention in the context of coffee shop. In this model, perceived quality consists of product, service, and experience quality, and relationship quality consists of satisfaction, trust, and commitment, and performance consists of revisit intention. More specially, this study identifies whether perceived quality plays a mediating role in the relationship between perceived quality and relationship quality and the direct/indirect effects of perceive quality on intention to revisit. Research design, data and methodology: The survey was conducted from September 1 to 30, 2019. The data were collected from 320 respondents and analyzed using structural equation modeling (SEM) with AMOS program. Results: The findings are as follows. First, quality perception of coffee specialty stores had a statistically positive effect on relationship quality, indicating supports H1. Therefore, customers can know that they are aware of the quality of coffee specialty stores, including quality of service and experience as well as products, and that they form relationship quality with coffee specialty stores. Second, relationship quality between coffee shops and customers had a significant positive effect on performance. Thus, H2 was supported. The results show that if the coffee shop does not consider relationship quality as important, customer loyalty decreases, the number of customers decreases, and the number of customers who switch to another coffee shop increases, which can lead to a threat to the coffee shop. Third, in the case of hypothesis H3, it was found that there was a partial mediating effect of satisfaction and trust between quality perception and reuse intention of coffee specialty stores, so hypothesis H3 was partially supported. As commitment appears to have no mediating effect, it can be said that customers who use coffee shops are not only difficult to maintain as regular customers of a particular coffee shop, but also have ample room to move to other coffee shops. Conclusions: Although many scholars point out the importance of service quality, few studies were conducted in the context of the Korean food service industry (including coffee shops). From this perspective, this study tested several hypotheses that the quality (product, service, experience) perceived by customers can have a positive effect on relationship quality and performance (re-visit intention), either directly or indirectly. The findings of this study demonstrate that if the manager of a coffee shop understands the characteristics of quality perceived by customers and the role of relationship quality, the effect of quality perceptions on customers can be maximized in order to maintain the relationship with customers.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Nuclear Engineering and Technology
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• v.25 no.4
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• pp.539-547
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• 1993

A stochastic approach using continuous time Markov process is presented to model the one-dimensional nuclide transport in fractured rock media as a further extension for previous works［1-3］. Nuclide transport of decay chain of arbitrary length in the single planar fractured rock media in the vicinity of the radioactive waste repository is modeled using a continuous time Markov process. While most of analytical solutions for nuclide transport of decay chain deal with the limited length of decay chain, do not consider the case of having rock matrix diffusion, and have very complicated solution form, the present model offers rather a simplified solution in the form of expectance and its variance resulted from a stochastic modeling. As another deterministic way, even numerical models of decay chain transport, in most cases, show very complicated procedure to get the solution and large discrepancy for the exact solution as opposed to the stochastic model developed in this study. To demonstrate the use of the present model and to verify the model by comparing with the deterministic model, a specific illustration was made for the transport of a chain of three member in single fractured rock medium with constant groundwater flow rate in the fracture, which ignores the rock matrix diffusion and shows good capability to model the fractured media around the repository.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.409-414
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• 2023

We described how to implement character expressions and animations that play an important role in expressing emotions and personalities in low-polygon character animation. With the development of the video industry, character expressions and mouth-shaped lip-syncing in animation can realize natural movements at a level close to real life. However, for non-experts, it is difficult to use expert-level advanced technology. Therefore, We aimed to present a guide for low-budget low-polygon character animators or non-experts to create mouth-shaped lip-syncing more naturally using accessible and highly usable features. A total of 8 mouth shapes were developed for mouth shape lip-sync animation: 'ㅏ', 'ㅔ', 'ㅣ', 'ㅗ', 'ㅜ', 'ㅡ', 'ㅓ' and a mouth shape that expresses a labial consonant. In the case of facial expression animation, a total of nine animations were produced by adding highly utilized interest, boredom, and pain to the six basic human emotions classified by Paul Ekman: surprise, fear, disgust, anger, happiness, and sadness. This study is meaningful in that it makes it easy to produce natural animation using the features built into the modeling program without using complex technologies or programs.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.