• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.304-309
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• 2013

This paper proposes a intelligent approach on range decision of figure of merit. Unknown range of the underwater target and the non-fixed signal excess make the uncertainty for the tracking process. Using the input data of signal excess related to the range, we establish the rule of the fuzzy set and the original data acquired by sonar can be transformed to the fuzzified data set. To reduce the error arisen from the unexpected data, we use the new data transformed in fuzzy set. The piecewise relations of the min value, max one, and the mean one are calculated. The three values are used for the expected range of the underwater target. By analysing the fluctuation of the data, we can expect the target's position and the characteristics of the maneuvering. The examples are presented to show the performance and the effectiveness of the proposed method.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2756-2768
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• 1997

The software Process can be defined as the set of activities, rules, procedures, techniques and tools used within the production of software. A software process model is a conceptual representation of a real world software Process and can be described by process programming languages. In this paper, we present the language constructs of SimFlex designed for object-based software process programming. The design of SimFlex is based on the object concept, so that it can model complex software processes concisely both in syntax and semantics. Since the language constructs of SimFlex are derived from the analysis of major PSEEs and their associated process programming languages, SimFlex includes the core characteristics required for a desirable object-based process programming language. In addition, SimFlex is designed to act as a template software process definition language which could be included in specific PSEEs through customization appropriate to those PSEEs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2354-2360
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• 2009

Healthcare is a field where ubiquitous computing is most widely used. We propose a mining-based healthcare multi-agent system for ubiquitous computing environments. This proposed scheme select diagnosis patterns using mining in the real-time biosignal data obtained from a patient's body. In addition, we classify them into normal, emergency and be ready for an emergency. This proposed scheme can deal with the enormous quantity of real-time sensing data and performs analysis and comparison between the data of patient's history and the real-time sensory data. We separate Association rule exploration into two data groups: one is the existing enormous quantity of medical history data. The other group is real-time sensory data which is collected from sensors measuring body temperature, blood pressure, pulse. Proposed system has advantage that can handle urgent situation in the far away area from hospital through PDA and mobile device. In addition, by monitoring condition of patient in a real time base, it shortens time and expense and supports medical service efficiently.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.453-462
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• 2010

The switch from analog to digital broadcast television is extended rapidly. The DTV can offer multiple programming choices, interactive capabilities and so on. Moreover, with the spread of Internet, the information exchange between the communities is increasing, too. These facts lead to the new TV service environment which can offer customized TV programs to personal/community users. This paper proposes a 'Customized Digital TV System for Individuals/Communities based on Data Stream Mining' which can analyze user's pattern of TV watching behavior. Due to the characteristics of TV program data stream and EPG(electronic program guide), the data stream mining methods are employed in the proposed system. When a user is watching DTV, the proposed system can control the surrounding circumstances as using the user behavior profiles. Furthermore, the channel recommendation system on the smart phone environment is proposed to utilize the profiles widely.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.69-80
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• 2007

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques with better performance.

• The Journal of Information Systems
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• v.30 no.1
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• pp.123-149
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• 2021

Purpose The purpose of this study is to define the anti-ESG activities of companies recognized by media by reflecting ESG recently attracted attention. This study extracts keywords for ESG controversies through association rule mining. Design/methodology/approach A research framework is designed to extract keywords for ESG controversies as follows: 1) From DeepSearch DB, we collect 23,837 articles on anti-ESG activities exposed to 130 media from 2013 to 2018 of 294 listed companies with ESG ratings 2) We set keywords related to environment, social, and governance, and delete or merge them with other keywords based on the support, confidence, and lift derived from association rule mining. 3) We illustrate the importance of keywords and the relevance between keywords through density, degree centrality, and closeness centrality on network analysis. Findings We identify a total of 26 keywords for ESG controversies. 'Gapjil' records the highest frequency, followed by 'corruption', 'bribery', and 'collusion'. Out of the 26 keywords, 16 are related to governance, 8 to social, and 2 to environment. The keywords ranked high are mostly related to the responsibility of shareholders within corporate governance. ESG controversies associated with social issues are often related to unfair trade. As a result of confidence analysis, the keywords related to social and governance are clustered and the probability of mutual occurrence between keywords is high within each group. In particular, in the case of "owner's arrest", it is caused by "bribery" and "misappropriation" with an 80% confidence level. The result of network analysis shows that 'corruption' is located in the center, which is the most likely to occur alone, and is highly related to 'breach of duty', 'embezzlement', and 'bribery'.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.451-460
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• 2018

Open source software is used in various ways when developing new softwares all around the world. It requires rights and responsibilities as a form of an open source software license. Because the license is a contract between original software developers of the open source software and users, we must follow it and extremely cautious to avoid copyright infringement. In particular, we must verify license compatibility when we develop new software using the existing open source softwares. However, license violation issues always occur and lead to lawsuits so that they are having an adverse effect on the open source software ecosystem. Thus, in this paper, we propose a method, OSLC-Vid, to identify license violations whether compatibility issues exist between open source softwares. The proposed method is verified by the experiments to detect actual license violation cases.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.95-104
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• 2019

Recently, software education has been emphasized as a key element of the fourth industrial revolution. Many universities are strengthening the software education for all students according to the needs of the times. The use of online content is an effective way to introduce SW education for all students. However, the provision of uniform online contents has limitations in that it does not consider individual characteristics(major, sw interest, comprehension, interests, etc.) of students. In this study, we propose a recommendation method that utilizes the directional similarity between contents in the boolean view history data environment. We propose a new item-based recommendation formula that uses the confidence value of association rule analysis as the similarity level and apply it to the data of domestic paid contents site. Experimental results show that the recommendation accuracy is improved than when using the traditional collaborative recommendation using cosine or jaccard for similarity measurements.