• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.877-880
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• 2003

In a competitive and globalized business environment, the need for the green products becomes stronger. To meet these trends, the environmental assessment besides delivery, cost and quality of products should be considered as an important factor in new product development phase. In this paper. a knowledge-based approximate life cycle assessment system (KALCAS) for the collaborative design environment is developed to assess the environmental impacts in context of product concept development. It aims at improving the environmental efficiency of the product using artificial neural networks consisting of high-level product attributes and LCA results. The overall framework of the collaborative environment including KALCAS is proposed. This architecture uses the CO environment to allow users on a wide variety of platforms to access the product data and other related information. It enables us to trade-off the evaluation results between the objectives of the product development including the approximate environmental assessment in the collaborative design environment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.203-210
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• 2004

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems - content-based recommending and collaborative filtering. Collaborative filtering recommender systems have been very successful in both information filtering domains and e-commerce domains, and many researchers have presented variations of collaborative filtering to increase its performance. However, the current research on recommendation has paid little attention to the use of time related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest. This paper proposes dynamic fuzzy clustering algorithm and apply it to collaborative filtering algorithm for dynamic recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes. The results of the evaluation experiment show the proposed model's improvement in making recommendations.

• Educational Technology International
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• v.16 no.1
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• pp.1-30
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• 2015

The purpose of this research is to propose a novel social LMS developed for group collaborative learning with a think-aloud tool integrated for sharing cognitive processes in order to improve group collaborative learning performance. In this developmental research, the system was designed with three critical elements: the think-aloud element supports learners through shared cognition, the social network element improves the quality of collaborative learning by forming a structured social environment, and the learning management element provides a understructure for collaborative learning for student groups. Moreover, the three critical elements were combined in an educational context and applied in three directions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.101-107
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• 2014

This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.

• IE interfaces
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• v.18 no.3
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• pp.221-233
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• 2005

e-Manufacturing can be referred to as a system methodology enabling the integration of manufacturing operations and IT technologies to achieve objectives of an enterprise. It is recently regarded as a powerful solution to survive in a chaotic marketplace. While conducting an e-Manufacturing project, we first needed to capture collaborative processes conducted by multiple participants in order to define functions of a system supporting them. However, pervasive process modeling techniques including IDEF3, Petri nets, and UML are not sufficient for modeling collaborative processes. Therefore, we first briefly investigate several process modeling methods including aforementioned modeling methods and ARIS focusing on the collaboration point of view. Then, we propose a new modeling method referred to as Collaborative Process Modeling (CPM) to clearly describe collaborative processes. Also, we develop and illustrate a rule for transforming collaborative process models into Marked Graph models to use the analysis power of the Petri nets. Using CPM empowers us to develop collaborative process models with simple notations, understand and facilitate the realization of the collaboration, and verify models before rushing into the development.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.53-63
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• 2004

There have been a lot of studies on problem based instruction and collaborative learning environment, but there is a lack of the studies on a system that offers a collaborative learning environment for problem based instruction. Thus I developed and applied a collaborative instruction system for web based problem based instruction. The system was comprised of the collaborative instruction room and the problem based instruction room so that the students could solve the problems by groups through collaborative instruction. The developed system was applied to the actual learning. As a result, the system contributed to the students' improvement in academic achievements, drew a high degree of responses from them, and had positive effects on their affective domain.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• Convergence Security Journal
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• v.6 no.3
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• pp.127-133
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• 2006

Some Research is actively being done on a web-based collaborative learning system. This is changes in educational paradigm in the knowledge information age. A web-based collaborative learning system for question making is to improve the effect of studying through positive interactions between colleagues and to motivate studying through group competitions. This system is designed to active and self-leading studying when a learner do collaborative learning for question making in group. This system can help initiate and active studying to learner through a course of collaborative learning for question making. It can be used to achieve collaborative learning in various ways.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.239-250
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• 2003

Recent recommender system user a method of combining collaborative filtering system and content based filtering system in order to slove the problem of the Sparsity and First-Rater in collaborative filtering system. In this paper, to make up for the prediction accuracy in hybrid Recommender system, the harmonic mean weight(CBCF_harmonic_mean) is used for calculating the user similarity weight. After setting up the threshold as 45 considering the performance of content based filtering, we apply significance weight of n/45 to user similarity weight. To estimate the performance of the proposed method, it if compared with that of combing both the existing collaborative filtering system and the content- based filtering system. As a result, it confirms that the suggested method is efficient at improving the prediction accuracy as solving problems of the exiting collaborative filtering system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.83-88
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• 2001

To get the items that a buyer wants in Internet auction. he must search for the items through several auction sites. When the bidding starts, he(the buyer) needs to connect to these auction sites frequently so that he can monitor the bid stats and re-bid. A reserve-price auction reduces the number of connections, but this limits the user's bidding strategy. Another problem is equity between the buyer and the seller. Both the buyer and the seller should profit together within proper limits. In this paper, we propose an auction agent system using a collaborative mobile agent and a brokering mechanism called MoCAAS (Mobile Collaborative Auction Agent System), which mediates between the buyer and the seller and executes bidding asynchronously and autonomously. This reduces connection costs. offers more intelligent bidding and solves the equity problem.