• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.246-250
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• 2005

Recommand Systems are being used by an ever-increasing number of E-Commerce to help customers find products to purchase. Recommend Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different customers tastes. In this paper, we design and build a Recommend System using the historical customer movie purchase transactions and extracts the knowledge needed to make association recommendations to new customers.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.70-74
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• 1998

In this paper, by means of Lyapunov second method, we analyze the stability of IP control servo systems in the time domain for the first time. Based on the results on the stability analysis, the design rule to select the gain of IP control is suggested such that the maximum error of output to the nominal system is guaranteed for all uncertainty and load variations. An example of a speed control of brushless dc motor given to prove the unusefulness of the gain design rule.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.162-165
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• 1996

In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.18-24
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• 1998

This paper attempts to improve domestic natural gas pricing system, thereby optimizing social welfare. This is done by deriving theoretical frameworks of natural gas pricing, which make use of both Ramsey component pricing rule and Efficient component pricing rule based on the theory of marginal cost. Allocative efficiency and social welfare between gas prices derived from the three pricing mechanism, present Cost-based pricing, Ramsey component pricing rule and Efficient component pricing rule, are analysed and compared in the case study. For the city gas, allocative efficiency of Cost-based pricing is higher than that of Ramsey component pricing rule and Efficient component pricing rule. In contrast, for the natural gas consumed for power generation, allocative efficiency of Cost-based pricing is lower than the other two pricing systems. It also turns out that social welfare is improved by the prices driven from Ramsey component pricing rule and Efficient component pricing rule rather than present Cost-based pricing.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Management & Information Systems Review
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• v.31 no.3
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• pp.21-46
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• 2012

This paper attempts to offer an effective strategy of hedge fund based on trade probability control in the futures market. By using various technical indicators, we create an association rule and transforms it into a trading rule to be used as an investment strategy. Association rules are made by the combination of various technical indicators and the range of individual indicator value. Adjustments of trade probabilities are performed by depending on the rule combinations and it can be utilized to establish an effective investment strategy onto the risk management. In order to demonstrate the superiority of the investment strategy proposed, we analyzed a profitability using the futures index based on KOSPI200. Experiments results show that our proposed strategy could effectively manage and response the dynamics investment risks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.10-20
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• 2020

This paper presents a method to measure the similarity of assigned jobs in the gravure printing operation based on the chromaticity and color sequence, and order the jobs accordingly. The proposed dispatching rule can be used to fulfill diverse manufacturing site requirements because the parameters can be adjusted to prioritize chromaticity and color sequence. In general, dispatching rules either ignore the job-changing time or require that the time be clearly defined. However, in the gravure printing operation targeted in this study, it is difficult to apply the general dispatching rule because of the difficulties in quantifying the job-changing time. Therefore, we propose a method for generalizing assignment rules of the job planner, allocating relative similarity among assigned jobs, and determining the sequence of jobs accordingly. Chromaticity priority is determined by the arrangement of the color assignments in the printing operation; color sequence priority is determined by the addition, deletion, or change in a specific color sequence. Finally, the job similarity is determined by the dot product of the chromaticity and color sequence priorities. Implementation of the proposed dispatching rule at an actual manufacturing site showed the planner present the same job order as that obtained using the proposed rule. Therefore, this rule is expected to be useful in industrial sites where clear quantification of the job-changing time is not possible.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.15-19
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• 2004

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.