• International Journal of Safety
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• v.4 no.2
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.565-584
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• 2024

More than 1 million automobile insurance repairs occur per year globally, and the related repair costs add up to astronomical amounts. Insurance companies and repair shops are spending a great deal of money on manpower every year to claim reasonable insurance repair costs. For this reason, promptly predicting insurance claims for vehicles in accidents can help reduce social costs related to auto insurance. Several recent studies have been conducted in auto insurance repair prediction using variables such as photos of vehicle damage. We propose a new model that reflects auto insurance repair characteristics to predict auto insurance repair claims through an association rule method that combines gradient descent and location information. This method searches for the appropriate number of rules by applying the gradient descent method to results generated by association rules and eventually extracting main rules with a distance filter that reflects automobile part location information to find items suitable for insurance repair claims. According to our results, predictive performance could be improved by applying the rule set extracted by the proposed method. Therefore, a model combining the gradient descent method and a location-based association rule method is suitable for predicting auto insurance repair claims.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.242-252
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• 2006

Data mining is the exploration and analysis of huge amounts of data to discover meaningful patterns. One of the most important data mining problems is association rule mining. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm is proposed that is based on a link structure between transactions. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.121-124
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• 2003

It is a major concern of e-shopping mall managers to satisfy a variety of customer's desire by recommending a proper commodity to the expected purchaser. Customer information like customer's fondness and idiosyncrasy in shopping has not been used effectively for the customers or the suppliers. Conventionally, e-shopping mall managers have recommended specific items of commodities to their customers without considering thoroughly in a customer point of view. This study introduces the ways of a choosing and recommending of commodities for customer themselves or others. A similarity measure between one member's idiosyncrasy and the other members' is developed based on the rule base and the case base. The case base is improved by recognizing and learning the changes of customer's desire and shopping trend.

• Journal of Power System Engineering
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• v.3 no.1
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• pp.45-49
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• 1999

Lots of products are made in various working conditions, depending on the size and the shape of them. Usually, at first, the die for new items had been designed on the basis of experience and know-how, and then modified through trial and error. At a die design stage most of drawings have been drawn manually. Recently some forging companies save design time by repeated utilization of standardized parts with registered data base. In this study the automated die design technique for forward extrusion of axisymmetric products is developed. A standardized die system is proposed from the investigation of ones employed frequently in the metal forming field and the design rules for cold extrusion die. A design example of forward extrusion die is given and discussed.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1269-1279
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• 2008

Data mining is the technique to find useful information in huge databases. One of the well-studied problems in data mining is exploration for association rules. An association rule technique finds the relation among each items in massive volume databases by several interestingness measures. An important and useful classification scheme of interestingness measures may be based on user-involvement. This results in two categories - objective and subjective measures. This paper present some relative interestingess measures to compare with degrees of association for two groups. A comparative study with some relative interestingness measures is shown by numerical example. The results show that the relative net confidence is the best relative interestingness measure.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.330-337
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• 2015

A bucket-brigade is a way of transporting items where items are passed from one person to the next. The operation of the bucket brigade imitates the cooperative behavior of ants when brood, food or other resources are moved. Koo (2009) presented a bucket brigade-based assembly cell where each worker follows a simple rule: perform assembly operations on a product until the next worker downstream takes it over; then go back to the previous worker upstream to take over a new assembly job. In this way, the flow line is self-balanced without any predetermined job assignment. However, there are some productivity losses related to hand-off and blocking. This paper examines the hand-off and blocking losses and presents a new bucket brigade-based assembly cell where working areas for each assembler is restricted with the help of buffer interfaces. Simulation experiments are used to validate the performance of the new assembly system.

• Korean Management Science Review
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• v.30 no.3
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• pp.17-31
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• 2013

The purpose of this paper is to illustrate the importance of identifying and considering 'purchase dependence' when purchase of an item is dependent on availability of other items demanded in the same order. This paper is the first study to develop an inventory model for purchase-dependent inventory systems. Through simulation experiments, we demonstrate that the developed inventory model incurs less inventory operations cost than other inventory models that ignore purchase dependence. For empirical validation of the developed inventory model, the actual inventory data at the Hyundai Engine Europe Service Center is used. We explain the process of identifying purchase dependencies among items through a data mining technique. The empirical study results in similar results to the simulation experiment, demonstrating that the developed inventory model is applicable to real situations.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.249-255
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• 2016

Recently, transaction data is accumulated everywhere very rapidly. Association analysis methods are usually applied to analyze transaction data, but the methods have several problems. For example, these methods can only consider one-way relations among items and cannot reflect domain knowledge into analysis process. In order to overcome defect of association analysis methods, we suggest a transaction data analysis method based on probabilistic graphical model (PGM) in this study. The method we suggest has several advantages as compared with association analysis methods. For example, this method has a high flexibility, and can give a solution to various probability problems regarding the transaction data with relationships among items.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.1
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• pp.119-130
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• 1989

This paper presents average travel time models automated warehousing system where the stacker crane transports only one pallet at a time with the tchebychev travel, I/O point is located at the cornor of the rack, and items are stored by the class-based storage assignment rule. In this study, the racks are treated as the continuous rectangle in time and a statistical approach was used to develop the models. In order to test the proposed models, average travel times determined by the models are compared with the true values for various rack shapes.