• Journal of Fisheries and Marine Sciences Education
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• v.27 no.6
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• pp.1546-1555
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• 2015

This study was performed to analyze the item characteristics of competency inventory for designer (CID), which Gil (2011) developed for measurement of design competency. To accomplish the purpose of this study, general partial credit (GPC) model based on polytomous item response theory was applied. The findings were as follows: First, CID is a reliable instrument for measuring design competency. Second, most items of CID have low item discrimination and average item difficulty according to the GPC model. Especially, there are some problems to have low item discrimination in view of validation. To improve the goodness of CID, we will need to examine why CID has low item discrimination.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.241-249
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• 2002

Making traditional plan of target marketing based on Association Rule has brought restriction to obtain the target of marketing. This paper is to present Inverse Association Rule as a new association rule for target marketing. Inverse Association Rule does not use information about relation between items that customers purchase like Association Rule, but use information about relation between items that customers do not pruchase. By adding Inverse Association Rule to target marketing, we generate new marketing rule to look for new target of marketing. From new marketing rule, this paper is to show direct marketing about target item and indirect marketing about another item associated with target item to sell target item. The reason is that sales of the item associated with target item have an influence on sales of target item.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.193-207
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• 2001

When designing a test set, we need to consider constraints on items that are deemed important by item developers or test specialists. The constraints are essentially on the components of the test domain or abilities relevant to a given test set. And so if the test domain could be represented in a more refined form, test construction would be made in a more efficient way. We assume that relationships among task abilities are representable by a causal model and that the item response theory (IRT) is not fully available for them. In such a case we can not apply traditional item selection methods that are based on the IRT. In this paper, we use entropy as an uncertainty measure for making inferences on task abilities and developed an optimal item selection algorithm which reduces most the entropy of task abilities when items are selected from an item pool.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.173-179
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• 1992

This paper is concerned with preventive maintenance model for the items whose failures are dependent on their wear level. The previous maintenance models have used time as their decision variable, but it is not appropriate for the case which have wear dependent processes for their failures. In this paper, we consider an operating item which is under periodic review and which is subject to degradation. The scheduled maintenance (overhaul ) is based on the level of item degradation rather time. A functional equation for the total expected cost over an infinite horizon period is formulated and solved.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.11-20
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• 1998

Testability analysis computes controllabilities and observabilities of all lines of a circuit and then evaluates fault coverage. The values of controllability and observability as well as fault coverage produced by testability analysis are used for applications of testability analysis. ITEM was evaluated as a fault coverage tool. But the values of controllability and observability at all lines of circuits must be estimated as a performance measure of testability tools for another application such as partial scan. In this paper, partial scan method based on sensitivity analysis which estimates relative improvement of detectability of circuits after scanning a flip-flop is used for performance evaluation of ITEM. Performance of ITEM, with respect to testability values on each net, has been measured by comparing ITEM and STAFAN. Partial scan performance achieved by ITEM is very similar to that of STAFAN, but ITEM takes less CPU time. Therefore ITEM is very efficient for partial scan application because ITEM runs faster for very large circuits in which execution time is critical.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.669-675
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• 2002

In recent years most of personalized recommender systems in electronic commerce utilize collaborative filtering algorithm in order to recommend more appropriate items. User-based collaborative filtering is based on the ratings of other users who have similar preferences to a user in order to predict the rating of an item that the user hasn't seen yet. This nay decrease the accuracy of prediction because the similarity between two users is computed with respect to the two users and only when an item has been rated by the users. In item-based collaborative filtering, the preference of an item is predicted based on the similarity between the item and each of other items that have rated by users. This method, however, uses the ratings of users who are not the neighbors of a user for computing the similarity between a pair of items. Hence item-based collaborative filtering may degrade the accuracy of a recommender system. In this paper, we present a new approach that a user's neighborhood is used when we compute the similarity between the items in traditional item-based collaborative filtering in order to compensate the weak points of the current item-based collaborative filtering and to improve the prediction accuracy. We empirically evaluate the accuracy of our approach to compare with several different collaborative filtering approaches using the EachMovie collaborative filtering data set. The experimental results show that our approach provides better quality in prediction and recommendation list than other collaborative filtering approaches.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.172-180
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• 2013

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

• Journal of Fashion Business
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• v.22 no.2
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• pp.134-149
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• 2018

The number of children wearing make-up is increasing. "Children's cosmetics" is not a legal term though it is commonly used. The purpose of this study is to analyze discussions on children's cosmetics based on news articles found on the internet. This study also identifies what products are being distributed as children's cosmetics. Keyword searches were conducted using internet portal sites. Information was extracted from news articles and Best Item 100 for children's cosmetics. The results of analyzing news articles and Best Item 100 lists are as follows : 1. There were two main discussion topics in news articles. The first topic was related to marketing(the branding and trends of children's cosmetics). The other topic was about government regulations(side effects, harmful ingredients, control, regulations, attention, proper product usage, product categorization, and the overall safety of children's cosmetics). By 2014, many articles had covered government control and regulation. However, since 2017, news articles have focused on the product categorization and the concern for overall safety has dramatically increased. 2. Three different product categories have appeared in the Best Item 100; they are cosmetics, toys, and other products. In market, consumers recognized children's cosmetics as cosmetics and also as toys. Between 2017 and 2018's Best Item, other products are dramatically down, color cosmetics and single cosmetics are on the rise, and the purchase of domestic products has increased.

• International Journal of Contents
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• v.5 no.1
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• pp.51-55
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• 2009

In this paper, we present a structure of personalized e-Learning system to study for a test formalized by uniform multiple-choice using on/off line mixed estimations as is the case of Driver :s License Test in Korea. Using the system a candidate can study toward the license through the Internet (and/or mobile instruments) within the personalized concept based on IRT(item response theory). The system accurately estimates user's ability parameter and dynamically offers optimal evaluation problems and learning contents according to the estimated ability so that the user can take possession of the license in shorter time. In order to establish the personalized e-Learning concepts, we build up 3 databases and 2 agents in this system. Content DB maintains learning contents for studying toward the license as the shape of objects separated by concept-unit. Item-bank DB manages items with their parameters such as difficulties, discriminations, and guessing factors, which are firmly related to the learning contents in Content DB through the concept of object parameters. User profile DB maintains users' status information, item responses, and ability parameters. With these DB formations, Interface agent processes user ID, password, status information, and various queries generated by learners. In addition, it hooks up user's item response with Selection & Feedback agent. On the other hand, Selection & Feedback agent offers problems and content objects according to the corresponding user's ability parameter, and re-estimates the ability parameter to activate dynamic personalized learning situation and so forth.