• Journal of Korean Society for Quality Management
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• v.5 no.2
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• pp.21-28
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• 1977

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.138-139
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• 2002

• Journal of Korean Society for Quality Management
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• v.11 no.1
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• pp.10-17
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• 1983

This paper studies item-life test plans with the specified item mean life $T_1$ (MTBF) - Producer's risk ${\alpha}$ and item mean life $T_2$ (MTBF, $T_2$ < $T_1$) - Consumer's risk ${\beta}$ when the probability of item survival follows the Weibull distribution (known shape parameter) as a expansion of [1]. And Operating Characteristic Curves and Average Life-testing Times of item-life test plans are computed for this paper and [1]. Cost analysis procedures are same as [1]. These results are computed by using computer program written in Level II Basic for Apple II Plus Micro-computer. Both this paper and [6] reduce the life-testing time for Weibull distribution in comparision with Exponential distribution, but results of [6] were computed for different criterions from this paper.

• Physical Therapy Korea
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• v.18 no.3
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• pp.94-102
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• 2011

The purpose of this study was to review existing assessment tools for patients with low back pain and improve them through combination. A total of 314 patients with low back pain participated. Their condition was assessed using the Oswestry Disability Questionnaire (ODQ), the Quebec Back Pain Disability Scale (QBPD), and the Back Pain Functional Scale (BPFS). Rasch analysis was applied to identify inappropriate items, item difficulties, and the separation index. In this study, the 'sex life' item of the ODQ (10 items) and the 'sleeping' item of the BPFS (12 items) showed misfit statistics, whereas all items of the QBPD (20 items) were appropriate. After combining the ODQ, QBPD and BPFS, Rasch analysis was applied. The 'pain intensity', and the 'sex life' item of the ODQ and the 'throw a ball' item of QBPD showed misfit statistics. These 3 items were retained for further analysis. The remaining 42 combined ODQ-QBPD-BPFS items were arranged according to difficulty. For all subjects, the most difficult item was 'pain intensity', whereas the easiest was 'take food out of the refrigerator'. As the separation index of 42 combined ODQ-QBPD-BPFS was higher than that of the three questionnaires separately, difficulty of items varied with some need for rearrangement. The results of this study confirmed the possibility and need for a new back pain disability assessment tool, and produced one. Further study is needed to refine the questionnaire in consideration of psychosocial and occupational factors.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.877-885
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• 2011

A collaborative filtering(CF) is the most widely used technique in recommender system. However, CF has sparsity and scalability problems. These problems reduce the accuracy of recommendation and extensive studies have been made to solve these problems, In this paper, we proposed a method that uses a weight so as to solve these problems. After creating a user-item matrix, the proposed method analyzes information about users who prefer the item only by using data with a rating over 4 for enhancing the accuracy in the recommendation. The proposed method uses information about the genre of the item as well as analyzed user information as a weight during the calculation of similarity, and it calculates prediction by using only data for which the similarity is over a threshold and uses the data as the rating value of unrated data. It is possible simultaneously to reduce sparsity and to improve accuracy by calculating prediction through an analysis of the characteristics of an item. Also, it is possible to conduct a quick classification based on the analyzed information once a new item and a user are registered. The experiment result indicated that the proposed method has been more enhanced the accuracy, compared to item based, genre based methods.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.96-104
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• 2020

As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.

• Journal of Gifted/Talented Education
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• v.12 no.4
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• pp.26-44
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• 2002

We have developed a tool and solution to test creativity level for science field. This test tool was considered 7 creativity elements. In development process, it was verified for contents validity, clarity of the item etc. The test developed in this study was analyzed item analysis after applying for 332 middle school students. As a results of item analysis, it showed meaningful(validity: 92%, item difficulty: $42%{\sim}73%$, reliability: 0.84, item discriminating power: $0.22{\sim}0.70$)over the level of a standard basis. This means that the test tool was useful in the test process of creativity level for science.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.5
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• pp.61-69
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• 2015

We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.23-33
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• 2007

The collaborative filtering has used the nearest neighborhood method based on the preference and the similarity using the Pearson correlation coefficient. Therefore, it does not reflect content of the items and has the problems of the sparsity and scalability as well. the item-based collaborative filtering has been practically used to improve these defects, but it still does not reflect attributes of the item. In this paper, we propose the user and item based collaborative filtering using the classification property and Naive Bayesian to supplement the defects in the existing recommendation system. The proposed method complexity refers to the item similarity based on explicit data and the user similarity based on implicit data for handing the sparse problem. It applies to the Naive Bayesian to the result of reference. Also, it can enhance the accuracy as computation of the item similarity reflects on the correlative rank among the classification property to reflect attributes.