• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1345-1352
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• 2014

Data mining is the representative methodology to obtain meaningful information in the era of big data.By Wikipedia, association rule learning is a popular and well researched method for discovering interesting relationship between itemsets in large databases using association thresholds. It is intended to identify strong rules discovered in databases using different interestingness measures. Unlike general association rule, inverse association rule mining finds the rules that a special item does not occur if an item does not occur. If two types of association rule can be simultaneously considered, we can obtain the marketing information for some related products as well as the information of specific product marketing. In this paper, we propose a balanced attributable relative accuracy applicable to these association rule techniques, and then check the three conditions of interestingness measures by Piatetsky-Shapiro (1991). The comparative studies with rule accuracy, relative accuracy, attributable relative accuracy, and balanced attributable relative accuracy are shown by numerical example. The results show that balanced attributable relative accuracy is better than any other accuracy measures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.159-164
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• 2013

It is well known that a single moving camera video is capable of extracting the 3-dimensional position of an object. With this in mind, current research performed image-based monitoring to establish a floating structure model using a camcorder system. Following this, the present study extracted frame images from digital camcorder video clips and matched the interest points to obtain relative 3D coordinates for both regular and irregular wave conditions. Then, the researchers evaluated the transformation accuracy of the modified SURF-based matching and image-based displacement estimation of the floating structure model in regular wave condition. For the regular wave condition, the wave generator's setting value was 3.0 sec and the cycle of the image-based displacement result was 2.993 sec. Taking into account mechanical error, these values can be considered as very similar. In terms of visual inspection, the researchers observed the shape of a regular wave in the 3-dimensional and 1-dimensional figures through the projection on X Y Z axis. In conclusion, it was possible to calculate the displacement of a floating structure module in near real-time using an average digital camcorder with 30fps video.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.445-455
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• 2011

A probabilistic classification framework is presented that can combine temporal contextual information derived from an existing land-cover map in order to improve the classification accuracy of land-cover classes that can not be discriminated well when using spectral information only. The transition probability is computed by using the existing land-cover map and training data, and considered as a priori probability. By combining the a priori probability with conditional probability computed from spectral information via a Bayesian combination rule, the a posteriori probability is finally computed and then the final land-cover types are determined. The method presented in this paper can be adopted to any probabilistic classification algorithms in a simple way, compared with conventional classification methods that require heavy computational loads to incorporate the temporal contextual information. A case study for crop classification using time-series MODIS data sets is carried out to illustrate the applicability of the presented method. The classification accuracies of the land-cover classes, which showed lower classification accuracies when using only spectral information due to the low resolution MODIS data, were much improved by combining the temporal contextual information. It is expected that the presented probabilistic method would be useful both for updating the existing past land-cover maps, and for improving the classification accuracy.

• Journal of Nutrition and Health
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• v.47 no.4
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• pp.287-299
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• 2014

Purpose: The goal of this study was to examine body image satisfaction and perception according to weight status, and to investigate those associations with dietary habits and nutritional status among preadolescent children. Methods: Body image and dietary habits and intake were assessed in 134 elementary school students in grade 4. Children were categorized according to normal and overweight or obese groups. Figure rating scales were used to assess body image perception (identification of perceived current body size) and dissatisfaction (difference between perceived current body size and ideal body image). Results: There were sex differences in body image perceptions. Normal-weight girls, overweight or obese girls and boys were more likely to desire a leaner body size than their perceived body size compared with normal-weight boys. Body image satisfaction and perception showed an association with weight status. More overweight or obese children indicated dissatisfaction or underestimation of body image than normal-weight children. Children with body image dissatisfaction due to heavier perceived body size than ideal body image showed lower frequencies of consumption of meals and vegetables, compared to those who were satisfied with their body image. Children who underestimated their body image were more likely to have a lower frequency of breakfast and meal regularity and a higher frequency of eating out of home or food deliveries than those with accurate body image perception. In addition, body image underestimation showed an association with lower intakes of protein, dietary fiber and calcium, and the higher percentage of calories derived from fat. Conclusion: Body image dissatisfaction as well as underestimation in children before puberty showed an association with overweight or obesity, and was also related to unhealthy dietary habits. These findings highlight the importance of accurate perception and satisfaction with body image in preadolescent children in order to prevent development of obesity in adolescents and adults.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.403-408
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• 2007

In a large corpus-based speech synthesizer, a break, which is a parameter influencing the naturalness and intelligibility, is used as an important feature during a unit selection process. Japanese is a language having intonations, which ate indicated by the relative differences in pitch heights and the APs(Accentual Phrases) are placed according to the changes of the accents while a break occurs on a boundary of the APs. Although a break can be predicted by using J-ToBI(Japanese-Tones and Break Indices), which is a rule-based or statistical approach, it is very difficult to predict a break exactly due to the flexibility. Therefore, in this paper, a method is to conduct a unit search by dividing breaks into two types, such as a fixed break and a flexible break, in order to use the advantages of a large-scale corpus, which includes various types of prosodies. As a result of an experiment, the proposed unit selection method contributed itself to enhance the naturalness of synthesized speeches.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.