• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.3
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• pp.131-141
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• 2013

Stock identification of Todarodes pacificus collected in the East Sea, Yellow Sea and East China Sea during the period from September to December in 2011 was analyzed by morphometric characters and mitochondrial DNA (mtDNA) cytochrome oxidase subunit I (COI) gene nucleotide variations. Frequency distributions of mantle length was analyzed by morphological method with measuring size of T. pacificus. Then each stock was estimated to confirm their maturation for mean mantle length comparing with mean mature mantle length 20-22 cm. According to morphologic stock identification, it is estimated that the northern part of East Sea is categorized as summer stock and the rest parts, including mid /southern part of the East Sea, northern part of the East China Sea and northern part of the West Sea were autumn stock. For genetic analysis, a total 49 haplotypes were defined by 33 variable nucleotide sites. From the extensive haplotype diversity, limited nucleotide diversity and star-like shape of haplotype network, T. pacificus appears to have undergone rapid population expansion from an ancestral population with a small effective population size. Although pair-wise Fst estimates which represent genetic difference among groups were low, there are relatively remarkable difference of Fst between middle and southern part of the East Sea. Although middle part of the East Sea and southern part of the East Sea were situated at the East Sea, genetically separated groups were appeared.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.329-337
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• 2017

In USA and UK, the standards of both reverberation time and background noise level have been established for the appropriate aural environment in classrooms. In order to realize this, guidelines for architectural planning and interior finishing have been also suggested. However, in Korea, there has hardly been any guidelines for satisfying background noise criteria and investigation about sound insulation performance of current walls of classrooms. The present study investigates the structure of outer wall and walls between classrooms of two middle schools in order to analyze the sound insulation performance against both exterior and interior noises. Acoustic parameters including transmission loss, standardized sound level difference, and signal to noise ratio have been measured and analyzed for sound insulation performance of walls and flanking noises. As a result, concerning the walls in between classrooms, it was found that walls of dry construction have greater sound insulation performance rather than the walls of wet construction especially in mid and high frequency bands. Also, It was revealed that thermopane, insulated pair glass, of outer walls, has greater sound insulation performance than the double window consisted of two single pane glass. Regarding flanking noises, the standards were exceeded when all windows, or windows and doors front onto corridor were opened. It denotes that students could be disturbed with the sound transmission by the interior noises.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.