• Title/Summary/Keyword: 패턴감성

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Comparison of Virulence in Xylitol-Sensitive and -Resistant Streptococcus mutans to Different Concentrations of Xylitol (자일리톨 처리 농도에 따른 자일리톨 감성균주와 내성균주의 독력 비교)

  • Im, Sang-Uk;Ahn, Sang-Hun;Song, Keun-Bae
    • Journal of dental hygiene science
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    • v.11 no.5
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    • pp.411-416
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    • 2011
  • Streptococcus mutans (S. mutans) is the major causative bacteria in dental caries. Xylitol is effective anticarious natural sugar substitute by inhibiting the virulence of S. mutans. However, long-term xylitol consumption leads to the emergence of the xylitol-resistant (XR) strains which means xylitol is no more inhibited their growth. We therefore confirmed the general characteristics and the virulence factors of the xylitol-sensitive (XS) and XR S. mutans for different concentrations of xylitol. S. mutans KCTC 3065 was maintained in TYE medium containing 0.4% glucose with 1% xylitol during 30 days at $37^{\circ}C$, 10% $CO_2$ to form XR strain. The strains were transferred to new medium every 24 hr and the same procedures without xylitol were repeated for the formation of XS S. mutans. Both XS and XR were cultured in different concentrations of xylitol (0%, 0.1% and 1%) then, cell growth, acid production and mRNA expression of gtf genes were analyzed. Xylitol reduced the cell growth of XS S. mutans in dose-dependent manner, but not reduced that of XR. Xylitol inhibited acid production of XS in dose-dependent manner, but not inhibited that of XR. Xylitol reduced the gtfB and gtfD mRNA expression of XS S. mutans which genes synthesized soluble and insoluble extracellular polysaccharides, but not reduced that of XR. These results indicate that the virulence of XR S. mutans is different characters of XS strains, which suggests XR strains may have different cariogenicity of XS strains. Further study is needed to explain the mechanism related to extracellular polysaccharide in the XR strains.

Applying QFD in the Development of Sensible Brassiere for Middle Aged Women (QFD(품질 기능 전개도)를 이용한 중년 여성의 감성 Brassiere 개발)

  • Kim Jeong-hwa;Hong Kyung-hi;Scheurell Diane M.
    • Journal of the Korean Society of Clothing and Textiles
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    • v.28 no.12 s.138
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    • pp.1596-1604
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    • 2004
  • Quality Function Deployment(QFD) is a product development tool which ensures that the voice of the customer needs is heard and translated into products. To develop a sensible brassiere for middle-aged women QFD was adopted. In this study the applicability and usefulness of QFD was examined through the engineering design process for a sensible brassiere for middle-aged women. The customer needs for the wear comfort of brassiere was made by one-on-one survey of 100 women who aged 30-40. The customer competitive assessment was generated by wearing tests of 10 commercial brassieres. The subjective assessment was conducted in the enviornmental chamber that was controlled at $28{\pm}1^{\circ}C,\;65{\pm}3\%RH.$ As a results, we developed twenty-one customer needs and corresponding HOWs for the wear comfort of brassiere. The Customer Competitive Assessment was generated by wearing tests of commercial brassiere. The subjective measurement scale and dimension for the evaluation of sensible brassiere were extracted from factor analysis. Four factors were fitting, aesthetic property, pressure sensation, displacement of brassiere due to movement. The most critical design parameter was wire-related property and second one was stretchability of main material of brassiere. Also, wearing comfort of brassiere was affected by the interaction of initial stretchability of wing and support of strap. Engineering design process, QFD was applicable to the development of technical and aesthetic brassieres.

Query-based Answer Extraction using Korean Dependency Parsing (의존 구문 분석을 이용한 질의 기반 정답 추출)

  • Lee, Dokyoung;Kim, Mintae;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.25 no.3
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    • pp.161-177
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    • 2019
  • In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.