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  <title>Repository Collection:</title>
  <link rel="alternate" href="https://oak.chosun.ac.kr/handle/2020.oak/18240" />
  <subtitle />
  <id>https://oak.chosun.ac.kr/handle/2020.oak/18240</id>
  <updated>2026-04-09T15:09:35Z</updated>
  <dc:date>2026-04-09T15:09:35Z</dc:date>
  <entry>
    <title>Simultaneous Optimization for Robust Parameter Design Using Signal-to-Noise Ratio</title>
    <link rel="alternate" href="https://oak.chosun.ac.kr/handle/2020.oak/18828" />
    <author>
      <name>Yong Man Kwon</name>
    </author>
    <id>https://oak.chosun.ac.kr/handle/2020.oak/18828</id>
    <updated>2024-04-26T07:04:27Z</updated>
    <published>2019-12-31T15:00:00Z</published>
    <summary type="text">Title: Simultaneous Optimization for Robust Parameter Design Using Signal-to-Noise Ratio
Author(s): Yong Man Kwon
Abstract: Taguchi's robust parameter design is an approach to reduce the performance variation of quality characteristics in products and processes. In robust design, the signal-to-noise ratio (SN ratio) was used to find the optimum condition to minimize the variation of quality characteristics as much as possible and bring the average of quality characteristics closer to the target value. In this paper, we propose a simultaneous optimization method based on a linear model of the SN ratio as a method to find the optimal condition of the control factor in case of multi-characteristics. In addition, the proposed method and the existing method were compared and studied by taking actual cases.</summary>
    <dc:date>2019-12-31T15:00:00Z</dc:date>
  </entry>
  <entry>
    <title>IoT 기반 빅데이터 효율성 향상을 위한 하둡기반 플랫폼 설계</title>
    <link rel="alternate" href="https://oak.chosun.ac.kr/handle/2020.oak/18831" />
    <author>
      <name>장경성</name>
    </author>
    <author>
      <name>배상현</name>
    </author>
    <id>https://oak.chosun.ac.kr/handle/2020.oak/18831</id>
    <updated>2024-04-26T07:04:27Z</updated>
    <published>2019-12-31T15:00:00Z</published>
    <summary type="text">Title: IoT 기반 빅데이터 효율성 향상을 위한 하둡기반 플랫폼 설계
Author(s): 장경성; 배상현
Abstract: IoT 및 사물인터넷 기반 빅데이터 시스템을 구축하는 경우 발생하는 빈번한 전송에 따른 데이터 오류율과 자원의 비효 율적 이용율을 극복하기 위하고 오픈소스기반 하둡시스템의 문제점을 극복하기 위한 본 연구에서는 순수 하둡을 기반으 로 적용된 결과를 분석하고 하둡 2.x대 버전을 기준으로 빅데이터 시스템의 용량을 산정한 가이드를 제시하고 용량 산정 의 기준을 에코 소프트웨어 적용 플랫폼을 제안한다.</summary>
    <dc:date>2019-12-31T15:00:00Z</dc:date>
  </entry>
  <entry>
    <title>The Impact of COVID-19 on Distribution Company in Korea</title>
    <link rel="alternate" href="https://oak.chosun.ac.kr/handle/2020.oak/18830" />
    <author>
      <name>Da Hye Lee</name>
    </author>
    <author>
      <name>In Hong Chang</name>
    </author>
    <id>https://oak.chosun.ac.kr/handle/2020.oak/18830</id>
    <updated>2024-04-26T07:04:27Z</updated>
    <published>2019-12-31T15:00:00Z</published>
    <summary type="text">Title: The Impact of COVID-19 on Distribution Company in Korea
Author(s): Da Hye Lee; In Hong Chang
Abstract: As the COVID-19 outbreak has prolonged, social distancing movements are encouraged and non-face-to-face lifestyle trends are spreading. As a result, it is necessary for general restaurants and small marts to provide delivery services like large-scale distribution companies. Large distribution companies have also suffered significant economic losses from COVID-19 because epidemiological investigations were conducted after the distribution center was closed due to the outbreak of COVID-19 in several large domestic distribution companies. In this thesis, in order to examine whether COVID-19 actually affects distribution companies, we attempt to understand the relationship between the number of confirmed cases per month and the sales share and growth rate by categories of distribution companies.</summary>
    <dc:date>2019-12-31T15:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Emotion Modeling for Emotion-based Personalization Service</title>
    <link rel="alternate" href="https://oak.chosun.ac.kr/handle/2020.oak/18829" />
    <author>
      <name>Tae Yeun Kim</name>
    </author>
    <author>
      <name>Sang Hyun Bae</name>
    </author>
    <id>https://oak.chosun.ac.kr/handle/2020.oak/18829</id>
    <updated>2024-04-26T07:04:27Z</updated>
    <published>2019-12-31T15:00:00Z</published>
    <summary type="text">Title: Emotion Modeling for Emotion-based Personalization Service
Author(s): Tae Yeun Kim; Sang Hyun Bae
Abstract: This study suggests the emotion space modeling and emotion inference methods suitable for personalized services based on psychological and emotional models. For personalized emotion space modeling taking into account the subjective disposition based on the empirical assessment of the personal emotions felt by the personalization process of emotion space was used as a decision support tool, the Analytic Hierarchy Process. This confirmed that the special learning to perform personalized emotion space modeling without considering the subjective tendencies. In particular to check the possible reasoning based on fuzzy emotion space modeling and sensitivity for the quantification and vague human emotion to it based on the inherent human sensitivity.</summary>
    <dc:date>2019-12-31T15:00:00Z</dc:date>
  </entry>
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