• Title/Summary/Keyword: 감성 트리

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Research Representative Color Image Emotion Emotional Image Size Changes through Tree (영상 이미지 색채 감성트리를 통한 대표감성크기 변화 연구)

  • Lee, Yean-Ran;Park, Hyo-Jin
    • The Journal of the Korea Contents Association
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    • v.15 no.11
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    • pp.10-17
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    • 2015
  • Emotional computer that you want to study in a regular number change is the continuing sensitivity. Emotional Computing manner the sensibilities numbered and emotions were running through the trees. Emotional assessment of emotional sensibility computing was used as the coordinates of the key effects of the James A. Russell (Core Affect). Emotional tree runs purpose was to verify the correlation of sensitivity and emotion computing tree. Emotional tree attributes experiment color, brightness, saturation was configured with. When 50% brightness increase, about pleasure (X-axis) has increased by 10.49 points. Brightness 50%, GREEN 50% increase in the degree of pleasure (X-axis) of 10.49 points, tone (Y axis) has increased by 15.85 points. Brightness 50%, GREEN 50% increase in the degree of pleasure (X-axis) of 10.49 points, tone (Y axis) has increased by 15.85 points. Brightness 50% of the free-extent (X-axis), BLUE 50% when the tone (Y axis), pleasure extent (X-axis) of 10.49 points, tone (Y axis) as much as 14.65 points sensibilities have changed. When representatives emotions size changes have increased 50% brightness, color RED 50%, increased 5.4% Emotional excitement, emotion depressed declined -4.2%. 50% brightness, color GREEN 50% increase in emotional excitement had increased to 8.6%, declined by -5.5% this melancholy sensibility. Representative emotion and emotional changes increase or decrease the size of the emotional attributes were analyzed by quantitative methods. After the happy emotions number is needed to study more similar to the human emotion through the execution of the video image emotion emotional tree computing.

Sentiment Analysis System Using Stanford Sentiment Treebank (스탠포드 감성 트리 말뭉치를 이용한 감성 분류 시스템)

  • Lee, Songwook
    • Journal of Advanced Marine Engineering and Technology
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    • v.39 no.3
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    • pp.274-279
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    • 2015
  • The main goal of this research is to build a sentiment analysis system which automatically determines user opinions of the Stanford Sentiment Treebank in terms of three sentiments such as positive, negative, and neutral. Firstly, sentiment sentences are POS tagged and parsed to dependency structures. All nodes of the Treebank and their polarities are automatically extracted from the Treebank. We train two Support Vector Machines models. One is for a node level classification and the other is for a sentence level. We have tried various type of features such as word lexicons, POS tags, Sentiment lexicons, head-modifier relations, and sibling relations. Though we acquired 74.2% in accuracy on the test set for 3 class node level classification and 67.0% for 3 class sentence level classification, our experimental results for 2 class classification are comparable to those of the state of art system using the same corpus.

A 3-D trimming system for bias-cut apparels (고감성 의류제조를 위한 3-D 입체 트리밍 시스템)

  • 김주용
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 2003.05a
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    • pp.19-22
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    • 2003
  • 경사의 사선방향으로 절단하여 봉제된 바이어 컷 직물로 제조된 의류는 일반 의류와 달리 우아한 외관을 타나낸다. 이 직물의 단점은 형태안정성이 나빠서 완전제품의 형태가 패턴의 형의 형태와 다른 모양을 나타내는 경우가 많으며, 특히 밑단 부분이 불균일하게 쳐지므로, 가지런히 절단하는 트리밍공정을 부가적으로 거치게 된다. 현재의 봉제 공정에서는 트리밍 단계를 모두 숙련공들의 수작업에 의존한 평면 커팅 방법으로 수행하기 때문에 작업자의 피로도에 따라 불량품이 양산되고 그에 따른 소비자들의 불만 요인도 높아지므로, 본 연구에서는 치수 조절 마네킹을 제조하여 실제 의복 착용 상태를 그대로 재현 한 후, 회전시키면서 스스로 회전하는 자동 커터에 의해서 공간상에서 트리밍 하는 3-D 입체 시스템을 개발하였다. 이 시스템에 의하여 제조된 의류는 트리밍 라인이 균일하고 매끄러우며, 안감이 밖으로 밀려나오는 경우가 없어 외관상 품위가 있는 외관을 나타내었다. 또한 수작업에 비하여 제조 속도가 훨씬 빠르므로 봉제 후공정에서의 의류 제조 시간을 획기적으로 단축하는 신속 생산 시스템의 요소로서 충분히 사용될 수 있는 가능성을 나타낸다.

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Video image analysis algorithms with happy emotion tree (영상 이미지 행복 감성 트리를 이용한 분석 알고리즘)

  • Lee, Yean-Ran;Lim, Young-Hwan
    • Cartoon and Animation Studies
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    • s.33
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    • pp.403-423
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    • 2013
  • Video images of emotional happiness or unhappiness, stress or emotional division of tranquility in the form of a tree is evaluated by weighting. Representative evaluation of the video image brightness contrast sensitivity ratings 1 car happy, unhappy or nervous, calm and refined with two car dependency, sensitivity to visual images are separated. Emotion Recognition of four compared to the numerical data is measured by brightness. OpenCV implementation through evaluation graph the stress intensity contrast, tranquility, happiness, unhappiness with changes in the value of four, separated by sensitivity to computing. Contrast sensitivity of computing the brightness according to the input value 'unhappy' to 'happy' or 'stress' to 'calm' the emotional changes are implemented. Emotion computing the regularity of the image to calculate the sensitivity localized computing system can be controlled according to the emotion of the contrast value of the brightness changes are implemented. The future direction of industry on the application of emotion recognition will play a positive role.

Emotional Tree Using Sensitivity Image Analysis Algorithm (감성 트리를 이용한 이미지 감성 분석 알고리즘)

  • Lee, Yean-Ran;Yoon, Eun Ju;Im, Jung-Ah;Lim, Young-Hwan;Sung, Jung-Hwan
    • The Journal of the Korea Contents Association
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    • v.13 no.11
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    • pp.562-570
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    • 2013
  • Image of emotional pleasure or displeasure, tension or emotional division of tranquility in the form of a tree is evaluated by weighting. Image representative evaluation of the sensitivity of the brightness contrast ratings 1 car pleasure, displeasure or stress or emotional tranquility and two cars are separated by image segmentation. Emotion Recognition of four compared to the numerical data is measured by brightness. OpenCV implementation through evaluation graph the stress intensity contrast, tranquility, pleasure, displeasure, depending on changes in the value of the computing is divided into four emotional. Contrast sensitivity of computing the brightness depending on the value entered 'nuisance' to 'excellent' or 'stress' to 'calm' the emotional changes can give. Calculate the sensitivity of the image regularity of localized computing system can control the future direction of industry on the application of emotion recognition will play a positive role.

A 3-D Trimming System for Bias-Cut Apparels (고감성 의류제조를 위한 3-D 입체 트리밍 시스템)

  • 김주용
    • Science of Emotion and Sensibility
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    • v.7 no.2
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    • pp.157-161
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    • 2004
  • The clothing by bias-cut fabric are outstanding in their shilluettes mainly due to their high level of drapability. The clothing, however, need a specific cutting process for being even trimming line skirts. The 3-D trimming system developed in the study has been successful in making high-quality skirts with extremely even trimming line. The system is expected to make quality apparel in shorter manufacturing time.

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The Study on Establishing Structure of Emotional Information for Emotional Design (감성디자인에 있어서 감성정보의 모델구축에 관한 연구)

  • Hyeong, Seong-Eun;Kim, Hyeon-Jeong;Hong, Jeong-Pyo
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 2007.05a
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    • pp.77-80
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    • 2007
  • 본 연구는 감성정보에 대한 모델을 구축하기 위하여 감성 정의를 "알고 있는 지식" 으로 규정하고 진행하였고, 감성디자인의 프로세스를 구축하기 위하여 암묵지식 이론과 이분법을 응용하여 감성요소를 추출하였다. 실험 방법에 있어서는 대상 주제에 대한 피험자가 표현하는 내용을 정리하고 이것에 대해 연구자가 이분법을 반복하여 실시하였으며, 압축된 분할 기준을 만들고 결과를 수량화 3류에 의한 해석과 샘플스코어를 직관적으로 이해하기 쉬운 트리 구조로 정리하였다. 이러한 감성 정보과정의 반복을 통하여 감성 디자인 프로세스를 구축하였다.

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Music Emotion Control Algorithm based on Sound Emotion Tree (감성 트리 기반의 음악 감성 조절 알고리즘)

  • Kim, Donglim;Lim, Bin;Lim, Younghwan
    • The Journal of the Korea Contents Association
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    • v.15 no.3
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    • pp.21-31
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    • 2015
  • This thesis proposes the emotions acquired after listening to the music as an emotion model composed of 8 types of emotions, based on the emotion model studied previously. The 5 musical factors selected, that affect the emotion, are tempo, dynamics, amplitude change, brightness, and noise. According to the emotion model composed of 8 types of emotions, 160 songs categorized into the 8 types of emotions were selected, and the actual data was extracted and analyzed. Through the analysis of actual data, an emotion equation made of weighted value of 5 factors was derived, and an algorithm that can predict the emotion by mapping on the 2-dimensional emotion coordinate system through the emotion equation was designed. Also, a way of controlling emotion by moving the coordinates on the 2-dimensional emotion coordinate system was suggested.