http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
중앙지와 지방지의 한진 중공업 노사갈등 보도 분석:프레임 분석을 중심으로
김은이 ( Eunyi Kim ) 인천대학교 사회과학연구원 2013 사회과학연구 Vol.4 No.-
his study is a comparative study of news frames between national and local newspapers with regard to the Hanjin Heavy & Construction’s labormanagement conflict. For the analysis, we selected < Chosunilbo > and < Choongangilbo > as conservative national newspapers, < Hankyereh > as a progressive national newspaper, and < Busanilbo > & < Kukjeilbo > as local newspapers which not only approximated geographically to Busan and Yongnam province where the Hanjin Heavy Industry & Construction company is located. The study found that the conservative national newspapers and the progressive national newspaper showed not only the very different angles, but also the disparate ideological differences, regarding the labor-management conflict. Contrary to the national newspapers, < Busanilbo > & < Kukjeilbo > as local newspapers focused more on the principle of news value and neutral reporting than ideological leaning. The conservative national newspapers did not pay much attention to the conflict, compared to the progressive national newspaper and the local newspapers. . The conservative national newspapers were more negative to the workers’ side, while the progressive national newspaper was more positive to the management in their news reporting. On the other hand, the local newspapers showed negative attitudes toward the workers and the management to a similar extent, and maintained the neutral attitude toward both sides. In terms of frame analysis, the responsibility frame and the conflict situation frame were more pronounced in the conservative national newspapers, while the workers supporting frame and the Hope bus cheering frame were more pronounced in the progressive national newspaper and the local newspapers. Interestingly, it turned out that the conservative national newspapers were supporting the existing power group, while they paid much less attention to the labor-management conflict. Lastly, the result suggested that the newspapers which has a geographical proximity or which are ideologically progressive tend to be neutral, and perform a role of conflict coordinator in the labor-management conflict issue.
김나연(Nayeon Kim),신윤희(Yunhee Shin),김수정(Soojeong Kim),김지인(Jeein Kim),정갑주(Karpjoo Jeong),구현진(Hyunjin Koo),김은이(Eunyi Kim) 한국정보과학회 2007 정보과학회논문지 : 소프트웨어 및 응용 Vol.34 No.9
본 논문에서는 신경망을 이용하여 텍스타일 영상으로부터 인간의 감성을 인식할 수 있는 시스템을 제안한다. 자동감성인식 시스템의 구현을 위해 220장의 텍스타일 영상을 수집한 후, 일반인 20명을 대상으로 설문조사를 실시하였다. 이를 통해 텍스타일 영상에서의 패턴과 감성간의 상관관계, 즉 특정 패턴이 사람의 감성에 영향을 준다는 것을 발견하였다. 따라서 본 연구에서는 텍스타일 영상에 포함된 패턴의 인식을 위해 신경망을 이용하였으며, 이때 패턴 정보의 추출을 위해 두 가지 특징 추출 방법을 사용한다. 첫 번째는 auto-regressive method를 이용한 raw-pixel data extraction scheme(RDES)을 사용하는 것이고, 두 번째는 wavelet transformed data extraction scheme(WTDES)을 사용하는 것이다. 제안된 시스템의 유용성을 증명하기 위해서 실제 100장의 텍스타일 영상을 감성을 인식하는데 사용했다. 그 결과 RDES와 WTDES를 사용했을 때 각각 71%와 90%의 인식률로, WTDES를 사용했을 때가 RDES를 사용했을 때보다 더 좋은 성능을 보였다. 데이타 추출방법에 따라 다소 차이가 있었지만 전체적으로 약 81%의 정확도를 보였다. 이러한 실험 결과는 제안된 방법이 감성인식 기반으로 텍스타일 데이타를 검색 할 수 있는 시스템 및 다양한 산업 분야에 응용 가능함을 보여주었다. This paper proposes a neural network based approach for automatic human emotion recognition in textile images. To investigate the correlation between the emotion and the pattern, the survey is conducted on 20 peoples, which shows that a emotion is deeply affected by a pattern. Accordingly, a neural network based classifier is used for recognizing the pattern included in textiles. In our system, two schemes are used for describing the pattern; raw-pixel data extraction scheme using auto-regressive method (RDES) and wavelet transformed data extraction scheme (WTDES). To assess the validity of the proposed method, it was applied to recognize the human emotions in 100 textiles, and the results shows that using WTDES guarantees better performance than using RDES. The former produced the accuracy of 71%, while the latter produced the accuracy of 90%. Although there are some differences according to the data extraction scheme, the proposed method shows the accuracy of 80% on average. This result confirmed that our system has the potential to be applied for various application such as textile industry and e-business.