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양희규(Yang, Hee-kyu) 계명대학교 인문과학연구소 2014 동서인문학 Vol.0 No.48
The spirit of liberal arts is related to the good and happy life, not to the worldly success. The ideals of education of Gandhi School places emphasis on "good life" or "classical happiness", not on the worldly success. Gandhi School teaches the four essential elements for happiness such as love, liberty, wisdom, and health. All human beings have functions of sentiment, will, and rationality. The proper operation and balance of those functions make a good and healthy life. Gandhi School is founded based on the spirit of liberal arts, especially on the philosophy of Gandhi. The spirit of the resistance without violence, which is so-called Gandhian-style resistance, which Tolstoy, Gandhi, and Martin Luther King insisted, desperately oppose the education based on worldly success. How could schools cultivate love, liberty, wisdom, and health? To cultivate a good habit is the key point. Most children already have wrong and bad habits on emotion, reason, and body. Gandhi School places emphasis on development and formation of good habits and personality. The curriculum of Gandhi School places emphasis on self-discovery through programs such as target education, club activities, internship policy, and graduation work. Students could freely choose classes which they want to learn and in doing so students come at a true knowledge of oneself and seek self-fulfillment. Teachers function as a humanistic therapist, coach, coordinator, education programmer, not just a teacher who teaches subjects. The class of Gandhi School is a kind of software of education where students learn what they really want, not just theorized teaching knowledge.
양희규 ( Hui-gui Yang ),염상길 ( Sanggil Yeoum ),추현승 ( Hyun-seung Choo ) 한국정보처리학회 2019 한국정보처리학회 학술대회논문집 Vol.26 No.1
제안 시스템은 모바일 사용자를 공공 인터넷 환경에서 사용 유형별로 분류 및 관리 가능한 서비스를 제공한다. 비콘을 이용하여 시스템 사용자의 위치와 서비스공간의 근접성을 확인한다. 또한 사용자의 서비스 사용 형태 및 성향에 따라 서비스 제공 장소에서 네트워크에 연결된 사물 인터넷 기기 및 기타 기기에 대한 접근 권한을 미리 정의하고 이를 제공할 수 있다. 본 연구는 이러한 시스템을 제안하고 구현된 결과를 바탕으로 시스템의 기능을 분석한다.
CT 이미지 세그멘테이션을 위한 3D 의료 영상 데이터 증강 기법
고성현,양희규,김문성,추현승 한국인터넷정보학회 2023 인터넷정보학회논문지 Vol.24 No.4
Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.