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The Reception of W. B. Yeats in China
Zeyuan Hu 한국예이츠학회 2020 한국예이츠 저널 Vol.62 No.-
예이츠는 중국에서 1910년대부터 소개되고, 번역되고 연구되었다. 수용 범주는 3가지인데, 지배자에 항거하는 민족주의 작가, 신중국시의 현대적 규범으로서의 예이츠, 중국문학의 국제화를 위해 공부하는 세계문학으로서의 그의 작품성, 등이 다. 중국에서의 예이츠의 변모는 외국문학의 수용은 정치적, 문화적, 문학적 요소 등이 상호작용하는 역동적 과정이라는 점을 가장 잘 보여준다. W. B. Yeats has been introduced, translated, and studied in China since 1910s. The reception of Yeats in China falls into three aspects: as nationalist writer who fights against the colonial oppression, as model modernist for Chinese New Poetry, and his work as world literature, which Chinese writers study for the internationalization of literature in China. The metamorphosis of Yeats in China best shows that the reception of foreign literature is a highly dynamic process in which various factors, which are political, cultural, and literary, interact with each other.
Improved DT Algorithm Based Human Action Features Detection
Hu, Zeyuan,Lee, Suk-Hwan,Lee, Eung-Joo Korea Multimedia Society 2018 멀티미디어학회논문지 Vol.21 No.4
The choice of the motion features influences the result of the human action recognition method directly. Many factors often influence the single feature differently, such as appearance of the human body, environment and video camera. So the accuracy of action recognition is restricted. On the bases of studying the representation and recognition of human actions, and giving fully consideration to the advantages and disadvantages of different features, the Dense Trajectories(DT) algorithm is a very classic algorithm in the field of behavior recognition feature extraction, but there are some defects in the use of optical flow images. In this paper, we will use the improved Dense Trajectories(iDT) algorithm to optimize and extract the optical flow features in the movement of human action, then we will combined with Support Vector Machine methods to identify human behavior, and use the image in the KTH database for training and testing.
Human Motion Recognition Based on Spatio-temporal Convolutional Neural Network
Zeyuan Hu,Sange-yun Park,이응주 한국멀티미디어학회 2020 멀티미디어학회논문지 Vol.23 No.8
Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.42% and 67.82%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.