Search

Your search keyword '"Zou, Shuang"' showing total 466 results
Start Over Author "Zou, Shuang"
466 results on '"Zou, Shuang"'

35. Genomes of leafy and leafless Platanthera orchids illuminate the evolution of mycoheterotrophy

Author

Li, Ming-He, Liu, Ke-Wei, Li, Zhen, Lu, Hsiang-Chia, Ye, Qin-Liang, Zhang, Diyang, Wang, Jie-Yu, Li, Yu-Feng, Zhong, Zhi-Ming, Liu, Xuedie, Yu, Xia, Liu, Ding-Kun, Tu, Xiong-De, Liu, Bin, Hao, Yang, Liao, Xing-Yu, Jiang, Yu-Ting, Sun, Wei-Hong, Chen, Jinliao, Chen, Yan-Qiong, Ai, Ye, Zhai, Jun-Wen, Wu, Sha-Sha, Zhou, Zhuang, Hsiao, Yu-Yun, Wu, Wan-Lin, Chen, You-Yi, Lin, Yu-Fu, Hsu, Jui-Ling, Li, Chia-Ying, Wang, Zhi-Wen, Zhao, Xiang, Zhong, Wen-Ying, Ma, Xiao-Kai, Ma, Liang, Huang, Jie, Chen, Gui-Zhen, Huang, Ming-Zhong, Huang, Laiqiang, Peng, Dong-Hui, Luo, Yi-Bo, Zou, Shuang-Quan, Chen, Shi-Pin, Lan, Siren, Tsai, Wen-Chieh, Van de Peer, Yves, and Liu, Zhong-Jian

Published
2022
Full Text
View/download PDF

40. Theoretical and Experimental Study of Floating Foundation Vibration Reduction System Based on Deep Neural Network

Author

Zhu Longji, Wenliuhan Heisha, and Zou Shuang

Subjects
Floating foundation, dynamic nonlinear model, air spring, recurrent convolution neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A floating foundation vibration reduction system with an air spring as a vibration isolation element has been widely used in the foundation of large ultra-precision instruments. With the excellent vibration isolation performance of the air spring, its complex dynamic nonlinear behavior is always a research difficulty. In this paper, a dynamic model of a floating foundation vibration reduction system based on the restoring force of air spring is derived. The structure of the recurrent convolution neural network (RCNN) is proposed based on combining the working characteristics of a convolution neural network and a long short-term memory neural network, and the dynamic model of a floating foundation vibration reduction system is established with the restoring force of the air spring calculated by the RCNN as the input. Finally, a test experiment was designed to compare the dynamic characteristics of the traditional numerical model and three deep neural network models in the floating foundation. The results show that convolution neural network, long short-term memory, and RCNN models could predict the vibration response of floating foundation vibration reduction system, and the RCNN model had better performance for a floating foundation.

Published
2021
Full Text
View/download PDF

47. Wolfberry genomes and the evolution of Lycium (Solanaceae)

Author

Cao, You-Long, Li, Yan-long, Fan, Yun-Fang, Li, Zhen, Yoshida, Kouki, Wang, Jie-Yu, Ma, Xiao-Kai, Wang, Ning, Mitsuda, Nobutaka, Kotake, Toshihisa, Ishimizu, Takeshi, Tsai, Kun-Chan, Niu, Shan-Ce, Zhang, Diyang, Sun, Wei-Hong, Luo, Qing, Zhao, Jian-Hua, Yin, Yue, Zhang, Bo, Wang, Jun-Yi, Qin, Ken, An, Wei, He, Jun, Dai, Guo-Li, Wang, Ya-Jun, Shi, Zhi-Gang, Jiao, En-Ning, Wu, Peng-Ju, Liu, Xuedie, Liu, Bin, Liao, Xing-Yu, Jiang, Yu-Ting, Yu, Xia, Hao, Yang, Xu, Xin-Yu, Zou, Shuang-Quan, Li, Ming-He, Hsiao, Yu-Yun, Lin, Yu-Fu, Liang, Chieh-Kai, Chen, You-Yi, Wu, Wan-Lin, Lu, Hsiang-Chai, Lan, Si-Ren, Wang, Zhi-Wen, Zhao, Xiang, Zhong, Wen-Ying, Yeh, Chuan-Ming, Tsai, Wen-Chieh, Van de Peer, Yves, and Liu, Zhong-Jian

Published
2021
Full Text
View/download PDF

49. A probabilistic life prediction framework for vibration fatigue of turbine blade based on refined polynomial chaos expansion.

Author

Fang, Jie, Liu, Chengxuan, Li, Kaiyang, Zou, Shuang, Sun, Bing, and Li, Yi

Subjects
POLYNOMIAL chaos, FATIGUE life, MONTE Carlo method, TURBINE blades, VIBRATION tests, FINITE element method, FATIGUE testing machines
Abstract

Vibration fatigue is a typical form of multiaxial fatigue. In this study, initial vibration fatigue tests were conducted on rocket turbine blades, and significant dispersion in fatigue life was found. Consistent with this finding, a novel probabilistic life prediction framework was proposed. This framework integrates sensitivity analysis and sequential sampling technology and introduces polynomial chaos expansion as a computationally efficient alternative to finite element analysis. And a continuous mechanics‐based damage evolution model was employed to examine the vibration fatigue life of turbine blades. The findings validate the effectiveness of the framework, as no significant difference was found between the experimental results and simulated predictions at the 95% confidence level. Furthermore, comparison with the Monte Carlo simulation indicated that this framework achieves comparable prediction accuracy, while significantly reducing the required samples by 2 orders of magnitude, which effectively addresses the fatigue problem of small sample data. This framework enables rapid and accurate multiaxial fatigue probabilistic life prediction, which holds important implications for the reliability design of reusable spacecraft. Highlights: This study is a novel probabilistic framework for predicting fatigue life of small sample data.The polynomial chaos expansion (PCE)‐based framework achieves the Monte Carlo simulation (MCS)‐level accuracy with less than 1% samples.The vibration fatigue tests have verified the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results