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您的检索式：作者名="Tiansheng Ye"
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| 1 | Treadmill step training promotes spinal cord neural plasticity after incomplete spinal cord injury显示文摘A large body of evidence shows that spinal circuits are significantly affected by training,and that intrinsic circuits that drive locomotor tasks are located in lumbosacral spinal segments in rats with complete spinal cord transection.However,after incomplete lesions,the effect of treadmill training has been debated,which is likely because of the difficulty of separating spontaneous stepping from specific training-induced effects.In this study,rats with moderate spinal cord contusion were subjected to either step training on a treadmill or used in the model(control) group.The treadmill training began at day 7 post-injury and lasted 20 ± 10 minutes per day,5 days per week for 10 weeks.The speed of the treadmill was set to 3 m/min and was increased on a daily basis according to the tolerance of each rat.After 3 weeks of step training,the step training group exhibited a significantly greater improvement in the Basso,Beattie and Bresnahan score than the model group.The expression of growth-associated protein-43 in the spinal cord lesion site and the number of tyrosine hydroxylase-positive ventral neurons in the second lumbar spinal segment were greater in the step training group than in the model group at 11 weeks post-injury,while the levels of brain-derived neurotrophic factor protein in the spinal cord lesion site showed no difference between the two groups.These results suggest that treadmill training significantly improves functional recovery and neural plasticity after incomplete spinal cord injury. | Tiansheng Sun Chaoqun Ye Jun Wu Zhicheng Zhang Yanhua Cai Feng Yue | 2013 | Neural Regeneration Research2013，8，27： | 5 |
| 2 | Reservoir prediction using multi-wave seismic attributes显示文摘The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes,and these problems become much more serious in multi-wave seismic exploration.Data redundancy will increase the burden on interpreters,occupy large computer memory,take much more computing time,conceal the effective information,and especially cause the 'curse of dimension'.Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance.In order to solve these problems,we study methods of principal component analysis (PCA),independent component analysis (ICA) for attribute optimization and support vector machine (SVM) for reservoir prediction.We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction.The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP-and PS-wave attributes,compared with that based on traditional PP-wave attributes,can improve the prediction accuracy. | Ye Yuan Yang Liu Jingyu Zhang Xiucheng Wei Tiansheng Chen | 2011 | Earthquake Science2011，24，4： | 1 |
| 3 | Dimension Reduction Based on Sampling显示文摘Dimension reduction provides a powerful means of reducing the number of random variables under consideration.However,there were many similar tuples in large datasets,and before reducing the dimension of the dataset,we removed some similar tuples to retain the main information of the dataset while accelerating the dimension reduc-tion.Accordingly,we propose a dimension reduction technique based on biased sampling,a new procedure that incorporates features of both dimensional reduction and biased sampling to obtain a computationally efficient means of reducing the number of random variables under consid-eration.In this paper,we choose Principal Components Analysis(PCA)as the main dimensional reduction algorithm to study,and we show how this approach works. | Zhuping Li Donghua Yang Mengmeng Li Haifeng Guo Tiansheng Ye Hongzhi Wang | 2023 | 国际计算机前沿大会会议论文集2023，，1： | 0 |
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