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您的检索式：作者名="Wenkai Chu"
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| 1 | Association Between Tp‐e/QT Ratio and Prognosis in Patients Undergoing Primary Percutaneous Coronary Intervention for ST‐Segment Elevation Myocardial Infarction显示文摘 | Xiangmei Zhao Zhouliang Xie Yingjie Chu Lei Yang Wenkai Xu Xianzhi Yang Xiaoyu Liu Lixiao Tian | 2012 | Clin Cardiol2012，，9： | 1 |
| 2 | Land cover classification in a mixed forest-grassland ecosystem using LResU-net and UAV imagery显示文摘Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally excels in medical and building classification, but its usefulness in mixed forest-grassland ecosystems in semi-arid to semi-humid climates is unknown. This study proposes a new semantic segmentation network of LResU-net in which residual convolution unit (RCU) and loop convolution unit (LCU) are added to the U-net framework to classify images of different land covers generated by UAV high resolution. The selected model enhanced classification accuracy by increasing gradient mapping via RCU and modifying the size of convolution layers via LCU as well as reducing convolution kernels. To achieve this objective, a group of orthophotos were taken at an altitude of 260 m for testing in a natural forest-grassland ecosystem of Keyouqianqi, Inner Mongolia, China, and compared the results with those of three other network models (U-net, ResU-net and LU-net). The results show that both the highest kappa coefficient (0.86) and the highest overall accuracy (93.7%) resulted from LResU-net, and the value of most land covers provided by the producer’s and user’s accuracy generated in LResU-net exceeded 0.85. The pixel-area ratio approach was used to calculate the real areas of 10 different land covers where grasslands were 67.3%. The analysis of the effect of RCU and LCU on the model training performance indicates that the time of each epoch was shortened from U-net (358 s) to LResU-net (282 s). In addition, in order to classify areas that are not distinguishable, unclassified areas were defined and their impact on classification. LResU-net generated significantly more accurate results than the other three models and was regarded as the most appropriate approach to classify land cover in mixed forest-grassland ecosystems. | Chong Zhang Li Zhang Bessie Y.J.Zhang Jingqian Sun Shikui Dong Xueyan Wang Yaxin Li Jian Xu Wenkai Chu Yanwei Dong Pei Wang | 2022 | Journal of Forestry Research2022，33，3： | 1 |
| 3 | Uncertainty Visualisation of a 3D Geological Geometry Model and Its Application in GIS-Based Mineral Resource Assessment:A Case Study in Huayuan District,Northwestern Hunan Province,China显示文摘This paper reports an application of uncertainty visualisation of a regional scale(1:50000)3 D geological geometry model to be involved in GIS-based 3 D mineral potential assessment of the Xiangxibei lead-zinc mineral concentration area in northwestern Hunan District,China.Three-dimensional(3 D)geological modelling is a process of interpretation that combines a set of input measurements in geometry.Today,technology has become a necessary part of GIS-based deep prospecting.However,issues of sparse data and imperfect understanding exist in the process so that there are several uncertainties in 3 D geological modelling.And these uncertainties are inevitably transmitted into the post-processing applications,such as model-based mineral resource assessment.Thus,in this paper,first,a big-data-based method was used to estimate the uncertainty of a 3 D geological model;second,a group of expectations of geological geometry uncertainty were calculated and integrated into ore-bearing stratoisohypse modelling,which is one of the major favourable parameters of assessment for Lead-Zinc(Pb-Zn)deep prospectivity mapping in northwestern Hunan;and finally,prospecting targets were improved. | Nan Li Cangbai Li Wenkai Chu Keyan Xiao Weihan Tao Xianglong Song Rui Cao Yang Xu Mingjing Fan | 2021 | Journal of Earth Science2021，32，2： | 1 |
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