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  • 收录类型=SCI-EXPANDED x
  • 人物=蒋娴 x

5 条 记 录,以下是 1-5

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A New Tree-Level Multi-Objective Forest Harvest Model (MO-PSO): Integrating Neighborhood Indices and PSO Algorithm to Improve the Optimization Effect of Spatial Structure被引量:12收藏 分享
作者:Qiu, Hanqing[1,2,3] Zhang, Huaiqing[1,2,3] Lei, Kexin[1,2,3] Hu, Xingtao[4] Yang, Tingdong[1,2,3]
机构:Chinese Acad Forestry;NFGA;Natl Long term Sci Res Base Huangfengqiao Forest M;Guizhou Normal Univ
来源:FORESTS  2023
关键词:forest planning   stand spatial structure   selection thinning priority   PSO algorithm   neighborhood-based indices  
An Automatic Identification Method of Crested Ibis (Nipponia nippon) Habitat Based on Spatiotemporal Density Detection被引量:2收藏 分享
作者:Jiang, Xian[1,2] Yang, Tingdong[1,2] Liu, Dongping[3,4] Zheng, Yili[5] Chen, Yan[1,2]
机构:Chinese Acad Forestry;Natl Forestry & Grassland Adm;Chinese Acad Forestry;Chinese Acad Forestry;Beijing Forestry Univ
来源:ANIMALS  2022
关键词:crested ibis   habitat   overnight site   foraging site   spatial density   temporal density  
Study on the Correlation between the Activity Trajectory of Crested Ibis (Nipponia nippon) and Meteorological Changes被引量:0收藏 分享
作者:Li, Fan[1,2] Liu, Xiaoxiao[3] Jiang, Xian[1,2] Guan, Li[3] Liu, Dongping[4,5]
机构:Chinese Acad Forestry;Natl Forestry & Grassland Adm;Beijing Univ Technol;Chinese Acad Forestry;Chinese Acad Forestry
来源:APPLIED SCIENCES-BASEL  2024
关键词:Crested Ibis   habitat   meteorological changes  
Spatial- and Temporal-Trajectory Analysis of the Crested Ibis (Nipponia nippon) by Fusing Multiple Sources of Data被引量:0收藏 分享
作者:Zhou, Yulong[1] Jiang, Xian[2] Chen, Zhanlong[1,3,4]
机构:China Univ Geosci;Chinese Acad Forestry;China Univ Geosci;China Univ Geosci
来源:ANIMALS  2023
关键词:behavioral analysis   night roosting points   anniversary activity   trajectory data clustering   trajectory data supplementation   habitat prediction  
AnimalEnvNet: A Deep Reinforcement Learning Method for Constructing Animal Agents Using Multimodal Data Fusion被引量:0收藏 分享
作者:Chen, Zhao[1,2] Wang, Dianchang[1,2] Zhao, Feixiang[1,2] Dai, Lingnan[1,2] Zhao, Xinrong[1,2]
机构:Beijing Forestry Univ;Natl Forestry & Grassland Adm;Chinese Acad Forestry
来源:APPLIED SCIENCES-BASEL  2024
关键词:deep reinforcement learning   AnimalEnvNet   multimodal data fusion   animal movement behaviour mode   CNN   LSTM  
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