Search Results - "Liu, Lushi"

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  1. 1

    Prospectivity Mapping for Magmatic-Related Seafloor Massive Sulfide on the Mid-Atlantic Ridge Applying Weights-of-Evidence Method Based on GIS by Lushi Liu, Jilong Lu, Chunhui Tao, Shili Liao

    Published in Minerals (Basel) (01-01-2021)
    “…The Mid-Atlantic Ridge belongs to slow-spreading ridges. Hannington predicted that there were a large number of mineral resources on slow-spreading ridges;…”
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    Journal Article
  2. 2

    Rice leaf chlorophyll content estimation with different crop coverages based on Sentinel-2 by Liu, Lushi, Xie, Yichen, Zhu, Bingxue, Song, Kaishan

    Published in Ecological informatics (01-07-2024)
    “…Chlorophyll content is an important index for evaluating the health and productivity of crops, and environmental stress on them. The real-time, rapid, and…”
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    Journal Article
  3. 3

    GIS-based Mineral Prospectivity Mapping of Seafloor Massive Sulfide on Ultraslow-spreading Ridges: A Case Study of Southwest Indian Ridge 48.7°–50.5° E by Liu, Lushi, Lu, Jilong, Tao, Chunhui, Liao, Shili, Chen, Shengbo

    “…With the depletion of mineral resources on land, seafloor massive sulfide deposits have the potential to become as important for exploration, development and…”
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    Journal Article
  4. 4

    Fuzzy Forest Machine Learning Predictive Model for Mineral Prospectivity: A Case Study on Southwest Indian Ridge 48.7°E–50.5°E by Liu, Lushi, Lu, Jilong, Tao, Chunhui, Liao, Shili, Su, Cheng, Huang, Nan, Xu, Xing

    “…Investigating the ocean environment where seafloor massive sulfide (SMS) deposits are located is not only technically intricate but also costly. Therefore,…”
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    Journal Article
  5. 5

    Micro-Bristle Robot Design Via Different Surrogate Model Optimization Methods by Shi, Yifan, Jing, Xiao, Liu, Lushi

    “…In this paper, we optimize the locomotion speed of a micro-bristle robot using three surrogate model optimization methods: Kriging method, Bayesian method, and…”
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    Conference Proceeding
  6. 6

    Improved initial alignment algorithm of SINS on shaking base based on Kalman filter by Shi, Ling-Feng, Hou, Zhi-Yong, Shi, Yifan, Liu, Lushi

    “…In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars use various nonlinear filtering…”
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    Conference Proceeding