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Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
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地理科学与资源研究... [20]
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期刊论文 [59]
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Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction
期刊论文
OAI收割
REMOTE SENSING, 2024, 卷号: 16, 期号: 12, 页码: 18
作者:
Jiang, Jiawei
;
Wang, Jun
;
Liu, Yiping
;
Huang, Chao
;
Jiang, Qiufu
  |  
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2024/09/02
temperature structure prediction
temperature structure reconstruction
spatiotemporal window ocean
satellite observations
spatiotemporal attention mechanism
Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models
期刊论文
OAI收割
FRONTIERS IN FORESTS AND GLOBAL CHANGE, 2023, 卷号: 6, 页码: 1249300
作者:
Guo, Qingchun
;
He, Zhenfang
;
Wang, Zhaosheng
  |  
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2024/01/04
extreme atmospheric temperature
artificial neural network
deep learning
CNN-GRU
CNN-LSTM
prediction
training algorithm
forest
High-Cycle Fatigue Properties and Life Prediction of ZK30 Magnesium Alloy at Room and Elevated Temperatures
期刊论文
OAI收割
JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE, 2023, 页码: 9
作者:
Xu, Zikuan
;
Huang, Lixin
;
Liu, Rui
;
Zhan, Hongyi
;
Zhang, Peng
  |  
收藏
  |  
浏览/下载:13/0
  |  
提交时间:2024/01/08
fatigue life prediction
fatigue strength
high-cycle fatigue
high temperature
magnesium alloy
High-temperature fatigue damage mechanism and strength prediction of vermicular graphite iron
期刊论文
OAI收割
INTERNATIONAL JOURNAL OF FATIGUE, 2023, 卷号: 168, 页码: 13
作者:
Chen, Y.
;
Pang, J. C.
;
Zou, C. L.
;
Li, S. X.
;
Zhang, Z. F.
  |  
收藏
  |  
浏览/下载:46/0
  |  
提交时间:2023/05/09
Vermicular graphite iron
High-temperature fatigue
Microstructures
Damage mechanism
Fatigue strength prediction
Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors
期刊论文
OAI收割
MATERIALS, 2023, 卷号: 16, 期号: 1, 页码: 13
作者:
Guo, Yiyun
;
Rui, Shao-Shi
;
Xu, Wei
;
Sun, Chengqi
;
Sun CQ(孙成奇)
  |  
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2023/02/09
machine learning
nickel-based superalloy
fatigue strength prediction
temperature
stress ratio
Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model
期刊论文
OAI收割
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 卷号: 61, 页码: 15
作者:
Ren, Yibin
;
Li, Xiaofeng
  |  
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2023/12/13
Predictive models
Atmospheric modeling
Sea ice
Numerical models
Arctic
Ocean temperature
Data models
Deep learning
Pan-Arctic
physically constrained loss function
sea ice concentration (SIC) prediction
subseasonal scale
A quantitative model based on grey theory for sea surface temperature prediction
期刊论文
OAI收割
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 卷号: 10, 页码: 17
作者:
Meng, Fanyu
;
Gu, Jilin
;
Wang, Ling-en
;
Qin, Zhibin
;
Gao, Mingyao
  |  
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2022/12/30
sea surface temperature
grey theory GM(11|sin) power model
genetic algorithm
prediction
A quantitative model based on grey theory for sea surface temperature prediction
期刊论文
OAI收割
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 卷号: 10, 页码: 17
作者:
Meng, Fanyu
;
Gu, Jilin
;
Wang, Ling-en
;
Qin, Zhibin
;
Gao, Mingyao
  |  
收藏
  |  
浏览/下载:28/0
  |  
提交时间:2022/12/30
sea surface temperature
grey theory GM(11|sin) power model
genetic algorithm
prediction
Correction of Overestimation in Observed Land Surface Temperatures Based on Machine Learning Models
期刊论文
OAI收割
JOURNAL OF CLIMATE, 2022, 卷号: 35, 期号: 16, 页码: 5359-5377
作者:
Liu, Fa
;
Wang, Xunming
;
Sun, Fubao
;
Wang, Hong
;
Wu, Lifeng
  |  
收藏
  |  
浏览/下载:35/0
  |  
提交时间:2022/09/21
Land surface temperature
Machine learning model
Climate regions
Prediction
Correction
Correction of Overestimation in Observed Land Surface Temperatures Based on Machine Learning Models
期刊论文
OAI收割
JOURNAL OF CLIMATE, 2022, 卷号: 35, 期号: 16, 页码: 5359-5377
作者:
Liu, Fa
;
Wang, Xunming
;
Sun, Fubao
;
Wang, Hong
;
Wu, Lifeng
  |  
收藏
  |  
浏览/下载:33/0
  |  
提交时间:2022/09/21
Land surface temperature
Machine learning model
Climate regions
Prediction
Correction