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Chinese Academy of Sciences Institutional Repositories Grid
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期刊论文 [118]
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Analysis of the influence of trust in opposing opinions: An inclusiveness-degree based Signed Deffuant-Weisbush model
期刊论文
OAI收割
INFORMATION FUSION, 2024, 卷号: 104, 页码: 11
作者:
Jiang, Bo
;
Zhao, Yiyi
;
Dong, Jianglin
;
Hu, Jiangping
  |  
收藏
  |  
浏览/下载:45/0
  |  
提交时间:2024/02/22
Deffuant-Weisbush model
Inclusiveness-degree function
Signed network
Opinion formation
Black swan phenomenon
Modelling micropit formation in rolling contact fatigue of bearings with a crystal plasticity damage theory coupled with cohesive finite elements
期刊论文
OAI收割
ENGINEERING FRACTURE MECHANICS, 2024, 卷号: 297, 页码: 16
作者:
Han, Xinqi
;
Li, Shuxin
;
Sun, Chengqi
;
Lu, Siyuan
;
Sun CQ(孙成奇)
  |  
收藏
  |  
浏览/下载:81/0
  |  
提交时间:2024/04/02
Micropit formation
Rolling contact fatigue
Transgranular crack growth
Crystal plasticity model
Kinetic studies of the secondary hydrate formation in porous media based on experiments in a cubic hydrate simulator and a new kinetic model
期刊论文
OAI收割
FUEL, 2024, 卷号: 358, 页码: 14
作者:
Xiao, Chang-Wen
;
Li, Xiao-Sen
;
Li, Gang
;
Yu, Yi-Song
;
Lv, Qiu-Nan
  |  
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2025/11/15
Secondary hydrate formation
Cubic hydrate simulator
Kinetic model
Hydrate morphology
Heterogeneous manner
Distributed Formation Control for a Multirobotic Fish System With Model-Based Event-Triggered Communication Mechanism
期刊论文
OAI收割
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2023, 卷号: 70, 期号: 11, 页码: 11433-11442
作者:
Dai, Shijie
;
Wu, Zhengxing
;
Zhang, Pengfei
;
Tan, Min
;
Yu, Junzhi
  |  
收藏
  |  
浏览/下载:33/0
  |  
提交时间:2023/11/17
Formation control
model-based eventtriggered mechanism (ETM)
robotic fish
underwater multiagent system
Quantifying the Nitrogen Sources and Secondary Formation of Ambient HONO with a Stable Isotopic Method
期刊论文
OAI收割
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 卷号: 57, 期号: 43, 页码: 16456-16464
作者:
Zong, Zheng
;
Wang, Tao
;
Chai, Jiajue
;
Tan, Yue
;
Liu, Pengfei
  |  
收藏
  |  
浏览/下载:33/0
  |  
提交时间:2024/01/26
HONO nitrogen sources
HONO formation
nitrogenisotope
oxygen isotope
Bayesian model
Quantifying the Nitrogen Sources and Secondary Formation of Ambient HONO with a Stable Isotopic Method
期刊论文
OAI收割
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 卷号: 57, 期号: 43, 页码: 16456-16464
作者:
Zong, Zheng
;
Wang, Tao
;
Chai, Jiajue
;
Tan, Yue
;
Liu, Pengfei
  |  
收藏
  |  
浏览/下载:33/0
  |  
提交时间:2024/09/14
HONO nitrogen sources
HONO formation
nitrogenisotope
oxygen isotope
Bayesian model
Quantifying the Nitrogen Sources and Secondary Formation of Ambient HONO with a Stable Isotopic Method
期刊论文
OAI收割
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 卷号: 57, 期号: 43, 页码: 16456-16464
作者:
Zong, Zheng
;
Wang, Tao
;
Chai, Jiajue
;
Tan, Yue
;
Liu, Pengfei
  |  
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2024/11/02
HONO nitrogen sources
HONO formation
nitrogenisotope
oxygen isotope
Bayesian model
Experimental investigation and modelling of the hydrate formation kinetics based on hydrate pore-scale distribution in porous media
期刊论文
OAI收割
CHEMICAL ENGINEERING SCIENCE, 2023, 卷号: 280, 页码: 14
作者:
Xiao, Chang-Wen
;
Li, Xiao-Sen
;
Li, Gang
;
Yu, Yang
;
Weng, Yi-Fan
  |  
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2025/11/14
Hydrate technology
Kinetic model
Hydrate formation
Reaction area
History-matching method
Quantitative evaluation model of shale oil adsorption: A case study of the first member of Cretaceous Qingshankou Formation in northern Songliao Basin, NE China
期刊论文
OAI收割
PETROLEUM EXPLORATION AND DEVELOPMENT, 2023, 卷号: 50, 期号: 5, 页码: 1137-1150
作者:
Li, Jinbu
;
Wang, Min
;
Lu, Shuangfang
;
Liu, Liang
;
Li, Ming
  |  
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2024/09/14
shale oil
adsorbed oil
occurrence state
mobility
Cretaceous Qingshankou Formation
Evaluation model
Songliao Basin
From shallow sea to deep sea: research progress in underwater image restoration
期刊论文
OAI收割
FRONTIERS IN MARINE SCIENCE, 2023, 卷号: 10, 页码: 24
作者:
Song, Wei
;
Liu, Yaling
;
Huang, Dongmei
;
Zhang, Bing
;
Shen, Zhihao
  |  
收藏
  |  
浏览/下载:30/0
  |  
提交时间:2023/10/07
shallow-sea image restoration
deep-sea image restoration
image formation
physical model
prior
deep learning