中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
沈阳自动化研究所 [200]
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期刊论文 [330]
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Cadmium accumulation in tropical island paddy soils: From environment and health risk assessment to model prediction
期刊论文
OAI收割
JOURNAL OF HAZARDOUS MATERIALS, 2024, 卷号: 465, 页码: 14
作者:
Guo, Yan
;
Yang, Yi
;
Li, Ruxia
;
Liao, Xiaoyong
;
Li, Yonghua
  |  
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2024/03/25
Cadmium
Pollution assessment
Probabilistic health risk
XGBoost
Structure and evolution of global lead trade network: An industrial chain perspective
期刊论文
OAI收割
RESOURCES POLICY, 2024, 卷号: 90, 页码: 104735
作者:
Chen, Wei
;
Zhang, Jianing
;
Yu, Zhaoyuan
;
Zhao, Xiquan
  |  
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2024/04/08
Lead trade
Trade network
Lead industrial chain
Supply chain security
Complex network
Unveiling the spatial differentiation drivers of major soil element behavior along traffic network accessibility
期刊论文
OAI收割
ENVIRONMENTAL POLLUTION, 2024, 卷号: 342, 页码: 12
作者:
Wang, Siyu
;
Xiong, Zhunan
;
Han, Xiaoxiao
;
Wang, Lingqing
;
Liang, Tao
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/01/22
Soil element behavior
Traffic artery accessibility
Urbanization
Soil metal pollution
Spatial differentiation
Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications
期刊论文
OAI收割
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 1, 页码: 18-36
作者:
Ding Wang
;
Ning Gao
;
Derong Liu
;
Jinna Li
;
Frank L. Lewis
  |  
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2024/01/02
Adaptive dynamic programming (ADP)
advanced control
complex environment
data-driven control
event-triggered design
intelligent control
neural networks
nonlinear systems
optimal control
reinforcement learning (RL)
A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing
期刊论文
OAI收割
Machine Intelligence Research, 2024, 卷号: 21, 期号: 2, 页码: 400-410
作者:
Dezheng Wang
  |  
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2024/04/03
Multi-scale, feature extractor, deep neural network (DNN), multirate sampled industrial processes, prediction
Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks
期刊论文
OAI收割
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 982-995
作者:
Tao Wang
;
Qiming Chen
;
Xun Lang
;
Lei Xie
;
Peng Li
  |  
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2024/03/18
Convolutional neural networks (CNNs)
deep learning
image processing
oscillation detection
process industries
Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems
期刊论文
OAI收割
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1227-1238
作者:
Qinchen Yang
;
Fukai Zhang
;
Cong Wang
  |  
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/04/10
Adaptive neural control (ANC)
deterministic learning (DL)
neural network (NN)
robot manipulators
Temporal and Spatial Changes of Hydrographic Connectivity with the Development of Agriculture, Industry, and Urban Areas: A Case Study of the Yellow River Basin in Henan Province during the Last Two Decades
期刊论文
OAI收割
WATER, 2023, 卷号: 15, 期号: 24, 页码: 4245
作者:
Li, Zhiying
;
Wei, Chaoyang
;
Zhou, Jianli
;
Yang, Fen
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/01/26
Henan section of the Yellow River Basin
modified normalized difference water index
hydrographic connectivity
Set Pair Analysis
Rethinking the dynamic of global production networks: Integrate the influence of suppliers and towards a flexible strategy making causal mechanism
期刊论文
OAI收割
GLOBAL NETWORKS-A JOURNAL OF TRANSNATIONAL AFFAIRS, 2023, 卷号: N/A, 页码: 17
作者:
Zheng, Zhi
  |  
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2024/01/02
causal mechanism
global production networks
interfirm capability transformation
strategy-making
supplier capability
Macro-influencers or meso-influencers, how do companies choose?
期刊论文
OAI收割
INDUSTRIAL MANAGEMENT & DATA SYSTEMS, 2023, 页码: 20
作者:
Lv, Junjie
;
Yang, Ruyu
;
Yu, Jianye
;
Yao, Wenjing
;
Wang, Yuanzhuo
  |  
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2023/12/04
Influencer marketing
Social commerce
Social media
Information dissemination
Social evolutionary game