scholarly journals Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity

RSC Advances ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 817-829
Author(s):  
Ge He ◽  
Tao Luo ◽  
Yagu Dang ◽  
Li Zhou ◽  
Yiyang Dai ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Emulsion Polymerization ◽  
Process Model ◽  
Mechanistic Model ◽  
Polymerization Kinetics ◽  
Data Driven ◽  
Programming Approach ◽  
Process Conditions ◽  
Model Based ◽  
Mooney Viscosity

The process model comprised of a mechanistic model based on emulsion polymerization kinetics and a data-driven model derived from genetic programming is developed to correlate the feed compositions and process conditions to NBR Mooney viscosity.

scholarly journals Correction: Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity

RSC Advances ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 6395-6395
Author(s):  
Ge He ◽  
Tao Luo ◽  
Yagu Dang ◽  
Li Zhou ◽  
Yiyang Dai ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Programming Approach ◽  
Mooney Viscosity

Correction for ‘Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity’ by Ge He et al., RSC Adv., 2021, 11, 817–829, DOI: 10.1039/D0RA07257E.

Semantics of OWL-S process model based on temporal description logic

2013 ◽  
Vol 33 (1) ◽  
pp. 266-269 ◽  
Author(s):  
Ming LI ◽  
Shiyi LIU ◽  
Fuzhong NIAN
Keyword(s):  
Process Model ◽  
Description Logic ◽  
Model Based

The Evaluation of Efficient Process Model Based on Survey

2018 ◽  
Author(s):  
Thulasee Krishna Dr. S. ◽  
Sreekanth Dr.S. ◽  
Dharanidhar K. N.
Keyword(s):  
Process Model ◽  
Efficient Process ◽  
Model Based

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals Multi-Inputs and Multi-Outputs equivalent model based on data driven controller for a robotic system

2020 ◽  
Vol 53 (2) ◽  
pp. 9784-9789
Author(s):  
Josué Gómez ◽  
Chidentree Treesatayapun ◽  
América Morales
Keyword(s):  
Robotic System ◽  
Data Driven ◽  
Equivalent Model ◽  
Model Based

Data-driven Model-based Detection of Malicious Insiders via Physical Access Logs

2019 ◽  
Vol 29 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Carmen Cheh ◽  
Uttam Thakore ◽  
Ahmed Fawaz ◽  
Binbin Chen ◽  
William G. Temple ◽  
...  
Keyword(s):  
Data Driven ◽  
Model Based ◽  
Malicious Insiders ◽  
Access Logs ◽  
Physical Access
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