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Liu S, Wei X, Sun W, Wang C, Li W, Ma L, Liu Q. Coking Prediction in Catalytic Glucose Conversion to Levulinic Acid Using Improved Lattice Boltzmann Model. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Siwei Liu
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, P.R. China
- University of Chinese Academy of Sciences, Beijing 100049, P.R. China
| | - Xiangqian Wei
- Laboratory of Basic Research in Biomass Conversion and Utilization, Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230026, P. R. China
| | - Weitao Sun
- Laboratory of Basic Research in Biomass Conversion and Utilization, Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230026, P. R. China
| | - Chenguang Wang
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, P.R. China
| | - Wenzhi Li
- Laboratory of Basic Research in Biomass Conversion and Utilization, Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230026, P. R. China
| | - Longlong Ma
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, P.R. China
| | - Qiying Liu
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, P. R. China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, P.R. China
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Kim YW, Yu HJ, Kim JS, Ha J, Choi J, Lee JS. Coronary artery decision algorithm trained by two-step machine learning algorithm. RSC Adv 2020; 10:4014-4022. [PMID: 35492670 PMCID: PMC9048707 DOI: 10.1039/c9ra08999c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/17/2020] [Indexed: 11/21/2022] Open
Abstract
A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed. The first step of the algorithm is based on the Gaussian progress regression model and is trained by a synthetic model using CFD analysis. The second step of the algorithm is based on a support vector machine with patient data, including flow characteristics and biometric features. Consequently, the accuracy of the FFR estimated from the first step of the algorithm was similar to that of the CFD-based method, while the accuracy of DEC in the second step was improved. This improvement in accuracy was analyzed using flow characteristics and biometric features. A two-step machine learning (ML) algorithm for coronary artery decision making is introduced, to increase the data quality by providing flow characteristics and biometric features by aid of computational fluid dynamics (CFD).![]()
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Affiliation(s)
- Young Woo Kim
- Department of Mechanical Engineering
- Yonsei University
- Korea
| | - Hee-Jin Yu
- Department of Mechanical Engineering
- Yonsei University
- Korea
| | - Jung-Sun Kim
- Division of Cardiology
- Severance Cardiovascular Hospital
- Yonsei University College of Medicine
- Korea
| | - Jinyong Ha
- Department of Electrical Engineering
- Sejong University
- Korea
| | - Jongeun Choi
- Department of Mechanical Engineering
- Yonsei University
- Korea
| | - Joon Sang Lee
- Department of Mechanical Engineering
- Yonsei University
- Korea
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