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19 October 2023 Agent model for calculating gas radiation properties based on gradient boosting decision tree algorithm
Taiye Liu, Chengpo Mu, Nan Wu, Yang He
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Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270934 (2023) https://doi.org/10.1117/12.2684562
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
The calculation of gas radiation properties has important applications in many fields such as infrared target detection, remote sensing atmospheric transmittance calculation and so on. Traditionally, the line by line calculation method is used for the calculation of gas radiation properties. However, this method is inefficient for the calculation of large scale flow field radiation data, so this paper proposes a method using a pre-training agent model based on gradient boosting decision tree algorithm instead of the line by line calculation method. After experimental comparison, it is found that the new model based on gradient boosting decision tree algorithm greatly improves the computational efficiency compared with the traditional line by line calculation method on the basis of guaranteed computational accuracy, and greatly reduces the storage space occupation compared with the pre-tabulation method. It can replace the line by line model to a certain extent when dealing with the needs of extensive and complex flow field calculations and it can greatly reduce the consumption of computing time.
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Taiye Liu, Chengpo Mu, Nan Wu, and Yang He "Agent model for calculating gas radiation properties based on gradient boosting decision tree algorithm", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270934 (19 October 2023); https://doi.org/10.1117/12.2684562
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KEYWORDS
Absorption
Machine learning
Decision trees
Data modeling
Databases
Engineering
Transmittance
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