An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx
Author:
Affiliation:
1. University of Bologna,Industrial Engineering Department,Bologna,Italy
2. Head of Powertrain Development Testing,Ferrari S.p.A,Maranello,Modena,Italy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10219091/10219092/10219122.pdf?arnumber=10219122
Reference16 articles.
1. Lightgbm: A highly efficient gradient boosting decision tree;ke,2017
2. Predictive modeling of engine emissions using machine learning: A review;khurana,2020
3. Permutation importance: a corrected feature importance measure
4. Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions
5. A Random Forest Algorithmic Approach to Predicting Particulate Emissions from a Highly Boosted GDI Engine
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Development of an Automatic Pipeline for Data Analysis and Pre-Processing for Data Driven-Based Engine Emission Modeling in a Real Industrial Application;SAE Technical Paper Series;2024-04-09
2. AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle;SAE International Journal of Engines;2024-01-09
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