1 code implementation • 12 Aug 2024 • Xinyi Song, Kennedy Odongo, Francis G. Pascual, Yili Hong
In this paper, we comprehensively compare different machine learning and deep learning methods under different performance metrics on the classification of solar cell EL images from monocrystalline and polycrystalline modules.
no code implementations • 30 Nov 2023 • Simin Zheng, Lu Lu, Yili Hong, Jian Liu
This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data.
no code implementations • 15 Nov 2023 • Li Xu, Yili Hong, Eric P. Smith, David S. McLeod, Xinwei Deng, Laura J. Freeman
We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained.
no code implementations • 9 Nov 2021 • Yili Hong, Jiayi Lian, Li Xu, Jie Min, Yueyao Wang, Laura J. Freeman, Xinwei Deng
We also describe recent developments in modeling and analysis of AI reliability and outline statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification, and discuss how those topics can be related to AI reliability, with illustrative examples.
no code implementations • 2 Feb 2021 • Yili Hong, Jie Min, Caleb B. King, William Q. Meeker
In this paper, we use recurrent disengagement events as a representation of the reliability of the AI system in AV, and propose a statistical framework for modeling and analyzing the recurrent events data from AV driving tests.
no code implementations • 10 Oct 2020 • Jiayi Lian, Laura Freeman, Yili Hong, Xinwei Deng
Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics.