Guangyuan Pan
Orcid: 0000-0003-0115-6659
According to our database1,
Guangyuan Pan
authored at least 19 papers
between 2014 and 2024.
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Bibliography
2024
Development of an Automated Global Crash Prediction Model With Adaptive Feature Selection of Deep Neural Networks.
IEEE Trans. Ind. Informatics, October, 2024
2023
A Dimensionality-Reducible Operational Optimal Control for Wastewater Treatment Process.
IEEE Trans. Neural Networks Learn. Syst., September, 2023
Neural Comput. Appl., April, 2023
An Adaptive Hybrid Attention Based Convolutional Neural Net for Intelligent Transportation Object Recognition.
IEEE Trans. Intell. Transp. Syst., 2023
Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine.
IEEE Access, 2023
TRFN: Triple-Receptive-Field Network for Regional-Texture and Holistic-Structure Image Inpainting.
Proceedings of the Neural Information Processing - 30th International Conference, 2023
Proceedings of the Neural Information Processing - 30th International Conference, 2023
Traffic Accident Forecasting Based on a GrDBN-GPR Model with Integrated Road Features.
Proceedings of the Neural Information Processing - 30th International Conference, 2023
Road Meteorological State Recognition in Extreme Weather Based on an Improved Mask-RCNN.
Proceedings of the Neural Information Processing - 30th International Conference, 2023
2022
IET Image Process., 2022
2021
2020
Road safety performance function analysis with visual feature importance of deep neural nets.
IEEE CAA J. Autom. Sinica, 2020
Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data.
CoRR, 2020
2019
Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation.
CoRR, 2019
2018
Winter Road Surface Condition Recognition Using A Pretrained Deep Convolutional Network.
CoRR, 2018
2017
A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data.
CoRR, 2017
2014
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014