Zhongyang Han
Orcid: 0000-0002-3397-4915
According to our database1,
Zhongyang Han
authored at least 15 papers
between 2016 and 2024.
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Bibliography
2024
A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry.
IEEE CAA J. Autom. Sinica, October, 2024
Graph-Frequency Domain Kalman Filtering for Industrial Pipe Networks Subject to Measurement Outliers.
IEEE Trans. Ind. Informatics, May, 2024
Distributed robust scheduling optimization for energy system of steel industry considering prediction uncertainties.
Inf. Sci., 2024
Ingredient Planning for Copper Industry: A Deep Reinforcement Learning-Based ε-Constrained Multi-Objective Optimization Framework.
Proceedings of the IEEE Congress on Evolutionary Computation, 2024
2023
A Multicriteria Evaluation and Cascaded Optimization Framework for Integrated Energy System of Steel Industry.
IEEE Trans. Ind. Electron., 2023
2022
Hierarchical Granular Computing-Based Model and Its Reinforcement Structural Learning for Construction of Long-Term Prediction Intervals.
IEEE Trans. Cybern., 2022
A hybrid granular-evolutionary computing method for cooperative scheduling optimization on integrated energy system in steel industry.
Swarm Evol. Comput., 2022
2020
Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input.
IEEE CAA J. Autom. Sinica, 2020
A Granular Computing-Based Hybrid Hierarchical Method for Construction of Long-Term Prediction Intervals for Gaseous System of Steel Industry.
IEEE Access, 2020
2019
Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization.
IEEE CAA J. Autom. Sinica, 2019
Proceedings of the 2019 IEEE Intl Conf on Dependable, 2019
2018
IEEE Trans. Cybern., 2018
2017
An optimized oxygen system scheduling with electricity cost consideration in steel industry.
IEEE CAA J. Autom. Sinica, 2017
2016
IEEE Trans. Cybern., 2016
Granular-computing based hybrid collaborative fuzzy clustering for long-term prediction of multiple gas holders levels.
Inf. Sci., 2016