A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 1
IEEE/CAA Journal of Automatica Sinica
| Citation: | C. Liu, Y. Wang, C. Yang, and W. Gui, “Multimodal data-driven reinforcement learning for operational decision-making in industrial processes,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 252–254, Jan. 2024. doi: 10.1109/JAS.2023.123741
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| [1] |
J. Huang, Z. Li, and Z. Zhou, “A simple framework to generalized zero-shot learning for fault diagnosis of industrial processes,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1504–1506, 2023. doi: 10.1109/JAS.2023.123426
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| [2] |
L. Hu, K. Chan, X. Yuan, and S. Xiong, “A variational bayesian framework for cluster analysis in a complex network,” IEEE. Trans. Knowl. Data Engineering, vol. 32, no. 11, pp. 2115–2128, 2020. doi: 10.1109/TKDE.2019.2914200
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| [3] |
J. Wang, Q. Zhang, and D. Zhao, “Highway lane change decision-making via attention-based deep reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 567–569, 2022. doi: 10.1109/JAS.2021.1004395
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| [4] |
P. Zhou, T. Chai, and J. Sun, “Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system,” IEEE. Trans. Contr. Syst. T., vol. 21, no. 1, pp. 162–175, 2013. doi: 10.1109/TCST.2012.2182996
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| [5] |
Y. Yuan, X. Luo, M. Shang, and Z. Wang, “A Kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data,” IEEE Trans. Cyber., vol. 53, no. 9, pp. 5788–5801, 2023. doi: 10.1109/TCYB.2022.3185117
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| [6] |
X. Luo, H. Wu, Z. Wang, J. Wang, and D. Meng, “A novel approach to large-scale dynamically weighted directed network representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 9756–9773, 2022. doi: 10.1109/TPAMI.2021.3132503
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| [7] |
X. Luo, H. Wu, and Z. Li, “Neulft: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors,” IEEE. Trans. Knowl. Data Engineering, vol. 35, no. 6, pp. 6148–6166, 2023.
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| [8] |
L. Hu, Y. Yang, Z. Tang, Y. He, and X. Luo, “FCAN-MOPSO: An improved fuzzy-based graph clustering algorithm for complex networks with multi-objective particle swarm optimization,” IEEE. Trans. Fuzzy. Syst., vol. 31, no. 10, pp. 3470–3484, 2023. doi: 10.1109/TFUZZ.2023.3259726
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| [9] |
Y. Wang, S. Li, C. Liu, K. Wang, X. Yuan, C. Yang, and W. Gui, “Multi-scale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process,” IEEE Trans. Cyber., 2023.
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| [10] |
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, USA: MIT press, 2018.
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| [11] |
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in Proc. 35th Int. Conf. Mach. Lear., 2018, pp. 1861–1870.
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