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 13 Issue 7
Jul.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Contents
L. Cao, F. Yang, and Y. Wang, “Causal representation learning for trustworthy industrial process modeling,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1758–1760, Jul. 2026. doi: 10.1109/JAS.2025.125678
Citation: L. Cao, F. Yang, and Y. Wang, “Causal representation learning for trustworthy industrial process modeling,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1758–1760, Jul. 2026. doi: 10.1109/JAS.2025.125678

Causal Representation Learning for Trustworthy Industrial Process Modeling

doi: 10.1109/JAS.2025.125678
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    X. Ma, T. Chen, and Y. Wang, “Dynamic process monitoring based on dot product feature analysis for thermal power plants,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 563–574, 2025. doi: 10.1109/JAS.2024.124908
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    P. Song, J. Wang, C. Zhao, and B. Huang, “From static and dynamic perspectives: A survey on historical data benchmarks of control performance monitoring,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 300–316, 2025. doi: 10.1109/JAS.2024.124902
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    L. Cao, J. Wang, J. Su, Y. Luo, Y. Cao, R. D. Braatz, and B. Gopaluni, “Comprehensive analysis on machine learning approaches for interpretable and stable soft sensors,” IEEE Trans. Instrum. Meas., vol. 74, pp. 1–17, 2025.
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