Forthcoming articles

International Journal of System Control and Information Processing

International Journal of System Control and Information Processing (IJSCIP)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of System Control and Information Processing (2 papers in press)

Regular Issues

  • A joint data-driven process monitoring method using knowledge propagation based on manifold clustering   Order a copy of this article
    by Chuanfang Zhang, Kaixiang Peng, Jie Dong 
    Abstract: Compared with existing process monitoring approaches, a joint data-driven method using knowledge propagation based on manifold clustering is proposed for fault detection, which utilizes historical data containing knowledge information (labeled data). The main contributions of this work are as follows: (1) Two transformation matrices are derived based on manifold learning and clustering method. (2) Different from conventional data-driven fault detection method, knowledge propagation based on manifold clustering is used to extract the features of unlabeled data. (3) According to extracted features, the fault detection approach is proposed. The proposed method is applied to Tennessee Eastman (TE) process. The simulation results indicate that the proposed monitoring scheme can effectively monitor the working conditions of the process and identify fault types.
    Keywords: Joint data-driven; Process monitoring; Fault detection; Manifold clustering; Knowledge propagation.

  • Parameter optimization research for hydraulic turbine regulating system based on CGABC algorithm   Order a copy of this article
    by Xiaoxia Tan, Jing Chen, Gonggui Chen 
    Abstract: Artificial bee colony (ABC) algorithm is regularly taken in the parameter optimization of the hydraulic turbine regulating system (HTRS). The chaotic and global ABC (CGABC) algorithm is proposed to overcome the slow convergence speed and low convergence precision that existed in standard ABC algorithm. Among them, global optimal is used to enhance the global exploration ability, and chaos optimization is adopted to increase the diversity of bee colony. The simulation results show that compared with fuzzy strategy, ZN, DE and ABC algorithm, the proposed CGABC algorithm significantly improves the dynamic transition process of HTRS. Simultaneously, the simulation experiment is carried out on HTRS with different governor parameters and controlled parameters when it is under the frequency and load disturbance condition. The results show that the dynamic characteristics and robustness is determined by the parameter selection, and the appropriate combination of parameters allows HTRS to achieve optimal dynamic performance and robustness.
    Keywords: Hydraulic turbine regulating system; CGABC algorithm; parameter optimization; frequency and load disturbance condition; dynamic performance; robustness.