Browsing by Author "Yang, Shuang-Hua"

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    Networked control systems and wireless sensor networks: theories and applications.
    (Taylor & Francis, 2008-11-01) Yang, Shuang-Hua; Cao, Yi
    Abstract This special issue aims to provide innovative research work that has recently been carried out in networked control and wireless sensor networks, including both theoretical developments, experimental and/or application research. Wired and wireless networks have been used as a platform in the remote monitoring and control. The applications cover a wide range from natural monitoring to ambient awareness, from military to surveillance, and from industrial plants to domestic home environments. This preface first introduces the basic concepts of networked control and wireless sensor networks, then presents the challenges in these areas, and finally summarises the five papers included in the special issue.
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    Predictive control for the ALSTOM gasifier problem.
    (Institution of Electrical Engineers, 2006-05-09T00:00:00Z) Seyab, R. K. A.; Cao, Yi; Yang, Shuang-Hua
    Model predictive control (MPC) has become the first choice of control strategy in many cases especially in the process industry because it is intuitive and can explicitly handle MIMO (multiple input multiple output) systems with input and output constraints. The authors implemented a simple MPC algorithm based on the state space formulation to control the ALSTOM gasifier. Among three operating conditions of the plant, 0% load condition is identified as the worst case. A linearised state space model at 0% load condition of the non-linear plant is adopted as the internal model for performance prediction. Because of this choice, the control system comfortably achieves performance requirements at the most difficult load condition. Meanwhile, the case study shows that the model is also adequate to pass all tests under other load conditions specified in the benchmark problem. The MPC algorithm uses standard formulation and off-the-shelf software with a few tunable parameters. Thus, it is easy to implement and to tune to achieve satisfactory performance.
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    A review of kernel methods for feature extraction in nonlinear process monitoring
    (MDPI, 2019-12-23) Pilario, Karl Ezra; Shafiee, Mahmood; Cao, Yi; Lao, Liyun; Yang, Shuang-Hua
    Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.