Yoonsik Kim

A study on the evaluation of structural controllability in chemical processes

Abstract Controllability should be considered at the design stage before control system is designed, as it is an inherent property of process. Structural information makes controllability assessment possible by giving insights into the pathways of disturbances in the process. In this study, a simple procedure to evaluate controllability using relative order analysis and structural decomposition is suggested to select design alternatives. The effectiveness of the proposed method is validated by comparing the results with the analyses that were performed by using dynamical simulation.

A study on the evaluation and improvement of structural controllability of chemical processes

Abstract The control performance in a chemical process does not depend on the controller but also on process structure itself. Therefore, dynamic characteristics of a process should be considered at the design step before control system design is fixed. Controllability means the dynamic characteristics of a process itself to operate safely without violating constraints to achieve various design objectives in the presence of uncertainties. Structural information may give insights into the pathways of disturbances in the process. In this study, a simple controllability evaluation and improvement procedure is suggested to screen out design alternatives using relative order analysis and structural decomposition and to improve the controllability. By comparing the results from the proposed method withthe case of dynamical simulation, the effectiveness of the proposed method was shown.

Skin detection based on multi-seed propagation in a multi-layer graph for regional and color consistency

We propose a new skin detection method based on multi-seeds propagation in a multi-layer graph representation of an image. Initially, some of nodes in the graph are set to be foreground or background seeds based on a simple Bayesian skin detector, and they are propagated through the graph to find the skin probability in the manner of semi-supervised learning. The graph is designed to consider not only local and global coherence but also to consider the color consistency by constructing a multilayer graph of image and cluster layers. Extensive experiments on several datasets are conducted, which demonstrate that our method outperforms the existing methods in terms of various quantitative measures, such as accuracy, precision, recall and F-measure.

A study on the improvement of controllability of chemical processes based on structural analysis

Abstract Controllability should be considered at the design stage before control system is designed, as it is an inherent property of process. Structural information makes controllability assessment possible by giving insights on the pathways of disturbances in the process. In this study, a simple procedure to evaluate controllability is suggested to select design alternatives. This procedure is useful in screening out the design alternatives before using detailed controllability evaluation methods such as dynamic simulation.

Qualitative Fault Diagnosis with System Decomposition: Application to a Large-Scale Boiler Plant

Abstract This study suggests a systematic method to decompose a large-scale process into sub-processes and then diagnose them. It is based on qualitative fault diagnosis of fault-effect tree model and for the minimization of knowledge base and flexible diagnosis. The new node, called a gate-variable, is introduced to connect the cause-effect relationships of each sub-process. Off-line analysis is performed to construct fault-effect trees of gate-variables. And, diagnosis strategy is modified to get the same result without system decomposition. The method is illustrated with a fault diagnosis system for a boiler plant.

Evaluation of Structural Controllability in Chemical Processes Using Relative Order

Abstract The control performance of a chemical process is determined by process structure as well as the performance of controllers. Therefore, the concept of “controllability” should be introduced in the early design stage to maximize the control performance. Structural information makes controllability evaluation possible by giving insights into the pathways of disturbances in the process. In this study, a simple controllability evaluation procedure is suggested to screen out design alternatives using relative order analysis and structural decomposition. The effectiveness of the proposed method was validated by comparing the results with the case of dynamical simulation.