Chung Yuan Christian University

About: Chung Yuan Christian University is a education organization based out in Taoyuan City, Taiwan. It is known for research contribution in the topics: Membrane & Fuzzy logic. The organization has 9819 authors who have published 11623 publications receiving 213139 citations. The organization is also known as: Tiong-gôan-tāi-ha̍k & CYCU.

Flame Images for Oxygen Content Prediction of Combustion Systems Using DBN

Abstract: As an increasingly popular method in the machine learning field, deep learning is applied to industrial combustion processes in this work. Using easily available color flame images obtained by the charge-coupled device (CCD), a soft sensor system based on deep learning is proposed to predict the outlet oxygen content online. Unlike the traditional principal component analysis which only extracts linear features, a multilayer deep belief network (DBN) is designed to extract the nonlinear features for a better description of the important trends in a combustion process. With the DBN-based multilevel representation of the CCD flame images, more useful information about the physical properties of a flame can be characterized. Sequentially, in a supervised fine-tuning stage, two DBN-based regression models are simply constructed to obtain the nonlinear relationship between the flame images and the outlet oxygen content. The advantages of the proposed deep learning-based analyzing and modeling method are demons...

Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers

Abstract: To enhance the fault diagnosis abilities for the dissolved gas analysis (DGA) of the power transformers, this paper proposes a novel adaptive fuzzy system for the incipient fault recognition through evolution enhanced design approach. Complying with the practical gas records and associated fault causes as much as possible, a fuzzy reasoning algorithm is presented to establish a preliminary fuzzy diagnosis system. In the system, an evolutionary optimization algorithm is further relied on to fine-tune the membership functions of the if-then inference rules. To make the diagnosis system intensively compact and the inference process more understandable, a pruning scheme is then developed to filter out the insignificant or redundant rules. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Taiwan Power Company (TPC).

Optimal design for fuzzy controllers by genetic algorithms

Abstract: Fuzzy control has been applied to various industrial processes; however, its control rules and membership functions are usually obtained by trial and error. Proposed in this paper is an optimal design for membership functions and control rules simultaneously by a genetic algorithm (GA). GAs are search algorithms based on the mechanics of natural selection and natural genetics. They are easy to implement and efficient for multivariable optimization problems, such as fuzzy controller design. The simulation result shows that the fuzzy controller thus designed can achieve good performance merely by using a few fuzzy variables.