@article{(Open Science Index):https://publications.waset.org/pdf/8191,
	  title     = {A Hybrid Scheme for on-Line Diagnostic Decision Making Using Optimal Data Representation and Filtering Technique},
	  author    = {Hyun-Woo Cho},
	  country	= {},
	  institution	= {},
	  abstract     = {The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {1},
	  year      = {2013},
	  pages     = {112 - 115},
	  ee        = {https://publications.waset.org/pdf/8191},
	  url   	= {https://publications.waset.org/vol/73},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 73, 2013},
	}