An Application of Fuzzy c-Means Clustering to FLC Design for Electric Ceramics Kiln

ABSTRACT- This paper presents an application of fuzzy c-means clustering to designing the fuzzy logic controller for the temperature control in electric ceramics kiln. This research aims to controlling the temperature in firing step of burning the ceramic products which were coated with black, intensely red and green chemical substances. The experimental results show that the fuzzy c-means clustering designed FLC gives better temperature characteristics when compared to the conventional FLC and the hard c-means clustering designed FLC

Building the pelvic endometriosis knowledge base software

The objective of this study is to develop a software-based medical expert system supporting the diagnosis of pelvic endometriosis. This system was developed to facilitate the creation of knowledge and inference engine. The diagnostic process used the interactive backward chaining inference algorithm. The medical knowledge data base was represented as production rules which represented in tree structures. The system was designed to interact with users in question information format. The clinical data from medical records of Gynecological out-patient clinic at HRH Maha Chakri Sirindhorn Medical Center were applied to the system by physician retrospectively. In this study, 35 medical records of women diagnosed with pelvic endometriosis were reviewed. The three most common presenting symptoms were dysmenorrhea, chronic pelvic pain and infertility, respectively. All of the patients were investigated with transvaginal sonography. Twenty-one patients had no histological studies. The clinical data of 30 patients accounted for 85.7 % were recorded successfully to the medical expert system. The diagnosis of these patients from the system corresponded with the previous data from the medical records of established pelvic endometriosis. Taken together, these data suggest that this medical expert system is a good tool to facilitate the decision making process in the diagnosis of pelvic endometriosis

Extraction Blood Vessels from Retinal Fundus Image Based on Fuzzy C-Median Clustering Algorithm

An automated method detection and extraction of blood vessels in retinal images would be described. The proposed algorithm is composed of three steps: matched filtering, fuzzy c-median (FCMED) clustering and label filtering. First, the matched filter technique is to enhance visualization of the blood vessels in retinal image. Secondly, the FCMED clustering is to keep the spatial structure of vascular tree segments. Finally, label filter technique is used to remove misclassified pixels. The algorithm has been tested on twenty sets in ocular fundus images, and experimental results are compared with clinically generated vessel segmentation and are evaluated in terms of sensitivity and specificity. This method performs well in analyzing anatomical structures in retinal image