A Study On Interval Type-2Fuzzy Clustering Algorithms With Their Applications In Electric Traction Monitoring System

Because of its humanoid logic language and ease of implementation, fuzzy clustering becomes the most popular method among all clustering analysis methods and has been widely used in image segmentation, large-scale data analysis, data mining, pattern recognition, etc. Fuzzy theory provides a theoretical foundation for fuzzy clustering analysis. With the development of fuzzy theory, the defect of handling uncertainties in type-1fuzzy set gradually emerged over recent years. Therefore the type-2fuzzy set with ability of handling uncertainties became immediate areas of research focus. Type-2fuzzy set has demonstrated superior to type-1fuzzy set in application in fuzzy control and other areas. However, the use of type-2fuzzy set in fuzzy clustering analysis is still in its infancy.There are some researches on combining type-2fuzzy set with fuzzy clustering and have proposed interval type-2and general type-2fuzzy clustering algorithms. But the computation complexity in type-2fuzzy set restricts the development and application of type-2fuzzy clustering algorithms. Through the in-depth study of interval type-2fuzzy clustering algorithm, an optimization method is proposed in this paper to enhance the computing efficiency and ability of managing large-scale data. An initialization method for cluster center is proposed and the calculation process of type-reduction has been optimized to elimate calculating redundancy. A mass of experimental results show that the optimized interval type-2fuzzy clustering algorithm has about40%improvement in operation efficiency compared with traditional interval type-2algorithm.Fuzzy clustering always treats diverse data types, and single clustering algorithm cannot meet the requirements of clustering different data. As a result, the application of type-2fuzzy clustering algorithms requires combination of different research background. As a main direction of application for fuzzy clustering, image segmentation is the most important process in object recognition, image understanding, computer vision, et al. A summary for existing fuzzy clustering algorithms in image segmentation is given in this paper, and the utilization of existing modified methods in interval type-2algorithm is discussed either. Consider the validity evaluation for interval type-2fuzzy clustering is still blank, the validity functions for type-1fuzzy clustering are extended to a generalized version to evaluate interval type-2fuzzy clustering algorithms. Besides, by taking into account the relevance among pixels in images, the spatial membership function is proposed to represent spatial neighborhood information. The utilization of spatial membership function in interval type-2fuzzy clustering algorithm gives the algorithm the ability of better segmenting pixels with noise and pixels along the edges. The experiments on synthetic and medical images verified the effectiveness of the modified algorithm.As the only connection component between catenary and pantograph, pantograph slide plays an important role in providing impetus for locomotives. With the speed-up in railway operation, the abrasion of pantograph slide is getting worse. Thus it’s a vital task in railway safety inspection to detect the state of pantograph slide. With the development of intelligent railway monitoring system, digital image processing has become an important method to detect the state of pantograph slide. As a critical process in image object extraction and post-processing, the image segmentation results will directly affect the detection results. The traditional image detection methods are susceptible to noise, weather, lighting and other factors, which make the detected edges of pantograph slide discontinuous. To enhance the ability of suppressing the interference of noise and other environmental factors as well as to improve the detection accuracy, the modified interval type-2fuzzy clustering algorithm is used to segment pantograph images. Besides, the algorithm is utilized in the identification of catenary pole number to locate the failt point. The detection results validate the advantages of using interval type-2fuzzy clustering image segmentation process.