Partition KMNN-DBSCAN Algorithm and Its Application in Extraction of Rail Damage Data. (13th July 2022)

• Record Type: Journal Article
• Authors: Li, Yujun; Yang, Zhi; Jiao, Shangbin; Li, Yuxing
• Other Names: Jafarzadeh Ghoushchi Saeid Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-07-13
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/4699573
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : In order to realize intelligent identification of rail damage, this paper studies the extraction method of complete damage ultrasonic B-scan data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN). Aiming at the problem that the traditional DBSCAN algorithm needs to manually set the Eps and Minpts parameters, a KMNN-DBSCAN (K-median nearest neighbor DBSCAN) algorithm is proposed. The algorithm first uses the dataset's own distribution characteristics to generate a list of Eps and Minpts parameters and then determines the optimal Eps and Minpts through an optimization strategy to achieve complete self-adaptation of the two parameters of Eps and Minpts. In order to further improve the clustering performance of the algorithm, the partition idea is introduced, and the partition KMNN-DBSCAN algorithm is proposed to solve the problem that the clustering results of the DBSCAN algorithm are inconsistent with the actual categories on datasets with uneven density. The experimental results show that the KMNN-DBSCAN algorithm has higher clustering accuracy and silhouette coefficient (SC) for the D037 dataset ultrasound information group (UIG) division; compared with the KMNN-DBSCAN algorithm, the proposed partition KMNN-DBSCAN algorithm has higher clustering accuracy, F-Measure, and SC values. The partition KMNN-DBSCAN algorithm achieves accurate division of all damage UIG on the damaged B-scan data with large density differences, and completes the effective extraction of complete damage data.