Detection of double defects for plate-like structures based on a fuzzy c-means clustering algorithm

2018 ◽  
Vol 18 (3) ◽  
pp. 757-766 ◽  
Author(s):  
Shaojie Chen ◽  
Shaoping Zhou ◽  
Chaofeng Chen ◽  
Yong Li ◽  
Shuangmiao Zhai
Keyword(s):  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Circular Array ◽  
Location Error ◽  
Direct Wave ◽  
Fuzzy C Means ◽  
Single Defect ◽  
Fuzzy C Means Clustering ◽  
Signal Processing Algorithms ◽  
Novel Method

A variety of signal processing algorithms have been proposed to detect and locate defects in plate-like structures. However, the signal-to-noise ratio in these algorithms is too small especially in the reflection wave from the boundary, which further degrades the accuracy of localization of defects. A novel method for localization of defects is proposed in this article, based on the direct wave and fuzzy c-means clustering algorithm. To verify its effectiveness, experiments using the parallel linear and circular array are conducted, respectively. The experimental results show that the proposed method not only accurately locates single defect but also locates double defects in plate-like structures, and by comparing with the current discrete elliptic imaging algorithm, its location error of single defect is reduced from 20–25 mm to 0–3 mm and double defects is also reduced from 60–90 mm to 0–3 mm.

scholarly journals Optimal Combination of Aircraft Maintenance Tasks by a Novel Simplex Optimization Method

2015 ◽  
Vol 2015 ◽  
pp. 1-19
Author(s):  
Huaiyuan Li ◽  
Hongfu Zuo ◽  
Dan Lei ◽  
Kun Liang ◽  
Tingting Lu
Keyword(s):  
Clustering Algorithm ◽  
Optimization Method ◽  
Optimal Combination ◽  
Maintenance Cost ◽  
The Novel ◽  
Simplex Optimization ◽  
Fuzzy C Means ◽  
Clustering Model ◽  
Fuzzy C Means Clustering ◽  
Novel Method

Combining maintenance tasks into work packages is not only necessary for arranging maintenance activities, but also critical for the reduction of maintenance cost. In order to optimize the combination of maintenance tasks by fuzzy C-means clustering algorithm, an improved fuzzy C-means clustering model is introduced in this paper. In order to reduce the dimension, variables representing clustering centers are eliminated in the improved cluster model. So the improved clustering model can be directly solved by the optimization method. To optimize the clustering model, a novel nonlinear simplex optimization method is also proposed in this paper. The novel method searches along all rays emitting from the center to each vertex, and those search directions are rightlyn+1positive basis. The algorithm has both theoretical convergence and good experimental effect. Taking the optimal combination of some maintenance tasks of a certain aircraft as an instance, the novel simplex optimization method and the clustering model both exhibit excellent performance.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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