scholarly journals Soft morphological filtering using hypergraphs

2021 ◽  
Vol 1085 (1) ◽  
pp. 012038
Author(s):  
Nuja M Unnikrishnan ◽  
Mini Tom ◽  
V Bino Sebastian ◽  
K V Thomas
Keyword(s):  
Morphological Filtering

scholarly journals Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Chao Zhang
Keyword(s):  
Frequency Conversion ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Operating Efficiency ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Morphological Filtering ◽  
Diagnosis Method ◽  
Simulation Signal

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.

A performance enhanced time-varying morphological filtering method for bearing fault diagnosis

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109163
Author(s):  
Bingyan Chen ◽  
Dongli Song ◽  
Weihua Zhang ◽  
Yao Cheng ◽  
Zhiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Time Varying ◽  
Filtering Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Morphological Filtering ◽  
A Performance

Morphological filtering of noisy images

1990 ◽  
Author(s):  
Lasse Koskinen ◽  
Jaakko T. Astola ◽  
Yrjo A. Neuvo
Keyword(s):  
Noisy Images ◽  
Morphological Filtering

Based the Morphological Filtering BP Algorithm of SAR Image Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1486-1490
Author(s):  
Ding Ding Jiang ◽  
De Rong Cai ◽  
Qiang Wei
Keyword(s):  
Image Recognition ◽  
Learning Algorithm ◽  
Image Interpretation ◽  
Practical Significance ◽  
Sar Image ◽  
Detection Capability ◽  
Rotation Invariant ◽  
Sar Images ◽  
Diverse Background ◽  
Morphological Filtering

SAR image recognition is an important content of of aviation image interpretation work. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. As can be seen through the experimental results, the algorithm can not only adapt to the complex and diverse background environment, and has a displacement of the same continuous moving target detection capability, telescopic invariant and rotation invariant features.

scholarly journals Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Huaiguang Liu ◽  
Liheng Zhang ◽  
Shiyang Zhou ◽  
Li Fang
Keyword(s):  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Adaptive Threshold ◽  
Segmentation Method ◽  
Tissue Segmentation ◽  
Adaptive Clustering ◽  
Morphological Filtering ◽  
Key Factor ◽  
The Relationship

The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced K -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional K -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

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