Research on electrochemical discharge machining based on image features and SVM algorithm

Bian, Jianxiao; Ma, Baoji

doi:10.3233/jifs-189551

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Pseudo cracks can be removed according to the relevant geometric features of each connected area. This requires measuring the feature set of each connected area in the image, and deleting the unqualified connected areas according to the needs of the feature algorithm [8]. These features include: perimeter, that is, the number of pixels contained on the boundary of the connected domain; Area, that is, the total number of pixels contained in the connected domain;…”

Section: Connected Domain Feature Descriptionmentioning

confidence: 99%

Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology

Zhang

2023

J. Phys.: Conf. Ser.

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After entering the 21st century, China’s economic development is changing with each passing day, and the total economic volume has ranked second in the world. After the quantitative problem is solved, the qualitative problem becomes more and more important. In order to make the expressway be used healthily and for a long time, simple and efficient expressway maintenance methods become extremely important. In this paper, based on the study of the predecessors’ related research on road crack identification, based on digital image processing technology, the material detection algorithm for expressway cracks is studied. The advantages and disadvantages of the existing algorithms are analyzed, and the main types of cracks in the expressway are analyzed. The causes and characteristics of each type of cracks are understood, and the problems of current road crack detection and identification algorithms are analyzed. Through the experimental test analysis of the crack material detection algorithm, various types of crack detection images are selected from the horizontal and vertical crack detection results to form a test sample image library. The validity of the classification algorithm is tested, and the test results verify the validity of the algorithm in this paper.

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Section: Connected Domain Feature Descriptionmentioning

confidence: 99%

Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology

Zhang

2023

J. Phys.: Conf. Ser.

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“…We also extract the gray level co-occurrence matrix by windowing according to the similar method of extracting the spectral gray level average feature. Then, after calculating the gray level co-occurrence matrix of each windowed pixel, calculate the average energy value and average correlation value of each windowed pixel according to the previous calculation formula of energy and correlation characteristics [14][15].…”

Section: Figure 2 Fusion Process Of Spectral and Texture Featuresmentioning

confidence: 99%

Image Features Fused with BP Neural Network

2022

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Because of the remarkable advantages of computer neural network(NN), it has been widely used in the field of image feature(IF) fusion recognition, and the research involves multiple research fields. In addition to image recognition or image classification, it has been studied in computer vision, industrial control and other aspects. This paper focuses on the analysis of the IF of the fusion BP NN. This paper briefly analyzes the fusion of IF, spectral features and texture features, proposes BP NN algorithm, and applies it to image fusion design; Finally, through experimental tests, the detection accuracy of fusion BP NN and traditional BP algorithm in IF fusion is compared and analyzed, which verifies the effectiveness and feasibility of the BP NN algorithm in this paper.

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“…Support vector machine (SVM) is a machine leaning method based on statistic learning theory and has a good classification ability for small-sample, non-linear, high-dimension problems [20]. SVM has been widely researched and applied in many fields, such as pattern recognition [21], regression estimation [22], image recognition [23], text classification [24], fault diagnosis [25]. However, SVM classification accuracy is heavily depends on the SVM parameters, such as the penalty parameter C and the kernel parameter γ of RBF kernel function, and it is very difficult to find out the optimal SVM parameters.…”

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confidence: 99%

Analog circuit diagnosis based on support vector machine with parameter optimization by improved NKCGWO

Song,

Chen,

Cai

et al. 2023

Analog Integr Circ Sig Process

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Support Vector Machine (SVM) is a widely used machine learning method in analog circuits fault diagnosis. However, SVM parameters such as kernel parameters and penalty parameter can seriously affect the classification accuracy. The current parameter optimization methods have some defects, such as low convergence speed, easy to fall into local optimal solution and premature convergence. In view of this, an improved grey wolf optimization algorithm (GWO) based on nonlinear control parameter strategy, the first Kepler' law strategy and chaotic search strategy (NKCGWO) is proposed to overcome the shortcoming of the traditional optimization methods in this paper. In NKCGWO method, three strategies are developed to improve the performance of GWO. Thereafter, the optimal parameters of SVM are obtained using NKCGWO-SVM. To evaluate the performance of NKCGWO-SVM for analog circuits diagnosis, two analog circuits are employed to fault diagnosis. The proposed method is compared with GA-SVM, PSO-SVM and GWO-SVM. The experimental results show that the proposed method has higher diagnosis accuracy than the other compared methods for analog circuits diagnosis.

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Research on electrochemical discharge machining based on image features and SVM algorithm

Cited by 4 publications

References 27 publications

Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology

Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology

Image Features Fused with BP Neural Network

Analog circuit diagnosis based on support vector machine with parameter optimization by improved NKCGWO

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