The use of a novel auto-focus technology based on a GRNN for the measurement system for mesh membranes

Chen, Chin‐Sheng; Weng, Chi-Min; Lin, Chih-Jer; Liu, Hsiao-Wei

doi:10.1007/s00542-015-2473-z

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Process. GRNN [10][11][12] is a kind of feedforward neural network with mentors learning, belonging to radial basis function neural network (RBF). Because of the advantages of strong nonlinear mapping ability, good local approximation ability, fast learning speed, simple parameter adjustment during programming, good generalization ability, and good robustness, GRNN has been widely used in engineering.…”

Section: Grnn-based Fault Identificationmentioning

confidence: 99%

Fault Identification Method of Diesel Engine in Light of Pearson Correlation Coefficient Diagram and Orthogonal Vibration Signals

Zhao

Zeng

et al. 2019

Mathematical Problems in Engineering

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In order to select fault feature parameters simply and quickly and improve the identification rate of diesel engine faults by using the vibration signals, this paper proposes a diesel engine fault identification method on the basis of the Pearson correlation coefficient diagram (PCC Diagram) and the orthogonal vibration signals. At first, the orthogonal vibration acceleration signals are synchronously acquired in the direction of the top and side of the cylinder head. And the time-domain feature parameters are extracted from the orthogonal vibration acceleration signals to obtain the Pearson correlation coefficient (PCC). Then, the correlation coefficient diagram used to do feature parameter screening is constructed by selecting the feature parameters with the correlation coefficient of more than 0.9. Finally, generalized regression neural network (GRNN) is adopted to classify and identify fuel supply fault in diesel engine. The results show that using the PCC Diagram can simplify the selection process of the feature parameters of the orthogonal vibration signals quickly and effectively. It can also improve the fault identification rate of diesel engine significantly with the help of adding the newly proposed cross-correlation coefficient of the orthogonal vibration signals into the GRNN input feature vector set.

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Section: Grnn-based Fault Identificationmentioning

confidence: 99%

Fault Identification Method of Diesel Engine in Light of Pearson Correlation Coefficient Diagram and Orthogonal Vibration Signals

Zhao

Zeng

et al. 2019

Mathematical Problems in Engineering

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“…Chen et al have published a research on the application of autofocus technology in the mesh membrane measurement system. Using an automatic system to replace manual mesh membrane and noncoplanar mesh membrane arrays can not only reduce costs but also save a lot of time [10]. Li et al discussed the important role of autofocus in the tool preset measuring machine based on computer vision.…”

Section: Introductionmentioning

confidence: 99%

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

Zhang

2021

Journal of Sensors

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When performing digital image processing, the most critical technology that affects its use effect is the autofocus technology. With the advancement of science and the development of computer technology, autofocus technology has become more and more widely used in various fields. Autofocus technology is a key technology in robot vision and digital video systems. In order to allow digital image processing technology to better serve humans, it is necessary to further improve the focus evaluation function algorithm. This article focuses on the imaging principle of defocused images, using different evaluation functions to analyze and process the experimental images to observe the changes in image clarity. Through the introduction and analysis of the existing evaluation function, it can be known that the focus evaluation function will directly affect the quality of digital image processing. Therefore, it is best to choose unimodality, unbiasedness, low noise sensitivity, wide coverage, and a small amount of calculation. For the evaluation function, the Laplacian gradient function is an ideal choice. However, because the current digital image processing technology is not perfect enough, the focus function is still prone to multiextreme problems when the image is severely defocused and the high-frequency components in the image are missing; the balance between image processing speed and focus accuracy also still needs to be improved. Therefore, this paper studies the autocontrol microscope focus algorithm based on digital image processing, analyzes the principle of visual image imaging, and makes some improvements to the microscope focus algorithm. Through experiments, it can be seen that the real-time data of the original Laplace function in the edge-obvious target is 76.9, and it reaches 77.6 after improvement. The improved algorithm can better maintain the single-peak state during the focusing process, which improves the image processing efficiency while ensuring the measurement accuracy.

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Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm

et al. 2018

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The use of a novel auto-focus technology based on a GRNN for the measurement system for mesh membranes

Cited by 4 publications

References 9 publications

Fault Identification Method of Diesel Engine in Light of Pearson Correlation Coefficient Diagram and Orthogonal Vibration Signals

Fault Identification Method of Diesel Engine in Light of Pearson Correlation Coefficient Diagram and Orthogonal Vibration Signals

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm

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