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Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

Nuclear Science and Techniques 2022年第6期33卷 89-101页

作者： Hao-Ran Liu Zhuo Zuo Peng Li Bing-Qi Liu Lan Chang Yu-Cheng Yan State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China The Engineering and Technical College of Chengdu University of Technology Leshan 614000China Southwestern Institute of Physics Chengdu 610225China Chengdu University Chengdu 610106China

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination *** experiments were conducted in two *** the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise *** each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination *** the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient *** experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise ***,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise ***,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise ***,based on the results,the parameter selection strategy of the PCNN was *** conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination *** does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

关键词： pulse coupled neural network Zero crossing Frequency gradient analysis Vector projection Charge comparison Neutron and gamma-ray discrimination pulse shape discrimination

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Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

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The Journal of China Universities of Posts and Telecommunications 2018年第3期25卷 55-64页

作者： Hu Ling Chang Xia Qian Wei School of Mathematics and Information Science North Minzu University

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

关键词： edge detection modulus maxima pulse coupled neural network wavelet transform non-subsampled contourlet transform

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Irregular Segmented Region Compression Coding Based on pulse coupled neural network

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Semiconductor Photonics and Technology 2006年第2期12卷 110-116,130页

作者： MA Yi-de QI Chun-liang QIAN Zhi-bai SHI Fei ZHANG Bei-dou College of Information Science ＆ Engineering Lanzhou University Lanzhou 730000 CHN China Jiuquan Satellite Launch Center Lanzhou 732750 CHN

An irregular segmented region coding algorithm based on pulse coupled neural network(PCNN) is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approximate gray values can be activated simultaneously. One can draw a conclusion that PCNN has the advantage of realizing the regional segmentation, and the details of original image can be achieved by the parameter adjustment of segmented images, and at the same time, the trivial segmented regions can be avoided. For the better approximation of irregular segmented regions, the Gram-Schmidt method, by which a group of orthonormal basis functions is constructed from a group of linear independent initial base functions, is adopted. Because of the orthonormal reconstructing method, the quality of reconstructed image can be greatly improved and the progressive image transmission will also be possible.

关键词： pulse coupled neural network Segmentation Orthonormal basis Compression coding possible.

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Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

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Journal of Bionic Engineering 2021年第3期18卷 711-720页

作者： Xue Wang Zhanshan Li Heng Kang Yongping Huang Di Gai College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China ZICT Technology Co. Ltd.Shenzhen 518063China

Medical image segmentation is a challenging task especially in multimodality medical image *** this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image ***,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image *** image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy *** the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the *** modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN *** experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

关键词： grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation

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PCNN based image processing and feature extraction of dual-bypass gas metal arc weld pool

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China Welding 2013年第4期22卷 1-7页

作者：张刚樊丁薛诚石王于黄健康 Key Laboratory of Non-ferrous Metal Alloys and Processing The Ministry of Education Lanzhou Universityof Technology' Lanzhou 730050 State Key Laboratory of Gansu Advanced Non-ferrous MetalMaterials Lanzhou University of Technology Lanzhou 730050

In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for control of the welding process and realizing the automatic welding, the measurement system of DB-GMA W process was established and the weld pool image was obtained by passive vision. Then, three image processing algorithms, Sobel, Canny, and pulse coupled neural network （PCNN） were detailed and applied to extracting the edge of the DB-GMA weld pool. In addition, a scheme was proposed for calculating the length, maximum width and superficial area of the weld pool under different welding conditions. The compared results show that the PCNN algorithm can be used for extracting the edge of the weld pool and the obtained information is more useful and accurate. The calculated results coincide with the actual measurement well, which demonstrates that the proposed algorithm is effective, its imaging processing time is required only 20 ms, which can completely meet the requirement of real-time control.

关键词： DB-GMAW weld pool pulse coupled neural network image processing

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Improved image filter based on SPCNN

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Science in China(Series F) 2008年第12期51卷 2115-2125页

作者： ZHANG YuDong WU LeNan School of Information Science & Engineering Southeast University Nanjing 210096 China

By extraction of the thoughts of non-linear model and adaptive model match, an improved Nagao filter is brought. Meanwhile a technique based on simplified pulse coupled neural network and used for noise positioning, is put forward. Combining the two methods above, we acquire a new method that can restore images corrupted by salt and pepper noise. Experiments show that this method is more preferable than other popular ones, and still works well while noise density fluctuates severely.

关键词： Nagao filter pulse coupled neural network image smoothing image de-noising salt and pepper noise,edge preserving

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