A direct method of nuclear pulse shape discrimination based on principal component analysis and support vector machine

Zhang, Zhao Hui; Hu, Cong; Fan, Xinyang; Liao, Bin; Li, Y.T.; Zhu, Jiajun; Zhang, L.Q.

doi:10.1088/1748-0221/14/06/p06020

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…In order to predict ice thickness using the temperature decay of the ice surface, a mapping relationship must be established between temperature elevation characteristics and ice thickness. The prediction of ice thickness will be a regression problem, and the solution methods will include support vector machines (SVM) [35][36][37] and BP neural network. Infrared thermal image resolution is 640 × 512, and the region of ice accretion contains more than 10,000 pixels, whose temperature elevation feature can be used as sample data.…”

Section: Icing Thickness Predictionmentioning

confidence: 99%

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Lü

Qingying

Pan

et al. 2022

IET Image Processing

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The present study investigated ice accretion thickness under non-incoming flow icing conditions on the ground using an infrared thermography system that converts infrared radiation temperature. Two back propagation (BP) neural network models were developed to measure ice thickness. Both theoretical model and polynomials were employed to fit the icing surface temperature elevation sequence to extract the pixel-level temperature attenuation characteristics, which were served as the input to the BP neural networks. The prediction method of ice thickness by the BP neural network was analysed from three perspectives of sensitivity, dimension, and precision. In addition, K-nearest neighbour (KNN) and support vector regression (SVR) algorithms were compared with BP neural network. In terms of prediction effect, the BP neural network performed the best. The verification of the BP neural network based on the characteristics of the theoretical model proved that the method can effectively predict the thickness of ice accretion, and its prediction error does not exceed 10%.

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Section: Icing Thickness Predictionmentioning

confidence: 99%

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Lü

Qingying

Pan

et al. 2022

IET Image Processing

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“…However, PCA cannot identify the classification of the samples and expands the difference among all samples. In reference [7], a direct method to discriminate nuclear pulse shapes based on PCA and support vector machine (SVM) is also reported in a paper, its fault diagnosis can be fundamentally solved as a case of PSD. Yet in some cases, this method may response slowly.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for scintillation detector based on BP neural network

Xie¹,

Yan²,

Li³

et al. 2021

J. Inst.

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This article gives a scintillation detector fault diagnosis method based on BP neural network. From the aspect of output signals of scintillation detectors, the wavelet packet transform is used to extract the energy characteristic vectors which are treated as the input of BP neural network, and a training database is established as well as BP neural network parameters are optimized. Then the method is employed to establish a fault recognition model and fault types can be concluded. Finally, the simulation data are compared with those of two other methods (the statistical diagnosis method and an method based on multi-classification support vector machine). The experimental results illustrate that the application of proposed method can improve the fault diagnosis accuracy of scintillation detectors effectively.

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“…[9]Suggested the need for an iris scanner for person recognition. [10] suggested the use of face recognition systems in security p laces like defense. [11] presented in their work how image processing techniques are helpful in the face recognition system.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN

Rao

Harshitha

Basha

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

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Nowadays in a situation like the Covid19 pandemic it is very sensitive to use biometric systems for attendance monitoring of employees. The reason is covid19 spreads from one person to another easily with a biometric system. It has become necessary for any organization to maintain an attendance monitoring system without taking fingerprints of any employee or a student. The automatic Face recognition system is best to alternate for the biometric system. An advanced automatic face recognition technique is proposed in this paper with the classification technique using Bag of Visual Words (BOVW) and Multi-Layer Perceptron (MLP) based Back Propagation Neural Network (BPNN). An Accuracy of 91% is achieved with the proposed methodology.

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A direct method of nuclear pulse shape discrimination based on principal component analysis and support vector machine

Cited by 10 publications

References 17 publications

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Fault diagnosis method for scintillation detector based on BP neural network

Hybrid Computerized Face Recognition System Using Bag of Visual Words and MLP-Based BPNN

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