Background Interference Removal Algorithm for PIV Preprocessing Based on Improved Local Otsu Thresholding

Due to background light fluctuation,noise interference,voltage fluctuation,and other factors,there will be noise interference of different intensities in the background of the collected image.In this paper,a PIV image background interference removal algorithm based on improved neighborhood Otsu processing is proposed.The algorithm proposed in this paper separates the particle image from the background interference through the adaptive neighborhood improved Otsu threshold segmentation method and uses the common PIV analysis tools PIVLab and para PIV to analyze the flow pattern after the interference is removed.The experimental results demonstrated that the proposed algorithm can obviously improve the quality of PIV results in terms of both PSNR and SSIM in the case of background light interference,and the increase in average performance is nearly 50%compared with traditional preprocessing algorithms,which solves the problem of large flow pattern analysis error caused by poor background light removal effect in the case of irregular grating and other background light interference only using traditional preprocessing.

Neutron-gamma discrimination method based on blind source separation and machine learning

The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20).