SIMULATION STUDIES OF THE MOLIERE RADIUS FOR EM CALORIMETER MATERIALS

The Monte-Carlo calculations of the Moliere radius (RM) for some homogeneous and heterogeneous media used in electromagnetic calorimetry in the energy range from 50 MeV to 10 GeV are presented in detail. The obtained results, the uncertainties in determining RM, estimations of the absorbed energy, methods for approximating the absorbed energy, and the accuracy of the results are discussed as well. Some RM are shown for calorimeter proto-types of the Spin Physics Detector experiment (SPD). A one-parameter function of the Moliere radius dependence on the absorber-scintillator thickness ratio is obtained.

Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks

This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.