Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
Na.................................
We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical\nguns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband\ndetection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic\nstripping algorithm, and better detection rate and the miss rate are achieved...............
Modern medicine is unthinkable without X-rays. Accurate diagnosis, leading\nto effective treatment, is largely based on precise X-ray examinations. The\ncreation of new, modern equipment and various medical procedures that\nmeet the increased requirements are a priority in our time. X-ray examinations\nare of particular importance for the orthopedic and traumatological\nclinics, where they provide information about presence of a fracture in the\npatientâ??s body, about the concrete operation performed or about the effect of\na suitable treatment. Along with their benefits X-rays have also a harmful effect....
This article uses the Monte Carlo method and MCNP5 software to first simulate\nthe X-ray energy spectrum of the tungsten target and the silver target. On\nthis basis, using lead, tungsten and tungsten alloys (90% tungsten, 7.1% nickel,\nand iron 2.9%) as an X-ray shielding material, the shielding efficiency of\nthese three materials at different thicknesses is calculated, and the results\nshow that tungsten and tungsten alloy have better shielding effect than lead.\nFor the X-rays of different energies generated by the tungsten target and the\nsilver target, in order to achieve the same shielding effect, the X-rays generated\nby the tungsten target require a thicker shielding material....
The purpose of this paper is to obtain the pore distribution of asphalt mixture accurately by nondestructive technology. Specimens\nprepared with four gradations of asphalt mixtures were scanned using X-ray computed tomography (CT) which was used to\nmeasure air void sizes at different depths within specimens. The air void distributions of obtained CTimages were analyzed using\nring blocking segmentation combining Otsuâ??s method, which provided an accurate estimate of air voids in asphalt mixtures....
Loading....