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The influence of Re on the propagation of a (0 10)[1 0 1] crack in the Ni/Ni3Al interface, including crack propagation velocity, crack-tip shape, and dislocation emission, is investigated using a molecular dynamics method with a Ni-Al- Re embedded-atom-method potential. The propagation velocity of the crack noticeably decreases at 5K when 3 or 6 at% Re atoms are added into the Ni matrix. At 1033 K, the crack tip becomes blunter and emission of dislocations becomes easier with Re addition, owing to the larger bond strength between Re and Ni atoms. Furthermore, we calculate the unstable stacking energy (ɣus), surface energy (ɣs), and adhesion work (Wad) of the interface. When Re atoms are randomly doped into a Ni matrix, ɣs/ɣus increases correspondingly. This means that Re addition decreases brittleness and improves ductility. The calculation also shows that ɣus is not affected by Re-Ni atomic interaction, and that Re-Re atomic interaction has some effect on ɣus. In addition, Wad increases with Re addition, and a small increase in Wad results in a larger decrease in crack velocity. This indicates that Re-Ni atomic interaction restrains crack propagation velocity at low temperature. [ABSTRACT FROM AUTHOR]

Moving vehicle detection and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a novel Marr wavelet, kernel-based background modeling method and a background subtraction method based on binary discrete wavelet transforms (BDWT) are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. The background and current frame are transformed by BDWT, and moving vehicles are detected in the binary discrete wavelet transforms domain. For the shortages of RGB (Red, Green, Blue) or HSV (Hue, Saturation, Value) color space-based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space and edge detection is proposed. The original data of the shadow according to the characteristics of the YCbCr space is chosen, and then, combined with edge detection, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after detection and obtaining the center of the object. An actual road test shows that the algorithm can effectively remove the influence of pedestrians and cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring. [ABSTRACT FROM AUTHOR]