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  • 1.
    Liang, Zhenyu
    et al.
    Northeastern Univ, CHN.
    Liang, Li
    Northeastern Univ, CHN.
    Cai, Yuze
    Shenyang Urban Construct Univ, CHN.
    Bertoni, Alessandro
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    The influence factors of the stability of tailings dam based on multi-source information fusion method2019In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 37, no 3, p. 3365-3372Article in journal (Refereed)
    Abstract [en]

    The BP neural network algorithm is used to construct a stable slope angle prediction model for open-pit mines, which can successfully predict the ultimate slope angle of the mine. At the same time, a multi-tool combination of Surpac, Madis, and Flac3D is used to create a numerical model for stable slope angle in mines. The model is used to test the final slope angle prediction structure for stable mines. Through a comprehensive analysis of the ore deposit model technology constructed by the series of realms, the economic parameters involved in the generation of realm boundaries are known, and specific primaries are pointed out with the error requirements of each parameter analyzed in the realm of series. A concrete solution is put forward to the issue of realm gap. From the point of view of the power and responsibility parameters, the series of realm production methods are analyzed, and the rapid generation methods of the realm of open-pit mine series are pointed out.

  • 2.
    Pan, Nan
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Kan, L.
    Kunming University of Science and Technology, CHI.
    Liu, Y.
    Kunming SNLab Tech Co., CHI.
    Fu, W.
    Xiangyang Public Security Department Wuhan Railway Public Security Bureau, CHI.
    Hou, Z.
    Criminal Investigation Department of Railway Public Security Bureau, CHI.
    Li, G.
    Shijiazhuang Public Security Bureau, CHI.
    Qian, J.
    Kunming University of Science and Technology, CHI.
    Fu, X.
    Kunming University of Science and Technology, CHI.
    Nonlinear tool traces fast tracing algorithm based on single point laser detection2019In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 36, no 2, p. 1109-1120Article in journal (Refereed)
    Abstract [en]

    There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm. © 2019 - IOS Press and the authors. All rights reserved

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