Browsing by Subject Deep learning

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Showing results [1 - 2] / 2
  • Authors: Minhhuy Le;  Advisor: Cong-Thuong Pham; Jinyi Lee;  Co-Author: - (2021)

    Magnetic flux leakage testing (MFLT) is an important nondestructive testing method for the detection and evaluation of defects in magnetic materials. Magnetic field distribution in an MFLT system is usually simulated by the finite element method (FEM), which required large memory, high computation, and complication of the meshing process. In this paper, an alternative simulation method will be proposed using a deep neural network (DNN). The DNN method provides an easy way of simulation by feeding only the distribution of supplied current and the physical properties such as magnetic permeability without the need for the meshing process. Defects with arbitrary sizes were simulated under...
  • Authors: Chao, Xu; Thien Van, Luong; Luping, Xiang; Shinya, Sugiura;  Advisor: -;  Co-Author: - (2022)

    A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the cha...