The aim of this paper is the development of a bridge diagnosis system for daily bridge maintenance. The system includes the technology of wireless sensor network, signal processing, and structure analysis of a bridge. We collect the vibration data caused by external force such as wind pressure, running vehicles and etc through wireless sensor network and then use independent component analysis (ICA) and spectral analysis to analyze the data for extracting character frequency. The diagnosis technology is developed to understand the phenomena like deterioration and corrosion of a bridge through the measurement result. So the diagnosis system can evaluate the health condition of a bridge and conduct a comprehensive deliberation on repair and reinforcement work. We apply this diagnosis system to field experiments at Kokura, Kitakyushu city in Japan and successfully extract the character frequency of a bridge.
Using near-infrared (NIR) light with 700–1200 nm wavelength, transillumination images of small animals and thin parts of a human body such as a hand or foot can be obtained. They are two-dimensional (2D) images of internal absorbing structures in a turbid medium. A three-dimensional (3D) see-through image is obtainable if one can identify the depth of each part of the structure in the 2D image. Nevertheless, the obtained transillumination images are blurred severely because of the strong scattering in the turbid medium. Moreover, ascertaining the structure depth from a 2D transillumination image is difficult. To overcome these shortcomings, we have developed a new technique using deep learning principles. A fully convolutional network (FCN) was trained with 5,000 training pairs of clear and blurred images. Also, a convolutional neural network (CNN) was trained with 42,000 training pairs of blurred images and corresponding depths in a turbid medium. Numerous training images were provided by the convolution with a point spread function derived from diffusion approximation to the radiative transport equation. The validity of the proposed technique was confirmed through simulation. Experiments demonstrated its applicability. This technique can provide a new tool for the NIR imaging of animal bodies and biometric authentication of a human body.
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