Study on two-dimensional distribution of X-ray image based on improved Elman algorithm |
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| Institution: | 1. College of Physics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China;2. Department of Information Science and Engineering, Wanfang College of Science and Technology HPU, Zhengzhou 451400, China;3. Infrared Optoelectronic Science and Technology, Key Laboratory of Henan Province, Xinxiang 453007, China;4. China Astronaut Research and Training Center, Beijing 100094, China;1. Shanghai University, School of Materials Science and Engineering, Shanghai 200444, China;2. Shanghai Institute of Ceramics, Chinese Academy of Science, Shanghai 201800, China;1. School of Material Science and Engineering, Shanghai University, Shanghai, 200444, China;2. Shanghai Institute of Ceramics, Chinese Academy of Science, Shanghai, 201800, China;1. NDT Department, Soreq Nuclear Research Center (NRC), Yavna 81800, Israel;2. Physicalisch-Technische Bundesanstalt (PTB), Braunschweig 38116, Germany;1. Department of Physics, Chifeng University, Chifeng 024001, China;2. Department of Physics, Tsinghua University, Beijing 100084, China |
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| Abstract: | The principle of the X-ray detector which can simultaneously perform the measurement of the exposure rate and 2D (two-dimensional) distribution is described. A commercially available CMOS image sensor has been adopted as the key part to receive X-ray without any scintillators. The correlation between the pixel value (PV) and the absorbed exposure rate of X-ray is studied using the improved Elman neural network. Comparing the optimal adjustment process of the BP (Back Propagation) neural network and the improved Elman neural network, the neural network parameters are selected based on the fitting curve and the error curve. The experiments using the practical production data show that the proposed method achieves high accurate predictions to 10−15, which is consistent with the anticipated value. It is proven that it is possible to detect the exposure rate using the X-ray detector with the improved Elman algorithm for its advantages of fast converges and smooth error curve. |
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| Keywords: | Elman neural network X-ray detection CMOS sensor BP neural network |
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