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- 저자 : Peizhen Li (검색결과 2 건)
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Zheng Lu,Xiaoyi Chen,Xiaowei Li,Peizhen Li 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.62 No.1
An effective design approach for Multiple Tuned Mass Dampers (MTMDs) in pedestrian bridges was proposed by utilizing the transfer function to obtain each TMD’s optimum stiffness and damping. A systematic simulation of pedestrian excitations was described. The motion equation of a typical MTMD system attached to a Multi-degree-of-freedom (MDOF) system was presented, and the transfer function from the input pedestrian excitations to the output acceleration responses was defined. By solving the minimum norm of the transfer function, the parameters of the MTMD which resulted in the minimum overall responses can be obtained. Two applications of lightly damped pedestrian bridges attached with MTMD showed that MTMDs designed through this method can significantly reduce the structural responses when subjected to pedestrian excitations, and the vibration control effects were better than the MTMD when it was considered as being composed of equal number and mass ratios of TMDs designed by classical Den Hartog method.
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Yanzhi Qi,Cheng Yuan,Qingzhao Kong,Bing Xiong,Peizhen Li 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.6
Implementing unmanned aerial vehicles (UAVs) on concrete surface-crack inspection leads to a promising visual crack detection approach. One of the challenges for automated field visual cracking inspection is image degradation caused by the rain or fog and motion blur during data acquisition. The present study combines two deep neural networks to address the image degradation problem. By using the Variance of Laplacian algorithm for quantifying image clarity, the proposed deep neural networks can remarkably enhance the sharpness of the degraded images. After vision enhancement process, Mask Region Convolutional Neutral Network (Mask R-CNN) was developed to perform automated crack identification and segmentation. Results show a 8~13% enhancement in prediction accuracy compared to the degraded images, indicating that the proposed deep learning-based vision enhancement method can effectivey identify and segment concrete surface cracks from photos captured by UAVs.
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