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2 2024

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Phase unwrapping method based on improved U-net network

Li Jiansu, Zhang Jingjing, Dang Changying, Chen Haowen, Yang Zhao, Zhao Junsheng

Optica Applicata

2024

Vol. 54, nr 4

523--539

In order to solve the problem of unwrapping the phase in digital holographic reconstruction, a new convolutional architecture is proposed. The proposed method takes the U-net network as a framework, and incorporates the lightweight deep learning network of Mobilenetv1 in the encoding part to reduce the model complexity, the number of parameters, and the cost of computation; and proposes a complex dual-channel convolutional block in the decoding part instead of the 3×3 convolution in the original U-net network, which fully incorporates the features in the decoding process. (abbreviated as DC-UMnet network) Meanwhile, the loss value is calculated using the SmoothL1Loss function, and the activation function uses ReLU6. Finally, the simulated dataset containing noise is used for training; and the experimentally obtained wrapped phase maps is used for verifying. The simulation results show that under different degrees of noise conditions, compared with the DCT method and the deep learning phase unwrapping network, the structural similarity index values of the DC-UMnet network are improved by 0.416 and 0.064; and the normalized root-mean-square errors are reduced respectively by 13.2% and 5.8%. Through the actual measurement data, the proposed network model of the feasibility and good noise reduction ability are verified, which can realize digital holographic phase unwrapping in a simple, fast and efficient way.

Study on the working mechanism of self-resetting performance of superelastic SMA fiber-reinforced ECC beams

Yang Zhao, Wu Qing, Qi Xiaolong, Xiang Jichao

Archives of Civil and Mechanical Engineering

2024

Vol. 24, no. 3

art. no. e180, 2024

This study aimed to master the self-resetting performance and working mechanism of superelastic shape memory alloy fibers reinforced engineered cementitious composite (SMAF-ECC) beams. Four SMAF-ECC pre-cut beams were fabricated. The three-point bending test was used to analyze the changes in mid-span deflection and crack width under different shape memory alloy (SMA) fibers lengths and end forms. And the stress of SMA fiber was analyzed by ABAQUS. Finally, the working mechanism of SMA fiber was revealed. The results showed that SMAF-ECC specimens perform well in deformation recovery and crack closure. Among them, the maximum deflection self-recovery rate of the specimen was 83.6%. And compared to the engineered cementitious composite (ECC) specimen, the residual crack width was reduced by 76.62% to 84.01%. In addition, reducing the length and diameter of the SMA fiber increased its stress. However, the length of SMA fiber had little effect on the deflection self-recovery rate of SMAF-ECC specimens, and the maximum difference between the specimens with different SMA fiber lengths was only 1.1%. The working mechanism of SMA fiber included four stages: linear elastic, descent, platform, and deformation strengthening stage. After the SMA fiber entered the platform phase, the residual deformation of the specimen was significantly reduced. When entering the deformation strengthening stage, the SMA fiber not only improved the bearing capacity of the specimen, but also provided greater restoring force, considerably improved the specimen’s self-resetting performance. This paper offered a theoretical basis for the design of self-resetting beams.