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Due to factors such as illumination changes，camera jitter and dynamic background，existing foreground subtraction algorithms cannot achieve good segmentation results in complex scenes. To solve this kind of problems，this paper proposes a subtraction algorithm based on a fully convolutional siamese neural network，which can accurately segment the foreground with only two arbitrary frames. Specifically，the input two images are divided into the base image and the image to be segmented. The algorithm uses the fully- convolutional siamese network to get the similarity metric map of input frames. The similarity metric map contains information about changes in pixels of the image to be segmented relative to the base image. Then， the similarity metric map is fused with the image to be segmented，and the encoder-decoder network is used to achieve end-to-end foreground subtraction results. The paper evaluates the proposed algorithm on the CDnet2014 dataset to prove its effectiveness. [ABSTRACT FROM AUTHOR]

Due to the irregular features of the airport pavement cracks with variable shapes，narrow widths， different lengths，and free⁃form spatial trends，the existing algorithms perform not well. To solve this problem，this paper constructs a neural network model based on deformable convolution and feature fusion （DFNet）. Firstly， the deformable convolution module is used to enhance the learning of fracture morphological characteristics by feature extraction network. Secondly，the multi⁃scale convolution module captures the global information of the fracture under different receptive fields. Finally，the feature fusion module is used to extract the characteristics of different levels of the fracture，through combining low⁃level and high⁃level features of cracks to achieve accurate segmentation of cracks on airport pavements. On the collected actual airport pavement crack data set，comparative experiments are carried out with other six existing algorithms. The proposed algorithm reaches 90.95% on the F1⁃Score of pixel⁃level segmentation， which is better than those of all other compared algorithms. The DFNet algorithm improves the ability of detecting cracks in airport pavements. Experimental results show that the proposed algorithm better meets the actual engineering requirements. [ABSTRACT FROM AUTHOR]

In global friction measurement, thin oil film technique can well represent the distribution of surface friction. Irradiated with ultraviolet light of a specific wavelength, the oil film with different thickness of the fluorescent color molecules will emit different brightness. Using this principle, the thickness of the oil film can be calculated by detecting the gray value of the excited fluorescent oil film.In this paper, BP neural network and extreme learning machine（ELM）neural network are used to build models to complete the prediction of the relationship between the fluorescence oil film thickness and gray level, and Hopfield neural network is used to complete the identification of the corresponding parameters. The experimental results show that the prediction errors of ELM neural network model, BP neural network model and interpolation model are 5.150%, 5.485% and 5.935%, respectively. Through Hopfield neural network identification, the parameter error rates of the influence factors such as light source power, optical distance and exposure coefficient are controlled at about 1%, which meets the requirements of practical engineering application. Compared with the traditional interpolation method, the higher precision can be obtained by using the neural network, which provides a feasible method for the study of the gray and thickness of the fluorescent oil film. [ABSTRACT FROM AUTHOR]

Aircraft structure damage seriously affects the aircraft flight safety. In order to effectively identify aircraft composite structure damage, a new method combining general regression neural network (GRNN) and extreme learning machine (ELM) of composite structure damage diagnosis is proposed in this paper. Firstly, the data of fiber optic sensor on composite material laminated plates are gathered and pre-processed after striking and stretching on composite laminated plates. Secondly, strain information is decomposed by variational mode decomposition (VMD), and intrinsic mode functions (IMFs) are obtained. Meanwhile, the singular entropy feature of each IMF is derived. Then, a feature vector is built by kernel independent component analysis (KICA). Finally, the fusion feature vector is used to bui_d GRNN-ELM identification model. Experimental data verify the effectiveness of the GRNNELM method, and the result shows that the GRNN-ELM model can realize aircraft composite structure damage identification more effectively compared with ELM and GRNN models, respectively, thus it has a good engineering application value. [ABSTRACT FROM AUTHOR]