One dimensional silver nanowires (AgNWs) were grown on carbon fiber (CF) by a facile polyol method. Fourier transform infrared spectrometer (FTIR), laser Raman spectrometer (Raman), field-emission scanning electron microscopy (FESEM), X ray diffraction instrument (XRD), energy dispersive spectrometer (EDS), and X-ray photoelectron spectrometer (XPS) were carried out to reveal the structure, morphology, and formation mechanism of the CF-AgNWs. It was found that AgNO 3 concentration of 1.5 mM, reaction temperature of 1608C, and reaction time of 120 min were appropriate conditions for growth of AgNWs on CF. Moreover, a mechanism was suggested that the cysteamine on CF acted as nucleation centers for growth of silver nanoparticles and then small sized silver nanoparticles reduced from silver nitrate were grown on CF via the silver bonding to sulfur. Through an Ostwald ripening process, small sized silver nanoparticles were grown into larger particles. With the assistance of polyvinylpyrolidone (PVP), these larger particles were directed to grow in a definite direction to form nanowires. It was found that the resistance of CFAgNWs was decreased to 19.5 X, compared with that of CF (102.6 X) with the same quality. Thus, the CF-AgNWs was added into waterborne polyurethane (WPU) to improve the electrical and dielectric properties of WPU. Results showed the WPU/CF-AgNWs composite presented a lower percolation threshold than WPU/CF composite. When the content was 2.5 wt %, the volume resistivity of the WPU/CF-AgNWs (1.90 3 10 4 X cm 21 ) was lower by approximately three orders of magnitude than that of WPU/CF (4.19 3 10 7 X cm 21 ). When the content was 2.5 wt %, the dielectric constant and dielectric loss of the WPU/CF-AgNWs were improved to 15.24 and 0.21, which were 34.5 and 40.8% higher than that of WPU/CF.

Vanishing point detection plays an important role in camera calibration and 3D scene reconstruction. There are usually a lot of parallel lines in the real scene. Vanishing point is the intersection point of these spatial parallel lines projected onto the image. Commonly used Hough algorithm to detect vanishing points, which has high complexity and low efficiency. This paper proposes a vanishing point detection algorithm based on optimization of line set. Firstly, the LSD algorithm is used to detect the line. Secondly, the extracted line set is optimized to remove the invalid interference line in the image, which improves the accuracy of vanishing point detection. Thirdly, K-means algorithm is used to cluster and group the optimized line set, which improves the overall efficiency of the algorithm. Finally, random sampling fitting algorithm is used to fit the grouped line set to calculate the precise vanishing point. Compared with Hough algorithm, the running speed of this algorithm is improved by 19% in the actual scene. The experimental results show that the algorithm has low complexity and short running time.

Aiming at the problems that traditional fire smoke recognition methods in a low recognition accuracy, a fusion network based on VGG16 is proposed, which use channel attention mechanism and contain Dense Blocks network to extract smoke features. To avoid the loss of smoke features, channel attention mechanism in backbone network is automatically to learn the importance of feature in this network. The experiment results show that the accuracy of this network is 3.0% higher than VGG16 neural network, and which is effective and feasible in smoke recognition tasks.