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Automatic detection of industrial product damage using machine learning is a promising research area. At the same time, various machine learning methods based on convolutional neural networks have a very important role in the application of visual automatic detection. Therefore, the machine visionbased automatic detection of high-speed railway rail damage has received widespread attention. This paper proposes an efficient detection method for the damage of high-speed railway rails called SCueU-Net. For the first time, the combination of U-Net graph segmentation network and the saliency cues method of damage location is applied to the task of high-speed railway rail damage detection. The experimental results show that our method has a detection accuracy rate of 99.76%, which is 6.74% higher than the recent method in damage identification accuracy, which improves the detection efficiency of rail damage significantly. INDEX TERMS High-speed railway, machine learning, data augmentation, rail damage detection. I. INTRODUCTION

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Sunspots with strong magnetic fields are the most important manifestations of solar activity, appearing as dark features in the photosphere observed in continuum images. We proposed an artificial intelligence technology called the simulated annealing genetic (SAG) method, which combined the genetic algorithm and simulated annealing algorithm to self-adaptively derive dual thresholds for detecting the umbra and penumbra of sunspots simultaneously. Full-disk continuum intensity images obtained from Solar Dynamics Observatory/Helioseismic Magnetic Imager (HMI) at a cadence of four hours from 2010 May to 2016 December were used. The detection results showed that the dual thresholds derived by the SAG method have outstanding performance in segmenting the umbra and penumbra from the photosphere with a satisfactory robustness efficiently. The boundaries of the umbra and penumbra were finely delineated, even for sunspots at the extreme solar limb. The total sunspot areas, umbral areas, and penumbral areas match very well with the data reported from HMI Debrecen Data (HMIDD), with the correlation coefficients reaching 0.99, 0.99, and 0.95, respectively. The mean ratios of umbra to sunspot areas per year ranged from 0.159 to 0.233. The ratios decreased with an increase in solar activity, which implies that the ratio was related to the solar activity level.

To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R 2 ) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%-6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.

In order to obtain high-performance aluminum alloy parts fabricated by selective laser melting, this paper investigates the relationship between the process parameters and microstructure properties of AlSi10Mg. The appropriate process parameters are obtained: the layer thickness is 0.03 mm, the laser power is 370 W, the scanning speed is 1454 mm/s, and the hatch spacing is 0.16 mm. With these process parameters, the ultimate tensile strength of the as-printed status is 500.7 ± 0.8 MPa, the yield strength is 311.5 ± 5.9 MPa, the elongation is 7.7 ± 0.5%, and the relative density is 99.94%. After annealing treatment at 275 °C for 2 h, the ultimate tensile strength is 310.8 ± 1.3 MPa, the yield strength is 198.0 ± 2.0 MPa, and the elongation is 13.7 ± 0.6%. The mechanical properties are mainly due to the high relative density, supersaturate solid solution, and fine dispersed Si. The supersaturate solid solution and nano-sized Si formed by the high cooling rate of SLM. After annealing treatment, the Si have been granulated and grown significantly. The ultimate tensile strength and yield strength are reduced, and the elongation is significantly improved.