To reveal the effect of vein-bionic surface textures on the tribological behavior of cylindrical roller thrust bearings (CRTBs) under starved lubrication, six kinds of leaves (Forsythia, Clausena lansiu, Ash, Purple leaf plum, Pipal and Apricot) were chose and their simplified patterns were fabricated on the shaft washers of CRTBs using laser surface texturing. The coefficients of friction (COFs) of vein-bionic textured bearings were measured using a vertical universal wear test rig. Their mass losses and worn surfaces were also characterized. The results show that: There is important influence of the symmetry of vein-bionic textures and the number of secondary veins on the friction and wear properties of vein-bionic textured CRTBs under starved lubrication. Compared to the smooth group, the COFs and mass losses of vein-bionic textured bearings are all reduced. Among all groups, the tribological performance of bearings with a pattern inspired from Ash is the best. Its wear loss is reduced by 16.23% and its COF is reduced by 15.79%. This work would provide a valuable reference for the raceway design and optimization of roller rolling element bearings.

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

The main black land conservation measure in China is the straw return to the fields. The processing of high-resolution images collected by aerial photography of UAVs through image stitching technology can provide image information for achieving fast and accurate detection of straw cover over large areas. The classical SIFT algorithm has many drawbacks, such as high dimensionality of feature descriptors, high computational effort, and low matching efficiency. To solve the problems above, this study proposes an improved algorithm. First, the method down sampled the high-resolution images before detecting the features to reduce the number of feature points and improve the efficiency of feature detection. Then, matching among feature points is achieved by gradient normalization-based feature descriptors to improve the matching accuracy. Next, the Progressive Sample Consistency algorithm eliminates the mismatch points and optimizes the transformation model. Finally, the images are fused with optimal stitching combined with fade-in and fade-out to achieve high-quality stitching. The comparative experimental results show that compared with the traditional SIFT and the speed-up robust feature algorithms, the algorithm has the advantage of the speed and good robustness to angle rotation, and makes full use of the texture information and the detail information, so it has higher accuracy. Compared with the traditional methods, the panoramic stitching image quality herein is excellent and can be applied to subsequent straw cover detection, the straw cover error is ≤3%, meeting the demand for large-area straw cover detection. Overall, the method proposed herein achieves an ideal balance between accuracy and efficiency; and outperforms other widely used and superior methods.INDEX TERMS Aerial images, down sampling, SIFT operator, panoramic stitching, straw coverage rate.

Flow field in aluminum reduction cells is very important for the heat transfer, the mass transfer and the moment transfer. The gas-liquid-liquid flow in aluminum reduction cells was studied numerically by ANSYS and CFX. The study indicates that the anode bubbles present as a shallow layer. The bath-metal interface deformation of gasliquid-liquid three-phase flow model is larger than that of liquid-liquid two-phase flow model. The velocity magnitude and circulation flow direction of bath flow in the channels are affected by the anode bubbles. The anode bubbles play a major role in bath flow of the channels' domain only. The Lorentz force plays a major role in bath flow of other domain. The conclusion indicates that the gas-liquid-liquid three-phase model is necessary for the numerical simulation of flow field in aluminum reduction cells and the three-phase model provides basis for further optimization.