In clinical endoscopic diagnosis, the endoscopic image quality is reduced due to interference. The interference factors mainly include local overexposure and shadow of images caused by light source irradiation angle and specular reflection caused by tissue fluid. As the primary task in developing a computer-aided diagnosis (CAD) model, high-performance image enhancement algorithms can help them obtain a more accurate recognition rate. This study takes the endoscopic images of Barrett’s esophagus (BE) collected under narrow-band imaging as the research object. An adaptive brightness equalization algorithm and a specular reflection suppression algorithm are proposed to preprocess datasets, and the existing segmentation model confirms the algorithm. The experimental results show that the image enhanced by the algorithm has a certain degree of improvement in the overall and local dark areas and texture details, eliminates the specular reflection, and effectively improves the recognition accuracy of the segmentation model. The proposed algorithm improves the Mean Intersection over Union (MIoU) by 10.8% and the F 1 -Score by 8.76% on the self-built BE datasets. It shows that it can play an excellent auxiliary diagnostic role and obtain higher diagnostic efficiency in clinical diagnosis.

Gear transmission is one of the important links in mechanical transmission system, and its meshing stability and lubrication reliability are the keys to ensure the smooth operation of transmission box. The gear meshing conditions under different lubrication parameters including incident distance, incident angle, incident diameter and others were tested to obtain the impact effect of oil on the gear. The data regression method was used to analyze the test data. The variation rule of gear transmission torque with different lubrication parameters was obtained, and the incident diameter was concluded as the critical influencing factor. The study could provide technical support for gear lubrication design considering oil impact.