Due to the rapid development of powder technology around the world, powder materials are being widely used in various fields, including metallurgy, the chemical industry, and petroleum. The turbo air classifier, as a powder production equipment, is one of the most important mechanical facilities in the industry today. In order to investigate the production efficiency of ultrafine powder and improve the classification performance in a turbo air classifier, two process parameters were optimized by analyzing the influence of the rotor cage speed and air velocity on the flow field. Numerical simulations using the ANSYS-Fluent Software, as well as material classification experiments, were implemented to verify the optimal process parameters. The simulation results provide many optimal process parameters. Several sets of the optimal process parameters were selected, and the product particle size distribution was used as the inspection index to conduct a material grading experiment. The experimental results demonstrate that the process parameters of the turbo air classifier with better classification efficiency for the products of barite and iron-ore powder were an 1800 rpm rotor cage speed and 8 m/s air inlet velocity. This research study provides theoretical guidance and engineering application value for air classifiers.

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information. Most existing methods in this domain fall short of harnessing the limited information available in monocular 3D detection tasks. They typically provide only a single detection outcome, omitting essential uncertainty analysis and result post-processing during model inference, thus limiting overall model performance. In this paper, we propose a comprehensive framework that maximizes information extraction from monocular images while encompassing diverse depth estimation and incorporating uncertainty analysis. Specifically, we mine additional information intrinsic to the monocular 3D detection task to augment supervision, thereby addressing the information scarcity challenge. Moreover, our framework handles depth estimation by recovering multiple sets of depth values from calculated visual heights. The final depth estimate and 3D confidence are determined through an uncertainty fusion process, effectively reducing inference errors. Furthermore, to address task weight allocation in multi-task training, we present a versatile training strategy tailored to monocular 3D detection. This approach leverages measurement indicators to monitor task progress, adaptively adjusting loss weights for different tasks. Experimental results on the KITTI and Waymo dataset confirm the effectiveness of our approach. The proposed method consistently provides enhanced performance across various difficulty levels compared to the original framework while maintaining real-time efficiency.

Unmanned surface vehicles (USVs) have been widely used for a wide range of tasks in the past decades. Accurate perception of the surrounding environment on the water surface under complex conditions is crucial for USVs to conduct effective operations. This paper proposes a radar-vision fusion framework for USVs to accurately detect typical targets on the water surface. The modality difference between images and radar measurements, along with their perpendicular coordinates presents challenges in the fusion process. The swaying of USVs on water and the extensive areas of perception enhance the difficulties of multi-sensor data association. To address these problems, we propose two modules to enhance multi-sensor fusion performance: a movement-compensated projection module and a distance-aware probabilistic data association module. The former effectively reduces projection bias during the alignment process of radar and camera signals by compensating for sensor movement using measured roll and pitch angles from the inertial measurement unit (IMU). The latter module models target regions guided by each radar measurement as a bivariate Gaussian distribution, with its covariance matrix adaptively derived based on the distance between targets and the camera. Consequently, the association of radar points and images is robust to projection errors and works well for multi-scale objects. Features of radar points and images are subsequently extracted with two parallel backbones and fused at different levels to provide sufficient semantic information for robust object detection. The proposed framework achieves an AP of 0.501 on the challenging real-world dataset established by us, outperforming state-of-the-art vision-only and radar-vision fusion methods.