2results about How to "Large scale preparation" patented technology

The application relates to a method for preparing a deformable reinforcing metal matrix composite material by a pressure infiltration technology, and relates to a method for preparing a metal matrix composite material. In order to solve the problems of poor plasticity of the metal matrix composite material and difficulty in preparing a deformable reinforcing aluminum matrix composite material by a self-exhaustion pressure infiltration technology, the application mixes reinforcing body powder with polysilazane, and then sintering, the polysilazane is cracked at high temperature and a ceramic skeleton is generated, the structural strength of the preform in a high-temperature state is improved, and the success rate of the self-exhaustion pressure infiltration process for preparing the deformable reinforcing metal matrix composite material is effectively improved. The application realizes the function of preparing a heterogeneous aluminum alloy by the pressure infiltration technology, and has better strength-plasticity matching compared with a homogeneous alloy with the same composition.

The application discloses a semantic segmentation method based on phase profile constraint and belongs to the technical field of computer vision. The method uses a calibrated camera-projector system to collect a two-dimensional texture image and a deformed fringe image of a to-be-measured scene under the same field of view; the deformed fringe image is solved through multi-step phase shifting and Gray code technology to obtain an absolute phase distribution and calculate a gradient field; according to a preset mutation condition, a physical edge profile reflecting the depth change of an object is extracted from the gradient field; the physical edge profile is mapped into the two-dimensional texture image based on the corresponding relationship of pixel coordinates to accurately define the semantic object area, the corresponding category label is given, and finally a semantic label mask image is generated. The application uses physical phase information to correct the uncertainty of visual texture edges, effectively solves the edge extraction problem under a complex light scene, realizes the automatic generation of semantic segmentation data, and improves the training precision and robustness of a semantic segmentation model.