Abstract: A hoist for transferred materials in an underground auxiliary transportation system and a method thereof. The hoist includes a hoist body, guiding plates mounted at two ends of the hoist body through bolts, twist locks mounted at four ends of the hoist body, a driving unit configured to drive twist locks to rotate, transmission assemblies configured to connect the twist locks with the driving unit, ejector pins configured to prevent the twist locks from rotating mistakenly, and a sensor configured to control the hoist to operate and provide protection safety.

Abstract: The present disclosure provides a hoist for transferred materials in an underground auxiliary transportation system and a method thereof. The hoist includes a hoist body, guiding plates mounted at two ends of the hoist body through bolts, twist locks mounted at four ends of the hoist body, a driving unit configured to drive twist locks to rotate, transmission assemblies configured to connect the twist locks with the driving unit, ejector pins configured to prevent the twist locks from rotating mistakenly, and a sensor configured to control the hoist to operate and provide protection safety. The hoist provided by the present disclosure can satisfy the ground-to-air and ground-to-ground transferring requirements for underground materials, and the safety protection sensors can prevent a misoperation of the hoist, can adapt to standard containers of different sizes, has a certain adaptability to narrow terrains, and can improve the stability of the overall mechanism in cooperation with the rope-pulling mechanism.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.