2results about How to "Mitigation mismatch" patented technology

The utility model relates to a conversion component, a dual-voltage battery pack and an electric tool system.The technical scheme includes that the conversion component is used for being connected with the dual-voltage battery pack and a first electric tool and converting the voltage of the dual-voltage battery pack into the voltage matched with the first electric tool, and the dual-voltage battery pack is provided with a first guide groove and comprises a main body part and a second guide groove; a terminal groove is formed in the housing; the male terminal is connected to the main body part and at least part of the male terminal is exposed out of the main body part; the female terminal is positioned in the terminal groove; and the second guide groove is formed in the side wall of the main body part and can be communicated with the first guide groove, so that the effect of improving the compatibility between the battery packs is achieved.

The application discloses a medical image segmentation method capable of learning a spectrum prior, and belongs to the technical field of medical image processing. The method acquires a medical image and performs pretreatment, and then inputs a double-branch diffusion model trained to extract image condition features; a mask noise state and a spectrum noise state are initialized, and joint reverse diffusion denoising is performed on the two states based on the image condition features; a mask diffusion branch in the double-branch diffusion model is used to generate a mask prediction result, and a spectrum diffusion branch is used to generate a learnable spectrum prior; in the joint reverse diffusion denoising process, the learnable spectrum prior dynamically guides the generation of the mask prediction result through space-spectrum collaborative updating, and finally a medical image segmentation result is obtained according to the denoised mask prediction result. In the training stage, the double-branch diffusion model uses an analytical spectrum anchor point and a structured spectrum statistical target to constrain the spectrum diffusion branch, so that the generated learnable spectrum prior is different from a fixed analytical spectrum.