7results about How to "Preserve continuity" patented technology

This invention discloses an anisotropic sparse deconvolution enhancement method and system for ultrasound tumor image segmentation. The method includes: establishing a local convolution model for ultrasound RF images based on the anisotropic characteristics of the ultrasound xz imaging plane; constructing an axis-aligned elliptical symmetric anisotropic point spread function (PSF), whose lateral and axial spread scales are obtained through physical parameter conversion or data-driven methods; constructing an ultrasound sparse reconstruction objective function incorporating the anisotropic PSF, adding non-negativity constraints, introducing a weighted Hessian continuity term and an L1 sparse prior term in the xz plane direction, and optimizing the solution to obtain the sparse reconstruction result; using this result as input, employing an RL iterative deconvolution method based on the anisotropic PSF, and achieving image boundary sharpening and noise suppression through PSF flip kernel ratio iteration; inputting the enhanced image into a tumor segmentation network to complete training and inference, thereby achieving ultrasound tumor image segmentation.

This invention discloses a stable torque transmission system for a photovoltaic tracking bracket, comprising a main shaft, a circular tube synchronous shaft, a torque transmission connecting sleeve, an inner reinforcing member, and a flexible limiting structure. The end of the circular tube synchronous shaft is locally shaped to form a shaped torque transmission section including a shaped torque transmission area and an arc-shaped stabilizing area retaining the original circular tube's outer arc contour. The torque transmission connecting sleeve is fitted onto the outer wall of the shaped torque transmission sections of two adjacent circular tube synchronous shafts to achieve torque transmission between the synchronous shafts through surface-to-surface mating. The inner reinforcing member is disposed inside the shaped torque transmission section to enhance the local strength and anti-instability capability when the end is connected to the reducer. The flexible limiting structure is connected between the main shaft and the circular tube synchronous shaft to limit the radial displacement of the synchronous shaft and allow it to rotate relative to the main shaft. This invention balances the stable torque transmission of the shaped surface with the stability of the circular tube structure, reducing off-center wear in the connection area and improving the long-term operational reliability of the system.

The invention relates to the technical field of oil product detection, in particular to a diesel oil Raman spectrum detection method and device based on a convolutional neural network, computer equipment and a computer program product. The method provided by the invention comprises the following steps: acquiring a Raman spectrum of diesel oil to be detected; the Raman spectrum is input into a pre-trained diesel oil detection model, a detection result output by the model is obtained, and the detection result represents the alcohol substance doping possibility of the to-be-detected diesel oil; wherein the diesel oil detection model comprises a multi-scale convolution branch structure, and the multi-scale convolution branch structure comprises three parallel convolution branches with different convolution kernel sizes. According to the method provided by the invention, through parallel execution of three convolution branches, feature information of various scales in the spectrum can be synchronously extracted, the recognition and extraction capability of the model on weak signal features is remarkably enhanced, and efficient and accurate recognition of alcohol doping behaviors in diesel oil is realized.

The application belongs to the technical field of energy storage modeling, and discloses a large disturbance transient simulation modeling method and system for an electrochemical energy storage system, which comprises the following steps: establishing a slow dynamic model, an alternating current side electromagnetic transient model and a direct current side energy storage battery equivalent circuit model, wherein the alternating current side electromagnetic transient model outputs a fast state variable; decomposing the fast state variable into a quasi-steady component and a deviation component, and establishing a linearized dynamic equation of the deviation component; resetting the deviation component when a grid voltage suddenly changes, superimposing the reset deviation component and the quasi-steady component to obtain an initial value of the fast state variable; and solving the slow dynamic model, the linearized dynamic equation of the deviation component and the direct current side energy storage battery equivalent circuit model simultaneously with the initial value of the fast state variable to obtain a transient dynamic response of the energy storage system in the whole process. The application completely retains the fast dynamic details and state continuity in the initial stage of the fault, and the simulation result is closer to the real physical response.

The application discloses a compression method of encoded video data, which comprises the following steps: extracting at least one first enhancement layer video frame from first encoded video data; re-encoding the first enhancement layer video frame to obtain a second enhancement layer video frame; and taking the second enhancement layer video frame and a base layer video frame in the first video data as second compressed video data, wherein the first video data is scalable video coding data. The compression method of the application does not change the structure of the video data before and after compression, retains the real-time and continuity of the video data, is compatible with the frame rate stability requirements of various players, and reduces the capacity of the video data without losing the video data information.

A wet clutch friction plate oil groove structure based on depth gradient optimization relates to the technical field of wet clutches and comprises a friction product body, a first annular oil groove, a second annular oil groove and a third annular oil groove are sequentially formed in the friction plate body in the radial direction, and a plurality of radial oil grooves distributed in the circumferential direction are formed in the friction plate body in the third annular oil groove. A first V-shaped oil groove is formed between every two adjacent radial oil grooves, the first oil groove and the second oil groove, and a second V-shaped oil groove is formed between every two adjacent radial oil grooves, the second oil groove and the third oil groove. The vertex of the first V-shaped oil groove is located on the first oil groove, two end points of the first V-shaped oil groove are communicated with the intersection point, the vertex of the second V-shaped oil groove is located on the third oil groove, two end points of the second V-shaped oil groove are communicated with the intersection point, and the intersection point is the communication point of the radial oil groove and the second oil groove. The deep oil grooves are formed in the outer ring area so that the heat dissipation efficiency can be improved; a shallow oil groove is adopted in the inner ring area to maintain the structural rigidity, and lubricating oil is guided to flow secondarily so as to further improve the heat exchange effect.

The invention discloses a low-illumination urban rail train key part defect image reconstruction and defect detection method and system based on improved SwinIR. The method comprises the following steps: S1, constructing a data set; s2, constructing an improved SwinIR low-illumination urban rail train key part defect image reconstruction model; s3, designing a loss function, and training the improved SwinIR low-illumination urban rail train key part defect image reconstruction model by using the training set and the verification set to obtain a trained optimal model and weight; then testing the trained optimal model by using a test set, and evaluating a test result; s4, inputting the urban rail train key part defect image into the trained improved SwinIR low-illumination urban rail train key part defect image reconstruction model to obtain a final super-resolution reconstruction image; and S5, inputting the super-resolution reconstructed image into the YOLO detection model for defect detection, and outputting a detection result.