6results about How to "Reduce repetitions" patented technology

The application provides an X-band broadband high-gain radar metasurface antenna and a design method thereof. The metasurface antenna comprises, from top to bottom, a metasurface layer, an intermediate metal layer and a lower microstrip feed line layer. The metasurface layer comprises at least one metasurface unit. An asymmetric chamfer structure is designed on each metasurface unit. A center fork-shaped slot is arranged at the center of each metasurface unit, and four embedded parasitic branches are arranged at four side edges, respectively. Rectangular coupling slots corresponding to each metasurface unit are arranged on the intermediate metal layer. Feed lines corresponding to each metasurface unit are arranged on the lower microstrip feed line layer. The design method provided in the application can improve the design efficiency.

The invention relates to a preparation method of a composite material box body with an inner boss, in particular to a preparation method of a SiC / SiBCN ceramic matrix composite material box body with an inner boss. In order to overcome the defects that in the prior art, the preparation process of a composite box body with an inner boss is complex, holes are formed in areas with different thicknesses, and hole filling fiber bundles are prone to deformation and dislocation after being pressed, so that the structure is unstable. The preparation method of the SiC / SiBCN ceramic matrix composite box with the inner boss sequentially comprises the steps of SiC fiber prefabricated body preparation, high-temperature treatment, boron nitride interface layer preparation, Si3N4 matrix deposition, SiBCN matrix preparation, Si3N4 matrix deposition and the like, the SiC fiber prefabricated body is prepared from a U-shaped prefabricated body, a concentric-square-shaped prefabricated body and an exterior prefabricated body, and the stability of the mounting boss is improved.

The invention relates to a dynamic test parameter optimization method and device for a material test and computer equipment. The method comprises the steps of obtaining an experiment target of a material experiment and an experiment process of the material experiment, and generating initial experiment formula parameters of the material experiment through a formula parameter optimization strategy; executing the material test through the experiment process based on the initial experiment formula parameters to obtain current experiment data, and identifying experiment abnormal information of the material test through an experiment dynamic analysis strategy based on the current experiment data; and on the basis of the experiment abnormal information, through an experiment parameter optimization strategy, generating current optimization experiment parameters of the material experiment, replacing the initial experiment formula parameters with the current optimization experiment parameters, and returning to execute the above steps until the material experiment is completed. By adopting the method, the experiment efficiency of a material experiment can be improved.

The invention discloses an adaptive path training system and method based on capability evolution prediction, and the system comprises a data collection module which is used for collecting the real-time behavior data of a target training object in a training process; the capability state construction module is used for constructing capability state data according to the real-time behavior data; the capability trajectory prediction module is used for performing capability evolution prediction on the capability state data according to a preset time sequence prediction model to obtain a behavior evolution trajectory sequence; the training path generation module is used for constructing a target function on the basis of a preset training task set according to the behavior evolution trajectory sequence and in combination with training time and training cost, and solving the target function on the basis of a preset strengthening algorithm to obtain an optimal training path; and the training execution module is used for sequentially executing the corresponding training tasks based on the optimal training path. The technical problem that in the prior art, a training path is fixed, and a training object cannot be dynamically adjusted is solved.

The invention provides an image splicing method and device, electronic equipment and a storage medium, and relates to the technical field of microscopic imaging. Comprising the following steps: determining an overlapping region between a first cell image and a second cell image to be spliced; performing intra-region division on the overlapped region to obtain a target processing region; according to the target processing area, the pixel information of the first cell image and the pixel information of the second cell image, accumulated cost information of each pixel point in the target processing area is determined; determining associated pixel points corresponding to the pixel points according to the accumulated cost information of the effective neighborhood pixel points corresponding to the pixel points; and determining a target splicing position in the overlapping area according to the end point pixel point in the target processing area and the associated pixel points corresponding to the pixel points. Based on the method, a better splicing position can be determined, seamless splicing of the image is satisfied, cells are not cut, and the problem of repeated cells in the spliced image caused by cell movement is reduced.

This invention discloses a method for classifier parameter identification based on Extreme Learning Machine (ELM). Step 1: Divide the classification dataset into training and test datasets. Step 2: Construct an ELM model and use a modified whale optimization algorithm to obtain the optimal initialization parameters for the multi-novel ELM. Step 3: Train the ELM model online using the training dataset, evaluate the model using the structural risk loss function, identify and update the output weights, and complete the classification training of the obtained dataset to refine the ELM model parameters. Step 4: Input the test dataset to be classified into the multi-novel ELM model trained online in Step 3, and identify the category of the test dataset online. If new data is input, repeat Step 3 to classify the newly input classification dataset. This invention improves the classification accuracy of the ELM model.