10results about How to "Clear texture" patented technology

The utility model belongs to the technical field of leather processing equipment, and particularly relates to stamping equipment for motorcycle cushion leather, which is arranged on a working table and comprises a support frame fixedly connected to the working table, a stamping bottom plate arranged at the lower part of the support frame and used for supporting the leather, and a stamping component arranged at the top of the support frame and used for stamping the leather, the device further comprises a pressure control system electrically connected with the imprinting assembly. The precise pressure control system and the precise positioning system ensure that the leather is uniform in pressure and accurate in position in the embossing process, so that embossed patterns and textures are clear and firm, and the percent of pass and the quality of products are improved.

The utility model discloses a kind of multi-effect synergic anti-wrinkle non-deformation pearl plush forming device, belong to pearl plush processing technical field, its technical scheme main points include support frame, the inner side of the support frame is provided with conveying mechanism, hot setting mechanism is welded in the both sides of support frame top, the hot setting mechanism includes mounting bracket, screw rod, threaded sleeve, servo motor, connecting rod, rotating roller, several heating rods, setting roller and temperature sensor, heating rod in hot setting mechanism is evenly distributed in rotating roller surface, cooperate temperature sensor real-time monitoring temperature, and adjust through PLC feedback, ensure that setting roller surface temperature is stably in optimum range, both guarantee that fabric fiber is fully set, avoid damage or deformation caused by overheating, setting roller is driven screw rod to adjust height by servo motor, can be flexibly adjusted with the interval of conveying belt according to fabric thickness, ensure that pressure is uniform, make the texture of pearl plush surface set clear, form consistent, improve product appearance texture.

The application discloses a preparation method of concave-convex texture ceramic tiles with uniform color and luster, and comprises the following steps: A, blank is pressed into a blank body by roller forming, and the blank body layer is obtained after first drying; B, printing ink of a mold is printed according to a preset pattern, digital face glaze is sprayed, and the concave-convex texture layer is formed after second drying; C, color ink is printed according to the preset pattern, and the inkjet printing layer is formed; D, digital protection glaze is sprayed, and the protection glaze layer is formed after third drying and fourth drying; E, kiln firing is performed, and the concave-convex texture ceramic tiles with uniform color and luster are obtained after polishing. The preparation method of the concave-convex texture ceramic tiles with uniform color and luster is simple in steps and high in operability, and under the premise of solving the black edge problem and ensuring the uniform color and luster of the ceramic tiles, the concave-convex texture effect with clear texture, high consistency and high fidelity is obtained on the surface of the ceramic tiles.

The application relates to the field of image processing and discloses a ground penetrating radar image sharpening method based on super-resolution reconstruction, which firstly carries out pretreatment on acquired original radar image data. In view of the problems of low-frequency ground penetrating radar image blurring and low resolution, the application proposes using an improved Real-ESRGAN super-resolution network to carry out image reconstruction. Core improvements include: a degradation model conforming to a GPR physical imaging mechanism is designed, which is used to more truly simulate low-quality image generation in the training stage; a hybrid loss function containing a depth-weighted pixel loss, a structure mask-weighted Charbonnier loss and an A-scan direction consistency loss is constructed, so that the recovery capability for deep weak signals, key structural features and echo waveform consistency is enhanced. The application can effectively improve the resolution and visual clarity of the ground penetrating radar image, enhance structural details and depth information, and provide a more reliable image basis for subsequent intelligent analysis tasks such as target recognition and disease detection.

The application discloses a model training method, a crack detection method, a computing device and a readable storage medium, and relates to the field of weld detection. The model training method comprises the following steps: training a model based on a StyleGAN3 architecture through an original sample set to obtain a virtual image sample set corresponding to a weld crack image, wherein the number of weld crack images of the virtual image sample set is greater than that of the original sample set; and training an initial model based on the virtual image sample set to obtain a weld crack detection model, wherein the initial model comprises a Faster RCNN model with a Swin Transformer network as a backbone network, and the initial model comprises an FPN module and an ACmix attention mechanism module. The FPN architecture and the ACmix attention mechanism are introduced according to the characteristics of the weld crack to improve the model, thereby improving the detection accuracy of the weld crack.

This invention relates to a Gaussian super-resolution reconstruction method based on physically consistent sparsity and frequency awareness, comprising: performing feature extraction and saliency prediction on the spacecraft image to be processed to obtain a multi-scale deep feature tensor and a foreground saliency mask; using the foreground saliency mask to perform spatial feature modulation on the multi-scale deep feature tensor to obtain a modulated multi-scale deep feature tensor; decoding the feature vector corresponding to each spatial position of the modulated multi-scale deep feature tensor to predict the corresponding Gaussian kernel parameters; based on the foreground saliency mask, performing a physically consistent hard gating operation on the opacity of all Gaussian kernel parameters to generate a sparsified set of Gaussian kernel parameters; and based on the sparsified set of Gaussian kernel parameters, obtaining the final super-resolution result image through differentiable Gaussian sputtering. This method can obtain high-quality arbitrary-scale super-resolution reconstruction results while meeting the high-performance requirements of spaceborne edge devices.

The application relates to the field of dyeing and finishing processes of textiles, and particularly discloses a short-process polyester-cotton school uniform fabric dyeing and finishing process which comprises the following steps: S1, knitted double-sided cloth is first subjected to enzyme treatment by adding enzymes and glacial acetic acid, without washing and liquid discharge, so as to obtain pretreated fabric; S2, dyeing auxiliaries and water are added to the pretreated fabric, acid is added to adjust the pH, then dyes are added to perform dyeing treatment, and after refining, dyed fabric is obtained; S3, the dyed fabric is subjected to reduction cleaning and cloth washing, and after drying, finished fabric is obtained; the process has the advantages of efficient treatment, high dyeing rate and good mechanical performance of the fabric.

This invention belongs to the field of thin-layer chromatography (TLC) identification technology for traditional Chinese medicine, and specifically relates to a rapid TLC identification method and application for American ginseng and ginseng. The TLC identification method includes the following steps: taking a test sample, adding a solvent, sonicating, and taking the supernatant as the test solution; taking American ginseng and ginseng reference materials, adding solvents respectively, sonicating, and taking the supernatant as American ginseng reference material solutions and ginseng reference material solutions; taking ginsenoside Rg1, ginsenoside Rf, ginsenoside Re, ginsenoside Rd, and pseudoginsenoside F11 reference standards, adding solvents, mixing, and obtaining reference solution; finally, performing TLC analysis and result determination on the test solution, reference material solution, and reference solution. This invention is simple to operate, highly efficient and rapid, has good repeatability and high accuracy, and is low in toxicity and durable, and can simultaneously detect adulteration, substitution, and confusion in American ginseng and ginseng.

The application discloses a kind of based on triangulated mesh differentiable rendering animated face three-dimensional reconstruction method, this method takes single-view or multi-view portrait head portrait video as input, single-frame reconstruction and sequence frame reconstruction are successively carried out, finally output the portrait head portrait basic model of triangular mesh format, face main joint position information and control weight, face expression mixed shape and multiple face color map.The geometric structure of the face three-dimensional model reconstructed by the method is accurate, the color map is high in definition, and the animation can be edited by joint parameters and expression mixing, which can meet the needs of film and television, game industry for face animation model.

The invention discloses an airport runway foreign matter self-adaptive focusing method based on scene perception. The method comprises the following steps that 1, multiplying power calculation and focal length calibration are conducted on an optical camera used for airport runway foreign matter detection; 2, according to the distance between the foreign matter in the runway and the optical camera, an evidence obtaining multiplying power and a corresponding initial focal length are set, and a runway area in the image is extracted; 3, collecting video image data of the optical camera, and calculating an image quality coefficient of the runway area; and step 4, driving a lens of the optical camera to adjust the focal length in a progressive increase mode and a progressive decrease mode according to a preset step length, calculating the quality coefficient of the runway area under each focal length in real time, determining the optimal focusing distance corresponding to the maximum quality coefficient, and driving the camera to be switched to the focusing distance to complete the scene perception-based airport runway foreign matter self-adaptive focusing.