9 results about "Laplace operator" patented technology

This invention discloses a method for locating abnormally overheating components on circuit boards based on thermal images. Starting from the characteristics of the thermal image itself, it obtains difference images under normal and abnormal conditions, and performs image registration using an accelerated robust feature registration algorithm optimized based on the Laplacian operator. This ensures that the difference images under abnormal and normal conditions are well aligned after registration, resulting in a clear differential thermal image. Then, outlier detection is performed on the differential thermal image using the Laplacian distribution, obtaining a differential thermal image after outlier detection, thus initially identifying abnormally overheating regions and locating the abnormally overheating components. Finally, kernel density estimation is used to determine the number of K-means clusters, and the K-means algorithm is used to segment the differential thermal image after outlier detection, obtaining a K-means segmented image. Morphological opening-closing operations are then performed on the image to remove minute lines. The thermal image segmentation results of this invention can accurately provide information on abnormally overheating components, enabling accurate location and identification of these components, helping to narrow down the defect range and providing defect judgment information.

This invention discloses an underwater target saliency detection method and system based on polarization multi-stage fusion, belonging to the field of computer vision and underwater image processing technology. The method acquires four-angle polarization images of turbid water and calculates the degree of linear polarization, then normalizes them to generate a polarization-enhanced image. Subsequently, high-frequency edge components are extracted through brightness enhancement and the Laplacian operator, and a preprocessed image is obtained by injecting learnable weights. A pre-trained deep neural network is then used to fuse the original image and the preprocessed image, outputting a saliency prediction map. This invention fully exploits polarization features, effectively suppresses scattering noise, and significantly improves the precision of underwater target edge segmentation.

The application belongs to the field of piezoelectric driver nonlinear modeling, and particularly discloses a piezoelectric driver creep nonlinear dynamics model and a construction method and system thereof. c (s)=s ‑μ ; wherein s is a Laplace operator; the identification method of the parameter mu includes: constructing input voltage signals with different frequencies omega i , calculating input voltage signal amplitudes U i ; according to the input voltage signals, obtaining output displacement signals of the piezoelectric driver, and simultaneously calculating output displacement signal amplitudes D i ; calculating the amplitude ratio A i of D i and U i ; obtaining a discrete data point set (x i , y i ) which satisfies: according to the linear fitting of the discrete data point set (x i , y i ), determining the slope of the fitted straight line, and determining the parameter mu according to the slope. The model constructed based on the fractional order involves fewer unknown parameters, is easy to identify, and at the same time, the modeling method is not limited by special discrete points in the time / frequency domain, and has higher universality.

The present application relates to the technical field of deep gas risk exploration, and particularly relates to a deep low exploration area volcanic reservoir prediction method, device, equipment and medium. The method comprises the following steps: according to the reservoir sensitive parameters of a research area, a post-stack virtual well constrained inversion is selected to obtain a low frequency model of a full low frequency band of a virtual well; a geological model is established based on the low frequency model of the full low frequency band and seismic interface information; based on the reflection characteristics of the volcanic rock in the research area, a Laplace operator frequency division configuration attribute is used to depict the volcanic rock body; according to the geological model and the volcanic rock depiction result, a configuration attribute of a slice position of a target layer is extracted, low frequency modeling is carried out based on the configuration attribute constraint to obtain a configuration attribute volume controlled low frequency model; and inversion is carried out based on the configuration attribute volume controlled low frequency model to obtain a volcanic reservoir prediction result of the research area. The present application realizes the spatial distribution depiction of irregular geological bodies, and the depiction of special geological bodies is clearer, which has an important supporting role for risk well deployment.

The invention discloses an image segmentation method of a regional active contour model based on a Laplacian operator. According to the method, an adaptive-Laplacian operator is introduced, and a corresponding energy functional is used as a length regular term of a level set function, so that effective regularization of the level set function is realized. The regularization mechanism not only can suppress false contours generated in the curve evolution process, but also can adaptively adjust the diffusion process, so that noise interference in the level set evolution process is effectively suppressed, image edge structure details are kept, and segmentation robustness and accuracy are improved. Experimental results show that the method has high segmentation precision and robustness in complex natural image segmentation.

A composite part curing state prediction method based on Laplace spectrum can establish a curing state prediction model for any shape of composite part. The method first divides the composite part model into grids; secondly, according to the type of the divided grid, the corresponding Laplace operator is defined, and a group of Laplace spectrum reflecting the frequency domain information of the part geometry is solved; then N groups of curing process data and the corresponding composite part curing state data are obtained; finally, based on the Laplace spectrum, a Laplace kernel integral module is constructed, and then a neural operator model is established, and the model parameters are trained through the obtained data samples, so that the curing state prediction model of the composite part can be obtained. The present application can be widely applied to the curing state prediction task of any shape of composite part, and the obtained curing state prediction model can realize the rapid prediction of the curing state of the composite part, and can be used for curing process optimization, curing process monitoring and other scenes.

This invention discloses a real-time aerial small target detection method based on phase awareness and frequency domain enhancement. First, a spatial-frequency dual-stream decoupling module simultaneously captures the target's local spatial semantics and high-frequency gradient structure during the feature extraction stage, and a hard-coded structural prior is constructed using the Scharr operator. Then, an absolute texture feature is extracted using a low-level frequency enhancer combined with a Log-Gabor filter. Based on this, a semantically gated phase interaction mechanism is used to strip away the easily disturbed amplitude spectrum in the frequency domain and reconstruct the structural prior using pure phase features. This is combined with a high-level semantic mask to achieve strong noise resistance across scales. Finally, at the detection head output, a frequency domain edge-gated hybrid expert module based on the Laplacian operator is used to achieve pixel-level dynamic feature refinement. This invention effectively revives sub-pixel-level target details and suppresses background false alarms, significantly improving the recall rate of aerial small target detection while maintaining extremely high real-time inference efficiency.

The application belongs to the technical field of scientific computing and artificial intelligence, and in particular to a random partial differential equation regular structure feature construction method and system. The integral operation of the regular structure feature is transferred from the physical space to the Fourier space. The Fourier diagonalization property of the Laplace operator is used to convert the integral of the partial differential equation coupled at each space point into the accurate solution of the scalar ordinary differential equation independent of each wave number. On the applicable periodic domain or the region processed by the periodization, the spectral accuracy is realized. Further, the serial recursive format is expanded into the causal convolution matrix form in the Fourier space. The integral factor power is pre-calculated and the three-dimensional causal convolution tensor is assembled. Through the parallel pipeline of batch fast Fourier transform, de-aliasing, tensor contraction causal convolution and batch inverse fast Fourier transform, the memory occupation is significantly reduced and the calculation efficiency is improved. The feature calculation efficiency and approximation accuracy of the machine learning system based on the regular structure feature construction are improved.