4 results about "Gaussian radial basis function" patented technology

The invention relates to the technical field of photoelectric display, and provides an area array chromaticity data multi-point correction method based on a two-dimensional Gaussian basis RBF interpolation method, and the method comprises the steps: obtaining a first chromaticity data array of a measured surface and second chromaticity data of M correction points in the measured surface; constructing a two-dimensional Gaussian radial basis function interpolation model sharing a group of shape parameters based on the positions of the correction points; solving a weight parameter of each Gaussian basis function according to the first chrominance data array and the second chrominance data at the correction point; and performing point-by-point correction on the first chrominance data array by using the solved model. According to the method, fitting precision and smoothness of chromaticity space nonlinear change are effectively balanced through adaptive shape parameters, the chromaticity relation consistency is kept by adopting a multi-channel coupling correction mode, and correction precision and efficiency are effectively improved.

The invention provides a hyperspectrum-based citrus leaf lesion diagnosis system and a hyperspectrum-based citrus leaf lesion diagnosis method, and the hyperspectrum-based citrus leaf lesion diagnosis system and the hyperspectrum-based citrus leaf lesion diagnosis method disclosed by the invention have the advantages that on the basis of analyzing hyperspectral imaging data of citrus leaves without diseases, lack of nutrients, black spots and yellow shoot; three parameters of yellow wave band reflectivity, infrared wave band slope and inflection point wavelength are used as characteristic quantities, and classification of four types of blades is realized by applying a support vector machine (RBF-SVM) classification model based on a Gaussian radial basis kernel function. The method solves the problems that when citrus tree disease information is detected through a field detection method at present, long-time observation with eyes is needed, subjective judgment of observers is depended, and misjudgment is likely to be caused; according to the present invention, the problem that the citrus tree disease information detection by using the chemical detection method needs the special person to detect by using the professional equipment so as not to accurately and rapidly detect each production stage of the citrus tree can be solved, and the citrus leaf disease can be rapidly and accurately diagnosed.

The invention provides a wheat protein quantitative prediction method and system based on a near infrared spectrum, and relates to the technical field of wheat detection.The method comprises the steps that wheat near infrared spectrum data are obtained and preprocessed; a bidirectional gating circulation unit is used for extracting time sequence characteristics; carrying out dimension mapping through the projection layer; and inputting the projection features into a KAN model improved based on a Gaussian radial basis function to carry out nonlinear regression so as to obtain a protein content prediction value. According to the method, through a BiGRU and improved KAN mixed architecture, the method has strong sequence modeling capability and high-precision nonlinear regression capability, meanwhile, the model interpretability is enhanced through feature importance analysis, the problem that in the prior art, the generalization capability and the interpretability of the model are insufficient is effectively solved, and a reliable scheme is provided for rapid nondestructive testing of the wheat quality.

The present application relates to the field of computer vision, and more particularly to a face emotion classification system based on multi-modal image recognition, comprising a multi-modal data acquisition module, a data preprocessing module, a face detection and target detection module, a feature extraction module, a global context modeling module and an emotion classification output module, wherein the present application introduces a KAN nonlinear channel mapping mechanism after the visual Transformer feature output, utilizes the function approximation capability based on the Gaussian radial basis function to perform a channel-by-channel nonlinear transformation on the multi-scale features, realizes the mapping from the linear semantic representation to the nonlinear semantic space, and thus enhances the expression capability of the model on the micro-expression changes and the fine-grained emotion features; the present application introduces a state space modeling mechanism in the global context modeling module, and combines the KAN gating nonlinear modulation mechanism to realize the linear complexity modeling on the long-range dependency relationship, and simultaneously enhances the nonlinear expression capability in the state space.