15 results about "Gaussian radial basis function" patented technology

Embodiments of the present disclosure provides a method and device for constructing a multi-dimensional coupled vibration identification model for a roll system of a strip mill, the method comprising: performing preprocessing, signal decomposition, and reconstruction of the multi-dimensional data for the roll system of the strip mill monitored through technical monitoring by multiple sensors during a working process of the strip mill, and performing feature extraction of the reconstructed data using a wavelet packet energy approach; obtaining an initial multi-dimensional coupled vibration feature local identification model for the roll system of the strip mill based on Support Vector Machine (SVM) by using a Gaussian radial basis function as a kernel function of the SVM model; and training the initial multi-dimensional coupled vibration feature local identification model for the roll system of the strip mill using a multi-source heterogeneous information fusion approach based on the feature vector to obtain the multi-dimensional coupled feature identification model for the roll system of the strip mill.

The invention discloses a mechanical arm trajectory planning method and system and a medium, and belongs to the field of mechanical arm trajectory planing.The method comprises the steps that an image set of injection objects in different postures is collected, and trajectory data of mechanical arm injection are collected; marking an injection area in the image set, and training a target recognition model based on YOLOv8 by using the marked image set; recognizing an injection area in the current image set by using a target recognition model, and taking the center point of the injection area as an injection point; an improved dynamic motion primitive DMP algorithm is used for planning the motion track of the mechanical arm returning to the current position after injection is completed from the current position to the injection point; the improved dynamic motion primitive DMP algorithm is obtained by introducing a speed item scaling factor and superposing a plurality of nonlinear Gaussian radial basis functions; in view of easy failure of a mechanical arm motion trajectory planning algorithm in the prior art, the adaptability of the algorithm to the situation that the head and the tail of the trajectory are the same is improved.

The invention relates to power load prediction, in particular to a dynamic hierarchical granularity model power load prediction method considering time sequence factors. According to the method, the fitting capability of periodic features, trend changes and sudden influences in load fluctuation is remarkably enhanced, and the problem of feature loss caused by neglect of a time sequence dynamic weight of an existing model can be solved. Comprising the following steps: S1, performing time sequence characteristic analysis on historical power load data, and converting an original non-stationary time sequence into a stationary time sequence through difference and logarithm transformation; s2, obtaining a cleaned power load data set; s3, based on the data cleaned in S2, selecting a Gaussian radial basis function (RBF) as a kernel function, and analyzing temperature, holiday identification and linear change trend key influence factors at the same time; s4, performing dynamic multi-level granulation on the data set, and dynamically adjusting the particle level according to the data mixing degree and the particle density; and S5, fusing the time sequence kernel function and the influence factor through a decision function, and predicting the future power load.

The invention discloses a truck suspension parameter optimization method based on an adaptive optimization Gaussian radial basis proxy model, which comprises the following steps of: 1, constructing a multi-body dynamics simulation model of a truck by using original truck parameters, and determining actually measured data, truck parameters and a modeling theory required for establishing the multi-body dynamics simulation model of the truck; 2, determining key suspension parameters which have obvious influence on the vibration acceleration of the truck by utilizing a VIP method; 3, based on the training data, constructing an AO-GRBF proxy model of the transverse and vertical vibration acceleration of the vehicle body, and searching an optimal key suspension parameter by adopting an NSGA-II genetic algorithm; and 4, based on the actual data of the truck, verifying the effectiveness of the (AO-GRBF) optimization method. According to the optimization method based on the AO-GRBF proxy model, the global fitting precision of the model is effectively improved, and the efficiency and accuracy of suspension parameter optimization are improved.

The invention discloses a radial basis function boundary optimization and multivariate quadratic quasi-interpolation combined data fitting method, which comprises the following steps of: firstly, selecting m data points at two ends from a given data set, fitting boundary data by using Gaussian radial basis function interpolation, and adjusting function characteristics through shape parameters; and fitting all data including the boundary data by using a multivariate quadratic quasi-interpolation method, calculating a weight function of the data and the corrected data, and combining a boundary fitting function to obtain a final result. The method provided by the invention solves the problems that the traditional interpolation calculation is complex, the numerical value is unstable and the boundary processing precision is low, the calculation complexity is low, and the numerical value is stable and reliable; the application effect in the fields of mechanical design, fluid dynamics simulation and the like is remarkable, compared with traditional polynomial fitting, the boundary fitting error is reduced by at least two orders of magnitude, and the calculation efficiency is greatly improved.

The invention belongs to the field of textile process automatic design, and particularly relates to a weaving production process automatic generation method. According to the invention, multi-mode full-process weaving workshop data are collected through a high-frequency sensor. After abnormal data is eliminated, filling a time sequence vacancy through a Gaussian radial basis function; according to a real weaving workshop, a virtual environment for intelligent agent optimization is built; minimization of product errors and minimization of production time are used as global optimization targets, and a deep reinforcement learning algorithm is used to enable an intelligent agent to learn an optimal production strategy reaching the targets from a historical production process; a user customizes the organization structure, the luster color, the fabric hand feeling, the stiffness and the bulkiness of the target fabric through the interactive interface module, and the intelligent agent automatically generates process parameters of all links of the weaving process according to the characteristics of the target fabric. The development efficiency and quality of textile manufacturing are improved, and the method is suitable for rapid production and optimization of a complex weaving process.

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 relates to the field of infrared image processing, and discloses an AI automatic distance measurement method based on an infrared image model, which comprises the following steps: deploying a high-precision thermal infrared imager, and collecting multi-scene and multi-distance infrared images; preprocessing the original infrared image; establishing a data set containing a plurality of typical scenes according to the preprocessed image, and marking the image; an improved yolov5 model is adopted to train the data set, and accurate target detection frame information is obtained; sampling different environment temperature image samples and corresponding actual physical distance data to form a ranging training sample; inputting the accurate target detection frame information and the distance measurement training sample into a designed Gaussian radial basis function neural network model for training to obtain a physical distance measurement model, and compensating the error of the system by using the physical distance measurement model to obtain the distance between the target and the thermal infrared imager; the method has the beneficial effects that the ranging precision is improved, the environmental adaptability is enhanced, the real-time performance is excellent, and the deployment is simple and convenient.

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 discloses a topological design method for a symmetric microstructure composed of a multiphase material. The topological design method comprises the following steps: establishing a level set function representing boundaries of all parts in an object to be designed by taking an Euler space as a reference design domain; constructing an integral form about a functional in the reference design domain, and calculating to obtain a volume integral of the to-be-designed object; distributing binary codes to each level set function in the reference design domain to describe different material phases to obtain a color level set; performing interpolation conversion on each level set function in the color level set by adopting a Gaussian radial basis function to obtain a parameterized form; and constructing a microstructure topological optimization model of the object to be designed according to the color level set and the volume integral, and obtaining a microstructure unit with an auxetic function according to the optimization model. The functional metamaterial can be effectively used in the structure of a complex curved surface structure, and the integrated design of the impact resistance function and the bearing performance is achieved.

The invention discloses a single cell sequencing data analysis method based on Bayesian modeling, which is suitable for low expression gene identification and biological heterogeneity detection. The method comprises the following steps: S1, data acquisition and preprocessing; s2, establishing a Bayesian hierarchical model, and modeling technical noise and biological heterogeneity at the same time; s3, introducing a Poisson model and Poisson-negative binomial model combined strategy, and fitting and expressing a mean value and variance relationship by using a Gaussian radial basis function so as to accurately describe gene variation; s4, carrying out posterior inference by utilizing Markov chain Monte Carlo sampling to identify HVG and LVG genes; and S5, under the condition of lack of the spike-in gene, introducing a batch effect and a cross-batch sharing prior, and constructing a non-spike-in expansion model. Compared with a traditional method, the method has the advantages that the accuracy and robustness of expression variation recognition are improved, and the method is suitable for single cell data analysis of complex biological systems such as tumors and immune microenvironments.

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 invention discloses a method for expanding rutting data of asphalt pavement based on a radial basis neural network. First, the data of the performance detection of asphalt pavement and the influencing factors of the performance of asphalt pavement are collected, and the data is preprocessed. Then, according to the obtained data, feature generation is carried out, and the features highly correlated with rutting data are screened out according to the Pearson correlation coefficient. Subsequently, a radial basis neural network expansion model is constructed, the center of the Gaussian radial basis function is determined by the K-means algorithm, the radius of the Gaussian radial basis function is determined by the k-nearest neighbor algorithm, the weight matrix from the hidden layer to the output layer is determined, and the optimal model is achieved by adjusting the number of neurons in the hidden layer. The expanded data is predicted and verified. Finally, the data expansion model is extended to each pavement data, and based on its characteristic data, the expansion of rutting data of asphalt pavement based on a radial basis neural network is realized.

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.

The invention provides a blade cold and hot state deformation online measurement method based on a blade tip timing technology. The method comprises the following steps: step 1, establishing a blade tip contour deformation model; 2, based on the light spot triggering position and radius, a Gaussian radial basis function is adopted to represent a light spot; 3, setting an angle window function to restrain the Gaussian radial basis function; and 4, constructing an optimization objective function, and taking the maximum value of the blade top contour in the windowed Gaussian radial basis function. According to the invention, non-contact, high-precision and full-parameter static deformation measurement of the rotating blade in a high-temperature and high-pressure narrow space is realized.