10 results about "Nonparametric regression" patented technology

The invention discloses a remote sensing data normalization method and system based on geographically weighted nonparametric regression. The method comprises the following steps: establishing a geographically weighted nonparametric regression estimation model; obtaining a target image and a reference remote sensing image set, and forming a remote sensing image pair based on the target image and the corresponding reference remote sensing image; preprocessing a target image in the remote sensing image pair to obtain a resampled target image consistent with the reference remote sensing image in spatial resolution; judging a variable coefficient in each local window in the resampled target image and the reference remote sensing image, and screening out homogeneous pixels meeting conditions based on the variable coefficient and a preset threshold to form a homogeneous pixel set; and based on the geographically weighted nonparametric regression estimation model and the homogeneous pixel set, performing pixel-by-pixel calculation on a to-be-normalized image to obtain a normalization result. Remote sensing data with large breadth, high time resolution, consistent spectrum and consistent radiation can be used as a radiation reference, and normalization of any target image is realized.

The invention provides an attitude trajectory optimization method and system based on a self-adaptive sliding window. The method comprises the following steps: acquiring visual media data and decoding the visual media data into a time sequence image frame sequence; performing attitude estimation on each frame of image to obtain a coordinate set of each attitude feature point; caching a coordinate set of the historical attitude feature points to form a variable-length dynamic trajectory cache queue; constructing a sliding window according to the coordinate sets of the attitude feature points of the current frame and the previous frame; performing abnormal data elimination on each coordinate track; performing B spline fitting on each coordinate track; sequentially executing multi-stage optimization on the coordinate track; and updating the sliding window frame by frame to obtain a sequential sequence of the optimized attitude feature points. The method has the beneficial effects that the window radius is dynamically adjusted based on the motion intensity, the window is expanded at low speed to enhance noise suppression, and the window is reduced at high speed to reduce delay; and nonparametric regression fitting is carried out by adopting a cubic B spline, so that the modeling capability of a nonlinear motion track is enhanced, and the track smoothness is improved.

The present invention relates to the technical field of power grid assessment and optimization. Disclosed are a progressive planning-based power grid evaluation method and system based on a combined assessment theory. The method comprises: using a combined weighting method to perform weight correction on an obtained index by; obtaining an assessed value on the basis of a similarity theory-based assessment method; performing standardization processing on the index by means of non‑dimensionalization processing; and performing evaluation on the basis of non-parametric regression. By means of a combined subjective-objective weighting method, the present invention ensures the objectiveness and fairness of assessment results. By means of a deep learning model, in particular an LSTM network and an attention mechanism, the efficiency and accuracy of assessment are improved. The systematic method of the present invention has wide prospects for application. In the present invention, by means of the design of automated optimization of assessment parameters, adaptive adjustment can be performed on the basis of historical data and real-time data, so that assessment results are closer to actual values, thereby improving the practicality of assessment.

The present invention discloses a progressive power grid planning evaluation method and system based on a combination assessment theory, and relates to the technical field of power grid assessment and optimization. The method includes: correcting a weight of an obtained index by using a combination weighting method; obtaining an assessed value based on a similarity theory assessment method; standardizing an index by dimensionless processing; and performing evaluation based on nonparametric regression. The present invention ensures objectivity and fairness of an assessment result and the efficiency and the accuracy of assessment are improved. The present invention may be adaptively adjusted, making the assessment result closer to reality.

The risk evaluation apparatus evaluates the risk of a machine learning model. The risk evaluation apparatus includes a recording unit, a loss function regression model acquirer, an attack noise addition unit, an error acquisition unit, and an evaluation unit. The recording unit records a set of predetermined loss functions and a set of pairs of data and labels predetermined. The loss function regression model acquirer determines a regression model of the loss function in the vicinity of data by nonparametric regression. The attack noise addition unit creates attack data that is an Adversarial Example using the regression model. The error acquisition unit determines the error between the output of the machine learning model when the data is input and the output of the machine learning model when the attack data is input. The evaluation unit evaluates the risk based on a set of errors.

The application discloses a building deformation prediction method based on a nonparametric regression model, and comprises the following steps: 1, obtaining the measured time series data of a building monitoring point, and performing noise reduction pretreatment on the data by using a sliding average method; 2, establishing a nonparametric regression model for the pretreated data, and performing autocorrelation test on irregular terms in the nonparametric regression model; 3, performing parameter optimization on SVR by using a fish-eagle optimization algorithm, and then establishing an SVR irregular term regression prediction model by using optimal parameters to fit irregular terms; 4, obtaining a final nonparametric regression model; and 5, performing deformation prediction on the building. The fish-eagle optimization algorithm can obtain a global optimal solution of an optimization problem, the optimization result is independent of initial conditions and the algorithm is independent of a solution domain, has strong robustness, can be well used for solving model parameters, and the method can realize accurate deformation prediction.

The application discloses a remote sensing data normalization method and system based on geographical weighted nonparametric regression, and the method comprises the following steps: building a geographical weighted nonparametric regression estimation model; obtaining a target image and a reference remote sensing image set, and forming a remote sensing image pair based on the target image and the corresponding reference remote sensing image; preprocessing the target image in the remote sensing image pair to obtain a resampled target image with a spatial resolution consistent with that of the reference remote sensing image; judging the coefficient of variation in each local window of the resampled target image and the reference remote sensing image, and screening out qualified homogeneous pixels based on the coefficient of variation and a preset threshold to form a homogeneous pixel set; and performing pixel-by-pixel calculation on the image to be normalized based on the geographical weighted nonparametric regression estimation model and the homogeneous pixel set, so as to obtain a normalization result. The remote sensing data with high width, high time resolution, consistent spectrum and consistent radiation can be used as a radiation reference benchmark to realize the normalization of any target image.

The application discloses a kind of narrowband internet of things system terminal working state timer dynamic configuration method, belong to satellite communication technical field, comprising: constructing the time delay power consumption multi-objective optimization problem of narrowband internet of things system terminal under low-orbit satellite internet of things scene;Based on the time delay power consumption multi-objective optimization problem, obtain the support vector machine nonparametric regression model of narrowband internet of things system terminal working state timer parameter to time delay and power consumption;The support vector machine nonparametric regression model of time delay and power consumption is substituted into the time delay power consumption multi-objective optimization problem, and the second generation non-dominated sorting genetic algorithm is used to solve, and the pareto front optimal solution set of time delay power consumption multi-objective optimization problem is obtained;From the pareto front optimal solution set, determine the working state timer parameter value that satisfies the time delay power consumption demand of current scene, according to the working state timer parameter value that satisfies the time delay power consumption demand of current scene determined to dynamic configuration timer parameter.

The invention discloses an industrial load abnormal value identification and processing method and system based on nonparametric regression, and the method comprises the steps: collecting industrial load data, and constructing a daily load vector data set according to the collected industrial load data; calculating a power consumption mode classification threshold, and dividing the power consumption mode into a basic power consumption mode and a special power consumption mode; extracting a daily load feature vector of a basic power utilization mode, calculating load similarity, classifying load levels according to the similarity, extracting a center load vector from different types of power utilization loads, calculating a data abnormal load domain, and screening out abnormal loads; and correcting the abnormal value by using a conventional weighted averaging method and a weighted averaging method based on load level mapping. According to the invention, for daily load data information of any time span, abnormal value identification and correction can be simply and effectively realized.

The invention belongs to the field of metabolic syndrome onset risk prediction, and particularly relates to a metabolic syndrome onset risk prediction system and method based on baseline amino acid. According to the scheme, the system comprises an acquisition unit, a model construction unit and a prediction unit, wherein the acquisition unit is used for acquiring various baseline amino acid data, demographic information data and onset risk data of a subject; the model construction unit is used for respectively constructing a logistic regression model obeying binomial distribution single amino acid prediction and a BKMR model and a qgcomp model for multiple amino acid combination prediction by taking multiple baseline amino acids and demographic information as independent variables and taking the onset risk as dependent variables; various data acquired by an acquisition unit are used for fitting a logistic regression model, a BKMR model and a qgcomp model, and meanwhile, the fitting process of the three prediction models is regulated and controlled by checking model convergence. According to the scheme, a Bayesian kernel nonparametric regression method and a quantile g parameter calculation method are used for predicting the onset risk of the metabolic syndrome through baseline amino acid.