42 results about "Polynomial regression model" patented technology

The invention discloses a performance prediction and fault alarm method for a photovoltaic power station. The method comprises the following steps of: a, setting the station of the power station; b, setting the operation mode of the power station; c, judging whether required real-time data or historical data exists or not; d, predicting the performance of the power station through an experience model if the state in the step a is that a new photovoltaic power station is required to be designed and the required real-time data or historical data in the step c does not exist, and predicting the performance of the power station through a data drive performance model if the required real-time data or historical data in the step c exists; e, predicting the performance of the power station through the data drive performance model or a polynomial regression model if the photovoltaic power station in the step a is operated and the required real-time data or historical data in the step c exists, and predicting the performance of the power station through the experience model if the required real-time data or historical data in the step c does not exist; f, comparing actual performance with the predicted performance, and performing fault alarm; and g, correcting the models on line by a Kalman filtering method and returning to the step c, and otherwise, directly returning to the step e. By the method, solar energy resources can be utilized to the maximum extent, and power utilization cost can be reduced; and the accuracy of performance prediction and fault diagnosis is improved.

Polynomial regression models are used to reduce errors in measurements of pseudorange between a GNSS satellite and a receiving station; for data compression by replacing a large number of measurements with a small number of coefficients of the model polynomial, optionally combined with modeling residuals; for extrapolating usefully accurate estimates of future range between the GNSS satellite and the receiving station; and for providing usefully accurate estimates of future coefficient values of the polynomial regression models themselves.

Polynomial regression models are used to reduce errors in measurements of pseudorange between a GNSS satellite and a receiving station; for data compression by replacing a large number of measurements with a small number of coefficients of the model polynomial, optionally combined with modeling residuals; for extrapolating usefully accurate estimates of future range between the GNSS satellite and the receiving station; and for providing usefully accurate estimates of future coefficient values of the polynomial regression models themselves.

The invention discloses an anti-seismic optimization design method of a building anti-seismic support-hanger. The method includes the steps: building a three-dimensional space analysis model of the building structure-anti-seismic support-hanger; selecting maximal space of the anti-seismic support-hanger and a minimum cross-sectional area of an inclined strut, and calculating maximum acceleration reaction of the anti-seismic support-hanger; calculating a floor response spectrum of a building structure, and correcting the floor response spectrum according to the maximum acceleration reaction toobtain a corrected floor response spectrum; building quadratic polynomial regression models among the maximum acceleration reaction of the anti-seismic support-hanger, the space of the support-hangerand the cross-sectional area of the inclined strut according to the corrected floor response spectrum; determining optimized design values of the space of the support-hanger and the cross-sectional area of the inclined strut. According to the method, polynomial regression models among earthquake actions of the anti-seismic support-hanger, the space of the support-hanger and the cross-sectional area and the support-hanger can be rapidly and accurately built, design parameters of the anti-seismic support-hanger are optimized, calculation result accuracy is ensured, and optimization design efficiency is greatly improved.

The embodiment of the present application belongs to the technical field of image processing, and relates to a real-time image color correction method, device, system, computer equipment, and storage medium. The method includes: acquiring raw data and performing data preprocessing, and the raw data includes raw image data and Standard color card data, the standard color card data includes at least two gray color block data; Calculate the RGB three-channel mean value of each color block included in the standard color card based on the original image data, and the gray color block Fitting the average value of the three channels of RGB to obtain fitting parameters; using the fitting parameters to correct all the color blocks of the standard color card, and calculating the correction parameters through the polynomial regression model; based on the fitting parameters and correction The parameter performs real-time color correction on the original image data, and then displays the color-corrected image data. The embodiments of the present application can improve the accuracy and speed of image color correction.

A search method for a high-energy-efficiency neural network architecture, including: Step 1: Construct a polynomial regression model of hierarchical running time and power; Step 2: Calculate the overall energy consumption of the neural network architecture based on the hierarchical running time and power; Step 3: Discrete Continuous search space; Step 4: Add energy consumption as one of the search objectives to the neural network architecture search process. The present invention accurately discovers high-energy-efficiency network architectures by means of machine search and reduces unnecessary search overhead. In the measurement of network architecture energy consumption, the polynomial regression model is used to predict the energy consumption of a specific architecture; in the architecture design process, machines are used instead of manual methods to automatically search for architectures that meet the requirements, making the design process more scientific; using continuous search space And the search method based on gradient descent, and on the basis of only searching for high-precision neural network architecture, a high-energy-efficiency target is added to improve search efficiency and optimize search results.

The invention relates to a method and a device for determining energization data for an actuator of an injection valve of a motor vehicle, wherein a control unit is supplied with input data and the control unit determines the energization data taking into account the input data, wherein the control unit Energy supply data were also determined by using a polynomial regression model (4).