7 results about "Fitting Problems" patented technology

The present application relates to geodetic and engineering surveying technical field, specifically to a kind of plane coordinate point cloud fitting arbitrary close degree error ellipse in control network design stage.The method includes: the pre-processing of control point coordinate point cloud, removes abnormal value;Accurate spatial precision evaluation density field is constructed to calculate grid point density value;According to the specified confidence, the density threshold value is calculated, the contour is extracted and the error ellipse parameter is fitted.The present application solves the ellipse fitting problem of any specified confidence of non-normal distribution point cloud by adaptive bandwidth kernel density estimation, and significantly improves the reliability of precision evaluation under complex scene.

The invention relates to the technical field of geodetic surveying and engineering surveying, in particular to a method for fitting an ellipse with any adaptation error through plane coordinate point clouds in the design stage of a control network. Comprising the steps of preprocessing control point coordinate point cloud, and removing abnormal values; constructing an accurate spatial precision evaluation density field to calculate a grid point density value; and calculating a density threshold according to a specified confidence coefficient, extracting a contour line contour, and fitting an error ellipse parameter. According to the invention, through adaptive bandwidth kernel density estimation, the ellipse fitting problem of any specified confidence of the non-normal distribution point cloud is solved, and the precision evaluation reliability in a complex scene is significantly improved.

The invention discloses a blade tip clearance and arrival time synchronous measurement method and device based on symmetric inverse function fitting, and the method comprises the steps: S1, a self-adaptive signal interception stage: carrying out the preprocessing of an original voltage signal collected by a capacitive sensor, including baseline correction, signal normalization and symmetric linear segment interception; s2, a symmetric inverse function fitting stage: constructing an inverse function data set and performing symmetric linear fitting; the symmetric linear fitting is that joint symmetric linear fitting is carried out on two inverse function data sets, and a peak moment, namely arrival time, and scale parameters are calculated through an objective function of a minimum time residual error; s3, at a least square linear fitting stage, the baseline offset, the peak moment and the scale parameter are substituted into a direct Gaussian fitting equation, a nonlinear fitting problem is converted into a linear fitting problem, the linear fitting problem is solved by adopting a least square method, and a closed-form solution of the signal amplitude is obtained; and inverting a blade tip clearance value based on a pre-calibrated relation curve between the signal amplitude and the blade tip clearance.

The present invention discloses a multi-objective optimization method for the aerodynamic shape of a second-segment wing of a hypersonic winged reentry vehicle. Based on multi-fidelity data fusion using a deep learning neural network, the method fits the linear and nonlinear relationships between high-fidelity and low-fidelity aerodynamic data, thereby improving the accuracy and reliability of aerodynamic parameters at various flight state points during the design of the second-segment wing shape of the vehicle. On this basis, a proxy model constructed using a radial basis function neural network algorithm is combined with a simulated annealing optimization method, which has better applicability for complex nonlinear fitting problems in the field of aerodynamic shape design, while improving the globality and robustness of the optimization results of the geometric shape design variables, thereby providing a new, efficient and accurate design tool for hypersonic winged reentry vehicles.

The invention discloses a self-supervision real image denoising method based on a dual random double sampling mode. Comprising the following steps: providing a random double-sampling method of dual denoising branches to solve a noise fitting problem and generate more sub-images for training; introducing complementary attributes of dual branches into the design of a loss function, and proposing a dual double-sampling cross loss function; putting forward a fixed'subgraph-sampling 'strategy according to a mode collapse phenomenon in a test process; in the pre-training and testing process, designing an artifact remover to further remove chessboard artifacts; and training the de-noising network of the dual branch structure through the training steps to obtain a de-noising model. And then, inputting an image with noise into the trained network model, and performing corresponding test steps to obtain a high-quality de-noised image. According to the method, clean images are not needed for training, the performance of the method is better than that of an existing self-supervision denoising method, and the method is an effective real image denoising method.

The invention provides a geological sample detection data dynamic correction method and system, and belongs to the technical field of element measurement. The method comprises the following steps: acquiring initial energy spectrum detection data of a geological sample to be detected, initializing the detection data according to the signal-to-noise ratio of the initial energy spectrum detection data, dynamically correcting the variable window width, equally dividing a region corresponding to the variable window width into N sub-regions, calculating a characteristic value of the detection data of each sub-region, and fitting and denoising the characteristic values; and adjusting the width of the detection data dynamic correction variable window, and correcting the geological sample detection data based on the stored de-noised characteristic value. The variable window width is dynamically corrected only by adjusting the detection data, and the input variables of the correction method or algorithm are reduced; initializing the dynamic correction variable window width of the detection data through the signal-to-noise ratio of the initial energy spectrum detection data, and matching the data correction and the data pollution degree; the characteristic values are fitted by adopting a fitting formula, so that the over-fitting problem is not easy to occur.

This invention belongs to the field of quantum computing technology and relates to a method for modeling and optimizing composite noise in the quantum circuits of NISQ devices. Based on the Lindebrade master equation, a composite noise model integrating relaxation noise and phase noise is constructed, and the coupling effect of multi-source noise is simulated using Kraus operators. An improved zero-noise extrapolation technique is proposed, employing a zero-noise neural network extrapolation method. A multilayer perceptron is used to replace the analytical extrapolation function, transforming error correction from a fitting problem into a data-driven prediction problem. Using the one-dimensional transverse field Ising model as a benchmark, a ground state preparation and dynamic evolution circuit is constructed in a quantum cloud computing platform and Cqlib environment. Multi-noise level data is generated using a time scaling strategy to train the neural network. This invention reduces the average absolute error by 92.2% across the entire parameter region, including ferromagnetic phases, paramagnetic phases, and quantum critical points, demonstrating superior accuracy and stability compared to traditional extrapolation methods.