4 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 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 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.

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.