7 results about "Singular spectrum analysis" patented technology

The application belongs to the technical field of microseismic signal processing, and in particular to a low signal-to-noise ratio microseismic signal denoising method and system. The method comprises the following steps: collecting multiple seismic time sequence signals; reconstructing the multiple seismic time sequence signals by using multivariate singular spectrum analysis to obtain reconstructed signals; splicing the reconstructed signals and the original multiple seismic time sequence signals to obtain a two-channel input data set; and inputting the two-channel input data set into a U-Net network for end-to-end recovery. The application significantly improves the recovery capability and phase preservation of weak seismic signals in an ultra-low signal-to-noise ratio environment.

This invention discloses a plate motion analysis method based on GNSS and SLR stations of the Eurasian Plate, belonging to the field of geodesy and surveying engineering technology. The method includes: acquiring raw coordinate time-series data from GNSS and SLR stations and preprocessing them; decomposing the preprocessed sequence using singular spectrum analysis (SSA) to reconstruct and separate trend, periodic, and noise terms; performing Fast Fourier Transform (FFT) on the reconstructed signal to extract frequency domain features; establishing a comprehensive model of linear velocity plus periodic correction by combining time and frequency domain features; and analyzing the motion characteristics of the Eurasian Plate based on this model to achieve motion trend prediction and anomaly detection. This invention, through the combination of SSA and FFT, significantly improves the accuracy of motion parameter estimation and the ability to identify weak signals, providing a reliable method for plate motion analysis and earthquake prediction.

The application belongs to the technical field of pet management, and particularly relates to a chip positioning method and system for urban pet dog management, which comprises the following steps: S1, acquiring real-time motion data of a pet dog, constructing motion entropy according to an acceleration change rate in the motion data, and calculating an adaptive data interception window length at a current time by using the motion entropy; S2, intercepting an acceleration modulus sequence in the adaptive data interception window length, decomposing and reconstructing the acceleration modulus sequence by using singular spectrum analysis technology, and obtaining a behavior sequence after removing noise; and S3, constructing a multi-dimensional feature sequence containing acceleration and angular velocity, and calculating the curvature of each point in a standard abnormal behavior template. The application effectively distinguishes playing from attacking through statistical transformation of physical characteristics, and reduces the false positive rate while taking into account the calculation energy efficiency and detection sensitivity.

The application relates to the technical field of LED light control, in particular to an LED dimming control method and system based on light closed-loop feedback. The application proposes the following scheme: a group of orthogonal multi-frequency micro-modulation sequences are superimposed on the baseline driving of a target light dose trajectory, the amplitude-phase response characteristics of the light channel are excited, and the timing signals output by the light sensor are collected. The timing signals are subjected to multi-scale Hankel embedding and singular spectrum analysis, the main subspace under different windows is extracted, and the common intersection direction is obtained on the Grassmann manifold to obtain a real narrowband response. The response is input into a parameterized radiation transmission prior model, hidden variables such as optical window transmittance, scattering intensity and film forming state are solved, an equivalent light dose estimate value is calculated, and the baseline driving of the LED array is dynamically corrected according to the value, so that stable closed-loop control of the real light dose is realized.

This invention relates to the field of novel power system stability, and discloses a data-driven dynamic monitoring method and system for subsynchronous oscillations in wind farms. The method includes: analyzing system stability characteristics from an energy perspective and defining characteristic quantities that can quantify the oscillation stability level of the wind farm grid-connected system; employing a singular spectrum analysis algorithm to improve the adaptive variational mode decomposition method, accurately extracting the time-frequency signals of the oscillation modes, and achieving rapid identification of oscillation stability; and applying a comprehensive subjective and objective evaluation method to establish an evaluation model and quantitatively assess the impact of relevant influencing factors on the energy characteristics of the dominant subsynchronous oscillation mode. The quantitative assessment method for subsynchronous oscillation stability proposed in this invention is highly practical, capable of assessing system stability and analyzing the characteristics of the dominant oscillation mode using only measurement data. It provides guidance for the quantitative assessment of subsynchronous oscillation stability, disturbance location, and oscillation suppression in practical engineering projects.

The application discloses a kind of based on pattern recognition's multi-source remote sensing image classification method, comprising the following steps: S1, obtains source domain remote sensing image, target domain remote sensing image;S2, using singular spectrum analysis extracts source domain spectral feature and target domain spectral feature, using spatial filtering extracts source domain spatial feature and target domain spatial feature;S3, introduce spectral weight matrix mechanism;S4, introduce edge weight mapping mechanism;S5, input improved MDANet model, introduce geodesic mutual attention mechanism, output source domain fusion feature and target domain fusion feature;S6, construct segmentation loss;S7, by fusion generates joint loss function, and updates parameter, and outputs target domain classification chart.The application effectively alleviates the domain shift problem caused by imaging condition, sensor difference and ground object distribution change of multi-source remote sensing image, realizes high-precision, strong robustness automatic classification of ground object class under cross-domain scene.