12 results about "Eigenvalue distribution" patented technology

The invention provides a multi-sensor joint optimization SLAM method and system based on adaptive weight. The method comprises the following steps: synchronously acquiring observation data of a laser radar, a visual sensor and an inertial measurement unit; determining the degradation state of each sensor, and correcting the uncertainty matrix of state estimation based on the degradation determination result; calculating information entropy based on the corrected eigenvalue distribution of the uncertainty matrix of each sensor, and combining determinant calculation of the matrix to obtain a quantitative reliability evaluation value; dynamically generating a fusion weight according to the quantitative reliability evaluation value so as to suppress the influence of the sensor which is judged to be degraded; and carrying out weighted fusion on the observation residual errors of the multiple sensors based on the fusion weight, and solving the optimal pose and map estimation of the system.

This invention proposes a direction-of-arrival (DOA) estimation method based on a sparse circular array using the golden ratio. The method defines the antenna position and three-dimensional coordinates using the golden ratio, constructing a sparse array. A DOA vector is established based on the azimuth and elevation angles, and the path difference and phase difference are calculated. These are then combined with multi-shot data to form a covariance matrix. To improve robustness under low snapshot and low signal-to-noise ratio conditions, adaptive diagonal loading is employed to adjust the noise eigenvalue distribution and enhance the distinguishability between the signal and noise subspaces. Addressing the issues of traditional MUSIC algorithms' accuracy depending on step size and poor real-time performance, a three-step search is designed: a coarse search to locate local maxima, a ternary trigonometric iteration to narrow the interval, and a fine search followed by quadratic surface fitting to eliminate grid errors. This method effectively suppresses sparse array grating lobes, balancing high accuracy and real-time performance.

The present application belongs to the technical field of image recognition, and particularly relates to a diamond quality detection method and system based on image recognition, comprising the following steps: obtaining a diamond image to be detected, calculating the structure tensor in the local neighborhood of each pixel of the diamond image, constructing an initial structure guide map according to the eigenvalue distribution, and recording the difference between the maximum eigenvalue and the minimum eigenvalue of the structure tensor as an anisotropy degree map; performing multi-scale shear wave transformation on the diamond image, extracting high-frequency subband coefficients at each scale to calculate local energy, and fusing the local energy at each scale to obtain a multi-scale edge saliency map; and fusing the initial structure guide map and the multi-scale edge saliency map to generate a composite guide map. The present application avoids the edge blurring phenomenon caused by traditional filtering, and improves the completeness of diamond surface defect extraction and the accuracy of overall quality detection.

The present application is to solve the problems of spectrum leakage, large loss of useful signal and insufficient interference locking precision in the existing narrow-band interference suppression technology; a narrow-band interference suppression method for Beidou navigation is provided, comprising the following steps: S101: discrete signal sequence sampling and segmented caching; S102: constructing an adaptive signal covariance matrix; S103: performing fast eigenvalue decomposition on the covariance matrix; S104: determining the number of interference components based on the eigenvalue distribution; S105: constructing an interference subspace projection matrix; S106: performing orthogonal projection to suppress narrow-band interference; S107: signal gain compensation and output reconstruction; S108: detecting whether the interference environment has changed dramatically; all components pointing to the interference in the signal space are filtered through the orthogonal projection matrix, avoiding the convergence problem of time-domain adaptive filtering, and effectively dealing with the strong narrow-band interference scene with high power density.

The present disclosure belongs to the technical field of hybrid DC power transmission system stability, and particularly relates to a hybrid DC instability discrimination method and system based on mode resonance, comprising: establishing a full-order state space model of a hybrid DC power transmission system, constructing a system dynamics model under the time scale of a DC control system, linearizing the obtained system dynamics model to obtain a linearized model of the hybrid DC power transmission system; calculating eigenvalues of the linearized model and participation factors of the eigenvalues, screening an alternative resonance mode set according to the obtained eigenvalue distribution, and system parameters strongly related to the alternative resonance mode set; modifying system parameters of the hybrid DC power transmission system, drawing root loci of system eigenvalues, judging whether mode resonance occurs and determining resonance strength of the mode resonance; if mode strong resonance occurs and the resonance strength is greater than a set threshold, it is determined that the system will be unstable, and system parameters at the resonance point are recorded.

The application discloses a kind of based on closed loop efficiency's heterogeneous directed network key node identification method, system and storage medium.The method is first based on node inherent function index and link synergistic conduction efficiency constructs full-factor adjacency matrix, then obtains network eigenvalue distribution by characteristic spectrum analysis, and based on matrix index constructs system efficiency index, to represent the abundance and quality of network closed loop path;Further utilize the product of left and right eigenvector components corresponding to the principal eigenvalue calculates the participation of each node to system efficiency, and calculates the node disturbance sensitivity under the closed loop efficiency change of node removal disturbance, combined with node closed loop participation, form comprehensive importance index, to realize key node identification and sorting.Compared with prior art, the application can simultaneously fuse node function, link efficiency and closed loop structure characteristics, has higher identification precision and stronger explanation ability to key node in heterogeneous directed network, can be applied to the robustness evaluation, resource allocation and structure optimization of complex synergistic system.

According to one aspect, a method for elaborating a machine learning model for the classification of time series signals is proposed comprising obtaining features of training time series signals associated with different indicated classes, calculating a distinction coefficient between classes for each feature and calculating a distinction coefficient between classes for each combination of features from a distribution of the values of the features for each group of time series signals, ranking the features according to the distinction coefficient of each feature and the distinction coefficient of each combination of features, and training the machine learning model by taking as input for this model at least one feature chosen according to the ranking.

A functional near-infrared spectral signal enhancement and classification method based on spatiotemporal autocorrelation and attention encoding is proposed to address the limitations of limited sample size and insufficient generalization ability of classification models in functional near-infrared spectral signal data. This method is applicable to the auxiliary diagnosis of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). The method first preprocesses the raw near-infrared spectral data, extracting oxyhemoglobin signals and calculating spatial autocorrelation parameters, channel-level temporal autocorrelation parameters, and the eigenvalue distribution of the functional connectivity matrix. Then, enhanced data is generated based on the spatiotemporal autocorrelation model. Finally, the raw and enhanced data are input into the STEAFNet deep learning model for accurate classification. This invention generates high-quality enhanced data, expands the sample size, and maintains high fidelity. Combined with deep learning, it improves classification accuracy and generalization ability, providing technical support for clinical applications.

This invention belongs to the field of wireless communication technology, specifically relating to a sparse three-dimensional antenna array-assisted beamforming method based on preset element positions. By constructing a rigorous physical consistency constraint model and employing hyperbolic tangent mapping and the Log-Sum-Exp function to smoothly reconstruct non-smooth geometric constraints, a highly efficient and stable joint alternation optimization algorithm is successfully designed. This invention overcomes the spatial degree-of-freedom bottleneck of traditional rigid planar arrays and fixed three-dimensional arrays in complex multipath environments. Under the premise of strictly adhering to stringent physical constraints such as element movement boundaries, intra-layer anti-coupling, and inter-layer anti-blocking, it achieves a significant leap in channel capacity and deep optimization of eigenvalue distribution, providing a complete and highly physically feasible technical solution for future high-capacity, highly robust millimeter-wave communication systems.

The application discloses a planar array FDA-MIMO radar main lobe interference suppression method, and the Capon technology is used to estimate target signal power to dynamically adjust a DL loading factor; based on eigenvalue distribution of a sample covariance matrix, an adaptive subspace dimension selection strategy is introduced to balance robustness and main lobe suppression performance under different mismatch conditions; the beamformer can adjust the projection subspace and the diagonal loading coefficient according to actual conditions, and can solve the mismatch problem of the steering vector caused by the distance-angle error, the array element position error, the frequency offset and the coherent local scattering, effectively suppress the main lobe interference while improving the beam robustness, obtain better SINR performance, and has wide application value and promotion prospect.

The invention provides a random simple complex network containment synchronization analysis method based on spectral moments, and relates to the technical field of control and information. The method comprises the following steps: firstly, deriving an analytical expression of first three-order expected spectral moments of an augmented Laplacian matrix, and representing the analytical expression as a function of high-order network local structure properties, so as to establish a quantitative corresponding relation between the expected spectral moments and the network local structure; and then, based on the obtained expected spectral moment set, proposing a piecewise linear reconstruction strategy, performing triangular approximation on feature root distribution of the augmented Laplacian matrix, further estimating a minimum feature root and a maximum feature root, and accordingly realizing pinning synchronization judgment and performance prediction of the random simple complex network. Finally, numerical simulation is carried out by taking a random simple complex network formed by a Lorenz system as an example, and the accuracy and effectiveness of the method are verified.

The invention relates to the technical field of wireless communication, and discloses a non-cooperative signal detection method and device based on a random tensor theory, a medium and equipment. According to the method, signals are collected and preprocessed through multiple antennas, high-dimensional tensor data are constructed, a tensor sample covariance matrix is calculated, eigenvalue distribution is analyzed, eigenvalue statistics are calculated, a detection threshold value is determined, and whether target signals exist or not is judged through comparison of the eigenvalue statistics and the threshold value. Updating the signal data in real time by adopting a dynamic window method, and repeating the step from tensorization processing to signal judgment to realize real-time monitoring and tracking of the target signal; the preprocessing is used for optimizing the data quality, and the high-dimensional tensor data is obtained by performing tensor processing on the preprocessed signal; priori information of a main user signal is not needed, the detection performance is excellent under the conditions of low signal-to-noise ratio, uncertain noise and the like, signals can be accurately recognized, the number change can be indicated, and the method is suitable for scenes of cognitive radio networks, radar detection and the like.