17 results about "Matrix iteration" patented technology

The invention discloses a pulse signal source tracking method based on a robust Kalman filter. The method comprises the following steps: reading a target state vector and error covariance matrix at a previous moment, and observing a noise covariance matrix; a variational Bayesian parameter before iteration, a target state vector and an error covariance matrix are initialized; starting iteration, and calculating the quadratic form sufficient statistics of the observation error and the forecast error; updating variational Bayesian parameters; updating the forecast error covariance matrix and the observation noise covariance matrix; calculating a target state vector and an error covariance matrix of the iteration; when the variation of the target state vector of the current iteration and the target state vector of the last iteration is smaller than the tolerance, ending the iteration, and outputting the target state vector at the current moment, the error covariance matrix and the observation noise covariance matrix; compared with a deterministic integral sampling method, the method provided by the invention adopts adaptive volume sampling to realize higher tracking precision under lower calculation cost.

The present invention discloses a method and system for blur removal and jitter correction in visual monitoring of super high-rise buildings, comprising the following steps: a visual sensor acquires a video stream at a fixed frame rate, and locates an initial region of interest (ROI) by mapping prior information; the spatial gradient field is calculated for the ROI image of the current frame, and a Gaussian-weighted two-dimensional gradient structure tensor matrix is ​​constructed; for the clear image sequence that passes blur detection, variational mode decomposition is then used in the time domain to decouple the displacement time series into eigenmode functions of different frequencies, and the low-frequency components are reconstructed to preserve the true deformation of the building; the reconstructed low-frequency displacement signal is output as the final monitoring result. The present invention employs a lightweight algorithm design throughout, significantly reducing the matrix operation dimension of subsequent processing through a dynamic ROI clipping mechanism; the structural tensor eigenvalues ​​are solved using a direct algebraic analytical method, avoiding complex matrix iterative decomposition.

The application discloses a self-iteration-based deep subspace training method and device, which comprises the following steps: a pre-training step, in which a first optimization function is minimized to train a deep subspace network, so as to initialize a similarity matrix; a training step, in which when the iteration number t is less than a first iteration number, a self-expression coefficient matrix is fixed, the second iteration number is iterated, the deep subspace network parameters are updated by minimizing a second optimization function; the deep subspace network parameters are fixed, the third iteration number is iterated, the self-expression coefficient matrix is updated by minimizing a third optimization function, the similarity matrix generated in the tth iteration is updated according to the updated self-expression coefficient matrix, and finally the update of the similarity matrix in the first t iterations is realized; and the iteration number t is increased by 1, and the training step is repeated. The application introduces the idea of self-iteration, supervises the training of the model through the results of the previous training, and can discard the previous training results, so that the memory and the calculation resources are saved.

This application discloses a method, device, and medium for reconstructing the absorption coefficient of biological tissue, relating to the field of tissue optical parameter measurement. The method includes: segmenting a time-resolved analog signal and a measured signal according to a preset time window segmentation rule; constructing a depth-time window coupling weight matrix based on the time window and photon path information; determining the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix; determining whether the overall error meets a preset iteration termination condition; if not, updating the sensitivity matrix based on the photon path information, and iteratively updating the current absorption coefficient using the depth-time window coupling weight matrix and the sensitivity matrix; determining a new time-resolved analog signal based on the updated absorption coefficient and photon path information; and continuing until the preset iteration termination condition is met. This application can improve the reliability, accuracy, and practicality of absorption coefficient reconstruction.

Embodiments of the present disclosure disclose a self-adaptive single-bit quantization SAR imaging method and device, a storage medium and a product. The method comprises: iteratively reconstructing an image matrix, in each iteration, transforming the image matrix to an echo domain based on a threshold matrix, an original echo signal matrix and an inverse imaging operator, performing single-bit quantization on the image matrix in the echo domain to obtain an echo residual; performing matched filtering on the echo residual using an imaging operator and then performing gradient descent to obtain a gradient descent result matrix; performing threshold shrinkage on the gradient descent result matrix based on a weight matrix and a fractional order sparse norm to obtain an updated image matrix; updating the weight matrix based on the updated image matrix; updating the threshold matrix using a delta modulation algorithm; and obtaining a SAR imaging result when a maximum number of iterations is reached. The method can compensate for the loss of amplitude information and improve the SAR imaging accuracy by adaptively updating the threshold and the fractional order sparse norm constraint while only retaining the signs of the echo signals.

The application relates to the technical field of aircraft simulation training and discloses a multi-modal man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map containing a flight environment situation graph and a device operation health graph through a hierarchical feature extraction network; an incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm, and outputs an incremental state prediction result; a parameter optimization layer initializes a tabu search population according to the prediction result, iteratively optimizes operation rule library parameters through a dynamic performance evaluation matrix, a decision generation module fuses the optimized rule library parameters and real-time multi-modal feature maps, generates a flight operation instruction sequence based on a dynamic confidence score, an execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes integer programming of the instruction sequence in combination with operation image deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The invention discloses an array mutual coupling self-correction DOA method based on sparse off-network, and belongs to the field of array signal processing. The method comprises the following steps: constructing a sparse off-network DOA estimation system model considering an array mutual coupling effect; initializing a mutual coupling coefficient vector and an off-grid parameter vector, and reconstructing a signal sparse matrix by using a smooth norm according to a system model; estimating an off-network parameter vector according to the reconstructed signal sparse matrix and a known initialized (or after last iteration) mutual coupling matrix; estimating a mutual coupling matrix from a system model by using a method based on gradient descent by using the reconstructed signal sparse matrix and the estimated off-network parameter vector; and after iteration meets a stop condition, obtaining DOA estimation of the signal according to the signal sparse matrix, the mutual coupling coefficient matrix and the off-network parameter vector after iteration. According to the method, a better DOA estimation effect can be obtained on the premise that the array antenna has an unknown mutual coupling effect.

This invention discloses a five-dimensional seismic data reconstruction method based on preconditioned Riemann gradient descent, belonging to the field of seismic data processing technology. The method involves performing a Hankel transform on fixed-frequency four-dimensional seismic data to construct a fourth-order block Hankel matrix; performing hard thresholding on the fourth-order block Hankel matrix to obtain a low-rank approximation matrix, and calculating the Euclidean gradient at the low-rank approximation matrix in the current iteration; calculating a preconditioner based on the diagonal part of the Euclidean gradient outer product; calculating the projection of the Euclidean gradient onto the tangent space of the Riemann manifold based on the Euclidean gradient and the preconditioner, and updating the low-rank approximation matrix based on the projected gradient to obtain the low-rank approximation matrix for the next iteration; iterative updates continue until a termination condition is met; the low-rank approximation matrix from the last iteration is inversely transformed into a fourth-order tensor, and the reconstructed five-dimensional seismic data is obtained based on the fourth-order tensor and the frequency components in the five-dimensional seismic data. This method can improve the reconstruction efficiency and quality of five-dimensional seismic data.

The application discloses a SOMP off-grid direct positioning method with dictionary matrix iterative optimization, and belongs to the technical field of passive positioning. The application uses a uniform linear array as a base station receiving device, monitors a signal source by using a distributed base station structure, and collects signal source receiving data. The application carries out noise reduction preprocessing on the signals received by the array, and then uses the noise-reduced signals to carry out matching search on a spatial grid to obtain the position of the signal source. The application uses a Taylor iterative optimization dictionary matrix in a matching process, and reduces the compensation deviation of a traditional off-grid algorithm. Compared with the traditional off-grid algorithm, the estimation result of the application is more accurate, and the application has important application value.

This application provides a method and apparatus for detecting unknown cross-site scripting (XSS) based on test script generation. It innovatively constructs a Markov chain-driven script generation mechanism and achieves intelligent construction of test scripts through atomic element sequence analysis and random walk strategies. A filtering analysis mechanism based on DOM tree difference is designed, combining multi-dimensional tag extraction and node localization to establish a filtering rule recognition strategy. A matrix iterative optimization mechanism is introduced, achieving adaptive optimization of the test script through dynamic updating and convergence calculation of the state transition matrix. This method effectively addresses the shortcomings of traditional techniques in script generation, filtering analysis, and optimization adjustment, significantly improving the detection effect of unknown XSS.

The invention discloses an intelligent agricultural product sorting method based on physical feature iterative optimization and a related device, and belongs to the technical field of agricultural product sorting. The method comprises the following steps: collecting a surface reflection image and a near-infrared transmission image of an agricultural product to be sorted; using homomorphic filtering to carry out anti-wrinkle preprocessing on the surface reflection image; extracting physical feature vectors of the two images; iterative optimization is carried out based on the physical characteristic matrix, an optimal judgment threshold parameter combination is obtained, feature extraction and judgment are carried out on agricultural product images collected in real time, and an execution mechanism is controlled to complete the sorting action according to encoder signals and a delay compensation mechanism. According to the method, morphological interference is solved through homomorphic filtering, interpretability detection of quality is achieved through white-box physical characteristic quantification, top-speed self-adaption of parameters is achieved through matrix iteration, and therefore precise and intelligent sorting of complex agricultural products is achieved.

The application discloses a kind of underwater target tracking methods based on multivariate skew laplace distribution modeling, the motion state data of underwater target is collected using the mode of sonar sensor, and the target position measurement information is obtained by the mode of coordinate system conversion;Underwater target motion model is established, the state space equation of underwater target is determined, the mathematical characteristics of underwater noise are analyzed, and the measurement model of target under non-gaussian noise is established;Based on multivariate skew laplace distribution, the non-gaussian noise is modeled, the mixed parameters, shape parameters and scale matrix of noise are solved under the variational bayesian framework, the target state and noise covariance matrix are iteratively updated, and the target motion state is iteratively updated using Kalman filtering estimation, after the iteration number, the estimated value of underwater target position and speed and the estimated value of covariance matrix are output.The application has better robustness and estimation accuracy, and does not need to select the degree of freedom parameter, and can be better applied to the tracking of target.

The invention relates to the technical field of software and artificial intelligence, in particular to a software development method based on natural language orchestration, which comprises the following steps of: S1, in response to a natural language demand, identifying a business entity and an action, mapping the entity into a library of a colored Pearl network, defining a color set, mapping the action into transition, and establishing a color set; constructing an incidence matrix according to the input consumption and the output of the Tokenn; and S2, iteratively constructing a branch process based on the incidence matrix, calculating a state identifier of a local configuration of a new event, and marking a cut-off event when the state identifier is repeated with a historical event so as to generate an acyclic finite complete prefix structure. According to the method, verification and supervision control synthesis are carried out through the Pearl network, and then a mathematical-level anti-deadlock section is automatically woven into a business code, so that the problems that traditional generative development mostly depends on direct generation and concurrency control of a large model, the model lacks global logical reasoning ability, and the development efficiency is low are solved. And therefore, the problem of frequent random deadlock of software in a high-concurrency environment is solved.

The present application relates to the technical field of geophysical exploration, in particular to a borehole-ground electromagnetic inversion method, device and storage medium. The method comprises: setting prior information; calculating borehole-ground electromagnetic response; calculating model data fitting term; calculating model regularization term containing constraint matrix based on minimum support gradient; solving gradient and Hessian matrix of objective function; iteratively solving to obtain model increment; determining update step length to obtain updated model; using the updated model to obtain borehole-ground electromagnetic response and calculate data fitting difference; judging whether the data fitting difference meets the convergence condition and whether the iteration number k meets the convergence condition; if one of them meets the condition, the iteration is exited and the model is output. The present application enhances the ability of borehole-ground electromagnetic to depict deep formation interface, improves the precision of borehole-ground electromagnetic inversion of deep formation interface, and can more intuitively and clearly invert the interface between formations at different depths.

The invention discloses a power grid fault identification method, system and device based on a fuzzy theory and an improved D-S evidence theory, and a storage medium, and relates to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining the original data of a power grid, including fault voltage and current, and the action state information of a protection relay and a circuit breaker; and carrying out noise reduction on the fault voltage and current, and classifying the denoised data by using a support vector machine classification model optimized by a fruit fly algorithm to obtain an electrical quantity fault diagnosis result. Switching value information is mined through timing constraint screening, and error information is discriminated and corrected; matrix iteration is carried out on the corrected switching value to obtain a fault probability, and finally, an electrical quantity diagnosis result and the switching value fault probability are fused to accurately judge a fault. According to the method, the electrical quantity information with relatively good timeliness and accuracy and the switching quantity information with relatively good redundancy are subjected to information synthesis, so that the characteristics of the information can be brought into full play, and a fault element can be accurately and quickly judged.

The application relates to the technical field of group decision-making, and discloses a group decision-making model based on enterprise management and preference relation relative projection, which comprises a data acquisition and initialization module, a group synthesis and consensus evaluation module, a double-drive dynamic weight calculation module, an expert mutual evaluation relation updating module and an iterative convergence and decision generation module, and can acquire an initial preference relation matrix and a mutual evaluation matrix; a group synthesis preference matrix is constructed based on current expert weights, and a global consensus level is analyzed; the objective consensus contribution degree and the subjective social trust degree of the experts are calculated, then the expert weights are updated by fusion, and the mutual evaluation matrix is updated; iteration is performed until convergence, a decision scheme ranking and an expert weight distribution report are generated based on the final group synthesis preference matrix; and the group decision-making efficiency based on enterprise management and preference relation relative projection can be improved.