6 results about "Covariance function" patented technology

This invention provides an underdetermined mode identification method based on block term decomposition and adaptive kurtosis harmonic separation, belonging to the field of structural health monitoring and operating mode identification technology. The method acquires multi-channel vibration responses, calculates the covariance matrices under different time delays, and stacks them into a third-order tensor. Block term tensor decomposition is used to extract the time-domain autocovariance function sequence of each potential component. The natural frequencies and damping ratios of each component are initially estimated using local peak finding and logarithmic decay methods. The minimum truncation length is adaptively calculated based on the coupling relationship between period and damping ratio. Kurtosis values ​​are calculated for the truncated autocovariance sequence, and harmonic interference is eliminated based on a kurtosis threshold while retaining the true structural modes. Finally, mode shapes are extracted from the spatial block matrix of the retained components, outputting high-precision natural frequencies, damping ratios, and mode shapes. This invention can achieve accurate identification of dense modes and high-damped modes under complex operating conditions with severe underdeterminacy and harmonic interference, improving the robustness of operating mode parameter identification.

The present application relates to geophysics and geodetic data processing technical field, disclose the GNSS time series analysis and modeling method considering variable amplitude, the method comprises the following steps: S1, obtain the GNSS coordinate time series;S2, time series is modeled as the Gaussian process defined by mean function and covariance function;S3, construct the mean function describing long-term linear trend;S4, construct the product kernel by the periodic kernel and the non-periodic smooth kernel multiplication as the covariance function, to unify the periodicity and time-varying amplitude of the signal modeling;S5, the maximum likelihood estimation method is used to solve the hyperparameter in model;S6, the time series is decomposed by using the optimized model, and the time-varying amplitude periodic signal is obtained.The present application can integrally model the periodic signal and its time-varying amplitude by constructing the Gaussian process product kernel, so as to realize the accurate separation of signal component.

This invention relates to the field of steel rolling, specifically disclosing a method, system, computer equipment, and medium for predicting rail rolling force. The method includes: collecting on-site data of the target rail production line; selecting multiple reduction parameters from the third pass as model input factors; selecting corresponding rolling force parameters as model output factors; and dividing the data into training and testing sets; constructing a Gaussian process regression model, defined as f(x) ~ GP[m(x), k(x, x')], where m(x) is the mean function, k(x, x') is the covariance function, and the observation equation of the model is y = f(x) + ε, where y is the model output factor, x and x' are the model input factors, ε is Gaussian white noise, and ε ~ N(0, σ n ²), σ n ² represents the noise variance; the Gaussian process regression model is trained and optimized using the training dataset to obtain a trained prediction model; the prediction accuracy of the trained prediction model is verified using the test set; if the prediction accuracy reaches a threshold, the prediction model is used to predict the rail rolling force.

This invention provides a configuration method for RAG systems based on multi-objective Bayesian optimization, belonging to the technical field of RAG systems. This invention obtains an initial observation dataset by randomly sampling initial configurations from a structured search space using a multi-objective evaluation function. For three performance indicators, independent Gaussian process models are established to fit the mean and covariance functions of the objective function. Based on the current Pareto front and the posterior prediction distribution of the Gaussian process model, the expected hypervolume improvement acquisition function is calculated, and the configuration with the largest improvement value is selected for iterative evaluation and updating. Finally, non-dominated solutions are identified to form the Pareto optimal configuration set. This invention solves the technical problem of complex configuration parameter space and conflicts between multiple performance indicators in retrieval-enhanced generation systems, which makes it difficult to automatically obtain the Pareto optimal configuration set.

The application relates to a target RCS prediction method based on a Gaussian process regression and a two-dimensional agent model, which comprises the following steps: a three-dimensional rectangular block is established, and parameters are set; a data set is acquired; a composite covariance function is constructed, and a Newton gradient method is used to optimize and solve hyperparameters; a two-dimensional agent model is constructed according to the optimized hyperparameters, a covariance vector and a covariance matrix of the agent model are calculated, and a prediction result is obtained; and the prediction result is evaluated according to evaluation indexes. The two-dimensional agent model has good performance; through training of training data, a corresponding relationship between input and output is found, and a test set is used to realize a prediction function; overall, time consumed is obviously less than that of a traditional electromagnetic scattering algorithm, and calculation cost is saved; the method is not only suitable for a radar signal analysis field, but also suitable for remote sensing research, geophysical science and the like, and analysis cost is low.

This invention discloses a multi-objective optimization method and related equipment for small-sample selected area laser melting process parameters, relating to the field of metal additive manufacturing technology. The method includes: acquiring a sample dataset obtained from selected area laser melting forming experiments under a preset combination of laser power and scanning speed parameters; constructing a relative density prediction model and a crack density prediction model using a Gaussian process regression method, and training the relative density prediction model and crack density prediction model respectively using the sample dataset; wherein the covariance function used in the Gaussian process regression method is the Matrn kernel function, and a white noise kernel is introduced into the covariance function; based on the trained relative density prediction model and crack density prediction model, searching for the optimal process parameters in the process parameter space using a Bayesian optimization algorithm. This invention can efficiently and accurately achieve multi-objective optimization of selected area laser melting process parameters.