5 results about "Parameter distribution" patented technology

The invention provides a ship lock structure simulation parameter correction method and device and electronic equipment, and the method comprises the steps: obtaining historical displacement actual measurement data of a ship lock structure, and forming standardized actual measurement data; determining to-be-corrected parameters according to the ship lock structure simulation model; performing global search on the to-be-corrected parameters by using a covariance matrix adaptive evolution algorithm, and obtaining distribution characteristic parameters which enable the matching degree between a displacement response predicted value output by the model and standardized measured data to be optimal through adaptive learning of the correlation between the parameters and iterative optimization so as to determine initial parameter distribution of the to-be-corrected parameters; constructing an augmented state space model in combination with the to-be-corrected parameters and the displacement response predicted value; and taking real-time ship lock displacement data as an observation value, and adopting a Kalman filtering algorithm to perform recursive assimilation on augmented state variables in the spatial model so as to update and correct to-be-corrected parameters. The real-time ship lock displacement data can be assimilated, and the to-be-corrected parameters in the ship lock structure model can be dynamically updated and corrected.

The present application belongs to the field of urban underground pipeline damage sonar evaluation, and discloses a pipeline cracking damage sonar intelligent evaluation method, which extracts two characteristic parameters of sonar echo signal correlation and energy, analyzes the parameter distribution under different damage states by using GMM, and constructs a probability density distribution benchmark library; in the test application stage, the GMM probability density distribution of the characteristic parameters is calculated, the Frechet similarity with the benchmark library is calculated, and the state with the largest similarity is taken as the damage state. The present application uses the training data to construct the GMM probability density distribution benchmark library, uses the similarity between the test data and the training data to carry out probabilistic damage evaluation, and with the increase of the monitoring data, the accuracy of the model will be higher and higher.

This invention discloses a method and system for constructing the probabilistic stability domain and inverting parameter uncertainties in milling chatter, belonging to the field of CNC machining and cutting dynamics prediction technology. The method first solves for a given dynamic parameter vector using a fully discrete method. i Stability boundary of the spindle speed-axial depth of cut plane; then use multiple sets i Boundary training agent model to quickly predict arbitrary i The boundary. Given i When distributing parameters, a proxy model is invoked via Monte Carlo sampling to generate a flutter probability distribution map and a preset probability. p The stability boundary is determined. For a limited amount of experimental data, a hierarchical Bayesian model is constructed, with and as hyperparameters as priors. A likelihood function is built using experimental stability / instability labels. The posterior distribution is obtained through Markov chain Monte Carlo sampling, and the probabilistic stability region is updated. Variance is estimated by reducing the likelihood using common random numbers. This invention significantly reduces computational overhead, enabling rapid construction of the probabilistic stability region and parameter distribution inversion, thus aiding in the selection of processing parameters and risk control.

This invention discloses a bridge construction deflection sensitivity analysis method, comprising the following steps: Step S1, establishing a historical database; Step S2, standardizing each parameter in the historical database; Step S3, based on the standardized parameter data, taking the average of all data for that parameter, and generating random numbers with a sample size of N from the parameter distribution; Step S4, integrating a hybrid learner; Step S5, establishing a three-dimensional deflection model of the main beam using the hybrid learner; Step S6, obtaining multiple deflection values ​​as sample output values ​​using the arithmetic mean median method; Step S7, calculating the conditional expectation of each parameter with respect to the sample output values; Step S8, calculating the magnitude of the influence of the parameters on the main beam deflection using the Sobol sensitivity analysis system. This invention, by analyzing the influence of each parameter on the main beam deflection, guides the strict observation and control of parameters with a significant impact during construction, thereby avoiding resource waste and reduced construction efficiency.

The invention relates to a method (100) for identifying a parameter distribution for modeling a technical system (50), comprising the following steps: - determining (101) a nominal parameter estimate on the basis of provided measurement data and at least one pre-given system dynamics model of the technical system (50); - determining (102) a distribution of parameter variations on the basis of a quantification of the uncertainty due to parameters of the nominal parameter estimate; - providing (103) the nominal parameter estimate and the distribution of parameter variations as a basis for modeling the technical system (50) taking into account the uncertainty due to parameters.