9 results about "Akaike information criterion" patented technology

The application discloses a site micro-vibration vibration source identification method based on machine learning, signals measured by acceleration sensors at the edge of a measured site and acceleration sensors at the center of the measured site are processed in sequence, including denoising, transient impact signal extraction, feature extraction, clustering processing of dynamic time warping distance of the transient impact signal based on a feature matrix, GMM modeling, obtaining a model, verifying the number of independent Gaussian components of the model, selecting the number of components with the minimum value as the optimal model according to the Akaike information criterion value, and performing maximum a posteriori probability identification on the transient impact signals captured by the sensors; GMM parameter comparison is performed on sets that may belong to the same category, two sets with highly overlapped models are merged into the same set, and the set is used as vibration data sets generated around the site and having an impact on the center position of the site.

This invention relates to a method for highlighting spike discharge data of intracranial electroencephalogram (EEG) signals. The method involves collecting background noise data from the patient's brain without neuronal discharges, preprocessing the background noise data, automatically selecting the optimal order of an autoregressive (AR) model using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), estimating the AR model coefficients using the Yul-Walker equation, and constructing a background noise model. The intracranial EEG signals to be processed are then subjected to high-pass filtering. A short-time Fourier transform (STFT) and window-based frame-by-frame processing strategy are used to subtract the spectrum of the signal from the noise. By adjusting the parameters of the spectral subtraction, noise removal and preservation of neuronal signal features are achieved.

This invention discloses a multi-information source mechanism calculation method based on earthquake early warning station network observations. The method includes selecting velocity and acceleration recorded waveforms to form an input database; obtaining the P-wave initial motion polarity through manual picking and template matching techniques; calculating the source mechanism using a grid scanning method, and using solutions with an inconsistency ratio below a threshold as preliminary solutions; manually screening P-wave and S-wave phase waveforms; and jointly using initial motion polarity, S / P amplitude ratio, and body wave waveform as constraints to calculate the probability value of each preliminary solution. The probability distribution of clustered solutions is obtained through the Akaike information criterion and cluster analysis to obtain the optimal source mechanism calculation result. This invention, based on joint inversion calculations using acceleration records from early warning stations and velocity records from seismic stations, lowers the lower limit of the magnitude of earthquakes with computable source mechanisms. The comprehensive use of three types of information constraints improves the reliability of the results, and the calculation of the probability distribution of clustered solutions reduces the adverse effects of multiple solutions, thus improving the stability of the results.

The present application belongs to the field of blast furnace system health management and prediction, and specifically discloses a blast furnace degradation model calibration and residual life prediction method under a noise environment. The method is based on a fractional Brownian motion with historical dependence, constructs an initial degradation model of the blast furnace system, estimates the model parameters by the maximum likelihood estimation method, and determines the optimal model structure of the blast furnace system temperature change by the Akaike information criterion. In addition, the present application also designs a model calibration trigger mechanism; when the mechanism is not triggered, the Bayesian fusion particle filter is used to estimate the potential degradation state of the system and update the model parameters; when the mechanism is triggered, a prediction error model is constructed and the degradation model structure is calibrated to adapt to complex working conditions. Finally, the probability distribution function of the residual life of the blast furnace system is derived, and the residual life prediction of the blast furnace system is carried out according to the calibrated degradation model.

Embodiments of the present application relate to the field of microseismic monitoring, and disclose a microseismic first arrival picking method, device, equipment and storage medium. The center time point of the time window of the microseismic signal is calculated according to the propagation path of the microseismic signal and the preset layer velocity, and the length of the time window is determined according to the center time point and the preset number of time sampling points, so as to constrain the time window of the first arrival point of the microseismic signal picked by the Akaike information criterion method, and the layer velocity is constantly updated in an iterative manner, and the time window is further updated, so as to determine the best first arrival point of the microseismic signal. Compared with the traditional Akaike information criterion method, the application has better noise immunity, can avoid the "multi-peak" phenomenon of the Akaike information criterion method under the condition of low signal-to-noise ratio, and thus avoids the misjudgment of the first arrival point.

The present invention relates to the technical field of non-destructive testing, and provides a nonlinear system-based structural damage testing method and apparatus. The method comprises: applying an excitation force to a test object under test, so that said test object vibrates under the excitation force, performing signal acquisition on said test object by means of a laser vibrometer to obtain an excitation signal and a response signal, using the excitation signal and the response signal together with a plurality of imported order combinations to construct initial NARX models corresponding to different orders, screening out a target NARX model from among the plurality of initial NARX models on the basis of the Akaike information criterion and optimizing the target NARX model to obtain an NARX model under test, comparing order information in the NARX model under test with order information in a reference NARX model, and determining, on the basis of an order comparison result, whether said test object has a defect. A nonlinear model is constructed by means of signal data capable of expressing nonlinearity of a test object under test, parameter optimization is performed on the model, so that the model more accurately represents characteristics of said test object, and then whether said test object has a defect is determined.

A method and a device for structural damage detection in a nonlinear system are provided. The method includes: applying an excitation force to a specimen to be detected to induce a vibration phenomenon in the specimen to be detected, and performing signal collection on the specimen to be detected by a laser vibrometer to obtain an excitation signal and a response signal; constructing, according to the excitation signal, the response signal and imported order combinations, initial NARX models corresponding to different orders; selecting, based on an Akaike information criterion, a target NARX model corresponding to an optimal order combination; optimizing the target NARX model to obtain a NARX model to be detected; comparing order information of the NARX model to be detected with order information of a benchmark NARX model, and determining, based on an order comparison result, whether there is a defect in the specimen to be detected.

This invention discloses a method for constructing a multilayer ionospheric model based on prior constraints, comprising the following steps: obtaining the ionospheric electron density matrix output by the NeQuick-G model; processing the ionospheric electron density matrix using principal component analysis, and determining the number of ionospheric layers based on the cumulative contribution rate of the principal components; clustering the ionospheric electron density and corresponding height using the K-means clustering algorithm based on the number of layers to obtain the height and boundary of each layer; obtaining simulated total electron content data based on the height and boundary of each layer; selecting the optimal polynomial order for each layer and each modeling time period from multiple candidate polynomial orders using the Akaike information content criterion, and constructing a polynomial function model for each layer and each time period; constructing an ionospheric model using the original GNSS observation data based on the number of layers, the height and boundary of each layer, and the polynomial function model, and solving the ionospheric model parameters.

The application discloses a hydrological time series cycle identification method based on spectral peak guidance and iterative waveform matching, which comprises the following steps: obtaining a stationary sequence based on the Akaike information criterion adaptive detrending; iteratively extracting cycles, using residual power spectrum in each round, retaining strong spectral peaks and generating real candidate cycles in the neighborhood to reduce the candidate set capacity; adopting phase binning median interpolation to construct a non-parametric waveform, correcting significance test by multiple comparison, and screening the optimal cycle; correcting the amplitude by joint least squares fitting, determining acceptance according to the Bayesian information criterion drop, and determining continuous extraction by white noise test; and finally outputting the significant cycle and the corrected component. Through the iterative process of spectral peak guidance, waveform matching, residual updating and re-guidance, the application can automatically identify real cycles and complex waveforms, suppress long cycle artifacts and over-extraction, and improve the accuracy and robustness of hydrological cycle identification.