6 results about "Scatter matrix" patented technology

The invention provides a gas concentration time sequence distribution prediction method based on multivariable data driving. Based on the theoretical relationship between the mean square displacement and the mean scattering angle cosine of neutrons in an infinite homogeneous medium, the mean square displacement is directly counted through Monte Carlo simulation, the mean scattering angle cosine is reversely deduced, and a first-order scattering matrix meeting the mean square displacement conservation is constructed accordingly. In order to be suitable for a finite geometric model, a correction factor is further introduced to correct an average scattering angle cosine obtained by a traditional method. A verification result shows that the method remarkably reduces the calculation deviation of effective proliferation factors, improves the neutron flux distribution precision, and is particularly suitable for high-precision multi-group calculation of high-anisotropy fast spectrum reactor cores such as pebble-bed high-temperature gas cooled reactors and sodium cooled fast reactors. According to the method, the time sequence probability distribution prediction of the gas concentration can be realized, the prediction uncertainty is quantified, the risk indexes such as the over-limit probability are directly output, and the scientificity and the reliability of coal mine safety early warning are improved.

There is a desire for dimension compression of training data by linear discriminant analysis to be achieved by secure computation. An AI analysis model generation system according to the disclosed technology comprises a secure computation device and a user terminal. The secure computation device acquires data with n rows and m columns encrypted so as to be able to be securely computed, the data comprising n m-dimensional vectors to which class classifications are assigned. For each class, encrypted vectors belonging to the class are securely computed to obtain an encrypted sum vector and an encrypted sum-of-products matrix. The user terminal decrypts the encrypted sum vector and the encrypted sum-of-products matrix to obtain a plaintext sum vector and a plaintext sum-of-products matrix, uses the plaintext sum vector and the plaintext sum-of-products matrix to obtain a within-class scatter matrix and a between-class scatter matrix, and uses the within-class scatter matrix and the between-class scatter matrix to obtain a transformation matrix with m rows and d columns.

The invention belongs to the technical field of forest remote sensing, and discloses a P-band SAR polarization characteristic-based forest type identification method, which comprises the following steps of: solving a coherence matrix and a covariance matrix on the basis of a pre-processed polarization scattering matrix, and carrying out wavelength dependence correction on basic radar statistical characteristics for a forest three-layer vertical superposition scattering structure formed by a P band; extracting feature subsets of a scattering mechanism related to a forest vertical structure, integrating the corrected basic features and adding polarization phase difference and scattering mechanism combination coefficient feature dimensions to form a complete polarization feature set covering multiple types of features, and extracting various terrain parameters of a target forest region based on DEM data to obtain a complete polarization feature set covering multiple types of features; performing terrain sensitivity analysis on the preliminarily screened feature subset and dividing sensitivity grades, constructing an adaptive terrain correction model in combination with P-band under-forest surface scattering proportion characteristics, and introducing a radar wave polarization state correction term to complete feature radiation normalization correction.

The application provides a well logging data anomaly detection method, which comprises the following steps: step 1, constructing a well logging data sample set; step 2, performing system initialization; step 3, performing feature extraction layer training; step 4, outputting classification layer training; and step 5, performing anomaly value identification. The well logging data anomaly detection method introduces a plurality of feature extraction layers, and introduces an intra-class scatter matrix when the feature extraction layers are constructed, so that abstract features with resolution can be obtained. When the output classification layer is trained, a risk weighting matrix is introduced, so that the robustness of the training result to noise is improved.

This invention discloses a kernelized inverse nearest neighbor discriminant analysis method, comprising the following steps: obtaining training image samples; mapping the input data to a high-dimensional space using a Gaussian kernel function; obtaining the representations of the intra-class and inter-class scatter matrices of the training image samples in the high-dimensional space using kernel tricks and the inverse nearest neighbor algorithm; learning the projection matrix that maximizes the inter-class scatter matrix and minimizes the intra-class scatter matrix through feature decomposition; extracting features from the training and test image samples using the projection matrix; and classifying the test samples using the nearest neighbor algorithm. This invention, for the first time, extends the inverse nearest neighbor linear discriminant analysis method to a high-dimensional space to solve the classification problem of nonlinear data. It proposes using a Gaussian kernel function for high-dimensional mapping, kernelizing the algorithm, and using kernel tricks for non-explicit mapping derivation. High-dimensional inverse nearest neighbors are established in the space after Gaussian function mapping to obtain the scatter matrix, wherein the kernel trick is used to realize the distance representation in the high-dimensional space.

The invention discloses a method for identifying adulteration of fish oil by combining Raman spectrum with multivariate statistical analysis. The method comprises the following steps: collecting the Raman spectrum of fish oil to be detected by using a Raman spectrometer; the collected Raman spectrum is subjected to preprocessing of denoising, baseline removal and normalization; dividing the spectral data into a training set and a test set according to a ratio of 7: 3; on the basis of traditional PCA, an intra-class scattering matrix and an inter-class scattering matrix are calculated, generalized Rayleigh quotients are calculated according to the intra-class scattering matrix and the inter-class scattering matrix, and sorting is carried out according to the generalized Rayleigh quotients; and training the linear discriminant analysis model by using data of the training set, and taking a result of the test set as a final classification result. Raman spectrum and machine learning are combined, solution consumption is low, sample preparation steps are simple, complex sample pretreatment is not needed, analysis time is short, and rapid detection and classification can be achieved. The analysis time is short, the classification accuracy is high, and the classification accuracy of the fish oil with the adulteration proportion of 5% reaches 95% or above.