893 results about "Gaussian" patented technology

The present invention lies in the field of 3D surveying, mapping, and imaging. In particular, the present invention relates to the rotational alignment of 3D datasets. Embodiments include an apparatus method and program for rotational and optionally also translational alignment of 3D datasets. The 3D datasets being stored as point clouds, transformed into vector sets, and the vector sets being represented as a unit sphere or Gaussian sphere and compared for best alignment. The found best alignment is used to rotate the two 3D datasets into rotational and translational alignment with one another.

The invention discloses a multi-background modeling method for speaker recognition, relating to a background modeling method in speaker recognition. The method is characterized by comprising the steps of: firstly, dividing training data according to a vocal tract length bending coefficient of voice, respectively training a UBM (Universal Background Model) in each group of data, then obtaining a target speaker GMM (Gaussian Mixture Model) by means of the self-adaption of each background model, obtaining multiple groups of GMM and UBM models, when recognizing a speaker, carrying out calculation on test data by each group of GMM and UBM models to obtain logarithm likelihood ratio fractions, and finally selecting a minimum logarithm likelihood ratio fraction from the logarithm likelihood ratio fractions to output. By adopting the invention, delicate depiction can be carried out on the background model, thereby improving the accuracy rate on speaker recognition.

Casing signals generated by an EMAT in a borehole are processed using at least two orthogonal band-limited filters. The band-limited filters may include Gaussian or Cauchy Wavelet filters. By using the Hilbert transform, an envelope of the filtered signals is determined and amplitudes and arrival times of individual arrivals are estimated. These can be used to estimate casing and cement properties.

The invention discloses an atmospheric pollution traceability diffusion analysis system and method based on a Gaussian diffusion model, and the system employs an analogue simulation module which comprises GIS information, pollution diffusion simulation and visual rendering, carries out the simulation of a regional environment through the information, forms a GIS map, and according to the monitoring data in combination with the Gaussian diffusion model after terrain correction, through an image processing technology, pollution diffusion concentration changes are overlaid on a GIS map, wind field distribution after topographic correction is obtained according to topographic data and meteorological data, then a Gaussian diffusion model formula is selected, the concentration influence of each pollution source on a grid center point of an evaluation area is obtained, and an accumulated concentration value is calculated. And visual rendering is performed on a GIS map according to the calculated pollutant concentration value of the central point of the grid, and a visual result is superposed with a basic geographic information base map of the evaluation area to realize visual display of a prediction result.

The invention relates to a lane changing model parameter optimization method based on a mixed Gaussian-hidden Markov model. Operation states of a vehicle and surrounding vehicles are analyzed, and a driving lane changing intention recognition method based on the hidden Markov model is established, that is, prediction of an implicit driver lane changing intention is achieved by using lane changingcharacterization parameter observation states capable of being observed; a highway vehicle database with concentrated NGSIM data serves as the basis, a lane changing characterization parameter sampleis extracted, training and verification are carried out on the hidden Markov model by using an HMM tool box programming algorithm in MATLAB, and the number of mixed Gaussian models and the number of states are optimized by using a particle swarm algorithm. The number of the mixed Gaussian models and the number of the states are optimized by using the particle swarm algorithm, so that the accuracyof driver lane changing intention recognition is improved; better accuracy is finally obtained for the prediction result of the driver lane change intention.

The invention relates to a calculation method for predicting the multi-polar expansion attribute of a dipeptide model through a BP neural network. The calculation method for predicting the multi-polar expansion attribute of the dipeptide model through the BP neural network comprises the following steps that the structures of different dipeptide conformations are optimized through quantum mechanics calculation software Gaussian, and physicochemical parameters of the dipeptide conformations and the distance between atoms are calculated; the physicochemical parameters of part of the atoms of the dipeptide conformations and the distance between the atoms are selected for training the BP neural network, so that the physicochemical parameter of the BP neural network is obtained; the other dipeptide conformations serve as a testing set to verify a prediction result based on the BP neural network. According to the calculation method for predicting the multi-polar expansion attribute of the dipeptide model through the BP neural network, quantum mechanics calculation is conducted through the BP neural network instead of quantum mechanics calculation Gaussian software, the physicochemical parameter information such as the energy and the multi-polar distance of dipeptide can be quickly provided according to different conformations on the basis of molecular mechanics simulation based on force field information. Calculation time is greatly shortened and calculation quantity is greatly reduced within the acceptable error range, and the precision in the dynamic simulation process is greatly improved.

A first party has a data vector x and a second party has a classifier defined as a set of multivariate Gaussian distributions. A secure inner dot product procedure is applied to each multivariate Gaussian distribution and the data vector x to produce a vector ai for the first party and a vector bi for the second party for each application. The secure inner dot product is then applied to each vector bi and the data vector x to produce a scalar ri for the first party and a scalar qi for the second party for each application. A summed vector of elements [(a1x+q1+r1), . . . , (aNx+qN+rN)] is formed, and an index I of the summed vector for a particular element having a maximum value is the class of the data vector x.

The invention discloses an encrypted malicious traffic detection method. According to the method, a Wreshark tool is utilized to process a traffic packet; filtering out invalid IP checksums, preprocessing the sample set and marking malicious / benign tags; performing preliminary feature extraction on the preprocessed traffic packet; constructing three feature subsets for the preliminarily extracted features, and standardizing and encoding the three feature subsets; carrying out feature dimension reduction on each type of feature subsets by adopting a machine learning or principal component analysis method; respectively establishing a random forest, an XGBoost classifier model and a Gaussian naive Bayes classifier model for the three feature subsets; the three classifier models are combined according to a Stacking strategy to form a DMMFC detection model; performing stream fingerprint fusion on the three feature subsets to form a sample set, dividing the sample set into a training set and a test set, and training a model; testing the model, and evaluating the test effect of the DMMFC model by using the evaluation indexes of the accuracy rate, the F1 score and the false alarm rate; encrypted malicious traffic detection is performed by adopting a method of combining multi-feature fusion and a Stacking strategy, and the method has relatively high detection capability.

The subject invention leverages standard probabilistic inference techniques to determine a log-likelihood for a conditional Gaussian graphical model of a data set with at least one continuous variable and with data not observed for at least one of the variables. This provides an efficient means to compute gradients for CG models with continuous variables and incomplete data observations. The subject invention allows gradient-based optimization processes to employ gradients to iteratively adapt parameters of models in order to improve incomplete data log-likelihoods and identify maximum likelihood estimates (MLE) and / or local maxima of the incomplete data log-likelihoods. Conditional Gaussian local gradients along with conditional multinomial local gradients determined by the subject invention can be utilized to facilitate in providing parameter gradients for full conditional Gaussian models.

The invention discloses a digital image measurement method based on feature matching. The method comprises the following steps: selecting a to-be-calculated point of the to-be-measured area; acquiringa speckle image; determining an area to be measured on the speckle image; selecting a point to be calculated in the area to be measured; dividing the point to be calculated into sub-regions, dividingeach point to be calculated into a square subarea with the side length of m, and extracting feature descriptors from all pixels in each sub-region, performing feature matching through a PatchMatch feature matching algorithm based on the feature descriptors, calculating initial displacement based on a feature matching result, and executing reverse combined Gaussian Newton iterative algorithm real-time displacement and strain in parallel based on the initial displacement.