34 results about "Analysis of covariance" patented technology

The invention discloses a dos attacking and defending method based on the data mining technique and a system thereof. The system comprises an anomaly detection module, a data mining engine module, a trust IP list filter module and a flow control module. The anomaly detection module adopts an efficient analysis covariance method to analyze the current network flow so as to judge the current system is abnormal or not, the data mining engine module uses a correlation analysis method to pick up a trust IP list to be used for filtering a data packet. The attacking system overcomes the shortcomings of the traditional system based on the trust IP list filtration, and can effectively differentiate normal flow and abnormal flow when defending the attack. The data mining engine module of the system picks up and transmits the IP trust list and the attribute value list with high efficiency so as to cause the system to defend the dos attack (Dos/DDoS) in real time.

The invention provides a geometric feature-based point cloud simplification method. The steps are as follows: by constructing the moving least square of the nearest neighbor point set of 3D (three-dimensional) sample points, the normal of the sample points is calculated, and moreover, by analyzing the neighbor point set according to covariances, the curvature of the sample points is estimated; by analyzing the normal voted tensor of the sample points, the feature edge intensity of the sample points is calculated, and accordingly, point cloud data are decomposed into a strange-edge part and a non-strong-edge part; by utilizing MeanShift clustering, surface area geometric feature similarity clustering is carried out on the non-strong-edge part; according to a curvature threshold and a search radius, the strange-edge part and each cluster are resampled, and thereby curvature-adaptive simplification is completed. According to the curvature threshold and the search radius, the method carries out curvature-adaptive simplification guaranteeing flat area-sampling density on point cloud data. Therefore, the method can be adopted to carry out high-quality simplification keeping feature boundaries and curved surface details on point cloud data.

The invention discloses a process quality prediction system and a method thereof. When a processing apparatus performs a process on a target, the process is measured by a measurement apparatus to receive a process value. The process value and several previous quality data collected from the measurement apparatus are used to predict the quality of the product which is processing inline. The method is composed of a moving window, a stepwise regression scheme and an analysis of covariance (ANCOVA). The drift and shift of process are overcome by the moving window. A key variable set is selected by the stepwise regression scheme and a virtual model is identified by the analysis of covariance.

The invention discloses a local curved surface change factor based scattered point cloud data compaction processing method. The local curved surface change factor based scattered point cloud data compaction processing method comprises the steps of 1 reading measured point cloud data, 2 calculating a central point of a point cloud, 3 searching dynamic K neighborhood points of the central point based on cubic grids and accordingly establishing the topological relation of scattered point cloud, 4 adopting a variance component method to calculate curved surface change factors of a k neighborhood of the central point, 5 determining the compaction rate of each cubic grid in the k neighborhood of the central point and performing even compaction in within a k neighborhood range. The topological relation of the scattered point cloud is established by establishing the dynamic K neighborhood point information of the scattered point cloud. Complicated curvature calculation is replaced by the curved surface change factors. The compaction ratio is adjusted according to the curved surface change factors Xi, even compaction within the k neighborhood range is achieved, the detail characteristic of high curvature can be protected, and planar characteristic of low curvature is also protected when the compaction degree is high. Point cloud data processing and curved surface reconstruction efficiency and accuracy are improved.

A method of identifying differentially-expressed genes includes deriving an analysis of variance (ANOVA) or analysis of covariance (ANCOVA) model for expression data associated with a number of genes; from the ANOVA or ANCOVA model, deriving a linear regression model defined at least in part by an observation vector representative of an observed subset of the gene-expression data, a design matrix of regressor variables, a vector of regression coefficients representing gene contribution to the observation vector, and a measurement error vector; and to the linear regression model, applying a hierarchical selection algorithm to designate a subset of the regression coefficients as significant regression coefficients, the selection algorithm representing at least one of the observation vector, the design matrix, and the measurement error vector as being hierarchically dependent on parameters having predetermined probabilistic properties, wherein the designated subset corresponds to a respective subset of the genes identified as differentially expressed.

A method of detecting variance by regression model has the following steps. Step 1 is preparing the FDC data and WAT data for analysis. Step 2 is figuring out what latent variable effect of WAT data by Factor Analysis Step 3 is utilizing Principal Component Analysis to reduce the number of FDC variables to a few independent principal components. Step 4 is demonstrating how the tools and FDC data affect WAT data by Analysis of covariance model, and constructing interrelationship among FDC, WAT and tools. The interrelationship can point out which parameter effect WAT significantly. By the method, when WAT abnormal situation happened, it is easier for engineers to trace where the problem is.

The invention discloses a satellite non-linear relative movement deviation propagation analysis method. The method comprises steps that a main satellite and a secondary satellite are assigned, a reference satellite absolute track state at the initial time, a two-satellite nominal relative movement state and a probability density function of initial relative movement state deviation are inputted, first-order and second-order state transfer tensors used for analyzing and forecasting a satellite relative movement state and deviation are calculated according to a non-linear relative movement equation considering J2 perturbation, a covariance analysis method is utilized for Gauss distribution, an analytic result is further outputted, otherwise, Gauss and model calculation is utilized, and an analytic result is outputted. A J2 perturbation item and a second-order non-linear item are considered, the method can be utilized for long-time high-precision analysis forecast of relative movement state deviation of two distant satellites, the acquired deviation information can be used for formation satellite bump probability calculation and bump early warning, and the method further has properties of accurate and reasonable design, small method analysis computational complexity and good applicability to actual engineering tasks.

The invention provides a high-performance rendering method of three dimensional (3D) point sampling data, comprising the following steps of: estimating normal direction information of a 3D sampling point by constructing a local implicit surface of the nearest neighborhood set of the 3D sampling point in combination with a differential geometry theory; determining oval expression of the 3D sampling point on a tangent plane through analyzing the neighborhood set via covariance; drawing the 3D sampling point data by oval weighting filtering with excellent anti-aliasing effect so as to acquire a data rendering effect with high quality; and designing a single-pass drawing algorithm of the 3D point sampling data according to order of depth values of the sampling points under the current visual angle so as to accelerate rendering speed. By using the method, the 3D sampling data are rendered in high performance and high quality.

The present invention provides a system for evaluating a correlation between a plurality of time-series data. The system includes a calculating section which calculates characteristic quantities indicating symmetry of a diagram drawn by a graph, on which data values included in the plurality of time-series data are plotted in a multidimensional space in which each coordinate axis represents the data value of each time-series data; and an evaluating section which evaluates the correlation between the plurality of time-series data, based on the calculated characteristic quantities.

A method for analyzing abnormalities in a semiconductor processing system includes performing an analysis of variance on a production history associated with each of a plurality of tools at each of a plurality of process steps for each of a plurality of processed wafers, and identifying key process steps. A regression analysis on a plurality of measurements of the plurality of wafers at each process step is performed and key measurement parameters are identified. An analysis of covariance on the key measurement parameters and key process steps, and the key process steps are ranked based on an f-ratio, therein ranking an abnormality of the key process steps. Further, the plurality of tools associated with each of the key process steps are ranked based on an orthogonal t-ratio associated with an analysis of covariance, therein ranking an abnormality each tool associated with the key process steps. The invention also provides 2D/3D analysis for abnormal tools and stage diagnosis.

The invention discloses a brain interval covariance analysis method for functional magnetic resonance data processing. The method includes the following steps that preprocessing is carried out, wherein individual differences are eliminated firstly to obtain statistical parameter images; then regions of interest are designated according to the images; registration and standardization are carried out on brain mappings by means of an SPM software package; according to experimental design, an estimation model is appointed, so that a statistical result is obtained; according to distributed situations of activated brain intervals, precise anatomic sites of the activated regions are found out, and the regions of interest are designated; effective connection analysis based on the regions of interest is carried out, wherein a function regulation model of a correlation analysis of a brain loop is established through correlation analysis of signal values. Specifically, the relation among the regions of interest is found out through comparison changes, relative to control, of tasks of different regions of interest. Interdependency among the brain intervals can be dynamically reflected in the process of performing the tasks through time covariance analysis and time dependence connection dynamic analysis.

The invention provides a short-term daily load prediction method considering steel future price and real-time electricity price factors. The method comprises the following steps of determining a correlation coefficient among the steel future price, the real-time electricity price, the date type and the load by using a covariance analysis method, and determining the steel future price, the real-time electricity price and the date type value in the short-term load prediction input data by using the correlation coefficient based on the load value; performing standardization processing on the input data, and establishing a short-term load prediction input data training set; based on the short-term load prediction input data training set, training the long-term and short-term memory model of load prediction to obtain a short-term load prediction model with appropriate parameters; and inputting the load values in the test set into a short-term load prediction model with appropriate parameters to complete short-term daily load prediction of the load with impact. Steel futures are used as input quantities, and errors of short-term daily load prediction caused by impact loads are reduced.

The invention discloses an analysis method and device of a work order. The method includes: obtaining a plurality of target work orders to be analyzed; determining influencing factor data corresponding to each of the job links in each of the target work orders, calling a one-way covariance analysis algorithm and a multi-way variance analysis algorithm to analyze the influence factor data, and determining the time-consuming data of the obtained significant factor data in the operation links corresponding to the significant factor data in the plurality of target work orders; calling a multiple comparison check algorithm to analyze the plurality of the time-consuming data to obtain extreme data; determining a target job conforming to the extreme data as a target job in an abnormal state. Theanalysis result of the invention is based on the method of combining the one-factor covariance analysis algorithm and the variance analysis algorithm to analyze the influence of various factors on thetime-consuming data of the target work order operation link, so the method has objectivity and accuracy, and further provides a more effective basis for the monitoring work.

The invention provides an error analysis method for a semi-physical simulation system of an aircraft. On the basis of mathematical equations of a flight control system of the aircraft, the transfer function of a two-axis turntable and the transfer function of a three-axis turntable, and with the simultaneous aircraft and target relative kinematic equation and proportional guidance equation, mean and covariance equations are established based on the covariance analysis description function method, and the errors of the semi-physical simulation system of the aircraft are solved using the mean and covariance equations. The problem that the calculation error of the mathematical equations of the flight control system of the aircraft is not included in the existing method of missiles and other hardware-in-the-loop simulation system errors is solved. The errors obtained by the invention include the calculation errors of the mathematical equations of the flight control system, and can be usedto reveal abnormal phenomena in simulation experiment results or to modify simulation experiment results.

The invention discloses a point cloud completion method based on local covariance optimization. The method comprises the following steps: S1, acquiring a data set; s2, data preprocessing; s3, constructing a neural network model; s4, constructing the loss of the neural network model; s5, training and optimizing a neural network model; and S6, saving the model and model parameters, and by adopting the technical scheme, taking the incomplete point cloud as input, and outputting the complete point cloud which has a complete shape and is finer. In the feature coding stage, a disordered and complex topological relation between local points is analyzed by using covariance, and local geometric structure information is extracted by using different convolution kernels; the integrity of the shape and the structural similarity are considered, features of a missing structure are inferred by fusing multi-scale hierarchical features, and a global feature vector of the point cloud under the complete shape is obtained; in the decoding stage, not only can the expansion of the number of the point clouds be realized, but also the local geometric structure of the point clouds can be optimized, so that the finer complete point clouds are generated.

A modified principal component analysis technique is described herein for analysis of relatively small data sets for the detection of chromosomal aneuploidies and/or microdeletions. Unlike analysis techniques for microarray studies, the present technique uses a modified principal component analysis that does not involve performing a covariance analysis. The methods, systems, and apparatus described herein allow for significant reduction of data noise in tests for the detection of chromosomal aneuploidies and/or microdeletions, leading to fewer inconclusive results.

The invention provides an S-wave arrival time identification method based on a wavelet transform covariance model. According to the method, noise reduction processing is firstly carried out on an input wave signal, then continuous wavelet transformation is carried out on the signal after noise reduction, then covariance analysis, improved Rectilinity function processing and secondary filtering are sequentially carried out on data after continuous wavelet transformation, and finally, the arrival time of an S wave is determined according to a set threshold value. The system comprises the following modules: a micro-seismic signal excitation module, a micro-seismic signal receiving module and an upper computer. According to the S-wave arrival time identification method and system based on the wavelet transform covariance model, the process of the method is clear and easy to understand, the method can be used for S-wave arrival time identification of a single-component sensor signal, and the S-wave arrival time identification precision is high; the system is simple in structure, can be used for engineering field application, and solves the problem that the S wave arrival time of a single-component sensor is difficult to identify in mine practical application.