9 results about "Chi-squared distribution" patented technology

This invention relates to the field of nuclear safety equipment testing technology. It provides a method for optimizing parameters in accelerated reliability testing of nuclear safety-grade valves. The method involves the following steps: establishing a correlation model between accelerated stress and life parameters; calculating the acceleration factor and formulating an initial test plan; deriving the theoretical sample size based on the success-failure test method combined with confidence requirements; constructing an extended coefficient table using the range-extended test method to optimize test parameters; generating multiple accelerated test plans through chi-square distribution verification; eliminating plans that do not meet the life characteristic threshold; determining the optimal sample size, number of failures, and test cycle through comprehensive test cost and risk analysis; and finally outputting a complete test plan including the action interval time. This method solves the problems of limited test samples, insufficient failure data, low confidence levels, and difficulty in adapting accelerated models in existing high-reliability valve technologies.

This invention proposes a method for real-time pseudorange quality control in the BeiDou satellite-based augmentation system, comprising: calculating n pseudorange residuals at time t; determining whether the number of original pseudorange residuals n is greater than or equal to a preset parameter; subtracting the mean residual from the original pseudorange residual of each satellite; dividing the centered pseudorange residual of each satellite by the dual-frequency pseudorange noise; calculating the test statistic X; determining whether the test statistic X is less than the 95th quantile of the chi-square distribution with n-4 degrees of freedom; removing the satellite with the largest absolute value of the normalized pseudorange residual; completing the removal of pseudorange outliers; and obtaining the current pseudorange residual. This invention is real-time, meeting the high real-time requirements of differential integrity processing in the BeiDou satellite-based augmentation system. The embodiments of this invention can identify and remove small abnormal pseudoranges in the BeiDou interplanetary augmentation system, achieving good pseudorange quality control.

This invention discloses a method for constructing a statistical model of concrete dam deformation observation. The method includes preprocessing concrete dam deformation monitoring data; using a trial-and-error approach to determine the chi-square distribution degrees of freedom describing the weights of the influence of the dam surface temperature in the early stages; determining the optimal parameters of the LSTM network model using the GA algorithm based on the training sample set data; constructing a concrete dam deformation prediction model based on the optimal parameters obtained by iteratively solving the LSTM network model using the GA algorithm; and verifying the model's prediction accuracy. Based on the causal functional relationship between concrete dam deformation and its explanatory variables, this invention proposes a modeling method for concrete deformation prediction with good prediction accuracy by introducing the chi-square distribution and a GA-optimized LSTM network. This invention can effectively reduce the uncertainty in deformation prediction caused by upstream and downstream dam surface temperatures, quantitatively consider the influence of the lag effect of dam body temperature deformation, and fully explore the complex nonlinear functional relationship between concrete dam deformation and its explanatory variables, thus achieving good prediction accuracy.

The application provides a GNSS outlier detection method, system and storage medium based on residual multi-order chi-square distribution used on a satellite, comprising the following steps: step one, acquiring the position, velocity and satellite time in the earth-fixed system output by the GNSS; step two, converting the output data of the GNSS into orbital 14 plane roots; step three, calculating the residual and the residual rate of change to make a first heavy outlier judgment, then calculating one-dimensional chi-square test outliers, then calculating two-dimensional chi-square test outliers after the one-dimensional chi-square test is passed, calculating five-dimensional chi-square test outliers after the two-dimensional chi-square test is passed, if all the tests are passed, updating the parameters; if one test is not passed, determining that it is an outlier, and simultaneously counting the continuous invalid times, when the continuous invalid times are greater than a threshold value, the threshold value is adjusted along with the increase of the continuous invalid times, so that the system divergence is avoided. The application has the beneficial effects that the application can obviously improve the rejection rate of outliers, and provides correct data for the use on the satellite.

A coherent wind lidar echo signal spectrum simulation data generation method is provided, comprising: obtaining the bottom noise of the echo signal; removing the bottom noise from the echo signal spectrum data and counting the variance of the random noise; performing normalization processing on the random noise and performing Gaussian fitting to obtain the amplitude, Doppler shift and bandwidth of the echo signal; recording the Doppler shift difference between adjacent range gates to obtain the power spectrum peak energy of a single spectrum data and the correction amount of the distance; respectively counting the statistical parameters of the random variable, the power spectrum peak energy, the correction amount of the distance and the bandwidth; generating a group of wind speed profile graphs by using random walk and normal distribution characteristics of random variables, generating bandwidths under different range gates that satisfy the Lorentz distribution, power spectrum peak energies of a single simulated spectrum that satisfy the chi-square distribution, and distance correction amounts that satisfy the normal distribution; and generating a single simulated spectrum by a radar equation and repeatedly generating complete spectrum simulation data.

This invention relates to a method for detecting anomalies in non-stationary industrial processes based on slow feature decomposition and Koopman high-dimensional space prediction, belonging to the field of industrial process time-series anomaly detection technology. The method includes: using slow feature analysis to decompose industrial time-series data into fast and slow changing components; mapping the fast and slow features to a linearly observable Koopman high-dimensional space based on Koopman theory, constructing a deep prediction model to achieve stepwise prediction of the feature sequence; constructing a SPE statistic that approximately follows a weighted chi-square distribution by combining the deviation between the prediction results and the actual values, and setting SPE control limits for anomaly detection accordingly; introducing KL divergence to measure the distribution difference between the current operating condition and the normal operating condition, and adaptively updating the SPE control limits based on its magnitude; and during the online deployment phase of the model, calculating the SPE value in real time and comparing it with the control limits to achieve rapid identification and dynamic early warning of anomalies under non-stationary conditions.

The embodiment of the invention discloses a deception perception-based navigation management method, which comprises the following steps of: acquiring interested information which comprises signal power, attitude and direction of a GNSS (Global Navigation Satellite System) satellite; calculating a deception offset vector according to the interested information, and mapping the deception offset vector into eccentricity of non-central chi-square distribution; calculating a detection statistical magnitude, and calculating a missed detection probability according to the detection statistical magnitude; performing statistics on the detected deception signals according to a spatial direction to form a probability quality function, calculating spatial entropy according to the probability quality function, and calculating integrity weight loss according to the spatial entropy; calculating a position error expectation, and obtaining an over-limit probability according to the position error expectation; and determining a minimum protection level satisfying a constraint according to the integrity weight loss, the missed detection probability and the over-limit probability, and determining a navigation processing strategy according to the minimum protection level satisfying the constraint.

This invention belongs to the field of radar technology, specifically relating to a dual-station passive positioning and target identification method. The method involves: acquiring radar coordinates; transforming the measured angles of each node radar in the northeast-sky coordinate system to a unified rectangular coordinate system referenced by the northeast-sky coordinate system of the first node radar; establishing four measurement parameter equations based on the triangulation formula, and randomly selecting three equations as a set of measurement subsets, through which the target position can be calculated; transforming the angle errors of each node radar in the northeast-sky coordinate system into angle errors in the unified rectangular coordinate system, and then obtaining the angle error covariance matrix; calculating the covariance matrix of the target position vector difference and its Mahalanobis distance based on the target positions obtained from different subsets, and performing hypothesis testing on the target based on the characteristic that Mahalanobis distance follows a chi-square distribution; and determining whether the target is a real target by comparing the Mahalanobis distances of different points, effectively eliminating false intersections.

The invention is suitable for the technical field of geochemical exploration and data processing thereof, and provides a geochemical comprehensive anomaly identification method and system for a complex lithologic region, and the method comprises the following steps: selecting a clustering index for clustering based on preprocessed geochemical data; clustering the preprocessed geochemical data by adopting an expectation maximization clustering algorithm based on a clustering index, and decomposing mixed normal distribution into a plurality of sub-bodies; for each daughter, calculating a robust mahalanobis distance of each daughter sample by adopting a fast minimum covariance determinant method; and according to the robust mahalanobis distance of each daughter sample, determining a threshold value for distinguishing the geochemical background and the anomaly based on chi-square distribution, and further identifying the geochemical comprehensive anomaly. According to the method, the statistical effectiveness of the mahalanobis distance of the geochemical sample in the complex lithologic region can be recovered to a certain extent, so that a geochemical comprehensive anomaly recognition result has statistical significance.