10 results about "Mixed effects" patented technology

This invention belongs to the field of high-performance thermoplastic composite granule preparation and process optimization technology. Specifically, it relates to a method and system for determining the optimal process for twin-screw extrusion preparation of carbon fiber / polyetheretherketone (PEEK) granules based on numerical simulation and multi-objective optimization. A three-dimensional model of the internal flow channel of the twin-screw extruder is established, and discretization is performed using a mesh stacking method. Multiple simulation conditions are constructed within a preset temperature and extrusion speed range, and process safety constraints are established. The analytic hierarchy process (AHP) – approximation of ideal solution ranking method is used for index standardization, weight allocation, and comprehensive scoring. With process temperature and extrusion speed as decision variables, a non-dominated sorting genetic algorithm is used for multi-objective global search to obtain the Pareto optimal solution set, which is then ranked and optimized to output recommended process parameter combinations. This invention's method can reduce the risk of thermal degradation and fiber damage while considering the mixing effect, and reduces the cost and cycle time of trial and error.

This application belongs to the field of microgrid frequency control, specifically disclosing a model-free adaptive microgrid frequency control method to resist network attacks. The method includes the following steps: considering the mixed effects of denial-of-service attacks and spoofing attacks, a system output model incorporating attack coefficients is established, modeling the microgrid frequency control system as a discrete-time nonlinear system; dynamic linearization technology is used to transform the nonlinear system into an equivalent linear data model; for the linear data model, a partial pseudo-derivative estimation algorithm is designed, and a model-free adaptive control law incorporating a decay function is designed; the time-varying partial pseudo-derivative matrix is ​​iteratively updated using the partial pseudo-derivative estimation algorithm, and based on the updated partial pseudo-derivative matrix, the control input at the current moment is calculated using the model-free adaptive control law to control the microgrid frequency. This application can achieve effective frequency control even when the microgrid frequency control system model is unknown and faces mixed network attack threats.

The application discloses a biological rhythm-light interaction regulation method based on comprehensive cognitive ability index under value more conditions and an intelligent light environment regulation system, and belongs to the technical field of biological rhythm regulation and intelligent environment control. The method comprises the following steps: constructing an individualized comprehensive cognitive ability index, and fitting a solidified mixed effect model parameter based on pre-experiment data; obtaining a real-time biological rhythm phase of a person, combining the solidified parameter to calculate a cognitive ability benchmark value of the person under no light intervention, and determining a cognitive ability gap amount to be compensated; and under the physical constraint of light parameters, optimization solving is carried out to obtain optimal light intensity and color temperature, and a light source is driven to perform adjustment to change a physical light environment, and adaptive optimization is realized through a closed-loop feedback mechanism. The application realizes the transformation from a group statistical model to an individual real-time control strategy, can implement precise light regulation according to people and time, and effectively maintains cognitive ability and rhythm health during value more work.

The application discloses a kind of infrastructure flood resilience evaluation methods, comprising: using flood simulation coupling model to carry out the whole process simulation of urban flood in multiple scenarios to target area;From the simulation process, the spatial attribute index of blue-green-gray infrastructure is extracted as the independent variable, the flood dynamic index of different flood stages is extracted as the dependent variable, to form a sample data set, and the independent variable is divided into multiple independent variable groups according to function;Standardization is carried out on the independent variable and the dependent variable respectively, and data enhancement is carried out;Bayesian mixed effect model is constructed, prior distribution is set for model parameters, posterior distribution of parameters is obtained based on the enhanced sample data set using sampling algorithm, and the evaluation result is output according to the posterior distribution.The application can clearly reveal the dynamic action law of blue-green-gray infrastructure in initial rising period, flood peak period and recession period, output more stable and reliable phased dynamic resilience evaluation result, and significantly improve the safety guarantee level of urban flood control and drainage.

The present application relates to a kind of multi-detection equipment system error real-time alarm method and system, method includes step 1: four-dimensional tensor data construction and pre-processing;Step 2: equipment error modeling based on mixed effect model;Step 3: equipment mutual inspection and error matrix construction;Step 4: adaptive dynamic threshold calculation and alarm triggering;System includes the hardware layer for data acquisition, data layer, calculation layer, application layer.By constructing four-dimensional tensor integration multidimensional detection data, using mixed effect model accurately split equipment system error (bias, drift), based on equipment mutual inspection and form error matrix, combined with adaptive dynamic threshold, realize the real-time alarm of system error, whole process does not need physical calibration, only by data cross validation can guarantee detection accuracy.

The application provides a distortion correction method and device, electronic equipment and storage medium, wherein the method comprises: determining actual coordinates corresponding to a plurality of calibration points according to target images obtained by photographing the plurality of calibration points by a target camera; determining principal point coordinates and focal length of the target camera according to the actual coordinates corresponding to the plurality of calibration points; constructing a mixed effect model of the target camera based on the actual coordinates corresponding to the plurality of calibration points, the principal point coordinates and the focal length of the target camera, wherein the mixed effect model comprises a fixed effect model and a random effect model; and performing distortion correction on an imaging area of the target camera through the mixed effect model. The mixed effect model constructed by the application can capture global trends of distortion and correct local residual distortion, thereby achieving high-precision correction of full-field distortion, improving adaptability and robustness of distortion characteristics of different cameras, and being able to adapt to significant differences in regional distortion of large-field cameras.

This application provides a structural simulation analysis method and apparatus based on carboxymethyl cellulose multi-component mixing. The method includes: constructing multiple multi-component mixing models with the same external dimensions but different internal structures; performing fluid dynamics simulation on each multi-component mixing model to simulate the mixing process of multiple fluids and obtaining the corresponding mixing results; and determining the multi-component mixing model with the optimal mixing effect based on comparative analysis of the mixing results. This application, by constructing and simulating multiple multi-component mixing models with different internal structures in parallel and comparing and analyzing their mixing results based on fluid dynamics principles, can quickly and objectively select the mixing model with the optimal mixing efficiency.

This invention discloses a wind speed interpolation prediction method based on a functional spatial variable coefficient model. By constructing a functional spatial variable coefficient mixed effect model, the method uses a spatially dependent Matérn distribution family spatial correlation function to characterize spatial correlation in the principal component scores, fully drawing on information from geographically adjacent locations to analyze the spatial correlation of wind speeds between wind turbines. The spatial variable coefficient model can more effectively describe this spatial variation pattern and eliminate large-scale biases caused by data aggregation, thereby improving the accuracy of wind speed prediction. In addition, this invention can also effectively perform interpolation prediction for wind speed at any spatial location within the range.

The application discloses an alternating current contactor residual electric life prediction method based on a mixed effect model. The method introduces the competitive failure and degradation correlation characteristics of three-phase contactors into the residual electric life prediction method, and the obtained performance degradation model conforms to the actual performance evolution law of the product. The mixed effect degradation modeling method and the maximum likelihood estimation method and other technical means are adopted, so that the electric life prediction can be effectively performed by using the electric arc erosion amount data in the product use process. While the prediction precision is ensured, the method is simple to calculate, provides a theoretical basis for the reliability analysis of switching devices, is convenient for engineering and technical personnel to use, and has strong application value.

This application discloses a method, application method, and apparatus for determining a cognitive load prediction model, relating to the field of human-computer interaction technology. The method includes acquiring EEG signals, task difficulty of the main task, working memory capacity of the subjects, and performance data of secondary tasks from several subjects; preprocessing the EEG signals and extracting multi-dimensional EEG features; combining the secondary task performance data with task difficulty and working memory capacity, using a residual-mixed effects model to generate a comprehensive cognitive load score, and mapping it to a comprehensive cognitive load score level using a percentage method; constructing a time-series dataset based on the multi-dimensional EEG features and corresponding levels; using the multi-dimensional EEG features as input and the corresponding levels as labels, training a CNN-Bi-LSTM-Attention network using a cross-entropy loss function to obtain a trained cognitive load prediction model. This application can use this model to predict cognitive load levels.