12results about How to "Shorten forecast time" patented technology

The application discloses a method for predicting rectangular narrow-gap re-submerged liquid droplet entrainment behavior of a plate-shaped element, which comprises the following steps: in an offline stage, constructing and initializing a BP neural network, training and optimizing weight values and threshold values by taking experimental boundary conditions as input variables and liquid entrainment rate as an output variable, and predicting in an online stage. The application can rapidly and accurately calculate the liquid entrainment rate value suitable for the re-submerged working condition of the bottom of the plate-shaped element by training the input-output relationship through a large amount of data by using the artificial neural network prediction method.

The application provides a tool state detection method based on a main shaft current signal, comprising the following steps: designing an orthogonal experiment related to a cutting speed, a feed rate and a cutting depth; collecting a single-phase current of a main shaft motor driver by using a current sensor; collecting a wear amount of a rear tool face of a milling cutter after each experiment, fitting a tool wear curve according to the collected wear amount; performing denoising on the collected original signal of the single-phase current of the main shaft motor driver based on a third-fourth quantile method of a sliding window; extracting time-frequency domain features and time-frequency joint domain features of the single-phase current signal of the main shaft motor driver based on the preprocessed data; selecting n features from the time-frequency domain features and the time-frequency joint domain features as input of a prediction model by using grey correlation degree analysis; establishing a prediction model based on a least square support vector machine regression model, optimizing the prediction model by using a genetic algorithm, and predicting the tool wear amount based on the optimized prediction model.

The application discloses a kind of reparameterization and stereoscopic coding brain disease classification method and system, it is related to computer medical image analysis technical field, including: S1.data acquisition step;S2.data processing step;S3.Establish RepBoTNet network model;S4.Establish RepBoTNet-CESA network model;S5.model training step;S6.classification step.The RepBoTNet-CESA network model constructed in the application combines the advantages that CNN network is good at capturing local information and transformer network is good at integrating global information, while obtaining excellent performance indicators, reduces the calculation cost;By constructing RepBoTNet-CESA network model, more rich and effective features can be extracted, and the region of interest is less likely to be lost, and when analyzing the whole brain structure, the accuracy of disease classification can be effectively improved.

The present application relates to a kind of Helmholtz resonance material sound absorption performance prediction method, comprising: based on the sound absorption performance database between the structure parameters of different dimensions of Helmholtz resonance material and sound absorption coefficient, obtain training set and verification set;Using the data in training set trains the Helmholtz resonance material sound absorption performance prediction network based on machine learning, test verification is carried out using verification set, obtain Helmholtz resonance material sound absorption performance prediction model;The structure parameters corresponding to different dimensions of the Helmholtz resonance material to be measured are input into Helmholtz resonance material sound absorption performance prediction model, obtain the predicted value of the sound absorption coefficient of different dimensions of Helmholtz resonance material.Compared with prior art, the present application solves the problem that it is difficult to predict sound absorption performance by simulation technology due to too many parameters of acoustic Helmholtz resonance material, solves the problem of long simulation prediction time and low efficiency, and can efficiently and accurately predict the sound absorption coefficient of different dimensions of Helmholtz resonance material.

The present application relates to a kind of short-term wind speed prediction method based on secondary decomposition and hybrid network, first using the integrated mode decomposition of adaptive noise technique, wind speed historical data is decomposed into multiple components, using sample entropy and heat map for analysis to different components, it is divided into high-frequency component, mid-frequency component, low-frequency component, high correlation component.And then using singular spectrum analysis, high-frequency component is further decomposed into subcomponent.Prediction of subcomponent, mid-frequency component, key component uses the hybrid network proposed, is predicted, to ensure overall prediction accuracy;Low-frequency component is predicted using extreme learning machine, to further improve operation efficiency, finally, all prediction results are superimposed to obtain wind speed prediction result.

The invention relates to a battery processing method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a target internal resistance based on a target capacity retention ratio of a battery and a first mapping relation; wherein the first mapping relation is used for indicating a corresponding relation between the capacity retention ratio and the internal resistance; based on the target internal resistance and a second mapping relation, obtaining the number of available charge and discharge cycles of the battery; wherein the second mapping relation is used for indicating the corresponding relation between the number of charge and discharge cycles and the internal resistance, so that the cycle life of the battery is accurately predicted, and meanwhile, the prediction time of the cycle life of the battery is shortened.

The application discloses a kind of based on deep neural network's root canal therapy postoperative tooth mechanics performance fast, accurate prediction method.The method is first based on microcomputer tomography image, comprehensively utilizes Mimics Research 21.0, Geomagic Studio 2013 and SolidWorks 2017 software, reconstructs the high-precision, multi-component three-dimensional entity model of root canal therapy postoperative tooth.On this basis, using Python script in Abaqus 2022 software carries out parameterized finite element simulation, batch calculates the mechanical response of postoperative tooth under different pulp cavity filling materials.Subsequently, using the standardized data set containing integral point space coordinates, stress and pulp cavity filling material attributes, three deep fully connected neural networks with independent architecture design are trained.The method shortens the prediction time of single stress distribution from tens of seconds of finite element analysis to tens of milliseconds while ensuring high consistency between the prediction results and the finite element analysis results, realizes thousands of times of calculation acceleration, and lays a technical foundation for scientific and personalized diagnosis and treatment plan.

This application discloses a liveness detection method, a training method for a liveness detection model, and a corresponding apparatus. The main technical solution includes: acquiring a first image and a second image of a target object using a first camera and a second camera; obtaining depth map information based on the disparity between the first image and the second image; extracting image features and depth map features from the first image and the depth map information respectively using a liveness detection model; fusing the image features and depth map features to obtain multimodal fusion features; and using the multimodal fusion features to obtain a detection result indicating whether the target object is live. This application can effectively improve the accuracy of liveness detection.

The invention provides a tomato shelf life prediction method based on a hyperspectral and RGB imaging technology. The tomato shelf life prediction method comprises the following steps: S1, obtaining a hyperspectral image and an RGB image of a tomato sample; s2, preprocessing the hyperspectral image to obtain hyperspectral feature data; s3, extracting color feature data and texture feature data of the RGB image; s4, splicing the data obtained in the steps S2 and S3, performing standardization processing, and dividing the data into a training set, a test set and a verification set; s5, constructing an MLP model; screening the target feature set; s6, training the MLP model to obtain a tomato shelf life identification model; s6, obtaining a hyperspectral image and an RGB image of the detected tomato, performing preprocessing, and screening data according to the target feature set; and S7, splicing the screened color feature data, texture feature data and hyperspectral feature data of the detected tomatoes, inputting the spliced data into the tomato shelf life recognition model for recognition, and outputting the shelf life of the detected tomatoes. According to the invention, the accuracy of tomato shelf life identification is improved.

The application discloses a kind of comprehensive energy system multivariate load prediction method, comprising: obtaining comprehensive energy system data, according to the correlation analysis of each meteorological factor and load to comprehensive energy system data, and strong correlation history meteorological factor data is screened out;Build multivariate load coupling feature matrix;According to meteorological factor data, multivariate load coupling feature matrix is clustered using adaptive K-means clustering algorithm;The multivariate load coupling feature matrix after clustering is input into the Attention-BiGRU prediction model pre-trained to predict, and the load prediction result is obtained.Considering meteorological factors and the cross-coupling characteristics of multivariate load, the model complexity and prediction time are reduced, so that the final prediction result takes into account stability and accuracy.

This invention provides a short-term power grid load forecasting method based on the PSO-LSSVM algorithm, comprising: performing modal decomposition on the load data sequence to obtain multiple intrinsic mode function components; removing noise components from the intrinsic mode function components to obtain effective components; constructing a nonlinear prediction model for the effective components based on least squares support vector machine; processing the nonlinear prediction model through structure minimization to obtain a load forecasting model; optimizing the load forecasting model based on particle swarm optimization algorithm to obtain a hybrid prediction model; and predicting the power grid load characteristic data at the time of prediction based on the hybrid prediction model to obtain the predicted power grid load. By removing high-frequency noise from the power grid load data, the nonlinear variation trend of the short-term power grid load can be accurately determined. The optimization of the load forecasting model based on particle swarm optimization algorithm improves prediction accuracy and shortens prediction time.

The invention relates to the technical field of composite material damage evaluation and performance prediction, and discloses a method and a system for rapidly predicting thermal-mechanical-electrical response of a quartz / epoxy resin composite material under laser irradiation based on a vector quantization variational auto-encoder (VQ-VAE). According to the method, a cascade deep learning neural network architecture of a DNN prediction network and a VQ-VAE decoder is constructed, laser working condition parameters are mapped to a discrete potential space, and complete temperature field, stress field and dielectric constant parameters are rapidly reconstructed. Wherein the VQ-VAE adopts a vector quantization mechanism to extract material ablation phase change and damage features, the visual field resolution is adjusted by improving the size of a convolutional layer in an encoder, and extraction of small-scale damage features is achieved. According to the method, second-level forecasting from parameter input to full-field output is achieved, the calculation efficiency and the forecasting precision are remarkably improved, and the method is suitable for rapid performance evaluation and design optimization of the composite material under laser irradiation.