5 results about "Validation error" patented technology

The application provides a large-span steel structure closed loop analysis method and system, the analysis method comprises the following steps: constructing a finite element model and a design parameter space, identifying key design parameters and key regions affecting the first N modal strain energy of the structure through initial sensitivity and modal analysis, and establishing a mapping relationship between the two; according to the parameter sensitivity contribution, a non-uniform sampling strategy is formulated, the sampling is encrypted in the high sensitivity parameter subspace, an initial sample set is generated, based on the sample and the finite element response, a hierarchical mixed kernel Kriging surrogate model containing a global long-range kernel and a multi-region local short-range kernel is constructed, the local kernel weight increases as the input point approaches the high sensitivity region, the iteration termination and the performance limit state are set, the composite point adding criterion integrating the prediction variance, the limit state distance and the cross-validation error is used to iteratively update the sample and the model, and after the condition is met, the structure performance analysis is completed by using the surrogate model.

Digit validation check control for instruction execution. A process receives an instruction to perform one or more operations using one or more input values. The instruction includes a "No Validation" indicator to check whether the digit validation check control is enabled for instruction execution. The process executes the instruction based on the "No Validation" indicator, including determining whether the digit validation check control is enabled for instruction execution and performing the processing based on this determination.Based on the "No Validation" indicator, which is set to a defined value, the "Digit Validation Check" control is activated, and the processing includes forcing a "Digit Validation Error" indicator, which is output by the execution to indicate "No Digit Validation Error" with respect to the at least one input value.

This invention provides a deep learning-based method for predicting the deformation of foundation pit support piles, belonging to the field of foundation pit engineering safety monitoring technology. The invention establishes a monitoring network to continuously collect multi-dimensional feature data and horizontal deformation values ​​of support piles, recording timestamps; sets the time window length and prediction step size, generates samples by sliding along the time axis, and divides them into training and test sets; constructs an AM-TKAN initial model, determines the type and initial range of hyperparameters to be optimized, and generates several hyperparameter combinations as individuals; trains the model corresponding to each individual on the training set and calculates the error, iteratively updates the hyperparameters according to the Hippo optimization algorithm, and outputs candidate optimal hyperparameter combinations that meet the error requirements; uses a validation set to select the model with the smallest validation error as the final prediction model; inputs real-time data into the final prediction model and outputs the deformation prediction results. This invention achieves high-precision prediction of the horizontal deformation of foundation pit support piles.

This invention discloses an intelligent early warning method, device, and storage medium for offshore wind turbine foundation scour, belonging to the field of structural safety early warning technology. The method includes the following steps: obtaining the foundation structural parameters of the offshore wind turbine and establishing a finite element model; performing foundation scour simulation and collecting time-domain data of pile top displacement and pile nodal stress; establishing several surrogate models, dividing the time-domain data into training and validation sets, and obtaining training and validation results; using two validation indices, validation error and leave-one-out error, to select the best surrogate model; using different scour conditions as input parameters to obtain the wind turbine foundation response, comparing it with a set safety early warning threshold, and issuing a safety alarm if the wind turbine foundation response reaches or exceeds the safety early warning threshold. This invention can solve the problems of traditional experimental methods, such as the difficulty in reproducing initial conditions, the influence of experimental scale on measurement results, and high costs, as well as the shortcomings of finite element simulation in achieving full coverage of scour conditions.

A system for effort estimation in agile software development using multi-stage evolutionary stacking, consisting of: a data acquisition module configured to retrieve software effort records from one or more data set repositories containing historical data from software development projects with characteristics and actual effort values; a data preprocessing module that is operationally connected to the data acquisition module and is configured to receive the aforementioned software effort data sets from the data acquisition module, cleans the received data by removing inconsistencies with missing target values, and normalizes numerical input characteristics to a common range; a first-level ensemble module connected to the data preprocessing module, wherein the first-level ensemble module comprises a variety of heterogeneous basic learners, including a Random Forest model, a Support Vector Regression model, and an Extreme Gradient Boosting model, which generate predictions from each of the heterogeneous basic learners using the preprocessed data sets received from the data preprocessing module; A genetic algorithm optimization module connected to the first layer's ensemble module, configured to: encode weights as a normalized real-valued vector for each of the heterogeneous base learners; apply a fitness function to minimize the mean squared validation error and derive an optimal weight vector; assign optimized weights to the predictions of each of the heterogeneous base learners; and generate weighted predictions based on the optimized weights. a second-level meta-learning module connected to the optimization module of the genetic algorithm, configured to receive the weighted predictions from the optimization module of the genetic algorithm, processes the weighted predictions using a deep multilayer perceptron neural network to learn complex patterns and nonlinear interactions, and generates a final effort estimate for the software; an output processing module connected to the second-level meta-learning module, configured to receive the final effort estimate for the software and process and visualize the data to improve user understanding; and a user interface connected to the output processing module to receive the processed final effort estimate for the software, wherein the user interface is configured to display the processed and visualized final effort estimate for the software.