43 results about "Random parameters" patented technology

The invention discloses an AI model parameter initialization method based on model parameter and structure multi-modal fusion, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting historical pre-training model data of a cross-model architecture and a cross-data set, processing model parameters into a token sequence, training a Transform codec, converting a model structure into a graph, and training GAT to extract structural features; a multi-modal feature data set is constructed after a related network is frozen, structural features serve as core conditions, a conditional diffusion model DDPM is trained through AdaLN modulation and residual module coupling, and multi-modal feature fusion of'parameter feature-structural feature 'is established. In the reasoning stage, the structural features of the unknown model are extracted, the random parameters of the unknown model are combined with the submerged space shape determined by the encoder, sampling is conducted through the conditional diffusion model, anti-token processing is conducted through the decoder, and adaptive initialized model parameters are generated for the unknown model. According to the method, cross-model structure high-quality parameter initialization is realized, the model training time is shortened, and the large model training requirement is met.

The invention relates to the technical field of cold chain transportation environment control, and discloses a temperature and humidity control system for cold chain transportation, and the system comprises an environment modeling module which is used for constructing a heat conduction model of a cold chain space based on a random field theory and a finite element method, and outputting a stiffness matrix containing random parameters; the self-adaptive sensing network module is used for receiving the characteristic information of the stiffness matrix, dynamically optimizing sensor distribution positions, collecting multi-node temperature and humidity data and adding space-time labels; and the distributed data processing module is used for receiving the sensing data. According to the invention, through random field theoretical modeling and parameter-state joint estimation, the temperature field prediction precision is significantly improved and the system environment adaptability is enhanced; a distributed sensing network architecture and a heterogeneous calculation acceleration technology are combined, so that the cooperative control with optimal energy efficiency is realized while millisecond-level real-time response is ensured, and the performance bottleneck of a traditional cold chain temperature control system is comprehensively broken through.

The application discloses a time-varying reliability topology optimization method for a continuum structure, and comprises the following steps: constructing a time-varying random parameter initial model of the continuum structure based on continuum structure information; constructing a time-varying reliability topology optimization mathematical model according to the time-varying random parameter initial model; obtaining displacement, velocity and acceleration responses of the continuum structure based on the time-varying reliability topology optimization mathematical model; obtaining sensitivity information about random variables according to the displacement, velocity and acceleration responses; obtaining a global time-varying reliability index based on the sensitivity information; establishing a derivative relationship between an objective function and constraint conditions and design variables according to the global time-varying reliability index and an adjoint vector, and obtaining gradient information according to the derivative relationship; and optimizing the continuum structure according to the gradient information. The application realizes high-precision and high-efficiency reliability design of the continuum structure under a dynamic uncertain environment.

The invention relates to a traffic flow speed estimation method based on a Gaussian mixture model and hierarchical Bayesian, and belongs to the technical field of intelligent traffic systems. The method comprises four steps of data preparation and preprocessing, traffic state clustering division, hierarchical Bayesian random parameter model construction, and model verification and performance evaluation. Through deep fusion of the Gaussian mixture model and the hierarchical Bayesian model, data-driven traffic state identification and random parameter estimation are realized, traffic flow heterogeneity can be effectively described, model parameter explosion is avoided, and model prediction precision and generalization ability are improved.

The invention specifically discloses an ion thruster acceleration grid probability life evaluation method based on coupling competitive failure, and relates to the technical field of aerospace propulsion system life evaluation. The method comprises the following steps: firstly, obtaining deterministic parameters and random parameters of a thruster acceleration grid assembly; then based on an ion sputtering corrosion theory, considering material loss caused by charge exchange ion bombardment of the acceleration gate, and establishing a coupling corrosion model of the thickness and aperture of the acceleration gate; thirdly, constructing a competitive failure model, and realizing numerical solution of competitive failure life by dynamically iterating a corrosion state and a failure criterion; and finally, independently repeated random sampling is carried out based on probability distribution of random parameters, the random sampling is substituted into the competitive failure model to obtain an acceleration grid simulation life sample set, and statistical analysis is carried out on the life sample set to obtain a probability life evaluation result. The method solves the problem of errors caused by independent modeling of structural failure and electronic reflux failure in the prior art, and is helpful for reducing the system operation risk and improving the reliability of a spacecraft.

The application provides a turbine blade thermal barrier coating force-thermal coupling reliability evaluation method, which comprises the following steps: measuring and constructing a density distribution function of a thermal barrier coating microstructure and thermodynamic parameters, completing macroscopic complex turbine blade thermal barrier coating temperature field and strain field simulation, constructing a Monte Carlo sample space of random parameters at different positions of the blade, establishing a micro-scale force-thermal coupling finite element model and obtaining a life data set, building and training a deep learning agent model, and completing turbine blade thermal barrier coating reliability evaluation and sensitivity analysis. The reliability evaluation method provided by the application comprehensively considers the influence of the thermal barrier coating microstructure factor and the force-thermal coupling failure mechanism, solves the problems of high dimension, high nonlinear calculation cost and the like in the reliability evaluation of the complex blade thermal barrier coating, improves the accuracy and efficiency of the reliability evaluation, and provides an evaluation means for the safe application and optimized design of the thermal barrier coating.

This application discloses a method for calculating the thrust envelope of a solid rocket motor based on servo uncertainty, relating to the field of solid rocket motor technology. This method focuses on the gas valve servo system of a throat-operated solid rocket motor, performing uncertainty modeling of the motor drive system and transmission system, analyzing the main influencing parameters of servo system uncertainty, including transmission clearance, eccentric shaft friction coefficient, and other uncertainty parameters and their probability distributions; generating random parameter samples based on the uncertainty model, and using the servo system control model to solve for the actual throat displacement response under random samples, obtaining the engine thrust time history under single-valve and multi-valve cooperative working modes; and fitting the thrust uncertainty envelope by calculating the mean and standard deviation of the thrust response through Monte Carlo simulation. This application quantitatively characterizes the degree of influence of internal factors of the servo mechanism on thrust performance, providing a reference for ensuring precise thrust control of solid rocket motors.

The invention discloses a target export function parameter generation method and device, electronic equipment and a storage medium. The method comprises the following steps: determining an initial parameter structure through an assembly instruction of a target dynamic link library; generating random parameter data based on the initial parameter structure; recording an instruction coverage rate in a process of simulating and executing an export function based on the random parameter data; determining target parameter data of which the instruction coverage rate meets a preset condition; executing an optimization operation based on the target parameter data to determine a parameter value distribution range used for correcting the initial parameter structure; and correcting the initial parameter structure based on the parameter value distribution range, and generating a target derivation function parameter. A dynamic execution process combining static analysis and optimization operation is realized, the problems that effective input is difficult to generate and the instruction coverage rate is low in unsigned information DLL parameter identification are solved, and efficient and accurate identification of the derived function parameter structure is realized.

The invention discloses a time-varying thermal failure probability estimation method and system for a deep groove ball bearing, and belongs to the field of bearing design. The method comprises the following steps: constructing a basic parameter model containing a mixed random parameter vector and a limit state function; establishing a transient thermal network model to calculate time-varying temperature response; constructing an agent model to replace high-time-consumption heat generation rate calculation; a self-adaptive time node sampling strategy is adopted, a proxy model and a thermal network model are combined to process large-scale samples, and the time-varying thermal failure probability is estimated. The system comprises a basic parameter modeling module, a transient thermal network simulation module, an agent model construction module and a self-adaptive probability evaluation module which correspond to the system. According to the method, the time-varying and random dual uncertainty is fused, and the proxy model and the adaptive sampling strategy are utilized, so that the defects of conservative property of a traditional static evaluation method and low efficiency of a traditional probability simulation method are effectively overcome, and accurate evaluation of the thermal behavior of the bearing under the dynamic working condition and remarkable improvement of probability calculation efficiency are realized.

The invention provides a geological analysis parameter protection method and system based on logarithm mapping, and relates to the technical field of geological exploration data protection, and the method comprises the steps: obtaining an original R parameter, building a linear mapping relation g = aR + b, building logarithm transformation mapping based on the linear mapping relation, converting the original parameter into a transformed parameter value, and obtaining a parameter value; and the transformed parameter values are provided for the data processing party for geological exploration operation, and the data owner recovers the original parameters through an inverse transformation formula after receiving the data obtained by exploration of the data processing party. According to the method, through logarithm transformation, an exploration company cannot identify the stratum type through conventional linear fitting, and sensitive geological information is effectively protected; meanwhile, irreversibility of the transformation process is ensured by adopting random parameters, and a data owner can completely recover original geological parameters through an inverse transformation formula; on the premise of protecting the data security, the normal exploration operation flow and the data processing precision are not influenced.

The invention relates to the technical field of signal processing, in particular to a filtering method for dealing with a system with complex multiplicative noise and random time delay uncertainty, which designs a reasonable system model of a multi-sensor networked system meeting conditions, and adopts a maximum and minimum robust estimation principle, a de-randomization method and a virtual noise technology. The system is converted into a multi-model multi-sensor system only with uncertain noise variance, an augmented noise method, a non-negative definite matrix factorization method and a Lyapunov equation method are applied, two robust centralized fusion steady-state Kalman estimators are obtained, and the robustness of the estimators is further proved. The method can be used for solving the problem of robust fusion Kalman filtering of multi-sensor single-channel ARMA signals with random parameter matrixes, uncertain noise variance and networked random uncertainty.

This invention belongs to the field of power system demand-side management technology and discloses a probabilistic assessment method for load adjustability potential considering dual uncertainties. The method includes: constructing adjustability potential models for five load types: air conditioning, cold chain, energy storage, electric vehicles, and data centers; determining the random parameters of external influences on each load and quantifying their probability distribution based on historical data; introducing random execution variables for each load type to describe the uncertainty of execution behavior with probability distributions, thus obtaining a more realistic adjustability potential; performing random sampling calculations on the above models and parameters using the Monte Carlo method to aggregate and generate a total adjustability potential sample; and performing fitting analysis on the sample to obtain the probability function of the system's adjustability potential and conduct multi-dimensional evaluation. This invention achieves the quantification of load adjustability potential uncertainty through a three-layer probabilistic modeling of "physical behavior - random influence - random response," providing decision support for optimized power grid operation.

The application relates to a traffic flow speed estimation method based on a Gaussian mixture model and a hierarchical Bayesian method, and belongs to the technical field of intelligent transportation systems. The method comprises four steps of data preparation and preprocessing, traffic state clustering division, hierarchical Bayesian random parameter model construction, model verification and performance evaluation. Through the deep fusion of the Gaussian mixture model and the hierarchical Bayesian model, the application realizes data-driven traffic state recognition and random parameter estimation, can effectively depict traffic flow heterogeneity, avoids model parameter explosion, and improves the model prediction precision and generalization ability.

The application belongs to the technical field of structure optimization, and particularly relates to a stiffened plate structure reliability topology and size joint optimization method, which comprises the following steps: taking a three-dimensional stiffened plate structure as an object, selecting the mean value of each random parameter as the initial value, and performing deterministic topology optimization; performing structure reliability analysis based on an HMV algorithm, and searching for the current most likely failure point; judging whether the target converges or not, regularizing and simplifying the material layout of the topology optimization output structure; selecting the mean value of each random parameter as the initial value of the normalized steel structure of the layout explanation, and performing deterministic size optimization; performing structure reliability analysis based on the HMV algorithm, and searching for the current most likely failure point; judging whether the target converges or not, and obtaining the stiffened plate optimization structure which simultaneously satisfies the structure performance and reliability index. The application realizes the reliability topology and size joint optimization of the material distribution optimization and the production demand satisfaction while ensuring the reliability.

The invention discloses a bird repelling method, system and device based on multi-mode intelligent perception and collaborative repelling and a storage medium, and relates to the technical field of electric power facility protection and intelligent bird repelling, the method comprises the steps of continuously scanning a monitoring airspace, and constructing a standardized dynamic parameter sequence; calling a behavior prediction model to calculate the probability that the birds approach the intention, comparing the probability with a preset threshold value, and generating a bird repelling starting instruction when the probability exceeds the threshold value; if not, the low-power-consumption monitoring state is maintained; after the bird repelling starting instruction is triggered, a random algorithm is triggered, and a group of random parameters for uniformly controlling ultrasonic, sound, light and laser modules are generated; the parameters are analyzed, all the modules are synchronously driven to cooperatively work in a random combination mode for a period of random time, and the system automatically returns to a monitoring state after bird repelling is completed; according to the invention, intelligent sensing and multi-mode random collaborative driving of bird approaching behaviors are realized, the bird driving durability can be obviously improved, and safe and stable operation of electric power facilities is guaranteed.

This invention discloses a smart home energy management method based on deep adversarial inverse reinforcement learning, comprising the following steps: (1) Modeling the energy cost minimization problem of a smart home and designing the corresponding Markov decision process environment state and actions; (2) Constructing n sets of random parameter historical data based on a set of random parameter historical data and a rolling generation method; (3) Solving the above minimization problem using the n sets of random parameter historical data and an optimization algorithm to obtain n expert trajectories; (4) The discriminator trains a reward network based on the expert trajectories and the trajectories generated by the generator agent; Under the guidance of the reward network, the generator agent is trained using experience tuples and a proximal policy optimization algorithm; (5) Repeating step (4) until a stable agent policy is obtained; (6) Deploying the trained agent policy in a real environment. Compared with existing methods, the method of this invention can effectively reduce energy costs and improve user comfort.

The invention relates to the technical field of chip simulation verification, and discloses a Macro IP parameter verification method, device and equipment, and the method comprises the steps: obtaining a parameter identifier for generating a Macro IP; based on the verification target, judging whether a parameter represented by the parameter identifier is a random parameter, and if the parameter represented by the parameter identifier is judged to be the random parameter, constraining a parameter value of the random parameter to obtain a target random parameter and a random parameter expected value corresponding to the target random parameter; if the parameter represented by the parameter identifier is judged to be the preset parameter, modifying a parameter value corresponding to the preset parameter to obtain a target preset parameter and a preset parameter expected value corresponding to the target preset parameter; generating a register-level transmission code based on the target random parameter and the target preset parameter; and constructing a test case, and verifying whether the operation result of the register-level transmission code meets the expectation or not. Manual parameter modification can be replaced, automatic verification is realized, and verification efficiency is improved.

The application discloses a watershed runoff probability prediction method based on Bayesian deep learning and a differentiable physical model, and the method comprises the following steps: firstly, a digital elevation model is used to construct a watershed differentiable distributed computing architecture, a parameter inference network is connected with an HBV hydrological model and a Muskingum confluence equation in series to form an end-to-end differentiable computation graph; then, Bayesian inference is realized by introducing a random inactivation layer and an input disturbance mechanism, and a random parameter group and a random rainfall scenario conforming to a posterior distribution are generated; finally, parallel set physical evolution is performed, and a runoff probability prediction and a flood risk assessment interval are output; the method combines a physical mechanism and deep learning, realizes high-precision deterministic prediction and reasonable uncertainty quantification, and provides more reliable decision support for flood warning.

The present application provides a method, device, equipment and storage medium for reducing the risk of sticking, comprising: obtaining input data of a prediction model and sticking probability corresponding to the input data, wherein the input data comprises fixed parameters and controllable parameters; when the sticking probability is higher than a first sticking threshold, generating a random population according to the controllable parameters; after adaptive screening, crossover operation and mutation operation on the random population, obtaining random parameters; inputting the random parameters and the fixed parameters into the prediction model to obtain an optimized sticking probability; if the optimized sticking probability is lower than a second sticking threshold, using the random parameters as the controllable parameters to guide the deployment of horizontal well drilling; when the sticking probability is higher than the first sticking threshold, generating random parameters according to the controllable parameters and inputting the random parameters and the fixed parameters into the prediction model to obtain an adjusted sticking probability according to the prediction model; when the sticking probability is lower than the second sticking threshold, using the random parameters to guide the deployment of horizontal well drilling.

The invention relates to the technical field of power system control and stability analysis, in particular to a quantitative analysis method and device for control performance by source network uncertainty and a medium, and the method comprises the steps: generating a plurality of random parameter combinations based on a plurality of probability distribution models; automatically configuring the random parameter combination to a pre-established power system simulation model, driving the power system simulation model to complete simulation operation of corresponding times, and automatically extracting at least one controlled quantity data; and calculating at least two performance indexes on the basis of the extracted controlled quantity data, and establishing a mapping relation between the random parameter combination and the performance indexes on the basis of the data of each simulation frequency so as to quantify the influence degree of different uncertainty factors on each control performance index. Through the setting, the influence of the bilateral uncertainty of the source network on the control performance of the power system can be systematically quantified, and the problem that the coupling effect of the bilateral uncertainty cannot be comprehensively analyzed by a traditional method is effectively solved.

The invention discloses a water-rich depth foundation pit ground surface settlement prediction method, which comprises the steps of obtaining a point cloud data set of a to-be-constructed part of a water-rich area through three-dimensional scanning, and inputting parameters of a plurality of foundation pit construction schemes to generate corresponding construction simulation results; randomly generating sampling points based on the construction simulation result, and acquiring geological parameters of the sampling points; and foundation pit settlement simulation is carried out according to the geological parameters, and finally the evaluation indexes are output. Therefore, in combination with a three-dimensional scanning technology, three-dimensional point cloud data of the to-be-constructed area is obtained, so that before construction, according to a preliminary design scheme of foundation pit excavation input by an engineer, a plurality of random parameter fields are generated through a Monte Carlo method to complement lacked data, and according to actual sampling data and a simulation result, a simulation result is obtained; and generating a reliable comparison structure of foundation pit settlement prediction among the foundation pit schemes so as to assist personnel in selecting a construction scheme which is less prone to serious settlement in the future.

The invention provides a collapsible loess area highway underground cavity GPR forward modeling method, and relates to the technical field of forward modeling. The method comprises the following steps: S1, establishing a multi-layer geologic model for a collapsible loess area road; s2, establishing cavity models in the bottom layer of the multi-layer geologic model, wherein the cavity models comprise a regular cavity model and an irregular boundary cavity model; s3, generating random parameters of each stratum according to the dry and wet state of the ground, and completing the establishment of an underground cavity GPR forward modeling model; and S4, performing forward modeling in the gprMax to obtain a forward modeling result. According to the method, the cavity model can be quickly generated, the parameters (such as the dielectric constant and the conductivity) of the collapsible loess area stratum in the dry state and the wet state are dynamically simulated, the building efficiency of various forward modeling models is improved, the data sample size needed by GPR research is expanded, and more comprehensive data support is provided for underground cavity detection under the complex geological condition.

The application provides a submarine landslide risk prediction method and device, wherein the method comprises the following steps: dividing a first submarine slope into subsea areas, obtaining the depth, boundary and rock-soil material strength parameters of the subsea areas, selecting the subsea areas as source areas and target areas respectively, constructing a three-dimensional search grid and generating search points, obtaining a benchmark deterministic model through a three-dimensional slope stability model, generating rock-soil random parameters conforming to a logarithmic normal distribution, importing the benchmark model to calculate a safety factor, constructing a data set by using the safety factor of the three-dimensional search grid, the depth and the rock-soil random parameters, training a prediction model based on domain adversarial neural network transfer learning, and finally predicting the prediction safety factor of a second submarine slope according to the prediction model, so that the risk evaluation of the second submarine slope can be quickly completed.

The present disclosure provides a model adjustment method and device, a video generation method and related equipment. The method comprises: determining initial video noise to be denoised by a video generation model; performing multi-round noise prediction and denoising processing on the initial video noise and a noise intensity adjustment sequence by the video generation model to obtain video noise after the last round of denoising; wherein the noise intensity adjustment sequence is generated based on a random parameter and a prediction progress parameter corresponding to multi-round noise prediction; decoding the video noise after the last round of denoising to obtain a model adjustment video; adjusting model parameters of the video generation model according to the model adjustment video to obtain an adjusted video generation model. According to the embodiments of the present disclosure, a video generation model with better performance can be obtained, and the video quality generated by the video generation model is improved.

This invention discloses a backdoor attack method, apparatus, and medium based on spatial transformation. The method includes: randomly selecting a portion of image samples from the original dataset and performing a spatial transformation with set parameters, changing the label of the portion of image samples to the target label, and performing a spatial transformation with random parameters on the remaining benign image samples in the original dataset while keeping the labels unchanged, thereby processing the original dataset into a poisoned dataset; using the poisoned dataset for standard training of a deep learning classification model to construct a victim model with a hidden backdoor implanted; wherein, when the victim model performs classification prediction, its hidden backdoor can be activated by samples in the dataset to be classified that have undergone the spatial transformation with set parameters, causing the samples in the dataset to be classified that have undergone the spatial transformation with set parameters to be incorrectly predicted as the target label, while the remaining samples can be correctly predicted as the true label.

The present application belongs to the technical field of foundation pit excavation simulation, and discloses a method and system for predicting the displacement probability of foundation pit excavation under the condition of non-homogeneous multi-layer soil, comprising: dividing the multi-layer soil according to the engineering survey data, independently selecting key random parameters and their statistical characteristics for each layer; proposing a stratified differentiated KL expansion method, performing K-L expansion on each layer to generate a random field, and introducing a distance-weighted continuity constraint transition zone treatment at the interface between layers to ensure smooth transition of the parameters of the random field of adjacent soil layers at the interface; establishing a two-dimensional finite element model of the foundation pit in COMSOL Multiphysics software, and importing the stratified generated random field into the material parameters of the model; solving by MATLAB cyclic call to obtain the horizontal displacement of the wall after the foundation pit is excavated; finally, the probability prediction and uncertainty quantification of the horizontal displacement of the retaining wall are carried out based on statistical analysis.

This invention belongs to the field of nuclear reactor computational technology, specifically relating to a numerical calculation method for fuel rod fretting wear based on implicit compact difference. It includes the following steps: S1: The input process includes obtaining the deterministic and random parameters of the fuel rod and positioning grid, and constructing a mathematical model; S2: The processing process includes solving the constructed mathematical model; S3: The result is output. The beneficial effects of this invention are: the algorithm provided by this invention can accurately capture the dynamic response of the fuel rod, improve computational efficiency and stability, enhance nonlinear processing capabilities, and support multi-parameter uncertainty analysis, significantly improving the prediction accuracy and engineering applicability of fuel rod fretting wear.

The invention discloses a dynamic reliability analysis method for a tilt-rotor aircraft based on a direct probability integral method, and belongs to the technical field of aircrafts. Multi-body dynamics modeling and related constraint analysis are carried out on a tilt-rotor aircraft system, according to the mechanical constraint and stability requirements of the aircraft in the transition stage, the uncertainty of aerodynamic parameters in the actual operation process is considered, a variable weight sampling point strategy is adopted, sampling points are substituted into a multi-body dynamics equation, and the multi-body dynamics model is obtained. And establishing a model of the CATIA tilt-rotor aircraft and importing the model into ADAMS software for dynamic simulation to obtain a corresponding dynamic time-domain response, obtaining a dynamic time-domain response under random parameters, and calculating the extreme value reliability of the dynamic time-domain response of the tilt-rotor aircraft to obtain the dynamic reliability. According to the method, uncertainty quantification and efficient reliability analysis of the motion trail of the multi-degree-of-freedom nonlinear system under random excitation can be realized, the time-varying reliability problem of a mechanical system can be solved, and the calculation efficiency of dynamic reliability analysis of the system is improved.

The present application relates to a kind of reliability analysis method based on adaptive agent model and importance sampling, belong to the technical field of interventional medical stent, solve the low efficiency of medical stent reliability analysis in prior art, for the reliability analysis of mechanical thrombectomy stent, including the following steps: quantifying the random parameter probability distribution of mechanical thrombectomy stent, initialization algorithm parameter;Random sample is generated, and training sample set and candidate sample set are obtained;Kriging agent model of the finite element model of mechanical thrombectomy stent is constructed and trained, and updated hypersphere is established;The convergence of updated hypersphere radius is judged;The accuracy of the Kriging agent model after training is verified by leave-one-out cross-validation;Failure probability is calculated;Failure probability variation coefficient is calculated and convergence is judged, when judging convergence end program, failure probability is used for the safety evaluation and optimization design of mechanical thrombectomy stent, otherwise, candidate sample set size is expanded and random sample is regenerated.

This invention discloses a damage probability calculation method based on a machine learning classifier, comprising: establishing a damage probability calculation model for a fragmentation warhead against a target; performing a large number of random calculations on all random parameters affecting the calculation results according to the damage probability calculation model; approximating the damage probability results in a large number of case data into two classes (0 and 1) or three classes (0, 0.5, and 1); training the above case data using a classifier to obtain a damage probability prediction model; and using the damage probability prediction model to calculate the average single-shot damage probability of the fragmentation warhead against the target under specific projectile-target encounter conditions. Compared with conventional methods for calculating damage probability, this machine learning classifier-based damage probability calculation method ensures a certain level of accuracy while reducing the computational load and significantly shortening the calculation time.