85 results about "Uncertainty estimate" patented technology

The invention discloses a flowmeter self-calibration method and system based on big data analysis. The method comprises the steps that multi-source data collection is conducted, and multiple kinds of data are collected and stored in a big data platform with a block chain evidence storage function; data preprocessing: adopting multiple algorithms to improve data quality; feature extraction and analysis: identifying key factors and monitoring fluid states by means of multiple technologies; calculating calibration parameters, and considering various coefficients and uncertainty analysis; performing automatic calibration, encrypting and writing parameters after digital twin simulation, and reserving a copy; and verifying the calibration effect by adopting a multi-mode verification mechanism. The system comprises a plurality of functional modules which work cooperatively. According to the invention, the data acquisition comprehensiveness is improved, the calibration period is shortened, the production efficiency is improved, the data integrity and reliability are ensured, key factors are accurately identified, the fluid state is monitored in real time, the calibration parameter adjustment is simulated, and the calibration effect is verified in a multi-mode manner, so that the reliability and accuracy of a flowmeter measurement system are improved.

The invention provides an aviation transient electromagnetic fast Bayesian inversion method, device and equipment, and relates to the technical field of geophysical signal processing and analysis, and the aviation transient electromagnetic fast Bayesian inversion method comprises the steps: obtaining a resistivity model and the height of a transmitting coil; the resistivity model and the transmitting coil height serve as input data, electromagnetic response data serve as output data, a deep learning network model is trained, and a forward modeling model is obtained; coupling the forward modeling simulation model to a parallel Bayesian inversion framework to generate a target network model; and performing resistivity inversion and quantitative uncertainty analysis on to-be-analyzed aviation transient electromagnetic data based on the target network model to obtain an analysis result. By integrating a deep learning forward simulator into a Bayesian inversion framework, rapid Bayesian inversion and quantitative uncertainty estimation of aviation transient electromagnetic data can be realized, and the inversion efficiency is greatly improved.

The invention provides a method and system for measuring resistance in an underwater vehicle towing test, and relates to the technical field of underwater vehicle hydrodynamic performance testing, and the method comprises three links of gravity center adjustment, measurement data processing and uncertainty analysis. Firstly, it is ensured that the gravity center and the buoyancy center of a test model are consistent in height, model attitude stability is ensured, and resistance measurement errors caused by attitude changes are eliminated. Secondly, measuring the sailing resistance of the test model in the towing test, and post-processing the measured data to obtain the total resistance coefficient of the test model at the test temperature; and finally, quantizing errors of each link and synthesizing expansion uncertainty by adopting a deviation limit and precision limit combined uncertainty analysis method. Compared with a traditional single-index evaluation method, the method not only can reveal error sources and quantify the influence of each link on the measurement result, but also can accurately reflect the composite effect of errors, and ensures the reliability of the resistance measurement result.

The invention relates to the technical field of CPU card chips, and discloses a CPU card chip test method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of a CPU card chip, and obtaining an initial test data matrix; constructing a test parameter association diagram of the initial test data matrix and predicting missing data to obtain a complete test data matrix; performing principal component analysis on the complete test data matrix to obtain an effective test data set; constructing a multi-level fuzzy set according to the effective test data set to obtain fusion test data; and inputting the fusion test data into a variational auto-encoder for self-supervised pre-training, executing conditional interpolation by using a diffusion state space model to obtain a defect detection result and an uncertainty estimation value, and realizing optimal fusion of electrical characteristics and functional test data by using a multi-stage fuzzy set and an adaptive weight adjustment mechanism. Complementary advantages of dual-channel data are fully utilized, and a more comprehensive and accurate test result is provided.

In various examples, systems and methods for uncertainty estimation for object detection in autonomous and semi-autonomous systems and applications are provided. The systems and methods may use data from one or more sensors (e.g., camera(s) and / or LiDAR sensor(s) to generate a representation of features surrounding a machine. A model may be used to generate probabilities of objects being present in the representation of features and uncertainty estimates corresponding to the object presence probabilities. The uncertainty estimates may be used to identify scenes that are significantly different from the training data, detect errors in the bounding shapes for objects, and / or highlight areas where object detections may have been missed. The systems and methods may also be used to auto-label scenes associated with the representation of features, and the auto-labeled scenes may be used for training purposes.

The invention provides an end-to-end automatic driving behavior decision-making method and system considering uncertainty estimation, and the method comprises the steps: inputting image information into a convolutional neural network, carrying out the feature extraction, obtaining the state information of surrounding traffic participants, carrying out the feature fusion of the state information of a vehicle, and inputting the fusion features into a full connection layer; constructing an integrated quantile network to obtain complete uncertainty estimation of an agent decision result, wherein the complete uncertainty estimation comprises data uncertainty estimation based on an implicit quantile network and model uncertainty estimation based on a multi-model integrated network considering prior Bayesian estimation; evaluating the confidence coefficient of the agent decision result, if the confidence coefficient is high, outputting the optimal driving action of the vehicle predicted by the integrated quantile network, and if the confidence coefficient is low, switching to the braking strategy output; and a control module of the end-to-end automatic driving decision model agent is further input, and a control signal is output to enable the vehicle to execute different control actions. According to the invention, the reliability and credibility of end-to-end automatic driving behavior decision are improved.

Disclosed is a method and device for determining uncertainty measure of a classification model. The method includes: obtaining a sample data set for evaluating the uncertainty of a classification model, each sample data including an original evaluation sample and a corresponding classification label; inputting the original evaluation sample into the classification model to obtain a first predicted classification result, which includes a first predicted category and a first predicted probability; calculating the first prediction loss of the classification model according to the difference between the first predicted probability and the classification label to obtain a first uncertainty estimate; calculating the first calibration error of the classification model according to the difference between the first predicted category and the classification label and the first predicted probability to obtain a second uncertainty estimate; determining the uncertainty measure of the classification model based on at least the first uncertainty estimate and the second uncertainty estimate. The classification model uncertainty measure determination method according to the present application can achieve applicability in a wide range of application scenarios.

The invention provides a method and system for on-line determination of molten steel carbon content in converter steelmaking, and relates to the technical field of converter steelmaking, the method comprises the following steps: obtaining multi-source process data, the multi-source process data comprising exhaust gas components, furnace mouth flame multispectrum and process parameters; performing time synchronization and feature extraction on the multi-source process data to generate a feature vector; inputting the feature vector into a carbon content prediction model to obtain a carbon content prediction value and an uncertainty estimation value thereof; the molten pool temperature is obtained, and based on the molten pool temperature, the waste gas components and the technological parameters, a theoretical carbon content value is calculated through a thermodynamic carbon content calculation model; and carrying out weighted fusion on the carbon content predicted value and the theoretical carbon content value to generate a fused carbon content as a target carbon content. According to the method and system for online determination of the carbon content of the molten steel in converter steelmaking, the precision and reliability of online determination of the carbon content of the molten steel can be effectively improved, and powerful support is provided for intelligent production of converter steelmaking.

A computer-implemented machine learning (ML) method is provided. The method includes computing a labeling matrix by applying a set of labeling functions (LFs) to data points of an unlabeled dataset. A projected labels matrix is generated by computing, based on the labeling matrix, LFs labels projections to undefined labels. An uncertainty of a respective label of the each labeled data point is estimated for each labeled data point based on an output of the LFs and the LFs labels projections. Data points are selected depending on the uncertainty estimated for the respective label of the each data point, and a labeling request for the selected data points is submitted to an oracle and updating the labeling matrix according to responses of the oracle.

The invention discloses a high-temperature gas cooled reactor control rod reactivity value measurement method and system, and relates to the technical field of high-temperature gas cooled reactor debugging and operation, and the method comprises the following steps: arranging measurement equipment to lift a to-be-measured control rod group to an upper limit, maintaining the reactor power stable operation, and carrying out initial data acquisition; calculating the theoretical reactivity value and neutron kinetic parameters of the control rod in a specific state, quickly inserting the control rod group to be measured to a lower limit, and recording data change; the reactivity value of the control rod is calculated, and the reactivity value obtained through calculation is corrected according to the initial power state of the reactor; and carrying out uncertainty analysis on the corrected reactivity value, and comparing a measurement result with a theoretical calculation value to optimize a measurement strategy. According to the invention, the time required for measurement can be reduced, the measurement precision is improved, the accuracy of the measurement result is enhanced, the measurement strategy is more perfect through continuous optimization, and the measurement result is more reliable.

A method for training an artificial intelligence (AI) model includes receiving a training dataset and utilizing processing circuitry to train a Bayesian Neural Network (BNN) based on the dataset and a selected training algorithm including backpropagation. The method estimates aleatoric and epistemic uncertainty for each sample in the dataset. Based on these uncertainty estimates, weights are assigned to the samples, prioritizing those with lower aleatoric uncertainty. A conditioned training dataset is generated by increasing the weights of these prioritized samples. The AI model is then trained using this conditioned dataset. Finally, the pre-trained AI model outputs a prediction. This approach improves model performance by focusing on data with more predictable characteristics, reducing bias and enhancing prediction reliability.

The invention discloses a storage tank measurement data processing method, computer equipment and a storage medium, and relates to the technical field of measurement modeling, and the method comprises the steps: collecting multi-physics field operation parameter data, carrying out the filtering, interpolation and abnormity elimination, and obtaining a multi-dimensional original data set; establishing a digital twin model based on the data set and calculating an initial parameter set; collecting real-time observation data, and executing residual optimization inversion to obtain a correction parameter set; performing uncertainty analysis and confidence weighted fusion by using the correction parameter set to obtain a fusion measurement parameter set; constructing a multi-objective cost function and updating the weight by adopting an exponential gradient evolution algorithm to obtain feed-forward control intensity; and in combination with the correction parameter set and the feedforward control intensity, carrying out cross-storage-tank robust aggregation and gating judgment to obtain new digital twinborn model parameters. According to the method, self-adaptive fusion and model closed-loop evolution of multi-source metering information are realized through confidence weighted fusion and an exponential gradient evolution weight optimization mechanism and by executing cross-storage-tank robust aggregation.

The invention discloses an electronic injection fuel injection strategy intelligent optimization method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing multi-source working condition data of an engine, and generating a standard data set; s2, constructing a Bayesian neural network model, and outputting an integrated fuel injection parameter prediction value and an uncertainty estimation value; s3, optimizing model structure parameters and hyper-parameters by using an ant lion optimization algorithm to obtain an optimal structure and parameters; s4, training the model by using the optimal structure and parameters, and performing performance evaluation by using a standard data set; s5, collecting working condition data in real time, and outputting an optimal integrated fuel injection parameter prediction value and an uncertainty estimation value; and S6, periodically collecting feedback data, training the model in combination with the standard data set increment, and re-executing ant lion optimization in good time. According to the method, high-precision intelligent optimization of the oil injection strategy of the engine is achieved, the fuel economy is improved, emission is reduced, and the self-adaptive capacity under the complex working condition is enhanced.

A software tool for predicting candidate recipes for a food product using (a) a predictor model that is trained to output, for a given candidate recipe that is passed as input to the predictive model, (i) a predicted value of at least one target variable and (ii) predicted values of a given subset of assessment variables, and (b) a generator model that functions to: (1) train an underlying predictive model that is configured to output (i) predicted values of the at least one target variable for a space of possible recipes and (ii) uncertainty estimates for the predicted values, and (2) select candidate recipes from the space of possible recipes based on (i) a balancing between the predicted values and the uncertainty estimates output by the underlying predictive model and (ii) the set of constraints.

The invention discloses a mechanical arm visual servo and safe obstacle avoidance method based on a disturbance observer, and belongs to the field of visual servo control of robots. Establishing a second-order model of visual servo based on the image and constraint of visibility of the feature object; enveloping simplification of the mechanical arm is completed, and safety constraints related to the robot are established; regarding an item which is not easy to calculate in the visual servo model as uncertainty, and designing an extended state observer to observe the pixel speed and the uncertainty; establishing a CBF constraint according to a safe distance constraint, an angular velocity and an angular acceleration constraint under an ideal condition, and then establishing a CBF considering uncertainty; and finally, designing a reference controller considering uncertainty, and solving a safety optimization problem based on the CLF of the preset time and the optimization solving objective function. The method is high in efficiency and good in stability, it can be guaranteed that the robot arm can rapidly estimate the uncertainty, and the uncertainty estimation value can be applied to the safety constraint after being obtained.

The invention discloses a medical image segmentation model learning system based on voxel uncertainty. The medical image segmentation model learning system comprises five functional modules: an image acquisition module, a region-of-interest sketching module, an image preprocessing module, an uncertainty guidance image segmentation model training module and a medical image segmentation model testing module. According to the system, an end-to-end voxel uncertainty guided medical image segmentation model learning method is provided, an accurate segmentation result and reliable uncertainty estimation are generated under the condition that calculation burden and complexity are not increased too much, and the performance, robustness and interpretability of a medical image segmentation model are improved at the same time.

The invention discloses a damping adjusting method, device and equipment of an anti-snake-shaped shock absorber and a storage medium, belongs to the field of train shock absorption, and aims to consider that other disturbed quantities except the anti-snake-shaped shock absorber can be regarded as a whole (sum uncertainty) in relative head shaking angle information of a bogie. Therefore, the method comprises the following steps: firstly, determining a dynamic differential relational expression of the relative head shaking angle information of the bogie about the damping coefficient of the anti-serpentine damper and the sum uncertainty, and then determining a sum uncertainty estimator (used for outputting a sum uncertainty estimated value) of the bogie; according to the method, the target damping coefficient of the anti-snakelike damper can be determined based on the dynamic differential relation on the basis of the total uncertainty estimated value and the measured value of the relative head shaking angle information, so that the damping coefficient is dynamically adjusted, the method can adapt to complex and changeable operation conditions, and the transverse stability and riding comfort of a train are improved.

The invention provides an OTA test uncertainty analysis method and device, and the method comprises the steps: measuring an EIS pattern # imgabs0 # of a main antenna and an EIS pattern # imgabs1 # of a diversity antenna, carrying out the offset of # imgabs2 # according to a preset penalty term, obtaining a biased EIS pattern # imgabs3 #, obtaining a combined pattern # imgabs6 #, carrying out the calculation of a standard TIS calculation method, and obtaining a TIS value CTIStd-i-j, calculating to obtain a TIS value CTISSPOT-i-j by adopting a single-point compensation method, and calculating a difference value of the TIS value CTISSPOT-i-j; for each group of EIS directional diagrams and a preset penalty term, repeating the above calculation process to obtain a preset number of difference samples; taking the statistical distribution parameter value of the difference value sample as an uncertainty value introduced by the single-point compensation method in the multi-antenna receiving scene; judging whether the uncertainty value is within a preset limit value range or not; if yes, judging that the single-point compensation method meets the accuracy requirement of the OTA test in the multi-antenna receiving scene; and if not, judging that the single-point compensation method does not meet the accuracy requirement of the OTA test in the multi-antenna receiving scene.

Techniques for preparing datasets for geo experiments and improving accuracy of geo experiments are presented herein. The system can access a dataset of a plurality of geographic pairs. Additionally, the system can calculate a first outcome estimate based on a difference in response data and a difference in input data for a first geographic pair. Moreover, the system can calculate a plurality of experimental uncertainty estimates associated with the plurality of geographic pairs during an experimental time interval. The system can access historical data associated with the plurality of geographic pairs. Furthermore, the system can determine a beta value and a trim rate that reduces a sum of the plurality estimates. Subsequently, the system can remove, based on the first outcome estimate and the beta value, the first geographic pair from the plurality of geographic pairs to generate the first subset of geographic pairs.

The invention provides a wave-rider aircraft aerodynamic data uncertainty analysis method and device, and the method comprises the steps: obtaining the numerical calculation aerodynamic data of a wave-rider aircraft; factors influencing the uncertainty of the numerical calculation pneumatic data are determined; carrying out uncertainty analysis on the basis of pneumatic data, calculating model uncertainty by adopting a range method, designing an orthogonal test according to factors, calculating numerical value and input parameter uncertainty, and carrying out significance analysis on factor influence and interaction according to the orthogonal test; and calculating the total uncertainty according to the model uncertainty, the numerical value and the input parameter uncertainty, and verifying the reliability of the total uncertainty by adopting new sample data. According to the method, the uncertainty of the pneumatic data is comprehensively and effectively obtained, meanwhile, the influence factors of the uncertainty of the numerical calculation pneumatic data and the significance of the interaction are visually obtained, and support is provided for the reliability of the numerical calculation pneumatic data.

The invention relates to the technical field of image processing, and discloses an oral squamous cell carcinoma invasion depth measurement method and system based on artificial intelligence. The method comprises the steps of performing standardization processing on oral tissue slices through a three-dimensional preprocessing algorithm to obtain a tumor mask image and a confidence map, then performing multipoint sampling deployment of a virtual measurement agent on the tumor surface, detecting feasibility of an invasion path by using a tissue resistance model, performing multi-branch feature extraction and deep regression calculation through an OSCC-DepthNet network, and finally obtaining the tumor mask image and the confidence map through the OSCC-DepthNet network. And obtaining an invasion path and a depth value of each agent, fusing multi-agent measurement results by adopting a confidence weighting algorithm, and outputting an oral squamous cell carcinoma invasion depth value and an uncertainty estimation value. The technical problems that an existing oral squamous cell carcinoma invasion depth measurement method is insufficient in three-dimensional space measurement capacity, too high in calculation complexity, poor in real-time performance and lack of an effective uncertainty quantification mechanism are solved.

A computer-implemented method utilizing a continuous discrete recurrent Kalman network, wherein the method includes receiving, at an encoder, an input from one or more sensors, wherein the input includes one or more time series data associating data at one or more points in time; outputting, to a Kalman filter, a latent observation and uncertainty estimate in response to the input at the encoder; determining a latent state prior and latent state posterior utilizing the Kalman filter; and outputting, via a decoder, a filtered observation utilizing at least the latent state posterior.

The present application discloses a method for estimating uncertainty of a neural network model and related equipment, belonging to the field of data processing. The present application inputs data to be analyzed, performs prediction processing on the data to be analyzed based on a preset neural network model, and obtains analysis results of the data to be analyzed, wherein the analysis results include predicted values ​​of the data to be analyzed and uncertainty estimates of the preset neural network model; the preset neural network model is obtained by training undetermined parameters of target model parameters in a preset model to be trained, and the target model parameters are obtained by replacing original model parameters in the preset model to be trained with spike and slab distributions from point values; the analysis results are obtained by randomly sampling the target model parameters in the preset neural network model obtained after training, and predicting the data to be analyzed based on the target neural network model obtained by random sampling.

The present invention provides a model-based offline reinforcement learning training method that includes policy constraints and uncertainty estimation. First, the dynamic model is updated: the number of set members, the number of model forward predictions, and the model set, policy network, and value function network are initialized; static data are randomly sampled from the static data set and the model is updated based on it, and this step is repeated until the model converges. Secondly, the policy is updated: the sampled static data is predicted multiple times through the model to obtain predicted data and uncertainty estimates and the predicted rewards are subtracted from the uncertainty to obtain dynamic data; the dynamic data is put into the experience pool and static data and dynamic data are sampled from the static data set and the experience pool; the state in the dynamic data is input into the policy network and the model to obtain the prediction of the next state; the policy network and the value function network are updated through the static data, dynamic data, and the prediction of the next state, and the above-mentioned policy update steps are repeated until the policy network converges.

The application provides a rotary kiln energy consumption optimization method and system combined with visual recognition, comprising: preprocessing kiln operation data, and establishing a unified fusion feature representation; adopting a stacked long short-term memory network to process long-term dependence relationship, and simultaneously processing local time sequence mode through a multi-layer expansion convolution, and weighting and fusing the two kinds of features through a self-attention mechanism; in order to improve prediction reliability, training a plurality of basic models with different initializations and architectures, and applying a Bayesian model average technology to obtain a point estimate value, a prediction interval and an uncertainty estimate; based on the prediction model, constructing a graph structure representation of a kiln process parameter space, calculating a predicted coal consumption value of each parameter combination, thereby identifying an optimal process parameter combination, and realizing kiln energy efficiency optimization. The application improves coal consumption prediction accuracy and process parameter optimization efficiency.

The invention relates to an input parameter influence analysis method and system for nuclear power plant accident consequence evaluation, a medium and equipment. The method comprises the following steps: acquiring parameters required for evaluation; screening and setting parameters required by evaluation to generate a parameter information table; performing random sampling based on the input parameters to generate a random sampling matrix; calculating by using a random sampling matrix to obtain an accident consequence evaluation result; analyzing uncertainty and sensitivity based on the accident consequence evaluation result, and obtaining an uncertainty analysis result and a sensitivity analysis result of the input parameter; performing comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result; and determining an influence result of the input parameters according to the comprehensive analysis result. The uncertainty and sensitivity of the input parameters are analyzed, the influence degree of each parameter on the accident consequence evaluation result is effectively quantified, it is ensured that the nuclear power plant can make an accurate decision in time when a potential accident occurs, and the public and environment safety is effectively protected.

The invention relates to a belt weigher weighing precision optimization method based on uncertainty analysis. The method comprises the steps that multi-source reference data are acquired and synchronously collected, and standard weight information of a truck scale, real-time data information of a belt weigher and environmental parameter information of the same material batch are synchronously acquired; according to the standard weight information of the truck scale, the real-time data information of the belt weigher and the environmental parameter information, A-class uncertainty is calculated, a B-class uncertainty three-dimensional decomposition calculation model is constructed to calculate B-class uncertainty, and the uncertainty is synthesized to calculate the total uncertainty; component contribution analysis is carried out according to the error size of each component; gradient error two-dimensional control, wherein the gradient error two-dimensional control comprises hardware leveling, algorithm compensation, vibration control and temperature compensation; the comparison test is repeated after optimization, if the measurement error is not smaller than the expected threshold value, the gradient error two-dimensional adjustment control step is repeated until the measurement error is smaller than the expected threshold value, and the method has the advantages of achieving accurate targeted optimization of all error sources and the like.

This application describes a method for automatically labeling data samples with uncertainty quantification. The proposed method comprises the steps of simultaneously feeding an input data sample into an online object detection module with uncertainty estimation and an off-board object detector with uncertainty estimation; comparing the resulting data from both the online classification and uncertainty estimation module and the off-board classification and uncertainty estimation module; and deciding whether to discard the input data sample, send it for human labeling, or archive it along with the classification and uncertainty estimation from the off-board object detection module with uncertainty estimation.These uncertainty estimates are then used in the training phase of subsequent object detectors to weight different samples differently, with human-labeled samples and automatically labeled samples with high confidence receiving more weight.

The present invention belongs to the technical field of high-speed wind tunnel tests, and discloses a method for analyzing the uncertainty of drag measurement of a high-speed wind tunnel airfoil model. The method for analyzing the uncertainty of drag measurement of a high-speed wind tunnel airfoil model of the present invention comprises the following steps: installing an airfoil model and a wake rake; conducting a high-speed wind tunnel airfoil model drag measurement test; calculating the static pressure of the incoming flow of the high-speed wind tunnel; and calculating the uncertainty of drag measurement of the high-speed wind tunnel airfoil model. The method for analyzing the uncertainty of drag measurement of a high-speed wind tunnel airfoil model of the present invention, through rigorous theoretical derivation and high-speed wind tunnel tests, uses an uncertainty transfer method to establish a method for analyzing the uncertainty of drag measurement of a high-speed wind tunnel airfoil model, which can provide reliable uncertainty of drag measurement of an airfoil model, thereby improving the reliability of high-speed wind tunnel test results, and has engineering application value.

The invention discloses an uncertainty analysis method and system for ship quay wall effect hydrodynamic force and a medium. The method comprises the following steps that a test error source is determined; calculating the deviation limit of the ship quay wall effect hydrodynamic force; calculating the precision limit of the ship quay wall effect hydrodynamic force; and calculating the uncertainty of the ship quay wall effect hydrodynamic force. According to the method, test error sources of ships limiting water areas are classified, influence factors of water depth and ship-shore distance are introduced into measurement of hydrodynamic force deviation, a calculation method of ship model navigational speed uncertainty is provided based on a circulating water tank, and a function relationship between ship shore wall effect hydrodynamic force and each test error source is established. The reliable hydrodynamic uncertainty can be obtained, and technical support is provided for analyzing the uncertainty of test results.