16 results about "Bayesian estimator" patented technology

A signal processing apparatus includes: a data input unit to which image data is input; an output unit configured to output an output value based on the data input to the data input unit; an expectation feedback calculator configured to calculate a difference between an expectation based on the input data and the output value; and a Bayesian estimator to which information on the difference and information based on the image data are input and which is configured to perform machine learning in order to approximate the output value to the expectation based on the input information and to search for an optimum configuration.

The present application relates to a kind of wind speed prediction method based on coal mine underground gas data fluctuation similarity, belong to coal mine underground safety monitoring technical field, the method includes: at least 3 adjacent methane sensors of deployment in target roadway, collect gas concentration time series data and carry out pre-processing;Identify gas abnormal fluctuation event, extract the local and time sequence characteristics of fluctuation waveform by CNN-LSTM network, output high-dimensional feature vector;Fluctuation signal of the same source is matched using improved Siamese neural network, and the position time difference of characteristic waveform is calculated by DTW algorithm;Calculate single group wind speed in combination with sensor spacing, and obtain preliminary wind speed by weighted average;Through bayesian estimation, the optimization result of the gas concentration of multiple devices is fused, and then error feedback is realized to realize online optimization of model.The present application does not need to rely on special wind speed equipment, and has high prediction accuracy and strong robustness, can provide reliable data support for underground gas management and ventilation control.

The application discloses a kind of automobile wheel positioning parameter bayesian estimation system and method, to solve automobile wheel positioning parameter bayesian estimation and need pre-calibration before detection problem.Automobile wheel positioning parameter bayesian estimation system mainly by camera (1), wheel target (2), target connection clamp (3), clamp (4) and tripod (5).Tribrach (5) is placed on horizontal ground, target connection clamp (3) is the section U-shaped cuboid of steel plate processing and two solid and 120-140 degree obtuse angle cylinder welding parts, automobile wheel positioning parameter bayesian estimation method is by automobile wheel positioning parameter bayesian estimation data acquisition, automobile wheel positioning parameter bayesian estimation training and test, automobile wheel positioning parameter bayesian estimation weight vector optimization etc.composition of step, provide a kind of structure simple, detection precision is high, need not pre-calibrated automobile wheel positioning parameter bayesian estimation system and method.

Systems, methods, and computer program products described herein for building a prediction model with varying availability. Embodiments described herein build a prediction model that is initially trained with a selected subset of data characteristics (e.g., data characteristics that are commonly available in training data), and then augmented via Bayesian estimation with additional data characteristics when such data characteristics are available.

The application discloses a strategy for information exchange in an N-S / DSMC coupling algorithm by using a Bayesian estimation method, and comprises the following steps: in the information exchange between a DSMC calculation region based on the Bayesian estimation method and an N-S solving region, the moving temperature T tr , the rotating temperature T rot , the vibration temperature T vib and the density of the velocity gas are estimated by using DSMC statistical samples, and the estimated values are taken as the results of the DSMC and are used for information exchange with the N-S calculation. The application provides a strategy for information exchange in an N-S / DSMC coupling algorithm by using a Bayesian estimation method, can give better parameter estimation under the condition of less DSMC sampling sample amount while ensuring the consistency of the calculation results and the precision, provides the N-S solving, and effectively improves the information exchange efficiency.

A kind of ship model parameter online identification method and system based on bayesian estimation belong to ship model parameter identification neighborhood.Proposed to improve the precision of ship model parameter online identification.The technical points: the present application applies bayesian estimation theory to ship model parameter online identification, the posterior probability is obtained by likelihood function and parameter prior, the bayesian estimation result is obtained by maximum principle of posterior probability, then the data is weighted according to condition number and correlation coefficient, the precision of parameter identification is improved, the invention will be applied to real ship, and provide parameter basis for ship motion controller design and optimization.According to known online measurement data section, likelihood function is obtained;(according to bayesian maximum posterior theorem, the posterior probability is obtained by using prior and likelihood function, then the maximum value of posterior probability is obtained, the corresponding bayesian estimation result is obtained;The data is weighted according to condition number and correlation coefficient, the weighted posterior probability function is obtained, and the automatic weighting bayesian estimation method is obtained according to the above steps.

This invention discloses an external force estimation and compensation method based on power flow discrimination and Bayesian estimation, relating to the field of precision servo control technology. The main steps include: calculating the motor power flow in real time and intelligently discriminating driving, pullback, and other operating modes based on its sign; employing a dual-model Bayesian estimation architecture to couple the estimation of fast dynamic states and slowly changing system parameters, achieving effective separation of internal and external disturbances; based on the high-confidence estimation results, comprehensively calculating friction, efficiency asymmetry, and inertia compensation terms to form a total feedforward compensation torque; this torque, after undergoing multiple safety constraints such as amplitude, rate of change, and energy integral, is superimposed on the torque command of the main controller in an open-loop feedforward manner, ultimately driving the motor. This invention achieves high-precision compensation for internal friction losses in the motor system and pure estimation of external forces through intelligent mode discrimination based on power flow and dual-model Bayesian collaborative estimation.

The application discloses a kind of based on manned submersible's submarine pipeline leak source searching method and system, method includes: the system model of establishing including submarine oil spill diffusion model, submersible upper layer control model and oil concentration sensor model;Based on the estimation framework of bayes, fusion real-time measurement information, adopt particle filter iterative update the posterior probability distribution of leak source state;Based on posterior distribution, adopt exploration and development dual control strategy to construct objective function, solve optimal control quantity and expected target position;According to target position, based on the dynamics model of submersible, design based on interference observer's backstepping controller drive submersible accurate tracking.The application realizes the autonomous positioning of leak source under complex submarine environment, with strong adaptability, high search efficiency, the advantages of good anti-interference ability, suitable for emergency search task of sudden leakage.

The application relates to a commercial vehicle load spectrum threshold determination method based on a simulation goodness-of-fit criterion, which comprises the following steps: S1, extracting a load turning point capable of representing fatigue damage to form an initial load sample for analysis; S2, constituting an initial candidate threshold set, presetting a threshold, and further determining a threshold-exceeding model; S3, performing a Bayesian estimation method based on the distribution of the threshold-exceeding model; S4, performing model goodness-of-fit determination on the Bayesian estimation method result by using a technique for order preference by similarity to ideal solution (TOPSIS); and S5, determining an optimal compromise solution according to the model goodness-of-fit. The commercial vehicle load spectrum threshold determination method based on the simulation goodness-of-fit criterion realizes objective trade-off of multiple fitting criteria, ensures that the comprehensive fitting effect of the Bayesian estimation method is optimal, makes extrapolated extreme load more reliable, significantly improves the accuracy and reliability of engineering structure fatigue life prediction, and provides a brand-new technical approach for commercial vehicle load spectrum analysis.

This invention relates to the field of product reliability analysis technology, specifically to a reliability assessment method for a non-fixed shape parameter dependent competing failure model. The method includes the following steps: constructing a dependent competing failure model, designing an adaptive stepwise type II hybrid truncated constant-load life test, estimating parameter points based on test data, establishing an acceleration equation, calculating product reliability under normal stress, and performing interval estimation. This invention constructs a MOBG distribution description that assumes fixed shape parameters and does not consider the dependencies between unknown failure mechanisms and causes. It designs an adaptive stepwise type II hybrid truncated constant-load life test to obtain data, solves for parameters based on maximum likelihood estimation and Bayesian estimation, and combines the acceleration equation to obtain parameter estimates under normal stress. This leads to the calculation of product reliability and interval estimation, improving the accuracy and applicability of reliability assessment. It is suitable for reliability analysis of various products with multiple failure mechanisms and potentially unknown causes.

This application provides a method, system, device, and storage medium for measuring the external field parameters of a superconducting quantum system, relating to the technical field of quantum information computing. The method for measuring the external field parameters of a superconducting quantum system includes: performing quantum evolution at multiple preset evolution times on multiple first preset external field parameters based on a target Hamiltonian and an initial state to determine first quantum state data; determining a target probability set based on the first quantum state data; performing quantum evolution at multiple preset evolution times on second preset external field parameters based on the target Hamiltonian and the initial state to determine second quantum state data; performing joint time-domain Bayesian estimation based on the target probability set, the second quantum state data, and a Bayesian estimation framework to determine the target posterior probability distribution corresponding to the second preset external field parameters; and determining the target external field parameters based on the target posterior probability distribution and the second preset external field parameters. This application can achieve stable and high-precision measurement of unknown external field parameters.

The present invention relates to the field of ship engineering, and specially discloses a method for measuring space angle of a dredger draghead pipe, wherein the method collects in real time the motion data of the hull and the draghead pipe, removes high-frequency noise through median filtering, extracts features and constructs a comprehensive feature vector, separates nonlinear influences of hull motion and draghead pipe attitude by a fuzzy inference system, and then based on the real-time error feedback, corrects the attitude estimation, and dynamically optimizes the estimation and corrects system bias through Bayesian estimation and a particle filtering algorithm, thereby improving measurement accuracy; the system also evaluates the overall measurement accuracy by means of a machine learning model, and automatically adjusts measurement parameters and procedures to improve system adaptability and robustness; for inaccurate measurement results, the system will analyze the causes and perform space angle measurement again to ensure long-term stable operation.

This invention discloses an ecological causal modeling and collaborative optimization method based on large-scale Bayesian networks, relating to the field of ecological prediction. The method includes: acquiring multi-source historical environmental data of a target ecological region; constructing a Bayesian network model; training the Bayesian network model using the multi-source historical environmental data, wherein the Bayesian network model, based on the multi-source historical environmental data, uses a scoring search method to construct a directed acyclic graph (DAG) representing the causal relationships between variables; based on the DAG, Bayesian estimation methods are used to learn the Bayesian network parameters, and the conditional probability distribution of each variable is determined using a Bayesian Dirichlet equivalent consistent prior as the prior distribution; inputting the multi-source environmental data to be tested into the trained Bayesian network model, and determining the prediction result corresponding to the target variable based on the conditional probability distribution and the multi-source environmental data to be tested; and determining the ecological protection measures corresponding to the target ecological region based on the prediction results.

This application relates to a method, system, device, and medium for predicting ethical risks in multi-agent collaborative systems. The method includes: acquiring real-time interaction logs of multiple agents in the education field, obtaining a dynamic change sequence of interactions through time-series analysis; dividing time stages based on interaction frequencies exceeding a preset threshold, calculating a stage risk evolution index, and quantifying the mean value by combining it with core ethical risk indicators to generate an educational ethical risk early warning vector; based on this vector, training a Bayesian network using Bayesian estimation to obtain a joint probability distribution of risk evolution; generating risk evolution paths from the distribution using the Monte Carlo method, and identifying propagation path types using a machine learning classification model; inputting this type of time-series data into a pre-trained long short-term memory network, and weightedly fusing it with real-time interaction data to generate educational ethical risk prediction parameters. This method can improve the timeliness and accuracy of risk early warning, avoid related ethical risks, and contribute to the standardized and safe development of artificial intelligence education.

The present application relates to crane training technical field based on thermal imaging crane wisdom examination method, examination system and medium. Crane wisdom examination method includes: obtaining infrared heat source data, coordinate of crane structure node, and skeleton node coordinate of operator, and processing to obtain high-dimensional feature vector Z , linear mapping obtains preliminary score. Through fuzzy logic rule, bayesian estimation method is handled to X With X It is input into evaluation neural network to obtain secondary score. The preliminary score and the secondary score are fused to obtain the final score, if the final score is greater than the threshold, the examination passes. The present application combines infrared thermal imaging technology and skeleton recognition technology, can capture the dominant behavior of the examinee operation and its hidden influence on the equipment at the same time, and reflect this potential bad operation habit in the final score, so as to fully reflect the real-time state and actual ability of the operator.