18 results about "Bayesian algorithm" patented technology

This invention discloses an underwater acoustic anti-interference transmission method and system based on sparse time-frequency feature mapping. The method includes: at the transmitting end, adaptively generating a fractional-order linear frequency-modulated waveform with a specific time-frequency shear slope based on the Doppler state of the underwater acoustic channel, achieving physical-layer focusing of transmitted energy; at the receiving end, constructing a hybrid observation model containing wide-block sparse channel components and narrow-block sparse burst noise components, and using a dual-channel variational Bayesian algorithm to jointly iteratively infer the posterior probability distribution of environmental burst noise in the fractional-order domain; finally, recovering the original signal through soft-threshold interference cancellation and fractional-order channel equalization. This invention effectively solves the communication failure problem caused by high-dynamic Doppler diffusion and marine biological impulse noise interference in underwater acoustics by actively mapping the waveform and using heterogeneous sparse joint inference at the receiving end, significantly improving transmission reliability in harsh underwater acoustic environments.

This application discloses a method, system, device, and medium for generating game parameters, relating to the technical field of game design. The method includes: performing correlation mining on the multidimensional behavioral data to obtain an emotional state feature sequence; constructing a personalized emotional response model for the target player based on the emotional state feature sequence, and generating a target emotional state feature trajectory for the target player based on an emotional change template and the personalized emotional response model; acquiring historical game interaction data and constructing an emotional state prediction model based on the historical game interaction data; calling the emotional state prediction model and searching in the game plot parameter space using a Bayesian algorithm to generate target game plot parameters, such that the emotional state feature prediction sequence corresponding to the target game plot parameters matches the target emotional state feature trajectory. This application has the effect of enhancing the personalized immersive experience of players in the game plot.

The application discloses an energetic material processing parameter optimization method and system based on a force-heat coupling model, and relates to the technical field of intelligent simulation optimization based on specific calculation models. In view of the problems that 3D printing and mechanical processing data are disconnected, and common force-heat coupling models are not suitable for energetic materials in the prior art, the method of the application first collects 3D printing operation and initial state data of the grain, then constructs a force-heat two-way coupling intelligent calculation model special for energetic materials, adopts a Bayesian algorithm to multi-objective optimize processing parameters based on the model, and carries out model-driven closed-loop dynamic processing control. The method of the application realizes full data connection and adaptation to material characteristics, takes into account safety, precision and efficiency, and improves the processing stability and consistency of the energetic material.

The application discloses an intelligent health monitoring system based on electroencephalogram signals, and relates to the technical field of intelligent medical treatment, comprising the following steps: constructing a user personalized neural baseline model through small sample calibration and meta-learning; carrying out daily monitoring based on the model, and dynamically updating the model by using an online Bayesian algorithm to track long-term physiological drift of the individual; when significant neural baseline drift is detected, automatically matching and triggering intervention measures; constructing an anti-fact causal inference model to quantify the net effect of the intervention measures on the neural baseline; generating a personalized health insight report based on the net effect; and driving the iterative evolution of the neural baseline model through reinforcement learning by using closed-loop data containing drift, intervention measures and net effect, so that the application realizes intelligent health management with long-term self-adaptation, accurate quantitative intervention effect and self-evolution ability.

Disclosed in the present application are a method and apparatus for intelligent judgment of a shield tail seal failure. The method comprising: acquiring gap data between segments and a shield tail, external soil pressure data and grease state data; inputting the gap data into a shield attitude anomaly warning model, and outputting a first prediction result; inputting the external soil pressure data into an external soil anomaly warning model, and outputting a second prediction result, the external soil anomaly warning model being used for determining the turning point of a sudden change of an external soil pressure; inputting the grease state data into a grease anomaly warning model, and outputting a third prediction result, the grease anomaly warning model being used for determining, on the basis of the grease state data, whether grease contains water, whether the grease temperature is abnormal, and whether the grease pressure is abnormal; and on the basis of a Bayesian algorithm, fusing the first prediction result, the second prediction result and the third prediction result to output an overall prediction result.

This invention provides an AI-based multimodal network information operation and maintenance system, belonging to the field of network information operation and maintenance technology. The system collects heterogeneous operation and maintenance data such as network traffic, device status, audio alarms, and thermal imaging through a data acquisition unit. It utilizes a multimodal feature extraction module to extract various features in parallel, forming a unified multidimensional feature vector. A hierarchical anomaly detection architecture is established, deploying lightweight and deep anomaly detection models at the edge and cloud respectively. A multimodal feature fusion model intelligently fuses different modal features through an attention mechanism. An adaptive threshold dynamically adjusts the Bayesian algorithm to optimize the detection threshold in real time based on network status. A multimodal fusion decision engine is constructed to weight and fuse anomaly detection results and dynamically adjust modal weights, thus solving the technical problem of insufficient accuracy in multimodal operation and maintenance data fusion processing.

The application provides a rail transit station distribution transformer power quality comprehensive evaluation method and system, comprising: acquiring power quality index data within a given time; constructing a segmented nonlinear single score model according to the preprocessed power quality index data, and obtaining power quality single score according to the single score model; wherein the single score model is constructed based on the historical deviation data of each power quality index collected in the preset sliding window; the initial weight corresponding to each index is determined by the entropy weight method, and the Bayesian algorithm is used to fuse each initial weight to obtain the final weight; based on the final weight and the power quality single score, the comprehensive score result is obtained through the pre-constructed comprehensive score model, and the power quality comprehensive evaluation is carried out according to the comprehensive score result. The application comprehensively measures the power quality from multiple dimensions, and realizes objective and dynamic evaluation of the power quality of the rail transit station distribution transformer.

This invention relates to a method and apparatus for tuning control parameters of magnetic levitation bearings, a storage medium, and an electronic device, belonging to the technical field of magnetic levitation control parameters. The tuning method includes: S1, constructing a mathematical model of a PID parameter optimization problem; S2, generating a Bayesian optimization sample set, fitting a Gaussian process surrogate model, and globally exploring to generate a PID parameter subspace; S3, executing a dung beetle optimization algorithm within the subspace to obtain the optimal point (x) within the subspace. db y db S4, add the optimal point to the Bayesian optimization sample set and update the Gaussian process model; S5, repeat steps S2 to S4 until the condition is met, and output the optimal PID parameter x. db * By combining the global exploration capability of Bayesian algorithms with the local development capability of the dung beetle algorithm, the Bayesian optimization reduces the dependence on the initial sample size, while the dung beetle algorithm accelerates convergence within the subspace, thus balancing efficiency and accuracy.

This invention discloses a method, apparatus, and device for predicting the remaining life of an automotive drive shaft, relating to the field of remaining life prediction technology. The method includes: constructing a damage accumulation model for the automotive drive shaft and setting a prior probability distribution for the parameters to be corrected in the damage accumulation model; collecting multiple types of monitoring data of the automotive drive shaft during operation, and constructing a fusion feature vector to characterize the damage state of the automotive drive shaft based on the multi-type monitoring data; constructing a likelihood function based on the fusion feature vector to represent the mapping relationship between the fusion feature vector and the parameters to be corrected; calculating the posterior probability distribution of the parameters to be corrected using the likelihood function and the prior probability distribution based on a Bayesian algorithm, and correcting the damage accumulation model according to the posterior probability distribution; and calculating the current cumulative damage value of the automotive drive shaft to predict its remaining life. This invention corrects the parameters to be corrected in the damage accumulation model in real time, improving the accuracy of drive shaft life prediction.

The application relates to a traffic flow detection method based on improved YOLOv8 and DeepSORT, which comprises the following steps: S1, acquiring a training data set; S2, obtaining initial values of hyperparameters of a target detection model; S3, after the target detection model is trained based on the training data set for multiple times, whether a first exit condition is met is judged, if yes, step S6 is executed, otherwise, step S4 is executed; S4, a Pareto Bayesian algorithm is used to update the hyperparameters to obtain candidate values of multiple groups of hyperparameters, and a genetic algorithm is further used to process the obtained candidate values of the multiple groups of hyperparameters to obtain a candidate value of a group of hyperparameters to replace the hyperparameters in the target detection model; S5, whether a second exit condition is met is judged, if yes, step S6 is executed; S6, each video frame of a to-be-detected video is detected by using the target detection model to obtain traffic flow data. Compared with the prior art, the application has the advantages of improving the detection accuracy in an unmanned aerial vehicle scene.

This invention discloses a method and apparatus for DOA estimation based on a spatially smoothed weighted sparse Bayesian algorithm. The method includes: reconstructing and normalizing the covariance matrix of the received signal from the array and the theoretical covariance matrix, and considering off-network error to construct a sparse reconstruction DOA estimation model under off-network conditions; obtaining a weight vector based on the spatially smoothed Capon algorithm and the theoretical covariance matrix; updating the parameters of the sparse Bayesian learning in the sparse reconstruction DOA estimation model according to the weight vector to obtain the first parameter; performing adaptive grid refinement based on the first parameter and the bipartite grid interpolation method, and iteratively updating the parameters of the sparse Bayesian learning; ending the iteration when a preset convergence condition is met, and outputting the DOA estimate.

The application discloses a kind of offshore wind power prediction methods based on wind speed-power combination decomposition reconstruction, comprising: based on combination decomposition method, offshore wind power sequence is carried out stationary treatment;According to the complexity and similarity characteristics of component, the data after decomposition is reconstructed dimension reduction;According to the coupling correlation between offshore wind speed and power, power is decomposed and combined with LSTM to establish prediction submodel;According to the network structure characteristics of deep learning, the hyperparameters are optimized using Bayesian algorithm, and BO-LSTM model is constructed, and the final prediction value is obtained by superimposing the prediction results of each BO-LSTM submodel.The method of the application can improve the problems of strong data volatility, incomplete decomposition and large data size caused by decomposition in offshore wind power prediction, enhance the ability of neural network to explore the correlation between wind speed data and power data, and improve the accuracy of offshore wind power prediction.

This invention discloses a method for locating single-phase grounding fault sections using a centralized multi-factor comprehensive analysis at a master station. The method includes: acquiring the topology, terminal register, and operational information of suspected grounding fault lines in the distribution network; constructing alarm signal observers and characteristic quantity observers corresponding to terminal alarm signals, zero-sequence characteristic quantity telemetry, and virtual measurements extracted from fault recording files, respectively; dynamically constructing a fault detection factor set within the fault observers based on site conditions and the grounding fault scenario; using an enhanced multi-factor Bayesian algorithm to calculate the posterior probability of events for the two observers for each line and section; and employing the evidence consistency assumption to correct evidence conflicts caused by missed and false alarms from intelligent terminals; finally, selecting the line, section, and locating the fault section based on the posterior probability of events from the two observers. This invention is flexibly applicable to different automatic distribution terminal configuration schemes in the field and can achieve highly reliable single-phase grounding fault section location.

This invention relates to the field of heat exchanger heat transfer enhancement and simulation technology, specifically to a groove channel optimization method and related equipment based on Bayesian algorithm. The method involves setting constraints on the geometric parameters of the groove channel and the value range of the parameters to be optimized, and constructing a simulation model. By running the model, the Nusselt number and friction factor are extracted, and the comprehensive heat transfer performance factor is calculated as the objective function. An initial surrogate model of the objective function and the parameters to be optimized is established using the Bayesian algorithm. The surrogate model is iteratively updated until the termination condition is met. Finally, the maximum value of the comprehensive heat transfer performance factor during the iteration process is selected, and the corresponding parameter combination is output as the optimal solution, achieving synergistic optimization of heat transfer and flow resistance in the groove channel.

This invention discloses a method for constructing a compound retention time prediction model in high-performance liquid chromatography (HPLC), relating to the field of machine learning technology. The method includes generating a candidate sample dataset; training a lightweight gradient booster regression model using the candidate sample dataset as input, and finding the optimal hyperparameter combination corresponding to the dataset using a Bayesian algorithm; training the lightweight gradient booster regression model using the candidate sample dataset with 10-fold cross-validation under the corresponding optimal hyperparameter combination to obtain an evaluation index; and using the model corresponding to the optimal evaluation index as the HPLC compound retention time prediction model and retaining the candidate sample dataset. This invention achieves efficient hyperparameter optimization by constructing a multi-strategy data configuration pool, using a LightGBM regression model combined with a Huber Loss objective function, and employing Bayesian optimization, ultimately ensuring model stability through cross-validation.

This application presents a high-resolution velocity and angle measurement method based on a frequency-domain dimensionality reduction sparse Bayesian algorithm, applicable to radar signal processing in two-dimensional uniform rectangular array (URA) systems. The method constructs locally continuous frequency-domain feature matrices in both the azimuth and Doppler dimensions, and generates a dimensionality reduction matrix using eigenvalue decomposition, achieving bidirectional frequency-domain dimensionality reduction of the original observation data. Then, it combines this with the Sparse Bayesian Learning (SBL) algorithm to perform two-dimensional sparse spectrum reconstruction on the dimensionality-reduced observation signal, thereby obtaining accurate and high-resolution joint estimation results for angle and velocity. This method, combining frequency-domain dimensionality reduction and Bayesian sparse reconstruction, significantly reduces computational complexity while maintaining excellent super-resolution capability and noise resistance, making it particularly suitable for velocity and angle measurement scenarios with dense target groups and low signal-to-noise ratios.

The application discloses a method and system for license-exempt communication based on sparse vector coding and sparse Bayesian, and the method comprises the following steps: obtaining a transmission information vector of a user and performing sparse vector coding processing to obtain a pseudo-randomly expanded sparse vector; performing iterative calculation and updating on the pseudo-randomly expanded sparse vector by a block sparse Bayesian algorithm to obtain an active block index; and recovering non-zero indexes in the active block according to the active block index to realize license-exempt communication of the user satellite. The application can effectively identify active users and recover their data, thereby greatly reducing access delay and improving the spectrum utilization efficiency of the system. The application can be widely applied to the technical field of Internet of Things communication as a method and system for license-exempt communication based on sparse vector coding and sparse Bayesian.