90 results about "Bayesian filtering" patented technology

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention discloses a multi-source equipment health collaborative operation and maintenance method in a complex environment based on edge federation, and relates to the technical field of equipment collaborative operation and maintenance. Vibration acoustic emission current waveforms are mapped to a unified time-frequency grid at an edge gateway, and an encrypted sparse index is generated and uploaded; training a global model by combining a graph regular base network with a gradient direction and a distribution distance, and injecting fault information by using a new working condition protection door; after being issued by a sparse adaptation layer of double-temperature-zone distillation and random projection compression, fine adjustment is carried out on site under few samples through temperature gradual fusion and reversible orthogonal mapping, and only unit gradient direction and health labels are uploaded; the center adopts entropy constraint Bayesian filtering to fuse information to generate a health index, a maintenance schedule and a spare part plan are formed by integer programming according to confidence intensity mapping risk popularity, a result is differentially pushed and audited, and a closed loop of collection, learning, evaluation and decision is realized.

The invention relates to the technical field of blood oxygen detection, in particular to a pulse blood oxygen saturation detection method based on image processing, a transfer learning strategy and a domain adaptation network are introduced, adjustment can be carried out according to personalized physiological parameters of a user, and therefore the applicability to different crowds is improved. Especially for people with deep skin color, the measuring error is obviously reduced, and the error rate is reduced by more than 30%. A set of mechanism for calculating the current signal quality index Q in real time is designed, dynamic calibration is carried out in combination with a Bayesian filtering model, and it is ensured that a reliable blood oxygen saturation degree estimation value can be obtained even under the condition of the low signal-to-noise ratio. And when the signal quality is reduced, the current detection value is compensated by using historical data through the LSTM network prediction module, so that the accuracy and continuity of the result are further ensured.

The invention relates to the technical field of artificial intelligence prediction, in particular to an artificial intelligence-based teenager psychological health development trajectory prediction system, which comprises a data acquisition module, an emotion recognition module, a trajectory prediction module, a risk assessment module and an intelligent feedback module, and provides a feature extraction method based on a point cloud structure. Time sequence feature modeling is carried out by combining K nearest neighbor with Transform, and global feature extraction is carried out by using position sensing optimization and Max Pooling, so that key features of the mental health state of the teenagers are extracted more comprehensively; according to the method, a clustering-based bidirectional LSTM is combined with a GRU prediction model, firstly, DBSCAN is used for clustering, different mental health state modes are defined, Bayesian optimization and Bayesian filtering are adopted for improving the prediction precision, and finally more accurate mental health development trajectory prediction is provided.

Systems or techniques are provided for facilitating improved digital twin calibration for scientific instruments. In various embodiments, a system can synchronize, via execution of a state-wide Bayesian filter, a parametric state of a digital twin with a physical state of a scientific instrument. In various instances, the state-wide Bayesian filter can comprise a set of calibration iterations, each of which can comprise a Bayesian update to an entirety of the parametric state based on an iteration-common observable that is exhibitable by the scientific instrument and simulatable by the digital twin. In various cases, the scientific instrument can be a charged-particle microscope, the parametric state of the digital twin can be an aberration coefficient vector of the charged-particle microscope, and the iteration-common observable can be a Fourier transform of a convergent beam electron diffraction pattern of an amorphous carbon specimen captured by the charged-particle microscope.

An object detection system for a vehicle that estimates a location of a target object located in an environment surrounding the vehicle includes an ultra-wide band (UWB) sensor network including three or more anchors mounted to the vehicle in wireless communication with a tag mounted to the target object, a non-stereo camera system that captures image data representing the target object located in the environment surrounding the vehicle, and one or more controllers in electronic communication with the UWB sensor network and the non-stereo camera system. The one or more controllers includes one or more processors that execute instructions to fuse together a camera-based location of the target object and a UWB-based location of the target object by a Bayesian filter to estimate the location of the target object.

The invention provides a multi-level semantic map construction method based on multi-sensor fusion, and the method carries out the construction of an environment grid layer, and comprises the steps: constructing an environment grid map in real time through fusing perception data; scene semantic layer construction: extracting image scene semantic probability distribution by using a deep convolutional network, fusing time sequence observation through Bayesian filtering, and mapping a scene category to a grid unit by using an occupation probability model; constructing an object semantic layer, namely identifying an object by adopting a target detection network in which an information aggregation-distribution mechanism is introduced, extracting an object point cloud, and dynamically updating object semantic attributes of grid units through multi-source observation fusion; and scene atlas generation: constructing a hierarchical scene atlas which takes the marker object as a reference core and comprises a spatial topological relation. According to the method, the dynamic environment adaptability and the multi-modal data fusion precision of semantic mapping are improved, a more visual environment understanding mode is provided for the robot, and the practicability of the semantic map in robot positioning and navigation is improved.

The invention discloses a public building environment monitoring system based on the Internet of Things, and belongs to the technical field of safety monitoring management, and the public building environment monitoring system specifically comprises the steps that a feature extraction module obtains Wi-Fi RTT time delay features, Bluetooth RSSI features and UWB TDoA features in parallel through a multi-mode feature extractor, and marks environment quality marks for each type of features; the signal attenuation correction module performs joint correction on a Wi-Fi RTT refraction error, a Bluetooth signal absorption error and a UWB multi-path reflection error based on a physical attenuation model and an offline training environment response curve, and outputs a corrected feature vector; the filtering fusion module is used for firstly applying Kalman filtering in a time domain and then applying particle filtering in a space domain through a multilayer Bayesian filtering fusion engine, fusing the corrected feature vectors and generating a target dynamic grid cell point probability heat map; and selecting a unit with the highest posterior probability from the target dynamic grid unit grid point probability heat map as a final alarm position.

This invention relates to the field of hydraulic engineering, and more particularly to a method for safety assessment and optimization design of large-flow aqueduct structures. The method includes: collecting displacement, strain, and temperature monitoring sequences based on a triple probe, completing grouping, coordinate and time registration, and establishing a monitoring baseline; constructing a spatial model including support nonlinearity and fluid-structure boundary conditions, applying loading conditions, and obtaining response predictions; aligning monitoring and prediction in time and location, performing hierarchical Bayesian filtering inversion, and obtaining parameter posteriors and structural health; under the integration of in-service monitoring and accelerated degradation characteristics, using multi-task learning to generate material evolution and lifespan intervals, and automatically generating and simulating optimization schemes under reliability and maintainability constraints. This application forms a closed loop from assessment to design, effectively enhancing data comparability, traceability of uncertain information, and adaptability of the scheme to engineering projects.

The invention provides an autonomous exploration method and device for an indoor mobile robot. The method comprises the following steps: performing pose estimation on the indoor mobile robot based on point cloud data and sensor data fusion; processing the point cloud data by adopting a method of combining fan-shaped projection and binary Bayesian filtering, carrying out environment modeling and constructing a lightweight grid map; according to the environment information on the lightweight grid map, utilizing a ray edge grid analysis method, a Bayesian inference edge grid intersection segmentation method, a leading edge region segmentation method and a viewpoint sampling and evaluation method to detect a leading edge region of the explored region and segmenting the leading edge region into a plurality of sub-regions; generating an optimal viewpoint of each sub-region; exploring an unknown environment by using the optimal viewpoint and adopting a hierarchical exploration strategy combining global exploration and local exploration to obtain a planned path; and according to the planned path, generating a navigation trajectory by adopting a method of combining multi-constraint trajectory optimization and nonlinear model predictive control.

The invention relates to the technical field of market trend prediction, and discloses a market trend prediction system driven by macroeconomic indicators. An index acquisition module of the system dynamically acquires core economic indexes such as GDP growth rate, CPI, PMI and currency supply; the data preprocessing module is used for carrying out layered noise reduction processing on the multi-source heterogeneous data; the feature engineering module constructs a market sensitive feature set through spatio-temporal feature fusion; the prediction model building module is used for building a multi-layer nonlinear prediction model based on a deep belief network; the dynamic adjustment module adopts reinforcement learning to optimize a decision threshold value and combines a Markov chain to carry out state transition planning; and the feedback iteration module analyzes and predicts deviation through Bayesian filtering and realizes strategy updating. According to the method, deep learning and reinforcement learning technologies are creatively fused, the prediction precision is remarkably improved through a dynamic calibration mechanism, and the method can be widely applied to the macroeconomic analysis fields of financial investment, industrial planning and the like.

The invention relates to the technical field of intelligent transportation, and discloses an airport self-service baggage check-in intelligent identification system, which comprises a sensor module, a data processing module, a data processing module and a storage module, and is characterized in that the sensor module is used for acquiring physical state data of baggage; the data processing module is used for receiving the signal from the sensor and executing data preprocessing; the control module is used for generating position feedback according to the real-time luggage tracking data and providing a visual interface and abnormal alarm information; and the feedback module is used for transmitting feedback information to the control module and ensuring automatic processing of luggage tracking and abnormal management, and the data processing module predicts and updates the state of the luggage through Kalman filtering, Bayesian filtering and particle filtering algorithms. The luggage state is estimated in real time by adopting multi-sensor data fusion and advanced algorithms such as Kalman filtering, Bayesian filtering and particle filtering, and the system can accurately predict the position, speed and acceleration of luggage by integrating data of various sensors.

Computer-implemented method (30) for calculating an estimate of a parameter characterising concrete (e.g. slump) in a drum (101) of a concrete mixing vehicle (100), comprising the steps of: - receiving (31) a drum speed value indicative of a rotational speed of the drum; - receiving (31) a torque value indicative of a torque required to turn the drum; - providing (32) the drum speed value and torque value as input measurements to a relationship estimator Bayesian filter; - calculating (33) at least one vehicle parameter characterising a relationship between drum speed and torque using the relationship estimator Bayesian filter, wherein the relationship estimator Bayesian filter is configured such that the at least one vehicle parameter comprises a hidden state variable of the relationship estimator Bayesian filter; and - calculating (34) an estimated parameter value by providing the at least one vehicle parameter as an input to a parameter calculation model.

The present invention provides an obstacle avoidance path planning method and related devices for a construction robot. By using Bayesian filtering to fuse the state information of the target construction robot with environmental data, an occupancy grid map is obtained; the occupancy grid map is input into a trained lightweight convolutional neural network for feature extraction to generate environmental feature data; the environmental feature data and the current state of the target construction robot are input into a trained decision-making model for decision-making to obtain an action instruction. The target construction robot reaches the target position in the shortest time based on the action instruction, and the path passed by the target construction robot to the target position is the obstacle avoidance path of the target construction robot; by improving the accuracy and robustness of environmental perception, efficient obstacle avoidance and path planning in a complex environment are realized.

The invention discloses an installation error calibration method based on Student's T distribution and variational Bayes, which comprises the following steps: firstly, constructing an installation error calibration geometric model of an SINS / USBL system, defining coordinate systems, establishing an attitude transfer matrix between the coordinate systems, and designing a state equation and a measurement equation by taking installation error angles in three directions as state variables; time updating and measurement updating are carried out based on a Kalman filtering framework; the method comprises the following steps of: embedding Student's T distribution into a variational Bayesian filtering framework, alternately updating distribution parameters of a state variable, a noise covariance and an auxiliary variable through a variational iterative optimization process, maximizing a variational lower bound until convergence, and outputting an optimized installation error angle estimated value. According to the method, acoustic measurement noise is modeled by using the heavy tail characteristic of the SINS / USBL combined system, the interference of outliers on installation error angle estimation is remarkably inhibited, the positioning accuracy of the SINS / USBL combined system in a complex underwater environment can be effectively improved, and the calibration robustness is enhanced.

The invention discloses a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation Satellite System) tight coupling method and system. The method comprises the following steps: acquiring first state distribution of a first target system, wherein the first target system is a GNSS and INS tight coupling system; presetting a first algorithm, and performing state estimation operation based on the first state distribution in combination with the first algorithm; and correcting the first target system according to the state estimation operation result. By adopting the particle filtering technology, the nonlinear noise problem of the GNSS signal in a high electromagnetic interference environment can be effectively solved. Particle filtering serves as a recursive Bayesian filtering technology based on a Monte Carlo method, probability distribution is represented through a group of random samples (particles), and a posterior probability density function in a nonlinear and non-Gaussian noise environment can be well approached. Therefore, even under the condition that the GNSS signal quality is obviously reduced, the method provided by the invention can also provide a more accurate and robust positioning result.

The invention provides an NHC noise adaptive estimation method and a variational Bayesian filtering method, and the method comprises the steps: firstly, detecting whether a vehicle is in a turning state or not through a vehicle turning detection algorithm, and carrying out the adaptive estimation of an NHC lateral speed noise initial value; secondly, according to the motion state of the vehicle, a variational Bayesian method is adopted, and on the basis that lateral speed noise is modeled into Gaussian white noise or student T distribution thick tail noise, statistical characteristics of the measured noise are estimated in a real-time and self-adaptive mode; a complete combined filtering method is formed through the two methods, positioning errors caused by NHC failure are greatly reduced, and the method can be used independently and is high in applicability.

The invention relates to the technical field of obstacle avoidance path planning, and discloses an intelligent sports vehicle dynamic obstacle avoidance path continuous planning method comprising the following steps: S1, obtaining a racing track pre-stored map, and constructing a racing track reference coordinate system; relates to the technical field of obstacle avoidance path planning, and the method comprises the steps: predicting the probability distribution of the future position of an obstacle through employing a recursive Bayesian filtering algorithm based on the historical observation data of the obstacle, carrying out the modeling through multivariate normal distribution, converting the modeling into a geometric confidence region, and forming a confidence occupancy set covering the uncertainty of the obstacle; a space-time safety channel is generated through set operation by combining the intelligent vehicle appearance envelope sequence and the racing track boundary; the method does not need to depend on a single prediction track, and by depicting the uncertainty of obstacle movement, even if the actual position of the obstacle deviates from prediction, the intelligent vehicle can still run in the safety channel, so that the problems of planning failure and conflict between the vehicle and the obstacle caused by deterministic prediction of the actual position of the obstacle in the prior art are effectively relieved.

The invention relates to the technical field of satellite communication, and discloses a low-orbit satellite communication Doppler frequency offset processing method, system and device and a medium, and the method comprises the steps: separating a pilot signal from a received signal; taking the Doppler frequency as a first system state, taking the received signal as first observation data, and performing Doppler frequency offset estimation on the pilot signal by adopting a frequency offset estimation algorithm to obtain a frequency offset estimation value; taking the Doppler frequency as a second system state, taking the frequency offset estimation value as second observation data, and adopting a frequency offset tracking algorithm to perform Doppler frequency offset tracking on the pilot signal to obtain a frequency offset tracking value; and performing frequency offset compensation on the received signal according to the frequency offset tracking value to obtain the received signal after frequency offset compensation. According to the method, the Doppler frequency offset is subjected to streaming processing through Bayesian filtering in the estimation and tracking stages, the complexity of data storage and calculation is effectively reduced, the data updating rate is increased, and therefore the accuracy of low-orbit satellite data is effectively improved.

The invention provides a chip data detection method and system based on artificial intelligence, and relates to the technical field of chip detection, and the method comprises the steps: obtaining the dynamic power data of a chip; constructing a chip data detection model based on the random forest; judging whether the structural complexity of the chip is smaller than preset structural complexity or not; if yes, carrying out denoising processing on the dynamic power data by adopting a Bayesian filtering algorithm to obtain first denoised power data, and carrying out anomaly detection through a chip data detection model; and otherwise, de-noising the dynamic power data by adopting a bidirectional long-short time memory network to obtain second de-noised power data, acquiring logic activity data of the chip, and performing anomaly detection through a chip data detection model by jointly using the second de-noised power data and the logic activity data. According to the invention, the structural complexity is de-noised by using the Bayesian filtering algorithm and is processed by using the bidirectional long and short time memory network, so that the detection efficiency and accuracy are effectively improved.

The invention discloses an environment situation scanning three-dimensional reconstruction method based on infrared and SLAM, and belongs to the technical field of computer vision and three-dimensional reconstruction. According to the method, thermal radiation information and environment image information of a target environment are obtained through infrared sensing and visual imaging respectively, after feature extraction and matching processing are conducted on the two kinds of information, synchronous positioning and map construction are completed, and environment pose data and environment map data are generated; a sensing uncertainty probability model is established, after multi-source data denoising is completed through Bayesian filtering, feature level fusion is carried out to obtain fusion environment data, and finally a three-dimensional reconstruction model of a target environment is constructed based on the fusion environment data. According to the method, the full-process closed loop of environment situation scanning and three-dimensional reconstruction is realized, the application limitation of single sensing information is made up, the comprehensiveness of environment sensing data and the scene adaptability of three-dimensional reconstruction are improved, and the digital reconstruction requirements in various complex environments can be met.

The invention discloses a digital twin co-evolution method, and belongs to the field of structural health monitoring and prediction. Comprising the following steps: constructing life prediction digital twins; carrying out online load-damage hybrid monitoring and fusion diagnosis; the digital twin co-evolution based on the Bayesian filtering model is driven; collaborative updating of the crack propagation simulation finite element model and the stress intensity factor agent model is achieved; and performing crack propagation and life prediction based on the digital twin updated by co-evolution. Through a multi-model closed-loop co-evolution mechanism, the problem of model misalignment under the influence of a strong uncertain environment is effectively solved, the accuracy and reliability of structural damage diagnosis and life prediction are remarkably improved, and a core technical support is provided for implementing accurate condition-based maintenance.

A wireless communications system includes a network of acoustic modems for communicating messages between downhole equipment and a surface control and telemetry system. The acoustic modems include multi-channel acoustic receivers that are uniquely deployed along an acoustic transmission medium to provide spatial diversity. Acoustic signals received on the multiple receiver channels are combined and filtered using a Bayesian-type filter to reduce noise.

The invention discloses an extended target tracking method based on a long-short-term memory network. The method comprises the following steps: learning motion characteristics of a target by using the long-short-term memory network, capturing non-Markov characteristics of long-term motion of the target by using long-term memory, and capturing speed change characteristics of short-term motion of the target by using short-term memory; priori knowledge and a long and short term memory network are combined, and under a reasonable assumption condition, recursion forms of a prediction formula and an updating formula of the extended target tracking method are deduced based on a Bayesian filtering framework; a memory link, a prediction link and an updating link of an algorithm combining the neural network and the prior knowledge are constructed, and the three links are executed in a recursive mode to achieve tracking of the extended target. According to the method, the tracking precision of the extended target can be effectively improved.

This invention relates to the field of environmental perception fusion positioning technology, and provides a positioning method, system, and electronic device based on a Bayesian filtering neural network. The method includes: acquiring an observation waveform matrix generated by a base station after receiving a waveform signal transmitted by a target object at each sampling time; inputting the observation waveform matrix into a preset positioning model to obtain the positioning information of the target object; fitting the Bayesian filtering result to the positioning model using a recurrent neural network, which is trained using a cost function including motion likelihood and observation likelihood. The motion likelihood is generated based on the motion model of the target object sample, and the observation likelihood is generated based on the position information of the target object sample determined by a parameter estimation algorithm. This invention addresses the shortcomings of existing technologies where the application of Bayesian filtering algorithms in environmental perception fusion positioning requires approximation algorithms and complex derivations to obtain positioning results, thus affecting positioning speed and accuracy.

This invention relates to the field of satellite communication technology and discloses a method, system, device, and medium for processing Doppler frequency offset in low-Earth orbit (LEO) satellite communication. The method includes: separating a pilot signal from a received signal; using the Doppler frequency as a first system state and the received signal as first observation data, performing Doppler frequency offset estimation on the pilot signal using a frequency offset estimation algorithm to obtain an estimated frequency offset value; using the Doppler frequency as a second system state and the estimated frequency offset value as second observation data, performing Doppler frequency offset tracking on the pilot signal using a frequency offset tracking algorithm to obtain a tracked frequency offset value; and performing frequency offset compensation on the received signal based on the tracked frequency offset value to obtain a frequency offset-compensated received signal. This invention uses Bayesian filtering for streaming processing of the Doppler frequency offset during the estimation and tracking stages, effectively reducing the complexity of data storage and computation, increasing the data update rate, and thus effectively improving the accuracy of LEO satellite data.

The invention discloses a motion alignment method based on state-dependent Bayesian group filtering, which is used for solving the problems of rapid attitude change and state-dependent noise in high-dynamic initial alignment of a strapdown inertial navigation system. The method has two creative points. The method comprises the following steps: firstly, establishing an accurate initial alignment model containing an inertial sensor error by utilizing Lie group representation; an observation probability model is established by adopting a shaft angle model which is more in line with physical definition, and the rotation of the carrier is described and tracked more accurately. Secondly, in the process of designing a state correlation Lie group Bayesian filtering algorithm, exact composition of state correlation noise in the alignment model is deduced and analyzed, and observation noise and the state are decoupled through vector dot product and cross product; experimental results show that the method is obviously superior to the existing method in alignment precision and time.

The present invention discloses a moving target detection method, device and terminal equipment, including: obtaining an image to be detected, detecting a moving foreground area of ​​the image to be detected by a motion detection method; performing macroblock division processing according to the image to be detected and the moving foreground area to obtain a macroblock foreground image; calculating the mutual information between adjacent macroblocks according to the macroblock foreground image to obtain a mutual information correlation image; performing region growth processing on the macroblock foreground image according to the mutual information correlation image to obtain a processed macroblock foreground image; obtaining the historical time sequence information of each macroblock in the processed macroblock foreground image, combining with a Bayesian filter, judging whether each macroblock in the processed macroblock foreground image is a foreground; and obtaining a moving target area according to all macroblocks judged to be foregrounds. The technical solution of the present invention can have good robustness to noise, enhance anti-interference performance, and thus improve the accuracy of moving target detection.

The present disclosure provides a system (104) and a method (800) for vehicle localization in a tunnel, utilizing sensor data and tunnel-specific features. The system (100) is configured to determine the lateral and longitudinal positions of the vehicle (101) in real time. The lateral position is identified by updating a light probability distribution and applying Bayesian filtering to establish the vehicle's ego lanes. Furthermore, a real-time landmark identifier (ID) for lights is generated based on normalized tunnel features, including distances, light intensity, and tunnel geometry. The longitudinal position is determined by matching the generated landmark ID with predefined map data, enabling robust localization for navigation and thus allowing the vehicle (101) to be located in tunnels.

This invention discloses an intelligent tracking method for maneuvering swarm targets based on deep neural networks. This method incorporates deep learning into a Bayesian filtering framework, extracting the motion characteristics and process noise statistical characteristics of the swarm targets from measurement data using multiple deep neural networks. It then estimates the motion state transition matrix and process noise variance matrix of the swarm targets online, and uses Bayesian filtering to accurately estimate the motion state of the swarm targets' centroid. By modeling the swarm target contour as an ellipse centered at the centroid, the high-precision centroid motion state estimation results improve the contour estimation accuracy, ultimately achieving swarm target tracking. Compared to existing swarm target tracking methods, the proposed method does not require prior information to establish a target motion model, enabling high-precision tracking of non-cooperative maneuvering swarm targets in complex environments where prior information is lacking.