127 results about "Markov chain Monte Carlo" patented technology

A computer implemented stochastic inversion method for estimating model parameters of an earth model. In an embodiment, the method utilizes a sampling-based stochastic technique to determine the probability density functions (PDF) of the model parameters that define a boundary-based multi-dimensional model of the subsurface. In some embodiments a sampling technique known as Markov Chain Monte Carlo (MCMC) is utilized. MCMC techniques fall into the class of “importance sampling” techniques, in which the posterior probability distribution is sampled in proportion to the model's ability to fit or match the specified acquisition geometry. In another embodiment, the inversion includes the joint inversion of multiple geophysical data sets. Embodiments of the invention also relate to a computer system configured to perform a method for estimating model parameters for accurate interpretation of the earth's subsurface.

A method for automatically detecting and tracking multiple targets in a multi-camera surveillance zone and system thereof. In each camera view of the system only a simple object detection algorithm is needed. The detection results from multiple cameras are fused into a posterior distribution, named TDP, based on the Bayesian rule. This TDP distribution represents a likelihood of presence of some moving targets on the ground plane. To properly handle the tracking of multiple moving targets with time, a sample-based framework which combines Markov Chain Monte carlo (MCMC), Sequential Monte Carlo (SMC), and Mean-Shift Clustering, is provided. The detection and tracking accuracy is evaluated by both synthesized videos and real videos. The experimental results show that this method and system can accurately track a varying number of targets.

Markov Chain Monte Carlo (MCMC) sampling of elements of a domain to be sampled is performed to generate a set of samples. The MCMC sampling is performed over a search tree of decision sequences representing the domain to be sampled and having terminal nodes corresponding to elements of the domain. In some embodiments the MCMC sampling is performed by Metropolis-Hastings (MH) sampling. The MCMC sampling is constrained using a bound on nodes of the search tree. The constraint may entail detecting a node whose bound value ensures that an acceptable element cannot be identified by continuing traversal of the tree past that node, and terminating the traversal in response. The constraint may entail selecting a node to serve as a starting node for a sampling attempt in accordance with a statistical promise distribution indicating likelihood that following a decision sequence rooted at the node will identify an acceptable element.

The invention discloses an optimization design method for a step stress accelerated degradation test based on a Bayesian theory, and is applied to the technical field of the accelerated degradation test. The optimization design method comprises the steps as follows: firstly, determining product performance degradation and acceleration models, and based on the historical data, giving prior distribution of model parameters; secondly, determining an optimization design space, and forming a test scheme set; thirdly, creating an expected utility function or an expected loss function, determining optimization goals, and based on a Markov Chain Monte Carlo method, determining optimization goal values of designs in the test scheme set; and lastly, finding the optimal test scheme by using a curve fitting method. According to the optimization design method, the shortcoming of high possibility of larger deviation due to the implementation of the traditional (local) test optimization design method when the values of the model parameters are supposed to be known is avoided, and the optimization scheme obtained in the implementation of the test optimization design when the prior distribution of the model parameters is given is more reasonable and more actual.

The invention provides a method for evaluating operating reliability of a multi-model integrated aero-engine under multiple failure modes. The method comprises the steps of analyzing the multiple failure modes, establishing a multi-failure mode analyzing model, establishing an alternative evaluation model, judging advantages and disadvantages of each model, establishing a multi-model integrated evaluation model, analyzing the influence from the multiple failure modes to the operating reliability of the aero-engine by applying a Bayesian model averaging method, and carrying out simulation calculation on the operating reliability of the aero-engine by adopting a MCMC (Markov Chain Monte Carlo) algorithm. According to the method provided by the invention, typical failure modes, performance degradation failure, structural strength failure and outburst failure are respectively analyzed, one optimal model is selected from multiple alternative models, multiple models are integrated in one framework by applying a multi-model integration technology for evaluating the operating reliability of the aero-engine, and the evaluation accuracy of the operating reliability of the aero-engine can be increased.

The invention discloses a vehicle operating condition multi-scale predicting method based on the Markov chain. The method establishes a Markov chain prediction model for the vehicle operating condition. The method comprises the steps of computing a state transferring matrix by maximum likelihood estimation according to the history information of vehicle operating condition; performing the vehicle operating condition predicting of different time scales according to the obtained state transferring matrix by utilizing the Markov chain and Monte Carlo analogy method; restoring the predicted outcomes of different time scales into data under a history operating condition sampling frequency through linear interpolation; dividing the predicted outcomes of different time scales into different confidence grades according to simulated sample quantity, and computing the linear weight coefficient under different confidence grades of the predicted outcome every time by adopting a linear weighting method; and merging all the predicted values of each scale of predicted outcome every time according to the weight coefficients and merging the different scales of predicted outcomes under the original data frequency to obtain the vehicle operating condition multi-scale predicting outcome. The vehicle operating condition multi-scale predicting method based on the Markov chain can meet the predicting precision requirements of the vehicle operating condition and the requirements of vehicle real-time control.

A method for establishing fault diagnosis technique based on contingent Bayesian networks, comprising steps of: step (1) determining a domain of an unknown object to be reasoned; step (2) defining a model structure by adopting a first-order logic language; step (3) generating a Blog model; step (4) transforming the Blog model into the contingent Bayesian networks; step (5) defining the contingent Bayesian networks; step (6) learning parameters of the contingent Bayesian networks; and step (7) reasoning a fault of the contingent Bayesian networks by utilizing a Markov chain Monte Carlo Method. By the steps mentioned above, establishing fault diagnosis technique based on contingent Bayesian networks is achieved.

The invention discloses a differential privacy protection method for an online social network based on a stratified random graph. The differential privacy protection method comprises the following steps: inputting a network; constructing a tree structure of the network based on a stratified random graph model; sampling in the network through a Markov chain Monte Carlo method according to a preset privacy budget so as to obtain a sampled tree; taking the root node of the sampled tree as an initial current node; calculating an associated probability value of the current node according to the preset privacy budget; finding out a set of node pairs by taking the current node as the nearest father node in the network, and setting an edge among the set of node pairs according to the associated probability value; judging whether traversal of the sampled tree is completed or not, and if not, continuously traversing the next node in the sampled tree; and otherwise, outputting a purified network composed of edges arranged among all the sets of nodes and nodes thereof. According to the invention, the privacy protection problem of sensitive structural data information in the social network can be solved; differential privacy protection requirements can be satisfied; and simultaneously, the good data availability is kept.

The techniques and structures described herein generally relate to sampling from an available probability distribution to create a desirable probability distribution. This resultant distribution can be used for computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems.

The invention provides a transcranial ultrasound cerebral angiography super-resolution imaging method based on Markov chain Monte Carlo multi-object tracking. The transcranial ultrasound cerebral angiography super-resolution imaging method comprises the following steps: sampling an ultrasound plane wave echo signal received by a low-frequency transcranial dedicated ultrasound linear array energy converter, and converting into radio frequency data; according to the radio frequency data, generating an ultrasound cerebral angiography image; removing background information generated by time domainmedian filtering in the ultrasound cerebral angiography image, and keeping image information of angiography microbubbles; through the Markov chain Monte Carlo multi-object tracking, deducing an angiography microbubble trajectory; according to a trajectory set obtained by deduction, carrying out cerebral angiography super-resolution imaging. The invention puts forward the transcranial super-resolution imaging method based on Markov chain Monte Carlo. Compared with other super-resolution imaging methods, the transcranial super-resolution imaging method disclosed by the invention has the characteristics of high frame rate, low data acquisition amount and suitability for high-concentration angiography microbubbles. Compared with traditional low-frequency angiography imaging, the method disclosed by the invention is characterized in that a cerebral vascular resolution ratio can be greatly improved under a noninvasive condition.

The invention discloses an underwater auxiliary oil recovery control system and a dynamic positioning method thereof. Focusing on an uncertainty problem that a motion of the underwater auxiliary oil recovery robot is interfered by ocean current, combining the manufactured underwater auxiliary oil recovery robot, the invention establishes a dynamical model based on a fluid dynamic numerical simulation identification parameter method and performs six-degree-of-freedom dynamic positioning analyzing. A position and a heading of the underwater auxiliary oil recovery robot are obtained by a sensor;a state of the underwater robot is estimated in real time by adopting an adaptive unscented Kalman particle filtering algorithm based on a hybrid genetic algorithm-Markov chain Monte Carlo method (GA-MCMC); a fast terminal reaching law is introduced into a non-singular fast terminal sliding mode control to compensate thrust, so that influence caused by interference such as the ocean current is reduced; and then a force and torque distribution strategy is designed according to a positioning error. The method has a good dynamic positioning effect, and can quickly adjust the dynamic distributionstrategy after being interfered in order to reduce the interference impact caused by random ocean current.

The invention discloses a steering engine reliability simulation sampling method based on Markova chain Monte Carlo, which comprises four stages: 1, Markova process simulation, namely selecting the initial state of a Markova chain, determining a random transition sampling probability density function, determining the next state of the Markova chain and constantly repeating to generate random sample points, of which the limit distribution is asymptotically optimal, of an importance sampling density function; 2, kernel density estimation, namely selecting a kernel density function, determining a window width parameter and a local bandwidth factor and generating a mixed importance sampling probability density function by using a self-adaptive width and kernel density estimation method according to Markova state points; 3, importance sampling, namely performing importance sampling according to the mixed importance sampling probability density function generated in the second stage; and 4,statistical calculation, performing failure probability estimation according to the important sample points generated in the third stage and calculating the failure probability of the system. The method effectively solves the problems of low simulation efficiency, low precision and mixed system.

The invention relates to the field of failure prediction of electromechanical equipment, discloses a method for predicting remaining life of equipment based on an improved particle filter algorithm, and improves the precision of life prediction of equipment. The improved particle filter algorithm used in the present invention includes an importance sampling stage and a resampling stage. In the importance sampling stage, the unscented Kalman filter method is introduced to update particles, and the proposed distribution is generated, thereby suppressing particle degradation; An off-chain Monte Carlo step that suppresses sample depletion. The invention is applicable to lithium batteries, rolling bearings and gear boxes.

The invention discloses a load characteristics comprehensive classification method based on Markov Monte Carlo. The method includes the following steps: finding the voltage drop time point, carrying out load dynamic characteristics extraction and classification at the disturbance moment corresponding to the voltage drop time point; judging whether the change between the load classifications has a Markov property or not; dividing all data into uniform segments by time; establishing Markov chain's probability transfer matrix based on the maximum likelihood thought for each data segment; judging whether the numerical characteristics are changed or not: if no, go to step V; if yes, carrying out clustering on the load data in the time segment according to the numerical characteristics corresponding to the matrix, and obtaining the probability transfer matrix of the load data with changed numerical characteristics in each time segment ; carrying out Markov Monte Carlo simulation and describing the load change situation; processing the sequence reflecting the load classification conversion using the Hidden Markov Model (HMM). The method provided by the invention improves the Markov chain Monte Carlo simulation and effectively reduces the possibility of the matrix entering the stable state after iteration.

The invention discloses a linear selecting method in the computing process of hydrological frequency, which includes: firstly, respectively selecting reasonable parameter prior distribution types, initial parameter samples and likelihood functions according to known regional prior information, performing parameter posterior distribution sampling by means of AM-MCMC (adaptive metropolis-markov chain monte carlo) to obtain parameter posterior distribution sampling results corresponding to each line type; analyzing and quantitatively describing probability distribution of the parameter posterior distribution sampling results by means of the POME (principle of maximum entropy) to obtain various parameter posterior distribution formulas in different line types, utilizing the method of approximate summation among application parameters to substitute for linear edge distribution integral process, solving bayes factor Bji of the hydrological line type Mj relative to the line type Mi according to the following formula; and finally selecting and comprehensively analyzing the hydrological line types on the basis of solving the bayes factor Bji. By the linear selecting method, uncertainty of the parameters analyzing and describing are analyzed reasonably, so that analyzed and computed results are improved evidently.

The invention provides an ultrasonic low-frequency transcranial volume super-resolution radiographic imaging method and system. The volume imaging technique is applied to the field of transcranial angiography; a two-dimensional plane transducer emits ultrasonic plane wave into the skull; scanning is performed in two directions perpendicular to each other; scanned images are subjected to transcranial vascular small-region three-dimensional super-resolution imaging by means of a voxel nearest neighbors process and a multi-objective tracking algorithm based on Markov chain Monte Carlo so as to obtain real-time high-resolution three-dimensional imaging for cranial internal structure.

The invention belongs to the field of distributed generation power quality of a power distribution network of a power system and particularly relates to a system harmonic probability evaluating method based on the Markov chain Monte Carlo method (MCMC). The system harmonic probability evaluating method based on the MCMC comprises the steps that firstly, a harmonic injection current model of a point of common connection (PCC) is analyzed; secondly, the MCMC is deduced; thirdly, an MCMC sampling algorithm flow for harmonic probability evaluation of the power distribution network is put forward again; finally, typical quintuple harmonics and typical septuple harmonics injected into the PCC of the power distribution system are calculated according to the algorithm flow, based on the MCMC sampling method, for harmonic probability evaluation of the power distribution system, and thus a probability statistics characteristic value and a probability density curve of harmonic injection current of the PCC are obtained. According to the system harmonic probability evaluating method based on the MCMC, the influence of the ratio of linear loads to non-linear loads during different time periods is considered, solution is conducted according to the effective MCMC sampling method, the situation that historical measurement data and experience-based judgment results are excessively depended on is avoided, and thus results can be more comprehensive, more objective and more similar to the actual condition of the power distribution network.

The invention relates to a method and a system for recognizing hidden paid posters on the basis of fusion of behavior characteristic and content characteristic. The method includes acquiring initial data including the behavior characteristic and the content characteristic of a user, performing dimensional fusion of the behavior characteristic and the content characteristic by the aid of a Markov Chain Monte Carlo stochastic model so as to form a user characteristic vector, performing DBN(deep belief network) model training according to the user characteristic vector so as to obtain a DBN model, detecting the DBN model to judge whether the detection result meets preset standards or not, and ending if so, otherwise, generating a corresponding regulation order according to the detection result to regulate related parameters at the characteristic fusion stage and the DBN model training stage respectively, and continuously optimizing proportional allocation of the behavior characteristic and the content characteristic, selection of specific characteristics and regulation of iterations during the DBN model training process according to recognition precision rate, thereby achieving the optimal training effect and finally improving recognition precision rate and adaptability of recognition methods.

The invention discloses a photovoltaic output time sequence simulation method based on a multi-scene state transition matrix and conditional probability sampling. The photovoltaic output time sequencesimulation method is used for simulating and generating photovoltaic time sequence output considering seasonal characteristics, daily characteristics, weather characteristics and fluctuation characteristics. The method comprises the following steps: firstly, aiming at a monthly photovoltaic output sequence, taking FCM clustering as internal optimization, and taking DB (-)clustering effectivenessindex as external optimization to form an original photovoltaic output sequence scene with clearer data characteristics. Secondly, establishing photovoltaic output state transfer matrixes of differentscenes, generating a photovoltaic output time sequence through a Markov chain Monte Carlo method, in the process, carrying conditional probability sampling through the Copula theory, generating a photovoltaic output state value at the next moment, and superposing the fluctuation amount conforming to the original probability distribution characteristic. Compared with an existing model, the probability statistics characteristic and the time sequence characteristic of the data are more accurate, and the implementation process is simple and easy to implement.

The invention discloses a system harmonic probability evaluating method based on a Markov chain Monte Carlo method. The system harmonic probability evaluating method comprises the steps of: s1, establishing an h-time harmonic current model generated by individual harmonic with total number of m in a certain class composite load and a general current model of the h-time harmonic of the certain class composite load injected into a PCC point; s2, analyzing uncertain parameters of the current model according to a Bayes formula in a classical theory of statistics, acquiring more objective and accurate unknown quantity posteriori distribution after adjustment through derivation and combining with rough prior distribution of the uncertain parameters; s3, acquiring the Markov chain Monte Carlo method; and s4, arriving at a probability statistics characteristic value and a probability density curve of percentage of each harmonic current according to the Markov chain Monte Carlo method. The results obtained by adopting the system harmonic probability evaluating method based on the Markov chain Monte Carlo method are more comprehensive, more objective and more similar to the actual condition of a power distribution network.

The invention provides a method for automatically detecting a remote sensing ground object target based on a stochastic geometry model. The method solves the automatic detection problem of a target with a relatively complex structure but relatively singular geometric component features in a remote sensing image. The method comprises the following steps: establishing a plurality of classes of image representative sets comprising the remote sensing ground object target; constructing the stochastic geometry model aiming at a target to be processed by taking geometric components for forming the target as processing units; after constructing the stochastic geometry model of the target components, converting the automatic detection problem of the target into an optimal configuration problem of a stochastic target seeking process; estimating the maximum value of the non-parameter probability density by using a Markov chain Monte Carlo method; and finally, detecting the target by using the stochastic geometry model, judging whether the target exists in the tested image or not, ending and outputting a result that no target exists if no target exists, and processing the image by using the stochastic geometry model to obtain the detection result corresponding to optimal configuration and outputting the final detection position of the target if the target exists.

A method of optimizing production of a hydrocarbon-containing reservoir by measuring low-frequency Distributed Acoustic Sensing (LFDAS) data in said well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow and simultaneously measuring Distributed Temperature Sensing (DTS) data from said well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow. An initial model of reservoir flow is provided using the LFDAS and DTS data; the LFDAS and DTS data inverted using Markov chain Monte Carlo method to provide an optimized reservoir model, and that optimized profile utilized to manage hydrocarbon production from said well and other asset wells.