1131 results about "Monte carlo em" patented technology

A method, system and computer program product for choosing actions in a state of a planning problem. The system simulates one or more sequences of actions, state transitions and rewards starting from the current state of the planning problem. During the simulation of performing a given action in a given state, a data record is maintained of observed contextual state information, and observed cumulative reward resulting from the action. The system performs a regression fit on the data records, enabling estimation of expected reward as a function of contextual state. The estimations of expected rewards are used to guide the choice of actions during the simulations. Upon completion of all simulations, the top-level action which obtained highest mean reward during the simulations is recommended to be executed in the current state of the planning problem.

The invention provides a water quality model based regional environment risk assessment method. The water quality model based regional environment risk assessment method includes five steps of establishing a regional environment risk source information database, and utilizing a Monte Carlo algorithm to simulate discharge of risk source pollutants; selecting a water quality model, and constructing a water quality model which meets characteristics of an assessment region source intensity water quality response relationship; calculating vulnerability indexes of risk receptors by using a fuzzy integral method with the risk assessment goal of influence on water ecology, human health and social economy; utilizing the water quality model to predict the distribution of regional pollutant concentration under all possible source intensity situations, and analyzing dangerousness of risk sources; characterizing regional environment risks with a risk curve under the synthesis of dangerousness of the risk sources and vulnerability indexes of the receptors. According to the risk curve, regional environment risk assessment can be performed, high-risk regions, and key risk sources and vulnerary receptors are identified, so that a reference is provided for planning a targeted region risk prevention and control scheme.

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 analyzing the machining precision reliability sensitivity of a numerically-controlled machine tool, belongs to the field of machine tool precision design, and particularly relates to a space error modeling method of a three-axis machine tool and a method for analyzing the machining precision reliability and the machining precision reliability sensitivity of the machine tool. A multi-body system motion feature analysis method is used for setting up a space error model of the machine tool, and combined with a Monte-carlo digital analogy method for analyzing the machining precision reliability of the machine tool, and the degree of influence on the machining precision reliability from the wave action of all items of geometric errors of the machine tool, so that the critical geometric errors influencing the machining precision reliability are identified. Guiding advice can be given for designing, assembling and machining of the machine tool, and the machining precision reliability of the machine tool is improved fundamentally.

The invention discloses a wind farm risk evaluation method based on a Monte Carlo method. The wind farm risk evaluation method includes the steps: (1) building a wind speed sequence prediction model on the basis of a time sequence method by means of original data standardization, model recognition and parameter determination; and (2) evaluating the reliability of a large power grid system concerning a wind farm on the basis of the sequential Monte Carlo method by means of random sampling and reliability evaluation. Randomness of the wind speed is adequately considered on the basis of a large quality of wind power databases, and the method is high in speed by the aid of the Monte Carlo high-speed algorithm.

The invention discloses an active power distribution network reliability analysis method based on pseudo sequential Monte Carlo simulation and belongs to the technical field of power system control. The method utilizes a pseudo sequential Monte Carlo simulation method to perform reliability analysis on an active power distribution network, the operation time is shortened, and the analysis accuracy is also improved. In addition, characteristics of the active power distribution network are further fully considered in the method, states of a communication system and power electronic devices in power distribution automation are involved in system state sampling, intelligent operation is performed on the system in a normal state, processing is performed through a traditional passive power distribution network during breakdown, the method is consistent with the actual situation of the active power distribution network, and obtained reliability data have confidence level.

The invention provides a novel signal detection scheme aiming at a 60GHz millimeter wave non-linear communication system, wherein the scheme is based on the Bayesian statistical inference mechanism, and capable of effectively solving the problems of system nonlinear distortion and frequency selective multipath fading, and achieving united blind estimate of channel gains and source signals. The method of the detection of the signal of the 60GHz millimeter wave communication system designs an important function applied to the nonlinear system and therefore overcomes the limits of the nonlinear characteristics to the traditional bayes method, further approaches an actual probability distribution function (PDF) through a series of dispersed particles with weights based on the thoughts of the Monte Carlo sequential importance sampling (MC-SIS), and finally utilizes the particle filtering technology to achieve real-time estimating and multi-channel iteration replacement of code element signals (shown in a attached diagram). The method and the device of the detection of the signal of the 60GHz millimeter wave communication system can be applied to the detection of signals of the nonlinear system, improve transmission performance of the system, need no training sequence, and at the same time can achieve the real-time estimating and detection of the signals.

The invention provides a static safety probability assessment method of a new energy grid power system. A new energy force output model adopting wind speed, sunlight intensity and other meteorologicalelement time period features comprehensively reflects weather random fluctuation features and time change rules, the nondeterminacy of system operation is reflected through a probabilistic load flowmethod based on Monte Carlo simulation, and the new energy force output model and the probabilistic load flow method are combined to set up the static safety probability assessment method of the new energy grid power system. A system load flow out-of-limit probability index is put forward to analyze the weak link and high-risk time frame of the system. A system N-1 risk assessment index is put forward to assess the system N-1 risk level and the risk change condition caused by new energy access scene changes. Compared with traditional and deterministic system static safety analysis, the nondeterminacy of the new energy grid power system is considered, the probability of static safety is assessed through the probability assessment method and an index assessment system, more comprehensive information is provided for system operation, and reference is provided for medium-term and long-term planning, scheduling and the like of the power system.

The invention provides a compilation method for a reliability test load spectrum of an electric drive assembly mechanical system, and the method comprises the following steps: step 1, collecting userload data, carrying out preprocessing, and dividing road condition segments; step 2, determining five typical working conditions; step 3, selecting a reliability test cycle segment of each typical working condition; step 4, adopting Monte Carlo simulation to extrapolate damage targets of all parts in the full life cycle; step 5, calculating the number of cycles of each reliability test cycle segment covering multiple damage targets by using a multi-objective optimization method; step 6, splicing the reliability test cycle segments to obtain a reliability test cycle condition; and step 7, compiling to obtain a reliability test load spectrum according to the cycle index of the reliability test cycle segment and the reliability test cycle working condition. The load spectrum compiled by the method is associated with user load data and covers high damage strength, and reference and basis are provided for reliability design and verification of the electric drive assembly.

The invention relates to a method for tracking multi-target vehicles by adopting the MCMC (Markov Chain Monte Carlo) algorithm, comprising the following steps: modeling the multi-target vehicle tracking process by adopting the MCMC algorithm, establishing various preselection states, traversing the preselection states by using the Metropolis-Hastings sampling based simulated annealing algorithm, and selecting the preselection state with the maximum connection probability as the optimal solution. In the method, a limit is set to the transfer equivalence for the first time, and the parameters in the data association are estimated by the experimental data regression fitting, thus the optimal vehicle motion trajectory with maximum posteriori probability significance is obtained, and the vehicles are tracked. The invention solves the problems of frequent shielding splitting of the multi-target vehicles and has the advantages of high multi-target vehicle tracking precision and good real-time performance.

The invention relates to a Monte-Carlo simulation accelerating method and system based on cloud computing. The method comprises the steps that Hadoop and Monte-Carlo software are installed on a local computer, and the Hadoop is configured to run in a pseudo-distributed mode; a MapReduce procedure is written in the local computer, and a stimulation input text is manufactured; a machine image provided with the Hadoop and the Monte-Carlo software is manufactured at a cloud terminal, the manufactured machine image is utilized for instantiating a certain number of virtual servers, and Hadoop on all the virtual servers is configured to run in a fully-distributed mode to form a Hadoop cluster; the local MapReduce procedure and the stimulation input text are uploaded to the virtual servers, the MapReduce runs on the cloud terminal Hadoop cluster, and Monte-Carlo simulation distributed computation is carried out. The number and configuration of the virtual servers can be flexibly selected, and the method and system can be used at any place where a network has access.

The invention discloses a stock ground material volume measurement method and system, and the method comprises the steps: S1, obtaining the point cloud data of a stock ground material pile through a laser scanner; S2, extracting color features of the point cloud data, and performing coarse segmentation on a stock ground background region and a stock pile target region according to the color features to obtain point cloud data of the stock pile target region; S3, performing fitting registration on the point cloud data of the material pile target area by adopting a coarse registration method anda fine registration method; and S4, according to the point cloud data obtained through fitting registration, calculating the volume of the material pile by using a mode of combining a Monte Carlo method and an integral method, and completing volume measurement. According to the invention, the volume of the material pile can be rapidly and accurately obtained, automatic measurement of the materialvolume of the material pile is realized, the labor cost is reduced, and the accuracy is high.

The invention discloses an unmanned aerial vehicle obstacle avoidance and path planning method, which combines Monte Carlo tree search and contrast reinforcement learning algorithms, overcomes the problem of insufficient signals of a GPS (Global Positioning System) in a specific environment, and realizes the obstacle avoidance and path selection functions of an unmanned aerial vehicle in a complex environment. The method includes the following steps: (1) constructing an environment simulator; (2) the unmanned aerial vehicle obtains observation information in the simulator, and the observation information is processed by using a deep neural network; (3) performing coarse-grained path planning by utilizing Monte Carlo tree search, and generating stage target points in the advancing path of the unmanned aerial vehicle for training of a subsequent reinforcement learning algorithm; (4) learning a fine control strategy and fine-grained path planning of the unmanned aerial vehicle by using reinforcement learning; (5) accelerating unmanned aerial vehicle training based on comparative learning. According to the method provided by the invention, the unmanned aerial vehicle has an autonomous decision-making capability in a complex environment with high difficulty coefficient and large uncertain factors, and can cope with emergencies to a certain extent to complete specific tasks.

The invention relates to a turning repair decision optimization method for rail transit vehicle wheel sets. The turning repair decision optimization method includes: subjecting eight wheel of one carriage to wheel set abrasion data preprocessing; establishing wheel set abrasion data driving models such as a wheel diameter abrasion model, a rim thickness abrasion model and a wheel set turning repair proportion coefficient distribution model; performing Monte Carlo simulation on rail transit vehicle wheel set abrasion and turning repair to acquire expected wheel set service life under the combination of different rim thickness preventing turning repair value and turning repair recovery value. By means of a genetic algorithm in the simulation, the expected rim thickness value and wheel diameter value after one specific turning repair are optimized with the optimization objectives that the wheel diameter loss is as low as possible and is possibly close to the rim thickness recovery value after turning repair; optimal turning repair decisions, namely the optimized rim thickness preventing turning repair value and turning repair recovery value, can be finally analyzed according to the expected simulated service life of the wheel sets.

The invention provides a Monte Carlo simulation method for underwater uplink laser communication. The Monte Carlo simulation method for underwater uplink laser communication comprises the steps that the initial coordinates of photons are determined, that is, the initial quantity of the photons is obtained, a Gaussian light source of laser is sampled, and an initial light source is generated; a next coordinate position is calculated according to the previous photon coordinate position,that is, a random free step length of photons is generated, an initial scattering angle is generated by a scattering phase function, a random azimuth angle is determined, and an initial transmission direction cosine of the photons is determined; and whether the photons are scattered in the next step is judgedaccording to different heights of the photons. According to the Monte Carlo simulation method for underwater uplink laser communication, scattering and weight attenuation of photons in the transmission process are simulated, the scattering positions and weights of the photons are further calculated and judged, the underwater communication condition is accurately simulated, and important guiding significance is provided for improving the quality and stability of underwater communication.

The invention discloses a pneumoelectric interconnection system optimization operation method considering wind power uncertainty. The method comprises the steps: building a pneumoelectric interconnection system random optimization operation model through employing the minimization of the total operation cost of a pneumoelectric interconnection system as a target and combining with a set related operation constraint; aiming at the nonlinear power flow equation constraint of a natural gas network, adopting a linearization method for processing; describing the uncertainty of wind power output byusing a scene analysis method, generating a large number of wind power output scenes through a Monte Carlo simulation method, then applying a scene reduction method based on synchronous back substitution elimination to reduce the scenes to obtain a small number of representative scenes, and then converting the random optimization problem of the pneumoelectric interconnection system considering thewind power uncertainty into a mixed integer linear programming problem of a plurality of deterministic scenes to be solved. Under the condition that the wind power output is uncertain, the economicalefficiency and safety of the operation of the pneumoelectric interconnection system and the absorption capacity of the wind power are effectively improved through optimal scheduling.

The invention discloses a method and device for analyzing correlation among objects in a social medial network, which are widely applied to many important related fields. The method comprises the following steps of: 1, extracting low-level features of the objects; 2, performing high-level semantic learning on the extracted low-level features through a deep network to obtain high-level features of the objects; and 3, obtaining the correlation among the objects according to the high-level features of the objects. In the method, high-level abstract features are learnt out of content information of the objects at first, and then correlation modeling is performed on the basis of the high-level features. A concept of implicit features is introduced in the method, and under a framework of a generative model, the implicit features generate the content information (high-level features) of the objects and the implicit features of the objects interact to obtain correlation information among the objects the maximum probability. The method adopts a Monte Carlo approximate inference algorithm to deduce parameters and hidden variables of a model.

The invention discloses a spaceborne laser altimeter in-orbit moon elevation detection uncertainty evaluation method which comprises the following steps of: screening an area of which the terrain change is flat from a laser altimeter moon surface elevation detection full-moon area; eliminating a pseudo elevation and calculating the area elevation mean value and the standard deviation in the screened area; analyzing various uncertainty influence factors influencing the elevation detection and the distribution density; and carrying out laser altimeter in-orbit moon elevation detection uncertainty evaluation in combination with the various influence factors and by utilizing the ''Monte Carlo Method for Evaluation of Measurement Uncertainty''specification and giving the elevation results. The method has the advantage that the elevation detection uncertainty can be given to the spaceborne laser altimeter in-orbit moon surface terrain elevation detection according to the selected area to provide a basis for further analyzing the source of laser altimeter measurement error and provide a reference and a basis for making a high-precision moon digital elevation map.

The invention relates to a bolt connection joint surface stiffness identification method considering the uncertainty. The method comprises the following steps of S1, building a finite element model ofa bolt connection structure according to geometrical parameters and material parameters of a connected piece and a bolt connecting piece; S2, carrying out a modal test on bolt connection structures under different pre-tightening torques to obtain structural interval uncertainty modal parameters, namely, obtaining a test mode parameter interval; S3, on the basis of the finite element model of thebolt connection structure, building a response surface model between material parameters of a thin-layer unit and dynamic characteristics of the structure; S4, in combination with the response surfacemodel and a Monte Carlo method, obtaining a calculation mode parameter interval; and S5, building an objective function according to the test mode parameter interval and the calculation mode parameter interval, identifying an interval of the material parameters of the thin-layer unit of a joint surface, and realizing an equivalent finite element model of the bolt connection structure of the thin-layer unit. The problem of the uncertainty of the joint surface stiffness in numerical simulation of the bolt connection structure in engineering is solved.

The invention discloses a recommendation system noise filtering method based on information entropies and fuzzy C-means clustering. The method comprises steps that first, user historical scoring dataof a target recommendation system is collected and arranged; second, Monte Carlo stochastic simulation is utilized to construct sub data sets of the user scoring data, a recommendation algorithm is utilized to acquire information entropies and recommendation precision of different sub data sets; third, the information entropies are classified according to uncertainty levels, recommendation precision is classified according to recommendation precision levels, and an empirical model is constructed to determine the potential natural noise data proportion; fourth, fuzzy clustering analysis on allthe user scoring data sets is carried out, and noise data is identified and deleted; and fifth, a recommendation algorithm operates for all the scoring data sets, and a recommendation precision indexis utilized to evaluate recommendation quality. The method is advantaged in that quantization measurement of the user scoring information can be realized, and the proposed natural noise data filteringtechnology has certain universality and portability.

The invention discloses a method for identifying a named entity based on a policy value network, and belongs to the field of information processing. The method comprises the following steps of firstly, modeling a labeling process of named entity recognition into a Markov decision process (MDP) so that a model for identifying the named entity based on enhanced learning is provided, namely MM-NER. MM-NER is the first to apply the Monte Carlo Tree Search (MCTS) enhanced MDP model to named entity recognition (sequence tag task).A strategy value network is designed on the basis of an MDP state definition to obtain label probability and label sequence accuracy evaluation, and an MCTS is used for simulation, so that a label sequence with more global consciousness is searched out. In the inferenceprocess, the strategy value network is directly used to ensure that the identification effect is basically consistent with the tree search strategy, and the time complexity is greatly reduced. An experimental result of the present invention on the CoNLL2003 named entity identification data set demonstrates the effectiveness of the MM-NER with the K-step exploration decision mechanism.