56 results about "Gaussian network model" patented technology

A method of generating graphical models for providing security analysis in computer networks that in one embodiment includes the steps of generating a type abstract graph independent of particular networks that models abstract dependency relationships among attributes and exploits; generating network-specific attack graphs by combining the type abstract graph with specific network information; monitoring an intruder alert; and generating a real-time attack graph by correlating the intruder alert with the network-specific attack graph. The real-time attack graph can be generated using reachability checking, bridging, and exploit prediction based on consequence alerts and may further include the step of calculating the likelihood of queries using a Bayesian network model. The method may also include the steps of inferring unobserved attacks that may have been missed by intrusion detection sensors, and projecting on which hosts and using what exploits additional intruder attacks may occur. The method may further include the step of comparing alternate actions by computation, wherein the alternate actions include the step of patching some vulnerabilities, and wherein the specific network information includes network topology. The specific network information may also include firewall rules.

The invention relates to an evaluation model for evaluating the operation risk of the electrical network on the basis of a Bayesian network modeling method, and belongs to the technical field of scheduling automation of the power system. The method comprises that (1) a Bayesian formula based network model is established; and (2) the risk early-warning evaluation index and model are calculated. The evaluation model has the advantages that the electrical network risk early-warning evaluation model is based on the Bayesian network, the Bayesian network model has unique bidirectional inference technology, the operation risk of the electrical network is evaluated, influence of components on the operation risk of the electric network can be analyzed, weak links of the system are obtained, requirements for risk early-warning evaluation and analysis in practical electrical network service are met, and basis is provided for improvement of the system.

The invention discloses a Bayesian network compensation method for thermal error of a numerical control machine tool, which comprises the following steps: (1) a Bayesian network thermal error prediction model is constructed according to measured sample data; and (2) the real-time compensation of the thermal error of the machine tool is realized according to the prediction result of the Bayesian network model. The compensation system of the invention has a simple structure and reliable application; and the adopted Bayesian network modeling method, on one hand, uses the language of a graph theory to intuitively express the causal dependency relation among various factors which produce the thermal error, on the other hand, analyzes and utilizes the inherent correlation among the factors according to the principle of probability theory to reduce the calculation complexity of inferential prediction, and has the characteristics of intuitive expression, high modeling accuracy and self-adaptation.

The invention relates to a Bayesian network model based public transit environment dynamic change forecasting method. The Bayesian network model based public transit environment dynamic change forecasting method comprises the following steps of screening out various factors affecting public transit passenger flow fluctuation or travel time change; abstracting random jamming conditions of exterior environments and passenger flow or travelling time decision variables into nodes of a Bayesian network, determining a station set and the value range of the station set, and performing discretization on the historical information data of the station set and the value range of the station set; analyzing the influence relation between exterior environment jamming input nodes and passenger flow or travelling time decision nodes and establishing a Bayesian network structural diagram for public transit dynamic environment forecasting; determining a conditional probability table between determinant conditions and the decision nodes; computing the posterior probability when certain public transit passenger flow or travelling time occurs, and accordingly, achieving forecasting of public transit environment dynamic change. Combined with public transit incident detection under the environment of an Internet of vehicles, the Bayesian network model based public transit environment dynamic change forecasting method achieves a dynamic passenger flow time and space change forecasting function and provides data support for daily public transit operation and management.

The invention discloses a brain function magnetic resonance image classification method based on network centrality. The method comprises the following steps of: preprocessing a brain function magnetic resonance image, performing brain region segmentation, and extracting an average time sequence of each brain region; calculating a partial correlation coefficient between each average time sequence, and obtaining a partial correlation coefficient matrix; performing binarization on the partial correlation coefficient matrix to obtain a brain network model; calculating the network centrality of each node in the network; and classifying the brain function magnetic resonance image by utilizing an adaptive improvement classifier, and checking the adaptive improvement classifier by employing a leave-one-out cross validation testing method. The brain function network is established by utilizing the brain function magnetic resonance image, the brain function magnetic resonance image is classified by utilizing the network topology information, and the brain function magnetic resonance image can be accurately classified.

The present invention relates to accident probability evaluation, particularly provides a Bayesian network model based risk evaluation method for a road transportation accident, and aims to solve the problem that the influence of superposition and counteraction among root node factors on a final evaluation result is not considered in an existing method. For the purpose, the method comprises: acquiring factors related to the road transportation accident; constructing a Bayesian network model of the road transportation accident according to the factors; and estimating a risk probability of the road transportation accident according to the Bayesian network model. The method is characterized in that the Bayesian network model has a three-layer network structure, and the method evaluates the probability of an intermediate node according to the probability of a root node and evaluates the risk probability of the road transportation accident according to the probability of the intermediate node. According to the method, superposition and/or counteraction effects among the factors of the root node are considered when the Bayesian network model is constructed, so that the accuracy of final evaluation can be greatly improved.

The invention discloses a wireless cognitive network knowledge base constructing method based on a Bayesian network. A Bayesian network model is constructed by using perceived network performance parameters, so that the dependence among the parameters is obtained, and the conditional probability dependence is transformed into knowledge in a wireless cognitive network to be stored in a knowledge base for guiding a process of intelligent decision making. The process of constructing the knowledge base mainly comprises the steps of: perceiving the network performance parameters, discretizing and analyzing the parameters, constructing the Bayesian network model and transforming the knowledge to construct the knowledge base. The network performance parameters are collected through a perceiving process, and data is discretized and analyzed. Through a structure learning and parameter learning process, the Bayesian network model is constructed by using historical data. The conditional dependence among the parameters in the Bayesian network is transformed into the knowledge so that the knowledge base is constructed and updated.

The invention relates to a method for dynamically generating bus timetables on the basis of Bayesian network models. The method includes screening microscopic and macroscopic factors affecting dynamic generation of the bus timetables; building the double-layer microscopic and macroscopic Bayesian network models for dynamically generating the bus timetables, and in other words, building the Bayesian network models for forecasting dynamic variation of bus environments and the Bayesian network models for dynamically generating the bus timetables; predicting transport capacity and transport volume occurrence probabilities of various routes under the condition of random disturbance and reasons for unbalance of the transport capacity and the transport volumes of the various routes; combining scheduling policies with one another and generating possible timetable schemes around the target for timely evacuating passengers; computing various indexes for evaluating the quality of the timetables from the points of governments, enterprises and the passengers and evaluating the quality of the timetables. The method has the advantages that a function of dynamically adjusting the timetables according to variation of the bus environments can be implemented, and accordingly technical support can be provided for daily bus operation management.

The invention discloses a wind power climbing event probability prediction method and system based on a Bayesian network, and the method comprises the steps: mining the dependency relationship betweena wind power climbing event and related meteorological influence factors such as wind speed, wind direction, temperature, air pressure, humidity, and the like, and building a Bayesian network topological structure with the highest fitting degree with sample data; quantitatively describing a conditional dependency relationship between the climbing event and each meteorological factor, estimating the value of each conditional probability in a conditional probability table at each node of the Bayesian network, and forming a Bayesian network model for predicting the wind power climbing event together with a Bayesian network topological structure; deducing the probability of occurrence of each state of the climbing event according to the numerical weather forecast information of the mastered prediction time; the value of the corresponding conditional probability at each node is adaptively adjusted, so that the inferred conditional probability result of each state of the climbing event is optimized, and the compromise between the reliability and the sensitivity of the prediction result is realized.

The invention provides a monocular vehicle 3D target pose estimation method, system and terminal and a storage medium. The method includes: S01, establishing a basic network model; S02, acquiring a captured image, acquiring a labeled contour of an object to be labeled from the captured image, acquiring coordinate data of each vertex of the labeled contour, and taking plane coordinate data of eachvertex of the labeled contour as an input of a training basic network model to train the basic network model; and S03, inputting an image to be detected into the basic network model, and outputting the 3D labeled result of the captured image. The method establishes a fast detection based CNN network model, two-dimensional vehicle target detection and three-dimensional vehicle pose estimation are carried out, and the pose of the target is estimated while the target is detected. The SSD algorithm is extended to cover the whole 3D pose space, and only the synthetic model data is trained; and onlythe traditional vision sensor is needed, the hardware cost is very low, and the performance-price ratio is high.

A marine observation big data visualization analysis method based on a complex network comprises the steps of performing grid division on original marine observation big data, constructing daily average data in a grid into a single Gaussian model and a mixed Gaussian model, and obtaining nodes represented by probability feature vectors; Determining the similarity between any two nodes in the single Gaussian network and the multi-Gaussian network to obtain a similarity matrix; And setting a threshold value to obtain an adjacent matrix, calculating the degree, the clustering coefficient and thenode betweenness of each node according to the adjacent matrix, and visualizing or drawing the degree, the clustering coefficient and the node betweenness on double logarithm coordinates or on a map.According to the invention, the Gaussian mixture model is combined with the complex network theory for the first time; The invention provides a marine observation big data analysis and visualization method, the fluctuation of ocean motion reflected on the data is restored to the maximum extent, and model parameters are used for expressing high-dimensional ocean data, so that the defect that a network model constructed on the basis of Pearson similarity can only measure time sequence data is overcome, and the calculation speed is also improved.

The invention relates to a BNM-based (Bayesian network model-based) fishery forecasting method. The method includes the steps of discretizing historical ocean information datasets of fishery environment; establishing a table of conditional probability between Bayesian network structure charts and Bayesian network nodes; calculating a posterior probability distribution formula of fishery by the Bayesian network structure chart obtained by optimal learning algorithm; and forecasting the fishery by the obtained posterior probability distribution formula. The function of rapidly forecasting for the fishery can be realized by the BNM-based fishery forecasting method.

An automobile engine fault maintenance method based on Bayesian network models and multi-criteria decision analysis includes: using each fault category and each fault source under each fault category, which affect an automobile engine, to build Bayesian network models so as to obtain a first probability value of each fault category and a second probability value of each fault source; respectively determining standardized weight of each decision criterion through multi-criteria decision analysis according to the decision criteria; obtaining a first effect value of each fault category according to each decision criterion, the standardized weight and the first probability value, selecting the fault category with the highest first effect value, obtaining a second effect value of each fault source under the fault category with the highest first effect value, and selecting the fault source with the highest effect value in the second effect values to serve as the maintenance judging results.

The purpose of the invention is to provide a maintenance decision-making method for a diesel engine fuel oil system through cost analysis in combination with a Bayesian network model. The method comprises the steps of: firstly, establishing the Bayesian network model of the diesel engine fuel oil system, and carrying out fault diagnosis on the fuel oil system based on the model to obtain an occurrence probability of each fault; secondly, carrying out non-dimensional processing on factors which influence a maintenance operation cost of the fuel oil system by use of a standardization formula; thirdly, fusing a plurality of influence factors in maintenance operations by use of an RBF (Radial Basis Function) neural network, and evaluating a corresponding maintenance cost; and finally, comprehensively evaluating a fault occurrence probability and a maintenance cost through a multiplication formula, and ordering the maintenance operations according to a product decreasing rule to obtain an optimal maintenance strategy of the fuel oil system. According to the method, through the cost analysis in combination with the Bayesian network model, the fault probability and the maintenance cost are comprehensively evaluated, and a decision making is carried out on a maintenance strategy of the fuel oil system so that a decision-making result has reference value.

The invention discloses an image annotation method based on a convolutional neural network and binary coding features. The method comprises the following steps: constructing an Incepton V3 basic network model; intercepting a final pooling layer of the Incepton V3 network basic model; removing Logits and softmax functions of the Incepon V3 network basic model, and using a sigmoid function as an activation function of the last layer to obtain a modified first basic network model; adding two full connection layers on the first basic network model, and using a sigmoid function as an activation function of the last layer to obtain a multi-label classification network model; performing training learning on the training set by using a multi-label classification network model, and optimizing the weight of the multi-label classification network model; marking the feature vector set of the target image based on the trained multi-label classification network model to obtain multi-label probability output of the target image; and in combination with multi-label probability output, labeling the target image by adopting a TagProp algorithm. Multi-label labeling of images can be realized, the cost is low, and the efficiency is high.

The invention discloses a hopping sequence prediction system based on a graphical model. The system comprises a preprocessing module, a prediction module and a feedback adjusting module, wherein the preprocessing module is used for removing noise and bandwidth from an intercepted original hopping sequence; the prediction module is connected with the preprocessing module and used for reconstructing a phase space and constructing a prediction model; and the feedback adjusting module is connected with the preprocessing module and the prediction model and used for precision detection, feedback and model adjustment. According to the system, embedded dimension m and time delay phi are solved by adopting a Cao method and an autocorrelation method, and then the phase space is reconstructed; and the Markov boundary of query nodes is learnt on the basis of an improved Microsoft malware protection center (MMPC) algorithm, and then the prediction model is constructed. The embedded dimension m and the time delay phi are two key parameters for reconstructing the phase space, and the parameters acquired by using the autocorrelation method and the Cao method are stable and reliable; and a Bayesian network model is simplified through the Markov boundary, so that the prediction efficiency is high.

The invention discloses a vehicle multi-attribute detection method based on single-network multi-task learning. The method comprises the following steps of collecting and screening pictures; making adata set; carrying out network design on the basis of a Darknet deep learning framework, designing a network structure by adopting an end-to-end and one-stage non-cascade mode according to the characteristic of multiple attributes of the vehicle, and constructing a network model; carrying out model training, setting and adjusting model parameters, training a vehicle multi-attribute data set according to a designed network model, and carrying out data enhancement and multi-scale training during training; and carrying out model testing and model evaluation. The Darknet-based deep learning framework platform is designed according to the Darknet-based deep learning framework platform; a network model is built and is of an end-to-end one-stage non-cascade structure, the network improves the detection effect of multiple attributes of a vehicle by adopting the technologies of data enhancement, convolution kernel separation, multi-scale feature fusion and the like, and better real-time performance is achieved while the higher detection accuracy and recall ratio are achieved.

The invention discloses an industrial production process fault monitoring method based on a hierarchical non-Gaussian monitoring algorithm. The industrial production process fault monitoring method comprises the steps of collecting train data and to-be-detected data, calculating the cross-entropy between every two input variables, according to the cross-entropy, dividing all the input variables into various subblocks, building a non-Gaussian monitoring model in each subblock by utilizing a two-layer non-Gaussian monitoring algorithm to extract data of the non-Gaussian part in each subblock, and calculating control limits and a statistic amount of the data of the non-Gaussian parts; in each subblock, calculating data of the remaining Gaussian part to obtain the control limits and statisticamount of the Gaussian parts; conducting fault detection through the control limits and the statistic amount. The industrial production process fault monitoring method based on the hierarchical non-Gaussian monitoring algorithm is better than other traditional methods in fault detection of the non-Gaussian process, not only can the highly complex coupling relationship among variables be sufficiently considered, but also the non-Gaussian part of the data with unknown distribution characteristics can be extracted, and thus the fault detection in the chemical engineering process is more efficientand more accurate.

The invention provides a positioning error compensation method for a robot straight line shaft based on data driving, and belongs to the technical field of robot automatic assembly. According to the positioning error compensation method, a target ball is placed at the tail end of the robot straight line shaft, a plurality of mark points are arranged in a robot space, the robot is controlled to move the target ball to each mark point, and the nominal positions of all the mark points under a robot coordinate system are obtained to serve as input values of a training set; the actual positions ofall the mark points are measured, and difference values are obtained by comparing the nominal positions and the actual positions of all the mark points to serve as the spatial positioning errors of the mark points and serve as output values of the training set; a Gaussian process regression model is used for training, and a Gaussian error model after training is obtained; and the Gaussian error model is used for compensating the spatial positioning errors of a robot, and the compensated kinematic errors of the robot are obtained. According to the positioning error compensation method, the measuring process is simple and convenient, high-precision measuring results can be obtained, and therefore high-precision real-time online compensation for kinematic errors of an automatic hole making system is achieved.

The invention discloses a forwarding modeling method and device based on a spring network model, and the method specifically comprises the steps: setting a time sampling interval and a spatial sampling interval of forwarding modeling; carrying out the discretization of a to-be-simulated geologic model according to the spatial sampling interval, and obtaining a digitalized geologic model, wherein the digitalized geologic model is of a rectangular grid form or a cuboid grid form; determining the elastic parameters of all directions of the digitalized geologic model according to the spatial sampling interval; and updating the wave field value of the digitalized geologic model at each moment according to a seismic focus subwave function, the time sampling interval and the elastic parameters of all directions of the digitalized geologic model. The method overcomes the restrains of a conventional grid shape, can be suitable for square or cube grids, also can be suitable for rectangular or cuboid grids, can meet the requirements of actual production for a simulated grid, and also can preferably select the time and spatial sampling intervals according to the actual conditions, so as to improve the calculation efficiency.