104 results about "Hierarchical modeling" patented technology

Technique assesses the impact of physical circuit variations, specification parameter variation, or process variations on clock, signal, and power network performance and through a hierarchical modeling and hierarchical Monte Carlo simulation method.

The invention relates to a safety and stability control system modeling method in power system dynamic simulation. The method comprises the following steps of: performing hierarchical modeling on the safety and stability control system in a hierarchical decomposition mode; simulating an operation mode, fault information and a control strategy by adopting a ladder logic diagram to obtain a main station model and a sub-station model; and connecting a safety and stability control system model and a primary system model to realize the close-loop dynamic simulation of a large-scale power system which contains the safety and stability control system model. By the method, various different action behaviors and control characteristics of the safety and stability control system in the large-scale power system can be simulated accurately; the defects that conventional simulation software cannot reflect the action behaviors of the safety and stability control system are overcome; postmortem analysis of complex and severe accidents can be performed; and the requirements of the large-scale power system dynamic simulation on the simulation of the safety and stability control system can be met.

A method for categorizing a set of objects includes defining a set of categories in which at least one category in the set is dependent on another category in the set; organizing the set of categories in a hierarchy that embodies any dependencies among the categories in the set; for each object, assigning to the object one or more categories l₁ . . . l_P where l_iε{1 . . . L} from a set {1 . . . L} of possible categories, wherein the assigned categories represent a subset of categories for which the object is relevant; defining a new set of labels z comprising all possible combinations of any number of the categories, zε{{1},{2}, . . . {L},{1,2}, . . . {1,L},{2,3}, . . . {1,2,3}, . . . {1,2, . . . L}}, such that if an object is relevant to several categories, the object must be assigned the label z corresponding to the subset of all relevant categories; and assigning to the object the several categories and the subcategories of the several categories.

The invention provides an attitude and orbit control data analysis method based on a decision tree. The method comprises the following steps: attitude and orbit control data preprocessing, i.e., finishing remote measurement data deduplicating, remote measurement data sequencing, remote measurement data extraction and remote measurement data outlier rejection through the data preprocessing; hierarchical modeling of an attitude and orbit control, i.e., establishing an information and control flow chart of the attitude and orbit control, determining remote measurement variables related to current faults of the attitude and orbit control, and taking the variables as input variables for decision tree analysis; establishing a flow chart of the decision tree analysis; and decision tree modeling, i.e., creating a decision tree C5.0 algorithm model, defining a model name in the model, boosting algorithm test frequency, and trimming attributes and a minimum recording number of each sub branch. The method provided by the invention solves the problem of difficulties in satellite attitude and orbit control complex data analysis and has a certain guidance effect on satellite fault identification, diagnosis and anticipation.

The invention discloses a pipeline planning method and device based on data driven modeling and simulation optimization, and belongs to the field of production line planning and design. The method comprises a step of performing rapid analysis on each data of an assembly line based on data driving and building a data model, a step of building a simulation model based on the data model through hierarchical modeling and driving the simulation model to run, outputting an simulation result and evaluating a result according to set indicators, continuously optimizing and improving the unreasonable allocation of device resources, and quickly performing reasonable pipeline planning and design. According to the invention, the model is quickly established through data driving, the model has good flexibility and reusability, the simulation model can be driven by modifying external data to run different simulation tests, the flexibility, versatility and portability of the simulation model are enhanced, the planning efficiency is improved, and the efficiency is improved and the correctness is ensured on the basis of automatic rapid modeling.

The invention discloses a particle computer dynamic simulation method for a 3D garment, which realizes a fast and robust physical simulation effect through applying a hierarchical modeling and particle simulation method to a physical model of clothing and a human body. The method mainly comprises the steps of parameterized human body modeling, building a particle human model, building a hierarchical clothing model, and performing particle-based fast collision detection algorithm. The particle computer dynamic simulation method for the 3D garment provided by the invention improves on the basis of a position based simulation method, proposes to use a mesh reconstruction technology, standardizes the clothing mesh own form, and increases the robustness of a computing process; particles of different sizes and collision properties are used to realize a fast and robust discrete time point collision detection and processing method; a multi-level particle method is used, and a method of refining a surface and densely paving particles is introduced to prevent a phenomenon of passing through, thus a simulation effect of multi-layer clothing can be realized more truly; and a constraint solving method with priority is used, and the simulation effect can be realized by using fewer iterations.

The invention discloses a hierarchical structure-based neural-network machine translation model, and relates to natural language processing based on deep learning. A word alignment tool GIZA++ is used to carry out word alignment on parallel training sentence pairs, and then source language sentences are divided into clauses of monotonous translation according to punctuation and word alignment information; the above-mentioned obtained clause data are used to train a clause classifier; hierarchical structure modeling is carried out on the source language sentences of the parallel sentence pairs; and hierarchical structure decoding is carried out on target language sentences of the parallel sentence pairs. The sentences are divided into the clauses of monotonous translation, and then word-clauses-sentence hierarchical-modeling, attention mechanisms and decoding are carried out: and a bottom-layer recurrent neural network (GRU) encodes semantic representations of the clauses, an upper-layer recurrent neural network encodes information of the sentences, the bottom-layer attention mechanism is devoted to word-level alignment inside the clauses, and the upper-layer attention mechanism is devoted to clause-level alignment.

The invention relates to a method for realizing a three-dimensional facial animation based on layered modeling and multi-body driving. In the method, on the basis of constructing a layered facial model, independent driving is carried out according to the characteristics of each rigid or flexible body so as to simulate more true and more subtle expressions to synthesize driving effects of eyes and a mouth. In the embodiment of the invention, the three-dimensional facial animation can be acquired through simulation experiments only by respectively shooting a front photograph and a profile photograph of the face of a model, wherein the face of the model needs to be in a naturally relaxed state, two eyes look ahead and the head needs to be vertical to the ground. The method for realizing a three-dimensional facial animation based on layered modeling and multi-body driving is used as the key link of synthesizing a virtual human and has an important actual application value to human-machine interaction and a virtual human voice animation system.

The invention discloses an object-oriented power grid information physical system hierarchical modeling method. The method specifically comprises the following steps: 1) establishing a power grid CPSfive-layer structure model; 2) performing redefinition on the adjacent matrix to realize power grid CPS topology modeling; 3) establishing a power grid CPS object state transition model for describingan object state transition process; 4) adopting the idea of a coupling model to regard each node in a power grid CPS as an atomic model with an independent internal structure, behaviors and an I/O interface; a plurality of atomic models form a coupling model in the form of an interface based on a certain connection relationship, and finally, describing the power grid CPS as a network consisting of a plurality of coupling parts, so that the modeling of the power grid CPS hierarchical relationship is realized to describe the inter-hierarchical vertical relationship. The method can be used for analyzing a plurality of problems such as cross influence factors of the CPS physical space and the information space of the power grid, and is of great significance for realizing fusion modeling and comprehensive safety assessment of the information physical system of the power grid.

The invention relates to a method and a device for controlling the vibration in an automobile hierarchical modeling manner. The method comprises the following steps: sending the data of eight vertical and lateral road roughness sensors to a CPU processor, integrating the hierarchical modeling algorithm and obtaining the motion state of the centroid of suspended mass, the constrained force of suspended mass, and acceleration predictive values obtained by predicting and decoupling the vertical and lateral acceleration variable quantities of No.1 to No.4 suspension brackets, through the operation; respectively transmitting to eight vertical and lateral matrix control quantity operation module of No.1 to No.4 units; obtaining theoretical quantity of controllable actuators of No.1 to No.4 units after the synthesis is conducted; conducting the conversion operation of actuating quantity because the data received by each actuator generates the corresponding action. The invention has the advantages that the hierarchical control architecture built on the basis of considering the common influence of the vertical excitation and the lateral excitation of the road is converted into eight four-multiplied-four matrix, so as to simultaneously conduct the control quantity operation of four controllable actuators and the vibration control, thereby reducing online operation time and reaching the optimum vibration control effect of the whole automobile.

The invention discloses a radar system performance index dynamic evaluation method based on Bayesian machine learning. The method comprises the following steps: modeling a likelihood function of radartarget detection data into Gaussian distribution; modeling the prior distribution of the target performance index to be evaluated into Laplace distribution; performing hierarchical modeling on the target to-be-evaluated performance index by utilizing the Bayesian hierarchical probability model; and performing approximate solution on the posterior distribution of the to-be-evaluated performance indexes of each target by using a variational Bayesian expectation maximization method, thereby obtaining a posterior probability density function and the like. The Bayesian machine learning method is adopted to realize the dynamic evaluation of the radar performance indexes, the biggest advantage is that under the model assumption condition, the analyzed dynamic indication result can be obtained only by observing the data once, the test frequency can be obviously reduced, the test cost and period are reduced, the analyzed index dynamic change range is provided, and the availability and the robustness are obviously improved. Therefore, the applicability for the dynamic evaluation of the radar system performance indexes is good.

The invention provides an SDN (Software Defined Networking) based IEC 61850 communication oriented dynamic bandwidth distribution method. The SDN based IEC 61850 communication oriented dynamic bandwidth distribution method comprises a first step of performing hierarchical modeling for an SDN controller based on an IEC 61850 standard; a second step of mapping a network service in the SDN controller into an ACSI (Abstract Communication Service Interfaces) service; a third step of building a communication protocol stack structure of IEC 61850 based transformer substation IED (Intelligence Electric Devices), the SDN controller of the IEC 61850 standard and a switch of OpenFlow; and a fourth step of, by the SDN controller, controlling an infrastructure to distribute a corresponding bandwidth for each transformer substation IED through a network, and monitoring the bandwidth distribution condition. The SDN based IEC 61850 communication oriented dynamic bandwidth distribution method makes up the defect on a network sensing function of a smart grid automatic management system, distributes network bandwidth resources dynamically and in real time for flow with different priorities and guarantees the best delivery performance in an end-to-end communication process of the transformer substation.

The invention relates to a network combat target assignment method for an air defense missile weapon system. The network combat target assignment method comprises: firstly partitioning a commanding information system which completes a target assignment task into a battle layer and a tactic layer according to functions; secondly, establishing an assignment model of the battle layer according to time, space and substance constraint conditions and giving out a resolving and calculating scheme; then establishing an assignment model of the tactic layer on comprehensive shooting advantage degrees of targets by a firepower unit and giving out a resolving and calculating scheme; and finally, reporting the targets which are failed in interception and the targets incapable of being assigned to the battle layer by the tactic layer, and performing the next assignment. The hierarchical target assignment method provided by the invention has the advantages that the command information systems on different layers complete the assignment tasks with different degrees of fineness, namely the battle layer coarsely assigns the targets to a certain tactic layer under the jurisdiction of the battle layer and then the tactic layer subdivides the targets into the specific firepower unit. Through the hierarchical modeling method, an assignment model on each layer is simplified. Meanwhile, the scientificity and reasonability of the assignment principle guarantee the optimization of the assignment result.

The invention discloses an abnormal traffic monitoring method for a distributed control system. The method comprises the following steps: step 1) establishing a hidden Markov model based on time series analysis; step 2) collecting network traffic; step 3) preprocessing the traffic; step 4) performing optimal estimation on components of a state vector of the model by using a recursive algorithm based on Kalman filtering; and step 5) comparing the optimal estimated value with a set threshold, if the optimal estimated value is greater than the threshold, deeming that the network traffic is abnormal, otherwise, entering step 1). By adoption of the abnormal traffic monitoring method based on structural time series analysis provided by the invention, the industrial Ethernet traffic is divided into different components, hierarchical modeling is performed on the complex network traffic under the assistance of a state space model, thereby effectively improving the industrial network traffic detection accuracy, reducing the false alarm rate and effectively preventing the outbreak of worms.

The present invention discloses a reservoir modeling method for predicting an internal structure of a high-sinuosity meandering river reservoir. The method comprises the following steps of: step 1, acquiring reservoir structure unit data of an outcrop, modern deposition and a tank experiment; step 2, acquiring up-well reservoir structure unit parameters by means of well data analysis; step 3, establishing a structure unit description function by means of the acquired structure unit parameters; and step 4, by using structure unit data of the outcrop, the modern deposition, the tank experiment and a well point as constraints and adopting a hierarchical modeling method, establishing a structure unit model of the meandering river reservoir. The reservoir modeling method is beneficial for establishing a fine, object and quantitative three-dimensional model of the internal structure of the meandering river reservoir, oil-gas recovery can be greatly improved and the reservoir modeling method can be widely applied to the technical field of oil-gas exploration and development.

The invention provides a hierarchical modeling method of protein side chain prediction. The hierarchical modeling method of the protein side chain prediction comprises the following steps: (1) taking main chain information as input, executing a first layer reasoning unit, outputting a side chain torsion angle X1, (2) taking the main chain information and the side chain torsion angle X1 as input, executing a second layer reasoning unit, outputting a side chain torsion angle X2, (3) taking the main chain information, the side chain torsion angle X1 and the side chain torsion angle X2 as input, executing a third layer reasoning unit, outputting a side chain torsion angle X3, and (4) taking the main chain information, the side chain torsion angle X1, the side chain torsion angle X2 and the side chain torsion angle X3 as input, and executing a forth layer reasoning unit, outputting a side chain torsion angle X4. The invention further provides a practice process of the hierarchical modeling method for all the layer reasoning units based on the hierarchical modeling method. Meanwhile, for all the layer reasoning units and combining data bank network (DBN) models corresponding to all the layer reasoning units output by the practice process, the invention further provides a sampling process of the hierarchical modeling method.

The invention discloses a testability modeling method based on a hybrid diagnostic model. According to the method, on the basis of the hybrid diagnostic model, system level testability modeling is realized through adoption of a hierarchical modeling method; on this basis, component level testability modeling is realized through integrating a structure model, a test description model, a maintenance model and a diagnostic model. In the outline design stage of a device, module division and function description are realized through top-down modeling; and in the detailed design stage of the device, the detailed design of each function module is realized through bottom-up modeling. The method is significant in improving the testability and diagnostic level of the device, reducing the whole life cycle cost, accelerating upgrade and update of the device and effectively shortening the development cycle of the device. According to the method, the diagnostic strategy of the hybrid diagnostic model is optimized through adoption of a diagnostic method based on a particle swarm optimization algorithm. Through adoption of the method, fault isolation precision demanded by the user and the diagnostic strategy with relatively low average test cost can be generated. The method has the practical application value specific to the complex device.

The invention discloses a modeling method for a service reliability of an IT (Information Technology) architecture of a cloud data center based on a hierarchical colored generalized stochastic Petri net. For limitations present in research of the service reliability of the corresponding IT architecture, a system model of the hierarchical colored generalized stochastic Petri net (HCGSPN) is established by fusing a colored Petri net and a generalized stochastic Petri net by use of a hierarchical modeling method, not only is a processing course of the IT architecture for a service request depicted, but also a time variation characteristic of the external service request and a real-time resource usage condition and a node working state of the IT architecture are embodied. Therefore, the evaluation for the service reliability can be implemented and the resource configuration strategy for the service reliability can be further improved, and a reference is provided for designing and operationand maintenance of the IT architecture of the cloud data center.

A hierarchical modeling tool or process is provided that represents the fill range of behavior of a hydrocarbon system, accurately including uncertainties and potential events affecting the system. The potential events may include actions taken and information learned. The hierarchical modeling tool may be embedded within a decision support system or used in a stand-alone fashion. Disclosed aspects may link from accurate (high-detail) physics models to an accurate uncertainty representation, and then reduce the accurate uncertainty representation to a high-speed representation of both the physics model and the uncertainty that can be used in an optimizer.

A system for, and method of, generating block timing constraints and a timing model. In one embodiment, the system includes a hierarchical modeling tool configured to: (1) generate a model file, (2) receive at least one abstracted view margin, at least one timing environment margin and at least one operational margin for inclusion in the model file, (3) generate block implementation timing constraints employing the at least one timing environment margin and the at least one operational margin and (4) generate a block timing model employing the at least one abstracted view margin and the at least one operational margin.