199 results about "Dynamic modelling" patented technology

A method and apparatus for dynamic modeling of an object having material points. The method includes receiving signals from a sensor which correspond to respective material points; providing a volumetric model, having functions as parameters, representative of the object; and adapting the parameters to fit a changing model shape. This method of dynamic shape modeling dynamic motion modeling or both includes shape estimation and motion analysis. The apparatus includes a signal processor for receiving signals from a sensor, a second signal processor for providing a volumetric model having functions as parameters representative of the object and a third signal processor for receiving sensed signals and adapting the model and providing a dynamic representation.

The present invention provides a dynamic model directed angiography three-dimensional reconstruction method, pertaining to an intersectional field of digital image processing and medicine imaging, for satisfying special requests of auxiliary detection and surgery guidance for cardiovascular disease in clinical medicine. The invention includes steps of angiography image preprocessing, vascellum segmentation, vascellum skeleton and radii extraction, model directed vascellum base element recognition, vascellum matching and vascellum three-dimensional reconstruction. The invention also provides a cardiovascular dynamic model construction method including steps of cardiovascular slice data extraction, cordis static and dynamic model building, and static and dynamic model building. According to the invention, good angiography three-dimensional reconstruction result may be obtained, which effectively assists detection and surgery guidance for cardiovascular disease, thereby satisfying clinical requests.

The invention relates to a transformer substation field operation management and control system and method based on three-dimensional dynamic modeling. The method comprises the following steps: performing three-dimensional dynamic modeling on a transformer substation; accurate positioning; based on the three-dimensional digital model, carrying out intelligent management and control on an operation site through intelligent identification and early warning; based on a three-dimensional digital model, performing live-action rehearsal and equipment simulation training on typical production operation and safety accidents through a virtual reality system. According to the invention, visual data support is provided for field operation work and personnel training work; real-time positioning and track checking of personnel and equipment are realized, and visual technical support is provided for remote intelligent management and control; an intelligent management and control means is provided for field operation safety, dynamic simulation of an operation task plan, typical operation, dynamic rehearsal of safety accidents and disassembly and reassembly analogue simulation of typical equipment are achieved, a visual simulation scheme is provided for field operation, operation risks are reduced, immersive training experience is provided, and training efficiency is improved.

A system and method is taught to substantially automate forecasting for a hydrocarbon producing reservoir through integration of modeling module workflows. A control management module automatically generates static and dynamic offspring models, with static and dynamic modeling software, until a performance objective associated with the forecasting of the reservoir is satisfied. The performance objective can include an experimental design table to determine a sensitivity of a particular parameter or can be directed towards reservoir optimization, i.e., ultimate hydrocarbon recovery, net present value, reservoir percentage yield, reservoir fluid flow rate, or history matching error.

A technique to provide dynamic modeling, parameter estimation and optimization for processes having operating targets. The technique has an optimization cycle and a simulation cycle. The simulation cycle runs at a rate that is equal to or faster than the run rate of the optimization cycle. Both cycles obtain data from the field about the process. The optimization cycle provides a simulation execution of a dynamic process model and the results of that simulation are used to match the model response to the actual plant response. This matching determines if a dynamic parameter estimation and data reconciliation is needed for the model parameters. Optimized operating targets are determined using either the estimated and reconciled parameters and data or if the same is not needed the collected process data. Mathematical and heuristic assessment tools are used to determine if the optimized operating targets should or should not be invoked.

The embodiment of the invention provides an interest point recommendation method on user dynamic preference and an attention mechanism. The interest point recommendation method comprises the followingsteps: S1, obtaining a historical behavior record of a user, constructing a historical behavior sequence of the user, and dividing the historical behavior sequence of the user into a long-term historical behavior sequence and a short-term historical behavior sequence; S2, respectively inputting the long-term historical behavior sequence and the short-term historical behavior sequence into a long-term preference model and a short-term preference model to learn the long-term preference and the short-term preference of the user; S3, integrating the long-term preferences and the short-term preferences of the users to obtain final preferences of the users; and S4, calculating a score of the user to the place according to the final preference of the user, and recommending interest points to theuser according to the score of the user to the place. According to the interest point recommendation method, dynamic modeling of user preferences is realized, and accurate representation of the userpreferences can be obtained, and the interest point recommendation effect is improved.

A system and method is provided for identification, quantification, and analysis of risks within a supply chain infrastructure. A combination of risk event data, risk interaction definition, and risk assignment hierarchy is used to dynamically model the expected impact of risk to pre-defined performance measures within the system. The dynamic modeling is carried out with the help of the Monte Carlo simulation engine.

The invention discloses a Kalman filtering and data driving fusion battery SOC estimation method belonging to the battery SOC estimation method technology field. The invention provides a varied variance Kalman filter least square support vector machine (VVKF LSSVM) fusion method. Based on two equations of a KF, a noise variance, which is adapted to a current system state to the greatest extent, is set during every iteration, and a problem of declined precision caused by Kalman filtering noise variance initial value relied on artificial experience setting is solved. A least square support vector machine (LS SVM) is selected as the measurement equation of the KF, and by starting from a data angle, the SOC estimation method suitable for various batteries is completed by establishing a simple sample library, and the estimation precision is improved by dynamic modeling. A part of data in an NASA lithium battery data set and a CACLE lithium battery data set is used for experimenting to prove the superiority of the VVKF by comparing with the KF, and the validity of the whole method on the lithium battery SOC estimation.

A controller for predicting cruising speeds of a vehicle includes a processor and an extracting unit to extract feature data from segments of a prior trajectory of the vehicle, the feature data including cruising speeds of the vehicle and predictive feature data. The controller also includes a model generator to generate a probabilistic model associating the predictive feature data with the cruising speeds of the vehicle and a predicting unit to predict a cruising speed of the vehicle for a target segment, which is an upcoming cruising segment of the vehicle, by conditioning the probabilistic model on real-time predictive feature data of segments of a current trajectory.

A method, apparatus, and computer program product for modeling dynamics of a queue are disclosed. A queue law function can be used to determine a control function for use in a congestion control module in a network for dropping packets. A queue law function may be determined based upon traffic conditions in the network. First a quantity that is representative of the link utilization between first and second nodes is determined. If the link is fully utilized, a quantity that is representative of an average round transmission trip time for data to be sent from the first node to the second node and an acknowledgment to be received by the first node is calculated. The queue law function which is dependent on a data drop probability based upon the link utilization, the buffer size, and the average round trip transmission time is determined. From this queue law function, parameters for defining a control function can be derived. These parameters include the minimum buffer size and the maximum expected queue size during normal operation for the node.

The invention relates to a method for establishing a dynamic virtual data base in an analyzing and processing system, which comprises the following steps: (1) defining metadata; (2) constructing a dimension indexing table; (3) constructing data marts; (4) instantiating the data marts; (5) constructing and instantiating a plurality of the data marts; and (6) dynamically modeling and instantiating the virtual data base. The method collects service required data from an enterprise database, defines fields with analysis significance into the metadata, carries out dynamic modeling, dynamic extraction and conversion processing on the corresponding metadata, constructs a plurality of the data marts conforming to an analysis subject and instantiates the virtual data base so as to realize the zero storage of the data in the virtual data base, overcome contradictions between system equipment resources and continuously increased information quantities and meet the requirements of high efficiency and data real-time for a management decision database of a decider.

A method, program and system for mapping ASN.1 data to an object model are provided. The invention consists of identifying the start of a data stream and then generating a new object name for the data. Next, the data is parsed to get the identifier and the data length. Then the data's tag type is analyzed. If the tag type is simple, a class diagram is generated for the data and then the source code for the data is generated. If the tag type is not simple, a subroutine is called to further parse the data before generating the class diagram and source code.

A method and an apparatus for performing modeling of batch dynamics in processing of semiconductor wafers. The method includes performing the process on the first semiconductor wafer in a lot, the process being controlled by a tool model, and acquiring integrated metrology data related to the process of the first semiconductor wafer using an integrated metrology tool. The method further includes performing a lot dynamic modeling process based upon an analysis of the integrated metrology data, the lot dynamic modeling process comprising adjusting the tool model based upon analysis of the integrated metrology data, and performing the process on a second semiconductor wafer in the lot based upon the adjusted tool model.

Systems, methods, and non-transitory computer-readable media are disclosed for automatically generating an improved aircraft wing by modifying quantitative design variables of one or more airfoils based on joint analysis of aerodynamic and structural performance. In particular, the disclosed systems determine cross-sectional aerodynamic and structural performance metrics for identified airfoils. Based on cross-sectional metrics, the disclosed systems iteratively determine wing aerodynamic performance and deformation metrics across a span of the wing until arriving at an equilibrium deformation and corresponding aerodynamic metric. The disclosed systems can repeatedly analyze the aero-structural metrics for different quantitative design variables to explore a design space and generate an improved wing design while improving aerodynamic and structural performance.

The invention provides a content sensing method based on network stream behaviors. The content sensing method comprises the following steps: acquiring network flow in an outer network environment andextracting an observational characteristic as a training sample; training a model by utilizing the training sample; inputting a network stream with an unknown type into the model and identifying the content; carrying out incremental learning by utilizing recognized network stream flow and historical model parameters; updating the model parameters to ensure the continuity of model classes. According to the content sensing method provided by the invention, a dynamic modeling capability of a hidden Markovmoder model and a powerful nonlinear expression capability of a deep neural network are utilized; an experiment result shows the feasibility of the method and compares performance advantages of an existing technical scheme.

Embodiments of the invention provide a multi-source heterogeneous data fusion method and apparatus. The method comprises the steps of obtaining a plurality of objects configured by a user according todemands and object attributes corresponding to the objects; obtaining an API data set, and according to the objects, the object attributes and the API data set, performing dynamic modeling to obtainan OPL dynamic model, wherein the API data set is formed by API conversion of multi-source heterogeneous data; and according to the OPL dynamic model, obtaining correlation relationships between the objects and API data, and a correlation relationship among the objects, thereby realizing multi-source heterogeneous data fusion. The apparatus is used for executing the method. By obtaining the objects, the object attributes and the API data set, the OPL dynamic model is built; through the OPL dynamic model, the correlation relationships between the objects and the API data, and the correlation relationship among the objects are obtained, so that the multi-source heterogeneous data fusion is realized; and therefore, the data fusion cost and storage cost are reduced while the correlation relationship among the objects is revealed for the user.

A method and system are disclosed for generating a list of trips on an associated transportation network, the list ranked in accordance with time-dependent modeling of passenger preferences. User preferences of choosing a specific public transportation service or change point are modeled by a set of latent variables. Any actual trip on the network is converted into a set of pairwise preferences implicitly made by the passenger during the trip. Sequences of services matrices and change points matrices from the retrieved set of trips and non-negative factorization of the services and change points matrices is performed to smooth the matrices. The set of pairwise preferences are used to learn a ranking function and the output of a journey planner is re-ranked using the ranking function.

The invention discloses an expansion target tracking method based on GLMB (Generalized labelled multi-bernoulli) filtering and Gibbs sampling. The expansion target tracking method based on GLMB filtering and Gibbs sampling estimates the target number and the shape of the expansion target, provides a multiple expansion target tracking method under a labelled random finite sets (L-RFS) framework, and mainly includes two aspects: dynamic modeling of multiple expansion targets and tracking estimation of multiple expansion targets. The expansion target tracking method based on GLMB filtering and Gibbs sampling includes the steps: combined with a generalized label multi-bernoulli filter, establishing a measurement limit hybrid model of the expansion targets, by means of Gibbs sampling and Bayesian information criterion, deriving the parameters of the limit hybrid model to learn tracking of the state of the multiple expansion targets, using an equivalent measurement method to replace measurement generated from the expansion targets, and performing ellipse approximating modeling on the shape of the expansion targets to realize estimation of the shape of the expansion targets. The simulation experiment shows that the expansion target tracking method based on GLMB filtering and Gibbs sampling can effectively track the multiple expansion targets, can accurately estimate the state and theshape of the expansion targets, and can obtain the track of the targets.

The invention discloses a multi-target segmenting and tracking method based on a depth image and belongs to the fields of image processing, mode identification, computer application and the like. The method comprises the following steps: collecting a depth image; updating background modeling; searching for a background; judging whether the background is detected for the first time, if so, performing target segmenting, and if not, performing target dynamic tracking; and judging whether data are inputted, if so, updating the background modeling again by taking new data, and if not, ending. The high-efficient target segmenting treatment and target dynamic tracking treatment are utilized to efficiently improve the reliability of target identifying and tracking, and preparation information is supplied to subsequent treatments, such as further image analysis, dynamic modeling, and three-dimensional man-machine interaction.

The invention discloses a photovoltaic power interval prediction method combining a deep cycle neural network and parameter estimation, belonging to the technical field of photovoltaic power prediction. The method of photovoltaic power forecasting based on a long-term and short-term memory network firstly chooses the data of product day, ambient temperature, ambient humidity, wind speed and solarirradiance as the original data of photovoltaic power forecasting. The data of product day, ambient temperature, ambient humidity, wind speed and solar irradiance are selected as the original data ofphotovoltaic power forecasting. The confidence intervals of PV power values and predicted values corresponding to 24 hourly hours of the predicted day are outputted from the predicted model to 24 hourly hours of the predicted day for 365 days of the year. This method establishes a relationship between the current photovoltaic power change and the previous photovoltaic power change, realizes the dynamic modeling of the time series data, and can reflect the change law of photovoltaic power more fully, and realizes more accurate photovoltaic power prediction. The method is easy to operate, is high in practicability and has a high promotion value.

The invention relates to a geosteering method for dynamic modeling during drilling and dynamic adjustment of the track in the process of horizontal well drilling of oil drilling. According to the solution, computer software is used, mutual multi-well comparison is carried out on target layer hierarchical data, target layer result structural maps, earthquake target layer pickup and multilateral well target layer data, and a plane difference algorithm is used, so that a target layer initial three-dimensional geological model is built; a two-dimensional geological model is extracted according to the track of a horizontal well which is being drilled, the direction of a drill bit is adjusted according to the spatial relation between the drilling track and the two-dimensional geological model, the vertical depth between the top and the bottom of the portion, arranged on each key point, of a target layer is obtained during drilling according to the electronic logging characteristics or the gasometry characteristics of special key points encountering a drill, such as a marker layer, a landing point, a bottom outlet, a bottom inlet, a top outlet, a top inlet and a special layer in the target layer, and the thickness uniformity principle, the three-dimensional geological model is adjusted, a two-dimensional geological model is extracted according to the track of the horizontal well which is being drilled, and the hole deviation angle of the drill bit is modified according to the dip angle provided by the two-dimensional geological model; the process is repeated, and it is ensured that the drilling direction of the drill bit is kept to be within the controllable range all the time. The hit rate and the drilling-encounter ratio of well drilling are improved, the drilling cycle is shortened, and drilling efficiency is improved.

An apparatus and a method for a product-line description and verification is provided. The apparatus includes: a product-line architecture model conversion tool for classifying related architecture model components according to variant characteristics by analyzing inputted scenarios, generating a dependency relationship model by extracting a dependency relationship between architecture model elements based on a variant element, and generating a product-line architecture from an inputted architecture model based on the dependency relationship model; and a product-line architecture verification tool for receiving a scenario of a system requirement, mapping the scenario to related architecture model components, analyzing an architecture model dependency by analyzing static modeling information and dynamic modeling information of a product architecture, and generating an aspect model of the requirements, the scenario and the related architecture model components.

Some variations provide a method for real-time estimation of state of charge and state of power of a battery, comprising: (a) cycling a battery with a driving profile; (b) utilizing a recursive algorithm that relates battery terminal voltage to battery current, wherein the algorithm includes open-circuit voltage and a finite-impulse-response filter to dynamically model kinetic voltage; measuring the battery terminal voltage and the battery current at least at a first time and a second time during cycling; calculating battery open-circuit voltage and finite-impulse-response filter parameters; calculating battery state of charge based on the open-circuit voltage; and calculating battery state of power based on the open-circuit voltage and the finite-impulse-response filter parameters. An extended Kalman filtering technique is incorporated for real-time updating of FIR model parameters. Only a single FIR filter is necessary, making these methods applicable for battery-powered systems with limited computing and storage capabilities.

The invention provides a graph convolution behavior recognition method and device based on the skeleton joint points. The real-time skeleton extraction and recognition can be completed. The accuracy of human body behavior recognition is improved. A good score is obtained, and the scene requirement of indoor monitoring is met. The method comprises the following steps: (1) carrying out human skeleton extraction through an OpenPose method; (2) performing dynamic modeling based on the time sequence of the skeleton, and constructing a space-time topological graph; (3) improving the structure of thegraph convolution network, reducing the residual block of the graph convolution network into a time domain graph convolution residual unit and a space domain graph convolution residual unit for the difference of the spatial and temporal features of different actions, so that the network can better learn the spatial and temporal features of different actions, and performing feature extraction through the improved graph convolution network; and (4) performing behavior classification on the output features by using a Softmax classifier to obtain corresponding behavior category labels.

A method of modeling a diesel engine that is capable of multiple combustion modes and equipped with a turbocharger and EGR loop. The model comprises a set of equations, each equation representing one of the following as a time derivative: pressure at the intake manifold, pressure between the turbine and an intake manifold throttle, pressure at the exhaust manifold, the compressor power, and the fresh air fraction (the ratio of fresh air to EGR). The model is used to determine what engine conditions (such as throttle positions and turbocharger output) will result in desired pressures and fresh air fraction.

This invention relates to a system and method of three-dimensional scene dynamic model and real-time simulation. It includes: CPU constitute the suppositional scene model; scene initialization unit, data library paper to introduce suppositional scene, protract unit of scene to introduce scene connection image and initialize the three-dimensional model; the scene update unit updates the scene protracted by protract unit, and re-protract it. This method includes: A. build suppositional scene model; B. introduce suppositional data library paper; C. from connection scene picture; D. initialize the three-dimensional scene; E. protract three-dimensional scene; F. update the scene; G. re-protract this scene.

The invention relates to an enterprise data management system based on unified data models. The enterprise data management system comprises a dynamic modeling module, a data object defining module, a unified data center and a data show and management module. The dynamic modeling module is used for establishing a set of unified data models meeting the enterprise data management requirement for an enterprise. The data object defining module is used for defining data objects and data relations between the data objects, so that a data object set is formed. The unified data center is used for storing the data object set. The data show and management module is used for showing and managing all the data objects. By the adoption of the enterprise data management system, through the established unified data models, a user is assisted in defining all the data objects with a unified and standard format, the different data objects are related through the data relations, and all the data objects are managed uniformly through the unified data center; meanwhile, through the version management mechanism, management of process data objects is achieved, and the data objects can be easily retraced, diagnosed and changed.

The invention discloses a collaborative filtering video recommendation method for considering user preference dynamic changes. The method comprises the steps of data pre-processing, model training andsorting, wherein the data pre-processing is mainly that original data is processed to generate a formative leaning sample set required for model training; and a training model mainly learns user characteristics and video characteristics according to generated samples, and is mainly composed of a parameter matrix, a BPR model and a SimRank model. When a system is ready to recommend videos to users, a recommendation engine firstly reads the users and videos recorded by a background and corresponding metadata into a pre-processing module; then a training module firstly initializes to-be-learnedcharacteristic parameters, BPR leaning and SimRank learning are carried out respectively on input corresponding leaning samples according to the data pre-processing module; and lastly, the videos aresorted and recommended according to the trained user characteristics and video characteristics. The collaborative filtering video recommendation method for considering the user preference dynamic changes has the advantages that under the condition of not increasing the time complexity, the user preference is modeled dynamically, thereby improving the accuracy of recommendation.