195 results about "Dynamic modelling" patented technology

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 discloses an intelligent workshop transparent monitoring method. The method comprises the following steps: step A: constructing an intelligent workshop transparent monitoring platform, comprising: step A1: establishing an interconnection and interworking system of a virtual model and a real object; step A2: static-modeling a workshop; step A3: dynamic-modeling the workshop; and stepA4: integrating the model and a device; and step B: realizing the intelligent workshop transparent monitoring method, comprising: step B1: specifically implementing intelligent workshop three-dimensional simulation; step B2: associating the virtual model and a real object model; step B3: downloading instructions and feeding back data acquisition; and step B4: displaying data visually. The invention further discloses an intelligent workshop transparent monitoring system. The intelligent workshop transparent monitoring system comprises an MES module, a unit control module, an SCADA module and abut control network module.

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.

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.

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 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.

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.

Techniques for modeling distributed resources are described. According to one aspect of the techniques, a model is initiated at a rendezvous to include elements, each of the elements corresponding to a node (i.e., an instance of a resource) or relationships between or among the elements. In other words, a model is represented as a collection of elements and relationships between the elements, centrally virtualizing or modeling a collection of resources for a given task.

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 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.