1889 results about "Model method" patented technology

Method for automatically generating an implementation of input and output container reusable parts for a process model managed and executed by at least one computer system. The method of generating uses the specification of a process model extended by specifications associating the process model to a reusable part environment outside the workflow process environment and generates an implementation of said input and output containers as reusable parts residing within said reusable parts environment. The method of generating comprises an analysis of the specifications of said process model. Based on this analysis the method generates the associated input container reusable parts and associated output container reusable parts as implementations of said input and output containers.

Computational methods are used to create cardiovascular simulations having desired hemodynamic features. Cardiovascular modeling methods produce descriptions of blood flow and pressure in the heart and vascular networks. Numerical methods optimize and solve nonlinear equations to find parameter values that result in desired hemodynamic characteristics including related flow and pressure at various locations in the cardiovascular system, movements of soft tissues, and changes for different physiological states. The modeling methods employ simplified models to approximate the behavior of more complex models with the goal of to reducing computational expense. The user describes the desired features of the final cardiovascular simulation and provides minimal input, and the system automates the search for the final patient-specific cardiovascular model.

Methods and systems for performing measurements based on a measurement model integrating a metrology-based target model with a process-based target model. Systems employing integrated measurement models may be used to measure structural and material characteristics of one or more targets and may also be used to measure process parameter values. A process-based target model may be integrated with a metrology-based target model in a number of different ways. In some examples, constraints on ranges of values of metrology model parameters are determined based on the process-based target model. In some other examples, the integrated measurement model includes the metrology-based target model constrained by the process-based target model. In some other examples, one or more metrology model parameters are expressed in terms of other metrology model parameters based on the process model. In some other examples, process parameters are substituted into the metrology model.

Methods and systems for performing one or more functions for a specimen using output simulated for the specimen are provided. One system includes one or more computer subsystems configured for acquiring output generated for a specimen by one or more detectors included in a tool configured to perform a process on the specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a learning based model configured for performing one or more first functions using the acquired output as input to thereby generate simulated output for the specimen. The one or more computer subsystems are also configured for performing one or more second functions for the specimen using the simulated output.

Methods, mediums and systems are provided to enable a user to program the behaviors of a Unified Modeling Language (UML) model in a graphical programming or modeling environment, such as block diagram programming environment. The UML model is exported into a text-based format, which is subsequently imported into the graphical programming or modeling environment. The graphical programming or modeling environment represents the UML model using functional blocks so that the user can program the behaviors of the UML model. The graphical programming or modeling environment also enables the user to simulate or execute the UML model to determine the behaviors of the UML model

Method and system for computation of advanced heart measurements from medical images and data; and therapy planning using a patient-specific multi-physics fluid-solid heart model is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image patient data. A patient-specific computational heart model is generated based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data. The computational model includes biomechanics, electrophysiology and hemodynamics. To generate the patient-specific computational heart model, initial patient-specific parameters of an electrophysiology model, initial patient-specific parameters of a biomechanics model, and initial patient-specific computational fluid dynamics (CFD) boundary conditions are marginally estimated. A coupled fluid-structure interaction (FSI) simulation is performed using the initial patient-specific parameters, and the initial patient-specific parameters are refined based on the coupled FSI simulation. The estimated model parameters then constitute new advanced measurements that can be used for decision making.

Method and apparatus for registering an object of known predetermined geometry to scanned three dimensional data such that the object's location may be verified. Such a known object may comprise a less than ideal three-dimensional (3-D) digital object such as a tooth, a dental appliance (e.g., as a tooth bracket model) or other like object, including portions thereof. Knowledge of such an object's location is generally helpful in planning orthodontic treatment, particularly where the location of the object needs to be determined or confirmed or where incomplete or poor scan data is obtained. Aspects of the present invention provide methods of effectively verifying dental appliance location and displaying appliance locations using a computer and three-dimensional models of teeth.

A “near wellbore modeling” software will, when executed by a processor of a computer, model a localized area of a reservoir field which surrounds and is located near a specific wellbore in the reservoir field by performing the following functions: (1) receive input data representative of a reservoir field containing a plurality of wellbores, (2) establish a boundary around one specific wellbore in the reservoir field which will be individually modeled and simulated, (3) impose an “fine scale” unstructured grid inside the boundary consisting of a plurality of tetrahedrally shaped grid cells and further impose a fine scale structured grid about the perforated sections of the specific wellbore, (4) determine a plurality of fluxes/pressure values at the boundary, the fluxes/pressure values representing characteristics of the reservoir field located outside the boundary, (5) establish one or more properties for each tetrahedral cell of the unstructured grid and each cylindrical grid cell of the structured grid, (6) run a simulation, using the fluxes/pressure values at the boundary to mimic the reservoir field outside the boundary and using the fine scale grid inside the boundary, to thereby determine a plurality of simulation results corresponding, respectively, to the plurality of grid cells located inside the boundary, the plurality of simulation results being representative of a set of characteristics of the reservoir field located inside the boundary, (7) display the plurality of simulation results which characterize the reservoir field located inside the boundary, and (8) reintegrate by coarsening the grid inside the boundary, imposing a structured grid outside the boundary, and re-running a simulation of the entire reservoir field.

A modeling method for use in surgical navigation is provided. The method acquires a finite number of pre-defined points from a patient's bone and registers the points with a surgical navigation system. The navigation system generates and displays a three-dimensional image of a warped bone model that is manipulatable and accurate in at least the locations of the points taken and can be used to calculate the locations of bone cuts, implant positions and sizes, as well as display all of this information on the three-dimensional warped model.

A mobile station and methods are disclosed for diagnosing and modeling site specific effluent treatment facility requirements to arrive at a treatment regimen and/or proposed commercial plant model idealized for the particular water/site requirements. The station includes a mobile platform having power intake, effluent intake and fluid outflow facilities and first and second suites of selectably actuatable effluent pre-treatment apparatus. An effluent polishing treatment array is housed at the station and includes at least one of nanofiltration, reverse osmosis and ion-exchange stages. A suite of selectively actuatable post-treatment apparatus is housed at the station. Controls are connected at the station for process control, monitoring and data accumulation. A plurality of improved water treatment technologies is also disclosed. The modeling methods include steps for analyzing raw effluent to be treated, providing a field of raw effluent condition entry values and a field of treated effluent condition goals entry values, and utilizing said fields to determine an initial treatment model including a selection of, and use parameters for, treatment technologies from the plurality of down-scaled treatment technologies at the facility, the model dynamically and continuously modifiable during treatment modeling.

The invention provides a method and a system for predicting cardiovascular and cerebrovascular disease risks. The method comprises the following steps: step 1, defining problems of cardiovascular and cerebrovascular disease prognosis risk prediction, step 2, collecting health medical data of cardiovascular and cerebrovascular disease patients; step 3, preprocessing data, including data integration, data cleaning, and processing missing data; step 4, constructing features and selecting features, identifying potential risk factors; step 5, the identified risk factors and rehabilitation outcomes forming an input-output sample set, inputting the input-output sample set to a random forest algorithm for model training, and evaluating prediction performance of a model. The method and the system can obtain health medical data of cardiovascular and cerebrovascular disease patients, the data being required by a clinician input model, and a predicted rehabilitation outcome of a patient in a certain time period in future is obtained through the model. The method and the system can preferably predict prognosis risks, so as to realize personalized accurate rehabilitation treatment.

Method and apparatus for dynamically placing sensors in a 3D model is provided. Specifically, in one embodiment, the method selects a 3D model and a sensor for placement into the 3D model. The method renders the sensor and the 3D model in accordance with sensor parameters associated with the sensor and parameters desired by a user. In addition, the method determines whether an occlusion to the sensor is present.

Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by "clustering" similar tasks together. The task analysis model may be used to (i) help users complete a task (such help, for example, may be in the form of a gratuitous help function), and/or (ii) to target marketing information to users based on user inputs and the task analysis model. The present invention also provides a uniform semantic network for representing different types of objects in a uniform way.

Method for tracking an object recorded within a selected frame of a sequence of frames of video data, using a plurality of layers, where at least one object layer of the plurality of layers represents the object includes initializing layer ownership probabilities for pixels of the selected frame using a non-parametric motion model, estimating a set of motion parameters of the plurality of layers for the selected frame using a parametric maximization algorithm and tracking the object. The non-parametric motion model is optical flow and includes warping the mixing probabilities, the appearances of the plurality of layers, and the observed pixel data from the pixels of the preceding frame to the pixels of the selected frame to initialize the layer ownership probabilities for the pixels of the selected frame.

The invention discloses a gas pipeline leakage detecting and positioning device and a method based on transient model method, which belong to the technical field of failure diagnosis and flow safety security. The detecting device comprises a sensor group, a signal pre-processing device, an I/V plate, a collecting card, a voltage-stabilized power supply, and an operating condition machine; the detecting method includes of establishing a math model for the liquid flowing in the pipe, solving the flow parameters change in the pipe through a computer under a certain boundary condition, and comparing the model calculating values with the true measuring values at the pipe end; when the difference of the true measuring values and calculating values exceeds a certain range, then a leakage can be identified, and the leakage is positioned through a pressure gradient method. The invention provides a leakage detecting method based on the transient model method, and has the advantages of less investment, high sensitivity and good adaptability, and can be used for detecting and positioning gas leakage and leakage point for on-site gas pipeline.

Method, system and computer program product for learning classification model. The present invention provides a computer-implemented method for learning a classification model using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units is provided. The method includes: obtaining a combination of co-occurring labels expected to be appeared together for an input to the classification model; initializing the classification model with preparing a dedicated unit for the combination from among the plurality of the hidden units so as to activate together related output units connected to the dedicated unit among the plurality of the output units, each related output unit corresponding to each co-occurring label in the combination; and training the classification model using the one or more training data.

Methods and systems for redirecting the funds in a money transfer transaction are provided. The redirection of funds can be accomplished through a money redirection system. The redirection process can be initiated through a request from a recipient of the funds to change the destination of the money transfer from an intended destination to a new destination. The recipient can provide the money redirection system with a transaction identifier and with recipient identification information so that the money redirection system can verify that the recipient making the money redirection request is the recipient associated with the money transfer. After the money redirection system has verified the identity of the recipient, the money redirection system can direct a money transfer facilitator to redirect the funds.

A cross data segmentation federation learning modeling method, a server and a readable storage medium are provided. The method comprises the following steps: the server node sends the first model parameter to each working node, so that each working node obtains the gradient and loss cost by federated model training of its own data to be trained, and feeds back the gradient and loss cost; a gradient and a cost of loss of feedback are received from each working node; the first model parameter is updated based on the gradient and the loss cost to obtain a second model parameter, and whether the second model parameter converges or not is judged. If so, the second model parameter is taken as a standard model parameter. The invention sends model parameters, collects gradient and updates model parameters through server nodes, and the working nodes carry out federated model training at the same time, so that the problem of data leakage does not exist in the model training process according todifferent types of model parameters.

Method of constructing a geomechanical model of an underground zone intended to be coupled with a reservoir model allowing simulation of fluid flows in the zone, from a geological model of the zone discretized by a fine grid. Basically, geomechanical properties are associated with the various cells of the geological model on the basis of experimental data, the underground zone is discretized by a geomechanical grid with larger cells than the geological grid, and a scale change is applied to the geomechanical data included in the geological model in order to define equivalent geomechanical properties at the scale of the geomechanical grid of the zone. Application: improvement in the quality of coupled simulations between geomechanical and reservoir models.

The invention discloses an attention mechanism relationship comparison network model method. An attention relationship comparison network model for small sample learning under a small amount of labeled sample data is constructed. Based on a relational network architecture, the model is divided into a feature coding part, a feature combination part and a relational coding part, the feature coding module is used for extracting image feature information, and the feature combination part is used for recombining the extracted query image feature information with the training image feature information of each group to form a new combined feature map. The relation encoding module performs nonlinear metric learning of the network; by introducing an attention mechanism and a spectrum normalizationmethod into an end-to-end deep convolutional neural network model, the model has higher classification accuracy under the condition of small sample learning, the stability of a final training result of the model is improved, and the image classification accuracy of an existing model in small sample learning is improved.

The invention provides a modeling method of the characteristics of population space-time dynamic moving based on multisource data fusion. The modeling method comprises: A. inputting map data, mobile phone locating data and floating vehicle data into a system and managing data organization according to requirements; B. establishing a spatial analysis model of the characteristics of population moving based on the mobile phone locating data and the floating vehicle data; C. applying the spatial analysis model of the characteristics of the population moving to carry out multisource data fusion of the map data, the mobile phone locating data and the floating vehicle data to obtain integrated information of the characteristics of the population moving; and D. analyzing the characteristics of various population moving according to the integrated information of the characteristics of the population moving and publishing an analyzed result by the geographic information system. The modeling method can acquire data of urban population space-time dynamic distribution and moving characteristics with large data amount, high quality and space-time characteristics, obtain basis of accurate population distribution and population moving characteristics, and provide decision-making supports for urban planning, land use planning, transportation planning and the like.

The invention relates to a method for rapidly detecting the content of index components of Chinese medicinal materials by using near-infrared spectroscopy technology, which can realize the rapid detection of the comprehensive quality of Chinese medicinal materials. Collect the sample, collect the near-infrared diffuse reflectance spectrum of the sample, and preprocess the obtained spectrum, then use the detection method corresponding to the sample in the conventional method to measure the content of its index components, and combine the spectrum with the content of the index components, The quantitative analysis model is established by adopting the chemometrics method suitable for the analysis of the components of traditional Chinese medicine, the sample to be tested is crushed, and its near-infrared spectrum is scanned, and the spectrum is input into the quantitative analysis model to measure the content of the index components in the Chinese medicinal material. The whole process takes a short time, is fast and accurate, can be measured online, improves production efficiency, greatly saves manpower and material resources, and can generate huge economic and social benefits.

A computer system simulation method starts with algorithmically implementing a specification model independently of hardware architecture. High level functional blocks representing hardware components are connected together using a bus architecture-independent generic channel. The bus architecture-independent generic channel is annotated with timing and protocol details to define an interface between the bus architecture-independent generic channel and functional blocks representing hardware components. The interface is refined to obtain a CCATB for communication space. The read( ) and write( ) interface calls are decomposed into several method calls which correspond to bus pins to obtain observable cycle accuracy for system debugging and validation and to obtain a cycle accurate model. The method calls are replaced by signals, and the functional blocks representing hardware components are further refined to obtain pin/cycle-accurate models which can be manually or automatically mapped to RTL, or be used to co-simulate with existing RTL components.

A modeling method using a three-dimensional (3D) point cloud, the modeling method including extracting at least one region from an image captured by a camera; receiving pose information of the camera based on two-dimensional (2D) feature points extracted from the image; estimating first depth information of the image based on the at least one region and the pose information of the camera; and generating a 3D point cloud to model a map corresponding to the image based on the first depth information.