70 results about "Predictive simulation" patented technology

A system for mitigation of cyberattacks employing an advanced cyber decision platform comprising a time series data retrieval module, a directed computational graph module, an outcome simulation module, and an observation module. The time series data retrieval module monitors cybersecurity related data from multiple sources, and continuously monitors traffic on a client network. The directed computational graph module analyzes the retrieved data for baseline pattern determination, and analyzes the data for anomalous occurrences. The outcome simulation module performs predictive simulation transformations on data provided by other modules of the platform and provides results as needed. The observation module formats data to maximize impact of included information and data.

A materials characterization method models dynamic, non-linear, temperature-dependent stress, strain, hysteresis, creep, and loss of elasticity at high strain, both in test samples and in Finite Element Analysis (FEA). Incorporating universal properties of statistical mechanics and adapting domain models from ferromagnetics to the higher-dimensional realm of stress tensors, the model is applicable to polymers, rubbers, liquids, and metals in elastic and plastic deformation. The model quantifies the dynamics of both plastic and brittle failure. Apparatus and methods are shown for testing material samples and matching the computational model to sample characteristics, leading to a set of characterizing parameters and predictive simulations using those parameters. Though apparatus and testing protocols of the invention yield optimum characterizations, pre-existing data from conventional testing yield useful results.

A system for mitigation of cyberattacks employing an advanced cyber decision platform comprising a time series data retrieval module, a directed computational graph module, an outcome simulation module, and an observation module. The time series data retrieval module monitors cybersecurity related data from multiple sources, and continuously monitors traffic on a client network. The directed computational graph module analyzes the retrieved data for baseline pattern determination, and analyzes the data for anomalous occurrences. The outcome simulation module performs predictive simulation transformations on data provided by other modules of the platform and provides results as needed. The observation module formats data to maximize impact of included information and data.

A materials characterization method models dynamic, non-linear, temperature-dependent stress, strain, hysteresis, creep, and loss of elasticity at high strain, both in test samples and in Finite Element Analysis (FEA). Incorporating universal properties of statistical mechanics and adapting domain models from ferromagnetics to the higher-dimensional realm of stress tensors, the model is applicable to polymers, rubbers, liquids, and metals in elastic and plastic deformation. The model quantifies the dynamics of both plastic and brittle failure. Apparatus and methods are shown for testing material samples and matching the computational model to sample characteristics, leading to a set of characterizing parameters and predictive simulations using those parameters. Though apparatus and testing protocols of the invention yield optimum characterizations, pre-existing data from conventional testing yield useful results.< / PTEXT>

A focused-ultrasound or other procedure for treating a target within a tissue region can be planned iteratively by creating a treatment plan specifying a treatment location pattern and stimuli applied thereto, simulating the treatment, computationally predicting an effect of the simulated treatment, comparing the predicted effect against one or more treatment constraints (such as efficacy and / or safety thresholds), and, if a constraint is violated, repeating the simulation for an adjusted treatment plan.

A method of simulating a local system which is in interaction with other local systems within a global environment. The method includes generating, at a central location, a collection of global sets of values, each representing a possible future global state of the global environment at a future time, and the collection representing a variety of possible future global states. The method also includes transmitting the collection of global sets of values to the local systems. The local systems receive some of the collection, select a local set of parameters, and perform calculations on that data. The collection of values is used as a predictive simulation of the local system.

Described is a system for patient-specific rehabilitation that can be performed outside a clinic. The system monitors a patient in real-time to generate a quantitative assessment of a physical state and a motivational state of the patient using sensor data obtained from sensors. Predictions related to the patient are generated utilizing patient-specific biomechanical and neurocognitive models implemented with predictive simulations. Video feed of the patient is registered with sensor data and a set of simulation data. Rehabilitation guidance instructions are conveyed to the patient through dialog-based interactions.

The invention discloses a dangerous chemical road transportation drainage basin water environment risk assessment method, which adopts an environment sensitive receptor influence derivation method, takes an environment sensitive receptor as an assessment basis, screens an environment risk road section according to the influence degree of dangerous chemical leakage to be transported on the environment sensitive receptor, and grades the environment risk road section; identifies a plurality of environmental risk road sections according to the drafted hazardous chemical substance transportation route, and then performs the environmental risk assessment and environmental risk grade division one by one on the road sections to serve as an auxiliary decision basis for finally determining a hazardous chemical substance transportation route scheme. The invention further discloses a dangerous chemical road transportation drainage basin water environment risk assessment system. An environment riskassessment module identifies an environment risk receptor around the leakage path, an environment risk source and environment risk substance information; the risk source identified by the environmentrisk identification sub-module is evaluated on the basis of an environment risk source evaluation method, the environment risk level of the risk source is evaluated, and the decision-making module isassisted to realize the prediction simulation of the sudden environment event.

The invention discloses an image simulation method based on a continuous multiple-point geostatistics method, soft data and hard data. For the prediction simulation of permeability images, soft data and hard data are comprehensively utilized to provide a method simulating permeability distribution by using the continuous multiple-point geostatistics method so as to obtain a simulation result similar to the structure characteristics of training images. Compared with the condition that only hard data is used and condition data is not provided, the invention has the highest simulation precision when using soft data and hard data and has minimum CPU time and memory consumption. Compared with the two-point geostatistics method, the continuous MPS method can simulate permeability images more accurately, so that the invention can be widely applied to the application development fields, such as geothermal energy, oil and natural gas fields, coal bed gas and the like.

A method of simulating a local system which is in interaction with other local systems within a global environment. The method includes generating, at a central location, a collection of global sets of values, each representing a possible future global state of the global environment at a future time, and the collection representing a variety of possible future global states. The method also includes transmitting the collection of global sets of values to the local systems. The local systems receive some of the collection, select a local set of parameters, and perform calculations on that data. The collection of values is used as a predictive simulation of the local system.

The invention relates to an intelligent cerebral apoplexy risk monitoring system, aims at monitoring cerebral apoplexy risk by utilizing personal information (comprising age, gender, blood pressure, stature, weight, constitutional index and the like) of health examination, physical examination and hospital admission, and blood routine examination and blood biochemistry data, belongs to the application of artificial intelligence and big data in the field of health care, and belongs to the cross technical field of artificial intelligence, big data and health care. The invention mainly aims at providing a simple, practicable and high-correctness cerebral apoplexy risk monitoring system. The intelligent cerebral apoplexy risk monitoring system is established through carrying out over ten thousands of predictions, simulations and analysis assessments on the data of more than five hundred and ninety thousands of normal persons and more than twenty thousands of cerebral apoplexy patients by means of prediction simulation and big data value extraction technologies of artificial intelligence, and is capable of helping users and doctors to carry out cerebral apoplexy risk monitoring so as to provide hope for the users to monitor the cerebral apoplexy risks and finally keep away from cerebral apoplexy.

A system for insurance process management employing an advanced decision platform has been developed. A high speed data retrieval and storage module retrieves insurance related data from a plurality of sources. A predictive analytics module performs predictive analytics functions on normalized insurance related data. A predictive simulation module performs predictive simulation functions on normalized insurance related data. An interactive display module displays results of activity of the predictive analytics module and the predictive simulation module as pre-programmed by analysts of an investigation, and re-display results in ways differing by additional representation programming instructions over the course of a viewing session.

A system for investment vehicle quantification employing an advanced decision platform comprises a data retrieval module configured to retrieve investment related data. A predictive analytics module performs predictive analytics on investment data using investment specific and machine learning functions. A predictive simulation module performs predictive simulation functions on the investment data. An indexed global tile module retrieves geospatial and map overlay data, and serves as an interface for geospatial data requests. An interactive display module displays the results of predictive analytics and predictive simulation and both real world and simulated geospatial data.

The invention discloses a method for accelerating the finite element solution of the elastic deformation of an object mesh model. For a nonlinear elastic object model, given discrete is a coarse meshand a fine mesh formed by units and vertices, the displacement field of the coarse mesh is solved by a finite element method using a piecewise linear matrix function as a displacement interpolation function, and the displacement field of the fine mesh is solved by the finite element method using linear interpolation function as displacement interpolation function. The shape function of the finiteelement method corresponding to the coarse mesh is a specially designed piecewise linear matrix function, and the piecewise linear matrix function is a discontinuous function. The method is used for simulating the deformation of non-uniform and heterogeneous non-linear elastic objects, and the non-linear constitutive relation is also used for predictive simulation of heterogeneous elastic bodies on a coarse mesh to obtain acceleration of two to three orders of magnitude and make good approximation to the overall stiffness of the system.

Methods, systems, and computer storage media are provided for generating simulation graphs using real-time clinical data. A user may indicate one or more scenario variables to apply to an area of interest. A simulation graph is then generated that includes at least an indication of a scenario status. The scenario status indicates an expected result based on the one or more scenario variables selected by the user. The simulation graph may also include a baseline status indicating an expected result should no changes be made to a current environment. Multiple simulation graphs for varying areas of interest may be generated and compared to one another such that a user is able to quickly identify efficient solutions.

The present approach employs a context-aware simulation platform to facilitate control of a robot remote from an operator. Such a platform may use the prior domain / task knowledge along with the sensory feedback from the remote robot to infer context and may use inferred context to dynamically change one or both of simulation parameters and a robot-environment-task state being simulated. In some implementations, the simulator instances make forward predictions of their state based on task and robot constraints. In accordance with this approach, an operator may therefore issue a general command or instruction to a robot and based on this generalized guidance, the actions taken by the robot may be simulated, and the corresponding results visually presented to the operator prior to evaluate prior to the action being taken.

The invention discloses a dangerous chemical substance road transportation ecological environment risk assessment method, which comprises the following steps: (1) setting a risk assessment system based on a network, and (2) setting an environment risk assessment management module; (3) setting an environmental risk assessment sub-module; and (4) setting an environmental risk level evaluation modulefor determining the environmental risk level of the road section with the ecological environmental risk by combining the level of the ecological environmental sensitive receptor. The risk assessmentsystem comprises a basic information management module, an environment risk assessment management module, an environment risk assessment sub-module and an environment risk level assessment module which are mutually connected and run based on the network, and the level of the ecological environment sensitive receptor is combined with the road section with the ecological environment risk. Accordingto the invention, an ecological environment sensitive receptor influence derivation method is adopted to realize prediction simulation of an emergency environment event, which is helpful for improvingthe environment emergency disposal level.

A focused-ultrasound or other procedure for treating a target within a tissue region can be planned iteratively by creating a treatment plan specifying a treatment location pattern and stimuli applied thereto, simulating the treatment, computationally predicting an effect of the simulated treatment, comparing the predicted effect against one or more treatment constraints (such as efficacy and / or safety thresholds), and, if a constraint is violated, repeating the simulation for an adjusted treatment plan.

The invention relates to a coal mine underground gas extraction drill hole failure judgment method, and belongs to the technical field of coal mine gas extraction. The method comprises the following steps: S11, constructing a borehole gas extraction prediction model by comprehensively controlling coal seam gas parameters, coal and rock stratum environment conditions, extraction borehole parameters, a gas circulation theory and the like of a region; S12, comparing the drilling monitoring data with the prediction simulation result, and judging the drilling operation state; S13, determining a failure drill hole according to the judgment result, and determining a drill hole failure type by utilizing a failure drill hole judgment method; S14, performing drilling repair, coal seam permeability improvement and other technical measures or adjusting extraction drilling engineering parameters according to the drilling failure type, and guiding the drilling extraction of the next area. Accordingto the method, a basis is provided for carrying out technical measures such as failure drilling repair in a targeted mode, the extraction drilling utilization efficiency and the fine management technical level are effectively improved, and it is guaranteed that the coal mine gas extraction effect is smoothly connected with mine extraction.

A soft error rate (SER) associated with a design of a semiconductor circuit may be predicted based on implementing a simulation associated with the design. The simulation may include generating a simulation environment based on information indicating the design, performing a particle strike simulation based on the simulation environment to generate charge deposition information, and calculating a collected charge quantity from the charge deposition information. A determination may be made whether the SER predicted based on the collected charge quantity at least meets a threshold. The design may be modified, and the simulation repeated, if the predicted SER value meets a threshold value. A semiconductor circuit may be manufactured based on the design if the predicted SER value is less than the threshold value.

The invention discloses a Copula and stacked LSTM network coupled park multivariate load joint prediction method. The method comprises the following steps: analyzing nonlinear correlation between cooling, heating and power multivariat loads as well as between the loads and other influence factors such as air temperature by utilizing a Copula theory, selecting input elements for load prediction, carrying out data division on the selected input elements to obtain a training set and a test set, and inputting the training set into a stacked LSTM deep neural network model; training the stacked LSTMdeep learning network model under a Keras environment deep learning framework, and storing weight information trained by the stacked LSTM deep learning network model; loading the trained stacked LSTMdeep neural network model to perform prediction simulation on the test set, and obtaining cold, heat and electric load data in a typical season; and using a mean absolute value error MAPE and a Taylor inequality coefficient TIC to evaluate and predict the cold, heat and electric load prediction results. The stacked LSTM deep learning network model is adopted to predict the load, and the park multivariate load can be effectively and accurately predicted.

A system for multi-corporation venture plan validation employing an advanced decision platform comprises a data retrieval module configured to retrieve cooperative venture related data such as financial, operations and historical data related to the current cooperative plan. A predictive analytics module performs predictive risk functions on venture related data. A predictive simulation module performs predictive simulation functions on the venture risk data. An interactive display module displays the results of predictive analytics and predictive simulation according to pre-designated specifications of the analysts.

A method of using an ankle exoskeleton device is provided herein. The method includes collecting one or more biomechanical data points from an individual. The method also includes developing individualized musculoskeletal simulations based on the one or more biomechanical data points. In addition, the method includes creating predictive simulations by modeling effects of an ankle exoskeleton device on the individualized musculoskeletal simulations. The method also includes utilizing established device-user relationships with real-time measurements to adjust device control. Lastly, the method includes optimizing design and control parameters of the exoskeleton device based on the predictive simulations and user responses.

The invention discloses a demand response participated power distribution network three-phase imbalance and carbon emission treatment method. The method comprises the following steps of performing day-ahead load and new energy output prediction; simulating user behaviors to form flexible load power data, and obtaining a user basic load on the basis of the predicted user total load; a flexible load model of the electric vehicle and the air conditioner is constructed by considering load operation condition constraints; constructing a three-phase imbalance index of the power distribution network, and constructing a carbon emission index of the power distribution network based on different energy carbon emission factors; a multi-target optimization model is constructed by fusing the three-phase imbalance treatment target of the power distribution network and the carbon emission reduction target of the power distribution network; and solving an optimization problem by combining a heuristic optimization algorithm with power flow simulation software of the power distribution network so as to reduce network loss and carbon emission of the power distribution network. According to the method, demand response resources are fully utilized, carbon emission reduction of the power distribution network and reduction of the three-phase unbalance degree of the power distribution network are achieved, landing of the dual-carbon policy in the power distribution network is promoted, and the method has important value.

A system for insurance process management employing an advanced insurance management platform has been developed. A high speed data retrieval and storage module retrieves insurance related data from a plurality of sources. A data analysis module determines an activeness metric for an object, such as a physical asset, in order to categorize risk and also receives a plurality of individual, entity, and object data to create one or more cognitive maps which may analyzed to determine a propensity for risk. The data analysis module generates a cognitive density metric based on the activeness metric and cognitive map. A predictive analytics module performs predictive analytics functions on normalized insurance related data and using the cognitive density metric. A predictive simulation module performs predictive simulation functions on normalized insurance related data. As a result, the system can produce various models to determine risk and loss associated with an insured physical asset.

A system for insurance process management employing an advanced decision platform has been developed. A high speed data retrieval and storage module retrieves insurance related data from a plurality of sources. A predictive analytics module performs predictive analytics functions on normalized insurance related data. A predictive simulation module performs predictive simulation functions on normalized insurance related data. An interactive display module displays results of activity of the predictive analytics module and the predictive simulation module as pre-programmed by analysts of an investigation, and re-display results in ways differing by additional representation programming instructions over the course of a viewing session.

A real-time control system includes a simulation system to implement a predictive, look-ahead function that provides an operator with information that enables a higher level of situational awareness of the expected transitional events of future steps within the control program or sequence logic. The simulation system enables future steps and transitions to be monitored before they actually occur, which enables the operator to recognize and potentially take action, in a current time step, to alleviate the underlying cause of the problem, thus reducing the likelihood of or preventing a sequence stall of the control program.

A system for generating a cybersecurity profile, wherein a time series data retrieval and storage server retrieves information from a prospective client, and information previously gathered, and stored, from a plurality of sources; a directed computational graph analysis module performs graph analysis on the data from the time series data retrieval and storage server; and an automated planning service module performs predictive simulation analysis on data received from the directed computational graph.

The invention discloses a digital twin station system, a job scheduling method based on the system and application. The job scheduling method comprises: inputting processed static data and dynamic data of a physical station into a trained prediction model to obtain job prediction parameters; inputting the operation prediction parameters into a digital twin station simulation model, and performing operation process simulation to obtain simulation operation indexes; and making a scheduling decision based on the simulation operation index. According to the invention, a digital twin station system in which a physical station and a virtual station cooperatively interact is constructed, real-time connection and dynamic feedback between a physical system and a digital system are formed, and efficient and accurate prediction, simulation and decision scheduling can be carried out on station operation.

The invention relates to the field of communication, in particular to a network flow simulation and prediction method and device. The method comprises the following steps: generating a fault signal sequence for at least one network element or link; acquiring fault signals in the fault signal sequences of all network elements and links, and accumulating the acquired fault signals on the network topology; according to the fault occurrence time sequence, obtaining fault services corresponding to the fault signals; according to the change topological graph after each fault occurs, calculating the routing change of the fault service after each fault occurs, and generating a simulation transaction; recording the flow of each fault service after each simulation transaction is executed, and predicting the change of flow distribution in the network; and after the simulation transaction is executed, rolling back the suspended simulation transaction. According to the method, through simulated fault signals and real service calculation, flow simulation and prediction which simulate the real situation as much as possible are realized under the conditions that network hardware equipment is not changed and normal network services are not influenced.