4928 results about "Systems modeling" patented technology

A surgical robotic system automatically calibrates tubular and flexible surgical tools such as endoscopes. By accounting for nonlinear behavior of an endoscope, the surgical robotic system can accurately model motions of the endoscope and navigate the endoscope while performing a surgical procedure on a patient. The surgical robotic system models the nonlinearities using sets of calibration parameters determined based on images captured by an image sensor of the endoscope. Calibration parameters can describe translational or rotational movements of the endoscope in one or more axis, e.g., pitch and yaw, as well as a slope, hysteresis, or dead zone value corresponding to the endoscope's motion. The endoscope can include tubular components referred to as a sheath and leader. An instrument device manipulator of the surgical robotic system actuates pull wires coupled to the sheath or the leader, which causes the endoscope to articulate.

A system and method for providing data to a model of a building system includes a building control system with a communications network, a control subsystem for generating module deployment data and transmitting the module deployment data over the communications network, a memory for storing a three dimensional model of at least a portion of a building and a computer. A computer program executed by the computer includes computer instructions for associating module deployment data received from the communications network with the three dimensional model and modifying the three dimensional model based upon the module deployment data.

The invention provides an unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning. The method comprises the steps of deep coding-decoding full-convolution network segmentation system model setup, domain discriminator network model setup, segmentation system pre-training and parameter optimization, adversarial training and target domain feature extractor parameter optimization and target domain MRI brain tumor automatic semantic segmentation. According to the method, high-level semantic features and low-level detailed features are utilized to jointly predict pixel tags by the adoption of a deep coding-decoding full-convolution network modeling segmentation system, a domain discriminator network is adopted to guide a segmentation model to learn domain-invariable features and a strong generalization segmentation function through adversarial learning, a data distribution difference between a source domain and a target domain is minimized indirectly, and a learned segmentation system has the same segmentation precision in the target domain as in the source domain. Therefore, the cross-domain generalization performance of the MRI brain tumor full-automatic semantic segmentation method is improved, and unsupervised cross-domain adaptive MRI brain tumor precise segmentation is realized.

A system for analyzing an interactive voice response (IVR) system of a call processing center determines a complete sequence of events occurring within the IVR system. The IVR system being operable to automatically accept calls from callers and respond to input from the callers. The system models a call flow of the IVR system as a non-deterministic finite-state automaton. A recording of a plurality of calls to the call processing center is split, such that each of plural calls to the call center is in one audio file. For a plurality of calls, the system detects a complete sequence of DTMF and/or speech input to the IVR system, detects selected prompts issued by the IVR system, and inputs the detected sequence of DTMF and/or speech input as well as the detected prompts issued by the IVR system into the finite-state automaton to determine a call-event sequence for that call. The call-event sequence includes information regarding how that call left the IVR system.

A system for providing real-time modeling of an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from an element of the system. The virtual system modeling engine is configured to generate a predicted data output for the element. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output.

A technique for establishing usage control over digital assets such as computer files. The system model not only tracks authorized users' access to files, but monitors passage of such files to uncontrollable removable storage media or through network connections and the like which may indicate possible abuse of access rights. In accordance with a preferred embodiment, an autonomous independent agent process running at a point of use, such a background process in a client operating system kernel, interrupts requests for access to resources. The agent process senses low level system events, filters, and aggregates them. A policy engine analyzes sequences of aggregate events to determine when policy violations occur.

A control system simultaneously controls a multi-zone process with a self-adaptive model predictive controller (MPC), such as temperature control within a plastic injection molding system. The controller is initialized with basic system information. A pre-identification procedure determines a suggested system sampling rate, delays or “dead times” for each zone and initial system model matrix coefficients necessary for operation of the control predictions. The recursive least squares based system model update, control variable predictions and calculations of the control horizon values are preferably executed in real time by using matrix calculation basic functions implemented and optimized for being used in a S7 environment by a Siemens PLC. The number of predictions and the horizon of the control steps required to achieve the setpoint are significantly high to achieve smooth and robust control. Several matrix calculations, including an inverse matrix procedure performed at each sample pulse and for each individual zone determine the MPC gain matrices needed to bring the system with minimum control effort and variations to the final setpoint. Corrective signals, based on the predictive model and the minimization criteria explained above, are issued to adjust system heating/cooling outputs at the next sample time occurrence, so as to bring the system to the desired set point. The process is repeated continuously at each sample pulse.

Systems and methods for real-time modeling of a microgrid. In an embodiment, real-time data is acquired from a microgrid. Predicted data for the microgrid is generated using a first virtual system model of the microgrid, which comprises a virtual representation of energy sources within the microgrid. The real-time data and the predicted data are monitored, and a calibration and synchronization operation is initiated to update the first virtual system model in real-time when a difference between the real-time data and the predicted data exceeds a threshold. Parameters of the first virtual system model can be modified to create a second virtual system model, and aspects can be forecasted for the microgrid operating under the modified parameters of the second virtual system model. In a further embodiment, market price information can be received, and optimization solutions can be generated based on the market price information.

A highly automated non-confining system to continuously, or at selected time intervals identify, measure, monitor and manage the consumption behavior, substance intake, body weight and growth of individual animals in their usual production environment including range, pasture, feedlot, dairy and farm without disruption to typical behaviors in order to determine, analyze, model and predict a variety of conditions relating to animal health, productivity, efficiency and quality. A transmitter generally attached to the animal identifies the individual animal by a unique individual code. A weighing device measures animal weight while an animal is consuming substances. An antenna receives the unique signal from the transmitter and a transmitting and receiving device sends acquired signals to a computer and receives instructions from a computer. A computer acquires signals and modification factors incorporated in the computer generate an event or interval measurement of an animal's weight and gain, growth rate and substance consumption, and the animal behavior affecting the measurement. A visual identification mechanism marks cattle that the computer system has determined require intervention. The system models and predicts animal health and growth, performance, carcass characteristics, feed utilization, manure and methane output.

A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

A system for providing real-time modeling of protective device in an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from protective devices within the system under management. The virtual system modeling engine is configured to update a virtual mode of the system based on the status of the protective devices and to generate predicted data for the system using the updated virtual model. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output. The analytics engine is also configured to determine the bracing capabilities for the protective devices.

A computer system models human memory by deriving associations between objects, events, and the context of the computer user or users. These associations can be dynamically generated, changing depending on the behavior of the user and context. Examples of areas in which this system can be used include time management (e.g., a calendar that presents time-based groupings of objects), people management (e.g., use analysis of user communications to rank importance of people to the user, groupings, and associations between people/groups and other objects such as documents, email), and general computer management (e.g., use analysis of user behavior to identify important objects and objects that are related to a current focus and context of the computer user).

An inertial (“INS”)/GPS receiver includes an INS sub-system which incorporates, into a modified Kalman filter, GPS observables and/or other observables that span previous and current times. The INS filter utilizes the observables to update position information relating to both the current and the previous times, and to propagate the current position, velocity and attitude related information. The GPS observable may be delta phase measurements, and the other observables may be, for example, wheel pick-offs (or counts of wheel revolutions) that are used to calculate along track differences, and so forth. The inclusion of the measurements in the filter together with the current and the previous position related information essentially eliminates the effect of system dynamics from the system model. A position difference can thus be formed that is directly observable by the phase difference or along track difference measured between the previous and current time epochs. Further, the delta phase measurements can be incorporated in the INS filter without having to maintain GPS carrier ambiguity states. The INS sub-system and the GPS sub-system share GPS and INS position and covariance information. The receiver time tags the INS and any other non-GPS measurement data with GPS time, and then uses the INS and GPS filters to produce INS and GPS position information that is synchronized in time. The GPS/INS receiver utilizes GPS position and associated covariance information and the GPS and/or other observables in the updating of the INS filter. The INS filter, in turn, provides updated system error information that is used to propagate inertial current position, velocity and attitude information. Further, the receiver utilizes the inertial position, velocity and covariance information in the GPS filters to speed up GPS satellite signal re-acquisition and associated ambiguity resolution operations

A method and apparatus for determining a fluid level and/or output flow during operation of a centrifugal pump, are provided, which may be used for production of gas and/or oil from a well, and include a vector feedback model to derive values of torque and speed from signals indicative of instantaneous current and voltage drawn by the pump motor, a pump model which derives values of the fluid flow rate and the head pressure for the pump from torque and speed inputs, a pumping system model that derives, from the estimated values of the pump operating parameters, an estimated value of fluid level and other pumping system parameters. Controllers responsive to the estimated values of the pumping system parameters control the pump to maintain fluid level at the pump input, near an optimum level, or within a safe operating range and/or output flow from the pump.

A method of conducting a sequence of linked simulation operations, utilizing a computer-based simulation model, commences with the performance of a first simulation operation to generate an output condition. A further simulation operation, which is defined to sequentially follow the first simulation operation, is then formed utilizing the simulation model. The second simulation operation at least partially and automatically inherits the output condition generated by the first simulation operation as an input condition. In this way, the second simulation operation commences with a configuration captured from a preceding simulation operation as an input condition. Multiple simulation operations may at least partially inherit a configuration from the first simulation operation.

An optimal path selection system extracts a connection subgraph in real time from an undirected, edge-weighted graph such as a social network that best captures the connections between two nodes of the graph. The system models the undirected, edge-weighted graph as an electrical circuit and solves for a relationship between two nodes in the undirected edge-weighted graph based on electrical analogues in the electric graph model. The system optionally accelerates the computations to produce approximate, high-quality connection subgraphs in real time on very large (disk resident) graphs. The connection subgraph is constrained to the integer budget that comprises a first node, a second node and a collection of paths from the first node to the second node that maximizes a “goodness” function g(H). The goodness function g(H) is tailored to capture salient aspects of a relationship between the first node and the second node.

A system generates optimal global set points for an environmental management system. The system comprises a system model for modeling components of a thermal plant, an objective function for modeling a parameter of the thermal plant, and an optimization engine for optimizing the parameter modeled by the objective function. The system model is coupled to an input data collector for receiving building data and weather data corresponding to a particular site. The system model includes models for thermal plant system components that may be implemented using classical models or artificial intelligence models. Classical models are those models that are implemented using linear programming, unconstrained non-linear programming, or constrained non-linear programming methodologies. The artificial intelligence models are those models that may be implemented using a fuzzy expert control system with crisp and fuzzy rules, genetic algorithms for optimization, or neural networks.

A system for setting WLAN node operating parameters includes at least one access point (AP) radio and a plurality of nodes in wireless communication with the AP radio according to media access control (MAC) parameters. Each of the plurality of nodes has a quality of service (QoS) threshold for wireless communication with the AP radio that is a function of the MAC parameters and includes a controller configured to provide individualized control of the MAC parameters. The controller is programmed to measure current settings for at least a portion of the MAC parameters for the node, input the current settings for those MAC parameters of the node into a WLAN system model to generate a system model output, and adapt a setting of at least one of the MAC parameters for the node to meet the QoS threshold for the node based on the system model output.

This computer implemented software invention supervises networked system resources with the goal of maximizing service availability, providing on-demand and uninterrupted access to service, and minimizing the down time due to failures. It is a cluster-wide solution that co-ordinates the states and activities of resources, assigns availability roles, implements recovery from failures, and implements overall system policy. To do this, it maintains a system model of the system's physical and logical configuration and models the resources using managed objects that provides an extensive representation of the states, roles, and relationships of the systems resources.

Controlling the transformation of the microstructure of shape memory material implants. A surgeon can use the inventive energy delivery device to control the rate of implant shape change, the extent of implant shape change, as well as the force exerted on the surrounding tissue. The invention allows for the fine control of force when fixating osteoporotic bone and rate of bone transport. This system models or measures the heating profile of the implant and provides the surgeon a method to control the extent of microstructure phase transformation so that the rate, force or extent of tissue movement can be controlled individually or together.

The present invention relates to an Internet enabled, interactive financial portfolio risk modeling system. The system operates online, in a collaborative computing environment between the user and the portfolio development system. The portfolio generating system models the user's personal investment parameters into a user profile in terms of the user risk tolerance level, user investment style and user bull/bear attitude. The system further calculates Value At Risk (VAR) values for the user. The system filters various securities based on their VAR and Beta values and present two list of filtered securities, with opposing Beta values, matching the user profile. The present invention enables the user to swap securities in and out of his existing portfolio and receive an analysis of the effect of the swap on his portfolio. The model also generates an ideal portfolio based on the user profile. The present invention presents the user with an estimated value of his portfolio, based on a regression formula as well as a possible best and worst scenario based on statistical formulas particularly to computer implemented, Internet based financial modeling systems.

An internal combustion engine system includes an intake manifold, a combustion chamber, an exhaust manifold and exhaust gas recirculation apparatus for recirculating a portion of the exhausted gases from the exhaust manifold to the intake manifold. An estimate intake manifold oxygen concentration is determined from the air fraction within the intake manifold which is determined from an engine system model that provides interdependent air mass fractions at various locations within the engine system.

A method and data processing system assesses the security vulnerability of a network. A system object model database is created and supports the information data requirements of disparate network vulnerability analysis programs. Only the required data from the system object model database representing the network is imported to the programs, which then analyze the network to produce data results from each program. These data results are stored in a common system model database and within the data fact base. Goal oriented fuzzy logic decision rules are applied to determine the vulnerability posture of the network.