Abstract: A system and method for extracting a resource from a reservoir repeatedly alternates between injecting a fluid and injecting a gas into the reservoir. A rate and/or an amount of each of the fluid and the gas that is injected into the reservoir is defined by a first fluid-and-gas ratio function that designates different ratios as a function of time. The ratios designate the rate and/or the amount of the fluid that is injected into the reservoir to the rate and/or the amount of the gas that is injected into the reservoir. The rate and/or the amount at which the fluid and/or the gas is injected into the reservoir is changed according to the ratios designated by the first fluid-and-gas ratio function as time progresses.

Abstract: System includes one or more processors that are configured to perform iterations of the following until a predetermined condition is satisfied. The one or more processors are configured to select a modified trial schedule. The modified trial schedule is selected based on initial fluid-extraction data and initial trial schedules and, if available, prior modified trial schedules and prior modified fluid-extraction data from prior iterations. The one or more processors are configured to receive modified fluid-extraction data generated by execution of the modified trial schedule with a designated model of the reservoir. The one or more processors are also configured to update the surrogate model with the modified fluid-extraction data and the modified trial schedule. For at least a plurality of the iterations, the modified trial schedule is selected, at least in part, to reduce uncertainty in a sample space as characterized by the surrogate model.

Abstract: A system includes a schedule generator having one or more processors configured to obtain resource extraction parameters for extracting a resource from a reservoir. The resource extraction parameters include well creation parameters associated with drilling wellbores, well stimulation parameters associated with introducing fracturing fluid into the wellbores, and production parameters associated with extracting the resource through the wellbores. The schedule generator selects initial trial schedules having different values of the resource extraction parameters and receives initial resource output data generated by execution of the initial trial schedules with a designated reservoir model. The schedule generator generates a surrogate model based on the initial resource output data and the initial trial schedules and uses the surrogate model to perform iterations of selecting modified trial schedules until a predetermined condition is satisfied.

Abstract: A system includes a steam distributor configured to distribute steam received from a steam generator to multiple injection wells in a well pad for steam assisted gravity drainage (SAGD) resource production. The system also includes one or more processors configured to control the steam distributor to distribute the steam to the injection wells according to a resultant scheme including values representing multiple parameters for the SAGD resource production. The parameters include the allocated quantities of steam, pressures within multiple production wells associated with the injection wells, and time periods that the steam is directed into the injection wells. The one or more processors are configured to determine the resultant scheme by performing multiple iterations of a surrogate evaluation process until a stop criterion is met, and identifying the resultant scheme as the sample scheme of a final iteration prior to the stop criterion being met.

Abstract: A system includes a schedule generator having one or more processors configured to obtain resource extraction parameters for extracting a resource from a reservoir. The resource extraction parameters include well creation parameters associated with drilling wellbores, well stimulation parameters associated with introducing fracturing fluid into the wellbores, and production parameters associated with extracting the resource through the wellbores. The schedule generator selects initial trial schedules having different values of the resource extraction parameters and receives initial resource output data generated by execution of the initial trial schedules with a designated reservoir model. The schedule generator generates a surrogate model based on the initial resource output data and the initial trial schedules and uses the surrogate model to perform iterations of selecting modified trial schedules until a predetermined condition is satisfied.

Abstract: System includes one or more processors that are configured to perform iterations of the following until a predetermined condition is satisfied. The one or more processors are configured to select a modified trial schedule. The modified trial schedule is selected based on initial fluid-extraction data and initial trial schedules and, if available, prior modified trial schedules and prior modified fluid-extraction data from prior iterations. The one or more processors are configured to receive modified fluid-extraction data generated by execution of the modified trial schedule with a designated model of the reservoir. The one or more processors are also configured to update the surrogate model with the modified fluid-extraction data and the modified trial schedule. For at least a plurality of the iterations, the modified trial schedule is selected, at least in part, to reduce uncertainty in a sample space as characterized by the surrogate model.

Abstract: A system and method for extracting a resource from a reservoir repeatedly alternates between injecting a fluid and injecting a gas into the reservoir. A rate and/or an amount of each of the fluid and the gas that is injected into the reservoir is defined by a first fluid-and-gas ratio function that designates different ratios as a function of time. The ratios designate the rate and/or the amount of the fluid that is injected into the reservoir to the rate and/or the amount of the gas that is injected into the reservoir. The rate and/or the amount at which the fluid and/or the gas is injected into the reservoir is changed according to the ratios designated by the first fluid-and-gas ratio function as time progresses.

Abstract: A system for generating a threat alert in an infrastructure component is provided. The system includes at least three acoustic sensors disposed at a pre-determined spacing apart from each other on the infrastructure component, wherein each of the sensors is configured to detect a signal corresponding to an outcome that causes damage to the infrastructure component. The system also includes a processing circuitry coupled to each of the at least three acoustic sensors, wherein the processing circuitry configured to filter noise from the signal and generate a threat signal. The system further includes a monitoring center configured to generate a shock alarm in response to the threat signal.

Abstract: A system for generating a threat alert in an infrastructure component is provided. The system includes at least three acoustic sensors disposed at a pre-determined spacing apart from each other on the infrastructure component, wherein each of the sensors is configured to detect a signal corresponding to an outcome that causes damage to the infrastructure component. The system also includes a processing circuitry coupled to each of the at least three acoustic sensors, wherein the processing circuitry configured to filter noise from the signal and generate a threat signal. The system further includes a monitoring center configured to generate a shock alarm in response to the threat signal.

Abstract: A system for predicting fall risk for a resident is provided. The system includes a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors. One or more of the sensors comprise range control radar (RCR) sensors. The fall risk prediction subsystem further includes a fall analysis component configured to analyze the data measurements from one or more of the sensors and a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

Abstract: Method and systems for diagnosing and treating individuals having a mental health disease that provide health related information about one or more of the individuals wherein at least portion of the information comprises collected data on one or more of the individual's activities; analyzes the health related information according to assess the individual's mental health; and creates a treatment plan based at least in part on the individual's assessed state of mental health.

Abstract: A system for predicting fall risk for a resident is provided. The system includes a fall risk prediction subsystem, configured to predict a fall risk likelihood for a resident based on data measurements collected from one or more sensors. One or more of the sensors comprise range control radar (RCR) sensors. The fall risk prediction subsystem further includes a fall analysis component configured to analyze the data measurements from one or more of the sensors and a fall risk assessment component configured to predict the fall risk likelihood for the resident based on the analysis.

Abstract: The present invention provides a computer implemented method for maintaining a knowledge base. The method taking as input, text examples that are tagged with a structural markup language; and maps knowledge nodes in the knowledge base with the tagged examples to determine: (1) the knowledge nodes that best match the tagged examples, and (2) the knowledge nodes that are best connected to the tagged examples. The results are displayed to the user, who verifies that the tagged examples match the selected knowledge nodes. Based on the user response, indices of the knowledge nodes are updated. The method may further include determining discrepancies between the existing knowledge base and the tagged examples, and further displaying the discrepancies to the user. The user can update the missing information in the knowledge base to remove the discrepancies in the existing knowledge base.

Abstract: Method and systems for diagnosing and treating individuals having a mental health disease that provide health related information about one or more of the individuals wherein at least portion of the information comprises collected data on one or more of the individual's activities; analyzes the health related information according to assess the individual's mental health; and creates a treatment plan based at least in part on the individual's assessed state of mental health.

Abstract: Systems and methods, for delivering targeted content to specific individuals, that provide health related information about an individual; correlates the individual with the targeted content based at least in part on the health related information; and delivers the targeted content to the individual through a device for delivering media content.

Abstract: A method of integrating case based reasoning data and FMECA data for product life cycle support. The method comprises creating an access path to case based data using a FMECA classification. The creating includes receiving a case based record including heading data. The heading data includes data elements and the data elements are correlated with data fields in a FMECA database. The FMECA database is searched for an entry that includes one or more of the data elements. The creating further includes determining if the entry should include a reference to the case based record. A new pointer to the case based record is inserted if the determining results in finding that the entry should include a reference to the case based record. The method for integrating case based reasoning data and FMECA data further comprises receiving a request from a user to access the case based data. The case based data is accessed via the FMECA database using the access path in response to the request.

Abstract: A system for monitoring the condition of a vehicle has at least one wireless transmitter in on the vehicle, and in communication with the vehicle computer system. Data representative of operating parameters generated during the operation of the vehicle is downloaded to the transmitter, which transmits the data to a wireless receiver positioned proximal to a path of travel of the vehicle. The receiver is linked to a processor for transmission of the data to the processor for storage and processing if necessary. The system may also include at least one sensor, positioned proximal the path of travel, for detecting a physical phenomenon emanating from at least one vehicle component. The sensor generates a signal, which is transmitted to the processor. The processor is capable of analyzing the signal generating data indicative of operating condition of the vehicle component. The sensor, and/or receiver, mounted to a structure positioned proximal the path of travel of the vehicle.

Abstract: A method for performing failure mode and effects analysis throughout the product life cycle. The method comprises receiving incident data from a requestor. The incident data includes a requestor product and a requestor fault mode. A shared failure mode and effects analysis database is accessed and searched for an existing entry that includes the incident data. The contents of the existing entry are transmitted to the requestor in response to locating an existing entry.

Abstract: A method for performing failure mode and effects analysis throughout the product life cycle. The method comprises receiving incident data from a requestor. The incident data includes a requestor product and a requester fault mode. A shared failure mode and effects analysis database is accessed and searched for an existing entry that includes the incident data. The contents of the existing entry are transmitted to the requestor in response to locating an existing entry.

Abstract: A serviceability strategy is established that permits the definition of a plurality of service models for components, functions, subsystems and field replaceable units in a complex machine system, as well as the implementation of the models for rendering service as serviceable events and faults occur, and for improvement of the models over time. The models may be dedicated in such a manner as to be selectable for addressing detectable and isolatable root causes of serviceable events as they occur, and the proper models are selected as such events occur based upon predefined indicators. As service events arise over time and are addressed by recommendations made by each model, data is gathered and serves as the basis for evolving the models so as to more appropriately address root causes of serviceable events and faults, and to improve the selection of the models most apply suited to addressing serviceable events and faults.