Patent application number | Description | Published |
20080270329 | SYSTEMS AND METHODS FOR MARTINGALE BOOSTING IN MACHINE LEARNING - Boosting algorithms are provided for accelerated machine learning in the presence of misclassification noise. In an exemplary embodiment, a machine learning method having multiple learning stages is provided. Each learning stage may include partitioning examples into bins, choosing a base classifier for each bin, and assigning an example to a bin by counting the number of positive predictions previously made by the base classifier associated with the bin. | 10-30-2008 |
20080294387 | MARTINGALE CONTROL OF PRODUCTION FOR OPTIMAL PROFITABILITY OF OIL AND GAS FIELDS - A computer-aided lean management (CALM) controller system recommends actions and manages production in an oil and gas reservoir/field as its properties and conditions change with time. The reservoir/field is characterized and represented as an electronic-field (“e-field”). A plurality of system applications describe dynamic and static e-field properties and conditions. The application workflows are integrated and combined in a feedback loop between actions taken in the field and metrics that score the success or failure of those actions. A controller/optimizer operates on the combination of the application workflows to compute production strategies and actions. The controller/optimizer is configured to generate a best action sequence for production, which is economically “always-in-the-money.” | 11-27-2008 |
20090157573 | System And Method For Grading Electricity Distribution Network Feeders Susceptible To Impending Failure - A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs. | 06-18-2009 |
20110231213 | METHODS AND SYSTEMS OF DETERMINING THE EFFECTIVENESS OF CAPITAL IMPROVEMENT PROJECTS - The present application provides methods and systems for quantitatively predicting an effectiveness of a proposed capital improvement project based on one or more previous capital improvement projects representative of one or more physical assets and including one or more attributes that includes defining a first sample pool from the previous capital improvement project data in which said previous capital improvement project has been performed, defining a second sample in which the previous capital improvement project has not been performed, the second sample pool including one or more attribute values that are the same as, or similar to, the attribute values for the first sample pool, generating a performance metric for each of the first and second sample pools, comparing the performance metric from the first sample pool with the performance metric from the second sample pool to determine a net performance metric, and, generating a prediction of effectiveness of the proposed capital improvement project concerning based on said net performance metric. | 09-22-2011 |
20120072039 | Dynamic Contingency Avoidance and Mitigation System - The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks. A disclosed system includes one or more processors, each having respective communication interfaces to receive data from the infrastructure, the data comprising infrastructure network data, one or more software applications, operatively coupled to and at least partially controlling the one or more processors, to process and characterize the infrastructure network data; and a display, coupled to said one or more processors, for visually presenting a depiction of at least a portion of the infrastructure including any changes in condition thereof, and one or more controllers in communication with the one or more processors, to manage processing of the resource, wherein the resource is obtained and/or distributed based on the characterization of said real time infrastructure data. | 03-22-2012 |
20130073488 | METRICS MONITORING AND FINANCIAL VALIDATION SYSTEM (M2FVS) FOR TRACKING PERFORMANCE OF CAPITAL, OPERATIONS, AND MAINTENANCE INVESTMENTS TO AN INFRASTRUCTURE - Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data. | 03-21-2013 |
20130080205 | CAPITAL ASSET PLANNING SYSTEM - A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric. The system also includes a display, coupled to have the one or more processors, for visually presenting the net metric of infrastructure effectiveness, in which the assets for improvement are selected based, at least in part, on the net metric of infrastructure effectiveness. | 03-28-2013 |
20130138482 | CAPITAL ASSET PLANNING SYSTEM - A capital asset planning system for selecting assets for improvement within an infrastructure that includes one or more data sources descriptive of the infrastructure, one or more databases, coupled to the one or more data sources, to compile the one or more data sources, one or more processors, each coupled to and having respective communication interfaces to receive data from the one or more databases. The processor includes a predictor to generate a first metric of estimated infrastructure effectiveness based, at least in part, on a current status of the infrastructure, a second metric of estimated infrastructure effectiveness based, at least in part, on a user-selected, proposed changed configuration of the infrastructure, and a net metric of infrastructure effectiveness based, at least in part, on said first metric and said second metric. The system also includes a display, coupled to have the one or more processors, for visually presenting the net metric of infrastructure effectiveness, in which the assets for improvement are selected based, at least in part, on the net metric of infrastructure effectiveness. | 05-30-2013 |
20130158725 | ADAPTIVE STOCHASTIC CONTROLLER FOR DISTRIBUTED ELECTRICAL ENERGY STORAGE MANAGEMENT - A system for managing a battery in communication with an electrical grid that includes (a) a data collector to collect data representative of an electrical grid; (b) an ASC controller operatively coupled to the data collector and adapted to receive collected data therefrom, the ASC controller comprising a financial strategizer to send instructions based on the collected data; and (c) a battery controller operatively coupled to the ASC controller to receive the instructions transmitted by the ASC controller, the battery controller configured to dictate whether the battery receives electricity from, or transmits electricity to the electrical grid. | 06-20-2013 |
20130232094 | MACHINE LEARNING FOR POWER GRID - A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid. | 09-05-2013 |
20140156031 | Adaptive Stochastic Controller for Dynamic Treatment of Cyber-Physical Systems - Techniques for generating a dynamic treatment control policy for a cyber-physical system having one or more components, including a data collector for collecting data representative of the cyber-physical system, and adaptive stochastic controller including one or more models for generating a predicted value corresponding to available actions based on an objective function, and an approximate dynamic programming element configured to receive actual operation metrics corresponding to the available actions. The approximate dynamic programming element can learn a state-action map and generate a dynamic treatment control policy using the one or more models. | 06-05-2014 |
20140163759 | DIGITAL BUILDING OPERATING SYSTEM WITH AUTOMATED BUILDING AND ELECTRIC GRID MONITORING, FORECASTING, AND ALARM SYSTEMS - A system and method for monitoring status of an electrical grid and one or more building subsystems. The system includes sensors in communication with an electrical grid, buildings that provide data related to the building subsystems, and a digital building operating system that includes a processor that performs instructions to process the data, identify the status of the electrical grid and the building subsystem, predict one or more events based on the data, and provide recommendations to the buildings such as how to prevent the bad event. | 06-12-2014 |
20140249876 | Adaptive Stochastic Controller for Energy Efficiency and Smart Buildings - Techniques for managing one or more buildings, including collecting historical building data, real-time building data, historical exogenous data, and real-time exogenous data and receiving the collected data at an adaptive stochastic controller. The adaptive stochastic controller can generate at least one predicted condition with a predictive model. The adaptive stochastic controller can generate one or more executable recommendations based on at least the predicted conditions and one or more performance measurements corresponding to the executable recommendations. | 09-04-2014 |