Abstract: 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.

Abstract: A system and computer-implemented method of providing contingency analysis information for a utility service network that includes obtaining contingency analysis information from a plurality of external sources, integrally combining the contingency analysis information obtained from each of the plurality of external sources into a single application and prioritizing the contingency analysis information in a predetermined order, dynamically updating, the contingency analysis information obtained from each of the plurality of external sources and the prioritization of the contingency analysis information based on status information, and displaying the contingency analysis information to a user via a graphical user interface.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A film formation system 10 includes a processing chamber 15 bounded by sidewalls 18 and a top cover 11. In one embodiment, a susceptor 16 is rotatably disposed in the system 10, and overlaps with a first peripheral member 205 disposed around the sidewalls 18. A radiant heating system 313 is disposed under the susceptor 305 to heat the substrate 19. In another embodiment, the top cover 11 has equally spaced pyrometers 58 for measuring the temperature of the substrate 19 across a number of zones. The temperature of the substrate 19 is obtained from pyrometric data from the pyrometers 58.

Abstract: A method of treating a metal surface of a portion of a substrate processing system to lower a defect concentration near a processed surface of a substrate includes forming a protective coating on the metal surface, wherein the protective coating includes nickel (Ni) and a fluoropolymer. Forming the protective coating on the metal surface can further include forming a nickel layer on the metal surface, impregnating the nickel layer with a fluoropolymer, and removing fluoropolymer from the surface leaving a predominantly nickel surface so the fluoropolymer is predominantly subsurface. A substrate processing system includes a process chamber into which a reactant gas is introduced, a pumping system for removing material from the process chamber, a first component with a protective coating, wherein the protective coating forms a surface of the component which is exposed to an interior of the substrate processing chamber or an interior of the pumping system.

Abstract: 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.

Abstract: 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.

Abstract: 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 conce

Abstract: 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.

Abstract: A film formation system 10 has a processing chamber 15 bounded by sidewalls 18 and a top cover 11. In one embodiment, the top cover 11 has a reflective surface 13 for reflecting radiant energy back onto a substrate 19, pyrometers 405 for measuring the temperature of the substrate 19 across a number of zones, and at least one emissometer 410 for measuring the actual emissivity of the substrate 19. In another embodiment, a radiant heating system 313 is disposed under the substrate support 16. The temperature of the substrate 19 is obtained from pyrometric data from the pyrometers 405, and the emissometer 410.

Abstract: A method of treating a metal surface of a portion of a substrate processing system to lower a defect concentration near a processed surface of a substrate includes forming a protective coating on the metal surface, wherein the protective coating includes nickel (Ni) and a fluoropolymer. Forming the protective coating on the metal surface can further include forming a nickel layer on the metal surface, impregnating the nickel layer with a fluoropolymer, and removing fluoropolymer from the surface leaving a predominantly nickel surface so the fluoropolymer is predominantly subsurface. A substrate processing system includes a process chamber into which a reactant gas is introduced, a pumping system for removing material from the process chamber, a first component with a protective coating, wherein the protective coating forms a surface of the component which is exposed to an interior of the substrate processing chamber or an interior of the pumping system.

Abstract: One embodiment of a processing system for fabricating compound nitride semiconductor devices comprises one or more processing chamber operable with form a compound nitride semiconductor layer on a substrate, a transfer chamber coupled with the processing chamber, a loadlock chamber coupled with the transfer chamber, and a load station coupled with the loadlock chamber, wherein the load station comprises a conveyor tray movable to convey a carrier plate loaded with one or more substrates into the loadlock chamber. Compared to a single chamber reactor, the multi-chamber processing system expands the potential complexity and variety of compound structures. Additionally, the system can achieve higher quality and yield by specialization of individual chambers for specific epitaxial growth processes. Throughput is increased by simultaneous processing in multiple chambers.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An Innervated Stochastic Controller optimizes business decision-making under uncertainty through time. The Innervated Stochastic Controller uses a unified reinforcement learning algorithm to treat multiple interconnected operational levels of a business process in a unified manner. The Innervated Stochastic Controller generates actions that are optimized with respect to both financial profitability and engineering efficiency at all levels of the business process. The Innervated Stochastic Controller can be configured to evaluate real options. In one embodiment of the invention, the Innervated Stochastic Controller is configured to generate actions that are martingales. In another embodiment of the invention, the Innervated Stochastic Controller is configured as a computer-based learning system for training power grid operators to respond to grid exigencies.