Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

Abstract: An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An ingredient data system that ingests text and graphics of product labels associated with consumer products generally includes a memory having instructions stored thereon; and at least one processor to execute the instructions to transmit via a network a representation of a label view to a user interface on a client computing device that displays one or more of the master attributes associated with the first request, at least a portion of each of the images of one or more of the product labels of the consumer products having the one or more master attributes associated with the first request and at least a portion of the sales history, and at least a portion of each of the images of one or more of the product labels associated with the related consumer products and at least a portion of a sales history.

Abstract: An ingredient data system that ingests text and graphics of product labels associated with consumer products generally includes a memory having instructions stored thereon; and at least one processor to execute the instructions to transmit via a network a representation of a label view to a user interface on a client computing device that displays one or more of the master attributes associated with the first request, at least a portion of each of the images of one or more of the product labels of the consumer products having one or more of the master attributes associated with the first request, and at least a portion of each of the images of one or more of the product labels associated with the related consumer products having the at least one master attribute different from one or more of the master attributes associated with the first request.

Abstract: An example method comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events from the SCADA data, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: Energy, data and information is obtained about the state of the electromagnetic spectrum and the nature of terrestrial transmissions through the use of a remote spectrum sensing system. The disclosure comprises at least one satellite in orbit around Earth capable of sensing frequencies in use by terrestrial transmitters such as those used for radiocommunication or radiodetermination services. In addition, various processing functions are applied to the collected energy, data and information before and/or after they are relayed to at least one ground station in order to reveal a greater understanding of the state of the spectrum and nature of transmissions. The disclosure relates to the described system in multiple embodiments and the method for obtaining energy, data and information about terrestrially used spectrum with such a system.

Abstract: Disclosed embodiments provide a system and method for producing hydrocarbons from biomass. Certain embodiments of the method are particularly useful for producing substitute natural gas from forestry residues. Certain disclosed embodiments of the method convert a biomass feedstock into a product hydrocarbon by hydropyrolysis. Catalytic conversion of the resulting pyrolysis gas to the product hydrocarbon and carbon dioxide occurs in the presence of hydrogen and steam over a CO2 sorbent with simultaneous generation of the required hydrogen by reaction with steam. A gas separator purifies product methane, while forcing recycle of internally generated hydrogen to obtain high conversion of the biomass feedstock to the desired hydrocarbon product. While methane is a preferred hydrocarbon product, liquid hydrocarbon products also can be delivered.

Abstract: A method and system for matching an athlete with a selected striking tool. The method includes, and the system facilitates, receiving test data resulting from the athlete hitting a test target with a test striking tool to launch the test target. The test data is mathematically transformed to calculate a test functional group score. The test functional group score is compared with a database to match the test functional group score with matched previous data having a similar functional group score to the test functional group score. A selection parameter of the selected striking tool is defined to correspond with a value of the selection parameter associated with a strong performance metric in the matched previous data.

Abstract: In an embodiment of the present invention, a Response object collects and synchronizes information from other types of objects as may be implemented in a CRM system. For example, certain objects may include a Lead or Contact Object, a Campaign object, and an Opportunity Object that is synchronized into a Response object. In this way, the results for a given Lead or Contact can be directly attributed to the Campaign or Opportunity without having to guess as may be necessary in certain typical CRM systems.

Abstract: System and method for monitoring objects of interest including persons in an indoor or outdoor scene with high accuracy, strong privacy protection, high security, low volume data transmission and storage. The system includes: an embedded sensing device with multiple sensors and microprocessor(s) with built-in artificial intelligence as an end unit with parametric outputs, a controller that aggregate parametric outputs from multiple end units with local intelligence and configurable control outputs, and an optional gateway that aggregates multiple controllers output and connect with the network, cloud and/or human interface.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: In an embodiment of the present invention, a Response object collects and synchronizes information from other types of objects as may be implemented in a CRM system. For example, certain objects may include a Lead or Contact Object, a Campaign object, and an Opportunity Object that is synchronized into a Response object. In this way, the results for a given Lead or Contact can be directly attributed to the Campaign or Opportunity without having to guess as may be necessary in certain typical CRM systems.

Abstract: An exemplary method includes solving on a computing system an optimal power flow formulation for a plurality of generators in a power system. The solving includes computing using multi-threaded parallelism a plurality of constraints for the formulation, computing using multi-threaded parallelism a plurality of Jacobian functions of the constraints, and computing using multi-threaded parallelism a Hessian of Lagrangian functions. The method further includes outputting results of the solving, wherein the results comprise values of generation levels for the plurality of generators. Apparatus and program products are also disclosed.

Abstract: A method for generating electric substation load transfer control parameters includes adjusting elements in a fundamental scale matrix according to a condition change of a power grid, wherein the fundamental scale matrix is constructed based on the topology structure of the power grid, and the elements in the fundamental scale matrix represent switch information and risk values of paths between nodes of the power grid, wherein the switch information represents number of switching times required for connecting two nodes of the power grid; and performing operations on the adjusted fundamental scale matrix to generate switch information and risk values of paths for electric substation load transfer control, as electric substation load transfer control parameters.

Abstract: Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.

Abstract: Various embodiments manage energy generation in a power generation and distribution system. In one embodiment, a set of residual load data is obtained for a given period of time measured at one or more nodes within a power generation and distribution system. The set of residual load data encodes a set of power flow signals. The set of residual load data is analyzed. An amount of power contributed to the set of residual load data by at least one energy generator class is determined based on the analysis of the set of residual load data.

Abstract: A large-scale radio frequency signal information processing and analysis system that provides advanced signal analysis for telecommunication applications, including band capacity and geographical density determinations and detection, classification, identification, and geolocation of signals across a wide range of frequencies and across broad geographical areas. The system may utilize a range of novel algorithms for bin-wise processing, Rayleigh distribution analysis, telecommunication signal classification, receiver anomaly detection, transmitter density estimation, transmitter detection and location, geolocation analysis, telecommunication activity estimation, telecommunication utilization estimation, frequency utilization estimation, and data interpolation.

Abstract: A method, system, and computer program product to manage a network comprising a plurality of interconnected components are described. The method includes obtaining a set of all the components that are part of the network over time, and identifying one or more repeating patterns of components among the set of all the components as corresponding lower-level definitions to generate a hierarchical set of all the components. The method also includes obtaining time-varying information regarding topology and operational values within the network, and creating a representation of the network at a set of times based on the hierarchical set of all the components and the time-varying information.