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: Aggregation of noisy datasets into a master firmographic database. In an embodiment, firmographic records are received from a plurality of sources, and normalized into a common schema. One or more firmographic records may be cleaned by replacing a value of one or more fields in those firmographic record(s) with a value of those field(s) in another firmographic record. The firmographic records may then be clustered, and each of the clusters may be collapsed into a single conflated firmographic record based on a voting process. A master identifier may be generated for each conflated firmographic record, and the conflated firmographic records may be merged into a master firmographic database that is indexed by master identifiers.

Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.

Abstract: An example method comprises receiving an initial topology of an electrical, receiving a selection of a region of interest, determining one or more external equivalents of the electrical network that are external to the region of interest, determining one or more internal equivalents of the region of interest, calculating a sensitivity matrix based on electrical impedances of at least one of the one or more internal equivalents and based on an amount of power exchanged when in operation, determining a subset of the sensitivity matrix as indicating highly sensitive buses, receiving historical data regarding power flows, predicting power flow for each highly sensitive buses, comparing the predicted power flow to at least one predetermined threshold to determine possible network congestion, and generating a report regarding network congestion and locations of possible network congestion in the region of interest based on the comparison.

Abstract: An example method includes receiving time-series vibration data for multiple components of multiple wind turbines. Based on the time-series vibration data, multiple groups of wind turbines are defined, as well as first and second vibration thresholds for each group. Time-series vibration data for a component of a particular wind turbine is received. A particular group that includes the particular wind turbine, as well as particular first and second vibration thresholds are identified. A vibration of the component has exceeded the particular first vibration threshold is determined. A forecast of at least one of a period of time before the vibration may exceed the particular second vibration threshold and a future date at which the vibration may exceed the particular second vibration threshold is generated. An alert that includes the particular wind turbine and at least one of the period of time and the future date is generated and provided.

Abstract: A system and method for a psychological-based approach for organizational growth of a business is described herein. The method is executed by an engine, an application, a software program, a service, or a software platform on a computing device. The engine includes an artificial intelligence (AI) component. The method includes: capturing data associated with a project continuously and in real-time, combining the captured data with other data to create a data set, utilizing the data set to better predict and drive a resolution of one or more problems associated with the project, outputting a plurality of frameworks to address the one or more problems, receiving a selection from a user of a framework from the plurality of frameworks to address the one or more problems, executing a base build using the selected framework to address the one or more problems.

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.

Abstract: Systems, techniques, and computer-implemented processes for cycle life performance determination of batteries using non-invasive acoustic solutions. In one aspect, a battery inspection system includes a plurality of transducers, and a controller communicatively coupled to the plurality of transducers. The controller is configured to send one or more commands to a first subset of the plurality of transducers for transmitting acoustic signals through a battery cell, receive, from a second subset of the plurality of transducers, response signals in response to the acoustic signals transmitted through the battery cell, and determine a cycle life performance score for the battery cell based on at least the response signals, the score indicating an estimated number of charge-discharge cycles that the battery cell goes through prior to reaching a threshold retention capacity.

Abstract: Conventional television audience measurements are made with diaries or by imaging the area in front of a television and trying to identify the people in the images watching television. Unfortunately, diaries are only accurate if the audience members record entries in them, and image-based techniques are intrusive. The present techniques address these problems by using a viewer's wearable device to measure a viewer's proximity to a television. The wearable device emits or receives low-power beacon signals; measurements of the signal strength are used to calculate the viewer's distance to the television. If the viewer is close enough to the television and the television is on, the viewer may be engaged with the content on the television. This system is simple, non-intrusive, and can used to measure engagement with each television in a multi-television household.

Abstract: One or more aspects of the present disclosure are directed to a synchronized actuation system for holding samples (e.g., battery cells) in place such that ultrasonic sensors can make physical contacts with the samples to transmit and receive acoustic signals across the samples for purposes of making ultrasound measurements of the battery sample. The movement of sensors on both sides of a sample can be synchronized (as opposed to being handled via independent actuators) to improve signal repeatability.

Abstract: Currently, significant technological obstacles prevent the automation of campaign configuration. Embodiments overcome these obstacles using an artificial intelligence (AI) model (e.g., comprising a k-nearest neighbor algorithm) that determines historical segments that match a target segment, based on one or more segment attributes. One or more thresholding criteria of a performance filter are applied to campaign configurations, associated with these matching historical segments, to determine one or more campaign configurations to be recommended. If one of these recommended campaign configurations is accepted, campaign settings for a new campaign may be auto-populated according to the accepted campaign configuration, such that a new campaign, with expected high performance, can be easily configured.

Abstract: Technologies for determining healthcare relationships include a healthcare insight computing device communicatively coupled to multiple patient data providers. The healthcare insight computing device is configured to determine one or more relationships between received data and previously received patient data associated with one or more patients. The healthcare insight computing device is further configured to correlate the ingested data as a function of the determined relationships and enrich the correlated patient data to form a new data element. Additionally, the healthcare insight computing device is configured to transmit the new data element to a raw data cluster such that visual representations of the data elements of the raw data cluster and the relationships there between can be displayed. Additional embodiments are described herein.

Abstract: A handheld or mountable air measuring device includes a sensor assembly with sensors that measure airspeed simultaneously in two directions that are perpendicular to each other. The device includes a data protocol conversion board allowing the communication between the sensor assembly and a smart device included with or associated with the device. The smart device receives the airspeed data. The smart device includes a user interface that includes a map or layout of the environment within which the device is used. The data is displayed on the smart device on the map. The data can be processed locally or transmitted to a remote location for processing. One or more dashboards are generated from the data and accessible by the smart device or another computing device.

Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.

Abstract: An example method comprises receiving sensor data from multiple wind turbines. A wind turbine includes a gearbox, a generator, and multiple gearbox and generator subcomponents. Health indicators may be determined for the gearbox and generator subcomponents with varying lead times. The health indicators correspond to alerts for current or predicted problems of the gearbox and generator subcomponents and have either low severity, medium severity, or high severity risk levels. A machine learning model trained on sensor data may generate the alerts. The multiple wind turbines may be displayed in a list that may be sortable by health indicators for the gearbox subcomponents and the generator subcomponents. The list may be filterable by alerts for the gearbox subcomponents or the generator subcomponents.

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: Provided is an approach for fast and efficient analysis of acoustic signals to detect anomalies in battery cells and using the results of such analysis for intelligent selection of a subset of battery cells for in-depth quality control analysis. In one aspect, a system includes a first acoustic scanner configured to scan a battery cell; determine if the battery cell is potentially anomalous or not; and send the battery cell for a high-resolution scan if the battery cell is determined to be potentially anomalous.

Abstract: The present disclosure is directed to similarity-based defect detection in battery cells without retraining utilized neural networks to identify new and unknown defects. In one aspect, a method includes receiving, as first input into a trained neural network, at least one first signal representative of acoustic measurements of at least one reference battery cell; receiving, as second input into the trained neural network, a second signal representative of acoustic measurements of an under-the-test battery cell; performing, using the trained neural network, an analysis of the at least one first signal and the second signal to determine if the second signal has a threshold similarity to the at least one first signal; and generating, as an output of the trained neural network, a defect identification for the under-the-test battery cell, based on whether the second has the threshold similarity to the at least one first signal or not.

Abstract: Provided is a process, including: obtaining movement transactions without having server-side access to information by which the members of the population undergoing the changes in geolocation indicated by the movement transactions can be identified, either personally or pseudonymously; obtaining for movement transactions corresponding to a designated window of time, geographic-pathogen-risk scores of starting geolocations that include the starting geographic positions; updating for the movement transactions corresponding to the designated window of time, geographic-pathogen-risk scores of the ending geolocations based on both geographic-pathogen-risk scores of the starting geolocations involved in movement transactions ending at the ending geolocations and rates of traffic at the ending geolocations indicated by movement transactions ending or starting at the ending geolocations.