Abstract: Machine-learning-aided automatic taxonomy for web data. In an embodiment, a training dataset of annotated features is used to train a model to predict a class in a taxonomy of web-based activities. The features may be derived from a uniform resource locator (URL) of an online resource and associated metadata. During operation, the features may be extracted from the URL and metadata of each activity record in web data. The trained model may be applied to the extracted features for each activity record to predict a class within the taxonomy. The predicted taxonomic class may be stored in association with the URL that was extracted from the activity record to produce a taxonomized URL.

Abstract: The present disclosure relates to systems for performing autonomous and semi-autonomous analysis of business metrics, and for encrypting and decrypting communication of business metrics over a network. The following also discloses systems for conducting analysis on a system of record, including propensity scoring and modeling. Components and interfaces of the system are also disclosed herein.

Abstract: An apparatus for estimating a condition of a battery includes a mode identifying unit configured to identify a usage mode of the battery during a period of time and its corresponding attenuation curve, according to recorded data on battery usage, stored usage modes of the battery and attenuation curves corresponding to the various usage modes, the attenuation curve representing a change of a fully charged capacity of the battery with battery usage; and a condition estimating unit configured to calculate battery degradation according to the recorded data, the identified usage mode and its corresponding attenuation curve, the degradation representing a quantity of the fully charged capacity of the battery that is reduced over the battery usage. The condition of the battery is estimated so as to rationally judge the residual value of the battery in operation.

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: A system for quantifying viewer engagement with a video playing on a display includes at least one camera to acquire image data of a viewing area in front of the display. A microphone acquires audio data emitted by a speaker coupled to the display. The system also includes a memory to store processor-executable instructions and a processor. Upon execution of the processor-executable instructions, the processor receives the image data and the audio data and determines an identity of the video displayed on the display based on the audio data. The processor also estimates a first number of people present in the viewing area and a second number of people engaged with the video. The processor further quantifies the viewer engagement of the video based on the first number of people and the second number of people.

Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

Abstract: The present disclosure provides a non-invasive and acoustic signal-based approach for examining a quality of SEI formation for any given battery cell and providing an objective assessment thereof. In one example, the objective assessment may be provided as a score that may be referred to as a Solid Electrolyte Interphase (SEI) score for a given battery cell. In one aspect, a method includes transmitting acoustic signals through a battery cell via one or more first transducers, receiving response signals in response to the acoustic signals at one or more second transducers, determining a score indicative of quality of SEI formation in the battery cell based on analyzing the response signals, and outputting the score.

Abstract: A system and method to schedule inspections for a plurality of assets at a corresponding plurality of outdoor locations are described. The method includes retrieving location attributes of each of the plurality of assets, retrieving terrestrial geotagged images that include one or more of the plurality of assets, obtaining parameters associated with each of the plurality of assets based on information from the images, and determining risk factors associated with each of the plurality of assets based on the associated parameters and a failure mapping. The method also includes determining a risk score for each of the plurality of assets based on the corresponding risk factors, and scheduling inspections of the plurality of assets based on the risk score associated with each of the plurality of assets.

Abstract: A system and method of managing a network that includes assets are described. The method includes modeling the network as a directed graph with each of the assets represented as a node and determining alternative paths to each node from each available corresponding source of the node. The method also includes computing upstream robustness of each node, computing upstream robustness of the network, and computing downstream criticality of each node. Managing the network and each asset of the network is based on the upstream robustness and the downstream criticality of each node.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to determining a geographic site to be assessed for lightning activity over a period of time; obtaining weather information associated with the geographic site, the weather information being generated by a weather prediction model for the period of time, wherein the weather information comprises a grid-based representation of a weather map; determining one or more grid points to assess the geographic site for lightning activity; evaluating weather-related metrics associated with the one or more grid points; determining one or more likelihoods of the geographic site experiencing lightning activity during the period of time based at least in part on the evaluation; and providing weather forecast information for the geographic site, the weather forecast information including at least the one or more likelihoods of the geographic site experiencing lightning activity during the period of time.

Abstract: Provided is a process, including: obtaining, with a computer system, a next geolocation to be visited by a user or at which the user has arrived; obtaining, with the computer system, a geolocation-pathogen-risk score of the next geolocation; determining, with the computer system, a topic of message based on the geolocation-pathogen-risk score; determining, with the computer system, by a natural-language-text generation model, natural language text within the topic of the message; and causing, with the computer system, the natural language text of the message to be presented to the user.

Abstract: Systems and methods for acoustic signal based analysis, include obtaining acoustic response signal data of at least a portion of a battery cell, the acoustic response signal data comprising waveforms generated by transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals to the one or more acoustic excitation signals. One or more metrics are determined from at least the acoustic response signal data, the one or more metrics being determined based on correlation of the one or more metrics to one or more characteristics of battery cells and a reference model is generated from the one or more metrics. A test battery can be evaluated using the reference model. Actionable insights or recommendations can be generated based on the evaluation. The reference model can also be updated based on the evaluation.

Abstract: The present disclosure relates to systems for enhancing security of communications occurring over a network, including methods for encrypting and decrypting communications of data over the network. The following also discloses systems for conducting analysis on a system of record, such as a Customer Relationship Management platform, including propensity scoring and modeling. Components and interfaces of the system are also disclosed herein.

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.