Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of certificates for each instance of a subject product or service. The certificates can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.

Abstract: A pressure safety device is used with a bag valve mask (BVM) for preventing over-pressurization. The BVM includes a bag assembly having a bag connector for detachably mating to a mask connector on a patient mask. The pressure safety device has a housing with a bag port, a mask fitting, and a flow path from the bag port to the mask fitting. The bag port detachably connects to the bag connector on the BVM, and the mask fitting detachably connects to the mask connector on the BVM. The pressure safety device includes an automatic flow reduction valve located on the flow path in the housing and impedes flow when pressure on a bag connector side of the valve exceeds a maximum threshold value.

Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of certificates for each instance of a subject product or service. The certificate can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.

Abstract: A camera apparatus includes control circuitry configured to acquire an input color image of an agricultural field, detect one or more foliage regions, and generate output binary mask images of foliage mask indicating one or more foliage regions and a soil region. The control circuitry is configured to convert the input color image to a Hue, Saturation, Lightness (HSV) color space to obtain an HSV image. Thereafter, the control circuitry is configured to selectively adjust a hue component and convert back to the RGB color space to obtain a soil region-adjusted RGB image. Furthermore, generate an augmented color image by combining pixels of the soil region, with pixels of the one or more foliage regions and utilize the generated augmented color image in training of a crop detection (CD) neural network model to learn a plurality of different types of soil and a range of color variation of soil.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for filling gaps in electric grid models are enclosed. A method includes obtaining vector data representing first portions of paths of electric grid wires over a geographic region; converting the vector data to first raster image data that depicts an overhead view of the electric grid wires including a first set of line segments representing the first portions of the paths; processing the first raster image data using a gap filling model; obtaining, as output from the gap filling model, second raster image data including a second set of line segments corresponding to gaps included in the input raster image data and representing second portions of paths of the electric grid wires; and converting the second raster image data to vector data representing the first portions and the second portions of paths of the electric grid wires.

Abstract: The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for process validation, anomaly detection and in-process quality assurance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for identifying characteristics of electric grid assets are disclosed. A method includes obtaining a plurality of images, each image depicting at least one utility pole of an electric grid; detecting, in each of the plurality of images, keypoints of the at least one utility pole depicted in the image; determining, using the keypoints from at least two images of a particular utility pole, at least one measurement of the particular utility pole; determining, using the measurement of the particular utility pole, an electrical characteristic of an asset supported by the particular utility pole; and providing the electrical characteristic as an output. The two or more images include images of the particular utility pole captured from multiple different camera perspectives. The asset can include a capacitor, a transformer, a switch, a power line.

Abstract: In one aspect, there is provided a method for training a machine learning model to process a graph that represents an electrical system to infer, from the graph, one or more unknown electrical values within the electrical system. In particular, the method includes: obtaining data defining multiple graphs, each graph representing a respective electrical system topology, obtaining, for each electrical system topology and from an electrical simulation system, simulation results indicating an electrical behavior of the respective electrical system topology, and training the machine learning model to predict electrical behaviors of electrical systems including by applying data defining each graph as input to the machine learning model to obtain respective output inferences and adjusting machine learning model parameters responsive to comparisons between the output inferences with simulation results of corresponding electrical system topologies.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a machine-learning model for predicting event tags. The system obtains asset data for an electric power distribution system in a geographic area. The asset data includes: for each of a plurality of electrical assets of the electrical power distribution system, data indicating one or more characteristics of the electrical asset. The system further obtains sensor data for the electric power distribution system. The sensor data includes measurement data from a plurality of electric sensors. The system generates, by processing the asset data and the sensor data, partition data that includes, for each of the plurality of electrical assets, an assignment that assigns the electrical asset to one of a set of feeder networks.

Abstract: A pressure safety device is used with a bag valve mask (BVM) for preventing over-pressurization. The BVM includes a bag assembly having a bag connector for detachably mating to a mask connector on a patient mask. The pressure safety device has a housing with a bag port, a mask fitting, and a flow path from the bag port to the mask fitting. The bag port detachably connects to the bag connector on the BVM, and the mask fitting detachably connects to the mask connector on the BVM. The pressure safety device includes an automatic flow reduction valve located on the flow path in the housing and impedes flow when pressure on a bag connector side of the valve exceeds a maximum threshold value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid modeling using surfel data are enclosed. An electric grid wire identification method includes: obtaining a set of surface elements (surfels), wherein each surfel of the set of surfels represents a portion of a surface of an object in a geographic region; selecting, based on one or more surfel attributes, one or more surfels of the set of surfels that each represent a portion of a surface of an electric grid wire; generating a representation of the electric grid wire from the selected one or more surfels; and adding the representation of the electric grid wire to a virtual model of the electric grid. Obtaining the set of surfels can include obtaining ranging data of the geographic region; and generating the set of surfels from the ranging data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for developing electrical grid mapping. One of the methods includes obtaining a computer model of an electric power grid; generating a network graph representation of the computer model, wherein nodes of the network graph represent grid assets of the computer model and edges of the network graph represent wires connecting the grid assets; generating an initial prediction of links between nodes in the network graph by adding at least one edge to the network graph to obtain an over-connected graph; applying the over-connected network graph as input to a machine learning model to obtain an annotated network graph, the machine learning model configured to identify edges as positive links and negative links, and apply annotations to the edges indicating whether each edge is a positive or negative link; and updating the model based on the annotated network graph.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for configuring location-specific electrical load models while preserving privacy. A first machine learning model configured to predict electrical load curves of an electrical utility grid can be obtained from a server. Load values associated with a particular region of the electrical utility grid can be obtained. The load values can be applied as calibration input to the first machine learning model to produce first adjustment parameters for the first machine learning model. The first adjustment parameters can be provided to the server.

Abstract: This disclosure describes a system and method for effectively training a machine learning model to identify features in DAS and/or seismic imaging data with limited or no human labels. This is accomplished using a masked autoencoder (MAE) network that is trained in multiple stages. The first stage is a self-supervised learning (SSL) stage where the model is generically trained to predict data that has been removed (masked) from an original dataset. The second stage involves performing additional predictive training on a second dataset that is specific to a particular geographic region, or specific to a certain set of desired features. The model is fine-tuned using labeled data in order to develop feature extraction capabilities.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for predicting electrical component failure. A first sensor measurement of a component of an electrical grid taken at a first time can be obtained. A second sensor measurement of the component taken at a second time can be identified, and the second time can be after the first time. An input, which can include the first sensor measurement and the second sensor measurement, can be processed using a machine learning model that is configured to generate, based on one or more changes in one or more characteristics of the component as depicted in the second sensor measurement compared to the first sensor measurement, a prediction representative of a likelihood that the component will experience a type of failure during a time interval. Data indicating the prediction can be provided for presentation by a display.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid model error reduction are enclosed. A method includes obtaining graph data defining a graph including nodes and edges. Each node represents a component of an electric grid and is associated with respective node data representing electrical properties of the component of the electric grid, each edge represents a connection between components of the electric grid and is associated with respective edge data representing electrical properties of the connection, and the graph data includes one or more errors, each error including erroneous node data or erroneous edge data. The method includes processing the graph data using an error-correcting model trained to correct errors in the graph data; obtaining, as output from the error-correcting model, output graph data; and verifying accuracy of the output graph data by processing the output graph data using an electric grid simulator.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a classification neural network. The system generates, from a set of object-specific data, one or more meta-learning datasets for one or more respective initial training tasks. The system determines values for a set of meta parameters by performing meta-learning with a classification neural network on the one or more meta-learning datasets. The system obtains a set of labeled training examples for a characteristic-detection task. The system determines based at least on one of the values for the set of meta parameters and using the set of labeled training examples, target values for the network parameters for the classification neural network to perform the characteristic-detection task.

Abstract: A pressure safety device is used with a bag valve mask (BVM) for preventing over-pressurization. The BVM includes a bag assembly having a bag connector for detachably mating to a mask connector on a patient mask. The pressure safety device has a housing with a bag port, a mask fitting, and a flow path from the bag port to the mask fitting. The bag port detachably connects to the bag connector on the BVM, and the mask fitting detachably connects to the mask connector on the BVM. The pressure safety device includes an automatic flow reduction valve located on the flow path in the housing and impedes flow when pressure on a bag connector side of the valve exceeds a maximum threshold value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid asset detection are enclosed. An electric grid asset detection method includes: obtaining overhead imagery of a geographic region that includes electric grid wires; identifying the electric grid wires within the overhead imagery; and generating a polyline graph of the identified electric grid wires. The method includes replacing curves in polylines within the polyline graph with a series of fixed lines and endpoints; identifying, based on characteristics of the fixed lines and endpoints, a location of a utility pole that supports the electric grid wires; detecting an electric grid asset from street level imagery at the location of the utility pole; and generating a representation of the electric grid asset for use in a model of the electric grid.