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 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: 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: 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.

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, 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: 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: A particular voxel is identified within a volume and a hash table is used to obtain volumetric data describing the particular voxel within the volume. Values of x-, y- and z-coordinates in the volume associated with the particular voxel are determined an index value associated with the particular voxel is determined according to a hashing algorithm, where the index value is determined from summing weighted values of the x-, y- and z-coordinates, and the weighted values are based on a variable value corresponding to a dimension of the volume. A particular entry is identified in the hash table based on the index value, where the particular entry includes volumetric data, and the volumetric data identifies, for the particular voxel, whether the particular voxel is occupied.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for predicting connections in electric grid models are disclosed. A method includes obtaining geospatial data representing a geographic area that includes an electrical distribution system; and generating, from the geospatial data, asset data that represents characteristics of electrical distribution system assets. The asset data includes: load data representing electrical loads of the electrical distribution system; and node data representing nodes of the electrical distribution system. The method includes processing the asset data using a connection model that is configured to predict electrical connections between assets of the electrical distribution system; and obtaining, from the connection model; output data indicating predicted electrical connections between assets of the electrical distribution system. The geospatial data includes at least one of overhead imagery or street level imagery of the geographic area.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for identifying phases of electrical grid wires are disclosed. A method includes identifying, within an image of a utility pole, a cross-arm supporting multiple wires; identifying a cardinal orientation of the cross-arm based on characteristics of the image; and determining, based on the cardinal orientation of the cross-arm, an electrical phase for each of the wires supported by the cross-arm. Identifying the cardinal orientation of the cross-arm includes: determining an orientation of the cross-arm relative to an axis of a field of view of the image; determining a cardinal orientation of the axis of the field of view; and estimating the cardinal orientation of the cross-arm based on an angular difference between the orientation of the cross-arm relative to the axis of a field of view and the cardinal orientation of the axis of the field of view.

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: 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 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: A particular voxel is identified within a volume and a hash table is used to obtain volumetric data describing the particular voxel within the volume. Values of x-, y- and z-coordinates in the volume associated with the particular voxel are determined an index value associated with the particular voxel is determined according to a hashing algorithm, where the index value is determined from summing weighted values of the x-, y- and z-coordinates, and the weighted values are based on a variable value corresponding to a dimension of the volume. A particular entry is identified in the hash table based on the index value, where the particular entry includes volumetric data, and the volumetric data identifies, for the particular voxel, whether the particular voxel is occupied.