Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: Devices, systems, and methods obtain training images; generate image-alignment data based on the training images; cluster the training images based at least in part on the image-alignment data, thereby generating clusters of training images; and select one or more representative images from the training images based on the clusters of training images.

Abstract: Devices, systems, and methods obtain respective corresponding feature-detection scores for a plurality of areas in an image; calculate respective corresponding sorting scores for at least some areas of the plurality of areas; for the at least some areas of the plurality of areas, arrange the corresponding feature-detection scores in order of the corresponding sorting scores, thereby generating an order of sorted feature-detection scores; and assign respective detection scores to the at least some areas based on the order of sorted feature-detection scores and on three or more of the following: the respective corresponding feature-detection scores of the areas, a spectral threshold, a spatial threshold, and a neighborhood kernel.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact to at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

Abstract: A method and apparatus are provided for converting a Two-Dimensional (2D) video to a Three-Dimensional (3D) video is disclosed. The method includes detecting a shot including similar frames in the 2D video; setting a key frame in the shot; determining whether a current frame is the key frame; when the current frame is the key frame, performing segmentation on the key frame, assigning a depth to each segmented object in the key frame; and when the current frame is not the key frame, performing the segmentation on non-key frames, and assigning the depth to each segmented object in the non-key frames.

Abstract: Embodiments herein provide waste abatement apparatuses and methods for treating waste solutions derived from depleted or used plating solutions, such as from an electroless deposition process or an electrochemical plating process. The waste abatement systems and processes may be used to treat the waste solutions by lowering the concentration of, if not completely removing, metal ions or reducing agents that are dissolved within the waste solution. In one embodiment of a demetallization process, a waste solution may be exposed to a heating element (e.g., copper coil) contained within an immersion tank. In another embodiment, the waste solution may be exposed to a catalyst having high surface area (e.g., steel wool or other metallic wool) within an immersion tank. In another embodiment, the waste solution may be flowed through a removable, catalytic conduit (e.g., copper tubing) having an internal catalytic surface.

Abstract: Embodiments herein provide waste abatement apparatuses and methods for treating waste solutions derived from depleted or used plating solutions, such as from an electroless deposition process or an electrochemical plating process. The waste abatement systems and processes may be used to treat the waste solutions by lowering the concentration of, if not completely removing, metal ions or reducing agents that are dissolved within the waste solution. In one embodiment of a demetallization process, a waste solution may be exposed to a heating element (e.g., copper coil) contained within an immersion tank. In another embodiment, the waste solution may be exposed to a catalyst having high surface area (e.g., steel wool or other metallic wool) within an immersion tank. In another embodiment, the waste solution may be flowed through a removable, catalytic conduit (e.g., copper tubing) having an internal catalytic surface.

Abstract: Embodiments herein provide waste abatement apparatuses and methods for treating waste solutions derived from depleted or used plating solutions, such as from an electroless deposition process or an electrochemical plating process. The waste abatement systems and processes may be used to treat the waste solutions by lowering the concentration of, if not completely removing, metal ions or reducing agents that are dissolved within the waste solution. In one embodiment of a demetallization process, a waste solution may be exposed to a heating element (e.g., copper coil) contained within an immersion tank. In another embodiment, the waste solution may be exposed to a catalyst having high surface area (e.g., steel wool or other metallic wool) within an immersion tank. In another embodiment, the waste solution may be flowed through a removable, catalytic conduit (e.g., copper tubing) having an internal catalytic surface.