Partitioning assets for electric grid connection mapping

    公开(公告)号:US12045025B1

    公开(公告)日:2024-07-23

    申请号:US18179452

    申请日:2023-03-07

    CPC classification number: G05B19/042 G06N20/00 G05B2219/2639

    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.

    ENHANCED SYNCHRONIZATION FRAMEWORK
    22.
    发明公开

    公开(公告)号:US20240223714A1

    公开(公告)日:2024-07-04

    申请号:US18238272

    申请日:2023-08-25

    CPC classification number: H04N5/067 H04N23/56 H04N23/66

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that provides an enhanced synchronization framework. One of the methods includes a primary and a second device that receive configuration information which identifies one or more actions to be performed by the secondary device when it receives specified pulses of a sequence of pulses from the primary device. The primary device transmits a sequence of pulses. The primary and the secondary device receive a particular pulse from the sequence of pulses. The secondary device determines whether the particular pulse satisfies one or more predetermined criteria and generates an instruction based on the determination.

    Aggregating information from different data feed services

    公开(公告)号:US12013859B2

    公开(公告)日:2024-06-18

    申请号:US17842132

    申请日:2022-06-16

    Inventor: David Andre

    CPC classification number: G06F16/24556 G06F16/248 G06F40/30 G06F40/40

    Abstract: Implementations are described herein for aggregating information responsive to a query from multiple different data feed services using machine learning. In various implementations, NLP may be performed on a natural language input comprising a query for information to generate a data feed-agnostic aggregator embedding (FAAE). A plurality of data feed services may be selected, each having its own data feed service action space that includes actions that are performable to access data via the data feed service. The FAAE may be processed based on domain-specific machine learning models corresponding to the selected data feed services. Each domain-specific machine learning model may translate between a respective data feed service action space and a data feed-agnostic semantic embedding space. Using these models, action(s) may be selected from the data feed service action spaces and performed to aggregate, from the plurality of data feed services, data that is responsive to the query.

    Algorithmic correction for optical cross-coupling

    公开(公告)号:US11984935B2

    公开(公告)日:2024-05-14

    申请号:US17940417

    申请日:2022-09-08

    Inventor: Paul Csonka

    CPC classification number: H04B10/803 H04B10/1129 H04B10/6165

    Abstract: The disclosure provides a method for adjusting an optical link alignment of a first communication device with a remote device. The method includes transmitting or receiving an optical signal; receiving one or more measurements of at least one environmental factor at the first communication device or the remote device; and receiving or detecting an apparent amount of alignment of the optical signal. Then, by one or more processors of the first communication device, determining an estimated error attributable to optical cross coupling and an actual amount of alignment of the optical signal based on the apparent amount of alignment and the estimated error. Next, adjusting the first communication device based on the actual amount of alignment to correct for optical cross coupling.

    SYNCHRONIZING NETWORK REFERENCE TIME AMONG POWER LINE COMPONENTS

    公开(公告)号:US20240154782A1

    公开(公告)日:2024-05-09

    申请号:US17981193

    申请日:2022-11-04

    CPC classification number: H04L7/0008 H02J13/00002

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing electric grid timing coordination. The method can include: receiving, by a first electric grid device, a message from a second electric grid device, the message including a first clock value associated with an oscillator of the second electric grid device; determining, based on the first clock value, a second-first device time offset between the second electric grid device and the first electric grid device; and storing the second-first device time offset in a time offset table stored at the first electric grid device.

    PERIODICALLY COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING

    公开(公告)号:US20240152774A1

    公开(公告)日:2024-05-09

    申请号:US17979964

    申请日:2022-11-03

    CPC classification number: G06N5/022 G06N5/043

    Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modeling agents in multi-agent systems as reinforcement learning (RL) agents and training control policies that cause the agents to cooperate towards a common goal. A method can include generating, for each of a group of simulated local agents in an agent network in which the simulated local agents share resources, information, or both, experience tuples having a state for the simulated local agent, an action taken by the simulated local agent, and a local result for the action taken, updating each local policy of each simulated local agent according to the respective local result, providing, to each of the simulated local agents, information representing a global state of the agent network, and updating each local policy of each simulated local agent according to the global state of the agent network.

    RESOURCE EFFICIENT TRAINING OF MACHINE LEARNING MODELS THAT PREDICT STOCHASTIC SPREAD

    公开(公告)号:US20240135691A1

    公开(公告)日:2024-04-25

    申请号:US18493018

    申请日:2023-10-23

    CPC classification number: G06V10/776 G06V10/761

    Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

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