UNDERWATER NET MONITORING DEVICE
    12.
    发明公开

    公开(公告)号:US20240224956A9

    公开(公告)日:2024-07-11

    申请号:US18202578

    申请日:2023-05-26

    CPC classification number: A01K63/00 G01N19/08

    Abstract: A net monitoring system, including: a plurality of net monitoring devices, each net monitoring device including: a housing; a plurality of tensioning arms, each tensioning arm reversibly extendable through the housing and configured to reversibly secure to a net, each tensioning arm including a force sensor configured to generate a tension signal indicative of a tension applied to the corresponding tensioning arm; a tensioning mechanism configured concurrently retract the plurality of tensioning arms into the housing; an impulse generating device, configured to generate an impulse responsive to a command; and a communications device configured to receive the tension signals from the plurality of force sensors, and transmit the tension signals through water; and a controller, configured to: command at least one of the plurality of net monitoring devices to generate the impulse; receive the tension signals responsive to the command to generate the impulse; and determine, based on the received tension signals, a presence of a defect in a net on which the plurality of net monitoring devices are installed.

    SELF-CALIBRATING ULTRASONIC REMOVAL OF ECTOPARASITES FROM FISH

    公开(公告)号:US20240224952A9

    公开(公告)日:2024-07-11

    申请号:US18319246

    申请日:2023-05-17

    CPC classification number: A01K61/13 A01M29/18 G01N29/00 G06V40/10 G01N29/34

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-calibrating ultrasonic removal of sea lice. In some implementations, a method includes generating, by transducers distributed in a sea lice treatment station, a first set of ultrasonic signals, detecting a second set of ultrasonic signals in response to propagation of the first set of ultrasonic signals through water, determining propagation parameters of the sea lice treatment station based on the second set of ultrasonic signals that were detected, obtaining an image of a sea louse on a fish in the sea lice treatment station, determining, from the image, a location of the sea louse in the sea lice treatment station, and generating a third set of ultrasonic signals that focuses energy at the sea louse.

    Sample segmentation
    14.
    发明授权

    公开(公告)号:US12033329B2

    公开(公告)日:2024-07-09

    申请号:US17383278

    申请日:2021-07-22

    CPC classification number: G06T7/11 G06V10/26 G06T2207/10036 G06T2207/20224

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved image segmentation using hyperspectral imaging. In some implementations, a system obtains image data of a hyperspectral image, the image data comprising image data for each of multiple wavelength bands. The system accesses stored segmentation profile data for a particular object type that indicates a predetermined subset of the wavelength bands designated for segmenting different region types for images of an object of the particular object type. The system segments the image data into multiple regions using the predetermined subset of the wavelength bands specified in the stored segmentation profile data to segment the different region types. The system provides output data indicating the multiple regions and the respective region types of the multiple regions.

    DETERMINING OPTIMAL GRID INTERCONNECTIONS
    15.
    发明公开

    公开(公告)号:US20240223005A1

    公开(公告)日:2024-07-04

    申请号:US18398057

    申请日:2023-12-27

    CPC classification number: H02J13/00002 H02J3/00 H02J2203/20

    Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for determining optimal grid interconnections. A power grid model that includes a topological representation of a power grid and electrical specifications of grid components can be accessed. First interconnection data representing a first proposed interconnection to the power grid can be obtained. At least one other proposed interconnection to the power grid can be selected. A modified power grid model can be generated at least by incorporating the first proposed interconnection and the at least one other proposed interconnection into the power grid model. A simulation of the power grid can be executed using the modified power grid model to obtain simulated power grid data. A combined impact of the first proposed interconnection and the at least one other proposed interconnection on the power grid can be determined from the simulated power grid data.

    MODELS FOR ESTIMATING ETA AND DWELL TIMES FOR TRANSPORTATION OF OBJECTS

    公开(公告)号:US20240211822A1

    公开(公告)日:2024-06-27

    申请号:US18126789

    申请日:2023-03-27

    CPC classification number: G06Q10/047 G06Q10/0833 G08G1/0129 H04W4/80

    Abstract: Aspects of the disclosure provide for the use and training of a model configured to provide an estimated time of arrival (ETA) for transporting an object. Data may be received from a reader device. The data may identify a tracking tag and a first timestamp. Based on the received data, a location of the tracking tag at the first timestamp may be determined. A starting location and second timestamp for the tracking tag and a destination location and third timestamp for the tracking tag may be identified. A list including at least one dwell time and dwell location for the tracking tag may be identified. The model may be trained to output the ETA, a dwell time, and a dwell location based on the determined location, the starting location, the destination location, the first timestamp, the second timestamp, the third timestamp, and the list.

    Subsurface lithological model with machine learning

    公开(公告)号:US12007519B2

    公开(公告)日:2024-06-11

    申请号:US18167988

    申请日:2023-02-13

    CPC classification number: G01V20/00 G01V1/282

    Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.

    DISTRIBUTED SENSING USING FLUID NETWORK
    18.
    发明公开

    公开(公告)号:US20240184003A1

    公开(公告)日:2024-06-06

    申请号:US18528036

    申请日:2023-12-04

    CPC classification number: G01V1/133 G01V1/226 G01V1/301 G01V2210/1299

    Abstract: This disclosure describes a system and method for generating subsurface image data by inducing a first acoustic energy in a fluid contained within a pipe network at a predetermined location. The acoustic energy propagates through the pipe network and into a subsurface in which the pipe network is contained and is then recorded using an array of transducers. The recorded acoustic energy is provided as input to a machine learning algorithm to generate image data associated with the subsurface, which is used to generate a subsurface model for presentation in a graphical user interface.

    HIGH THROUGHPUT CHARACTERIZATION OF AGGREGATE PARTICLES

    公开(公告)号:US20240169030A1

    公开(公告)日:2024-05-23

    申请号:US17990569

    申请日:2022-11-18

    CPC classification number: G06F18/214 G06F18/251

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterization of aggregate particles. A method includes obtaining, from a set of low fidelity sensors, first sensor data of a first portion of particles; obtaining, from a set of high fidelity sensors, second sensor data of the first portion of particles, the second sensor data comprising a higher fidelity representation of characteristics of the first portion of particles than the first sensor data; training a characterization model using the first sensor data and the second sensor data, the training comprising: providing, as training data to the characterization model, the second sensor data; and processing the second sensor data with the characterization model to correlate the first sensor data with the second sensor data. The first sensor data can indicate shape characteristics of each particle; and the second sensor data indicates a surface area of each particle.

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