Characterising wave properties based on measurement data using a machine-learning model
AssigneeX Development LLC
InventorsThomas Robert Swanson, Riva Gulassa
Life of this patent
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Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for estimating wave properties of a body of water. A computer-implemented system obtains measurement data for a duration of time from an inertial measurement unit (IMU) onboard an underwater device, generates model input data based on at least the measurement data obtained at the plurality of time points, and processes the model input data to generate model output data indicating one or more wave properties using a machine-learning model. The system further determines, based on at least the one or more wave properties, whether the device is safe to be deployed.
Claims19
A computer-implemented method, comprising:
- obtaining measurement data for a duration of time from an inertial measurement unit (IMU) onboard an underwater device;
- generating model input data based on at least the measurement data obtained at the plurality of time points;
- processing the model input data to generate model output data indicating estimation of one or more wave properties using a machine-learning model having a plurality of model parameters learned by performing training using a plurality of training examples;
- and
- determining, based on at least the estimation of the one or more wave properties, whether the device is safe to be deployed,
- wherein the machine-learning model includes one or more of:
- a linear regression model, a linear discriminant model, a logistic regression model, a support-vector machine model, a decision tree model, a k-nearest neighbors model, or a neural network model.
A system comprising:
- one or more computers;
- and
- one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform:
- obtaining measurement data for a duration of time from an inertial measurement unit (IMU) onboard an underwater device;
- generating model input data based on at least the measurement data obtained at the plurality of time points;
- processing the model input data to generate model output data indicating estimation of one or more wave properties using a machine-learning model having a plurality of model parameters learned by performing training using a plurality of training examples;
- and
- determining, based on at least the estimation of the one or more wave properties, whether the device is safe to be deployed,
- wherein the machine-learning model includes one or more of:
- a linear regression model, a linear discriminant model, a logistic regression model, a support-vector machine model, a decision tree model, a k-nearest neighbors model, or a neural network model.
One or more computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform:
- obtaining measurement data for a duration of time from an inertial measurement unit (IMU) onboard an underwater device;
- generating model input data based on at least the measurement data obtained at the plurality of time points;
- processing the model input data to generate model output data indicating estimation of one or more wave properties using a machine-learning model having a plurality of model parameters learned by performing training using a plurality of training examples;
- and
- determining, based on at least the estimation of the one or more wave properties, whether the device is safe to be deployed,
- wherein the machine-learning model includes one or more of:
- a linear regression model, a linear discriminant model, a logistic regression model, a support-vector machine model, a decision tree model, a k-nearest neighbors model, or a neural network model.
Dependencies are inferred from the claim text.