A method and a controller unit for controlling motion of a watercraft with a hydrofoil) obtains information indicating shape of water surface in front of the hydrofoil. The controller unit further predicts wave acceleration of the watercraft using a neural network. Furthermore, the controller unit determines a target route and corresponding total acceleration of the watercraft under a set of constraints. The total acceleration is minimized when the watercraft travels according to the target route. The set of constraints includes: a first constraint that the hydrofoil stays within an interval relative to the water surface, and a second constraint relating to magnitude of acceleration derived from maximum AoA and the predicted wave acceleration. The controller unit calculates an AoA for the target route. Next, the controller unit sends a signal for adjusting the hydrofoil according to the AoA.

The invention relates to a buoyant photobioreactor arrangement, wherein the photobioreactor arrangement is buoyant on a surface water, and comprises a) a transparent photobioreactor container, b) a floating body that is buoyant on the surface water and c) a holding device arranged at or on the floating body holding the transparent photobioreactor container, the photobioreactor container being able to be lowered into the surface water by means of the holding device.

The invention relates to a buoyant photobioreactor arrangement, wherein the photobioreactor arrangement is buoyant on a surface water, and comprises a) a transparent photobioreactor container, b) a floating body that is buoyant on the surface water and c) a holding device arranged at or on the floating body holding the transparent photobioreactor container, the photobioreactor container being able to be lowered into the surface water by means of the holding device.

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.

Devices, systems and methods for real-time wave monitoring are described. One example system for real-time monitoring of wave conditions includes a plurality of buoys, wherein each of the plurality of buoys comprises a sensor array configured to continuously monitor one or more characteristics of the wave conditions, a transceiver configured to transmit, to a remote server, information corresponding to the one or more characteristics of the wave conditions over a wireless communication channel, and a tether that physically couples the buoy to an anchor, wherein the information from each of the plurality of buoys is combined with a user preference to provide a user with a message regarding the wave conditions in response to a user request, and wherein a duration between the user request and transmission of the information from each of the plurality of buoys is less than a predetermined value.

Devices, systems and methods for real-time wave monitoring are described. One example system for real-time monitoring of wave conditions includes a plurality of buoys, wherein each of the plurality of buoys comprises a sensor array configured to continuously monitor one or more characteristics of the wave conditions, a transceiver configured to transmit, to a remote server, information corresponding to the one or more characteristics of the wave conditions over a wireless communication channel, and a tether that physically couples the buoy to an anchor, wherein the information from each of the plurality of buoys is combined with a user preference to provide a user with a message regarding the wave conditions in response to a user request, and wherein a duration between the user request and transmission of the information from each of the plurality of buoys is less than a predetermined value.

An optimum engine configuration is determined, based on a predicted required power, for a seafaring vessel having a plurality of thrust engines. The predicted required power is determined by inputting vessel operational data, environmental data, and voyage data to a required power model. At least some of the vessel operational data and environmental data is received from a plurality of sensors positioned onboard the vessel. The optimum engine configuration is selected from a plurality of candidate engine configurations. Each candidate engine configuration includes a specified number of thrust engines running and a specified power output level of each thrust engine. The optimum engine configuration is selected based on a candidate total predicted fuel consumption of each candidate engine configuration. The candidate total predicted fuel consumption amount is determined as a sum of the engine-specific predicted fuel consumptions determined for each running thrust engine of that candidate engine configuration.

The present specification relates to image capture. More specifically, it relates to selective image capture for sensor carrying devices or floats deployed, for example, on the open sea. In one form, data is generated on the sensor carrying devices or floats by an on-board Inertial Measurement Unit (IMU) and is used to automatically predict the wave motion of the sea. These predictions are then used to determine an acceptable set of motion parameters that are used to trigger the on-board camera(s). The camera(s) then capture images. One consideration is that images captured at or near the peak of a wave crest with minimal pitch and roll will contain fewer obstructions (such as other waves). Such images provide a view further into the horizon to, for example, monitor maritime sea traffic and other phenomenon. Therefore, the likelihood of capturing interesting objects such as ships, boats, garbage, birds, . . . etc. is increased. These images may then be further processed and/or transmitted in a variety of manners.

The present specification relates to image capture. More specifically, it relates to selective image capture for sensor carrying devices or floats deployed, for example, on the open sea. In one form, data is generated on the sensor carrying devices or floats by an on-board Inertial Measurement Unit (IMU) and is used to automatically predict the wave motion of the sea. These predictions are then used to determine an acceptable set of motion parameters that are used to trigger the on-board camera(s). The camera(s) then capture images. One consideration is that images captured at or near the peak of a wave crest with minimal pitch and roll will contain fewer obstructions (such as other waves). Such images provide a view further into the horizon to, for example, monitor maritime sea traffic and other phenomenon. Therefore, the likelihood of capturing interesting objects such as ships, boats, garbage, birds, . . . etc. is increased. These images may then be further processed and/or transmitted in a variety of manners.

A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.