A method for predicting heaving motion parameters of a semi-submersible offshore platform based on heaving acceleration includes: in heaving motion of a semi-submersible offshore platform, representing heaving acceleration of the semi-submersible offshore platform based on a linear potential flow theory; considering a noise influence of a heaving motion measurement marine environment, a low-frequency influence caused by a slow change of the environment and an influence caused by a baseline drift error of an acceleration sensor, introducing a noise term, a low-frequency change term and a baseline drift error term, and uniformly representing the noise term, the low-frequency change term and the baseline drift error term by a unified Prony sequence; and removing a drift term from uniformly represented heaving acceleration, establishing a relationship between the heaving acceleration and heaving motion parameters in terms of the remaining Prony sequence with the drift term being removed, and estimating the heaving motion parameters.

A method for predicting heaving motion parameters of a semi-submersible offshore platform based on heaving acceleration includes: in heaving motion of a semi-submersible offshore platform, representing heaving acceleration of the semi-submersible offshore platform based on a linear potential flow theory; considering a noise influence of a heaving motion measurement marine environment, a low-frequency influence caused by a slow change of the environment and an influence caused by a baseline drift error of an acceleration sensor, introducing a noise term, a low-frequency change term and a baseline drift error term, and uniformly representing the noise term, the low-frequency change term and the baseline drift error term by a unified Prony sequence; and removing a drift term from uniformly represented heaving acceleration, establishing a relationship between the heaving acceleration and heaving motion parameters in terms of the remaining Prony sequence with the drift term being removed, and estimating the heaving motion parameters.

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.

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.

A non-transitory computer-readable storage medium storing a navigation monitoring program that causes at least one computer to execute a process, the process includes acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along a course.

A non-transitory computer-readable storage medium storing a navigation monitoring program that causes at least one computer to execute a process, the process includes acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along a course.

A method for evaluating a securing system for a floating structure, where the method includes collecting a plurality of metocean data from a plurality of metocean sensor devices during a current time period coinciding with a field operation, where the field operation is conducted from the floating structure that is stabilized by the securing system. The method can also include evaluating the metocean data using a plurality of algorithms. The method can further include determining, based on evaluating the metocean data, a condition of the securing system at the current time period.

The present invention discloses a collaborative design method using an event-triggered scheme (ETS) and a Takagi-Sugeno (T-S) fuzzy H.sub.∞ controller in a network environment. For the problem about the unmanned surface vehicle control based on a switching T-S fuzzy system under an aperiodic DoS attack, the present invention provides an H∞ controller design method based on the event-triggered scheme. The characteristics of the unmanned surface vehicle system under the DoS attack are analyzed, and external disturbance in the navigation process is added into an unmanned surface vehicle motion model to establish an unmanned surface vehicle switching system model. The stability of the system is analyzed by piecew se Lyapunov functionals, such that controller gain and event-triggered scheme weight matrix parameters are obtained, thus ensuring that a networked unmanned surface vehicle navigation system has the ability to resist the DoS attack and the external disturbance.

The present invention discloses a collaborative design method using an event-triggered scheme (ETS) and a Takagi-Sugeno (T-S) fuzzy H.sub.∞ controller in a network environment. For the problem about the unmanned surface vehicle control based on a switching T-S fuzzy system under an aperiodic DoS attack, the present invention provides an H∞ controller design method based on the event-triggered scheme. The characteristics of the unmanned surface vehicle system under the DoS attack are analyzed, and external disturbance in the navigation process is added into an unmanned surface vehicle motion model to establish an unmanned surface vehicle switching system model. The stability of the system is analyzed by piecew se Lyapunov functionals, such that controller gain and event-triggered scheme weight matrix parameters are obtained, thus ensuring that a networked unmanned surface vehicle navigation system has the ability to resist the DoS attack and the external disturbance.

A system assists the driving of a ship and is configured to estimate the structural loads of the ship due to the direct wave excitation, and structural loads of the ship due to the whipping effect caused by the wave slamming. The system includes at least one reference sensor adapted to provide an indication of a motion or stress magnitude at a predetermined point of the ship structure, and is further configured to calculate an estimate of the magnitude at the predetermined point in the ship structure, compare the indication of magnitude with the estimate of the magnitude so as to determine an offset value, and correct the estimates of the structural loads and/or the estimate of the magnitude on the basis of the offset value.