Abstract: The present disclosure provides a system and method for monitoring a floating vessel hull mooring system by determining one or more hull rotational motions of yaw, roll, and/or pitch that do not require independent knowledge of environmental conditions. The hull rotational motion of a secure and intact mooring system can be calculated and/or established experientially over time by measuring movement of the hull to characterize the hull rotational motion at given geographical positions. A compromised mooring system will result in different hull rotational motion of at least one of yaw, roll, and/or pitch. By monitoring the hull rotational motion for a given geographical position to be compared to the theoretical values (and/or previous recorded values), it is then possible to assess that at least a portion of the mooring system has been compromised and in at some embodiment indicate which portion of the mooring system has been compromised.

Abstract: The present disclosure provides a system and method for monitoring a floating vessel hull mooring system by determining one or more hull rotational motions of yaw, roll, and/or pitch that do not require independent knowledge of environmental conditions. The hull rotational motion of a secure and intact mooring system can be calculated and/or established experientially over time by measuring movement of the hull to characterize the hull rotational motion at given geographical positions. A compromised mooring system will result in different hull rotational motion of at least one of yaw, roll, and/or pitch. By monitoring the hull rotational motion for a given geographical position to be compared to the theoretical values (and/or previous recorded values), it is then possible to assess that at least a portion of the mooring system has been compromised and in at some embodiment indicate which portion of the mooring system has been compromised.

Abstract: The present invention provides a system and method of side-stepping the need to retrain neural network model after initially trained using a simulator by comparing real-world data to data predicted by the simulator for the same inputs, and developing a mapping correlation that adjusts real world data toward the simulation data. Thus, the decision logic developed in the simulation-trained model is preserved and continues to operate in an altered reality. A threshold metric of similarity can be initially provided into the mapping algorithm, which automatically adjusts real world data to adjusted data corresponding to the simulation data for operating the neural network model when the metric of similarity between the real world data and the simulation data exceeds the threshold metric. Updated learning can continue as desired, working in the background as conditions are monitored.

Abstract: The present disclosure provides a system and method for monitoring a floating vessel hull mooring system by determining one or more hull rotational motions of yaw, roll, and/or pitch that do not require independent knowledge of environmental conditions. The hull rotational motion of a secure and intact mooring system can be calculated and/or established experientially over time by measuring movement of the hull to characterize the hull rotational motion at given geographical positions. A compromised mooring system will result in different hull rotational motion of at least one of yaw, roll, and/or pitch. By monitoring the hull rotational motion for a given geographical position to be compared to the theoretical values (and/or previous recorded values), it is then possible to assess that at least a portion of the mooring system has been compromised and in at some embodiment indicate which portion of the mooring system has been compromised.

Abstract: The present invention provides a system and method of side-stepping the need to retrain neural network model after initially trained using a simulator by comparing real-world data to data predicted by the simulator for the same inputs, and developing a mapping correlation that adjusts real world data toward the simulation data. Thus, the decision logic developed in the simulation-trained model is preserved and continues to operate in an altered reality. A threshold metric of similarity can be initially provided into the mapping algorithm, which automatically adjusts real world data to adjusted data corresponding to the simulation data for operating the neural network model when the metric of similarity between the real world data and the simulation data exceeds the threshold metric. Updated learning can continue as desired, working in the background as conditions are monitored.