Response component analysis for sea state estimation using artificial neural networks and vessel response spectral data

Long, Nathan K.; Sgarioto, Daniel; Garratt, Matthew A.; Sammut, Karl

doi:10.1016/j.apor.2022.103320

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach aimed to leverage shared information across multiple related tasks to improve overall performance and adaptability of the model. In (Nathan K. Long, 2022), the WBA has been utilized for uninhabited surface vessels (USVs) to highlight the contribution of high-frequency wave detection in the spectrum. The designed Neural Network (NN) was input by vessel spectral response data of heave, pitch and roll instead of raw time series data, enabling compressing the information and generating wave properties as output.…”

Section: 1mentioning

confidence: 99%

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Majidiyan,

Enshaei,

Howe

et al. 2024

Preprint

View full text Add to dashboard Cite

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. Despite extensive research on the wave buoy analogy (WBA), real-time sea state estimation (SSE) has remained challenging due to the need for adequate data to construct a consistent probability density function of waves. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram form for subsequent data analysis. To achieve this, an adaptive ship response predictor and deep learning approach have been incorporated to classify seaway data while minimizing network complexity through feature engineering. Specifically, a Convolution Neural Network (CNN) and deep transfer learning have been employed for training purposes. The system's performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the proposed approach for a fully automated system. Furthermore, the deficits with the feature transformation of the existing data-driven SSE models have been discussed. This study provides a foundation for improving online sea state estimation and promoting the seaway acquisition for stationary marine units.

show abstract

Section: 1mentioning

confidence: 99%

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Majidiyan,

Enshaei,

Howe

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Disturbance observer based adaptive heading control for unmanned marine vehicles with event‐triggered and input quantization

Ning,

Wang,

Liu

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

SummaryThe primary objective of this paper is to enhance the efficient utilization of communication resources. To achieve this, the paper delves into the disturbance observer based adaptive heading control strategy for Unmanned Marine Vehicles with event‐triggered and input quantization. Furthermore, in order to mitigate the impact of slow time‐varying external disturbances within the control system, the disturbance observer is employed for estimation. Within the context of a networked control, control input is subjected to quantization via an input quantizer, and the process of input quantization is described by using a linear analytical model. Importantly, no foreknowledge of quantization parameters is necessary for the quantized feedback controllers. Subsequently, the sliding mode control method is combined with event‐triggered mechanismss to design quantization feedback control system. The stability of the closed‐loop system is established in line with the fundamental tenets of Lyapunov stability theory, validating the bounded nature of both observation and heading tracking control errors. The effectiveness of the proposed heading control scheme is further underscored through a series of simulation experiments.

show abstract

Investigation of Vessel Manoeuvring Abilities in Shallow Depths by Applying Neural Networks

Moreira,

Guedes Soares

2024

JMSE

View full text Add to dashboard Cite

A set of planar motion mechanism experiments of the Duisburg Test Case Post-Panamax container model executed in a towing tank with shallow depth is applied to train a neural network to analyse the ability of the proposed model to learn the effects of different depth conditions on ship’s manoeuvring capabilities. The motivation of the work presented in this paper is to contribute an alternative and effective approach to model non-linear systems through artificial neural networks that address the manoeuvring simulation of ships in shallow water. The system is developed using the Levenberg–Marquardt backpropagation training algorithm and the resilient backpropagation scheme to demonstrate the correlation between the vessel forces and the respective trajectories and velocities. Sensitivity analyses were performed to identify the number of layers necessary for the proposed model to predict the vessel manoeuvring characteristics in two different depths. The outcomes achieved with the proposed system have shown excellent accuracy and ability in predicting ship manoeuvring with varying depths of shallow water.

show abstract

Response component analysis for sea state estimation using artificial neural networks and vessel response spectral data

Cited by 4 publications

References 20 publications

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Disturbance observer based adaptive heading control for unmanned marine vehicles with event‐triggered and input quantization

Investigation of Vessel Manoeuvring Abilities in Shallow Depths by Applying Neural Networks

Contact Info

Product

Resources

About