Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves

Ren, Zhengru; Han, Xu; Yu, Xingji; Skjetne, Roger; Leira, Bernt J.; Sævik, Svein; Zhu, Man

doi:10.1016/j.ymssp.2022.109422

Cited by 57 publications

(19 citation statements)

References 26 publications

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“…In the future, we plan to use the obtained results for point scoring in the follow-up activity, which may affect the road map of different agents [ 37 , 38 ]. A more consistent unsupervised method will help assign specific scores of interest to points on the map in a fast and efficient manner.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Kılıç

Nielsen

2022

Sensors

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In this study, principal component analysis and k-means clustering (PCAKM) methods for synthetic aperture radar (SAR) data are analyzed to reduce the sensitivity caused by changes in the parameters and input images of the algorithm, increase the accuracy, and make an improvement in the computation time, which are advantageous for scoring in the follow-up plan. Although there are many supervised methods described in the literature, unsupervised methods may be more appropriate in terms of computing time, data scarcity, and explainability in order to supply a trustworthy system. We consider the PCAKM algorithm, which is used as a benchmark method in many studies when making comparisons. Error metrics, computing times, and utility functions are calculated for 22 modified PCAKM regarding difference images and filtering methods. Various images with different characteristics affect the results of the configurations. However, it is evident that the PCAKM becomes less sensitive and more accurate for both the overall results and image results. Scoring by utilizing these results and other map information is a gap and innovation. Obtaining a change map in a fast, explainable, more robust and less sensitive way is one of the aims of our studies on scoring points in the follow-up plan.

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Section: Discussionmentioning

confidence: 99%

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Kılıç

Nielsen

2022

Sensors

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“…x = [x, y, z, α, β, γ] T (16) where x, y and z are the origin position of the coordinate system {B} relative to the coordinate system {A}. α, β and γ are the Euler or azimuth angle of {B}.…”

Section: Inverse Kinematics Modelingmentioning

confidence: 99%

“…However, prediction performance was degraded once that current scenario became inconsistent within the preset dataset. Alternatively, time series analysis methods could update the parameters of ship motion and wave model in real-time [16,17]. Sparse regression was used to identify the parameters of 6-DOF ship motion exposed to waves in [18].…”

Section: Introductionmentioning

confidence: 99%

An Offshore Self-Stabilized System Based on Motion Prediction and Compensation Control

Liu

Yuan

Xiao

et al. 2023

JMSE

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The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship motion exposed to various sea conditions was established, and the parameters of the model were initialized and updated by offline and online learning historical data. Using the autoregressive model with the acquired parameters, the prediction of the ship’s motion was achieved. Then, a Self-Stabilized system platform composed of six electric cylinders in parallel was designed, and the corresponding inverse kinematics were established. The corresponding controller using the result of motion prediction as the input was also proposed to counteract the extra motion variables of the ship. Various experiments, by simulating different sea conditions, can be carried out. The results show that the average error of the motion prediction was less than 1%. The maximum error of the self-stabilizing control was 1.6°, and the average error was stable within 0.7°. The Self-Stabilized system was able to effectively compensate for the rocking motion of ships affected by waves, which was of great significance for improving the maritime safety guarantee and the intelligent level of shipborne equipment.

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“…AI has demonstrated its capacities in different fields [ 32 , 33 , 34 , 35 , 36 ]. For instance, the authors in [ 37 ] suggest a sparse regression-based, data-driven model identification for ships with 6 degrees of freedom. In [ 38 ], the authors propose a solution to optimize sensor mapping in smart homes based on deep adversarial transfer learning.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Boulouard

Ouaissa

et al. 2022

Sensors

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River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

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Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves

Cited by 57 publications

References 26 publications

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan

An Offshore Self-Stabilized System Based on Motion Prediction and Compensation Control

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

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