Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network

Zhang, Guoyin; Tan, Feng; Wu, Yanxia

doi:10.1109/access.2020.2993909

Cited by 60 publications

(27 citation statements)

References 25 publications

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“…The most applied models are neural networks, and examples of predicting ship responses are reported in [12], [13]. Besides, the long short-term memory deep neural network is also popular when dealing with time series predictions either as an end-to-end model [14] or a compensative model [15]. An example of utilizing clustering techniques is presented in [16].…”

Section: B Data-driven Predictionmentioning

confidence: 99%

Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction

Wang

Hatledal

et al. 2022

IEEE Trans. Ind. Inf.

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High-fidelity models capable of accurately predicting ship motion are critical for promoting innovation and efficiency in the maritime industry. However, creating an advanced model that comprehensively represents the system and its interaction with dynamic environments has always been challenging. Many models provide partial knowledge about a system. To handle the deficiency and improve model fidelity, we propose a hybrid modeling methodology, in which prior knowledge describing the ship dynamic effects is incorporated into a data-driven calibrator, yielding a representative model with high predictive capability. Enabled by the integration of model estimated ship states into the calibrator, the informative information could be interpreted and carried forward. Simulation and full-scale experiments are conducted on the research vessel Gunnerus to exemplify the concept. A best available numerical model and a neural network are prepared to be the foundation and calibrator, respectively. Experiment results show that the cooperative model greatly improves the predictive capability of the research vessel. From the ship modeling perspective, this study provides new insights by bridging the gap between two separate domains: model-based and data-driven.

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Section: B Data-driven Predictionmentioning

confidence: 99%

Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction

Wang

Hatledal

et al. 2022

IEEE Trans. Ind. Inf.

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show abstract

“…A similar sequential RBF network was used for multi-step predictions in relation to predictive control of a ship's course in Yin et al (2010). Recently, recurrent networks have also been used for predicting roll/pitch angles and heave motion Zhang et al (2020) Duan et al (2019) and horizontal motion Skulstad et al (2019). Support vector regression (SVR) models represent an alternative to NNs for creating data-based predictive models Li et al (2016).…”

Section: Related Workmentioning

confidence: 99%

A Co-operative Hybrid Model For Ship Motion Prediction

Skulstad¹,

Li²,

Fossen³

et al. 2021

MIC

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Dynamic models of ships have been widely used for model-based control and short-term prediction in the past. Identifying the parameters of such models has mainly been done through scaled model tests, full scale tests or computational fluid dynamics software. This is a challenging task due to the many aspects that influence the ship dynamic behaviour and thus one would expect a certain degree of mismatch between the actual motion of the ship and the modelled behaviour. The mismatch in the dynamic model may be due to unmodelled effects, but also the lack of measurements of waves and ocean current. To make up for the discrepancies the authors propose to create a co-operative hybrid model consisting of the dynamic model and a neural network, where the neural network predicts the acceleration error of the dynamic model. The approach is tested on real data originating from the Research Vessel (RV) Gunnerus performing a shutdown of thrusters during stationkeeping. The subsequent task is to predict the propagation of position and heading while drifting due to wind, wave and current forces. Comparing the motion of the real ship and the modelled ship, shows the improved prediction accuracy of the hybrid model.

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“…LSTM adds a Memory Cell structure in the neural node of the hidden layer of RNN for storing the past data and adds 3 gate structures, Output, Input, and Forget gates, for controlling the usage of past data. By forgetting the unusable data and memorize the novel data in a cell state, LSTM could transmit beneficial data in the succeeding time calculation [21]. The denotes the present state and ̃ indicates the temporary state.…”

Section: Activity Recognition Using Bilstm Modelmentioning

confidence: 99%

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

Al‐Wesabi¹,

Albraikan

Hilal

et al. 2021

IEEE Access

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In recent times, Human activity recognition (HAR) becomes a major challenging issue among computer vision applications in day to day lives. The HAR is mainly envisioned to be utilized with other technologies, namely Internet of Things (IoT) and sensor technologies. Due to the advancements of deep learning (DL) approaches, automated high level feature extraction process can be utilized to improve the HAR results. In addition, DL techniques can be employed in different domains of sensor enabled HAR. In this aspect, this study designs an optimal DL based HAR (ODL-HAR) model on sensor enabled IoT environments. The ODL-HAR technique aims to determine human activities in day to day lives using wearables and IoT devices. The proposed ODL-HAR technique involves different stages of operations namely data acquisition, data preprocessing, feature extraction, classification, and parameter optimization. The ODL-HAR technique uses a MobileNet-v2 model as a feature extractor and bidirectional long short-term memory (BiLSTM) model as a classifier. In order to optimally tune the hyperparameter involved in the BiLSTM model, chaos game optimization (CGO) algorithm is employed and thereby raises the recognition performance. The design of CGO algorithm for hyperparameter optimization of HAR shows the novelty of the work. A wide range of simulations takes place to point out the betterment of the ODL-HAR technique on two benchmark datasets. The experimental results portrayed the enhanced performance of the ODL-HAR technique over the other recent HAR approaches interms of different evaluation parameters.

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Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network

Cited by 60 publications

References 25 publications

Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction

Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction

A Co-operative Hybrid Model For Ship Motion Prediction

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

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