Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

Abebe, Misganaw; Shin, Yong-Woo; Noh, Yoojeong; Lee, Sang Bong; Lee, Inwon

doi:10.3390/app10072325

Cited by 43 publications

(15 citation statements)

References 23 publications

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“…Commonly used technologies for measuring ship speed include AIS [26], radar [27], lasers [28], and video-based speed measurements [29]. The emergence and advancement of the AIS system have provided robust technical support for acquiring ship navigation information [30].…”

Section: Techniques For Obtaining Information On Ship Speedmentioning

confidence: 99%

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Zhou

Zhao

Chen

et al. 2023

JMSE

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Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation.

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Section: Techniques For Obtaining Information On Ship Speedmentioning

confidence: 99%

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Zhou

Zhao

Chen

et al. 2023

JMSE

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“…A variety of studies have used AI to improve energy efficiency in maritime transport. While most of these studies focused on vessel speed optimization (Abebe et al, 2020;X. Yan, Wang, Yuan, Jiang, & Negenborn, 2018), others targeted optimizing ships' energy consumption in port (Man et al, 2020;Peng et al, 2020;R.…”

Section: Energy-efficiency Optimizationmentioning

confidence: 99%

“…ensemble methods to predict ship speed. Finally, the model optimized the actual ship route (Abebe et al, 2020).. Yan et al found the optimal speed of inland ships using the distributed parallel k-means clustering algorithm. Their proposed method contributes to lowering carbon dioxide emissions from vessels and decreasing energy consumption (Yan et al, 2018).…”

Section: Abebe Et Al Used a Decision Tree Regression Model And Fourmentioning

confidence: 99%

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Aylak¹

2022

European Journal of Science and Technology

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This study aims to identify current approaches in the usage of Artificial Intelligence (AI) methods for solving shipping problems. Recent advances in AI are being examined, and the way it is adapted to maritime logistics is reviewed. In this study, 66 papers dealing with AI in the maritime industry are reviewed bibliometrically. Research data were primarily sourced from databases of IEEE Xplore, Web of Science, ScienceDirect (Elsevier), Sciences Citation Index, Google Scholar, Springer, and journals. Selected papers are categorized and classified, and the outcomes of some noteworthy publications are discussed in detail. A comprehensive assessment is also presented, which highlights research gaps and forecasts future research orientations. Two possible areas in the maritime industry are proposed for further research using AI capabilities. Predictive analysis is the first domain, followed by energy efficiency optimization. In addition, Machine Learning (ML) and Operations Research (OR) have fostered a growing interest in automating the learning of heuristics to solve optimization problems to avoid the need for expensive and inefficient human labour to create highly specialized heuristics. Future research can take advantage of these new ML approaches to address Maritime Logistics problems utilizing the ever-increasing amount of data available. Future research on maritime logistics can also develop learning models based on the identified gaps.

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“…Nagalingam et al [17] presented an ensemble of extreme learning machine to estimate the longitudinal and side force coefficients as well as the yaw moment coefficient. Abebe et al [18] used various machine learning regression techniques, such as the linear regression, polynomial regression, decision tree regressor, gradient boosting regressor, extreme gradient boosting regressor, random forest regressor, and extra trees regressor techniques, to predict ship speed. The models were finally used for actual ship route optimization purposes.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

Lin

Zhao

et al. 2020

JMSE

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In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.

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Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

Cited by 43 publications

References 23 publications

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

The Impacts of the Applications of Artificial Intelligence in Maritime Logistics

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

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