Sea state identification based on vessel motion response learning via multi-layer classifiers

Tu, Fangwen; Ge, Shuzhi Sam; Choo, Yoo Sang; Hang, Chang Chieh

doi:10.1016/j.oceaneng.2017.08.047

Cited by 26 publications

(24 citation statements)

References 14 publications

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“…Even though machine learning or deep learning techniques have been widely used in other areas, they have rarely been applied to sea state estimation. Tu et al proposed a multi-layer classifier for sea state estimation in terms of wave height working on salient feature extracted from the time domain and frequency domain of the motion data of DP vessels [7]. Although this method does not rely on accurate mathematical models, it requires a lot of human involvement.…”

Section: Related Work a Onboard Measurements Based Sea State Estmentioning

confidence: 99%

“…Nevertheless, to the best of our knowledge, the previous model-free methods only considered the onboard measurement of dynamic positioning (DP) motion and only considered the height of the waves without considering the direction of the waves [3], [7]. DP motion, used in [3], [7], represents a special kind of maneuvering, which involves maintaining a fixed location or performing a very slow tracking task [8]. The use of this special maneuvering to estimate sea state lacks generality because most ships do not have a DP system, and those that do are generally moving forward when in operation.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

Cheng

Ellefsen

et al. 2020

IEEE Trans. Instrum. Meas.

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Sea state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependency on mathematical models of ships. As previous data-driven studies for sea state estimation only consider wave height and use the motion data from dynamic positioning vessels, this paper introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction, and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network blocks with dense connections between different blocks, channel attention modules and a feature attention module. The dense connections build shortcut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against state-ofthe-art approaches. Applying the SSENET on two datasets of zigzag motion for comparative studies shows the effectiveness of the proposed method.

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Section: Related Work a Onboard Measurements Based Sea State Estmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

Cheng

Ellefsen

et al. 2020

IEEE Trans. Instrum. Meas.

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“…It should be mentioned that all the above methods ship dependent, which means their methods can only be used for these specific vessels. Nevertheless, from [6], if the model depends only on ship motion data, it could be applicable to all types of vessels. In essence, ship motion data is time series data, and contains several frequencies which can describe the characteristics of environment.…”

Section: A Sea State Estimationmentioning

confidence: 99%

“…To address the problem of conventional methods, other researchers turned their attention to machine learning, using ship motion data and feature engineering techniques to extract temporal and frequency domain features from the data. For example, in [6], the sea state was estimated by a multi-layer random forest (RF) classifier. The method does not rely on accurate mathematical models, but requires many hand-crafted features that play a very important role in the classification results.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation

Cheng

Skulstad

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Developing a reliable model to identify the sea state is significant for the autonomous ship. This paper introduces a novel deep neural network model (SeaStateNet) to estimate the sea state based on the ship motion data from dynamically positioned vessels. The SeaStateNet mainly consists of three components: an Long-Short-Term Memory (LSTM) recurrent neural network to capture the long dependency in the ship motion data; a convolutional neural network (CNN) to extract time-invariant features; and a Fast Fourier Transform (FFT) block to extract frequency features. A feature fusion layer is designed to learn the degree affected by each component. The proposed model is applied directly to the raw time series data, without needing of any hand-engineered features. A sensitivity analysis (SA) method is applied to assess the influence of data preprocessing. Through benchmark test and experiment on ship motion dataset, SeaStateNet is verified effective for sea state estimation. The investigation on real-time test further shows the practicality of the proposed model.

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“…Although there is no specific guideline but considering the average lifetime of a WEC device, such systems are designed for an extreme event which can return only after 50 years. 37 In recent years, many studies have used cognitive classifiers, 38 vector maps, K-mean clustering, etc. for the identification of the extreme event and to estimate its chance of occurrence.…”

Section: Chakraborty Et Almentioning

confidence: 99%

Impact of extreme events on conversion efficiency of wave energy converter

Chakraborty

Majumder

2019

Energy Science & Engineering

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Sea state identification based on vessel motion response learning via multi-layer classifiers

Cited by 26 publications

References 14 publications

A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data

Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation

Impact of extreme events on conversion efficiency of wave energy converter

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