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

Abstract

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.

A Novel Channel and Temporal-Wise Attention in Convolutional Networks for Multivariate Time Series Classification

“…We plug the CT_CAM module into the FCN [9], single layer CNN (SLCNN), and the latest proposed DenseNet [4] and then compared this DenseNet_CT_CAM, FCN_CT_CAM, and SLCNN_CT_CAM with nine different baseline approaches, including common distance-based classifiers, bagof-patterns feature-based methods, and deep learning framework. and FCN are 128 and {128, 256, 128}.…”

“…SLCNN contains only one CNN block with the number of filter 128 while FCN consists of three CNN blocks with the number of filter {128, 256, 128}. The setting of DenseNet is the same with [4]. The attention module is stacked after each CNN block.…”

“…Multivariate time series classification (MTSC) is one of the most significant tasks. MTSC aims to predict classification labels for a certain multivariate time series data, which has many application scenarios in the real world such as clinical time series data analysis [1], human activity recognition [2], [3], sea state estimation [4], [5], and fault diagnosis in machinery system [6], [7].…”

A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data

“…The output is the estimated thruster condition of the vessel. Although there have been researches concerning using ship motion data in neural network for control [14], trajectory prediction [15] and sea state estimation [16], it is the first time that the ship motion data is used in neural network for thruster FDI. This approach does not require a dynamic model of vessel and no feature extraction process is needed since CNN can produce hierarchical representations from the raw data.…”

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

“…On the other hand, depending on the proposed controllers' nature, the control methods do not react quickly to sudden changes in environmental forces such as wind blows, unless the position predictor can recognize and take timely proper actions. To keep the vessel in the calm to extremely high sea state, the DPS compensates its heading and position changes by using the thrusters, where the sea state changes effect on the thruster's power consumption [36]. Accordingly, the thruster's control speed is increased or decreased due to the high and low-level of thrusting respectively.…”