Modeling of Ship Maneuvering Motion Using Neural Networks

In this paper, Neural Networks （NNs） are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.

Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks

This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.

Modeling of Multi-Freedom Ship Motions in Irregular Waves with Fuzzy Neural Networks

In this paper, the neural network technology is combined with the fuzzy set theory to model the wave-induced ship motions in irregular seas. This combination makes possible the handling of a non-linear dynamic system with insufficient input information. The numerical results from the strip theory are used to train the networks and to demonstrate the validity of the proposed procedure.

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.

Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks

Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics of traditional and artificial intelligent forecasting techniques are discussed and rationales for choosing WNN are explained. Secondly, the components and features of BDTI will be explicated. After that, the authors delve the determinants and influencing factors behind fluctuations of the BDTI in order to set inputs for WNN forecasting model. The paper examines non-linearity and non-stationary features of the BDTI and elaborates WNN model building procedures. Finally, the comparison of forecasting performance between WNN and ARIMA time series models show that WNN has better forecasting accuracy than traditionally used modeling techniques.

Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach

This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results.

A Hybrid Features Based Detection Method for Inshore Ship Targets in SAR Imagery

Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.

特征降维与融合的水声目标识别方法

为解决水声目标在强噪声环境下识别困难以及特征高维问题,本文提出一种将水声信号进行离散小波变换并提取其低频系数与重组一维梅尔倒谱系数融合的方法,以减少特征维度并弥补信息损失。利用1D-CNN-LSTM神经网络在DeepShip和ShipsEar 2个数据集上进行实验,识别准确率均在99%以上。结果表明:该算法能够有效抑制噪声,具备较强的鲁棒性。将所提算法应用到单船识别,实验结果表明该算法能够有效区分同类型的不同船舶。

基于神经网络的船舶阻力预报研究

常规代理模型的阻力预报是以主尺度比、船型系数等作为输入,相比于CFD计算时输入完整船型,其较低的信息密度导致代理模型预报精度较低。本文以4108个完整船型几何形状特征张量作为输入,采用神经网络作为代理模型,以船舶的总阻力系数作为输出,研究船型阻力的高维度、高精度预报方法。首先,将船型进行无量纲化处理,并提取特征张量作为输入;然后,建立神经网络模型,搭建输入层、隐藏层和输出层;最后,将船型的特征张量与总阻力系数输入神经网络,通过误差反向传播进行训练,直至损失函数值收敛。本文研究结果可为基于高维代理模型的阻力性能预报提供理论和技术支持。

Influence of Regular Wave and Ship Characteristics on Mooring Force Prediction by Data-Driven Model

The study of mooring forces is an important issue in marine engineering and offshore structures.Although being widely applied in mooring system,numerical simulations suffer from difficulties in their multivariate and nonlinear modeling.Data-driven model is employed in this paper to predict the mooring forces in different lines,which is a new attempt to study the mooring forces.The height and period of regular wave,length of berth,ship load,draft and rolling period are considered as potential influencing factors.Input variables are determined using mutual information(MI)and principal component analysis(PCA),and imported to an artificial neural network(NN)model for prediction.With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology,MI is found to be more appropriate to provide effective input variables than PCA.Although the three factors regarding ship characteristics are highly correlated,it is recommended to input all of them to the NN model.The accuracy of predicting aft spring line force attains as high as 91.2%.The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors.

Multipoint Heave Motion Prediction Method for Ships Based on the PSO-TGCN Model

During ship operations,frequent heave movements can pose significant challenges to the overall safety of the ship and completion of cargo loading.The existing heave compensation systems suffer from issues such as dead zones and control system time lags,which necessitate the development of reasonable prediction models for ship heave movements.In this paper,a novel model based on a time graph convolutional neural network algorithm and particle swarm optimization algorithm(PSO-TGCN)is proposed for the first time to predict the multipoint heave movements of ships under different sea conditions.To enhance the dataset's suitability for training and reduce interference,various filter algorithms are employed to optimize the dataset.The training process utilizes simulated heave data under different sea conditions and measured heave data from multiple points.The results show that the PSO-TGCN model predicts the ship swaying motion in different sea states after 2 s with 84.7%accuracy,while predicting the swaying motion in three different positions.By performing a comparative study,it was also found that the present method achieves better performance that other popular methods.This model can provide technical support for intelligent ship control,improve the control accuracy of intelligent ships,and promote the development of intelligent ships.

A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units

Water transportation today has become increasingly busy because of economic globalization.In order to solve the problem of inaccurate port traffic flow prediction,this paper proposes an algorithm based on gated recurrent units(GRUs)and Markov residual correction to pass a fixed cross-section.To analyze the traffic flow of ships,the statistical method of ship traffic flow based on the automatic identification system(AIS)is introduced.And a model is put forward for predicting the ship flow.According to the basic principle of cyclic neural networks,the law of ship traffic flow in the channel is explored in the time series.Experiments have been performed using a large number of AIS data in the waters near Xiazhimen in Zhoushan,Ningbo,and the results show that the accuracy of the GRU-Markov algorithm is higher than that of other algorithms,proving the practicability and effectiveness of this method in ship flow prediction.

A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

Accurate prediction of shipmotion is very important for ensuringmarine safety,weapon control,and aircraft carrier landing,etc.Ship motion is a complex time-varying nonlinear process which is affected by many factors.Time series analysis method and many machine learning methods such as neural networks,support vector machines regression(SVR)have been widely used in ship motion predictions.However,these single models have certain limitations,so this paper adopts amulti-model prediction method.First,ensemble empirical mode decomposition(EEMD)is used to remove noise in ship motion data.Then the randomforest(RF)prediction model optimized by genetic algorithm(GA),back propagation neural network(BPNN)prediction model and SVR prediction model are respectively established,and the final prediction results are obtained by results of three models.And the weights coefficients are determined by the correlation coefficients,reducing the risk of prediction and improving the reliability.The experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is superior to the single predictive model and more reliable.The mean absolute percentage error(MAPE)of the proposed model is 0.84%,but the results of the single models are greater than 1%.

A Novel SAR Image Ship Small Targets Detection Method

To satisfy practical requirements of high real-time accuracy and low computational complexity of synthetic aperture radar (SAR) image ship small target detection, this paper proposes a small ship target detection method based on the improved You Only Look Once Version 3 (YOLOv3). The main contributions of this study are threefold. First, the feature extraction network of the original YOLOV3 algorithm is replaced with the VGG16 network convolution layer. Second, general convolution is transformed into depthwise separable convolution, thereby reducing the computational cost of the algorithm. Third, a residual network structure is introduced into the feature extraction network to reuse the shallow target feature information, which enhances the detailed features of the target and ensures the improvement in accuracy of small target detection performance. To evaluate the performance of the proposed method, many experiments are conducted on public SAR image datasets. For ship targets with complex backgrounds and small ship targets in the SAR image, the effectiveness of the proposed algorithm is verified. Results show that the accuracy and recall rate improved by 5.31% and 2.77%, respectively, compared with the original YOLOV3. Furthermore, the proposed model not only significantly reduces the computational effort, but also improves the detection accuracy of ship small target.

基于个性化联邦学习的异构船舶航行油耗预测

船舶航行油耗的精准预测,对保护海洋环境、减少航运业运营成本起关键作用,但航运业船舶的数据私密性、及异构船舶的数据异质性,导致常规机器学习方法的预测效果有限。为此,提出一种基于类别型特征的梯度提升(CatBoost)联合个性化联邦学习(PFL)预测方法。首先,对本地不同数据源的船舶信息数据及海况数据进行数据融合和清洗过滤,以提高输入数据质量;其次,对本地融合数据用CatBoost进行特征选取,以去除冗余数据;随后,引入带个性化层的联邦学习(FedPer)框架,建立异构船舶航行油耗预测模型,以保证异构船舶的数据私密性;进一步,对基本层权重矩阵采用联邦平均算法(FedAvg)聚合参数并反馈,对个性化层权重矩阵由本地客户端采用深度前馈神经网络(DFNN)进行训练优化,以消除数据异质性的影响,提高预测精度。最后,结合实际异构船舶航行油耗算例进行对比实验,结果表明,相比于其他模型,CatBoost联合个性化联邦学习预测方法的预测精度更高,对降低异构船舶航行油耗具有一定的指导意义。

基于水动力载荷混合数据集的高精度神经网络代理模型构建

In this work,we constructed a neural network proxy model(NNPM)to estimate the hydrodynamic resistance in the ship hull structure design process,which is based on the hydrodynamic load data obtained from both the potential flow method(PFM)and the viscous flow method(VFM).Here the PFM dataset is applied for the tuning,pre-training,and the VFM dataset is applied for the fine-training.By adopting the PFM and VFM datasets simultaneously,we aim to construct an NNPM to achieve the high-accuracy prediction on hydrodynamic load on ship hull structures exerted from the viscous flow,while ensuring a moderate data-acquiring workload.The high accuracy prediction on hydrodynamic loads and the relatively low dataset establishment cost of the NNPM developed demonstrated the effectiveness and feasibility of hybrid dataset based NNPM achieving a high precision prediction of hydrodynamic loads on ship hull structures.The successful construction of the high precision hydrodynamic prediction NNPM advances the artificial intelligence-assisted design(AIAD)technology for various marine structures.

基于旋转不变性的高分辨率遥感影像船舶检测

近年来,随着高分辨率遥感影像和船舶智能化的发展,通过遥感技术在大范围内对船舶目标进行检测识别已在海洋监管和安全等领域发挥出重要的现实意义。考虑到人类的视觉回路系统中对外界特定目标有很强的方向选择性,借鉴视觉的方向选择性机制,将有助于提升舰船检测识别任务的性能。从3个方面来模拟这种视觉的方向性选择机制:对卷积层采用Gabor卷积核分解的方法来模拟视觉回路的方向性,使深度卷积网络具有方向不变性;通过采用方向回归的方式估计舰船目标的主方向,模拟方向性选择机制;结合方向性目标来提升舰船检测识别任务的性能。试验结果表明:与快速区域卷积神经网络(Faster R-CNN)、单步多框检测(SSD)和定向响应网络(ORN)方法相比,该方法能取得较好的效果,表现出潜在的优势,均值平均精度(mAP)可达到约98%。

双向门控循环单元在船舶轨迹预测中的应用

针对传统循环神经网络提取船舶轨迹序列特征能力不足,导致预测结果与实际轨迹之间的误差较大,影响船舶调度与航行安全的问题,将双向门控循环单元(Bidirectional Gated Recurrent Unit, Bi-GRU)神经网络应用到船舶轨迹预测中。利用Bi-GRU神经网络模型具有的前瞻特性以及大量船舶自动识别系统(Automatic Identification System, AIS)数据,提出基于Bi-GRU的船舶轨迹预测模型。结果表明,Bi-GRU的预测精度较门控循环单元(Gated Recurrent Unit, GRU)有明显提升,均方误差降低13.0%,均方根误差降低6.5%,平均绝对误差降低16.5%。研究成果可为提高船舶交通服务系统安全管理水平、判断船舶交通风险程度及智能船舶碰撞预警提供理论支撑。