Short-term Ship Research Articles

AIS Data-Based Hybrid Predictor for Short-Term Ship Trajectory Prediction Considering Uncertainties

AIS Data-Based Hybrid Predictor for Short-Term Ship Trajectory Prediction Considering Uncertainties

A fully adaptive time–frequency coupling model using self-attention mechanism based on deep operator network for very short-term forecasting of ship motion

Very short-term ship motion forecasting aims to predict future movements using historical ship motion data. While ship motion features both temporal and frequency characteristics, the latter is often neglected. This paper proposes a fully adaptive time–frequency coupling forecasting model using self-attention mechanism based on the Deep Operator Network (DeepONet), abbreviated as TFD. The multi-head attention layers enable the trunk net to adaptively learn the relationships between different frequencies in the frequency domain and assign varying weights accordingly. Thus, compared to the Fourier transform and multilayer perceptron-net enhance model based on DeepONet (FMD), which relies on manually specified filter frequencies, the TFD model is capable of fully learning the motion patterns in both the time and frequency domains, establishing nonlinear mapping relationships between them. It exhibits greater interpretability and generalization. The TFD model is tested for accuracy and generalization using ship motion data from the Iowa University experimental tank. The results indicate that, compared to the DeepONet and FMD, the TFD model reduces the mean square error (MSE) by up to 64.72% and 52.45%, with an average reduction of 55.57% and 42.47%. In terms of generalization, the forecasting MSE is reduced by up to 65.04% and 46.08%. Compared to the DeepONet and FMD, the proposed TFD model demonstrates significant improvements in forecasting horizon and generalization, providing a notable advantage in very short-term ship motion prediction applications.

A Short-Term Vessel Traffic Flow Prediction Based on a DBO-LSTM Model

To facilitate the efficient prediction and intelligent analysis of ship traffic information, a short-term ship traffic flow prediction method based on the dung beetle optimizer (DBO)-optimized long short-term memory networks (LSTM) is proposed. Firstly, according to the characteristics of vessel traffic flow, speed, and density, the traffic flow parameters are extracted from the AIS data; secondly, the DBO-LSTM model is established, and the optimal hyperparameter combinations of the LSTM are found using the DBO algorithm to improve the model prediction accuracy; then, taking the AIS data of a part of the coastal port area in Xiangshan as an example, we compare and analyze the results of the recurrent neural network, temporal convolutional network, LSTM, and DBO-LSTM prediction models; finally, the results are displayed and analyzed by visualization. The experimental results show that each error is reduced in predicting the flow parameter, speed parameter, and density parameter, and the accuracy reaches 95%, 92%, and 95%, respectively. After predicting the three parameters in the next 24 h, the accuracy rate reaches 93%, 91%, and 94%, respectively, compared with the real data, which surpasses the comparison model and achieves better prediction accuracy, verifying the feasibility and reasonableness of the proposed prediction model.

Premonition-driven deep learning model for short-term ship violent roll motion prediction based on the hull attitude premonition mechanism

Ship transit forecasting can provide advanced movement information to a multidimensional wave motion compensation system. However, traditional deep learning models based on data-driven or physical data have difficulty in accurately predicting the rapid variations in ship motion attitudes that frequently occur in complex ocean environments. Therefore, a premonition-driven deep learning model is proposed in this paper to significantly improve the prediction accuracy of ship violent roll motion. This model applies precursor information to predict premonitory values of ship prospective motion attitudes based on an integrated statistical regression model and then employs these premonitory values to revise the outputs of the deep learning time series model. First, a hull attitude premonition mechanism is presented based on biological premonition cognitive behaviour, which captures the correlation relationships between extreme values by downgrading the globally continuous time series prediction problem to a time-independent regression problem. The corresponding methods for predicting local extreme values and deep feedforward neural network cluster are established based on these correlation relationships between the extreme values to achieve precise premonitory values. Second, the time and amplitude variables of the extreme angle values are transformed to countdown time variables and sequential sets of premonitory angle values. Two types of premonition units and their respective premonitory long short-term memory neural networks are constructed to upgrade the regression problem to a globally continuous time series prediction problem. Finally, a single-step output recurrent strategy is applied to forecast the short-term ship motion attitude for fifteen seconds by considering eleven initially predicted points across the two datasets. In sixty-six simulation experiments, the mean absolute error, root mean squared error, and mean absolute percentage error of the premonition-driven long short-term memory neural networks are considerably lower than those of traditional deep learning models. These simulation experiments verify that the premonition-driven deep learning model can effectively forecast short-term violent ship roll motion and provide safeguards for cargo transfer.

Short-term ship roll motion prediction using the encoder–decoder Bi-LSTM with teacher forcing

The safety of maritime operations has become a paramount concern with the advancement of intelligent ships. Ship stability and safety are directly impacted by roll motion, making the prediction of short-term ship roll motion pivotal for assisting navigators in making timely adjustments and averting hazardous roll conditions. However, predicting ship roll motion poses challenges due to nonlinear dynamics. This study aims to predict short-term ship roll motion by leveraging the encoder–decoder structure of Bidirectional Long Short-Term Memory Networks (Bi-LSTM) with teacher forcing. The model is accomplished by employing an encoder–decoder structure to map input sequences to output sequences of varying lengths, and employing teacher forcing to enhance the network’s ability to extract information. To refine and analyze the prediction model, aspects such as the quantity of training data to guarantee model generalization, establishing apposite length relationships between input and output sequences, and assessing the model performance in various sea states are investigated. Additionally, comparative experiments assessing roll motion prediction for intervals of 10s, 30 s, 60 s, and 120 s are conducted to substantiate the necessity and effectiveness of the proposed network. The dataset originates from a commercial professional simulator developed by the Norwegian company Offshore Simulator Center AS (OSC).

Nonlinear Transient Switching Filter for Automatic Buffer Window Adjustment in Short-term Ship Response Prediction

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution.

Model identification of ship turning maneuver and extreme short-term trajectory prediction under the influence of sea currents

To improve the accuracy of extreme short-term prediction of ship motion under environmental disturbances, a method to identify the motion model of ship turning maneuver and predict the ship extreme short-term trajectory with the influence of ocean currents is studied. Under the influence of ocean currents, a ship motion response model is identified based on the least squares method, and a ship extreme short-term trajectory prediction model is established. The velocity distribution extraction algorithm, center point restoration algorithm, and weighted derivation algorithm are proposed to extract the current direction and the velocity of the turning test. By integrating the current information and ship response characteristics in the extreme short-term ship prediction model, the extreme short-term segmented prediction of the ship trajectory is realized, and the current information is corrected online according to the prediction errors. The real test results show that, compared with the prediction method based on the ship dynamic model, inertia derivation and current compensation, the proposed extreme short-term trajectory prediction method has higher accuracy. Specifically, the prediction error of the proposed method is reduced by 80.6%, 89.3%, and 54.7% than abovementioned methods, respectively.

A Novel Short-Term Ship Motion Prediction Algorithm Based on EMD and Adaptive PSO–LSTM with the Sliding Window Approach

Under the influence of variable sea conditions, a ship will have an oscillating motion comprising six degrees of freedom, all of which are connected to each other. Among these degrees of freedom, rolling and pitching motions have a severe impact on a ship’s maritime operations. An accurate and effective ship motion attitude prediction method that makes the prediction in a short period of time is required to guarantee the safety and stability of the ship’s maritime operations. Traditional methods are based on time domain analysis, such as the autoregressive moving average (ARMA) models. However, these models have limitations when it comes to predicting the nonlinear and nonstationary characteristics of real ship motion attitude data. Many intelligent algorithms continue to be applied in nonlinear and nonstationary ship attitude prediction, such as extreme learning machines (ELMs) and the long short-term memory (LSTM) neural network, as well as other deep learning methods, showing promising results. By using the sliding window approach, the time-varying dynamic characteristics of the ship’s motion attitude can be preserved better. The simulation results demonstrate that the proposed model performs well in terms of predicting the nonlinear and nonstationary ship motion attitude.

Short-term ship motion attitude prediction based on LSTM and GPR

The random motions of a ship due to the sea waves directly affects the safety and efficiency of marine operations. Accurate short-term ship attitude prediction plays a vital role in operational decision-making in future seconds. In this paper, a new hybrid prediction model of ship motion attitude is proposed based on Long Short-Term Memory (LSTM) neural network and Gaussian Process Regression (GPR). When dealing with nonlinear regression problems, LSTM model can get high-accuracy point prediction results, while GPR model with lower prediction accuracy can obtain interval prediction results with probability distribution significance. The LSTM-GPR model successfully combines the advantages of LSTM and GPR, and can obtain high-accuracy point prediction results and reliable interval prediction results at the same time. The prediction experiments of ship rolling angle and pitch angle are carried out under both motion and static conditions. The results show that the LSTM-GPR hybrid model can obtain reliable interval prediction results without reducing the forecasting accuracy of LSTM model, which verifies the effectiveness and advancement of the hybrid model.

Wave-Induced Vertical Motions and Bending Moments in Damaged Ships

The wave-induced vertical ship motions and bending moments of a double hull-oil tanker in realistic flooding conditions are studied. The scenarios investigated are represented by water ingress into the starboard ballast tanks for collision damage cases and both starboard and portside ballast tanks for grounding situations. Seakeeping computations are performed for eight damage scenarios and for the intact condition, each corresponding to different changes in displacement, trim, and heel. For each of the damage conditions, transfer functions of vertical motions and loads are calculated using a potential linear 3D panel hydrodynamic code in the frequency domain that includes effect of the motion of the water in flooded tanks. A MATLAB code is developed to facilitate automated hydrodynamic simulation of many damage scenarios. Verification of seakeeping results is performed by comparing transfer functions with results of the previous study. Wave-induced vertical responses of damaged ship are then compared to those of intact ship using two spectral-based methods originating from uncertainty analysis of wave loads, which are convenient tools to assess consequences of damage on short-term ship responses. Generally, observed trend is that vertical wave-induced responses of damaged ship converge toward those of intact ship with increasing wave period. Fairly small differences between responses of asymmetrically damaged ship with respect to the symmetrical incoming wave directions are found. The results of the study are an efficient method for seakeeping assessment of damaged oil tankers and the framework for evaluating consequences of damage scenarios, heading angles, and sea conditions on seakeeping responses of damaged ships. The results can be used to decide if the intact ship model can be used instead of the damaged one for the emergency response procedure or for the risk assessment studies when modeling and computational time represent important limitations.

Time-domain simulator for short-term ship manoeuvring prediction: development and applications

ABSTRACTThis paper presents a short-term time-domain algorithm for ship motion simulation. Plane motion was considered, and thus, a three degrees of freedom model was used. The main inputs of the model are: propellers speed, rudder angles and initial conditions (initial position/orientation and linear/rotational velocity). The outputs are final ship position and orientation after a given simulation time. The data available in the Voyage Data Recorder and the results of sea trials performed before the ship delivery were used to estimate the needed parameters, implementing a simple but reliable linear regression scheme. The proposed model was applied to Costa Concordia manoeuvring simulation in order to assess its reliability in terms of trajectory prediction. Finally, as an example of application, the simulator was implemented in a real-time manoeuvring predictor, which is capable of foreseeing the ship position up to 40 s ahead in time, supporting the Commander in emergency operations.

A real-time ship roll motion prediction using wavelet transform and variable RBF network

Real-time prediction of ship roll motion is vital for marine safety and efficiency of operations onboard the ship. However, ship roll motion is a complex time-varying nonlinear process which varies with various sailing conditions as well as time-varying environmental factors. To achieve precise real-time ship roll prediction, an ensemble prediction scheme is constructed by combining the discrete wavelet transform (DWT) method with the variable-structure radial basis function (RBF) network. The DWT is used to reduce the time-series data redundancies and carry the data information in few significant uncoupled sub-series, thus facilitate the identification and prediction by using the variable RBF networks. The variable RBF networks are used to represent time-varying dynamics with both the structure and parameters are tuned in real time. The DWT-transform-based variable RBF networks are used to represent the time-varying nonlinear dynamics of ship roll movement during ship maneuvering. The effectiveness of the proposed DWT-based real-time roll prediction scheme is demonstrated by short-term ship roll motion prediction experiments based on the actual ship roll motion measurements collected during sea test of M.V. YuKun.

Nonlinear computational methods for hydroelastic effects of ships in extreme seas

We present computational methods to assess slamming-induced hull whipping on sectional loads of ships in regular and irregular waves. The numerical methods solved the Reynolds-averaged Navier-Stokes (RANS) equations coupled with the nonlinear rigid body motion equations of the elastic ship hull. We numerically investigated three containerships in regular waves, in random irregular long crested waves, and in deterministic wave sequences. Comparisons to experimental measurements agreed favorably. We relied on different wave models, including second order Stokes waves and nonlinear wave fields obtained from the solution of nonlinear Schrodinger equations (NLS). Simulations in random irregular waves provided short-term ship response probability distributions under sea state conditions relevant for design loads.

大波高を避航することを考慮した船体応答の長期予測法

Distribution characteristics of wave occurrence probability influence long-term prediction results that have estimated by using short-term ship responses and a wave scattering table. Because the observation volume of data and methods of analyzing wave data in each scattering table of significant wave height and mean wave period are different, long-term prediction results depend on the scattering table to apply. Especially, the difference of the frequency count of higher wave height in the scattering table strongly affects the results. When a ship encounters a severe wave condition, the ship speed decreases (nominal speed loss). When the wave condition becomes more severe, the captain judges the deceleration and the change of the course to avoid the danger of the damage of the ship. A long-term prediction method that has modeled the nominal speed loss and the ship handling is necessary to obtain a realistic prediction value. In this paper, the characteristics of a higher wave height for variety of scattering tables are investigated. Additionally, this paper proposes a long-term prediction method that models a situation when a ship is operated in such a way so as to avoid higher waves. Finally, this paper investigates the influence on a long-term prediction when the occurrence probability of a higher wave height is changed. Language: ja

Structural Safety Assessment of a 1100 TEU Container Ship, Based on a Enhanced Long Term Fatigue Analysis

This paper is focused on an enhanced integrated method for structural safety assessment of maritime ships under extreme random wave loads. In this study is considered an 1100 TEU container test ship, with speed range 0 to 18 knots. The most comprehensive criteria for ships structural safety evaluation over the whole exploitation life is based on the long term ship structures analysis, that includes: stress hot-spots evaluation by 3D/1D-FEM hull models, computation of short term ship dynamic response induced by irregular waves, long term fatigue structure assessment. The analysis is enhanced by taking into account the ships speed influence on hydroelastic response. The study includes a comparative analysis on two scenarios for the correlation between the ships speed and waves intensity. The standard constant ship speed scenario and CENTEC scenario, with total speed loss at extreme waves condition, are considered. Instead of 20 years ship exploitation life estimated by classification societies rules from the long term structural safety criteria, the enhanced method has predicted more restrictive values of 14.4-15.7 years. The numerical analyses are based on own software and user subroutines. The study made possible to have a more realistic approach of ships structural strength assessment, for elastic and faster ships as container carriers, in compare to the standard one based only on naval rules, delivering a method with higher confidence in the designed structural safety.

Ship scheduling and container shipment planning for liners in short-term operations

Good short-term ship scheduling and container shipment planning are very important for liner operations; however, in Taiwan, most such carriers currently utilize a trial-and-error process. In this study, we employ network flow techniques to construct a model for such activities. A solution algorithm, based on Lagrangian relaxation, a subgradient method, and a heuristic for the upper-bound solution, is developed to solve the model. To demonstrate and to test how well the model and the solution algorithm apply in the real world, we performed a case study using operating data from a major Taiwanese marine shipping company. The test results show that the model and the solution algorithm could be useful references for ship scheduling and container shipment planning.

Effect of transfer function uncertainty on short-term ship responses

A survey is presented of the generally used theories for predicting wave-induced motions and loads on ships as well as of the available comparisons with experimental results. The published results are used to quantify a model uncertainty factor that is proposed here to correct the theoretic predictions for systematic deviations and to quantify their uncertainty. The non-linearity of the response is also considered and modelled. A formulation is developed to quantify the influence of the transfer function uncertainty on the short-term response variance.