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

Accurate prediction of ship motion is very important for ensuring marine 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 a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (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%.

Automatic speech recognition (ASR) systems have been widely deployed in modern smart devices to provide convenient and diverse voice-controlled services. Since ASR systems are vulnerable to audio replay attacks that can spoof and mislead ASR systems, a number of defense systems have been proposed to identify replayed audio signals based on the speakers' unique acoustic features in the frequency domain. In this paper, we uncover a new type of replay attack called modulated replay attack, which can bypass the existing frequency domain based defense systems. The basic idea is to compensate for the frequency distortion of a given electronic speaker using an inverse filter that is customized to the speaker's transform characteristics. Our experiments on real smart devices confirm the modulated replay attacks can successfully escape the existing detection mechanisms that rely on identifying suspicious features in the frequency domain. To defeat modulated replay attacks, we design and implement a countermeasure named DualGuard. We discover and formally prove that no matter how the replay audio signals could be modulated, the replay attacks will either leave ringing artifacts in the time domain or cause spectrum distortion in the frequency domain. Therefore, by jointly checking suspicious features in both frequency and time domains, DualGuard can successfully detect various replay attacks including the modulated replay attacks. We implement a prototype of DualGuard on a popular voice interactive platform, ReSpeaker Core v2. The experimental results show DualGuard can achieve 98% accuracy on detecting modulated replay attacks.

Semi-supervised learning (SSL) has gained significant attention in the domain of sea state estimation (SSE) due to its capacity to alleviate the reliance of deep learning models on extensive labeled datasets. While existing semi-supervised SSE methodologies leveraging pseudo-labeling have achieved promising results, they often overlook the challenges posed by high class imbalance and the prevalence of missing data in ship motion datasets, which restricts their broader applicability. In this article, we propose a novel SSL approach BalanceSSE based on the class-imbalanced ship motion data for SSE. This approach consists of three main modules: 1) the dynamic imputation (DIT); 2) the imbalance temporal-frequency learning (ITFL); and 3) the ClusterProx classifier (CL). The DIT module dynamically imputes incomplete ship motion data by assigning different weights to various dimensions data. The ITFL module employs time-frequency collaborative learning to generate pseudo-labels and integrate an adaptive confidence strategy to select high confidence pseudo-labels. This process is further enhanced by the CL module to produce better estimates. Experimental tests on UCR datasets and ship motion datasets demonstrate that BalanceSSE outperforms state-of-the-art methods. Ablation studies highlight the critical role of each module in BalanceSSE.

Ship motions are usually employed to analyze water transportation system. Interpolation of ship motions can be used for estimating the lost ship movement information, which is important for analyzing water traffic. Although some methods are proposed to interpolate ship trajectory, these methods are needed to improve interpolation accuracy. In addition, the course and speed of ship are not interpolated in previous studies. This article proposes a novel estimation algorithm for interpolating ship motions. First, the bilateral filtering is used to smooth the navigation trajectories, then a novel interpolation approach forecasts the vessel positions during the interpolation time period by taking into account the dynamic information of Automatic Identification System (AIS), and then forms the interpolation track of the moving vessel. In order to validate the proposed method, the AIS data of Yangtze River is collected from the AIS stations and the proposed restoration methods are verified by MATLAB under simulation. The results show that the proposed method can effectively remove the inaccurate ship motion data and interpolate the lost ship motion data.

In this paper, the disturbance applied to launcher hatch by ship motions is introduced to identify the vertical ship motion disturbance. Basically, ship motions are comprised of 6 degrees of freedom: roll, pitch, yaw, heave, surge and sway. In the case of the shipboard launcher hatch the coupled pitch, heave and roll are significant motions to be transformed to a vertical direction motion. The maximum acceleration values are obtained from the vertical motion model and the ship motion data in accordance with ship type and hatch location on the ship. We verify that the maximum pitch motion mainly influences the launcher hatch and also present the quantity of the maximum load disturbance by the ship's motion acceleration.

The ship motions in waves are regarded as a nonstationary random process because of the ship maneuvers, such as changes of the course and the speed, even on the assumption that the seaway can be considered as stationary stochastic process. In this paper, the recursive type of Bayesian modeling procedure is proposed and applied to online estimation of directional wave spectra based on ship motion data, which are pitch angle, roll angle and vertical acceleration. In the proposed method, the convergence of the estimates is not achieved every time step, but gradually achieved with time steps by using the ship motion cross spectra. To cope with the nonstationarity included in the time history data, instantaneous cross spectrum analysis based on T-VVAR (Time Varying Coefficient Vector Auto Regressive) modeling procedure is adopted. The T-VVAR model is transformed into the state space model and the time varying coefficients can be evaluated by using the Kalman Filter algorithm. Using the estimated time varying coefficients, the instantaneous ship motion cross spectra can be calculated every moment. Moreover, in order to estimate the directional wave spectra stably, a new constraint condition, which is based on the observed significant wave height data, is introduced into the Bayesian modeling procedure. To examine the reliability of the proposed procedure, onboard tests were carried out on nonstationary conditions. It is shown that the statistical values derived by integration of the estimated directional wave spectra agree well with the observed one.

Ships with partially filled liquid tanks, such as LNG carriers or FPSOs, are sensitive to sloshing in case they are exposed to waves. The effect of sloshing can have a pronounced effect on the ship motions, in particular roll in oblique seas. In this paper several methods are described which can be used to quantify this effect. The first is a linear diffraction method in which the effect of the liquid in the tank is modeled as a solid inertia of the fluid mass, an added mass and damping of the sloshing fluid and a hydrostatic free-surface correction to the GM. The response is easily computed in the frequency domain. The second is a time domain method in which the sloshing liquid in the tank is modeled with the CFD code ComFLOW. The forces exerted by the liquid on the tank walls are included in a time-domain simulation of the ship motions, based on linear potential flow for the outer domain (ship hull and ocean). The computed ship motions are again input for the motions of the liquid tank, generating a 2-way coupling between the dynamics of the tank and the ship. Both methods are applied to sloshing model tests, published by Molin (2008). In these tests, the response of a barge was measured with a completely filled and partially filled tank in beam seas. The results of the linear diffraction method agree reasonably well, but some differences in the roll response near the first sloshing mode are observed. The coupled time-domain method gives very good results for both low and high sea states.

Experimental investigations into the effect of at-sea conditions on ship airwake characteristics

Bayesian estimation of directional wave spectra based on ship motions

<i>The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.</i>

Based on Bayesian modeling procedure, a stochastic procedure is introduced. Using this procedure, directional wave spectra can be estimated by using the ship motion data which were recorded during the ship underway. The prior distribution in this procedure consists of two parts. The one is a stochastic constraint so called Gaussian smoothness prior distribution. And the other one is adopted to avoid excessive estimation for the directional wave spectra. Furthermore, the encounter frequencies are transformed into the true wave frequencies even in following seas.In order to examine the reliability of the proposed procedure, the onboard tests were carried out. And, the possibility of this procedure can be shown and problems encountered on applying are reported.

For the orthogonal frequency-division multiplexing (OFDM) system, one can estimate the channel responses (CRs) based on frequency-domain (FD) pilot symbols. On the other hand, one can also estimate the time-domain (TD) channel impulse response by inserting a unit pulse ahead of each OFDM block. However, there is no analytical comparison between the CR estimation based on FD pilot symbols and TD unit pulses. In this paper, we give the analytical comparison of the mean-squares errors (MSEs) for the CR estimation based on TD and FD pilot signals. For the CR estimation based on FD pilot symbols, the linear minimum mean-squares error (LMMSE) algorithm is optimal. We derive an MSE lower bound for the LMMSE algorithm among all possible distribution of pilot symbols. This MSE lower bound coincides with the MSE attained by the optimal CR estimation based on TD unit pulses. We also show that with the uniform distribution of pilot symbols, the resulted MSE is very close to the MSE lower bound for the LMMSE algorithm. Our results conclude that the optimal CR estimation based on TD and FD pilot signals attain almost the same MSEs.

: Beam steering angles of a shipboard phased array antenna are calculated, given the position of the ship, the position and attitude of the antenna on the ship, and the ship's bearing, roll, and pitch angles. The coordinate transformations were written in MATLAB; the pertinent software is added in the Appendix. An example of an application is given.

Shipborne stabilized platform is an important equipment to ensure the stability of shipborne equipment relative to inertial coordinate system. This paper presents a model predictive control strategy based on ship motion prediction (MPMPC) for ship stabilization platform. Firstly, the ship motion is simulated, and the autoregressive prediction model (AR model) is used to predict the ship motion. Then the kinematics analysis of the Shipborne stabilized platform is carried out and the mathematical model of the hydraulic drive unit (HDU) of the stable platform is established. Then the predicted ship motion is combined with model predictive control (MPC). The predicted trajectory of HDU can be obtained by the kinematics calculation of predicted ship motion. One part of the predicted trajectory is used to compensate the time delay of HDU, and the other part is used as the reference trajectory of the rolling optimization of MPC, instead of the reference trajectory using the measured ship motion at the current moment in traditional model predictive control. Compared with the reference trajectory using the measured ship motion at the current moment, the predicted trajectory of AR model can reflect the future state of the system better, and a better control sequence will be obtained by minimizing the objective function. Finally, the simulation and experiment show that the MPMPC has higher tracking accuracy than traditional MPC.

It has become a established theory that the ship motions in irregular waves are estimated from the transfer functions of ship motions and the Neumann spectra. But, according to this method we can only know the information of ship motions in irregular waves to be defined by spectra. Therefore it has a weak point, because we can not directly compare the actual ship performances in a certain wave height or a certain wind wave scale with the estimated qualities from it.This paper makes a proposal to exclude that fault of the conventional method. In the first place, the author investigated the relation between wind velocity and mean wave height in average sea states. Next, he tried on to substitute the assumed partially developed Neumann wave spectra for the average relations between wind velocity and mean wave heights. Hereupon, the significant wave heights of the assumed spectra are equal to the average wave height of actual average sea states. Moreover, he calculated the ship motions in that assumed spectra by the ordinary method. Then, it is considered that the calculated values by this method are equivalent the ship motions in average sea states.The obtained results show that the estimated values by the use of the fully developed Neumann spectra develop a tendency to over estimate, and that estimations give the inverse qualities at comparative calculations between various ship forms is possible.Then he predicts the critical ranges of the navigation in the averge sea state arising from severe ship motions or seasickness. Moreover, the cumulative occurance frequency of ship motions are predicted.The used ship forms for calculations are four kinds of destroyers. In this report, pitching and bow vertical accelerations in head seas are calculated.