Seconds In Future Research Articles

Mooring line failure diagnosis and motion control of semi-submersible platform based on the predictive model

Real-time prediction of deterministic platform motions in the coming future seconds was essential. The long-short-term memory networks (LSTM) and the fully connected neural networks were employed to establish the neural networks for motion predictions. The predicted results were compared with those calculated by AQWA. The results demonstrated that the predictive model could offer a fast and accurate prediction of the motion response of the semi-submersible platform. The sliding window model was built to predict motion without real input and monitor mooring line failure. The results showed that the predictive model based on the sliding window model can also achieve accurate prediction of platform motion. With the help of the predictive model, a control module written in Python was activated on the basis of the identification of mooring line failure. The control results show that the proportional-integral-derivative (PID) controller was very good at recovering the kinematic performance after mooring line failure and suppressing the continuous failure of the remaining mooring lines.

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.

The input vector space optimization for LSTM deep learning model in real-time prediction of ship motions

Vessel motions due to the ocean waves contribute to maritime operational safety and efficiency. Real-time prediction of deterministic ship motions in the coming future seconds is essential in decision-making when performing motions sensitive activities. The Long-Short Term Memory (LSTM) deep learning model provides a potential way for nonlinear ship motions prediction due to its capability in nonlinearity processing. Determination of a reasonable dimension of the input vector is critical in training the LSTM model. Conventionally, the optimal dimension for the input vector is selected by traversing an empirical preset range. Hence, it suffers both high computational cost and poor adaptation in determining the optimal input vector dimension. In the present work, an input vector space optimization method is proposed based on the dependence hidden in ship motion records of a sequence. Taking different correlation expressions into consideration, both the Impulse Response Function (IRF) based and Auto-correlation Function (ACF) based techniques are investigated for input vector space optimization. Numerical simulations are carried out for vilification and comparison purpose. The ACF technique is better in representing the auto-correlation hidden in the stochastic ship motions. And the ACF-based LSTM model performs better in both training efficiency and prediction accuracy.

Spirituality in Medical Acupuncture

What is spirituality? There have been and will continue to be so many differential definitions and explanations regarding this vitally important concept and questions over our present, past and future seconds, minutes, hours, days, weeks, months, years, centuries and millennia and there is no way to fully and finally document these heartfelt endeavors right here and right now as indicated in this present installation.