Zongsheng Wang

Significant wave height (SWH) is a key physical parameter influencing the safety of shipping, fisheries, and marine engineering projects, and is closely related to climate change and marine disasters. Existing models struggle to balance a high prediction accuracy with low parameter counts, and are challenging to deploy on platforms such as buoys. To address these issues, this study proposes an innovative method for SWH prediction by combining Singular Spectrum Analysis (SSA) with a residual correction mechanism in a Long Short-Term Memory (LSTM) network. This method utilizes SSA to decompose SWH time series, accurately extracting its main feature modes as inputs to the LSTM network and significantly enhancing the model’s ability to capture time-series data. Additionally, a residual correction module is introduced to fine-tune the prediction results, effectively improving the model’s 12 h forecasting accuracy. The experimental results show that for 1, 3, 6, and 12 h SWH predictions, by incorporating SSA and the residual correction module, the model reduces the Mean Squared Error (MSE), Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) by 60–95%, and increases the coefficient of determination (R²) by 2–60%. The proposed model has only 10% of the parameters for LSTM based on Variational Mode Decomposition (VMD), striking an excellent balance between prediction accuracy and computational efficiency. This study provides a new methodology for deploying SWH prediction models on platforms such as buoys, and holds significant application value in marine disaster warning and environmental monitoring.

This paper proposes a control method for an excavator under a limited area, which designs a prediction algorithm based on the distance between the excavator and the obstacle. Through this method, it can be ensured to the maximum extent that the excavator will not have safety accidents due to human operation errors and other reasons. The method is validated on a tracked excavator, confirming the effectiveness of the algorithm. Field tests showed that the excavator joints could be stopped within 20cm of the obstacle and were only allowed to move in the direction away from the obstacle.

Adaptive fuzzy control is employed in this paper for identification and modeling of AUV parameters, addressing the issues of inaccuracy and susceptibility to interference within the AUV mechanism model. This paper explores the application of fuzzy control in AUV modeling, with an adaptation law designed to respond to environmental changes. This law helps mitigate issues such as model inaccuracy and uncertainty by allowing for parameters to be adjusted dynamically. Furthermore, a sliding mode controller is introduced for enhanced robustness during hover operations. Through extensive simulations in MATLAB and SIMULINK, the efficacy and reasonableness of this method are demonstrated.