大型艦船模型在其他方面的應用

　　Application of Large Ship Models in Other Aspects

　　虛擬現實技術優化艙內空間：劉丹和王雯艷在 2023 年使用虛擬現實技術建立大型艦船艙內空間模型，優化艦船三維圖像模型中的特征參數，并將艦船內部的虛擬空間進行劃分，通過圖像分割技術結合虛擬現實技術對大型艦船的艙內空間分布進行優化，從而大幅度提升大型艦船的空間利用率，為船員今后的海上作業提供便利。

　　Virtual reality technology optimizes cabin space: Liu Dan and Wang Wenyan used virtual reality technology to establish a model of the cabin space of a large ship in 2023, optimize the feature parameters in the three-dimensional image model of the ship, and divide the virtual space inside the ship. By combining image segmentation technology with virtual reality technology, the distribution of cabin space of the large ship is optimized, thereby greatly improving the space utilization rate of the large ship and providing convenience for the crew's future maritime operations.

　　軌跡預測：Xianyang Zhang、Gang Liu 和 Chen Hu 在 2019 年針對大型艦船軌跡預測問題，討論了基于隱馬爾可夫模型（HMM）的軌跡預測問題。為了減少誤差積累對預測精度的影響，在 HMM 框架中加入小波分析，提出了一種基于小波的 HMM 軌跡預測算法（HMM-WA）。通過小波變換和單重構，將軌跡序列轉換為列向量，然后將其作為 HMM 的輸入。仿真結果表明，HMM-WA 算法與經典 HMM、線性回歸方法和卡爾曼濾波器相比，可以有效提高預測精度。

　　Trajectory prediction: Xianyang Zhang, Gang Liu, and Chen Hu discussed the trajectory prediction problem based on Hidden Markov Model (HMM) for large ships in 2019. In order to reduce the impact of error accumulation on prediction accuracy, wavelet analysis is added to the HMM framework, and a wavelet based HMM trajectory prediction algorithm (HMM-WA) is proposed. By using wavelet transform and single reconstruction, the trajectory sequence is transformed into column vectors, which are then used as inputs for HMM. The simulation results show that the HMM-WA algorithm can effectively improve prediction accuracy compared to classical HMM, linear regression methods, and Kalman filters.

　　垂直加速度預測：Yumin Su、Jianfeng Lin 和 Dagang Zhao 在 2020 年提出了一種基于循環神經網絡的長短期記憶（LSTM）和門控循環單元（GRU）模型的實時船舶垂直加速度預測算法。通過對大型船舶模型在海上進行自推進試驗，獲得了船首、中部和船尾的垂直加速度時間歷史數據，并通過 Python 對原始數據進行重采樣和歸一化預處理。預測結果表明，該算法可以準確預測大型船舶模型的加速度時間歷史數據，預測值與實際值之間的均方根誤差不大于 0.1。優化后的多變量時間序列預測程序比單變量時間序列預測程序的計算時間減少了約 55%，并且 GRU 模型的運行時間優于 LSTM 模型。

　　Vertical acceleration prediction: Yumin Su, Jianfeng Lin, and Dagang Zhao proposed a real-time ship vertical acceleration prediction algorithm based on recurrent neural network long short-term memory (LSTM) and gated recurrent unit (GRU) models in 2020. By conducting self propulsion tests on a large ship model at sea, historical data of vertical acceleration at the bow, middle, and stern were obtained, and the raw data was resampled and normalized using Python for preprocessing. The prediction results indicate that the algorithm can accurately predict the acceleration time history data of large ship models, and the root mean square error between the predicted value and the actual value is not greater than 0.1. The optimized multivariate time series prediction program reduces the computation time by about 55% compared to the univariate time series prediction program, and the running time of the GRU model is better than that of the LSTM model.

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