大型艦船模型在其他方面的應(yīng)用

　　Application of Large Ship Models in Other Aspects

　　虛擬現(xiàn)實(shí)技術(shù)優(yōu)化艙內(nèi)空間：劉丹和王雯艷在 2023 年使用虛擬現(xiàn)實(shí)技術(shù)建立大型艦船艙內(nèi)空間模型，優(yōu)化艦船三維圖像模型中的特征參數(shù)，并將艦船內(nèi)部的虛擬空間進(jìn)行劃分，通過圖像分割技術(shù)結(jié)合虛擬現(xiàn)實(shí)技術(shù)對(duì)大型艦船的艙內(nèi)空間分布進(jìn)行優(yōu)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(yè)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(yù)測(cè)：Xianyang Zhang、Gang Liu 和 Chen Hu 在 2019 年針對(duì)大型艦船軌跡預(yù)測(cè)問題，討論了基于隱馬爾可夫模型（HMM）的軌跡預(yù)測(cè)問題。為了減少誤差積累對(duì)預(yù)測(cè)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(yù)測(cè)算法（HMM-WA）。通過小波變換和單重構(gòu)，將軌跡序列轉(zhuǎn)換為列向量，然后將其作為 HMM 的輸入。仿真結(jié)果表明，HMM-WA 算法與經(jīng)典 HMM、線性回歸方法和卡爾曼濾波器相比，可以有效提高預(yù)測(cè)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(yù)測(cè)：Yumin Su、Jianfeng Lin 和 Dagang Zhao 在 2020 年提出了一種基于循環(huán)神經(jīng)網(wǎng)絡(luò)的長(zhǎng)短期記憶（LSTM）和門控循環(huán)單元（GRU）模型的實(shí)時(shí)船舶垂直加速度預(yù)測(cè)算法。通過對(duì)大型船舶模型在海上進(jìn)行自推進(jìn)試驗(yàn)，獲得了船首、中部和船尾的垂直加速度時(shí)間歷史數(shù)據(jù)，并通過 Python 對(duì)原始數(shù)據(jù)進(jìn)行重采樣和歸一化預(yù)處理。預(yù)測(cè)結(jié)果表明，該算法可以準(zhǔn)確預(yù)測(cè)大型船舶模型的加速度時(shí)間歷史數(shù)據(jù)，預(yù)測(cè)值與實(shí)際值之間的均方根誤差不大于 0.1。優(yōu)化后的多變量時(shí)間序列預(yù)測(cè)程序比單變量時(shí)間序列預(yù)測(cè)程序的計(jì)算時(shí)間減少了約 55%，并且 GRU 模型的運(yùn)行時(shí)間優(yōu)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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