大型舰船模型在其他方面的应用
发布时间：2025-01-22 来源：/
大型舰船模型在其他方面的应用
Application of Large Ship Models in Other Aspects
虚拟现实技术优化舱内空间：刘丹和王雯艳在 2023 年使用虚拟现实技术建立大型舰船舱内空间模型，优化舰船三维图像模型中的特征参数，并将舰船内部的虚拟空间进行划分，通过图像分割技术结合虚拟现实技术对大型舰船的舱内空间分布进行优化，从而大幅度提升大型舰船的空间利用率，为船员今后的海上作业提供便利。
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.
轨迹预测：Xianyang Zhang、Gang Liu 和 Chen Hu 在 2019 年针对大型舰船轨迹预测问题，讨论了基于隐马尔可夫模型（HMM）的轨迹预测问题。为了减少误差积累对预测精度的影响，在 HMM 框架中加入小波分析，提出了一种基于小波的 HMM 轨迹预测算法（HMM-WA）。通过小波变换和单重构，将轨迹序列转换为列向量，然后将其作为 HMM 的输入。仿真结果表明，HMM-WA 算法与经典 HMM、线性回归方法和卡尔曼滤波器相比，可以有效提高预测精度。
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.
垂直加速度预测：Yumin Su、Jianfeng Lin 和 Dagang Zhao 在 2020 年提出了一种基于循环神经网络的长短期记忆（LSTM）和门控循环单元（GRU）模型的实时船舶垂直加速度预测算法。通过对大型船舶模型在海上进行自推进试验，获得了船首、中部和船尾的垂直加速度时间历史数据，并通过 Python 对原始数据进行重采样和归一化预处理。预测结果表明，该算法可以准确预测大型船舶模型的加速度时间历史数据，预测值与实际值之间的均方根误差不大于 0.1。优化后的多变量时间序列预测程序比单变量时间序列预测程序的计算时间减少了约 55%，并且 GRU 模型的运行时间优于 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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