基于机器学习的方法,用于分析大型游轮中的振动和噪声
Wenwei Wu1, Tao He1, Xiaying Hao1
1China Ship Scientific Research Center, Wuxi, Jiangsu Province, China.
PloS one
|July 25, 2024
概括
机器学习识别了邮轮甲板的关键噪声频率,改善了舒适度评估. 这种方法有助于减少和控制船舶的振动和噪音,提高乘客的体验.
科学领域:
- 海洋工程 海洋工程是指海洋工程.
- 声学 声学 在声学方面
- 数据科学是数据科学.
背景情况:
- 游轮的舒适性对于乘客的满意度和利能力至关重要.
- 振动和噪音是游轮舒适度的关键指标.
- 对于振动和噪声数据的现有分析方法存在局限性.
研究的目的:
- 开发一种基于机器学习的方法来分析邮轮的振动和噪声数据.
- 识别与不同船甲板相关的基本噪声频率.
- 创建一个分类器来区分噪声样本.
主要方法:
- 利用机器学习算法进行特征排名,选择和分类.
- 分析了来自中国第一艘大型游轮的127个噪音样本.
- 设置一个分类任务,将甲板作为标签,频率作为特征.
- 使用随机森林和轻GBM进行分类和特征选择.
主要成果:
- 确定了大多数甲板的一个或两个基本噪声频率,部分通过统计方法验证.
- 开发了一个随机森林分类器,使用LightGBM识别的八个最佳频率特征.
- 对于噪声样本分类器,取得了0.3415的马修斯相关系数.
结论:
- 机器学习方法提供了对船舶振动和噪声的洞察性分析.
- 这些发现有助于减少和控制邮轮上的振动和噪声水平.
- 这项研究为研究船舶声学和舒适度提供了新的方向.
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