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通过使用简单的卷积网络,高效的船舶噪声分类具有积极的激励噪声和融合特征
Xu Lin1, Ruichun Dong2, Yuqing Zhao2
1College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China. linxu@sdust.edu.cn.
Scientific reports
|October 20, 2023
概括
这项研究引入了积极的激励噪声,以改善船舶噪声分析在水声遥感. 简单的CNN模型实现了高性能,克服了水下机器人应用的数据稀缺性.
科学领域:
- 水声遥感远程传感技术
- 水下声学 水下声学
- 信号处理 信号处理
背景情况:
- 船舶噪声分析对于使用水声遥感识别识别船舶至关重要.
- 数据稀缺性挑战了准确的船舶噪声分类模型的开发.
- 现有的方法通常依赖于复杂的网络结构,不适合资源有限的水下机器人.
研究的目的:
- 为了解决水声信号识别中的数据短缺问题.
- 开发一种简单但有效的基于卷积神经网络 (CNN) 的识别方法.
- 为了实现精确的船舶识别,用于有限的计算能力的水下机器人应用.
主要方法:
- 引入了积极的激励噪声,以增加有限的数据集.
- 采用基于CNN的架构用于水声信号识别.
- 评估了各种特征提取技术,并将性能与之前的研究进行了比较.
主要成果:
- 拟议的CNN方法实现了与先前研究相比或优于先前研究的性能.
- 简单的神经网络表现出高性能和出色的通用性.
- 即使添加噪音,也可以实现有效的特征提取.
结论:
- 简单的神经网络可以在没有复杂架构的情况下有效地分类水声信号.
- 积极激励噪音是减轻数据稀缺问题的可行策略.
- 开发的方法适用于水下机器人的现实应用.
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