在航行和通信中应用文本挖掘方法,以提高海上安全
Paulina Hatłas-Sowińska1, Leszek Misztal2
1Department of Mathematics Physics and Chemistry, Maritime University of Szczecin, Szczecin, Poland.
PloS one
|March 22, 2024
概括
本研究提出了一种新型的自然语言到本体学翻译模型,用于自主船舶导航系统. 经过验证的原型增强了海上通信和避免碰撞的能力.
科学领域:
- 海洋技术的海洋技术
- 人工智能的人工智能是人工智能.
- 本体学工程学 工程学 工程学
背景情况:
- 自主导航系统需要强大的通信处理.
- 自然语言接口对于海上无的人与系统的互动至关重要.
- 实体学为海运等复杂领域提供了结构化的知识表示.
研究的目的:
- 开发和验证一种模型,用于将自然语言命令转化为本体学,反之亦然,用于自动驾驶船只.
- 提高海上船舶的通信能力.
- 在关键的海上场景中改善决策过程,例如避免碰撞.
主要方法:
- 使用机器学习技术,特别是文本挖掘.
- 应用基础和高级的本体学属性用于知识表示.
- 开发一个基于规则的系统,用于命令序列分解和提取.
主要成果:
- 开发了一个功能性原型,实现自然语言到本体学翻译模型.
- 该模型在模拟的海上碰撞场景中成功实施和验证.
- 测试结果证实了系统架构和算法的正确性.
结论:
- 拟议的模型有效地将自然语言转化为自主海上导航的本体学.
- 开发的系统增强了海上通信任务,可以应用于航运.
- 经过验证的原型证明了该方法对现实世界海洋应用的可行性和准确性.
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