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基于深度自编码多维特征融合的物联网检测在体育训练中的应用
1School of Physical Education, Shandong University of Finance and Economics, Jinan 250002, China.
Preventive medicine
|July 19, 2023
概括
本研究介绍了一种使用可编程交换机和深度自编码的物联网 (IoT) 检测系统. 该系统提高了效率,并减少了物联网网络中的延迟,在体育训练模拟中的应用.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 机器学习 机器学习
背景情况:
- 现有的物联网 (IoT) 检测方法存在局限性.
- 需要更高效,更快速的物联网检测系统.
研究的目的:
- 提出一个新的物联网检测方案,利用可编程交换机和深度自编码.
- 设计一个集中的物联网检测系统,集成可编程交换机和机器学习.
- 应用这种新的物联网检测技术来增强体育训练模拟.
主要方法:
- 开发了一个系统,将可编程交换机与物联网MQTT协议以及多维特征融合算法相结合.
- 在可编程交换机上实现了一个物联网检测模块,用于前端数据预检测.
- 使用可编程交换机快速收集数据和决策 (重定向或丢弃数据包).
主要成果:
- 拟议的系统通过在可编程开关上处理信息来最大限度地减少数据包传输延迟.
- 该系统有效地预先检测故障数据,减少服务器负载.
- 在体育训练模拟中的应用证明了增强的实用性并满足了培训需求.
结论:
- 可编程交换机和深度自编码的集成在集中式物联网检测方面取得了重大进展.
- 这种方法提高了效率,减少了延迟,并提高了物联网网络的可靠性.
- 在体育训练中的成功应用凸显了开发的技术的多功能性和实际影响.
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