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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

132
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
132

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相关实验视频

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多传感器数据融合和CNN-LSTM模型用于人类活动识别系统

Haiyang Zhou1, Yixin Zhao1, Yanzhong Liu1

  • 1Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

这项研究引入了一种新的人类活动识别 (HAR) 系统,使用相机和毫米波雷达数据的组合. 融合方法在低光条件下显著提高了准确性,超过了仅使用摄像头的系统.

关键词:
美国有线电视新闻网 (CNN-LSTM)融合算法 融合算法人类活动的认可 人类活动的认可多传感器数据融合数据

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 传感器融合式传感器

背景情况:

  • 人类活动识别 (HAR) 对于老年护理至关重要,但基于摄像头的系统在低光下扎.
  • 现有的HAR系统面临准确性限制,特别是在具有挑战性的环境条件下.

研究的目的:

  • 开发一个改进的HAR系统,克服低光限制.
  • 通过传感器融合,提高HAR准确度并降低错误分类率.

主要方法:

  • 设计了一个混合系统,结合了摄像头和毫米波雷达传感器.
  • 改进的卷积神经网络-长期短期记忆 (CNN-LSTM) 模型被用于特征提取.
  • 研究了三个数据融合算法 (数据级,功能级,决策级).

主要成果:

  • 与仅使用摄像头的系统相比,合传感器数据显著提高了HAR准确度,在低光下提高了19.87%26.68%.
  • 数据级融合算法将错误分类率降低到2%6%.

结论:

  • 拟议的多传感器融合系统有效地提高了HAR在低光环境中的准确性.
  • 这种方法显示出在智能生活空间中可靠监控和减少活动错误分类的潜力.