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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
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相关实验视频

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SEA++:基于多图的高阶传感器对齐,用于多变量时间序列无监督域调整.

Yucheng Wang, Yuecong Xu, Jianfei Yang

    IEEE transactions on pattern analysis and machine intelligence
    |August 16, 2024
    PubMed
    概括

    本研究介绍了传感器对齐 (SEA) 方法,通过将传感器特征在本地和全球层面对齐来改进无监督域适应多变量时间序列数据. 在多变量时间序列无监督域调整中,SEA和SEA++实现了最先进的性能.

    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 信号处理 信号处理

    背景情况:

    • 无监督域调整 (UDA) 方法通过最小化域差异来减少标签依赖.
    • 由于传感器级分布变化,现有的UDA方法在多变量时间序列 (MTS) 数据方面遇到了困难.
    • 这种限制阻碍了UDA对MTS数据的有效性,这在现实应用中很常见.

    研究的目的:

    • 通过提出一个名为多变量时间序列无监督域调整 (MTS-UDA) 的新框架来应对MTS数据的UDA挑战.
    • 引入传感器对齐 (SEA) 来解决本地和全球传感器层面的域差异.
    • 通过结合先进的调整技术,增强对SEA++的拟议方法.

    主要方法:

    • 为MTS-UDA提出的SEnsor调整 (SEA) 框架.
    • 开发了内分特征对齐,以对齐传感器特征及其在本地传感器层面的相关性.
    • 设计的外特征对齐,以强制执行对全球传感器特征的限制,以实现全球传感器水平对齐.
    • 扩展了SEA到SEA++,并基于多图的高阶对齐,以增强特征和相关性对齐.

    主要成果:

    • 在6个公共MTS数据集上,SEA和SEA++展示了最先进的性能.

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  • 提出的方法有效地解决了在本地和全球传感器层面的域差异.
  • 经验结果证实了SEA和SEA++在MTS-UDA任务中的优越性.

    • 结论:

    • SEA和SEA++为多变量时间序列无监督域调整提供了有效的解决方案.
    • 传感器级对齐方法显著改善了MTS数据上的UDA性能.
    • 提出的方法推进了复杂时间序列数据的域调整领域.