由人工智能驱动的电快速短暂抑制,用于在感应智能近距离传感器中增强电磁干扰免疫力
Silvia Giangaspero1, Gianluca Nicchiotti1, Philippe Venier2
1iSIS Institute, HEIA-FR, HES-SO University of Applied Sciences and Arts Western Switzerland, 1700 Fribourg, Switzerland.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
使用神经网络 (NN) 的人工智能 (AI) 能够有效地过感应性近距离传感器中的电磁干扰 (EMI). 一个封闭的循环单元 (GRU) 模型在最小的内存下减少了70%的噪声,使未来的ASIC实现成为可能.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 传感器技术 传感器技术
背景情况:
- 感应式近距离传感器需要电磁干扰 (EMI) 免疫力,用于工业应用.
- 对于EMI缓解信号带宽的常规过器 (100 Hz1.6 kHz).
研究的目的:
- 研究神经网络 (NN) 在自动过来自传感器信号的EMI方面的有效性.
- 为了比较不同NN架构的EMI无声化.
- 为潜在的ASIC实施开发一个优化的NN模型.
主要方法:
- 分析和比较1D卷积NN,反复NN和混合NN模型.
- 基于NN模型的门式循环单位 (GRU) 的开发.
- 优化和压缩GRU网络以提高内存效率.
主要成果:
- 拟议的基于GRU的NN模型成功地拒绝了EMI扰乱的信号.
- 最终优化的网络将噪声降低70% (MSEred).
- 该模型只占用2KB的内存.
结论:
- 对于感应传感器中的EMI过,NN,特别是GRU型号,提供了一个强大的解决方案.
- 优化的GRU网络满足了潜在集成到特定应用集成电路 (ASIC) 的内存约束.
- 这种方法克服了传统过方法的带宽限制.
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