使用RSSI测量和人工智能评估旋转电机中的通信性能.
Sonia Ben Brahim1, Samia Dardouri2, Hanen Lajnef1
1InnoV'COM Laboratory-Sup'Com, University of Carthage, Ariana 2083, Tunisia.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种新的AI方法,用于评估使用信号强度在电机中的无线通信. 一个SVM模型在识别可靠的通信信号时达到97%的准确性.
科学领域:
- 电气工程 电气工程
- 无线通信系统无线通信系统
- 在工程领域的人工智能.
背景情况:
- 旋转式电机由于反射和金属表面而对无线通信具有挑战性的环境.
- 在这些动态系统中评估实时通信性能对于可靠性和效率至关重要.
- 现有的方法可能无法充分解决旋转机器内部信号传播的复杂性.
研究的目的:
- 开发和验证一种用于评估旋转电机中的无线通信性能的新方法.
- 利用接收信号强度指示 (RSSI) 和人工智能进行可靠的信号评估.
- 为了确定最有效的机器学习模型来分类信号质量在这个特定的应用程序.
主要方法:
- 利用接收信号强度指示 (RSSI) 测量用于实时信号监控.
- 实施了包括数据收集,预处理和特征提取在内的多阶段方法.
- 训练和评估各种机器学习模型,包括支持向量机 (SVM) 带有辐射基函数 (RBF) 内核.
主要成果:
- 使用RBF内核的支持矢量机 (SVM) 模型表现出卓越的性能.
- 在分类通信信号质量方面取得了97%的整体准确性.
- 高精度和回忆得分,以及混矩阵显示的最小错误分类率,证实了模型的稳定性.
结论:
- 拟议的人工智能驱动的方法有效评估旋转电机中的无线通信性能.
- 带有 RBF 内核的 SVM 是在这种环境中分析复杂 RSSI 数据的强大而准确的模型.
- 这种方法可以提高无线系统在苛刻的工业应用中的可靠性评估.
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