Elleathy Ahmad, Alhumaidan Faris, Alqahtani Mohammed, Almaiman Ahmed S, Ragheb Amr M, Ibrahim Ahmed B, Ali Jameel, Esmail Maged A, Alshebeili Saleh A
Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia.
KACST-TIC in Radio Frequency and Photonics (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia.
Sensors (Basel). 2023 May 24;23(11):5015. doi: 10.3390/s23115015.
This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college's gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder's existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB.
本文展示了一种基于应变的光纤布拉格光栅（FBG）、机器学习（ML）和自适应阈值处理的入侵检测系统，用于在低信噪比水平下将入侵者分类为无入侵者、入侵者或风。我们使用沙特国王大学工程学院一个花园周围制造并安装的一段真实围栏演示了入侵检测系统。实验结果表明，自适应阈值处理有助于提高机器学习分类器的性能，如线性判别分析（LDA）或逻辑回归算法在低光信噪比（OSNR）场景下识别入侵者存在的性能。当OSNR水平<0.5 dB时，所提出的方法可以达到99.17%的平均准确率。
