Pamukti Brian, Afifah Shofuro, Liaw Shien-Kuei, Sung Jiun-Yu, Chu Daping
Graduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Department of Electronics and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Sensors (Basel). 2024 Dec 25;25(1):47. doi: 10.3390/s25010047.
Distributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often involve high complexity and significant computational demands. Additionally, the backscattering method requires the signal to travel twice the normal distance, which can be inefficient. We propose an innovative interferometric sensing approach utilizing a Mach-Zehnder interferometer (MZI) combined with a time forest neural network (TFNN) for intrusion detection based on signal patterns. This method leverages advanced sensor characterization techniques and deep learning to improve accuracy and efficiency. Compared to the conventional one-dimensional convolutional neural network (1D-CNN), our proposed approach achieves an 8.43% higher accuracy, demonstrating the significant potential for real-time signal processing applications in smart environments.
分布式光纤传感器（DFOS）在入侵检测中越来越受欢迎，特别是在户外和受限区域。通过先进的信号处理和深度学习技术提高DFOS性能至关重要。虽然传统神经网络有效，但通常涉及高复杂性和大量计算需求。此外，后向散射方法要求信号传播的距离是正常距离的两倍，这可能效率低下。我们提出了一种创新的干涉传感方法，利用马赫-曾德尔干涉仪（MZI）与时间森林神经网络（TFNN）相结合，基于信号模式进行入侵检测。该方法利用先进的传感器表征技术和深度学习来提高准确性和效率。与传统的一维卷积神经网络（1D-CNN）相比，我们提出的方法准确率提高了8.43%，显示出在智能环境中实时信号处理应用的巨大潜力。
