Author Affiliations
Abstract
1 Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China
2 College of Physics and Hebei Key Laboratory of Photophysics Research and Application, Hebei Normal University, Shijiazhuang 050024, China
The realization of robust coherent energy transfer with a long range from a donor to an acceptor has many important applications in the field of quantum optics. However, it is hard to be realized using conventional schemes. Here, we demonstrate theoretically that robust energy transfer can be achieved using a photonic crystal platform, which includes the topologically protected edge state and zero-dimensional topological corner cavities. When the donor and the acceptor are put into a pair of separated topological cavities, the energy transfer between them can be fulfilled with the assistance of the topologically protected interface state. Such an energy transfer is robust against various kinds of defects, and can also occur over very long distances, which is very beneficial for biological detections, sensors, quantum information science, and so on.
Photonics Research
2020, 8(11): 11000B39
1 中北大学计算机与控制工程学院, 山西 太原 030051
2 电子测量技术国家重点实验室, 山西 太原 030051
3 中北大学机电工程学院, 山西 太原 030051
针对激光超声检测过程中, 缺陷的分类、定位及特征难以识别的问题, 根据激光超声遇到不同裂纹呈规律性变化的特点, 采用神经网络对在激光超声检测中出现的缺陷特征进行统计识别。对采集到的激光超声信号归一化和规范化后, 首先计算每个信号的均值、均方根值、峰值、峭度等十个统计特征, 并将这些特征组合成一个等长的特征向量, 然后采用径向基(RBF)神经网络识别。经过计算发现总的识别正确率为95%, 部分类型的缺陷识别可以达到100%, 较低的识别正确率也在80%以上。实验结果表明, 该方法能精确、高效地识别裂纹缺陷且对环境的适应能力比较好, 有助于实现对裂纹的定量检测。
统计特征 缺陷识别 神经网络 激光超声 statistic features flaw identification neural network laser ultrasonic
