機械学習（主成分分析による次元削減およびサポートベクターマシン）に基づいて、ブリルアン光相関領域反射計による動的歪の検出速度を大幅に向上させる手法を提案した論文が Optics Express に掲載されました。中国の常熟理工学院（Changshu Institute of Technology）の姚雨果博士との共同研究の成果です。
Y. Yao and Y. Mizuno, “Dynamic strain measurement in Brillouin optical correlation-domain sensing facilitated by dimensionality reduction and support vector machine,” Opt. Express, vol. 30, no. 9, pp. 15616-15633 (2022).
Brillouin optical correlation-domain sensing enables high-speed Brillouin gain spectrum (BGS) measurement at random positions along the optical fiber. To extract the Brillouin frequency shift (BFS) that reflects the real-time strain information, machine learning methods of principal components analysis (PCA) and support vector machine (SVM) are used in the signal processing for the BGSs. The performances of dimensionality reduction by PCA and SVM based on classification and regression are analyzed and compared. The experiment demonstrates an 8 kHz BGS acquisition repetition rate and an average BFS extraction time of 0.0104 ms, which is 27.3 times faster than the conventional method with no PCA. The proposed methods realize a real-time dynamic strain measurement at the frequency of 40 Hz.