Young Ho Kim

Distributed acoustic sensing (DAS) systems capture complex, nonlinear wave propagation across fiber-optic cables. Conventional event classification architectures, constrained by static physical topologies or isolated spatial grids, fail to effectively adapt to the dynamic feature relationships associated with such events, particularly when modeling complex spatiotemporal interactions across sensor arrays. To resolve these structural limitations, we introduce the Spectro-Spatial Dynamic Graph Neural Network (S2-DyGNN), whose architecture couples a two-dimensional frequency–time convolutional front-end with a dual-matrix graph neural network (GNN). First, the convolutional module extracts spectro-temporal features, explicitly capturing localized acoustic dynamics independent of inter-sensor interference. Subsequently, the graph module constructs a dual-matrix topology, fusing a static physical distance prior with a data-driven adjacency matrix that recalculates spatial connections frame by frame from input signals. When evaluated on a highly skewed nine-class DAS field dataset, S2-DyGNN outperformed other conventional models by achieving a peak macro-averaged F1-score of 86.6% and an overall accuracy of 94.0%. The dual-matrix graph topology prevented dominant background features from washing out sparse transient events, improving the minority “openclose” class F1-score to 55.7% compared to the 48.0% ceiling of a static graph topology. These results demonstrate that explicitly coupling localized spectro-temporal representations with physically anchored spatial topologies consistently outperforms models that process these domains in isolation, providing a highly robust and scalable solution for real-world continuous monitoring systems.

We investigated the effect of externally introduced 980 nm photobleaching on radiation-induced attenuation (RIA) in pure silica core (PSC) single-mode fibers and demonstrated that it enhanced the radiation-hard characteristics of PSC fiber based Raman-distributed temperature sensors (Raman-DTS) operating at 1550 nm. Gamma-ray irradiation experiments confirmed that 980 nm photobleaching effectively reduces the RIA of PSC fibers at 1550 nm. RIA spectra were measured in the wavelength range of 1400–1650 nm during gamma-ray irradiation to evaluate the influence of 980 nm photo-bleaching on the wavelength dependence of RIA. We also analyzed the spectral changes in RIA of the PSC fibers with different photobleaching power and showed that the wavelength dependence of RIA can be tuned by adjusting the bleaching power. Finally, we demonstrated that applying the external photo-bleaching strategy effectively mitigates radiation-induced temperature measurement distortion in Raman-DTS by reducing the RIA imbalance between the Raman Stokes and anti-Stokes scattering bands.

This paper presents a compact dynamic optical frequency domain reflectometry (D-OFDR) platform enabling millimeter-scale, distributed strain sensing for real-time structural health monitoring (SHM) of reinforced concrete subjected to seismic loading. The proposed D-OFDR interrogator employs a dual-interferometer architecture: a main interferometer for strain sensing and an auxiliary interferometer for nonlinear frequency sweep compensation. Both signals are detected by photodetectors and digitized via a dual-channel FPGA-based DAQ board, enabling high-speed embedded signal processing. A dual-edge triggering scheme exploits both the up-chirp and down-chirp of a 50 Hz bidirectional sweep to achieve a 100 Hz interrogation rate without increasing the sweep speed. Laboratory validation tests on stainless steel cantilever beams showed sub-hertz frequency fidelity (an error of 0.09 Hz) relative to conventional strain gauges. Shake-table tests on a 2 m RC column under incremental seismic excitations (scaled 10–130%, peak acceleration 0.864 g) revealed distinct damage regimes. Distributed strain data and frequency-domain analysis revealed a clear frequency reduction from approximately 3.82 Hz to 1.48 Hz, signifying progressive stiffness degradation and structural yielding prior to visible cracking. These findings demonstrate that the bidirectional sweep-triggered D-OFDR method offers enhanced real-time monitoring capabilities, substantially outperforming traditional point sensors in the early and precise detection of seismic-induced structural damage.

We present a dual-frequency Terahertz (THz) heterodyne interferometry system for nondestructive 3D profiling of large step heights without phase unwrapping. The system employs photonic generation of two THz frequencies around a center frequency of 306 GHz with tunable frequency spacing from 8 to 11.5 GHz, corresponding to synthetic wavelengths of 37.5 to 26.1 mm. Through phase-sensitive heterodyne detection, minimum detectable height differences of 6.7 μm at 10 GHz and 19.7 μm at 8 GHz were achieved. This allows for direct measurement of millimeter-scale discontinuities within a single 2π phase cycle. Furthermore, experimental validation in 3D-printed polylactic acid (PLA) samples demonstrates accurate measurements up to 27 mm with the measured refractive index of 1.638. Taking into account a frequency modulation of 8 GHz and the refractive index of PLA, step heights up to 29.3 mm are measurable within a single 2π phase cycle. The system also enables non-destructive profiling, passing through a 200-μm paper envelope that is an optically opaque material, and images embossed structures. This technique facilitates non-contact metrology for composite material inspection, automotive components, and multilayer coatings where optical methods fail due to material opacity.

Distributed acoustic sensing (DAS), which can provide dense spatial and temporal measurements using optical fibers, is quickly becoming critical for large-scale infrastructure monitoring. However, anomaly detection in DAS data is still challenging owing to the spatial correlations between sensing channels and nonlinear temporal dynamics. Recent approaches often disregard the explicit sensor layout and instead handle DAS data as two-dimensional images or flattened sequences, eliminating the spatial topology. This work proposes GraphDiffusion, a novel generative anomaly-detection model that combines a conditional denoising diffusion probabilistic model (DDPM) and a graph neural network (GNN) to overcome these limitations. By treating each channel as a graph node and building edges based on Euclidean proximity, the GNN explicitly models the spatial arrangement of DAS sensors, allowing the network to capture local interchannel dependencies. The conditional DDPM uses iterative denoising to model the temporal dynamics of standard signals, enabling the system to detect deviations without the need for anomalies. The performance evaluations based on real-world DAS datasets reveal that GraphDiffusion achieves 98.2% and 98.0% based on the area under the curve (AUC) of the F1-score at K different levels (F1_K-AUC), an AUC of receiver operating characteristic (ROC) at K different levels (ROC_K-AUC), outperforming other comparative models.

We report the feasibility of an OFDR system that is capable of high-speed distributed strain measurement for real-time health monitoring of concrete structures. When a concrete column was shaken by a seismic wave, the strain amplitude changes and frequency response were measured with a data comparison between the OFDR and strain gauge.