arXiv subjects

Wonhee Lee

Publications and source records attributed to Wonhee Lee.

5 recordsLinked to original sources

arXiv2026

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source. Despite its importance, most publicly available AIS datasets lack predefined anomaly labels, forcing prior studies to rely on either distribution-based rarity or domain rule/expert-assisted labeling. These approaches, however, face fundamental limitations: statistical rarity often fails to reflect practically critical events, while expert-based labeling is costly, subjective, and difficult to scale. Moreover, both paradigms tend to overlook interaction-driven hazards such as near-miss approaches between vessels. To address these challenges, we propose an equation-grounded anomaly taxonomy that is implementable under a limited AIS observation schema and extensible to other AIS datasets. Specifically, the taxonomy defines three anomaly types: unexpected AIS activity (A1), route deviation (A2), and close approach (A3), covering both single-vessel and inter-vessel anomalies. Building on this taxonomy, we introduce a unified score-synthesize-label pipeline that produces LLM-guided plausibility scores, uses them to synthesize anomalies, and assigns timestamp-level labels. To rigorously assess detection performance, we further design benchmark evaluation settings that account for variations in temporal-window length and anomaly-type composition, and evaluate a broad range of time-series models and anomaly detection models. Together, these contributions provide a systematic basis for evaluating maritime anomaly detection methods across different anomaly types. Our code is available at https://github.com/snudial/open-maritime-anomaly-detection.

cs.LG↗

arXiv2025

Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies

Graph neural networks have emerged as a powerful tool for learning spatiotemporal interactions. However, conventional approaches often rely on predefined graphs, which may obscure the precise relationships being modeled. Additionally, existing methods typically define nodes based on fixed spatial locations, a strategy that is ill-suited for dynamic environments like maritime environments. Our method introduces an innovative graph representation where timestamps are modeled as distinct nodes, allowing temporal dependencies to be explicitly captured through graph edges. This setup is extended to construct a multi-ship graph that effectively captures spatial interactions while preserving graph sparsity. The graph is processed using Graph Convolutional Network layers to capture spatiotemporal patterns, with a forecasting layer for feature prediction and a Variational Graph Autoencoder for reconstruction, enabling robust anomaly detection.

cs.LG↗

arXiv2019

Development of high-sensitivity chip calorimeters for cellular metabolic heat sensing

Cellular metabolic rate is a good indicator of the physiological state of cells and its changes, which can be measured by total heat flux accompanying metabolism. Chip calorimeters can provide label-free and high throughput measurements of cellular metabolic rate, however, lack of high power resolution and microfluidic sample handling capability has been preventing their wide applications. We report high-resolution chip calorimeters integrated with thin-film parylene microfluidics, which can reliably measure metabolic heat from mammalian cells with controlled stimuli. The molding and bonding technique allowed fast and reliable parylene microfluidic channel fabrications and highly sensitive vanadium oxide thermistor enabled temperature resolution as small as ~ 15 μK, which led to a three-orders-of-magnitude improvement in volume specific power resolutions. Measurements of metabolic heat were successfully demonstrated with adherent and nonadherent cells. We expect the chip calorimeter will provide a universal platform for fundamental cell-biology studies and biomedical applications including cell-based assay for drug discovery.

physics.bio-ph↗

arXiv2019

High throughput, automated prediction of focusing patterns for inertial microfluidics

Visual inspections for identifying focusing points in inertial microfluidic flows are prone to misinterpreting stable locations and focusing shifts in the case of non-trivial focusing patterns. We develop and deploy an approach for automating the calculation of focusing patterns for a general channel geometry, and thereby reduce the dependence on empirical/visual procedures to confirm the presence of stable locations. We utilize concepts from interpolation theory (to represent continuous force-fields using discrete points), and stability theory to identify "basins of attraction" and quantitatively identify stable equilibrium points. Our computational experiments reveal that predicting equilibrium points accurately requires upto $\times$10-20 times more refined force-maps that conventionally used, which highlights the spatial resolution required for an accurate representation of cross-sectional forces. These focusing patterns are validated using experimental results for a rectangular channel, and triangular channel with an apex angle of $90^\circ$. We then apply the approach to predict and explain focusing patterns and shifts for a $90^\circ$-isosceles triangular channel across a range of Reynolds numbers for $\frac{a}{H} = 0.4$ (particle-to-channel size ratio). We observe that the predicted focusing patterns match experiments well. The force-maps also reveal certain "clouds" of localized stable points, which aid in explaining the onset of bifurcation observed in experiments. The current algorithm is agnostic to channel cross-sections and straight/curved channels, which could pave way to generating a library of focusing patterns as a function of channel geometry, and $Re$, to assist in design of novel devices for tailored particle-streams.

physics.flu-dyn↗

arXiv2015

Remote sensing of pressure inside deformable microchannels using light scattering in Scotch tape

We present a simple but effective method to measure the pressure inside a deformable micro-channel using laser scattering in a translucent Scotch tape. Our idea exploits the fact that the speckle pattern generated by a turbid layer is sensitive to the changes in an optical wavefront of an impinging beam. A change in the internal pressure of a channel deforms the elastic channel, which can be detected by measuring speckle patterns of a coherent laser that has passed through the channel and the Scotch tape. We demonstrate that internal pressure can be remotely sensed with the resolution below 0.1 kPa within a pressure range of 3 kPa after calibration. With its high sensitivity, reproducibility, and easy applicability, the present method will find direct and diverse applications.

physics.optics↗