arXiv subjects

Hansol Yoon

Publications and source records attributed to Hansol Yoon.

3 recordsLinked to original sources

arXiv2026

Numerical-aperture transfer in holotomography with a deterministic diffusion prior

High-throughput holotomography often relies on long-working-distance, multiwell-compatible optics that reduce illumination numerical aperture (NA) and limit access to high spatial frequencies. Here we present ResShift-ODE, a deterministic diffusion-prior framework that transfers low-NA refractive-index (RI) tomograms to high-NA-equivalent volumes without modifying the acquisition hardware. We formulate low-NA-to-high-NA transfer as a diffusion-prior inverse problem under an explicit NA-limited Fourier-domain forward operator, distinct from suppressing artifacts within an already measured passband. The method extends residual-shifting diffusion to volumetric RI data and reformulates the reverse process as a probability-flow ordinary differential equation, enabling reproducible inference in five denoiser evaluations. On held-out emulated-pair test volumes, inferred volumes matched high-NA references with RI errors of 0.002-0.003 for >99% of voxels, while Fourier analysis confirmed measurement-anchored lateral-band recovery without filling the axial missing cone. 3D ResShift-ODE required five denoiser evaluations per volume, incurring ~5.8x the cost of a 3D U-Net while remaining ~166x faster than a 1000-step 3D denoising diffusion probabilistic model.

physics.optics↗

arXiv2022

Mathematical Models of Human Drivers Using Artificial Risk Fields

In this paper, we use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations. A risk field assigns a non-negative risk measure to the state of the system in order to model how close that state is to violating a safety property, such as hitting an obstacle or exiting the road. Using risk fields, we construct a stochastic model of the operator that maps from states to likely actions. We demonstrate our approach on a driving task wherein human subjects are asked to drive a car inside a realistic driving simulator while avoiding obstacles placed on the road. We show that the most likely risk field given the driving data is obtained by solving a convex optimization problem. Next, we apply the inferred risk fields to generate distinct driving behaviors while comparing predicted trajectories against ground truth measurements. We observe that the risk fields are excellent at predicting future trajectory distributions with high prediction accuracy for up to twenty seconds prediction horizons. At the same time, we observe some challenges such as the inability to account for how drivers choose to accelerate/decelerate based on the road conditions.

cs.LG↗

arXiv2021

Predictive Runtime Monitoring for Mobile Robots using Logic-Based Bayesian Intent Inference

We propose a predictive runtime monitoring framework that forecasts the distribution of future positions of mobile robots in order to detect and avoid impending property violations such as collisions with obstacles or other agents. Our approach uses a restricted class of temporal logic formulas to represent the likely intentions of the agents along with a combination of temporal logic-based optimal cost path planning and Bayesian inference to compute the probability of these intents given the current trajectory of the robot. First, we construct a large but finite hypothesis space of possible intents represented as temporal logic formulas whose atomic propositions are derived from a detailed map of the robot's workspace. Next, our approach uses real-time observations of the robot's position to update a distribution over temporal logic formulae that represent its likely intent. This is performed by using a combination of optimal cost path planning and a Boltzmann noisy rationality model. In this manner, we construct a Bayesian approach to evaluating the posterior probability of various hypotheses given the observed states and actions of the robot. Finally, we predict the future position of the robot by drawing posterior predictive samples using a Monte-Carlo method. We evaluate our framework using two different trajectory datasets that contain multiple scenarios implementing various tasks. The results show that our method can predict future positions precisely and efficiently, so that the computation time for generating a prediction is a tiny fraction of the overall time horizon.

cs.RO↗