arXiv subjects

Jee Won Lee

Publications and source records attributed to Jee Won Lee.

At least 19 recordsLinked to original sources

arXiv2026

VISKY: Virtual Inertia Skyhook Control for Semi-Active Suspension Systems Using Magnetorheological Dampers

This paper presents a Virtual Inertia Skyhook (VISKY) controller for magnetorheological (MR) dampers in semi-active suspensions. The proposed law is derived from a continuous sky-ground damping baseline augmented with acceleration feedback on the sprung and unsprung masses. In the closed-loop equations, these acceleration terms appear as a mass-like virtual inertia matrix rather than as a change in physical hardware. This interpretation motivates the VISKY name while making the underlying sky-ground hybrid structure explicit. Numerical evaluations under half-sine bump, representative ISO 8608 random-road and while-acceleration metrics relative to conventional Skygroundhook, with the largest gains appearing near the wheel-hop mode. The controller retains low computational overhead because it requires only algebraic force computation and bounded MR inversion.

eess.SY↗

arXiv2026

VEGA: Electric Vehicle Navigation Agent via Physics-Informed Neural Operator and Proximal Policy Optimization

We present VEGA, a vehicle-adaptive energy-aware routing system for electric vehicles (EVs) that integrates physics-informed parameter estimation with RL-based charge-aware path planning. VEGA consists of two copupled modules: (1) a physics-informed neural operator (PINO) that estimates vehicle-specific physical parameters-drag, rolling resistance, mass, motor and regenerative-braking efficiencies, and auxiliary load-from short windows of onboard speed and acceleration data; (2) a Proximal Policy Optimization (PPO) agent that navigates a charger-annotated road graph, jointly selecting routes and charging stops under state-of-charge constraints. The agent is initialized via behavior cloning from an A* teacher and fine-tuned with cirriculum-guided PPO on the full U.S. highway network with Tesla Supercharger locations. On a cross-country San Francisco-to-New York route (~4,860km), VEGA produces a feasible 20-stop plan with 56.12h total trip time and minimum SoC 11.41%. Against the controlled Energy-aware A* baseline, the distance and driving-time gaps are small (-8.49km and +0.37h), while inference is >20x faster. The learned policy generalizes without retraining to road networks in France and Japan.

cs.RO↗

arXiv2026

TIDI-GS: Floater Suppression in 3D Gaussian Splatting for Enhanced Indoor Scene Fidelity

3D Gaussian Splatting (3DGS) is a technique to create high-quality, real-time 3D scenes from images. This method often produces visual artifacts known as floaters--nearly transparent, disconnected elements that drift in space away from the actual surface. This geometric inaccuracy undermines the reliability of these models for practical applications, which is critical. To address this issue, we introduce TIDI-GS, a new training framework designed to eliminate these floaters. A key benefit of our approach is that it functions as a lightweight plugin for the standard 3DGS pipeline, requiring no major architectural changes and adding minimal overhead to the training process. The core of our method is a floater pruning algorithm--TIDI--that identifies and removes floaters based on several criteria: their consistency across multiple viewpoints, their spatial relationship to other elements, and an importance score learned during training. The framework includes a mechanism to preserve fine details, ensuring that important high-frequency elements are not mistakenly removed. This targeted cleanup is supported by a monocular depth-based loss function that helps improve the overall geometric structure of the scene. Our experiments demonstrate that TIDI-GS improves both the perceptual quality and geometric integrity of reconstructions, transforming them into robust digital assets, suitable for high-fidelity applications.

cs.GR↗

arXiv2026

SARA: Scene-Aware Reconstruction Accelerator

We present SARA (Scene-Aware Reconstruction Accelerator), a geometry-driven pair selection module for Structure-from-Motion (SfM). Unlike conventional pipelines that select pairs based on visual similarity alone, SARA introduces geometry-first pair selection by scoring reconstruction informativeness - the product of overlap and parallax - before expensive matching. A lightweight pre-matching stage uses mutual nearest neighbors and RANSAC to estimate these cues, then constructs an Information-Weighted Spanning Tree (IWST) augmented with targeted edges for loop closure, long-baseline anchors, and weak-view reinforcement. Compared to exhaustive matching, SARA reduces rotation errors by 46.5+-5.5% and translation errors by 12.5+-6.5% across modern learned detectors, while achieving at most 50x speedup through 98% pair reduction (from 30,848 to 580 pairs). This reduces matching complexity from quadratic to quasi-linear, maintaining within +-3% of baseline reconstruction metrics for 3D Gaussian Splatting and SVRaster.

cs.CV↗

arXiv2025

LiteVoxel: Low-memory Intelligent Thresholding for Efficient Voxel Rasterization

Sparse-voxel rasterization is a fast, differentiable alternative for optimization-based scene reconstruction, but it tends to underfit low-frequency content, depends on brittle pruning heuristics, and can overgrow in ways that inflate VRAM. We introduce LiteVoxel, a self-tuning training pipeline that makes SV rasterization both steadier and lighter. Our loss is made low-frequency aware via an inverse-Sobel reweighting with a mid-training gamma-ramp, shifting gradient budget to flat regions only after geometry stabilize. Adaptation replaces fixed thresholds with a depth-quantile pruning logic on maximum blending weight, stabilized by EMA-hysteresis guards and refines structure through ray-footprint-based, priority-driven subdivision under an explicit growth budget. Ablations and full-system results across Mip-NeRF 360 (6scenes) and Tanks & Temples (3scenes) datasets show mitigation of errors in low-frequency regions and boundary instability while keeping PSNR/SSIM, training time, and FPS comparable to a strong SVRaster pipeline. Crucially, LiteVoxel reduces peak VRAM by ~40%-60% and preserves low-frequency detail that prior setups miss, enabling more predictable, memory-efficient training without sacrificing perceptual quality.

cs.CV↗

arXiv2025

Hybrid Vision Servoing with Depp Alignment and GRU-Based Occlusion Recovery

Vision-based control systems, such as image-based visual servoing (IBVS), have been extensively explored for precise robot manipulation. A persistent challenge, however, is maintaining robust target tracking under partial or full occlusions. Classical methods like Lucas-Kanade (LK) offer lightweight tracking but are fragile to occlusion and drift, while deep learning-based approaches often require continuous visibility and intensive computation. To address these gaps, we propose a hybrid visual tracking framework that bridges advanced perception with real-time servo control. First, a fast global template matcher constrains the pose search region; next, a deep-feature Lucas-Kanade module operating on early VGG layers refines alignment to sub-pixel accuracy (<2px); then, a lightweight residual regressor corrects local misalignments caused by texture degradation or partial occlusion. When visual confidence falls below a threshold, a GRU-based predictor seamlessly extrapolates pose updates from recent motion history. Crucially, the pipeline's final outputs-translation, rotation, and scale deltas-are packaged as direct control signals for 30Hz image-based servo loops. Evaluated on handheld video sequences with up to 90% occlusion, our system sustains under 2px tracking error, demonstrating the robustness and low-latency precision essential for reliable real-world robot vision applications.

cs.RO↗

arXiv2025

Micro-splatting: Multistage Isotropy-informed Covariance Regularization Optimization for High-Fidelity 3D Gaussian Splatting

High-fidelity 3D Gaussian Splatting methods excel at capturing fine textures but often overlook model compactness, resulting in massive splat counts, bloated memory, long training, and complex post-processing. We present Micro-Splatting: Two-Stage Adaptive Growth and Refinement, a unified, in-training pipeline that preserves visual detail while drastically reducing model complexity without any post-processing or auxiliary neural modules. In Stage I (Growth), we introduce a trace-based covariance regularization to maintain near-isotropic Gaussians, mitigating low-pass filtering in high-frequency regions and improving spherical-harmonic color fitting. We then apply gradient-guided adaptive densification that subdivides splats only in visually complex regions, leaving smooth areas sparse. In Stage II (Refinement), we prune low-impact splats using a simple opacity-scale importance score and merge redundant neighbors via lightweight spatial and feature thresholds, producing a lean yet detail-rich model. On four object-centric benchmarks, Micro-Splatting reduces splat count and model size by up to 60% and shortens training by 20%, while matching or surpassing state-of-the-art PSNR, SSIM, and LPIPS in real-time rendering. These results demonstrate that Micro-Splatting delivers both compactness and high fidelity in a single, efficient, end-to-end framework.

cs.GR↗

arXiv2025

A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators

We present a hybrid surrogate model for electric vehicle parameter estimation and power consumption. We combine our novel architecture Spectral Parameter Operator built on a Fourier Neural Operator backbone for global context and a differentiable physics module in the forward pass. From speed and acceleration alone, it outputs time-varying motor and regenerative braking efficiencies, as well as aerodynamic drag, rolling resistance, effective mass, and auxiliary power. These parameters drive a physics-embedded estimate of battery power, eliminating any separate physics-residual loss. The modular design lets representations converge to physically meaningful parameters that reflect the current state and condition of the vehicle. We evaluate on real-world logs from a Tesla Model 3, Tesla Model S, and the Kia EV9. The surrogate achieves a mean absolute error of 0.2kW (about 1% of average traction power at highway speeds) for Tesla vehicles and about 0.8kW on the Kia EV9. The framework is interpretable, and it generalizes well to unseen conditions, and sampling rates, making it practical for path optimization, eco-routing, on-board diagnostics, and prognostics health management.

cs.LG↗

arXiv2025

MATT-GS: Masked Attention-based 3DGS for Robot Perception and Object Detection

This paper presents a novel masked attention-based 3D Gaussian Splatting (3DGS) approach to enhance robotic perception and object detection in industrial and smart factory environments. U2-Net is employed for background removal to isolate target objects from raw images, thereby minimizing clutter and ensuring that the model processes only relevant data. Additionally, a Sobel filter-based attention mechanism is integrated into the 3DGS framework to enhance fine details - capturing critical features such as screws, wires, and intricate textures essential for high-precision tasks. We validate our approach using quantitative metrics, including L1 loss, SSIM, PSNR, comparing the performance of the background-removed and attention-incorporated 3DGS model against the ground truth images and the original 3DGS training baseline. The results demonstrate significant improves in visual fidelity and detail preservation, highlighting the effectiveness of our method in enhancing robotic vision for object recognition and manipulation in complex industrial settings.

cs.GR↗

arXiv2024

EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamics

An onboard prediction of dynamic parameters (e.g. Aerodynamic drag, rolling resistance) enables accurate path planning for EVs. This paper presents EV-PINN, a Physics-Informed Neural Network approach in predicting instantaneous battery power and cumulative energy consumption during cruising while generalizing to the nonlinear dynamics of an EV. Our method learns real-world parameters such as motor efficiency, regenerative braking efficiency, vehicle mass, coefficient of aerodynamic drag, and coefficient of rolling resistance using automatic differentiation based on dynamics and ensures consistency with ground truth vehicle data. EV-PINN was validated using 15 and 35 minutes of in-situ battery log data from the Tesla Model 3 Long Range and Tesla Model S, respectively. With only vehicle speed and time as inputs, our model achieves high accuracy and generalization to dynamics, with validation losses of 0.002195 and 0.002292, respectively. This demonstrates EV-PINN's effectiveness in estimating parameters and predicting battery usage under actual driving conditions without the need for additional sensors.

cs.LG↗

arXiv2024

A study of the radio spectrum of Mrk 421

We present the results of a spectral analysis using simultaneous multifrequency (22, 43, 86, and 129 GHz) very long baseline interferometry (VLBI) observations of the Korean VLBI Network (KVN) on BL Lac object, Markarian 421 (Mrk 421). The data we used was obtained from January 2013 to June 2018. The light curves showed several flux enhancements with global decreases. To separate the variable and quiescent components in the multifrequency light curves for milliarcsecond-scale emission regions, we assumed that the quiescent radiation comes from the emission regions radiating constant optically-thin synchrotron emissions (i.e., a minimum flux density with an optically thin spectral index). The quiescent spectrum determined from the multifrequency light curves was subtracted from the total CLEAN flux density, yielding a variable component in the flux that produces the time-dependent spectrum. We found that the observed spectra were flat at 22-43 GHz, and relatively steep at 43-86 GHz, whereas the quiescent-corrected spectra are sometimes quite different from the observed spectra (e.g., sometimes inverted at 22-43 GHz ). The quiescent-corrected spectral indices were much more variable than the observed spectral indices. This spectral investigation implies that the quiescent-spectrum correction can significantly affect the multifrequency spectral index of variable compact radio sources such as blazars. Therefore, the synchrotron self-absorption B-field strength (B_SSA) can be significantly affected because B_SSA is proportional to the fifth power of turnover frequency.

astro-ph.HE↗

arXiv2023

Precessing jet nozzle connecting to a spinning black hole in M87

The nearby radio galaxy M87 offers a unique opportunity to explore the connections between the central supermassive black hole and relativistic jets. Previous studies of the inner region of M87 revealed a wide opening angle for the jet originating near the black hole. The Event Horizon Telescope resolved the central radio source and found an asymmetric ring structure consistent with expectations from General Relativity. With a baseline of 17 years of observations, there was a shift in the jet's transverse position, possibly arising from an eight to ten-year quasi-periodicity. However, the origin of this sideways shift remains unclear. Here we report an analysis of radio observations over 22 years that suggests a period of about 11 years in the position angle variation of the jet. We infer that we are seeing a spinning black hole that induces the Lense-Thirring precession of a misaligned accretion disk. Similar jet precession may commonly occur in other active galactic nuclei but has been challenging to detect owing to the small magnitude and long period of the variation.

astro-ph.HE↗

arXiv2023

Double SSA Spectrum and Magnetic Field Strength of the FSRQ 3C 454.3

We present the results of a radio multi-frequency ($\rm 3-340~GHz$) study of the blazar 3C~454.3. After subtracting the quiescent spectrum corresponding to optically thin emission, we found two individual synchrotron self-absorption (SSA) features in the wide-band spectrum. The one SSA had a relatively low turnover frequency ($ν_{\rm m}$) in the range of $\rm 3-37~GHz$ (lower $ν_{\rm m}$ SSA spectrum, LSS), and the other one had a relatively high $ν_{\rm m}$ of $\rm 55-124~GHz$ (higher $ν_{\rm m}$ SSA spectrum, HSS). Using the SSA parameters, we estimated magnetic field strengths at the surface where optical depth $τ=1$. The estimated magnetic field strengths were $\rm >7~mG$ and $\rm >0.2~mG$ for the LSS and HSS, respectively. The LSS emitting region was magnetically dominated before the June 2014 $γ$-ray flare. The quasi-stationary component (C), $\sim 0.6~{\rm mas}$ apart from the 43 GHz radio core, became brighter than the core with decreasing observing frequency, and we found that component C was related to the LSS. A decrease in jet width was found near component C. As a moving component, K14 approached component C, and the flux density of the component was enhanced while the angular size decreased. The high intrinsic brightness temperature in the fluid frame was obtained as $T_{\rm B, int} \approx (7.0\pm1.0) \times 10^{11}~{\rm K}$ from the jet component after the 2015 August $γ$-ray flare, suggesting that component C is a high-energy emitting region. The observed local minimum of jet width and re-brightening behavior suggest a possible recollimation shock in component C.

astro-ph.HE↗

arXiv2021

The intrinsic structure of Sagittarius A* at 1.3 cm and 7 mm

Sagittarius A* (Sgr A*), the Galactic Center supermassive black hole (SMBH), is one of the best targets to resolve the innermost region of SMBH with very long baseline interferometry (VLBI). In this study, we have carried out observations toward Sgr A* at 1.349 cm (22.223 GHz) and 6.950 mm (43.135 GHz) with the East Asian VLBI Network, as a part of the multi-wavelength campaign of the Event Horizon Telescope (EHT) in 2017 April. To mitigate scattering effects, the physically motivated scattering kernel model from Psaltis et al. (2018) and the scattering parameters from Johnson et al. (2018) have been applied. As a result, a single, symmetric Gaussian model well describes the intrinsic structure of Sgr A* at both wavelengths. From closure amplitudes, the major-axis sizes are ~704$\pm$102 $μ$as (axial ratio $\sim$1.19$^{+0.24}_{-0.19}$) and $\sim$300$\pm$25 $μ$as (axial ratio $\sim$1.28$\pm$0.2) at 1.349 cm and 6.95 mm respectively. Together with a quasi-simultaneous observation at 3.5 mm (86 GHz) by Issaoun et al. (2019), we show that the intrinsic size scales with observing wavelength as a power-law, with an index $\sim$1.2$\pm$0.2. Our results also provide estimates of the size and compact flux density at 1.3 mm, which can be incorporated into the analysis of the EHT observations. In terms of the origin of radio emission, we have compared the intrinsic structures with the accretion flow scenario, especially the radiatively inefficient accretion flow based on the Keplerian shell model. With this, we show that a nonthermal electron population is necessary to reproduce the source sizes.

astro-ph.HE↗

arXiv2021

Magnetic Field Strengths of the Synchrotron Self-Absorption Region in the Jet of CTA 102 During Radio Flares

CTA 102 is a blazar implying that its relativistic jet points towards Earth and emits synchrotron radiation produced by energetic particles gyrating in the magnetic field. This study aims to figure out the physical origins of radio flares in the jet, including the connection between the magnetic field and the radio flares. The dataset in the range 2.6-343.5 GHz was collected over a period of 5.5 years (2012 November 20-2018 September 23). During the data collection period, seven flares at 15 GHz with a range of the variability time-scale of roughly 26-171 days were detected. The quasi-simultaneous radio data were used to investigate the synchrotron spectrum of the source. We found that the synchrotron radiation is self-absorbed. The turnover frequency and the peak flux density of the synchrotron self-absorption (SSA) spectra are in the ranges of 42-167 GHz and 0.9-10.2 Jy, respectively. From the SSA spectra, we derived the SSA magnetic field strengths to be 9.20 mG, 12.28 mG, and 50.97 mG on 2013 December 24, 2014 February 28, and 2018 January 13, respectively. We also derived the equipartition magnetic field strengths to be in the range 24-109 mG. The equipartition magnetic field strengths are larger than the SSA magnetic field strengths in most cases, which indicates that particle energy mainly dominates in the jet. Our results suggest that the flares in the jet of CTA 102 originated due to particle acceleration. We propose the possible mechanisms of particle acceleration.

astro-ph.HE↗

arXiv2021

East Asian VLBI Network Observations of Active Galactic Nuclei Jets: Imaging with KaVA+Tianma+Nanshan

The East Asian very-long-baseline interferometry (VLBI) Network (EAVN) is a rapidly evolving international VLBI array that is currently promoted under joint efforts among China, Japan, and Korea. EAVN aims at forming a joint VLBI Network by combining a large number of radio telescopes distributed over East Asian regions. After the combination of the Korean VLBI Network (KVN) and the VLBI Exploration of Radio Astrometry (VERA) into KaVA, further expansion with the joint array in East Asia is actively promoted. Here we report the first imaging results (at 22 and 43 GHz) of bright radio sources obtained with KaVA connected to Tianma 65-m and Nanshan 26-m Radio Telescopes in China. To test the EAVN imaging performance for different sources, we observed four active galactic nuclei (AGN) having different brightness and morphology. As a result, we confirmed that Tianma 65-m Radio Telescope (TMRT) significantly enhances the overall array sensitivity, a factor of 4 improvement in baseline sensitivity and 2 in image dynamic range compared to the case of KaVA only. The addition of Nanshan 26-m Radio Telescope (NSRT) further doubled the east-west angular resolution. With the resulting high-dynamic-range, high-resolution images with EAVN (KaVA+TMRT+NSRT), various fine-scale structures in our targets, such as the counter-jet in M87, a kink-like morphology of the 3C273 jet and the weak emission in other sources, are successfully detected. This demonstrates the powerful capability of EAVN to study AGN jets and to achieve other science goals in general. Ongoing expansion of EAVN will further enhance the angular resolution, detection sensitivity and frequency coverage of the network.

astro-ph.GA↗

arXiv2020

Interferometric Monitoring of Gamma-ray Bright AGNs: OJ 287

We present the results of simultaneous multi-frequency imaging observations at 22, 43, 86, and 129\,GHz of OJ\,287. We used the Korean VLBI Network as part of the Interferometric MOnitoring of GAmma-ray Bright active galactic nuclei (iMOGABA). The iMOGABA observations were performed during 31 epochs from 2013 January 16 to 2016 December 28. We also used 15\,GHz OVRO and 225\,GHz SMA flux density data. We analyzed four flux enhancements in the light curves. The estimated time scales of three flux enhancements were similar with time scales of $\sim$50 days at two frequencies. A fourth flux enhancement had a variability timescale approximately twice as long. We found that 225\,GHz enhancements led the 15\,GHz enhancements by a range of 7 to 30 days in the time delay analysis. We found the fractional variability did not change with frequency between 43 and 86\,GHz. We could reliably measure the turnover frequency, $ν_{\rm c}$, of the core of the source in three epochs. This was measured to be in a range from 27 to 50\,GHz and a flux density at the turnover frequency, $S_{\rm m}$, ranging from 3-6\,Jy. The derived SSA magnetic fields, $B_{\rm SSA}$, are in a range from $0.157\pm0.104$ to $0.255\pm0.146$ mG. We estimated the equipartition magnetic field strengths to be in a range from $0.95\pm0.15$ to $1.93\pm0.30$ mG. The equipartition magnetic field strengths are up to a factor of 10 higher than the values of $B_{\rm SSA}$. We conclude that the downstream jet may be more particle energy dominated.

astro-ph.GA↗

arXiv2019

Kinematics of the M87 jet in the collimation zone: gradual acceleration and velocity stratification

We study the kinematics of the M87 jet using the first year data of the KVN and VERA Array (KaVA) large program, which has densely monitored the jet at 22 and 43 GHz since 2016. We find that the apparent jet speeds generally increase from $\approx0.3c$ at $\approx0.5$ mas from the jet base to $\approx2.7c$ at $\approx20$ mas, indicating that the jet is accelerated from subluminal to superluminal speeds on these scales. We perform a complementary jet kinematic analysis by using archival Very Long Baseline Array monitoring data observed in $2005-2009$ at 1.7 GHz and find that the jet is moving at relativistic speeds up to $\approx5.8c$ at distances of $200-410$ mas. We combine the two kinematic results and find that the jet is gradually accelerated over a broad distance range that coincides with the jet collimation zone, implying that conversion of Poynting flux to kinetic energy flux takes place. If the jet emission consists of a single streamline, the observed trend of jet acceleration ($Γ\propto z^{0.16\pm0.01}$) is relatively slow compared to models of a highly magnetized jet. This indicates that Poynting flux conversion through the differential collimation of poloidal magnetic fields may be less efficient than expected. However, we find a non-negligible dispersion in the observed speeds for a given jet distance, making it difficult to describe the jet velocity field with a single power-law acceleration function. We discuss the possibility that the jet emission consists of multiple streamlines following different acceleration profiles, resulting in jet velocity stratification.

astro-ph.HE↗