arXiv subjects

Jung Ho Park

Publications and source records attributed to Jung Ho Park.

10 recordsLinked to original sources

arXiv2026

Aircraft and Fleet Sizing for Regional Air Mobility: College Town Case Studies

We examine how aircraft seat configuration interacts with daily operation in Regional Air Mobility by applying a joint supply-demand optimization framework that simultaneously determines market share, fare, and flight schedule. The framework integrates a binary logit discrete choice model into a task assignment formulation, capturing passengers' mode choice between Regional Air Mobility and driving across spatiotemporal origin-destination pairs. We evaluate three U.S. college town corridors under 4-, 6-, and 8-seat configurations across cost scales from 0.4 to 1.0 and fleet sizes from 12 to 30 aircraft. Profitability and throughput serve as primary performance metrics, and we analyze pricing power, operating cost, and revenue to explain performance variation across markets. We find that larger aircraft configurations and fleet sizes do not improve profitability universally. Larger aircraft are preferred where economies of scale are favorable and demand is sufficient and directionally balanced. The best configuration in these case studies is the 4-seat in imbalanced markets and the 6-seat in balanced or dense markets.

eess.SY↗

arXiv2026

Dynamic Lane Allocation in UAM Corridors for Efficient Multimodal Door-to-Door Mobility

This article presents dynamic directional lane allocation in urban air mobility (UAM) corridors as a discrete-time mixed-integer linear program (MILP). This formulation activates, deactivates, and reverses lane direction as bi-directional airspace demand evolves. We model demand from disaggregate ground travel data by decomposing each trip into a multi-modal sequence with first-, middle-, and last-mile legs and routing the UAM-served middle-mile segment through a vertiport-side dispatch model. We use the San Francisco Bay Area as a case study by placing a multi-region spanning corridor between Contra Costa county and Silicon Valley. We find that the dynamic policy cuts unused airspace capacity by 5x, increases mean lane utilization from 36-48% to 67% at the same service level relative to baselines, and reduces commuting-population mean travel time by up to 21.6%. These results show that dynamic configuration of airspace capacity alleviates a significant percentage of the under-utilization issue of lane-based UAM airspace design and UAM concept of operations. This dynamic allocation also provides a safe, structural way to increase throughput, making UAM a more viable complement to multimodal door-to-door mobility systems.

eess.SY↗

arXiv2026

RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matching, which controls the holding intervals for batched sets of requests and vehicles, reveals an inherent trade-off between matching and pickup delays. The resulting environment with temporally varying request arrival patterns and dynamic congestion calls for more expressive networks with sufficient capacity to capture their non-stationarity. To address the limitations of existing methods that rely on shallow encoders that cannot capture dynamic supply-demand patterns and congestion effects, we introduce the Regime-Aware Spatio-Temporal Mixture-of-Experts (RAST-MoE) framework, which formalizes adaptive delayed matching as a regime-aware Markov Decision Process and equips RL agents with a self-attention MoE encoder. Instead of relying on a single monolithic network, our design allows different experts to specialize automatically in varying operational conditions, improving representation capacity while maintaining per-sample computation efficiency. Despite its modest size of only 12M parameters, our framework consistently outperforms strong baselines. On real-world Uber trajectory data from San Francisco, it reduces average matching delay by 10%, and pickup delay by 15%. In addition, it demonstrates robustness to unseen demand regimes, stable training behavior without reward hacking, and expert specialization to different regimes. This study shows the strength of MoE-enhanced RL for large-scale decision-making tasks with complex spatiotemporal dynamics.

cs.LG↗

arXiv2025

Weighted Multi-Prompt Learning with Description-free Large Language Model Distillation

Recent advances in pre-trained Vision Language Models (VLM) have shown promising potential for effectively adapting to downstream tasks through prompt learning, without the need for additional annotated paired datasets. To supplement the text information in VLM trained on correlations with vision data, new approaches leveraging Large Language Models (LLM) in prompts have been proposed, enhancing robustness to unseen and diverse data. Existing methods typically extract text-based responses (i.e., descriptions) from LLM to incorporate into prompts; however, this approach suffers from high variability and low reliability. In this work, we propose Description-free Multi-prompt Learning(DeMul), a novel method that eliminates the process of extracting descriptions and instead directly distills knowledge from LLM into prompts. By adopting a description-free approach, prompts can encapsulate richer semantics while still being represented as continuous vectors for optimization, thereby eliminating the need for discrete pre-defined templates. Additionally, in a multi-prompt setting, we empirically demonstrate the potential of prompt weighting in reflecting the importance of different prompts during training. Experimental results show that our approach achieves superior performance across 11 recognition datasets.

cs.LG↗

arXiv2024

Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering

When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and unified information about users. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement of the performance.

cs.IR↗

arXiv2023

Development of a thorium coating on an aluminium substrate by using electrodeposition method and alpha spectroscopy

A thin coating of thorium on aluminium substrates with the areal density of 110 to 130 $μg/cm^2$ is developed over a circular area of 22 mm diameter by using the electrodeposition method. An electrodeposition system is fabricated to consist of three components; an anode made of a platinum mesh, a cylindrical-shape vessel to contain the thorium solution, and a cathode in the form of a circular aluminium plate. The aluminium plate is mounted horizontally, and the platinum mesh is connected to an axial rod of an electric motor, mounted vertically and normal to the plane of the aluminium. The electrolyte solution is prepared by dissolving a known-weight thorium nitrate powder in 0.8 M HNO3 and isopropanol. The system is operated either in constant voltage (CV) or constant current (CC) mode. Under the electric field between the anode and cathode, thorium ions were deposited on the aluminium substrate mounted on the cathode. In the CV mode at 320, 360, and 400 V and in the CC mode at 15 mA, thorium films were formed over a circular area of the aluminium substrate. The areal density of thorium coating was measured by detecting emitted alpha particles. The areal density of thorium varied from 80 to 130 $μg/cm^2$ by changing the deposition time from 10 to 60 min. The results from the CV mode and CC mode are compared, and the radial dependence in the measured areal density is discussed for different modes of the electric field. The developed thorium coatings are to be used in the in-house development of particle detectors, fast neutron converters, targets for thorium fission experiments, and other purposes.

physics.ins-det↗

arXiv2022

Atlas flow : compatible local structures on the manifold

In this paper, we focus on the intersections of a manifold's local structures to analyze the global structure of a manifold. We obtain local regions on data manifolds such as the latent space of StyleGAN2, using Mapper, a tool from topological data analysis. We impose gluing compatibility conditions on overlapping local regions, which guarantee that the local structures can be glued together to the global structure of a manifold. We propose a novel generative flow model called Atlas flow that uses compatibility to reattach the local regions. Our model shows that the generating processes perform well on synthetic dataset samples of well-known manifolds with noise. Furthermore, we investigate the style vector manifold of StyleGAN2 using our model.

cs.CV↗

arXiv2022

Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs

The discovery of the disentanglement properties of the latent space in GANs motivated a lot of research to find the semantically meaningful directions on it. In this paper, we suggest that the disentanglement property is closely related to the geometry of the latent space. In this regard, we propose an unsupervised method for finding the semantic-factorizing directions on the intermediate latent space of GANs based on the local geometry. Intuitively, our proposed method, called Local Basis, finds the principal variation of the latent space in the neighborhood of the base latent variable. Experimental results show that the local principal variation corresponds to the semantic factorization and traversing along it provides strong robustness to image traversal. Moreover, we suggest an explanation for the limited success in finding the global traversal directions in the latent space, especially W-space of StyleGAN2. We show that W-space is warped globally by comparing the local geometry, discovered from Local Basis, through the metric on Grassmannian Manifold. The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.

cs.CV↗

arXiv2014

Localization of small perfectly conducting cracks from far-field pattern with unknown frequency

In inverse scattering problem, it is well-known that subspace migration yields very accurate locations of small perfectly conducting cracks when applied frequency is known. In contrast, when applied frequency is unknown, inaccurate locations are identified via subspace migration with wrong frequency data. However, this fact has been examined through the experimental results so, the reason of such phenomenon has not been theoretically investigated. In this paper, we analyze mathematical structure of subspace migration with unknown frequency by establishing a relationship with Bessel function of order zero of the first kind. Identified structure of subspace migration and corresponding results of numerical simulation answer that why subspace migration with unknown frequency yields inaccurate location of cracks and gives an idea of improvement.

math.NA↗

arXiv2014

Nanopatterning by Laser Interference Lithography: Applications to Optical Devices

A systematic review, covering fabrication of nanoscale patterns by laser interference lithography (LIL) and their applications for optical devices are provided. LIL is a patterning method with simple, quick process over a large area without using a mask. LIL is a powerful technique for the definition of large-area, nanometer-scale, periodically patterned structures. Patterns are recorded in a light-sensitive medium that responds nonlinearly to the intensity distribution associated with the interference of two or more coherent beams of light. The photoresist patterns produced with LIL are the platform for further fabrication of nanostructures and growth of functional materials which are the building blocks for devices. Demonstration of optical and photonic devices by LIL is reviewed such as directed nano photonics and surface plasmon resonance (SPR) or large area membrane reflectors and anti-reflectors. Perspective on future directions for LIL and emerging applications in other fields are presented.

cond-mat.mes-hall↗