arXiv subjects

Eunji Lee

Publications and source records attributed to Eunji Lee.

5 recordsLinked to original sources

arXiv2026

Geometry resolved atomic oxygen risk assessment for very low earth orbit spacecraft

Atomic oxygen (AO) is a major durability concern for spacecraft in very low Earth orbit (VLEO), yet orbit-averaged fluence does not resolve exposure on individual surfaces and internal components. This study develops a geometry-resolved AO assessment by coupling NRLMSISE-00, HWM07, and SYSTEMA ATOMOX. One-year simulations were performed for a 350 km circular Sun-synchronous orbit at LTAN 06:00 and 12:00 using a baseline spacecraft, a wedge-modified body, and two synthetic aperture radar antenna sub-arrays. The LTAN 12:00 orbit produced 8-10% higher orbit-averaged AO flux than LTAN 06:00. For the baseline geometry, the ram-facing surface accumulated 6.9-7.5 x 10^21 atoms/cm^2, whereas side and zenith/nadir surfaces received only 3-5% of the ram fluence. Material-specific erosion yields changed the component-level risk ranking: the CFRP zenith panel was predicted to erode by 15.1-16.2 um/year despite receiving much lower fluence than the ram-facing multilayer insulation. The wedge generated approximately one order of magnitude spatial variation through local shielding. Housing openings also allowed AO to reach internal printed circuit boards, with maximum annual fluences of 9.5 x 10^16 and 4.0 x 10^19 atoms/cm^2 in the H- and V-polarized antenna models, respectively. HWM07 winds produced 10-20% side-panel asymmetry, which decreased below 1% when winds were disabled. Comparison with MISSE-8 reproduced the measured zenith-to-ram ratio of approximately 4% but underpredicted wake exposure, identifying a limitation of ballistic ray tracing. These results demonstrate that VLEO AO durability requires coupled consideration of orbit, atmospheric winds, geometry, and material response.

physics.optics↗

arXiv2024

Habaek: High-performance water segmentation through dataset expansion and inductive bias optimization

Water segmentation is critical to disaster response and water resource management. Authorities may employ high-resolution photography to monitor rivers, lakes, and reservoirs, allowing for more proactive management in agriculture, industry, and conservation. Deep learning has improved flood monitoring by allowing models like CNNs, U-Nets, and transformers to handle large volumes of satellite and aerial data. However, these models usually have significant processing requirements, limiting their usage in real-time applications. This research proposes upgrading the SegFormer model for water segmentation by data augmentation with datasets such as ADE20K and RIWA to boost generalization. We examine how inductive bias affects attention-based models and discover that SegFormer performs better on bigger datasets. To further demonstrate the function of data augmentation, Low-Rank Adaptation (LoRA) is used to lower processing complexity while preserving accuracy. We show that the suggested Habaek model outperforms current models in segmentation, with an Intersection over Union (IoU) ranging from 0.91986 to 0.94397. In terms of F1-score, recall, accuracy, and precision, Habaek performs better than rival models, indicating its potential for real-world applications. This study highlights the need to enhance structures and include datasets for effective water segmentation.

cs.CV↗

arXiv2023

Deep-learning-driven end-to-end metalens imaging

Recent advances in metasurface lenses (metalenses) have shown great potential for opening a new era in compact imaging, photography, light detection and ranging (LiDAR), and virtual reality/augmented reality (VR/AR) applications. However, the fundamental trade-off between broadband focusing efficiency and operating bandwidth limits the performance of broadband metalenses, resulting in chromatic aberration, angular aberration, and a relatively low efficiency. In this study, a deep-learning-based image restoration framework is proposed to overcome these limitations and realize end-to-end metalens imaging, thereby achieving aberration-free full-color imaging for mass-produced metalenses with 10-mm diameter. Neural-network-assisted metalens imaging achieved a high resolution comparable to that of the ground truth image.

physics.optics↗

arXiv2023

Explainable Product Classification for Customs

The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most likely subheadings (i.e., the first six digits) of the HS code. The model also provides reasoning for its suggestion in the form of a document that is interpretable by customs officers. We evaluated the model using 5,000 cases that recently received a classification request. The results showed that the top-3 suggestions made by our model had an accuracy of 93.9\% when classifying 925 challenging subheadings. A user study with 32 customs experts further confirmed that our algorithmic suggestions accompanied by explainable reasonings, can substantially reduce the time and effort taken by customs officers for classification reviews.

cs.AI↗

arXiv2021

Classification of Goods Using Text Descriptions With Sentences Retrieval

The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office. This decision is crucial to importers and exporters, as it determines the tariff rate. However, similar to court decisions made by judges, the task can be non-trivial even for experienced customs officers. The current paper proposes a deep learning model to assist this seemingly challenging HS code classification. Together with Korea Customs Service, we built a decision model based on KoELECTRA that suggests the most likely heading and subheadings (i.e., the first four and six digits) of the HS code. Evaluation on 129,084 past cases shows that the top-3 suggestions made by our model have an accuracy of 95.5% in classifying 265 subheadings. This promising result implies algorithms may reduce the time and effort taken by customs officers substantially by assisting the HS code classification task.

cs.AI↗