arXiv subjects

Heejung Kim

Publications and source records attributed to Heejung Kim.

4 recordsLinked to original sources

arXiv2026

FISICA: A Deployed Service for Plantar-Pressure and Posture Assessment with Ontology-Grounded Recommendation

FISICA is a body-assessment and recommendation service running in production. One standing session with two photographs returns foot-loading measures, posture coordinates, a driven 3D avatar, a visual report, and ranked shoe and exercise candidates. Measurement comes from a purpose-built scale carrying 634 force-sensitive elements on a 1 cm grid and four load cells, and a rule-based evaluator controls every recommendation while a language model only explains the stored result. The method contribution is the avatar. Instead of mapping a measured angle onto a rig through a tuned gain, we measure the avatar with the same function used on the subject and solve until the two agree, on a sampling-invariant spinal metric that separated a normal from a kyphotic record by 7.2 degrees against 0.9 degrees for a single-joint formulation. In production, general APIs respond at a 0.023 s median, plantar-pressure analysis at 0.45 s, and recommendation at 2.16 s to 2.26 s with the rule-based portion under one second in every trial. The served keypoint graph reaches 0.960 PCK@0.2 on public data, and the catalog holds 699 shoes with 10,500 typed facts. An approved study supplies the radiographic reference for the validation still ahead.

cs.CV↗

arXiv2025

Inverse Design of Optimal Stern Shape with Convolutional Neural Network-based Pressure Distribution

Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the resistance and propulsion efficiency meet the design requirements. In this study, the designer designed a pressure distribution that meets the design requirements; this paper proposes an inverse design algorithm that estimates the stern shape using deep learning. A convolutional neural network was used to extract the features of the pressure distribution expressed as a contour, whereas a multi-task learning model was used to estimate various sections of the stern shape. We estimated the stern shape indirectly by estimating the control point of the B-spline and comparing the actual and converted offsets for each section; the performance was verified, and an inverse design is proposed herein

cs.LG↗

arXiv2024

Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

Purpose: We aimed to develop a data-driven multiomics approach integrating radiomics, dosiomics, and delta features to predict treatment response at an earlier stage (intra-treatment) for brain metastases (BMs) patients treated with PULSAR. Methods: We conducted a retrospective study of 39 patients with 69 BMs treated with PULSAR. Radiomics, dosiomics, and delta features were extracted from pretreatment and intra-treatment MRI scans and dose distributions. Six individual models and an ensemble feature selection (EFS) model were constructed using SVM and evaluated via stratified 5-fold cross-validation. The classification task distinguished lesions with >20% volume reduction at follow-up. We assessed performance metrics including sensitivity, specificity, accuracy, precision, F1 score, and AUC. Various feature extraction and ensemble selection scenarios were explored to enhance model robustness and reduce overfitting. Results: The EFS model, integrating features from pre-treatment radiomics and dosiomics, intra-treatment radiomics, and delta-radiomics, outperformed six individual models. It achieved an AUC of 0.979, accuracy of 0.917, and F1 score of 0.821. Among the top 9 features, six were derived from post-wavelet transformation, and three from original images. The discrete wavelet transform decomposes volumetric images into multi-resolution components, offering a more comprehensive characterization of underlying structures. Conclusion: Our study demonstrated the feasibility of employing a data-driven multiomics approach to predict tumor volume changes in BMs patients with PULSAR. The EFS model demonstrates enhanced performance compared with six individual models, emphasizing the importance of integrating both pretreatment and intra-treatment data. This application holds promise for optimizing BMs management, potentially mitigating risks associated with under- or over-treatment.

physics.med-ph↗

arXiv2023

Superconductivity of metastable dihydrides at ambient pressure

Hydrogen in metals is a significant research area with far-reaching implications, encompassing diverse fields such as hydrogen storage, metal-insulator transitions, and the recently emerging phenomenon of room-temperature ($\textit{$T_C$}$) superconductivity under high pressure. Hydrogen atoms pose challenges in experiments as they are nearly invisible, and they are considered within ideal crystalline structures in theoretical predictions, which hampers research on the formation of meta-stable hydrides. Here, we propose pressure-induced hydrogen migration from tetrahedral site ($\textit{T}$) to octahedral site ($\textit{O}$),forming $LaH_x^OH_{2-x}^{T}$ in cubic $LaH^2$.Under decompression, it retains $H_x^O$ occupancy, and is dynamically stable even at ambient pressure, enabling a synthesis route of metastable dihydrides via compression-decompression process. We predict that the electron phonon coupling strength of $LaH_x^OH_{2-x}^{T}$ is enhanced with increasing $\textit{x}$, and the associated $\textit{$T_C$}$ reaches up to 10.8 $\textit{K}$ at ambient pressure. Furthermore, we calculated stoichiometric hydrogen migration threshold pressure ($\textit{$P_C$}$) for various lanthanides dihydrides ($\textit{R}$$H_2$, where $\textit{R}$=Y, Sc, Nd, and Lu), and found an inversely linear relation between $\textit{$P_C$}$ and ionic radii of $\textit{R}$. We propose that the highest $\textit{$T_C$}$ in the face-centered-cubic dihydride system can be realized by optimizing the $\textit{O}$/$\textit{T}$-site occupancies.

cond-mat.supr-con↗