arXiv subjects

Junho Song

Publications and source records attributed to Junho Song.

14 recordsLinked to original sources

arXiv2026

RECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals

Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online personalization by updating the model on incoming samples. But in a stream, a basic question is left open: \emph{which samples should drive each update?} Using all buffered samples blurs the update with irrelevant segments. Using only the latest segment makes the update noisy and unstable. The most useful samples are recent, aligned with the current physiological state, and reliable enough to learn from. We propose \textbf{RECAST} (REcent \& Context-Aware Sampling for TTA), a lightweight sampling module for buffered TTA frameworks. RECAST builds each adaptation batch from three signals: temporal recency, contextual similarity, and predictive reliability. It changes only which samples are used, leaving the model and the training objective unchanged. On two blood-pressure datasets, RECAST improves estimation accuracy and trend tracking over baselines and ablations. The per-patient gains are statistically significant on both datasets, with broad improvement on the regular benchmark and gains concentrated on the hardest patients in the emergency-department setting. RECAST stays practical, adding only sub-second latency per segment on a single GPU and CPU core.

cs.AI↗

arXiv2026

Constructing a Hydrogen Line Library for Ly{\alpha} Emitters at Low Redshifts (z < 0.4): Estimating Dust Extinction and Assessing Paschen line Detectability with SPHEREx

Hydrogen recombination lines, one of the strongest emission lines from star-forming galaxies, are used to probe the early Universe as indicators of star formation rates and ionizing photon production rates. Ratios between different recombination lines provide clues to estimate dust attenuation, in addition to the H II region physical diagnostics. To prepare for future near-infrared spectral surveys and optical narrow-band imaging surveys aiming for hydrogen lines in different redshifts, we construct hydrogen recombination line libraries by compiling data from published literature. Our compilation includes 260 galaxies mostly at z < 0.4, of which at least one hydrogen recombination line is observed. The specific hydrogen emission lines under investigation encompass three Balmer lines (H{\alpha}, H\b{eta} and H{\gamma}) and Ly{\alpha} line. Of the compiled galaxies, we estimated dust extinction for 85 galaxies that have detections of at least two Balmer lines, by assuming the Case B recombination and Calzetti extinction curve, based on which Ly{\alpha} escape fraction is also inferred. For each of the 14 galaxies with detections of all three Balmer lines, we optimized the extinction curve so that it yields consistent E(B-V) values across the three possible Balmer line ratios, showing the variety in the slope of the dust attenuation curve. We further evaluated the feasibility of studying hydrogen emission line-selected galaxies with future spectral surveys such as SPHEREx by modeling the Paschen lines of our galaxy sample.

astro-ph.GA↗

arXiv2026

Reference-state System Reliability method for scalable uncertainty quantification of coherent systems

Coherent systems are representative of many practical applications, ranging from infrastructure networks to supply chains. Probabilistic evaluation of such systems remains challenging, however, because existing decomposition-based methods scale poorly as the number of components grows. To address this limitation, this study proposes the Reference-state System Reliability (RSR) method. Like existing approaches, RSR characterises the boundary between different system states using reference states in the component-state space. Where it departs from these methods is in how the state space is explored: rather than using reference states to decompose the space into disjoint hypercubes, RSR uses them to classify Monte Carlo samples, making computational cost significantly less sensitive to the number of reference states. To make this classification efficient, samples and reference states are stored as matrices and compared using batched matrix operations, allowing RSR to exploit the advances in high-throughput matrix computing driven by modern machine learning. We demonstrate that RSR evaluates the system-state probability of a graph with 119 nodes and 295 edges within 10~seconds, highlighting its potential for real-time risk assessment of large-scale systems. We further show that RSR scales to problems involving hundreds of thousands of reference states -- well beyond the reach of existing methods -- and extends naturally to multi-state systems. Nevertheless, when the number of boundary reference states grows exceedingly large, RSR's convergence slows down, a limitation shared with existing reference-state-based approaches that motivates future research into learning-based representations of system-state boundaries.

cs.LG↗

arXiv2025

CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics

Recognizing the need for explainable AI (XAI) approaches to enable the successful integration of AI-based ECG prediction models (AI-ECG) into clinical practice, we introduce a framework generating \textbf{Co}unter\textbf{F}actual \textbf{E}CGs (i,e., named CoFE) to illustrate how specific features, such as amplitudes and intervals, influence the model's predictive decisions. To demonstrate the applicability of the CoFE, we present two case studies: atrial fibrillation classification and potassium level regression models. The CoFE reveals feature changes in ECG signals that align with the established clinical knowledge. By clarifying both \textbf{where valid features appear} in the ECG and \textbf{how they influence the model's predictions}, we anticipate that our framework will enhance the interpretability of AI-ECG models and support more effective clinical decision-making. Our demonstration video is available at: https://www.youtube.com/watch?v=YoW0bNBPglQ.

cs.AI↗

arXiv2025

Frequency Response Data-Driven Disturbance Observer Design for Flexible Joint Robots

Motion control of flexible joint robots (FJR) is challenged by inherent flexibility and configuration-dependent variations in system dynamics. While disturbance observers (DOB) can enhance system robustness, their performance is often limited by the elasticity of the joints and the variations in system parameters, which leads to a conservative design of the DOB. This paper presents a novel frequency response function (FRF)-based optimization method aimed at improving DOB performance, even in the presence of flexibility and system variability. The proposed method maximizes control bandwidth and effectively suppresses vibrations, thus enhancing overall system performance. Closed-loop stability is rigorously proven using the Nyquist stability criterion. Experimental validation on a FJR demonstrates that the proposed approach significantly improves robustness and motion performance, even under conditions of joint flexibility and system variation.

cs.RO↗

arXiv2025

Possibility of using 3.3$\mu$m PAH luminosity as a molecular gas mass estimator

We present CO(1-0) observations of 50 star-forming galaxies at 0.01<z<0.35, for which 3.3$\,\mu$m PAH emission flux or its upper limit is available. A scaling relation between 3.3$\,\mu$m PAH luminosity and CO(1-0) luminosity is established covering ~2 orders of magnitude in total IR luminosity and CO luminosity, with a scatter of ~0.23 dex: $\mathrm{log}\,L_\mathrm{3.3}/\mathrm{L}_\odot=(1.00\pm0.07)\times\mathrm{log}\,L_\mathrm{CO(1-0)}^\prime/(\mathrm{K\,km\,s^{-1}\,pc^2})+(-1.10\pm0.70)$. The slope is near unity, allowing the use of a single value of $\langle\mathrm{log}\,(L_\mathrm{3.3}/L_\mathrm{CO(1-0)}^\prime)\rangle=-1.09\pm0.36~[\mathrm{L}_\odot/(\mathrm{K\,km\,s^{-1}\,pc^2})]$ in the conversion between 3.3$\,\mu$m PAH and CO luminosities. The variation in the $L_\mathrm{3.3}/L_\mathrm{CO}^\prime$ ratio is not dependent on the galaxy properties, including total IR luminosity, stellar mass, and SFR excess. The total gas mass, estimated using dust-to-gas ratio and dust mass, is correlated with 3.3$\,\mu$m PAH luminosity, in line with the prescription using $\alpha_\mathrm{CO}=0.8-4.5$ covering both normal star-forming galaxies and starburst galaxies. AGN-dominated galaxies tend to have a lower $L_\mathrm{3.3}/L_\mathrm{CO}^\prime$ than non-AGN galaxies, which needs to be investigated further with an increased sample size. The established $L_\mathrm{3.3}$-$L_\mathrm{CO}^\prime$ correlation is expected to be applicable to wide-field near-infrared spectrophotometric surveys that allow the detection of 3.3$\,\mu$m emission from numerous low-redshift galaxies.

astro-ph.GA↗

arXiv2024

Deep learning-based modularized loading protocol for parameter estimation of Bouc-Wen class models

This study proposes a modularized deep learning-based loading protocol for optimal parameter estimation of Bouc-Wen (BW) class models. The protocol consists of two key components: optimal loading history construction and CNN-based rapid parameter estimation. Each component is decomposed into independent sub-modules tailored to distinct hysteretic behaviors-basic hysteresis, structural degradation, and pinching effect-making the protocol adaptable to diverse hysteresis models. Three independent CNN architectures are developed to capture the path-dependent nature of these hysteretic behaviors. By training these CNN architectures on diverse loading histories, minimal loading sequences, termed \textit{loading history modules}, are identified and then combined to construct an optimal loading history. The three CNN models, trained on the respective loading history modules, serve as rapid parameter estimators. Numerical evaluation of the protocol, including nonlinear time history analysis of a 3-story steel moment frame and fragility curve construction for a 3-story reinforced concrete frame, demonstrates that the proposed protocol significantly reduces total analysis time while maintaining or improving estimation accuracy. The proposed protocol can be extended to other hysteresis models, suggesting a systematic approach for identifying general hysteresis models.

cs.LG↗

arXiv2024

CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains

Electrocardiogram (ECG) diagnosis remains challenging due to limited labeled data and the need to capture subtle yet clinically meaningful variations in rhythm and morphology. We present CREMA (Contrastive Regularized Masked Autoencoder), a foundation model for 12-lead ECGs designed to learn generalizable representations through self-supervised pretraining. CREMA combines generative learning and contrastive regularization via a Contrastive Regularized MAE loss, and employs a Signal Transformer (SiT) architecture to capture both local waveform details and global temporal dependencies. We evaluate CREMA on benchmark datasets and real-world clinical environments, including deployment scenarios with significant distribution shifts. CREMA outperforms supervised baselines and existing self-supervised models in both linear probing and fine-tuning evaluations. Notably, it maintains superior performance across diverse clinical domains, such as emergency care, highlighting its robustness under real-world conditions. These results demonstrate that CREMA serves as a scalable and reliable foundation model for ECG diagnostics, supporting downstream applications across heterogeneous and high-risk clinical settings.

cs.LG↗

arXiv2023

MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection

Detecting anomalies in real-world multivariate time series data is challenging due to complex temporal dependencies and inter-variable correlations. Recently, reconstruction-based deep models have been widely used to solve the problem. However, these methods still suffer from an over-generalization issue and fail to deliver consistently high performance. To address this issue, we propose the MEMTO, a memory-guided Transformer using a reconstruction-based approach. It is designed to incorporate a novel memory module that can learn the degree to which each memory item should be updated in response to the input data. To stabilize the training procedure, we use a two-phase training paradigm which involves using K-means clustering for initializing memory items. Additionally, we introduce a bi-dimensional deviation-based detection criterion that calculates anomaly scores considering both input space and latent space. We evaluate our proposed method on five real-world datasets from diverse domains, and it achieves an average anomaly detection F1-score of 95.74%, significantly outperforming the previous state-of-the-art methods. We also conduct extensive experiments to empirically validate the effectiveness of our proposed model's key components.

cs.LG↗

arXiv2023

Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, a community's seismic safety and resilience are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by discovering an implicit connection between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.

stat.AP↗

arXiv2023

Adaptive active subspace-based metamodeling for high-dimensional reliability analysis

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is leveraged to identify low-dimensional salient features of a high-dimensional computational model. A surrogate computational model is built in the low-dimensional feature space by a heteroscedastic Gaussian process. Active learning adaptively guides the surrogate model training toward the critical region that significantly contributes to the failure probability. A critical trait of the proposed method is that the three main ingredients-active subspace, heteroscedastic Gaussian process, and active learning-are coupled to adaptively optimize the feature space mapping in conjunction with the surrogate modeling. This coupling empowers the proposed method to accurately solve nontrivial high-dimensional reliability problems via low-dimensional surrogate modeling. Finally, numerical examples of a high-dimensional nonlinear function and structural engineering applications are investigated to verify the performance of the proposed method.

stat.AP↗

arXiv2021

Optimal Inspection of Network Systems via Value of Information Analysis

This paper develops computable metrics to assign priorities for information collection on network systems made up by binary components. Components are worth inspecting because their condition state is uncertain and the system functioning depends on it. The Value of Information (VoI) allows assessing the impact of information in decision making under uncertainty, including the precision of the observation, the available actions and the expected economic loss. Some VoI-based metrics for system-level and component-level maintenance actions, defined as "global" and "local" metrics, respectively, are introduced, analyzed and applied to series and parallel systems. Their computationally complexity of applications to general networks is discussed and, to tame the complexity for the local metric assessment, a heuristic is presented and its performance is compared on some case studies.

math.OC↗

arXiv2020

Probabilistic Performance-Pattern Decomposition (PPPD): analysis framework and applications to stochastic mechanical systems

Since the early 1900s, numerous research efforts have been devoted to developing quantitative solutions to stochastic mechanical systems. In general, the problem is perceived as solved when a complete or partial probabilistic description on the quantity of interest (QoI) is determined. However, in the presence of complex system behavior, there is a critical need to go beyond mere probabilistic descriptions. In fact, to gain a full understanding of the system, it is crucial to extract physical characterizations from the probabilistic structure of the QoI, especially when the QoI solution is obtained in a data-driven fashion. Motivated by this perspective, the paper proposes a framework to obtain structuralized characterizations on behaviors of stochastic systems. The framework is named Probabilistic Performance-Pattern Decomposition (PPPD). PPPD analysis aims to decompose complex response behaviors, conditional to a prescribed performance state, into meaningful patterns in the space of system responses, and to investigate how the patterns are triggered in the space of basic random variables. To illustrate the application of PPPD, the paper studies three numerical examples: 1) an illustrative example with hypothetical stochastic processes input and output; 2) a stochastic Lorenz system with periodic as well as chaotic behaviors; and 3) a simplified shear-building model subjected to a stochastic ground motion excitation.

stat.ML↗

arXiv2018

Hamiltonian Monte Carlo Methods for Subset Simulation in Reliability Analysis

This paper studies a non-random-walk Markov Chain Monte Carlo method, namely the Hamiltonian Monte Carlo (HMC) method in the context of Subset Simulation used for structural reliability analysis. The HMC method relies on a deterministic mechanism inspired by Hamiltonian dynamics to propose samples following a target probability distribution. The method alleviates the random walk behavior to achieve a more effective and consistent exploration of the probability space compared to standard Gibbs or Metropolis-Hastings techniques. After a brief review of the basic concepts of the HMC method and its computational details, two algorithms are proposed to facilitate the application of the HMC method to Subset Simulation in structural reliability analysis. Next, the behavior of the two HMC algorithms is illustrated using simple probability distribution models. Finally, the accuracy and efficiency of Subset Simulation employing the two HMC algorithms are tested using various reliability examples. The supporting source code and data are available for download at (the URL that will become available once the paper is accepted).

stat.CO↗