arXiv subjects

Wonjun Ko

Publications and source records attributed to Wonjun Ko.

7 recordsLinked to original sources

arXiv2026

Test-Beam Performance of the AstroPix Silicon Sensor for Imaging Calorimetry

AstroPix is a high-voltage CMOS HVCMOS monolithic active pixel sensor MAPS developed for future space-based gamma-ray missions. It is also a candidate technology for the imaging layer of the Barrel Imaging Calorimeter BIC in the ePIC experiment at the future Electron-Ion Collider EIC. We report the first AstroPix test-beam results obtained at the KEK Photon Factory Advanced Ring PF-AR and the CERN Proton Synchrotron PS T10 beam line in 2025, using the third prototype AstroPix-v3. AstroPix-v3 sensors were operated as both standalone tracking layers and imaging layers interleaved with prototype lead/scintillating-fiber Pb/SciFi calorimeter modules, using electron and hadron beams in the few-GeV/c momentum range. Event synchronization between the continuous readout of AstroPix-v3 and the trigger-based readout of the Pb/SciFi calorimeter was achieved using a common timestamp. The AstroPix-v3 sensors exhibit stable performance, reaching a maximum hit efficiency of 68 percent at a bias voltage of -400 V under pion-dominated beam conditions. When combined with the Pb/SciFi calorimeter, the AstroPix layers successfully capture the development of electromagnetic showers. Using Cherenkov-based particle identification, electron-induced events exhibit significantly higher hit multiplicities and broader spatial distributions than pion-induced events, thereby providing clear discrimination between electromagnetic and hadronic showers. These results demonstrate that AstroPix-v3 provides effective, high-granularity imaging of shower development and is well suited as an imaging layer in future calorimeter systems for both collider and space-based experiments.

physics.ins-det↗

arXiv2026

Beam test of a Pb/SciFi prototype for the Barrel Imaging Calorimeter at the Electron-Ion Collider

A Lead-Scintillating Fiber (Pb/SciFi) prototype for the Barrel Imaging Calorimeter (BIC) at the Electron--Ion Collider (EIC) was tested with electron beams at the CERN PS T10 beam line in August 2024. The prototype consisted of unit modules with a sampling structure of lead sheets and scintillating fibers, corresponding to a total depth of approximately $10.9\,X_{0}$. Beam tests were performed with electron momenta between 0.5 and 3~GeV/$c$ to evaluate the energy and timing performance of the prototype. This study characterizes the performance of a Pb/SciFi prototype and provides input for future beam tests, calibration and readout optimization, and the development of larger-scale prototypes.

physics.ins-det↗

arXiv2022

TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging

Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine learning as well as deep learning architectures for sleep staging. However, two key challenges hinder the practical use of these architectures: effectively capturing salient waveforms in sleep signals and correctly classifying confusing stages in transitioning epochs. In this study, we propose a novel deep neural network structure, TransSleep, that captures distinctive local temporal patterns and distinguishes confusing stages using two auxiliary tasks. In particular, TransSleep adopts an attention-based multi-scale feature extractor module to capture salient waveforms; a stage-confusion estimator module with a novel auxiliary task, epoch-level stage classification, to estimate confidence scores for identifying confusing stages; and a context encoder module with the other novel auxiliary task, stage-transition detection, to represent contextual relationships across neighboring epochs. Results show that TransSleep achieves promising performance in automatic sleep staging. The validity of TransSleep is demonstrated by its state-of-the-art performance on two publicly available datasets, Sleep-EDF and MASS. Furthermore, we performed ablations to analyze our results from different perspectives. Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep learning-based sleep staging.

cs.LG↗

arXiv2021

Deep Efficient Continuous Manifold Learning for Time Series Modeling

Modeling non-Euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields. Particularly, a symmetric positive definite matrix is being actively studied in computer vision, signal processing, and medical image analysis, due to its ability to learn beneficial statistical representations. However, owing to its rigid constraints, it remains challenging to optimization problems and inefficient computational costs, especially, when incorporating it with a deep learning framework. In this paper, we propose a framework to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space, by which it becomes feasible not only to efficiently solve optimization problems but also to greatly reduce computation costs. Further, for dynamic modeling of time-series data, we devise a continuous manifold learning method by systematically integrating a manifold ordinary differential equation and a gated recurrent neural network. It is worth noting that due to the nice parameterization of matrices in a Cholesky space, training our proposed network equipped with Riemannian geometric metrics is straightforward. We demonstrate through experiments over regular and irregular time-series datasets that our proposed model can be efficiently and reliably trained and outperforms existing manifold methods and state-of-the-art methods in various time-series tasks.

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arXiv2020

A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs

In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods. To be specific, we devise an actor-critic network such that an agent can determine which timepoints need to be used (informative) or discarded (uninformative) in composing the intention-related features in a given trial, and thus enhancing the intention identification performance. To validate the effectiveness of our proposed method, we conducted experiments with a publicly available big MI dataset and applied our novel mechanism to various recent deep-learning architectures designed for MI classification. Based on the exhaustive experiments, we observed that our proposed method helped achieve statistically significant improvements in performance.

eess.SP↗

arXiv2020

Multi-Scale Neural network for EEG Representation Learning in BCI

Recent advances in deep learning have had a methodological and practical impact on brain-computer interface research. Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely connected layers for classification. However, studies in neurophysiology have revealed that EEG signals carry information in different ranges of frequency components. To better reflect these multi-frequency properties in EEGs, we propose a novel deep multi-scale neural network that discovers feature representations in multiple frequency/time ranges and extracts relationships among electrodes, i.e., spatial representations, for subject intention/condition identification. Furthermore, by completely representing EEG signals with spatio-spectral-temporal information, the proposed method can be utilized for diverse paradigms in both active and passive BCIs, contrary to existing methods that are primarily focused on single-paradigm BCIs. To demonstrate the validity of our proposed method, we conducted experiments on various paradigms of active/passive BCI datasets. Our experimental results demonstrated that the proposed method achieved performance improvements when judged against comparable state-of-the-art methods. Additionally, we analyzed the proposed method using different techniques, such as PSD curves and relevance score inspection to validate the multi-scale EEG signal information capturing ability, activation pattern maps for investigating the learned spatial filters, and t-SNE plotting for visualizing represented features. Finally, we also demonstrated our method's application to real-world problems.

eess.SP↗

arXiv2019

Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI

In recent years, deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no concern of its practical use, hampered by time-consuming steps and a large data requirement. To this end, recent studies adopted a transfer learning strategy, especially domain adaptation techniques. Among those, to our knowledge, an adversarial learning has shown its potential in BCIs. In the meantime, it is known that adversarial learning-based domain adaptation methods are prone to negative transfer that disrupts learning generalized feature representations, applicable to diverse domains, e.g., subjects or sessions in BCIs. In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning. To be specific, we devise two operational components in a deep network that explicitly estimate mutual information between feature representations; (1) to decompose features in an intermediate layer into class-relevant and class-irrelevant ones, (2) to enrich class-discriminative feature representation. On two large EEG datasets, we validated the effectiveness of our proposed framework by comparing with several comparative methods in performance. Further, we conducted rigorous analyses by performing an ablation study in regard to the components in our network, explaining our model's decision on input EEG signals via layer-wise relevance propagation, and visualizing the distribution of learned features via t-SNE.

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