arXiv subjects

Youngjae Jeon

Publications and source records attributed to Youngjae Jeon.

4 recordsLinked to original sources

arXiv2026

Aharonov-Casher-induced electric quadrupole of charge-neutral particles

For charged particles, their orbital angular momentum (OAM) in solid have a direct magnetic manifestation as an orbital magnetization. For charge-neutral particles, however, the physical manifestation of the OAM in solid remains unclear. Here, we show that a charge-neutral particle carrying a magnetic moment couples to electric-field gradient through the Aharonov-Casher (AC) effect, thereby exhibiting the electric quadrupole in crystalline solids. This AC-induced electric quadrupole (AC-EQ) contains the scalar, toroidal dipole, and reduced quadrupole components, which are conjugate to the divergence, circulation, and shear of the electric field, respectively. As representative examples, we calculate the AC-EQ of magnons in two magnetic systems. In ferromagnetic pyrochlore, Dzyaloshinskii-Moriya interaction (DMI) induces a sizable AC-EQ, whereas in helical Fe langasite the AC-EQ emerges from the helical spin configuration even in the absence of DMI.

cond-mat.mes-hall↗

arXiv2026

Fine-Grained Open-Set Fault Diagnosis via Metric-Guided Time-Frequency Configuration Selection and Class-Specific Autoencoders

Reliable fault diagnosis of rotating machinery is essential for the safe and stable operation of industrial systems. Although deep learning methods perform well under closed-set conditions, real machinery may encounter previously unseen fault states. Existing open-set fault diagnosis (OSFD) methods remain limited in fine-grained severity diagnosis because they often rely on coarse type levels, heuristically selected Short-Time Fourier Transform (STFT) settings, and global class boundaries. We propose a fine-grained OSFD method that combines metric-guided data-centric (MGDC) STFT configuration selection with class-specific autoencoder (CSAE)-based anomaly rejection. MGDC screens candidate STFT configurations using the Silhouette score computed from spectrogram representations, identifying promising time-frequency representations before network training. The diagnostic model then uses a bank of CSAEs to learn compact class-specific manifolds for known degradation states. During inference, reconstruction-error-based class affinity identifies known classes, while a dual-criteria mechanism based on latent dimension-wise boundaries and class-specific reconstruction error rejects unknown samples. Experiments on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method achieves H-scores of 0.9924 and 0.9509 for fine-grained fault severity diagnosis. MGDC also identifies the best-performing configuration found by exhaustive search while evaluating only 9 of 38 candidates on CWRU and 2 of 39 candidates on PU, reducing the selection cost by factors of 5.69 and 29.87, respectively. These results indicate that the proposed method supports accurate open-set severity diagnosis with substantially lower configuration-selection cost.

eess.SP↗

arXiv2026

Comparing the orbital angular momentum and magnetic moment of magnons in the Kagome antiferromagnet with negative spin chirality

The orbital dynamics of magnons have recently drawn interest due to their potential roles in thermal and orbital transport phenomena in magnetic insulators. In this study, we investigate the orbital magnetic moment (OMM) and orbital angular momentum (OAM) of magnons in a Kagome antiferromagnet with negative vector chirality, focusing on the distinction between thermodynamic and wave-packet-based definitions. We compute the Berry curvature, the OMM, and the OAM in momentum space under an external magnetic field. Our results reveal a quantitative difference between the OMM and OAM, yet their associated Nernst coefficients exhibit similar temperature and field dependence in transport. Our results provide a quantitative comparison between the thermodynamic and wave-packet formulations of magnon orbital dynamics.

cond-mat.mes-hall↗

arXiv2025

A Robust Method for Fault Detection and Severity Estimation in Mechanical Vibration Data

This paper proposes a robust method for fault detection and severity estimation in multivariate time-series data to enhance predictive maintenance of mechanical systems. We use the Temporal Graph Convolutional Network (T-GCN) model to capture both spatial and temporal dependencies among variables. This enables accurate future state predictions under varying operational conditions. To address the challenge of fluctuating anomaly scores that reduce fault severity estimation accuracy, we introduce a novel fault severity index based on the mean and standard deviation of anomaly scores. This generates a continuous and reliable severity measurement. We validate the proposed method using two experimental datasets: an open IMS bearing dataset and data collected from a fanjet electric propulsion system. Results demonstrate that our method significantly reduces abrupt fluctuations and inconsistencies in anomaly scores. This provides a more dependable foundation for maintenance planning and risk management in safety-critical applications.

eess.SY↗