arXiv subjects

Younghwi Kim

Publications and source records attributed to Younghwi Kim.

3 recordsLinked to original sources

arXiv2026

FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting

Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g., PLCs, microcontrollers) with strict constraints on latency and memory, limiting parameters to a few thousand. Conventional deep architectures are often impractical here. We propose the Fourier-Efficient Adaptive Temporal Hierarchy Forecaster (FEATHer) for accurate long-term forecasting under severe limits. FEATHer introduces: (i) ultra-lightweight multiscale decomposition into frequency pathways; (ii) a shared Dense Temporal Kernel using projection-depthwise convolution-projection without recurrence or attention; (iii) frequency-aware branch gating that adaptively fuses representations based on spectral characteristics; and (iv) a Sparse Period Kernel reconstructing outputs via period-wise downsampling to capture seasonality. FEATHer maintains a compact architecture (as few as 400 parameters) while outperforming baselines. Across eight benchmarks, it achieves the best ranking, recording 60 first-place results with an average rank of 2.05. These results demonstrate that reliable long-range forecasting is achievable on constrained edge hardware, offering a practical direction for industrial real-time inference.

cs.LG↗

arXiv2025

Legacy Learning Strategy Based on Few-Shot Font Generation Models for Automatic Text Design in Metaverse Content

The metaverse consists of hardware, software, and content, among which text design plays a critical role in enhancing user immersion and usability as a content element. However, in languages such as Korean and Chinese that require thousands of unique glyphs, creating new text designs involves high costs and complexity. To address this, this study proposes a training strategy called Legacy Learning, which recombines and transforms structures based on existing text design models. This approach enables the generation of new text designs and improves quality without manual design processes. To evaluate Legacy Learning, it was applied to Korean and Chinese text designs. Additionally, we compared results before and after on seven state of the art text generation models. As a result, text designs generated using Legacy Learning showed over a 30% difference in Frechet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) metrics compared to the originals, and also exhibited meaningful style variations in visual comparisons. Furthermore, the repeated learning process improved the structural consistency of the generated characters, and an OCR based evaluation showed increasing recognition accuracy across iterations, indicating improved legibility of the generated glyphs. In addition, a System Usability Scale (SUS) survey was conducted to evaluate usability among metaverse content designers and general users. The expert group recorded a score of 95.78 ("Best Imaginable"), while the non expert group scored 76.42 ("Excellent"), indicating an overall high level of usability. These results suggest that Legacy Learning can significantly improve both the production efficiency and quality of text design in the metaverse environment.

cs.HC↗

arXiv2025

Distributed Lag Transformer based on Time-Variable-Aware Learning for Explainable Multivariate Time Series Forecasting

Time series data is a key element of big data analytics, commonly found in domains such as finance, healthcare, climate forecasting, and transportation. In large scale real world settings, such data is often high dimensional and multivariate, requiring advanced forecasting methods that are both accurate and interpretable. Although Transformer based models perform well in multivariate time series forecasting (MTSF), their lack of explainability limits their use in critical applications. To overcome this, we propose Distributed Lag Transformer (DLFormer), a novel Transformer architecture for explainable and scalable MTSF. DLFormer integrates a distributed lag embedding and a time variable aware learning (TVAL) mechanism to structurally model both local and global temporal dependencies and explicitly capture the influence of past variables on future outcomes. Experiments on ten benchmark and real world datasets show that DLFormer achieves state of the art predictive accuracy while offering robust, interpretable insights into variable wise and temporal dynamics. These results highlight ability of DLFormer to bridge the gap between performance and explainability, making it highly suitable for practical big data forecasting tasks.

cs.LG↗