Wonjong Kim

Feature selection is essential for improving classification performance and reducing overfitting in high-dimensional learning tasks. However, conventional importance-based methods often suffer from instability, model bias, and sensitivity to threshold settings. To address these limitations, we propose EFSHB (Ensemble Feature Selection using Hierarchical Binning), a hybrid ensemble framework that integrates importance-based sorting, bin-level greedy evaluation, iterative hierarchical refinement, and union-based integration of model-wise selected features. At each iteration, five tree-based models independently perform bin-wise greedy selection, and their selected subsets are merged through a union operation to form the feature set for the next iteration. This iterative process progressively refines the feature space while mitigating model-specific bias and promoting robust predictive performance across heterogeneous models. EFSHB was evaluated on nine high-dimensional benchmark datasets, including biomedical gene-expression, synthetic, proteomics, and speech-feature data. Across all datasets, EFSHB achieved the highest or near-highest classification accuracy, outperforming traditional Greedy Feature Selection (GFS), binning-based GFS (GFSB), and hierarchical binning GFS (GFSHB). On average, EFSHB improved accuracy for all classifiers, achieving mean gains of 14.0% over GFS and 13.3% over GFSHB. EFSHB also provided balanced feature reduction by avoiding excessive feature retention while preserving complementary informative features identified across models. In terms of computational efficiency, EFSHB reduced average feature selection time from 266 min (GFS) to 11 min, corresponding to a 24-fold speed-up. These results demonstrate that EFSHB achieves robust predictive performance and high computational efficiency, making it suitable for diverse high-dimensional applications.

Urban mobility systems increasingly depend on remote sensing and artificial intelligence to enhance traffic monitoring and safety management. This study presents a LiDAR-based framework for urban road condition analysis and risk evaluation using vehicle-mounted sensors as dynamic remote sensing platforms. The framework integrates deep learning based object detection with mathematically defined surrogate safety indicators to quantify collision risk and evaluate evasive maneuverability in real traffic environments. Two indicators, Hazardous Modified Time to Collision (HMTTC) and Searching for Safety Space (SSS), are introduced to assess lane-level safety and spatial availability of avoidance zones. LiDAR point cloud data are processed using a Voxel RCNN architecture and converted into parameters such as density, speed, and spacing. Field experiments conducted on highways and urban corridors in South Korea reveal strong correlations between HMTTC occurrences, congestion, and geometric road features. The results demonstrate that AI-driven analysis of LiDAR data enables continuous, infrastructure-independent urban traffic safety monitoring, thereby supporting data-driven, resilient transportation systems.

The field of digital healthcare is rapidly evolving due to advancements in artificial intelligence technologies, particularly deep learning. This study aims to explore new possibilities in telemedicine by applying mixed reality (MR) technology to emergency medical situations. The primary goal of this research is to develop an MR device-based remote medical support system that utilizes MR devices and high-speed wireless communication to transmit onsite conditions to medical control rooms, enabling emergency responders to provide treatment under remote guidance. The system leverages Microsoft's HoloLens 2 to capture and transmit patient biometric information and surrounding environments, utilizing deep learning algorithms for human skeleton recognition and object alignment. The generated 3D avatars and content are then used in remote medical consultations. This article highlights the potential medical applications of HoloLens 2, a cutting-edge consumer electronics device, demonstrating how this system can provide innovative medical solutions through consumer technology and open up new possibilities in the healthcare field.

This paper presents an efficient schedule method for maintenance, repair, and overhaul (MRO) tasks for aircraft engines using a constraint programming algorithm. Using data obtained from Korean Air’s MRO maintenance logs, we analyze and predict the optimal scheduling of regular inspections and fault repairs for various engine types. By proposing a proper modeling of the problem and preparing data for the constraint programming algorithm, we demonstrate superior performance in scheduling efficiency and resource utilization. The experimental results show an average utilization of 99.35%, and the method can even achieve 100% utilization in some cases.