O‐Joun Lee

Meta-Cobi: A Metadata-Augmented Vision-Language Framework for Multi-view Canine Body Condition Scoring

Jae-Eon Ahn, Ju-Won Seo, Kang-Min Kim, O‐Joun Lee

kr (code pays fourni par la source)

Publisher's Note: “Artificial neural networks in semiconductor device compact modeling: A review” [Appl. Phys. Rev. 13 , 021327 (2026)]

Minsun Cho, Deokho Jang, O‐Joun Lee, Chang‐Hyun Kim et autres

Media Discourse and Child Maltreatment in Korea: A Semantic Network Analysis of Policy-Driven Narrative Shifts

Yoewon Yoon, Hee-Ju Kang, Juae Park, Jooho Lee et autres

Recent increases in child maltreatment cases in South Korea have prompted policy reforms, including the implementation of the Inclusive-State Child Policy to strengthen child protection and prevention systems. Despite these efforts, public concern remains high due to continued reports of severe abuse …

kr, qa (code pays fourni par la source)

Artificial neural networks in semiconductor device compact modeling: A review

Minsun Cho, Deokho Jang, O‐Joun Lee, Chang-Hyun Kim et autres

This review examines the use of artificial neural networks (ANNs) in the development of compact models for emerging devices. As new device technologies emerge, there is a critical need for compact models that are not only accurate and computationally efficient, but also …

kr, ca (code pays fourni par la source)

A multi-branch network for cooperative spectrum sensing via attention-based and CNN feature fusion

Doi Thi Lan, Quan T. Ngo, Luong Vuong Nguyen, O‐Joun Lee

In cognitive radio (CR) systems, the accurate detection of spectrum holes is a cornerstone for efficient spectrum utilization. However, the increasing complexity of CR environments, particularly those with multiple primary users (PUs), has made precise spectrum sensing a paramount challenge. To address …

vn, kr (code pays fourni par la source)

Cross-Context Topic Evolution and Sentiment Dynamics of AI Discourse on Weibo and Twitter

Jinxi Huang, Juncheng Yu, O‐Joun Lee

Public discourse on artificial intelligence (AI) has expanded rapidly across social media platforms. Investigating its long-term topic evolution and sentiment dynamics across different social contexts can provide critical insights and practical implications for understanding AI-related public opinion. This study proposes a cross-context …

kr (code pays fourni par la source)

Energy-Efficient Resource Allocation in O-RAN Using Soft Actor-Critic

Tri‐Hai Nguyen, Huy T. Nguyen, Vo Nguyen Quoc Bao, Thanh-Dat Le et autres

Deep Reinforcement Learning (DRL) has recently become a powerful tool for addressing complex optimization problems in mobile networks. Among DRL methods, Soft Actor-Critic (SAC), an off-policy algorithm, stands out due to its sample efficiency and ability to learn stable and resilient policies. …

vn, us, kr (code pays fourni par la source)

AI-assisted ultrasonic system for non-invasive glucose classification in whole blood

Jeong Eun Lee, Hyeon-Ju Jeon, Min-Seo Kim, O‐Joun Lee et autres

Abstract Diabetes mellitus is a chronic disorder characterized by persistent hyperglycemia that damages multiple organs. With global prevalence rising, accurate, convenient, and non-invasive glucose monitoring is urgently needed. However, current diagnostic methods—such as point-sample tests and continuous glucose monitoring (CGM)—remain limited by …

kr (code pays fourni par la source)

Ground Penetrating Radar Image Analysis for Underground Barrier Detection by Combining YOLOv12 with Channel-wise Attention and Denoising Auto-Encoder

Tien-Bach-Thanh Do, Jun-Hee Cho, Jin-Hyouk Park, Ki-Nam Kim et autres

Accurate detection of underground barriers such as pipelines is crucial for urban safety and infrastructure management. Ground Penetrating Radar (GPR) image provides a non-destructive means for subsurface exploration, but its B-scan images often contain strong noise and clutter that hinder reliable recognition. …

kr, ca, vn (code pays fourni par la source)

Constructing a Companion Animal Disease Knowledge Graph by Utilizing LLMs in Data Preprocessing and Pseudo Annotation

Thien Thi Thu Nguyen, Eun-Soon You, Hyun Woo Kim, Van Thuy Hoang et autres

Companion animal medical data are abundant but fragmented, highlighting the need for knowledge graphs (KGs) to improve interoperability, analytics, and applications in veterinary medicine. However, recent veterinary KGs are narrow and often rely on manual or semi-structured data, limiting scalability, integration with …

kr, vn (code pays fourni par la source)

Mitigating Degree Bias in Graph Representation Learning With Learnable Structural Augmentation and Structural Self-Attention

Van Thuy Hoang, Hyeon-Ju Jeon, O‐Joun Lee

kr (code pays fourni par la source)

Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

Van Thuy Hoang, O‐Joun Lee

This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled molecules, the previous pre-training methods still rely on semantic subgraphs, …

kr (code pays fourni par la source)