Optimizing White Noise for Stability and Strategic Performance Enhancement of LLM-RAG Systems

Dayoung Kim, Young Myoung Ko

Retrieval-Augmented Generation (RAG) has emerged as a technique for improving the performance of language models (LLMs), where the inclusion of noisy documents ...

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DOI: https://doi.org/10.7232/JKIIE.2026.52.3.193

A Dynamic Inventory Routing Approach for the Public Bike Sharing Rebalancing Problem: A Case Study of Seoul

Jungyoon Koh, Seungtaek Kim, Junghyun Lee, Chungmok Lee

Seoul’s public bicycle system faces operational inefficiencies due to an imbalance in availability, resulting to bicycle shortages or surpluses at specific time...

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DOI: https://doi.org/10.7232/JKIIE.2026.52.3.208

An Analysis of the International Level of Machine Learning Application Research in Korean Manufacturing Quality Management

Sung Hwan Jung

This study evaluates South Korea’s global competitiveness in machine learning applications for manufacturing quality management in the context of the transition...

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DOI: https://doi.org/10.7232/JKIIE.2026.52.3.231

Design and Implementation of a Deep Learning–Based Multimodal Childcare Support System for Deaf Parents

Jeong-Tae Kim, Kyungdoh Kim

Recent advances in machine learning and Internet of Things (IoT) technologies have led to the development of childcare support services that monitor infants’ co...

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DOI: https://doi.org/10.7232/JKIIE.2026.52.3.245

Research of a Multi-Output Regression Model for Roll Gap Prediction in Roll-to-Roll Calendering of Dry Battery Electrodes

Janghyun Mun, Hongseok Yoo, Jaeseok Jang

This study develops a multi-output regression model to predict multi-stage roll gaps in a roll-to-roll calendering process for dry lithium-ion battery electrode...

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DOI: https://doi.org/10.7232/JKIIE.2026.52.3.257

Data-Level Robust Federated Learning for Refrigerator Temperature Prediction with Anomalous Customers

Jinsoo Bae, Soyeon Park, Jihyung Choi, Jihyun Kim, Seongil Ham, Bokung Lee, Pilseob Kang, Seoung Bum Kim

Refrigerator temperature prediction using neural networks can improve both energy efficiency and customer experience in smart home environments. Training high-q...

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DOI: https://doi.org/10.7232/JKIIE.2026.52.3.268