https://doi.org/10.5207/JIEIE.2026.40.3.157

Seung-Wan Park ; Yu-Rim Kang ; Yoon-Chul Lee ; Yong Woo Kim ; Hyeon Woo Kim ; Chang-Ju Park

This study proposes a dual-channel CCT-tunable LED lighting system for cyanosis observation, combining 3000 K and 5000 K LEDs with lookup table (LUT)-based current control for continuous correlated color temperature (CCT) variation from 3000 to 5000 K. Across the full range, the simulated average illuminance (215.2?224.1lx) and uniformity (0.754? 0.760) satisfied hospital lighting requirements, and color quality remained stable (Ra 92.3?93.8, Rf 89.5?90.2, Rf_skin 91.3?93.3). A CIEDE2000-based cyanosis color-difference index (CCDI), defined as the color difference between modeled normal and cyanotic skin under each spectral power distribution (SPD), increased from 0.86 (3000 K) to 1.54 (5000 K), while the redness (CIELAB a*) of rendered normal skin decreased from 15.4 to 12.4. Because cyanosis perceptibility (favored at higher CCT) and skin-color reproduction (favored at lower CCT) oppose each other, the tunable dual-channel design lets the operator balance the two through CCT control.

https://doi.org/10.5207/JIEIE.2026.40.3.164

Seo-Hoon Kim ; Jong-Bin Park

This study investigates the energy saving performance of a context aware predictive lighting control system applied to a real convenience store environment. A smart lighting system based on Closed Circuit Power Line Communication (CPLC) was deployed, and four lighting control strategies Zone Based Reactive Control, Product Focused Control, Rest Adaptive Control, and Trajectory Predictive Control were implemented and evaluated over a 61 day period. The predictive control strategy employs a first order Markov chain model to estimate user movement trajectories and proactively adjust lighting conditions. Energy consumption was analyzed in terms of hourly patterns and daily totals, and the performance of each control strategy was compared with a baseline scenario in which all lighting fixtures operated continuously at full output (18.2kWh/day). The results indicate that all control strategies are expected to achieve energy savings. Among them, Trajectory Predictive Control was estimated to reduce average daily lighting energy consumption to 11.1kWh/day, corresponding to an estimated energy saving of approximately 39.0% compared with the baseline condition. These results demonstrate the practical effectiveness of predictive lighting control for energy-efficient operation in small commercial spaces and highlight its potential applicability to retrofit lighting systems in commercial buildings.

https://doi.org/10.5207/JIEIE.2026.40.3.174

Geonu Jeon ; Seung-Yoon Lee ; Sun-Ho Yu ; Dong-Wook Kim ; Dong-Geun Lee

This paper presents the modeling and operational analysis of a DC distribution system based on PSCAD/EMTDC simulation. The proposed system represents a DC distribution system architecture integrating various power electronic converters, rotating loads, renewable energy sources such as photovoltaic generation, and a battery energy storage system (BESS). The system is modeled at the electromagnetic transient (EMT) level to capture switching dynamics and transient interactions among converters and loads. The DC distribution network consists of an AC/DC interlinking converter based on a solid-state transformer (SST), isolated and non-isolated DC/DC converters, motor drive systems, and BESS connected to a common DC bus. In addition, BESS-based operational strategies including peak load management and DC voltage stabilization using droop control are investigated. Simulation results demonstrate that the proposed DC distribution system model can effectively maintain DC bus voltage stability and support peak load reduction under various operating conditions. The developed PSCAD/EMTDC model provides a useful platform for analyzing the dynamic characteristics and control strategies of future DC power systems.

https://doi.org/10.5207/JIEIE.2026.40.3.182

Min-Sik Kim ; Eun-Hyeok Choi

Identifying the causes of failures or defects in mechanical equipment during operation is often difficult when using conventional continuous video recording systems. Such systems generate excessive data and are not effectively linked with control devices, which limits their usefulness for fault analysis. This paper presents an event-driven video monitoring and recording platform that integrates RTSP-based video streams with OPC UA-based control signals to support efficient fault and defect analysis in mechanical equipment. Unlike conventional continuous recording approaches, the system records only video segments around control events, reducing unnecessary data storage. Through experimental validation, synchronization between control events and video data was maintained within tens of milliseconds, while the recorded video size was approximately 1.41MB per event. These results show that the proposed approach can effectively support real-time monitoring and fault analysis in smart manufacturing environments.

https://doi.org/10.5207/JIEIE.2026.40.3.189

Eui-Chul Shin ; Kwang-Seo Park

This paper evaluates the adequacy of the N-2 reliability criterion for simultaneous double-circuit faults in Korea’s 765kV transmission lines. While international practices treat such events as low-probability contingencies and allow flexible operational measures, Korea applies a deterministic and strict no-interruption criterion. Simulation results indicate that the system remains stable under single-circuit faults, whereas loss of synchronism occurs under double-circuit faults. Although such events have significant system impacts, their occurrence probability is substantially reduced by design and protection measures. Therefore, a risk-based and performance-oriented approach is required to achieve a balanced consideration of reliability and economic efficiency.

https://doi.org/10.5207/JIEIE.2026.40.3.196

Jin-Soo Park ; Eun-Gyu GU ; SoonHo Choi ; Yoon-Sung Cho

In this paper, two international dynamic line rating (DLR) standards, IEEE 738 and CIGRE TB 601, are compared to improve the utilization of transmission capacity in response to increasing renewable energy curtailment. The study focuses on their differences in modeling convective heat loss. Using 24-hour meteorological data from South Korea’s southwestern coastal region, both methods were applied to a representative conductor model. The results showed a maximum ampacity difference of 158.6 A between the two models. For a 154kV line, this translated to a difference of up to 42.3MW (14.9%) in the three-phase active power transfer capacity. These findings suggest that the choice of DLR algorithm can significantly affect transmission capacity assessment, and that CIGRE TB 601 may provide a more suitable basis for secure grid operation under complex weather conditions.

https://doi.org/10.5207/JIEIE.2026.40.3.204

Seung-Hoon Kwon ; Eun-Hyeok Choi

This paper proposes a system architecture for implementing Physical AI in digital twin?based automation systems and experimentally evaluates its performance using a multi-process automated system (MPS). The proposed architecture separates rule-based safety control executed by a PLC from AI-driven decision-making, enabling collaborative operation within a closed-loop control framework. A digital twin environment is employed to generate synthetic data and optimize control parameters, while an IFM (Industry Foundation Module)?based learning structure enables continuous model adaptation using both real and virtual data. Experimental results demonstrate that the proposed system maintains production cycle time within ±10% of the target under varying process conditions, and achieves steady-state control performance with an average deviation of approximately ±5 seconds. The proposed system integrates Digital Twin-based simulation, IFM-based AI learning, and OPC UA-based closed-loop control to enable adaptive autonomous manufacturing in multi-process automation environments. These results validate the feasibility of applying Physical AI to multi-process automation environments and provide practical insights into system architecture and data flow design for autonomous manufacturing systems.

https://doi.org/10.5207/JIEIE.2026.40.3.212

Du-San An ; Koog-Hwan Oh ; Hyun-Il Kim ; Sang-Taek Lee

With the strengthening of environmental regulations in the maritime sector, hybrid electric propulsion systems have emerged as an effective solution for reducing emissions from small vessels. This study focuses on the commercialization and evaluation technologies of core electric propulsion equipment for hybrid small vessels. To evaluate the safety and reliability of the propulsion system, Failure Mode and Effects Analysis (FMEA) is performed for major subsystems and components. A weighted Risk Priority Number (RPN) method is applied to improve the limitations of the conventional RPN method. In addition, measurement and fault diagnosis modules are developed to collect and monitor operational data from the propulsion system in real time. A Hardware-in-the-Loop Simulation (HILS)-based safety diagnosis environment is constructed, and EMC and IP54 tests are conducted for the integrated control system and propulsion motor. The results demonstrate that the applicability of the proposed technologies for the stable operation and commercialization of hybrid electric propulsion systems for small vessels.

https://doi.org/10.5207/JIEIE.2026.40.3.222

Seong-Hun Kim ; Jin-Gyu Kim

Low-temperature plasma technology has recently gained increasing attention as a promising approach for microplastic degradation. However, most previous studies have primarily focused on removal efficiency and byproduct analysis, with limited emphasis on quantitative prediction of removal behavior. In this study, we proposed a predictive model combining reaction kinetics and Gaussian process regression to describe microplastic removal behavior under varying power and treatment time conditions. The proposed model accurately reproduced the experimental removal behavior and demonstrated reliable predictive capability in untrained power and time domains. Furthermore, the model was used to identify favorable operating conditions for achieving rapid microplastic removal with high energy yield. These findings suggest that the kinetics?Gaussian process regression model can serve as a useful tool for predicting low-temperature plasma-based microplastic removal behavior and for optimizing process operating conditions.

https://doi.org/10.5207/JIEIE.2026.40.3.228

Seo-Hoon Kim ; Jong-Bin Park

This study analyzes the temporal power balance and load matching characteristics of a small retail commercial building under different Zero Energy Building (ZEB) definitions. An EnergyPlus-based simulation model is developed for a 24-hour convenience store-type building using Seoul EPW weather data. The model includes refrigeration equipment, lighting, plug loads, and heating, ventilation, and air-conditioning (HVAC) systems. Photovoltaic (PV) and Energy Storage System (ESS) configurations are applied to evaluate energy self-sufficiency, PV self-consumption, grid dependency, and Load Match Index (LMI). The results show that continuous refrigeration loads cause persistent nighttime grid dependency. Although PV improves annual energy self-sufficiency to 0.584, a significant temporal mismatch between generation and demand remains. The application of ESS increases PV self-consumption from 0.824 to 0.952 and improves LMI from 0.281 to 0.382. In the expanded PV?ESS scenario, energy self-sufficiency reaches 1.012 and LMI increases to 0.812. However, achieving a high level of Load Match ZEB requires substantially larger PV and ESS capacities than those needed for Source ZEB. These results indicate that annual energy balance alone is insufficient to evaluate actual operational power self-sufficiency and highlight the practical limitations of on-site renewable energy deployment in small commercial buildings.