Honam University

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within ±1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25–30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18–88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships.

With a growing companion animal population and their extended lifespans in South Korea, the need for health examinations tailored to specific life stages is increasing. However, an objective standard for defining these life stages is currently lacking. To classify life stages for companion dogs and cats by identifying clusters in age-specific disease proportions derived from medical records, providing a data-driven foundation for health examination programs. We collected 505,667 medical records from 82 veterinary facilities in South Korea between 2020 and 2023. Diagnoses were standardized using GPT-4o and S-BioBERT. Following preprocessing, data from 27 facilities yielded 222,706 canine and 39,910 feline records for the final analysis. Principal component analysis and K-means clustering (K = 4) were applied to age-specific disease proportions to identify life stages. The 10 most highest-proportion diagnoses diseases were determined for each cluster. Canine life stages were classified as ≤ 1 year, 2-5 years, 6-10 years, and 11-15+ years. Feline life stages were 1-2 years, 3-8 years, 9-12 years, and 13-15+ years. In dogs, developmental diseases were common in the youngest age group, while chronic diseases were more prevalent in older groups. In cats, oral and urinary diseases were high-ranking, conjunctivitis was most common in the early stage, and chronic diseases increased with age. Age-specific diagnosis patterns support four practical life stages for dogs and cats in South Korea. These boundaries can inform evidence-based preventive examination schedules, animal health policy, and pet insurance product design.

This study proposes an artificial neural network-based diagnostic technique for diagnosing the aging status of the Insulated Gate Bipolar Transistor (IGBT), a core component of a three-phase inverter system installed in a stratospheric solar drone, and predicting their remaining useful life (RUL). Due to environmental constraints that make it difficult to collect actual operating data, 15,625 training data sets were constructed through MATLAB/Simulink-based inverter system simulations that reflect various IGBT aging conditions. The learning data was processed to use the change rate of the inverter three-phase voltage and voltage components (positive sequence, negative sequence, zero sequence) according to the symmetric coordinate method, based on the change in the on-state resistance (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_on</i>) value of the IGBT as input feature data. Based on this, we implemented and trained a classification model to predict the Risk Level and a regression model to predict the Combined Aging Index. The classification model achieved 94.49% overall accuracy and 97.61% accuracy for severe aging prediction, while the regression model attained a coefficient of determination (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of 0.8718, RMSE of 2.56, and MAE of 1.84, demonstrating strong predictive performance. Feature contribution analysis further identified the reverse-phase voltage change rate as the most critical indicator, accounting for 34.2% of the predictive power in inverter aging diagnosis. This study verified the robustness of the model through simulations that reflected a noisy operating environment. Future validation using operational data and the inclusion of various failure modes can enhance the generalization performance of the predictive model. The proposed technique can be used as a diagnostic technology for predictive maintenance and assurance of reliability in aircraft inverter systems, which require high reliability.

Injection molding processes generate large volumes of heterogeneous process data that reflect complex and dynamic manufacturing conditions, directly influencing product quality. Conventional quality control approaches based on fixed statistical thresholds and operator experience often fail to ensure stable and secure operation under such variability, leading to increased defect rates and reduced process reliability. Moreover, insufficient consideration of data integrity and process stability can compromise the robustness of data-driven manufacturing systems. This study proposes a secure machine learning framework for defect detection and quality enhancement in injection molding processes. The framework integrates systematic data preprocessing, correlation analysis, and an unsupervised learning approach based on a Gaussian Mixture Model (GMM) to achieve reliable defect classification. Key process parameters contributing to quality deviations are identified through statistical correlation analysis, and process data are clustered into one normal group and three abnormal groups corresponding to different defect types without requiring labeled datasets. By emphasizing data integrity, stable clustering behavior, and resilience to process variability, the proposed framework enhances manufacturing security and reduces the risk of misclassification. Experimental results using real injection molding process data demonstrate effective defect reduction, improved process parameter optimization, and enhanced operational efficiency. By enhancing defect detectability and reducing misclassification risks, the proposed framework supports safer and more human-centric manufacturing environments by improving operator trust, decision confidence, and preventive intervention capability.

Abstract Background Previous studies presented that newer generation stents implantation for coronary chronic total occlusion (CTO) improved clinical outcomes. However, there were limited data regarding comparison of the biodegradable polymer (BP) versus durable polymer (DP) drug-eluting stents (DESs) for CTO patients who underwent successful percutaneous coronary intervention (PCI). We compared major clinical outcomes following successful CTO PCI with two different type of DESs up to 2 years. Methods A total of 434 CTO patients enrolled from January 2012 were divided into two groups; BP stent group (n=228) and DP stent group (n=206). Individual and composite clinical outcomes were compared between the two groups up to 2 years. Results The baseline clinical characteristics were similar between the two groups except that BP stent group had higher prevalence of hypertension and dyslipidemia. After log-rank test showed that DP stent group had a higher incidence of total death, however after baseline adjustment by cox proportional hazards regression, there were no difference for individual and composite clinical outcomes up to 2 years (Table). Conclusions In our study, BP stent and DP stent showed similar individual and composite clinical outcomes in patients who underwent successful CTO PCI up to 2 years. Long-term and large-scale studies are needed for final conclusion.

Abstract Background Carvedilol and Bisoprolol are widely used β-blockers for cardiovascular disease management, but their comparative effectiveness in ST-segment elevation myocardial infarction (STEMI) patients with preserved left ventricular ejection fraction (LVEF ≥50%) and treated with drug-eluting stents (DESs) implantation is not well established. This study aimed to compare the long-term outcomes of these two different β-blockers. Methods We analyzed data from the Korea Acute Myocardial Infarction Registry (KAMIR) involving STEMI patients with preserved LVEF treated with DESs. Patients were grouped based on discharge prescription of Carvedilol or Bisoprolol and followed for up to 3 years. The primary endpoint was all-cause death, and the secondary endpoint was major adverse cardiac events (MACE), defined as a composite of all-cause death, myocardial infarction (MI), and revascularization. Inverse probability of treatment weighting (IPTW) was used to adjust for confounders. Results After IPTW adjustment, baseline characteristics were well balanced between the Carvedilol group (n=4,045) and the Bisoprolol group (n=3,998). At 1 year, the incidence of total death (0.9% vs. 1.0%, HR: 0.890, 95% CI: 0.570–1.392, P=0.650) and MACE (4.3% vs. 4.7%, HR: 0.895, 95% CI: 0.724–1.105, P=0.307) were similar between the two groups. Over the 3-year follow-up, the incidence of total death (3.1% vs. 2.5%, HR: 1.240, 95% CI: 0.951–1.618, P=0.121) and MACE incidence (9.5% vs. 9.6%, HR: 0.990, 95% CI: 0.852–1.149, P=0.909) remained comparable. Conclusions In STEMI patients with preserved LVEF treated with DESs, there were no significant differences in long-term clinical outcomes between Carvedilol and Bisoprolol.