This study proposes a weighted ensemble deep learning framework for accurately predicting the State of Health (SOH) of lithium-ion batteries. Three distinct model architectures—CNN-LSTM, Transformer-LSTM, and CEEMDAN-BiGRU—are combined using a normalized inverse RMSE-based weighting scheme to enhance predictive performance. Unlike conventional approaches using fixed hyperparameter settings, this study employs Bayesian Optimization via Optuna to automatically tune key hyperparameters such as time steps (range: 10-35) and hidden units (range: 32-128). To ensure robustness and reproducibility, ten independent runs were conducted with different random seeds. Experimental evaluations were performed using the NASA Ames B0047 cell discharge dataset. The ensemble model achieved an average RMSE of 0.01381 with a standard deviation of ±0.00190, outperforming the best single model (CEEMDAN-BiGRU, average RMSE: 0.01487) in both accuracy and stability. Additionally, the ensemble's average inference time of 3.83 seconds demonstrates its practical feasibility for real-time Battery Management System (BMS) integration. The proposed framework effectively leverages complementary model characteristics and automated optimization strategies to provide accurate and stable SOH predictions for lithium-ion batteries.

본 연구는 환경 요인을 바탕으로 절화용 국화 생장 예측을 위한 최적의 모델을 개발하는 것을 목표로 하였다. 이를 위해 13개의 모델(Linear Regression, Lasso Regression, Ridge Regression, ElasticNet Regression, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Neural Network, Decision Tree, Random Forest, XGBoost, AdaBoost, CatBoost, Stacking)의 성능을 R2, MAE, RMSE를 평가 지표 로 비교하였다. 단일 모델 중에서는 Decision Tree가 가장 우수한 성능을 보였으며, R2값은 0.90에서 0.91 사이였다. 앙 상블 모델 중에서는 CatBoost가 가장 높은 성능을 보였으며 (R2=0.90~0.92) Random Forest와 XGBoost 또한 유사한 성 능을 보였다. 전체적으로 트리 기반 앙상블 모델이 국화 생장 예측에 적합한 모델로 나타났다.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

This qualitative study examines amotivation in South Korean EFL students through the lens of activity theory. Using semi-structured interviews, data were collected from six elementary and secondary school students, aged 10 to 16 years (Grades 4 to 10), to explore key psychological constructs, including the ideal L2 self, the ought-to L2 self, the perceived meaning of English learning, parental expectations, and social pressures. The findings reveal that motivation in L2 learning is contextually constructed and often shaped retrospectively rather than serving as a precursor to proficiency. Notably, some students with minimal motivation still achieved high levels of English proficiency, suggesting that motivation is not necessarily an antecedent of L2 success but rather an outcome shaped by broader socioeducational forces. This challenges conventional models that assume a direct causal link between motivation and achievement. The study underscores the importance of contextual and sociohistorical influences in shaping L2 motivation and calls for a reevaluation of the rigid dichotomy between motivated and amotivated learners in competitive educational settings.

Purpose: This study evaluated the impact of a nursing simulation learning module for caring for patients with chronic obstructive pulmonary disease (COPD) on nursing knowledge, clinical competence, team psychological safety, and learning satisfaction among nursing students. Methods: A non-equivalent control-group pretest–posttest quasi-experimental design was used with 36 students (18 per group) assigned to either a simulation group or a lecture group. Data collected from June 8 to July 13, 2024, were analyzed using SPSS 27.0. Results: Nursing knowledge showed no significant between-group difference (F=1.32, p=.260) but improved over time (F=8.24, p=.007). Clinical competence showed a significant group-by-time interaction (F=58.33, p<.001). Team psychological safety (t=2.70, p=.012) and learning satisfaction (t=2.27, p=.030) were higher in the simulation group. Conclusion: These findings provide foundational data for developing simulation-based educational strategies in nursing curricula. The module may also be applied to the training of novice nurses in clinical settings, thereby contributing to enhanced nursing education and improved clinical practice.

This study explores a pedagogical approach to learning modern Greek imperative forms using machine translation and evaluates its relevance in language education. While imperatives frequently appear in textbooks and exams, they present challenges for beginners, highlighting the need for effective instruction. Machine translation can serve as a practical learning aid in this context. The study h as tw o k ey a ims: e valuating t he q uality of G reek-to-Korean imperative sentence translations from Google Translate and DeepL, and identifying effective learning activities for helping students recognize and acquire imperative forms, specifically in instructional texts. The analysis shows that although machine translation captures core meanings, it struggles with contextually accurate expressions and complex syntax. The study suggests using machine translation to familiarize beginners with imperative forms and support intuitive learning. For more advanced learners, comparing machine and human translations can promote deeper grammatical understanding. Ultimately, machine translation can function not only as a translation tool but also as a means for linguistic analysis and grammar awareness in second language learning.

본 연구는 2022 개정교육과정에 신설된 고등학교 ｢인간과 경제활동｣ 교과서의 탐구 활동 에서 교육과정의 교수·학습의 방향이 제시하고 있는 다양한 학습 방법이 구현되고 있는지를 분석한 것이다. 인간과 경제활동 교육과정은 경제학의 기본 원리나 개념에 대한 설명보다는 학생들이 다양한 체험 활동을 통해 경제 문제를 자신의 삶과 생활에 적용하도록 하였다. 본 연구에서 ｢인간과 경제활동｣ 교과서가 교육과정에서 제시한 다양한 체험 활동을 구현했 는지에 대한 분석 결과는 다음과 같다. 우선, 교수·학습 방법 중 가장 많이 사용된 것은 미디어 활용 학습, 협동 학습, 토의·토론 학습 순으로 나타났다. 다음으로, 게임 학습, 프로 젝트 학습, 시뮬레이션 학습, 인공지능 활용 학습 등 기존의 경제 교과서에서는 잘 다루지 않았던 교수·학습 방법들이 새롭게 시도되었다. 마지막으로, 교육과정에서 제시한 지역사회 경제 전문가와의 협력 학습은 다루어지지 않았다. 향후 ｢인간과 경제활동｣ 교과서에서 시도 된 다양한 체험 활동의 학습 효과에 대한 연구를 통해 경제에 대한 학생들의 흥미를 고취할 수 있기를 기대한다.

Seismically deficient reinforced concrete(RC) structures experience reduced structural capacity and lateral resistance due to the increased axial loads resulting from green retrofitting and vertical extensions. To ensure structural safety, traditional performance assessment methods are commonly employed. However, the complexity of these evaluations can act as a barrier to the application of green retrofitting and vertical extensions. This study proposes a methodology for rapidly calculating the allowable axial force range of RC buildings by leveraging simplified structural details and seismic wave information. The methodology includes three machine-learning-based models: (1) predicting column failure modes, (2) assessing seismic performance under current conditions, and (3) evaluating seismic performance under amplified mass conditions. A machine learning model was specifically developed to predict the seismic performance of an RC moment frame building using structural details, gravity loads, failure modes, and seismic wave data as input variables, with dynamic response-based seismic performance evaluations as output data. Classifiers developed using various machine learning methodologies were compared, and two optimal ensemble models were selected to effectively predict seismic performance for both current and increased mass scenarios.

Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

본 연구는 스캇(1893)의 훈민정음 기원론과 스캇(1887; 1893)의 한국 어 자모 제시 방법을 고찰하는 데 목적을 두고 있다. 이를 위해 스캇 (1893)을 번역하여 훈민정음 기원론을 살펴보았고, 스캇(1887; 1893)을 번역하여 한국어 자모 제시 방법을 살펴보았다. 그러한 결과 스캇(1893) 은 훈민정음의 기원을 산스크리트어에 두었다는 것을 알 수 있었고, 스 캇(1887; 1893)을 통해 스캇(1887)의 자모는 리델(1881)의 육필본을 스 캇(1893)은 리델(1881)의 인쇄본을 참고하였다는 것을 알 수 있었다. 또 한 스캇(1887)은 학습서의 틀을 갖춘 최초의 영어권 한국어 학습자를 위 한 학습서이며, 후속 연구자에게 큰 영향을 주었음을 알 수 있었다. 본 연구의 의의는 비교적 활발히 이루어지지 않고 있는 스캇의 한국어 학습 서 연구에 참여하였다는 것과 이 연구가 한국어 역사 교육의 기초 자료 가 될 수 있다는 것이다.

Given the hazards posed by black ice, it is crucial to investigate the conditions that contribute to its formation. Two ensemble machinelearning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were employed to forecast the occurrence of black ice using atmospheric data. Additionally, explainable artificial intelligence techniques, including Feature Importance (FI) and partial dependence Plot (PDP), were utilized to identify atmospheric conditions that significantly increase the likelihood of black ice formation. The machinelearning algorithms achieved a forecasting accuracy of 90%, demonstrating reliable performance. FI analysis revealed distinct key predictors between the algorithms: relative humidity was the most critical for RF, whereas wind speed was paramount for XGBoost. The PDP analysis identified the specific atmospheric conditions under which black ice was likely to form. This study provides detailed insights into the atmospheric precursors of frost/fog-induced black ice formation. These findings enable road managers to implement proactive winter road maintenance strategies, such as optimizing anti-icing patrol routes and displaying warnings on various message signs, thereby enhancing road safety.

본 연구는 A대학의 PBL기반 혁신교수법이 대학생의 학습성과와 수업 만족도에 미치는 영향을 실증적으로 분석하는 것을 목적으로 한다. 연구 대상은 2022년부터 2024년까지 PBL기반 혁신교수법이 적용된 강의를 수강한 514명의 학생이며, 사전-사후 설문을 통해 학습 역량 변화를 측 정하였다. 연구 결과, 의사소통능력, 문제해결능력, 자기주도적학습능력 모든 영역에서 유의미한 향상이 나타났으며, 수업 만족도 평균도 4.52로 높은 수준을 보였다. 특히, 학생 퍼실리테이터 활용, 팀 프로젝트 기반 학습, 경험학습 사이클 적용, 에듀테크 기반 수업 설계 등의 요소가 학습 성과 향상에 기여한 것으로 나타났다. 반면, 온라인 학습 도구 활용 만족 도는 상대적으로 낮아 향후 개선이 필요함이 확인되었다. 본 연구의 의 의는 다음과 같다. 첫째, 기존 강의식 교육의 한계를 극복하고, 학습자 중심의 문제 해결형 수업 모델이 실제 대학 교육 현장에서 효과적으로 구현될 수 있음을 실증적으로 검증하였다. 둘째, 학생 퍼실리테이터, 에 듀테크, 경험학습 사이클 등을 통합한 구체적인 운영 사례를 제시함으로 써, 타 대학에서도 적용 가능한 실천적 교수 전략을 제공하였다. 셋째, 혁신교수법의 효과성 평가 체계와 개선 방향(예: 퍼실리테이터 양성, 실 습형 수업과의 연계 등)을 제안함으로써 향후 교육 정책 수립에 기초자 료를 제공할 수 있는 기반을 마련하였다.