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 연구의 목적은 남북한 간 교류협력 재개에 대비하여 향후 민간 차원의 남북교역 추진 시 활용 가능한 표준계약서 조항을 도출하는 것 이다. 이를 위해 기존의 남북교역 거래 관행과 계약체결 실태를 조사하 고, 실제 남북교역 현장에서 사용되었던 분야별·업체별 계약서를 분석 하였다. 그동안 남북한 당사자 간 상거래는 남북관계의 특수성과 북한 리스크 요인 등으로 사업추진 과정에서 예측 불가능한 위험과 불확실 성이 지속되어 왔다. 이러한 상황에서 대외무역과 국제상거래 관행에 부합하는 남북한 간 표준계약 양식의 도입과 적용 필요성이 꾸준히 제 기되어 왔다. 본 연구는 남북교역 표준계약서를 제시하기 위해 기존 남 북교역 계약서상의 관행을 참고하되, 국제적으로 통용되는 무역계약 조 항의 관점에서 구성하였다. 즉, 중장기적으로 남북관계의 특수성보다는 국제규범 및 관례 등에 기초한 보편성을 중심으로 한 계약서 작성이 필요하다는 것에 주안점을 두었다. 이를 통해 남북교역 시 공정거래 관 행이 확립되고 신뢰관계 형성을 위한 초석이 될 수 있기를 기대한다.

This study proposes a weight optimization technique based on Mixture Design of Experiments (MD) to overcome the limitations of traditional ensemble learning and achieve optimal predictive performance with minimal experimentation. Traditional ensemble learning combines the predictions of multiple base models through a meta-model to generate a final prediction but has limitations in systematically optimizing the combination of base model performances. In this research, MD is applied to efficiently adjust the weights of each base model, constructing an optimized ensemble model tailored to the characteristics of the data. An evaluation of this technique across various industrial datasets confirms that the optimized ensemble model proposed in this study achieves higher predictive performance than traditional models in terms of F1-Score and accuracy. This method provides a foundation for enhancing real-time analysis and prediction reliability in data-driven decision-making systems across diverse fields such as manufacturing, fraud detection, and medical diagnostics.

Defective product data is often very few because it is difficult to obtain defective product data while good product data is rich in manufacturing system. One of the frequently used methods to resolve the problems caused by data imbalance is data augmentation. Data augmentation is a method of increasing data from a minor class with a small number of data to be similar to the number of data from a major class with a large number of data. BAGAN-GP uses an autoencoder in the early stage of learning to infer the distribution of the major class and minor class and initialize the weights of the GAN. To resolve the weight clipping problem where the weights are concentrated on the boundary, the gradient penalty method is applied to appropriately distribute the weights within the range. Data augmentation techniques such as SMOTE, ADASYN, and Borderline-SMOTE are linearity-based techniques that connect observations with a line segment and generate data by selecting a random point on the line segment. On the other hand, BAGAN-GP does not exhibit linearity because it generates data based on the distribution of classes. Considering the generation of data with various characteristics and rare defective data, MO1 and MO2 techniques are proposed. The data is augmented with the proposed augmentation techniques, and the performance is compared with the cases augmented with existing techniques by classifying them with MLP, SVM, and random forest. The results of MO1 is good in most cases, which is believed to be because the data was augmented more diversely by using the existing oversampling technique based on linearity and the BAGAN-GP technique based on the distribution of class data, respectively.

Molybdenum-tungsten (Mo-W) alloy sputtering targets are widely utilized in fields like electronics, nanotechnology, sensors, and as gate electrodes for TFT-LCDs, owing to their superior properties such as hightemperature stability, low thermal expansion coefficient, electrical conductivity, and corrosion resistance. To achieve optimal performance in application, these targets’ purity, relative density, and grain size of these targets must be carefully controlled. We utilized nanopowders, prepared via the Pechini method, to obtain uniform and fine powders, then carried out spark plasma sintering (SPS) to densify these powders. Our studies revealed that the sintered compacts made from these nanopowders exhibited outstanding features, such as a high relative density of more than 99%, consistent grain size of 3.43 μm, and shape, absence of preferred orientation.

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

This study investigates the effect of the DDL (Data-Driven Learning) approach on the English sentence writing ability of 6th graders in elementary school. To this end, a total of seven English textbooks were used to build a corpus. Five teachers were then asked to conduct five lessons using a weak version of DDL in their 6-grade EFL classrooms. Students were asked to complete a pre- and post-test and a pre- and post-survey, and a selected number of students and four of the five teachers had in-depth interviews with the researcher. The results are as follows: First, DDL using the textbook corpus was found to be adequate for helping elementary students improve their sentence-writing ability. Second, DDL had a significant effect on upper, middle, and lower level groups of students. Third, the students felt that DDL was neither unfamiliar nor difficult. Fourth, teachers with little teaching experience found it easy to conduct their classes using the DDL approach. This study implies that DDL is an effective approach to teaching communicative functions and language forms in the elementary English classroom and can be useful for all levels of elementary students.