Tuned Mass Dampers (TMDs) are widely used to mitigate structural vibrations in buildings and bridges. However, conventional optimization methods often struggle to achieve optimal performance due to the complexity of structural dynamics. This study proposes the NN-L-BFGS-B algorithm, which combines Artificial Neural Networks (ANNs) for global exploration and L-BFGS-B for local exploitation to efficiently optimize TMD parameters. A ten-story shear-building model with a TMD is used for validation. The proposed method achieves the lowest H₂ norm compared to previous studies, demonstrating improved optimization performance. Additionally, NN-L-BFGS-B effectively balances computational efficiency and accuracy, making it adaptable to various engineering optimization problems.

최근 지구온난화로 인한 피해가 심각해짐에 따라 화석연료 사용을 줄이고자 친환경 수소 에너지의 활용이 증가하고 있다. 이에 따라 수소의 저장 및 운송을 위한 수소 저장 용기의 수요가 확대되고 있으나, 현재 널리 사용되고 있는 강재 기반 저장 용기는 부식과 같은 내구성 저하 현상에 취약하다. 따라서 선행 연구는 지지부 부식에 따른 내진 성능 저하 문제를 해결하기 위해 부식 저항성 이 뛰어난 CFRP를 지지부 기둥을 적용하여 설계 하중에서 적용성을 검토하였다. 이때 본 연구는 CFRP의 강도-중량비가 높음을 고려 하여 기존 강재 구조물 지지부 ㄱ 단면 대비 높은 강성을 가진 H 단면과 ㅁ 단면을 지지부 기둥에 적용하여 연구를 수행하였다. 이때 실제와 가까운 해석 결과를 도출하기 위해 고유진동수 추출해석을 진행하여 감쇠 계수를 적용 시켰고, AC 156 인공 지진을 설계 하중 으로 적용한 결과, ㅁ 단면을 적용한 강재 기둥의 접합부 응력은 222.34 MPa로 기존 ㄱ 형강 대비 78.93%로 설계 하중에 만족함을 보였다. ㅁ 단면 적용 CFRP 기둥은 파손 지수(DI)를 통해 평가하였고, 이때 최대 DI는 수지 인장에서 발생하였으며, 그 값은 0.708로 파괴 기준 대비 29.2% 낮아 설계 하중에 만족함을 보였다. 또한, 기초 슬래브에서 쪼갬 인장 응력과 휨 인장 응력을 통한 평가를 진행 하였고, 현장 실험 결과와 마찬가지로 설계 하중에 휨 인장 파괴가 발생하는 것으로 확인하였다. 하지만 파단 시점은 CFRP에서 1.54배 오래 설계 하중에 견디는 것을 확인하여, 그 적용성을 확인하였다. 결론적으로 지진의 발생 빈도가 높아짐에 따라 수소 저장 용기의 안전성 확보가 시급하다. 따라서 기존 강재 대상 구조물의 부식으로 인한 강성 저하 문제를 해결하기 위해, 높은 내구성 및 부식 저항성 재료의 적용은 필수적이다. 동시에 기초 슬래브의 안전성 확보에 대한 연구가 추가적으로 수행되어야 한다.

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.

Nitrogen fertilizers are generally known to be of great help in improving crop yields, but excessive nitrogen fertilizer usage can not only destroy the environment but also negatively affect crop growth. This study aims to develop a decision-making system for optimal nitrogen fertilizer use for efficient production of Chinese cabbage (Brassica rapa), one of the major vegetables. The proposed system has the functions of detecting farmland based on satellite images, predicting cabbage yields and greenhouse gas (e.g., nitrous oxide) emissions according to nitrogen fertilizer use, and making decisions using the prediction results. To develop the proposed system, a generalized prediction model is developed using experimental data collected from South Korea, Egypt, India, Canada, Lithuania, and China, and the effectiveness of the proposed system is validated through experiments. As a result, the proposed system will enable farmers to conduct eco-friendly agricultural activities through appropriate nitrogen fertilizer use while stably maximizing productivity of Chinese cabbages.

The number of significant issues on many welding processes are often connected to high productivity and manufacturability at low costs. The research on welding processes in the literature has reported several research activities, but there is still scope for improvement in most industrial settings. The primary goal of this research is to determine the best super-TIG welding settings to use for groove welding. First, in order to determine the quality characteristics and risks associated with them, concepts and frameworks of quality by design (QbD) which is a new standard in pharmaceutical area in order to improve drug qualities were integrated into this process optimization. Second, stepwise experimental design approaches including a factorial design as well as a response surface methodology (RSM) were customized and performed for this specific automated super-TIG welding process. Third, based on experimental design results, the optimal operating conditions with both design space (i.e., acceptable range of operating conditions) and safe operating space (i.e., safe range of operating conditions) were obtained. Finally, a case study including QbD steps, stepwise experimental design approaches, design and operating spaces, the optimal factor settings, and their association validation results was conducted for verification purposes.

In the context of increasingly uncertain maritime logistics environments, container Demurrage and Detention (D&D) charges pose a significant challenge to both carriers and shippers. Traditional policies typically impose separate cost structures for container pickup (demurrage) and container return (detention), yet such separate impositions often fail to capture the interconnected nature of operational delays and the pervasive uncertainty present in hinterland container flows. This study addresses the problem of D&D decision-making under uncertainty by proposing a merged free time policy that integrates both D&D charges into a unified framework. By merging the free time allocated for both pickup and return processes, the proposed policy aims to enhance operational flexibility, reduce overall logistics costs, and provide a more predictable cost structure for carriers while improving service quality for shippers. To achieve these objectives, we develop a mathematical optimization model that incorporates stochastic pickup and return scenarios, thereby reflecting the uncertainties in container availability and transportation delays. The model embeds a strategic decision-making process between carriers and shippers through a hierarchical framework to jointly optimize free time allocations and penalty structures. Numerical experiments based on simulated data demonstrate that the merged free time policy outperforms traditional separate policies by improving container turnover efficiency and mitigating the negative impact of uncertainty on operational performance. Our findings offer valuable insights into cost management and risk reduction in maritime logistics and contribute to the literature by providing a comprehensive strategy for D&D management that supports more collaborative hinterland container operations and enhances overall supply chain resilience.