This study proposes a surrogate model framework that integrates finite element analysis and deep learning to rapidly estimate equivalent material properties of patterned sheets. Conventional homogenization methods can only be applied after the pattern geometry has been finalized, requiring additional modeling and simulation. In contrast, the proposed approach establishes a surrogate model in advance, enabling the immediate estimation of equivalent material properties once the pattern geometry is defined. A dataset of 5,000 cases was generated using simulations, and Bayesian hyperparameter optimization was applied to improve model performance. The surrogate model achieved R² values above 0.99 for all target properties, confirming high internal consistency. Experimental validation with patterned STS304 specimens yielded meaningful results, with all errors remaining within 15%, which demonstrates the reliability of the proposed surrogate model despite minor deviations caused by fabrication imperfections and limited training data. Despite these limitations, the proposed system enables instant estimation of equivalent properties from pattern geometries, offering significant reduction in computational cost and design time. This approach enhances design reliability and provides a practical tool for the application of patterned materials in industrial engineering.

Cordycepin is the principal bioactive compound produced by Cordyceps militaris and exhibits diverse pharmacological properties. However, cordycepin production is highly sensitive to cultivation conditions, leading to substantially variable production amounts and challenges in process optimization. An interpretable machine learning framework was established in this study to predict the cordycepin produced by C. militaris cultivated on Pinus densiflora sawdust. Three key cultivation parameters—input weight, growth weight, and particle size—were quantified using submerged mycelial culture. The cordycepin content was measured via high-performance liquid chromatography. Four predictive models (random forest, support vector machine, XGBoost, and artificial neural network) were optimized through a randomized hyperparameter search and evaluated using internal validation and Tropsha’s external quantitative structure-activity relationship criteria. The validation accuracy of XGBoost was the highest (root mean square error = 42.67 μg/mL), whereas the external performance of random forest was the most reliable (R² = 0.898). Shapley additive explanations revealed that input weight most strongly influenced cordycepin production, followed by growth weight and particle size, with distinct nonlinear and interaction-driven effects among the cultivation variables. Kernel density and dependence analyses confirmed the occurrence of multimodal production regimes associated with the substrate loading and particle size characteristics. Finally, the best-performing model was deployed through a streamlit-based graphical user interface, enabling the real-time prediction of cordycepin concentration with a 95% confidence interval. The results collectively demonstrate the utility of interpretable AI-driven modeling for unveiling complex biological responses, providing a practical decision-support tool for optimizing cordycepin production in fungal biotechnologies.

최근 고도화된 딥러닝 모형을 이용하여 하천 수질에 영향을 줄 수 있는 과도한 조류(algae) 발생을 예측하는 연구에 대한 관심이 지속되고 있으며, 모형의 구축에 사용되는 현장 측정 자료의 특성상 다양한 이상치를 포함할 수 있어 데이터의 이상치 관리 필요성이 높아지고 있다. 본 연구에서는 현장 자료의 이상치가 딥러닝 모형의 성능에 미치는 영향을 분석하기 위해 딥러닝 Long Short-Term Memory(LSTM) 모형을 이용하여 하천 조류 발생을 정량적으로 평가하는 지표인 클로로필-a를 예측하는 모형을 구축하였으며, 10%의 이상치를 포함한 자료와 이상치가 포함되지 않은 원본 자료로 학습된 모형의 성능을 비교하였다. 또한 딥러닝 기반 이상치 탐지 알고리즘인 Autoencoder(AE)를 이용하여 이상치를 제거한 후 모형의 성능에 미치는 영향을 비교하였다. 분석 결과 이상치를 포함하지 않은 자료로 학습된 Base 모형과 10%의 이상치를 포함한 자료로 학습된 모형의 Nash-Sutcliffe efficiency(NSE)가 각각 0.882 및 0.858로 나타나 이상치가 모형의 성능을 저하시킬 수 있음을 확인하였다. 한편 AE를 이용하여 이상치를 다양한 비율로(5–20%) 제거한 자료로 학습된 모형의 성능을 분석한 결과 NSE가 0.883–0.896으로 이상치의 제거에 따라 모형의 성능이 Base 모형과 유사한 수준으로 개선되는 것으로 나타났다. 본 연구에서는 이상치가 딥러닝 모형에 미치는 영향을 분석하고 이상치 탐지 모형의 활용에 따른 조류 발생 예측 딥러닝 모형의 성능 향상이 가능함을 확인하였다.

모빌리티 기기에 주로 사용되는 리튬 이온 배터리는 고장 시 심각한 인명 피해로 이어질 수 있어, 실시간으로 배터리의 건전성 상태 (State of Health, SOH)를 정확하게 추정하고 모니터링하는 것이 매우 중요하다. 본 논문에서는 TCN-GRU 하이브리드 딥러닝 신경망 기반 리튬 이온 배터리의 SOH 추정 방법을 제안한다. 제안한 모델은 모빌리티 온보드 시스템에 적용 가능하도록 배터리 관리 시스템 (BMS)에서 직접 측정 가능한 전류, 전압, 시간의 원시 데이터를 리샘플링하여 입력으로 사용하였다. 해당 입력 데이터는 과거와 현재 의 데이터만을 활용하여 학습하는 TCN 모델을 통해 국소적 용량 회복을 포함한 배터리 열화 과정에서 나타나는 비선형적인 특징을 효과적으로 추출함으로써 모델의 신뢰성을 향상시켰다. 추출된 특징은 GRU 모델에 입력되어 시간적 정보 및 패턴을 학습하며, 정밀 한 SOH 추정 결과를 도출하였다. 제안한 방법은 CALCE 배터리 열화 데이터를 기반으로 검증하였으며, 평가 지표인 MAE와 RMSE 는 모든 배터리 셀에 대해 각각 최대 0.55 및 0.7의 일관되고 우수한 성능을 보였다.

This study aimed to develop a model for accurately predicting the acute aquatic toxicity (48h- EC50) of chlorine disinfection by-products (DBPs). DBPs have caused environmental risks, but experimental toxicity data are difficult to obtain due to time, cost, and ethical constraints. Therefore, a deep learning model was developed using actual concentration-based data. Toxicity data for 139 aliphatic chlorinated compounds were from the OECD QSAR Toolbox and from aquatic toxicity test results provided by the japan ministry of the environment. Various concentration criteria, including nominal and measured concentrations, were encoded as additional inputs, and EC50 values were augmented via log transformation and structural string modifications to overcome small data limitations. The directed message passing neural network (D-MPNN) model, which considers bond directionality, was applied to reflect structural complexity accurately. Also, this model effectively reflected subtle structural differences and showed stable performance even with limited data. Comparisons between models with and without concentration criteria revealed that the model considering all concentration criteria had superior predictive accuracy. This result shows that concentration criteria are a critical factor in toxicity prediction. This study suggests a baseline model that works reliably even with small datasets reflecting realistic concentration criteria, showing its potential use for replacing some experiments and for screening toxic substances.

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.

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 study developed a deep learning-based software module for classifying the ripeness of bananas in real time as they move along a conveyor belt. A total of 5,286 images annotated with three ripeness stages, namely unripe, ripe, and overripe, were divided into training, validation, and test datasets at a ratio of 88:8:4. The datasets were used to train YOLOv5s and YOLOv5l object detection models over 50 epochs. The model performance was evaluated using box loss, object loss, class loss, and mean average precision (mAP). Both models exhibited decreasing loss values approaching zero and achieved mAP, precision, and recall scores exceeding 90%, thus indicating a robust classification performance without overfitting. The software module integrated with the trained YOLOv5l model accurately identified the ripeness stage of bananas in motion on the conveyor system without misclassification. Collectively, these findings indicate that the proposed system can be effectively applied to banana-processing lines for automated and accurate ripeness-based sorting.

본 연구는 딥러닝 기술을 활용하여 공동주택 외벽의 균열 탐지를 효과적으로 하기 위한 다양한 데이터 전처리 방법을 비교 분석하 였다. 특히, 표준 균열 데이터셋에 일반적으로 나타나지 않는 오탐균열을 식별하는 데 중점을 두고 있다. 이 연구는 탐지 정확도를 최 적화하기 위해 여러 이미지 전처리 기법을 적용한 결과를 비교한다. 객체 탐지를 위한 엣지 필터링과 RGB 색 필터 등을 이용한 색상 정규화를 결합한 방법을 집중적으로 검증하였다. 이러한 기술들은 실제 균열과 오탐균열을 구분하기 위해 적용되었으며, 이들의 탐 지 성능에 미치는 영향을 철저히 조사하였다. 효율적인 균열 탐지 모델을 찾기 위해 EfficientNet V2s 기반 모델을 적용하였다. RGB, YUV, LAB, HSV 네 가지 이미지 필터가 원본 이미지와 CLAHE 정규화된 이미지에 적용되었는데, 그 결과 단색 콘크리트 균열 탐지 에 효과적인 전통적인 정규화 방법이 공동주택 외벽 균열 탐지에는 제한적인 효과를 보인다는 것을 확인하였다. 또한, 단일 색 필터의 적용이 일관된 탐지 결과 개선 효과를 주지 않는다는 것을 밝혔다. 결국, 본 연구를 통해 다양한 이미지 정규화와 색 필터 조합의 균열 탐지 성능을 검증하였으며, 실제 균열과 오탐균열을 구분하는 탐지 성능 향상을 위해 추가적으로 다양한 접근의 연구가 필요하다는 것을 확인하였다.