In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning.

This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder.

This paper presents a study on the development of a hybrid modeling framework for the optimal operation of microgrids based on renewable energy resources. Accurate prediction of both renewable energy generation and consumer demand is crucial for the efficient management of renewable energy-based microgrids. The proposed hybrid modeling framework integrates a high-resolution physical model for forecasting renewable energy sources (solar and wind), a data-driven model for renewable energy prediction, and a hybrid forecasting model that combines both physical and data-driven approaches. Additionally, the framework incorporates a consumer demand model to further optimize grid operations. In this research, a hybrid prediction model was developed to enhance the accuracy of solar and wind power generation forecasts. The hybrid model leverages the complementary strengths of both physical and data-driven models. When historical data are insufficient, the physical model generates synthetic training data to improve the learning process of the data-driven model. Moreover, in cases where the data-driven model exhibits limited predictive accuracy due to insufficient training data, the physical model provides reliable forecasts, ensuring robust performance under various conditions. When sufficient real-world data are available, the Weighted Inverse Error Weighting (WIEW) strategy is applied to dynamically integrate the outputs of both models, significantly enhancing forecasting accuracy. Furthermore, a digital twin platform was implemented to operate and simulate each model, and a validation system for the digital twin platform and models was established using Software-in-the-Loop Simulation (SILS) and Power Hardware-in-the-Loop Simulation (PHILS) techniques. This study focuses on the development and validation of a hybrid model designed to improve the accuracy of solar and wind power generation forecasts for renewable energy microgrids.

Recently, generative models have rapidly advanced and are being applied to various domains beyond vision and large language models (LLMs). In the field of chemistry and molecular generation, deep learning-based models are increasingly utilized to reduce experimental exploration and research costs. In this study, we conducted research on Variational Autoencoder-based molecular generation and property prediction to screen for optimal molecules in the design of electrolyte additives for lithium-ion batteries. Using a dataset composed of promising electrolyte additive candidate molecules, we generated new molecules and predicted HOMO and LUMO values, which are key factors in electrolyte additive design. For approximately 1000 newly generated electrolyte additive candidate molecules, we performed DFT calculations to obtain HOMO and LUMO values and calculated the mean absolute error (MAE) between the predicted values from the trained model and the DFT-calculated values. As a result, the model demonstrated exceptionally low errors of approximately 0.04996 eV (HOMO) and 0.06895 eV (LUMO), respectively. This means that battery experts can receive recommendations for new molecules, refer to their predicted HOMO and LUMO values, and select potential electrolyte additives for further validation through experiments. By replacing the traditional electrolyte additive development process with deep learning models, this method has the potential to significantly reduce the overall development time and improve efficiency.