AIMC Topic: Models, Theoretical

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Surrogate Model Development for Digital Experiments in Welding.

Journal of visualized experiments : JoVE
The manufacturing industry heavily relies on welding processes to join materials, forming integral components across various sectors. Many aspects will influence the quality of the weld and finally affect the structure integrity of the weldment. Weld...

Development of intelligent hybrid controller for torque ripple minimization in electric drive system with adaptive flux estimator: An experimental case study.

PloS one
In order to ensure optimal performance of permanent magnet synchronous motors (PMSMs) across many technical applications, it is imperative to minimize torque fluctuations and reduce total harmonic distortion (THD) in stator currents. Hence, this stud...

Explainable machine learning for predictive modeling of blowing snow detection and meteorological feature assessment using XGBoost-SHAP.

PloS one
Accurate forecasting of blowing snow events is vital for improving numerical models of snow processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect blowing snow eve...

Electric vehicle braking energy recovery control method integrating fuzzy control and improved firefly algorithm.

PloS one
Braking energy recovery is crucial for improving the energy efficiency and extending the range of electric vehicles. If a large amount of braking energy is wasted, it will lead to problems such as reduced range and increased battery burden for electr...

Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach.

Journal of environmental management
Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management. This study quantifies the impacts of CC and HA on runoff and hydrological drought characteristics (HDC) in the Y...

The optimization and impact of public sports service quality based on the supervised learning model and artificial intelligence.

Scientific reports
Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework of this study is established. Secondly, the technic...

Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman.

Journal of environmental management
Accurate precipitation predictions are crucial for addressing climate change impacts on water resources, especially in arid regions like Oman. Therefore, this study evaluates three machine learning models-Random Forest (RF), Multilayer Perceptron Neu...

Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods.

Scientific reports
This study systematically analyzes the land use changes in Changsha City from 2000 to 2023. Three classification models-Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) were employed to evaluate the accu...

A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty.

Environmental monitoring and assessment
Accurate simulation of groundwater level is crucial for the sustainable management of water resources. However, the numerous uncertainties in input data, simulation model parameters, and physical processes, as well as the dependency between system va...

Multi-agent large language model frameworks: Unlocking new possibilities for optimizing wastewater treatment operation.

Environmental research
Wastewater treatment plants (WWTPs) are highly complex systems where biological, chemical, and physical processes interact dynamically, creating significant operational challenges. Traditional modeling approaches, such as Activated Sludge Models (ASM...