Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

Currently, solar photovoltaic (PV) systems are a priority for end-use decarbonization, aimed at reducing reliance on fossil fuels. However, PV systems are typically exposed to outdoor conditions, making them more susceptible to aging and damage. In this paper, a predictive maintenance approach that integrates digital twin technology with the copula-based model is proposed. This integration enables accurate simulation of the PV system’s condition and precise representation of the correlation between the power output of the digital twin and that of the actual system. Given the power output of the digital twin, predictive maintenance is performed based on the conditional cumulative distribution function (CDF) of the actual power output, which is derived from the copula model. A comprehensive case study was conducted to evaluate the performance of the proposed approach named OCAD (Optimal Copula-based Anomaly Detector), which achieved an accuracy of 92.51% and an F1-score of 92.13%. This significantly outperforms conventional models, including SVM, KNN, and ANN, demonstrating the effectiveness of the proposed predictive maintenance strategy.