The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

A sequential well placement strategy is important for field development planning under geological uncertainty, because reservoir conditions can change between drilling stages. Motivated by this challenge, this study proposes a hybrid framework that combines convolutional neural networks (CNNs) with a genetic algorithm (GA). The goal is to determine optimal well locations efficiently, while reducing reliance on full-physics reservoir simulation. The methodology uses OPM Flow to generate training datasets for two consecutive six-month periods. This allows the CNN proxy to learn the relationship between permeability realizations, well coordinates, and cumulative oil production. The trained proxies then guide the GA-based optimization in each period. Results for the Egg model show strong predictive performance in both stages. The coefficients of determination are 0.76 and 0.82 for training data, and 0.64 and 0.63 for testing data, in the first and second periods, respectively. In addition, the proxy-based optimization required only about 26% of the computational time of direct simulation in the first period, and roughly 15% in the second. Production estimates were maintained within a 5% error margin. Overall, the proposed sequential, proxy-assisted approach is accurate and computationally efficient for well placement optimization under geological uncertainty.