Fatigue driving is one of the main causes of traffic accidents. This paper proposes a fatigue detection method based on computer vision. The first is the introduction of an optimized algorithm, based on AdaBoost, to detect the face area, and then the ERT algorithm is used to achieve precise localization of the facial landmarks. Finally, a variety of fatigue features of eyes and mouth state associated with driving fatigue are extracted, and after the fusion of all these features, the fatigue driving detection is performed. The experimental results show that multi-feature detection is more accurate than single feature detection.
State of Efficiency (SOE) is proposed as a performance indicator of lithium-ion battery in terms of energy efficiency.• The degradation trajectory of SOE for NCA lithium-ion batteries is studied and a linear model is proposed to describe SOE degradation trend.• A number of factors that affect SOE have been identified and studied, including ambient temperature, discharge current, and cutoff voltage.
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