The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.

In this study, a loader drive axle digital model was built using 3D commercial software. On the basis of this model, the transmission efficiency of the main reducing gear, the differential planetary mechanism, and the wheel planetary reducing gear of the loader drive axle were studied. The functional relationship of the transmission efficiency of the loader drive axle was obtained, including multiple factors: the mesh friction coefficient, the mesh power loss coefficient, the normal pressure angle, the helix angle, the offset amount, the speed ratio, the gear ratio, and the characteristic parameters. This revealed the influence law of the loader drive axle by the mesh friction coefficient, mesh power loss coefficient, and speed ratio. The research results showed that the transmission efficiency of the loader drive axle increased with the speed ratio, decreased when the mesh friction coefficient and the mesh power loss coefficient increased, and that there was a greater influence difference on the transmission efficiency of the loader drive axle.

We propose a novel fuzzy control strategy for hybrid electric vehicles (HEVs) based on the feature selection genetic algorithm of multivariate data, which greatly shortens the selection time of the optimal parameters of the traditional genetic algorithm. Firstly, we take the fuel consumption and emission of an HEV as the optimization index, and develop a novel fuzzy control method considering parameters of the fuzzy controller with high correlation with the objective function, in which the membership function parameter is optimized by the feature selection genetic algorithm. Finally, the performances of the fuzzy control strategy for an HEV and the novel fuzzy control strategy optimized by the feature selection genetic algorithm under the New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS) cycle conditions are analyzed and compared. The results show that the proposed fuzzy control can greatly improve the fuel economy and reduce the emission of HEVs.

In this study, a zinc oxide (ZnO) film was deposited by sputtering on an indium tin oxide (ITO) glass substrate. ZnO nanorods were then grown on the film by the hydrothermal method, then assembled with a gold electrode to fabricate a nanogenerator. The ZnO nanostructure and nanogenerator were analyzed by field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), and the measurement of current-voltage characteristics. The results of FE-SEM show that the length of the ZnO nanorods increased with the growth time, and the optimal dimensions of the ZnO nanorods were a length of 2 μm and a diameter of 130 nm at the growth time of 6 h. In the XRD pattern, ZnO (002) and (103) peaks were observed at 2θ = 34.45 and 62.51°, respectively, confirming that the ZnO nanorods were grown on the substrate. The nanogenerator was driven by an ultrasonic wave to measure its voltage and current. The highest average current and voltage were 3.46 × 10 −6 A and 5.63 × 10 −2 V, respectively. These results indicate that the ZnO nanorods prepared by the hydrothermal method are suitable for the fabrication of a nanogenerator.

We propose a novel Tsallis cross-entropy thresholding method based on the premise that local long-range correlations rather than global long-range correlations may exist among the gray levels of pixels. The target and background of the image can be considered as two independent parts, and their information integrity can be maximized by the proposed method. Our experimental results show that this method can obtain better segmentation results than the minimum Tsallis cross-entropy thresholding method when considering global long-range correlations when segmenting images in which the object and background have no obvious correlations.