In order to reduce traffic accidents caused by fatigue driving, a fatigue driving detection algorithm is proposed based on deep learning and facial multi-index fusion from the driver $'\text{s}$ facial features. Because the scene in the actual driving process is very complex and changeable, this algorithm first improves the multi-task cascaded convolutional neural network (MTCNN) so that it can quickly and accurately locate the face and detect the facial key points. According to the facial key points, the driver’s eyes and mouth regions are determined. Second, these regions are input into the eyes and mouth state recognition network (E-MSR Net) for state recognition. The E-MSR Net is a depth separable convolution neural network that is improved and optimized based on MobilenetV2. Finally, the three facial features of eye closure rate (ECR), mouth opening rate (MOR), and head non-positive face rate (HNFR) are fused to judge the driver’s fatigue state. This algorithm can quickly and accurately make judgments in the face of complex and changeable scenes. At the same time, it can avoid the failure of the algorithm caused by the occlusion of the eyes or mouth due to wearing sunglasses or masks during driving. The accuracy of the proposed algorithm on the self-made data set achieved 97.5%, which proved the feasibility of the algorithm.

Aiming at the problem of convergence speed and chattering in sliding mode variable structure control of manipulator, an improved exponential reaching law and nonlinear sliding surface are proposed, and the Lyapunov function is used to analyze its stability. According to the dynamic model of the 6-DOF UR5 manipulator and the proposed reaching law and sliding surface, the corresponding control scheme is designed. The control performance of the proposed control scheme is verified by tracking the end trajectory of the manipulator on the MATLAB and CoppeliaSim robot simulation platform. The experimental results show that the proposed control scheme can not only significantly improve the convergence speed and make the system converge quickly, but also can effectively reduce the chattering of the system. Even in the presence of disturbance signals, fast and stable tracking can be achieved while ensuring the robustness of the system, and the chattering of the robotic arm system can be weakened to a certain extent. Compared with the classical control method based on the computational torque method and the traditional sliding mode control scheme based on the exponential reaching law, the proposed scheme has certain advantages in terms of tracking accuracy, convergence speed, and reducing system chattering, and effectively improves the overall control performance of the system.

Owing to the complex dynamics of quadrotor unmanned aerial vehicles (UAVs) and their susceptibility to unknown interferences in an actual working environment, the flight control accuracy of UAVs is extremely high. Moreover, their anti-interference ability is particularly important. This study designed a sliding-mode controller based on the extended state observer. The position control was obtained through the outer-loop position controller. The attitude control was determined through the inner-loop attitude controller. The input of the UAV system was obtained through the controller. The boundary-layer function was used to weaken the oscillatory response of the system, and the traditional extended state observer was improved to improve the response speed, robustness, and tracking accuracy of the controller. For the entire process, the input and output state information of the system and total internal and external disturbances were estimated in real-time through the extended state observer. A sliding-mode control law was designed to compensate for the estimated disturbance in real-time to realize attitude control. Finally, Lyapunov theory was used to confirm the stability of the system. The simulation results demonstrated the improved anti-interference and tracking ability of the designed controller.

Gesture fingertip detection plays a vital role in human-computer interaction applications such as VR and robot control. To improve the accuracy of fingertip detection in complex background, this paper proposes a fingertip detection algorithm based on a novel maximum discrimination HOG feature. Firstly, the Holistically-nested edge detection algorithm is used to detect the edge of hand images and non-hand images, and the HOG features of the contour edge are extracted to obtain the training set of positive and negative samples to reduce the influence of illumination, color, and texture on fingertip detection. Secondly, the maximum discrimination features are filtered from the positive sample set by a custom maximum discrimination feature filter and stored as a feature dictionary. The filtered maximum discrimination features and negative sample set are input into the XGBoost classifier for training and the voting rights classifier is obtained. Filtering maximum discrimination features can reduce the interference of irrelevant features and improve the performance of the classifier. Then, the KNN algorithm is used to find the best match in the dictionary, and the final fingertip position is obtained by the Meanshift algorithm. Finally, the algorithm in this paper is tested. The test results show that the accuracy of fingertip detection in this paper can reach 99 %, and the detected RMSE is kept in the range of 5 pixels, which is higher than that based on YCbCr skin color segmentation, YOLO target detection, and YOLO-YCbCr.

The present study proposes an active disturbance rejection sliding mode traction control system to deal with the dramatic change of joint angular acceleration and jitter attributed to non-uniform traction during terminal traction of the manipulator based on the use of human-computer interaction (HCI) device, as well as achieve the high-precision and high-speed terminal traction control. First, the traction trajectory generated by the HCI device is mapped into the motion space of the manipulator, and a low-pass filter is employed to filter out the high-frequency noises attributed to non-uniform traction. Second, the corresponding joint solution of the traction trajectory is solved by using the inverse kinematics algorithm. Then the nonlinear and strongly coupled manipulator is linearly decoupled. Lastly, the effect of non-uniform traction on the manipulator’s joints is considered as interference, and an active disturbance rejection sliding mode controller is introduced for each joint. To reduce the chattering, the output torque of the controller is processed with a low-pass filter. The stability of the controller is proved with the Lyapunov method. To verify the traction effect of the traction control system and its anti-interference ability for non-uniform traction, the trajectory tracking experiment with random jitter and the real terminal traction control experiment is performed on the UR5 manipulator in the virtual robot experiment platform. As revealed from the experimentally achieved results, the proposed controller can significantly limit the effect of non-uniform traction on each joint of the manipulator and achieve the fast, stable, and high-precision terminal traction control of the manipulator by the HCI device.

High-precision logging equipment is critical for measuring the borehole diameter and drilling offset in coal mining and petroleum drilling. We propose a module composition and positioning principle for an ultrasonic transducer based on an ultrasonic logging instrument for shaft sinking by drilling (ULISSD) for calculating the reflection distance. The logging distance, which is the primary performance index of a logging system, is determined by using the self-reception sensitivity and error of the ultrasonic transducer in a downhole system. To measure the error between the piezoelectric element of the transducer and the rubber seal of the borehole logging system, we developed an ultrasonic-transducer error-calibration device and a calibration method for a central-air-return-shaft-drilling project. This calibration device can eliminate the inherent error of the transducer and calculate the rate of propagation with high accuracy. The measurement error is reduced by approximately 1.5 mm; thus, the ULISSD measurement accuracy can be effectively improved in central-air-return-shaft drilling.