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Automatic detection and recognition of pavement distresses is the key to timely repair of pavement. Repairing the pavement distresses in time can prevent the destruction of road structure and the occurrence of traffic accidents. However, some other factors, such as a single object category, shading and occlusion, make detection of pavement distresses very challenging. In order to solve these problems, we use the improved YOLOv5 model to detect various pavement distresses. We optimize the YOLOv5 model and introduce attention mechanism to enhance the robustness of the model. The improved model is more suitable for deployment in embedded devices. The optimized model is transplanted to the self-built intelligent mobile platform. Experimental results show that the improved network model proposed in this paper can effectively identify pavement distresses on the self-built intelligent mobile platform and datasets. The precision, recall and mAP are 95.5%, 94.3% and 95%. Compared with YOLOv5s and YOLOv4 models, the mAP of the improved YOLOv5s model is increased by 4.3% and 25.8%. This method can provide technical reference for pavement distresses detection robot.
Aiming at the problems of model uncertainties and other external interference in trajectory tracking control of n-degree of freedom manipulators, a non-singular terminal sliding mode controller with nonlinear disturbance observer (NDO–NTSMC) trajectory tracking method is proposed. A nonlinear disturbance observer (NDO) is designed to forecast and compensate the system external interference, and a nonlinear gain is designed to make the observer error achieve the expected exponential convergence rate so that the feedforward compensation control is realized. Then, a non-singular terminal sliding mode controller (NTSMC) built on nonlinear sliding surface is designed to surmount the singularity fault of classic terminal sliding mode controller (TSMC). Therefore, the time required from any initial state to reach the equilibrium point is finite. In addition, the redesign of the sliding surface ensures the tracking accuracy rate of uncertain systems. Then, based on Lyapunov principle, we complete the stability analysis. Finally, the method is applied to a 2-DOF robotic manipulator model compared with other methods. In the simulation, the manipulator needs to track a continuous trajectory under the condition of joint friction disturbance. The simulation result shows that the torque output of the designed method is chattering-free and smooth, and the tracking effect is precise. Simulation results indicate that the proposed controller has the advantages of excellent tracking performance, strong robustness, and a fast response.
In the common application of ECG, a technique of detecting and analyzing ECG signal waveform based on deep learning for large samples of ECG signal is proposed to replace artificial image recognition. For higher accuracy, it’s necessary to improve the existing CNN classifiers. First, it’s to pre-process the ECG signal to remove noise for QRS detection. Secondly, it’s to extract the QRS complex features, which contain the R wave— with the largest amplitude, can be used to capture and locate protruding morphological data, then send the datasets to the model to train. Eventually, the results are obtained. Using the proposed Dropout-DCNN model, the overall accuracy of the detection reaches 99.52% when we compare it with manual ones marked by experts in the MIT-BIH library. It can be considered that this module better than other ones and can be applied in clinical practice.
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