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This paper proposes a new real-time target tracking method based on the open-loop monocular vision motion control. It uses the particle filter technique to predict the moving target’s position in an image. Due to the properties of the particle filter, the method can effectively master the motion behaviors of the linear and nonlinear. In addition, the method uses the simple mathematical operation to transfer the image information in the mobile target to its real coordinate information. Therefore, it requires few operating resources. Moreover, the method adopts the monocular vision approach, which is a single camera, to achieve its objective by using few hardware resources. Firstly, the method evaluates the next time’s position and size of the target in an image. Later, the real position of the objective corresponding to the obtained information is predicted. At last, the mobile robot should be controlled in the center of the camera’s vision. The paper conducts the tracking test to the L-type and the S-type and compares with the Kalman filtering method. The experimental results show that the method achieves a better tracking effect in the L-shape experiment, and its effect is superior to the Kalman filter technique in the L-type or S-type tracking experiment.

The train plug door is the only way for passengers to get on and off. The reliability of the doors has a direct impact on passengers’ safety and operational efficiency. In order to address the shortcomings of the post-analysis and poor real-time of current fault diagnosis methods for train plug doors, a fault diagnosis method based on sound recognition is proposed. To process the non-stationary sound signals, the empirical mode decomposition (EMD) method is applied to sound signal samples of train plug doors, and a series of intrinsic mode functions (IMFs) are obtained. Then, wavelet packet decomposition is utilized on each IMF to acquire more detailed information. And wavelet packet energy entropy features are obtained. The Fisher discrimination criterion is used to carry out a mathematical analysis to select the most significant features as discrimination features. Finally, multi-class support vector machine (multi-class SVM) is utilized to carry out classification and validation. And the prediction accuracy of the 67 test samples reaches 95.52%, which indicates the proposed fault diagnosis method for train plug doors is feasible. The proposed method also provides the possibility of automatic faults recognition. [ABSTRACT FROM AUTHOR]