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Metro Vehicle Door System (MVDS) is one of the most frequently used parts in rail transit. In this work, we propose an effective and visualized state detection technique of MVDS by utilizing feature extraction and matching, which is helpful to improve the safety and reliability of the door system. Five states of MVDS, the normal opening, opened, closing and closed state, as well as the anti-extrusion (also named as anti-drag) state, are to be detected in our work. In particular, we design an improved Speeded Up Robust Features (SURF) method to reduce the mismatching pairs by exploiting local space distance, which is crucial for practical usage. Furthermore, we present a scheme to detect the five states of MVDS mentioned above via our improved SURF. Finally, we conduct experiments in subjective and objective, and the resultant performance shows the effectiveness of our method.

Detecting the state of the Duffing oscillator, a type of well-known chaotic oscillator, deeply affects the accuracy of its application. Considering this, the present paper introduced a novel method for detecting the state of the Duffing oscillator. Binary outputs, simple calculation, high precision and fast response time were the main advantages of the phase space trajectory autocorrelation. Also, this study explained the largest Lyapunov exponent as well as a number of other methods commonly employed in detecting the state of the Duffing oscillator. The precision and effectiveness of the method introduced was compared with other well-known state detection methods such as the 0-1 test and the largest Lyapunov exponent.