A tilt-rotor aircraft can switch between two flight configurations (the helicopter configuration and the fixed-wing plane configuration) by tilting its rotors. In the process of rotor tilting, the nacelles which drive the rotors tilt together with the rotors. Because the mass of the nacelles cannot be ignored compared to the mass of the whole aircraft, the tilting of the nacelles is a coupling motion of the body and the nacelles. In order to better character the aircraft dynamics during the nacelle tilting, a multibody model is established in this paper. In this multibody model, Kane’s method is used to build a dynamic model of a tilt-rotor aircraft. The generalized rates are used to describe the movement of the body and the nacelles (with rotors). The generalized active forces and generalized inertial forces of both the body and the nacelles (with rotors) are obtained, respectively, and the first-order differential equations of the generalized rates are obtained. The longitudinal trim of the XV-15 aircraft is calculated according to the single-body model and our multibody model, in this paper, and the results verify the correctness of the multibody model. In the process of nacelle inclination angle command tracking, the multibody model can provide more information about the disturbance torque of the nacelle than the single-body model, and model inversion control based on the proposed multibody model can obtain a better tracking result than a PID control method only using nacelle angle feedback information.
Software defect prediction can help software testers to focus on software modules with more defects. Many ensemble methods have been proposed for software defect prediction to divide software modules into defect-prone and defect-free, and these ensemble methods have been proved to be more effective than single learning algorithms. A few ensemble approaches have been applied to predict the number of defects in software modules, and they also perform well in most cases. The good performance of ensemble approaches implies that ensemble algorithms might not only improve the accuracy of software defect classification models, but also improve the performance of defect ranking models. Therefore, we propose an ensemble method based on Yang et al.'s learning-to-rank approach in this paper. Experimental results show that the learning-to-rank-based ensemble approach performs better than the single learningto-rank approach, which means that the idea of ensemble can improve the performance of the learning-to-rank approach to sort modules in order of defect count. We also conduct a comparison study of ensemble approaches for cross-version defect prediction over 30 sets of cross-version data, which indicates that the ensemble technique of random subspace is more appropriate than boosting over these experimental data sets.
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