Research on the performance of buffer for landing gear based on the drop test

Based on the drop test of the articulated main landing gear of Seagull 300 light multifunctional amphibious airplane, a further study has been conducted to establish buffer performance under different air chamber pressures and attitude angles. Through comparative analysis of the test results, the influencing rule of air chamber pressure and attitude angle on the buffer performance parameters (system capacity, vertical load, buffer compression, system efficiency and buffer efficiency) was obtained. The results demonstrate that air chamber pressure has a significant effect on the buffer system efficiency, while the attitude angle influences the system capacity a lot. With air chamber pressure increasing system efficiency decreases first, then gradually increases after reaching its minimum at 2.15 MPa and decreases at last after reaching its maximum at 2.7 MPa. Buffer efficiency decreases first and then increases after reaching its minimum at 2.2 MPa. When the attitude angle is between 3 and 12 degrees, the smaller the attitude angle, the more energy the system absorbs and the better the buffer performance is. The rate of change of performance parameters varies linearly with attitude angle. With the increase of angle, system capacity, maximum vertical load and system efficiency increase, and the change rate of buffer compression decreases correspondingly. The rate of change of system efficiency has the fastest growth

Learning user-specific latent influence and susceptibility from information cascades

Predicting cascade dynamics has important implications for understanding information propagation and launching viral marketing. Previous works mainly adopt a pair-wise manner, modeling the propagation probability between pairs of users using n^2 independent parameters for n users. Consequently, these models suffer from severe overfitting problem, specially for pairs of users without direct interactions, limiting their prediction accuracy. Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively. This model requires much less parameters and thus could combat overfitting problem. Moreover, this model could naturally model context-dependent factors like cumulative effect in information propagation. Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015