In this paper, we propose a model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for music generation. We first convert MIDI-format music file into a musical score matrix, and then establish convolution layers to extract feature of the musical score matrix. Finally, the output of the convolution layers is split in the direction of the time axis and input into the LSTM, so as to achieve the purpose of music generation. The result of the model was verified by comparison of accuracy, time-domain analysis, frequency-domain analysis and human-auditory evaluation. The results show that Convolution-LSTM performs better in music genertaion than LSTM, with more pronounced undulations and clearer melody.
The problem of form-finding for the suspended cable is actually the problem of determining all key points' coordinates on main cable, which are by equilibrium relation on the horizontal force, main cable sagitta and lifting point force under the precondition of determining the endpoint's boundary conditions of cable segment. According from the static equilibrium relationship of cable element, based on the analysis of its analytical solution process, in this paper, the cable elements are divided into two types in accordance withthe vertical distribution load along the arc length and along the string length , the corresponding shape curve of cable element is the parabola and the catenary, and with parabolic results as its initial value for the iteration of nonlinear solution, then cable element eventually converge for the catenary. And based on the exact coordinates results ,the calculation method of the length without stress is presented , and compiled corresponding computational procedures. By comparing the results of form-finding and the cable-length in non-stress according to program compiled and the results from the finite element software and the measured value of Aizhai suspension bridge, compared with the nonlinear finite element method,it confirmed the method requireing smaller dividing element density, the convergence speed is quicker and the results can ensure the precision.
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