Effect of cross-wind on spatial vibration responses of train and track system

By taking cross-wind forces acting on trains into consideration, a dynamic analysis method of the cross-wind and high-speed train and slab track system was proposed on the basis of the analysis theory of spatial vibration of high-speed train and slab track system. The corresponding computer program was written by FORTRAN language. The dynamic responses of the high-speed train and slab track under cross-wind action were calculated. Meanwhile, the effects of the cross-wind on the dynamic responses of the system were also analyzed. The results show that the cross-wind has a significant influence on the lateral and vertical displacement responses of the car body, load reduction factor and overturning factor. For example, the maximum lateral displacement responses of the car body of the first trailer with and without cross-wind forces are 32.10 and 1.60 mm, respectively. The maximum vertical displacement responses of the car body of the first trailer with and without cross-wind forces are 6.60 and 3.29 mm, respectively. The maximum wheel load reduction factors of the first trailer with and without cross-wind forces are 0.43 and 0.22, respectively. The maximum overturning factors of the first trailer with and without cross-wind forces are 0.28 and 0.08, respectively. The cross-wind affects the derailment factor and lateral Sperling factor of the moving train to a certain extent. However, the lateral and vertical displacement responses of rails with the crnss-wind are almost the same as those without the cross-wind. The method presented and the corresponding computer program can be used to calculate the interaction between trains and track in cross-wind.

A new formant feature and its application in Mandarin vowel pronunciation quality assessment

In order to improve the Mandarin vowel pronunciation quality assessment, a nox/el formant feature was proposed and applied to formant classification for Chinese Mandarin vowel pronunciation quality evaluation. Formant candidates of each frame were plotted on the time-frequency plane to form a bitmap, and its Gabor feature was extracted to represent the formant trajectory. The feature was then classified by using GMM model and the classification posterior probability was mapped to pronunciation quality grade. The experiments of comparing the Gabor transformation based formant trajectory feature with several other kinds of traditionally used features show that with this method, a human-machine scoring correlation coefficient （CC） of 0.842 can be achieved, which is better than the result of 0.832 by traditional speech recognition techniques. At the same time, considering that the long-term information of formant classification and the short-term information of speech recognition technique are complementary to each other, it is investigated to combine their results with linear or nonlinear methods to further improve the evaluation performance. As a result, experiments on PSK show that the best CC of 0.913, which is very close to the correlation of inter-human rating of 0.94, is gotten by using neural network.

Use of Context Blocks in Genetic Programming for Evolution of Robot Morphology

The paper explores applications of genetic programming to co-evolution of morphology and low-level control. In most reasonably difficult tasks, facilitation provided by modularity has proved to be vital for successful application of genetic programming. However, the need for sharing data among nodes in the syntactic tree becomes especially acute when evolving modular programs. It has been shown before that it may be beneficial that modules themselves be node-attached. The paper presents extensions to standard genetic programming （the so-called contexts and context blocks） that allow for straight-forward storage, retrieval, transfer, and modification of data stored in the context of a syntactic tree, and shared by multiple nodes. Framework is thus provided for both： data sharing and node-attached modules. Finally, using context blocks, a genetic algorithm has been embedded within genetic programming to evolve values of constants. In genetic programming evolution of constants has been a long-standing problem. The paper shows how context blocks can be utilized to provide a more granular and flexible approach to their evolution. As shown in previous works, node-attached modules perform favorably when compared with existing approaches. Concerning evolution of context block constants, it is shown here that they too perform favorably when compared with ephemeral constants.