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The purpose of this paper was to level determine of mutual interaction and integration of the musical culture of She and Han nationalities into modernity through the study and coding of folk songs from four China geographical regions (Fujian, Zhejiang, Jiangxi, and Anhui provinces) using a new musical feature density map for machine music classification. The conducted simulations revealed that FIR-ELM is the best choice of the three classifiers (ELM, R-ELM, and FIR-ELM) analyzed as it is able to provide the highest classification accuracy. The simulations performed found that the low-frequency FIR-ELM is capable of 80.65% classification accuracy, as the FIR-ELM is an improved version of the ELM and thus performs better during the classification of folk songs. The findings of this study can be used as a basis for further research on the interaction and integration of the musical culture of these nationalities. [ABSTRACT FROM AUTHOR]

Influenced by various environmental and meteorological factors, wind speed presents stochastic and unstable characteristics, which makes it difficult to forecast. To enhance the forecasting accuracy, this study contributes to short-term multi-step hybrid wind speed forecasting (WSF) models using wavelet packet decomposition (WPD), feature selection (FS) and an extreme learning machine (ELM) with parameter optimization. In the model, the WPD technique is applied to decompose the empirical wind speed data into different, relatively stable components to reduce the influence of the unstable characteristics of wind speed. A hybrid particle swarm optimization gravitational search algorithm (HPSOGSA) combining conventional PSOGSA with binary PSOGSA (BPSOGSA) is utilized to realize the FS and parameter optimization simultaneously. The PSOGSA is employed to tune the parameter combination of input weights and biases in ELM, while BPSOGSA is exploited to select the most suitable features from the candidate input variables determined by a partial autocorrelation function for reconstruction of the input matrix for ELM. The proposed forecasting strategy carries out multi-step short-term WSF using mean half-hour historical wind speed data collected from a wind farm situated in Anhui, China. To investigate the forecasting results of the hybrid model, a lot of comparisons and analyses are executed. Simulation results illustrate that the proposed WPD-ELM model with FS and parameter optimization can effectively catch the non-linear characteristics hidden in wind speed data and provide satisfactory WSF performance. [ABSTRACT FROM AUTHOR]