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Prediction of blast-induced ground vibration using gene expression programming (GEP), artificial neural networks (ANNS), and linear multivariate regression (LMR)

Shakeri Jamshid, Shokri Behshad Jodeiri, Dehghani Hesam

Archives of Mining Sciences

2020

Vol. 65, no. 2

317--335

In this paper, an attempt was made to find out two empirical relationships incorporating linear mul-tivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.

Analysis of State-Space Model based Voice Conversion

Sun J., Zhang X.

Przegląd Elektrotechniczny

2011

R. 87, nr 10

373-376

A new State-Space Model (SSM) based voice conversion method has been proposed recently which outperforms the traditional Gaussian Mixture Model (GMM) method. Although the implementation process of the new method has been elaborated, the theoretical essence of this method has not been analysed clearly. In this paper an exhaustive analysis of the SSM based method is given theoretically and experimentally. Through these analysis, much simpler equivalence form and performance upper bound of the new method are obtained. Finally possible improvements are discussed.

Przedstawiono teoretyczna i eksperymentalną analizę nowego algorytm SSM przetwarzania sygnału mowy.