Abstract
Known as an ancient civilization, there exists a large amount of earthen archaeological sites in China. Various types of environment monitoring data have been accumulated waiting to be analyzed for the aim of future protection. In this paper, a non-stationary data processing strategy is proposed for the better understanding of such monitoring data. The kernel-based extreme learning machine (ELM) is utilized to preprocess the original data and restore the missing parts. Then a new non-uniform sampling time-frequency representation (TFR) is proposed to analyze the non-stationary characteristic of restored data from a signal processing perspective. The test data is the real environment monitoring data of the burial pit at the Yang Mausoleum of the Han dynasty. The experimental result shows that the proposed scheme can extract different information from the original data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhu, M.Z., Zhang, X.L., Qi, Y.: An adaptive STFT using energy concentration optimization. In: 10th International Conference on Information, Communications and Signal Processing, Singapore, Dec 2015
Sejdic, E., Djurovic, I., Jiang, J.: Time–frequency feature representation using energy concentration: an overview of recent advances. Digit. Sig. Proc. 19(1), 153–183 (2009)
Zhang, S.Q., Li, P., Zhang, L.G., Li, H.J., Jiang, W.L., Hu, Y.T.: Modified S transform and ELM algorithms and their applications in power quality analysis. Neurocomputing 185, 231–241 (2016)
Bai, Z., Huang, G.B., Wang, D.W., Wang, H.: Sparse extreme learning machine for classification. IEEE Trans. Cybern. 44(10), 1858–1870 (2014)
Silvestre, L.J., Lemos, A.P., Braga, J.P., Braga, A.P.: Dataset structure as prior information for parameter-free regularization of extreme learning machines. Neurocomputing 169, 288–294 (2015)
Han, M., Liu, B.: Ensemble of extreme learning machine for remote sensing image classification. Neurocomputing 149, 65–70 (2015)
Hu, J.G., Zhou, G.M., Xu, X.J.: Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data. Ecol. Model. 266, 86–96 (2013)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
Feng, G.R., Lan, Y., Zhang, X.P., Qian, Z.X.: Dynamic adjustment of hidden node parameters for extreme learning machine. IEEE Trans. Cybern. 45(2), 279–288 (2015)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans ON systems, Man, and Cybernetics-Part B 42(2), 513–529 (2012)
Acknowledgement
This research was supported in part by the National Natural Science Foundation of China (61301286) and the Fundamental Research Funds for the Central Universities (JB160210).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Qi, Y., Zhu, M., Zhang, X., Fu, F. (2018). Earthen Archaeological Site Monitoring Data Analysis Using Kernel-based ELM and Non-uniform Sampling TFR. In: Cao, J., Cambria, E., Lendasse, A., Miche, Y., Vong, C. (eds) Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-57421-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-57421-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57420-2
Online ISBN: 978-3-319-57421-9
eBook Packages: EngineeringEngineering (R0)