Capital Normal University

About: Capital Normal University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Terahertz radiation & Quantum entanglement. The organization has 11441 authors who have published 11988 publications receiving 159071 citations. The organization is also known as: Shǒudū Shīfàn Dàxué.

Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China

Abstract: Drought is a natural disaster that profoundly impacts all parts of the environment. Drought forecasting could provide technical support for drought risk prevention. This paper explored and compared the forecasting abilities of the autoregressive integrated moving average (ARIMA) in statistics, the wavelet neural network (WNN) and the support vector machine (SVM) in machine learning for drought forecasting in the Sanjiang Plain, China. The models used in this paper are based on the standard precipitation evapotranspiration index (SPEI) on the 12-month timescale. The SPEI was calculated using precipitation and temperature data collected during the period 1979–2016 from seven meteorological stations in the study area. Then, the SPEI series were predicted with the ARIMA, WNN and SVM models separately. The coefficient of determination (R2), mean-squared error (MSE), Nash–Sutcliffe efficiency coefficient (NSE) and Kolmogorov–Smirnov (K–S) distance, which is a nonparametric measure, were used to evaluate the performance of all models. A comparison between the raw data and predictions illustrates that the R2 and NSE values of the WNN model were 0.837 and 0.831, respectively; those of the SVM model were 0.833 and 0.827, respectively; and those of the ARIMA model were both > 0.9. Moreover, the ARIMA model had smaller MSE and K–S distance values than those of the other two models. Further, analysis of variance showed that the ARIMA model exhibited an obvious advantage over the other two models when forecasting drought in the Sanjiang Plain, China. Therefore, the method used for drought forecasting depends not only on the object of the data series but also on the underlying concepts of the models or algorithms and is a choice that should be made with caution.

A survey of methods incorporating spatial information in image classification and spectral unmixing

Abstract: Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.

Evolution equation of entanglement for bipartite systems

Abstract: We explore how entanglement of a bipartite system evolves when one subsystem undergoes the action of an arbitrary noisy channel. It is found that the dynamics of entanglement of such system is determined by the channel's action on the maximally entangled state, which includes as a special case the results for two-qubit systems [Konrad et al., Nat. Phys. 4, 99 (2008)]. In particular, for multiqubit or qubit-qudit systems, we get a general factorization law for the evolution equation of entanglement, with one qubit being subject to a noisy channel.