Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sam...Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sampling intervals on predictive performance of ANNs in forecasting exchange rate time series. It is shown that selection of an appropriate sampling interval would permit the neural network to model adequately the financial time series. Too short or too long a sampling interval does not provide good forecasting accuracy. In addition, we discuss the effect of forecasting horizons and input nodes on the prediction performance of neural networks.展开更多
Surface remeshing is widely required in modeling, animation, simulation, and many other computer graphics applications. Improving the elements' quality is a challenging task in surface remeshing. Existing methods ...Surface remeshing is widely required in modeling, animation, simulation, and many other computer graphics applications. Improving the elements' quality is a challenging task in surface remeshing. Existing methods often fail to efficiently remove poor-quality elements especially in regions with sharp features. In this paper, we propose and use a robust segmentation method followed by remeshing the segmented mesh. Mesh segmentation is initiated using an existing Live-wire interaction approach and is further refined using local mesh operations. The refined segmented mesh is finally sent to the remeshing pipeline, in which each mesh segment is remeshed independently. An experimental study compares our mesh segmentation method as well as remeshing results with representative existing methods. We demonstrate that the proposed segmentation method is robust and suitable for remeshing.展开更多
We use multifractal detrended fluctuation analysis （MF-DFA） method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen （OFC） earthquake model on assortative...We use multifractal detrended fluctuation analysis （MF-DFA） method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen （OFC） earthquake model on assortative scale-free networks. We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifraetal nature. Comparing the MF-DFA results for the original interevent time series with those for shuffled and surrogate series, we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation.展开更多
基金This research is Partially supported by NSFC, CAS. MADIS and RGC of Hong Kong.
文摘Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sampling intervals on predictive performance of ANNs in forecasting exchange rate time series. It is shown that selection of an appropriate sampling interval would permit the neural network to model adequately the financial time series. Too short or too long a sampling interval does not provide good forecasting accuracy. In addition, we discuss the effect of forecasting horizons and input nodes on the prediction performance of neural networks.
基金the National Natural Science Foundation of China(Nos.61772523,61372168,61620106003,and 61331018)supported by a Chinese Government Scholarship
文摘Surface remeshing is widely required in modeling, animation, simulation, and many other computer graphics applications. Improving the elements' quality is a challenging task in surface remeshing. Existing methods often fail to efficiently remove poor-quality elements especially in regions with sharp features. In this paper, we propose and use a robust segmentation method followed by remeshing the segmented mesh. Mesh segmentation is initiated using an existing Live-wire interaction approach and is further refined using local mesh operations. The refined segmented mesh is finally sent to the remeshing pipeline, in which each mesh segment is remeshed independently. An experimental study compares our mesh segmentation method as well as remeshing results with representative existing methods. We demonstrate that the proposed segmentation method is robust and suitable for remeshing.
基金Supported by Foundation for Outstanding Young and Middle-aged Scientists in Shandong Province under Grant No.BS2011HZ019State Key Laboratory of Data Analysis and Applications,State Oceanic Administration under Grant No.LDAA-2011-02the Fundamental Research Funds for the Central Universities under Grant No.201113006
文摘We use multifractal detrended fluctuation analysis （MF-DFA） method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen （OFC） earthquake model on assortative scale-free networks. We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifraetal nature. Comparing the MF-DFA results for the original interevent time series with those for shuffled and surrogate series, we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation.
