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Search Results: 1 - 10 of 120191 matches for " Xiaogang Wang "
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A Derivative-Free Optimization Algorithm Using Sparse Grid Integration  [PDF]
Shengyuan Chen, Xiaogang Wang
American Journal of Computational Mathematics (AJCM) , 2013, DOI: 10.4236/ajcm.2013.31003
Abstract:

We present a new derivative-free optimization algorithm based on the sparse grid numerical integration. The algorithm applies to a smooth nonlinear objective function where calculating its gradient is impossible and evaluating its value is also very expensive. The new algorithm has: 1) a unique starting point strategy; 2) an effective global search heuristic; and 3) consistent local convergence. These are achieved through a uniform use of sparse grid numerical integration. Numerical experiment result indicates that the algorithm is accurate and efficient, and benchmarks favourably against several state-of-art derivative free algorithms.

Theoretical Properties of Composite Likelihoods  [PDF]
Xiaogang Wang, Yuehua Wu
Open Journal of Statistics (OJS) , 2014, DOI: 10.4236/ojs.2014.43018
Abstract:

The general functional form of composite likelihoods is derived by minimizing the Kullback-Leibler distance under structural constraints associated with low dimensional densities. Connections with the I-projection and the maximum entropy distributions are shown. Asymptotic properties of composite likelihood inference under the proposed information-theoretical framework are established.

HYDROGEN-VACANCY INTERACTION IN IRON
WANG Xiaogang,
WANG
,Xiaogang

金属学报(英文版) , 1995,
Abstract: The discrete variational method (DVM)and cluster model are used to study the electronic structure and atomic configuration of the hydrogen-vacancy complex in iron.The optimum location of hydrogen atom in the monovacancy in iron is determined by minimizing the total energy and the bond characteristic between hydrogen and iron atoms is analyzed Meanwhile, the positron annihilation characteristics of the hydrogen -vacancy complex is also calculated.
Discrete Inequalities on LCT  [PDF]
Guanlei Xu, Xiaotong Wang, Xiaogang Xu
Journal of Signal and Information Processing (JSIP) , 2015, DOI: 10.4236/jsip.2015.62014
Abstract: Linear canonical transform (LCT) is widely used in physical optics, mathematics and information processing. This paper investigates the generalized uncertainty principles, which plays an important role in physics, of LCT for concentrated data in limited supports. The discrete generalized uncertainty relation, whose bounds are related to LCT parameters and data lengths, is derived in theory. The uncertainty principle discloses that the data in LCT domains may have much higher concentration than that in traditional domains.
Data Fusion Using Empirical Likelihood  [PDF]
Hsiao-Hsuan Wang, Yuehua Wu, Yuejiao Fu, Xiaogang Wang
Open Journal of Statistics (OJS) , 2012, DOI: 10.4236/ojs.2012.25070
Abstract: The authors propose a robust semi-parametric empirical likelihood method to integrate all available information from multiple samples with a common center of measurements. Two different sets of estimating equations are used to improve the classical likelihood inference on the measurement center. The proposed method does not require the knowle- dge of the functional forms of the probability density functions of related populations. The advantages of the proposed method are demonstrated through extensive simulation studies by comparing the mean squared errors, coverage proba- bilities and average lengths of confidence intervals with those from the classical likelihood method. Simulation results suggest that our approach provides more informative and efficient inference than the conventional maximum likelihood estimator if certain structural relationship exists among the parameters of relevant samples.
Laplacian spectral characterization of some graph products
Suijie Wang,Xiaogang Liu
Mathematics , 2010, DOI: 10.1016/j.laa.2012.05.003
Abstract: This paper studies the Laplacian spectral characterization of some graph products. We consider a class of connected graphs: $\mathscr{G}={G : |EG|\leq|VG|+1}$, and characterize all graphs $G\in\mathscr{G}$ such that the products $G\times K_m$ are $L$-DS graphs. The main result of this paper states that, if $G\in\mathscr{G}$, except for $C_{6}$ and $\Theta_{3,2,5}$, is $L$-DS graph, so is the product $G\times K_{m}$. In addition, the $L$-cospectral graphs with $C_{6}\times K_{m}$ and $\Theta_{3,2,5}\times K_{m}$ have been found.
Fully Convolutional Neural Networks for Crowd Segmentation
Kai Kang,Xiaogang Wang
Computer Science , 2014,
Abstract: In this paper, we propose a fast fully convolutional neural network (FCNN) for crowd segmentation. By replacing the fully connected layers in CNN with 1 by 1 convolution kernels, FCNN takes whole images as inputs and directly outputs segmentation maps by one pass of forward propagation. It has the property of translation invariance like patch-by-patch scanning but with much lower computation cost. Once FCNN is learned, it can process input images of any sizes without warping them to a standard size. These attractive properties make it extendable to other general image segmentation problems. Based on FCNN, a multi-stage deep learning is proposed to integrate appearance and motion cues for crowd segmentation. Both appearance filters and motion filers are pretrained stage-by-stage and then jointly optimized. Different combination methods are investigated. The effectiveness of our approach and component-wise analysis are evaluated on two crowd segmentation datasets created by us, which include image frames from 235 and 11 scenes, respectively. They are currently the largest crowd segmentation datasets and will be released to the public.
Convergent and Anti-diffusive Properties of Mean-Shift Method
Xiaogang Wang,Jianhong Wu
Statistics , 2012,
Abstract: An analytic framework based on partial differential equations is derived for certain dynamic clustering methods. The proposed mathematical framework is based on the application of the conservation law in physics to characterize successive transformations of the underlying probability density function. It is then applied to analyze the convergence and stability of mean shift type of dynamic clustering algorithms. Theoretical analysis shows that un-supervised mean-shift type of algorithm is intrinsically unstable. It is proved that the only possibility of a correct convergence for unsupervised mean shift type of algorithm is to transform the original probability density into a multivariate normal distribution with no dependence struture. Our analytical results suggest that a more stable and convergent mean shift algorithm might be achieved by adopting a judiciously chosen supervision mechanism.
Changes in Tropical Cyclone Number in the Western North Pacific in a Warming Environment as Implied by Classical Thermodynamics  [PDF]
Xiaogang Zhou, Chongjian Liu, Ying Liu, Hui Xu, Xiuming Wang
International Journal of Geosciences (IJG) , 2011, DOI: 10.4236/ijg.2011.21003
Abstract: Observational analyses show that the equatorial trough in the western North Pacific (WNP) is a well-known origin for tropical cyclones (TC) which have tended to weaken in intensity and decrease in number during the last several decades under global warming. A scientific problem then arises as to why higher sea surface temperatures (SSTs), one of the necessary conditions for typhoon genesis, can cause a weakened equatorial trough and a decreased TC number. In this paper, the WNP is taken as an example to illustrate a possible mechanism for the above-mentioned seemingly counterintuitive phenomena and explain the causality between the unusually heterogeneous pattern of SSTs in a warming environment and TC number in the WNP. This mechanism is based substantially on the second law of thermodynamics.
Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit  [PDF]
Guanlei Xu, Xiaotong Wang, Xiaogang Xu, Lijia Zhou, Limin Shao
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.43031
Abstract:

In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

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