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Search Results: 1 - 10 of 120321 matches for " Xiaoqing Wang "
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New MDS Euclidean and Hermitian Self-Dual Codes over Finite Fields  [PDF]
Hongxi Tong, Xiaoqing Wang
Advances in Pure Mathematics (APM) , 2017, DOI: 10.4236/apm.2017.75019
Abstract: In this paper, we construct MDS Euclidean self-dual codes which are ex-tended cyclic duadic codes. And we obtain many new MDS Euclidean self-dual codes. We also construct MDS Hermitian self-dual codes from generalized Reed-Solomon codes and constacyclic codes.
Stochastic Separated Continuous Conic Programming: Strong Duality and a Solution Method
Xiaoqing Wang
Mathematical Problems in Engineering , 2014, DOI: 10.1155/2014/896591
Abstract: We study a new class of optimization problems called stochastic separated continuous conic programming (SSCCP). SSCCP is an extension to the optimization model called separated continuous conic programming (SCCP) which has applications in robust optimization and sign-constrained linear-quadratic control. Based on the relationship among SSCCP, its dual, and their discretization counterparts, we develop a strong duality theory for the SSCCP. We also suggest a polynomial-time approximation algorithm that solves the SSCCP to any predefined accuracy. 1. Introduction Stochastic programming is one of the branches of optimization which enjoys a fast development in recent years. It tries to find optimal decisions in problems involving uncertain data, so it is also called “optimization under uncertainty” [1]. Since the problems in reality often involve uncertain data, stochastic programming has a lot of applications. Many deterministic optimization models have their stochastic counterpart; for example, the stochastic counterpart of linear programming is stochastic linear programming. In this paper, we consider the stochastic counterpart of a kind of optimization model called separated continuous conic programming ( ) which has the following form: Here the control and state variables (both are decision variables), and , are vectors of bounded measurable functions of time . , , are closed convex cones in the Euclidean space with appropriate dimensions, are vectors, are matrices, and the superscript denotes the transpose operation. was first studied by Wang et al. [2]. They developed a strong duality theory for under some mild and verifiable conditions and suggested an approximation algorithm to solve with predefined precision. has a variety of applications in robust optimization and sign-constrained linear-quadratic control. However, many applications of are stochastic in nature in the sense that the values of some parameters in the resulted models may change over time with some probability distribution. To incorporate this kind of randomness into the model, we introduce the following stochastic counterpart of which we call stochastic separated continuous conic programming ( ) problem: where is a random variable. is formulated with the similar idea as that of the stochastic linear programming [1, 3]. There are two stages in this problem; the values of some parameters in the second stage depend on the value of a random variable . Our goal in this paper is developing the strong duality for and suggesting a solution method to solve it approximately with predefined
Stepwise Confidence Interval Method for MTD Studies with Binomial Populations  [PDF]
Na Yu, Xiaoqing Tang, Hanxing Wang
Engineering (ENG) , 2013, DOI: 10.4236/eng.2013.510B095

Now we extend one method into a sequence of binomial data, propose a stepwise confidence interval method for toxic-ity study, and also in our paper, two methods of constructing intervals for the risk difference are proposed. The first one is based on the well-known conditional confidence intervals for odds ratio, and the other one comes from Santner“small-sample confidence intervals for the difference of two success probabilities”, and it produces exact intervals, through employing our method.

On Inversion of Continuous Wavelet Transform  [PDF]
Lintao Liu, Xiaoqing Su, Guocheng Wang
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.57071

This study deduces a general inversion of continuous wavelet transform (CWT) with timescale being real rather than positive. In conventional CWT inversion, wavelet’s dual is assumed to be a reconstruction wavelet or a localized function. This study finds that wavelet’s dual can be a harmonic which is not local. This finding leads to new CWT inversion formulas. It also justifies the concept of normal wavelet transform which is useful in time-frequency analysis and time-frequency filtering. This study also proves a law for CWT inversion: either wavelet or its dual must integrate to zero.

Stability and Local Hopf Bifurcation for a Predator-Prey Model with Delay
Yakui Xue,Xiaoqing Wang
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/252437
Abstract: A predator-prey system with disease in the predator is investigated, where the discrete delay is regarded as a parameter. Its dynamics are studied in terms of local analysis and Hopf bifurcation analysis. By analyzing the associated characteristic equation, it is found that Hopf bifurcation occurs when crosses some critical values. Using the normal form theory and center manifold argument, the explicit formulae which determine the stability, direction, and other properties of bifurcating periodic solutions are derived.
DOA estimation of wideband signals based on slice-sparse representation
Lu Gan and Xiaoqing Wang
EURASIP Journal on Advances in Signal Processing , 2013, DOI: 10.1186/1687-6180-2013-18
Abstract: In this article, the direction-of-arrival (DOA) estimation problem of wideband signal sources is studied. We pass the incident signals through a bank of narrowband filters to split the array outputs into several narrowband components. Then, a novel slice-sparse representation model of the joint narrowband array covariance data is proposed in the frequency domain to enforce joint sparsity in the concatenated covariance matrix of all frequencies. Based on the greed matching pursuit algorithm, a multiple measurement slices orthogonal matching pursuit algorithm is proposed to exploit the joint frequency processing in the case of wideband scenarios. The DOA estimation is achieved by joint processing of the array covariance data at different frequency bins. The estimated performance is compared with the representative DOA estimation methods. Simulation experiments are conducted to validate the effectiveness of the proposed method.
Image Processing Techniques in Shockwave Detection and Modeling  [PDF]
Suxia Cui, Yonghui Wang, Xiaoqing Qian, Zhengtao Deng
Journal of Signal and Information Processing (JSIP) , 2013, DOI: 10.4236/jsip.2013.43B019

Shockwave detection is critical in analyzing shockwave structure and location. High speed video imaging systems are commonly used to obtain image frames during shockwave control experiments. Image edge detection algorithms become natural choices in detecting shockwaves. In this paper, a computer software system designed for shockwave detection is introduced. Different image edge detection algorithms, including Roberts, Prewitt, Sobel, Canny, and Laplacian of Gaussian, are implemented and can be chosen by the users to easily and accurately detect the shockwaves. Experimental results show that the system meets the design requirements and can accurately detect shockwave for further analysis and applications.

The Relationship between Professional Identity and Career Maturity among Pre-Service Kindergarten Teachers: The Mediating Effect of Learning Engagement  [PDF]
Liying Zhang, Meirong Chen, Xiaoqing Zeng, Xinqiang Wang
Open Journal of Social Sciences (JSS) , 2018, DOI: 10.4236/jss.2018.66016
Abstract: Objective: To investigate the mediating role of leaning engagement in the relationship between professional identity and career maturity. Methods: 711 pre-service kindergarten teachers completed the Professional Identification Scale, the College Students’ Career Maturity Questionnaire and the Utrecht Work Engagement Scale-student. Results: 1) There are significant difference in professional identity and career maturity on grades, different level of parents’ education and whether part-time work. 2) Professional identity, career maturity, and learning engagement were significantly correlated with each other. 3) Career maturity partially mediated the relationship between professional identity and learning engagement. Conclusion: Professional identity directly influences learning engagement, and also influences learning engagement through the partial mediating effects of career maturity.
A Software Tool for Constructing Traditional Chinese Medical Expert Systems
Wang Nengbin,Liu Xiaoqing,Liu Guangfu
计算机科学技术学报 , 1988,
Abstract: It is an urgent task to hnplemeut a lot of expert systems to capture the valuable expertise ofexperienced doctors of traditional Chinese medicine.In order to meet the needs,a software tool isdeveloped.It features a unified diagnosis model,a specially designed knowledge representationlanguage and an efficient but effective inference engine.To implement an expert system,it isonly necessary to input the expert's knowledge expressed in knowledge representation languagewithout the design of any additional software.The time and effort required for implementing anexpert system are thus greatly saved.The software is very compact and can run onmicrocomputers e.g.IBM-PC/XT.Two traditional Chinese medical expert systems have beensuccessfully implemented with the tool.
Multi Features Combination for Pedestrian Detection
Bin Hu,Shengjin Wang,Xiaoqing Ding
Journal of Multimedia , 2010, DOI: 10.4304/jmm.5.1.79-84
Abstract: In this paper, we propose a new approach for pedestrian detection in crowded scene from static images. The method is based on hybrid features, one type of middle-level features, which compose of multi features include gradient features, Edgelet features and haar-like features, three low-level feature sets. The gradient features focus on the local point information, the Edgelet features focus on the local edge information and the haar-like features focus on the local region information of the image. We use two stages of Adaboost to train the final classifier. In the first stage, the whole image is divided into many small windows which all include numerous low-level features. Adaboost is used in each window to get one middle-level feature which composes of some best features including gradient features, Edgelet features and haar-like features in this window. Secondly, from all middle-level features, Adaboost is used again to get the final classifier. Experiment results on common datasets and comparisons with some previous methods are given.
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