摘要：
Directional sensor networks have a lot of practical applications, and target coverage is one of the most important issues. In this article, we study the target coverage problem in directional sensor network, where directional sensors can rotate freely around their centers and targets are attached with differentiated coverage quality requirements. Our goal is to maximize the lifetime of sensor network, satisfying the coverage quality requirements of all targets. We model it as the maximum cover sets problem and address it by organizing the directions of sensors into a group of non-disjoint cover sets, which can cover all targets, satisfying their coverage quality requirements, and then schedule them alternately to extend the network lifetime. It consists of two parts. First, since directional sensor has infinite directions by rotating continuously, we propose sensing direction partition algorithm to find all non-redundancy directions for each sensor according to the targets deployed within the sensing region of the sensor. Then, based on the result of the sensing direction partition algorithm, we propose an efficient heuristic algorithm for the maximum cover sets problem to organize the directions of sensors into a number of non-disjoint cover sets and allocate work time for each cover set. Besides, we get an upper bound of the optimal solution for the problem. Finally, simulation results are presented to demonstrate the performance of our algorithm.
摘要：
当前的服务选择方法大都假设所有服务的QoS(Quality of Service)属性值必须均为确定的实数,并未考虑QoS属性的模糊性。这个假设在实际应用需求中具有一定的局限性,还会丢失大量的数据信息。为增强QoS表达能力,将QoS属性值描述成精确数值型、区间数值型、模糊数值型。同时,用序关系向量表示用户对不同QoS属性的需求偏好,将其转换成用户对QoS属性的主观权重,并采用熵权法计算QoS属性的客观权重。在此基础上,采用相对优势度算法给出混合QoS属性的服务选择过程。最后通过模拟和实验验证,证明相对优势度算法的有效性与合理性。
作者机构：
[李畅] Hubei Province Key Laboratory for Geographical Process Analysing & Modelling, Central China Normal University, Wuhan;430079, China;College of Urban and Environmental Science, Central China Normal University, Wuhan;[马浩] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan;[董才林] School of Mathematics and Statistics, Central China Normal University, Wuhan
通讯机构：
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
作者机构：
[Dong, Cai-lin] Huazhong Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Zhou, Jie; Sun, Liu-quan] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China.;[Zhou, Jie] Capital Normal Univ, Sch Math, Beijing 100048, Peoples R China.
通讯机构：
[Sun, Liu-quan] C;Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China.
摘要：
Case-cohort sampling is a commonly used and efficient method for studying large cohorts.In many situations,some covariates are easily measured on all cohort subjects,and surrogate measurements of the expensive covariates also may be observed.In this paper,to make full use of the covariate data collected outside the case-cohort sample,we propose a class of weighted estimators with general time-varying weights for the additive hazards model,and the estimators are shown to be consistent and asymptotically normal.We also identify the estimator within this class that maximizes efficiency,and simulation studies show that the efficiency gains of the proposed estimator over the existing ones can be substantial in practical situations.A real example is provided.
期刊：
Applied Mechanics and Materials,2014年651-653:2241-2244 ISSN：1662-7482
通讯作者：
Yu, Ying
作者机构：
[Zou, Xianglin; Yu, Ying] Cent China Normal Univ, Sch Comp Sci, Wuhan, Hubei Province, Peoples R China.;[Dong, Cailin] Cent China Normal Univ, Sch Math, Wuhan, Hubei Province, Peoples R China.
通讯机构：
[Yu, Ying] C;Cent China Normal Univ, Sch Comp Sci, Wuhan, Hubei Province, Peoples R China.
会议名称：
3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)
会议时间：
JUL 26-27, 2014
会议地点：
Huhhot, PEOPLES R CHINA
会议主办单位：
[Yu, Ying;Zou, Xianglin] Cent China Normal Univ, Sch Comp Sci, Wuhan, Hubei Province, Peoples R China.^[Dong, Cailin] Cent China Normal Univ, Sch Math, Wuhan, Hubei Province, Peoples R China.
摘要：
With the aim to meet the requirements of multi-directional choice, the paper raise a new approach to the invariant feature extraction of handwritten Chinese characters, with ridgelet transform as its foundation. First of all, the original images will be rotated to the Radon circular shift by means of Radon transform. On the basis of the characteristic that Fourier transform is row shift invariant, then, the one-dimensional Fourier transform will be adopted in the Radon domain to gain the conclusion that magnitude matrixes bear the rotation-invariance as a typical feature, which is pretty beneficial to the invariant feature extraction of rotation. When such is done, one-dimensional wavelet transform will be carried out in the direction of rows, thus achieving perfect choice of frequency, which makes it possible to extract the features of sub-line in the appropriate frequencies. Finally, the average values, standard deviations and the energy values will form the feature vector which is extracted from the ridgelet sub-bands. The approaches mentioned in the paper could satisfy the requirements from the form automatic processing on the recognition of handwritten Chinese characters.
作者：
Asmhan Flieh Hassan;Cailin Dong;Zahir M. Hussain
期刊：
Journal of Computer Science,2014年10(11):2269-2283 ISSN：1549-3636
通讯作者：
Hussain, Z.M.
作者机构：
School of Mathematics and Statistics, HuaZhong Normal University, 152 Luoyu Road, Wuhan, 430079, China;Department of Mathematics University of Kufa, Najaf, Iraq;Department of Computer Science, University of Kufa, Najaf, Iraq;School of Engineering, Edith Cowan University, Australia
通讯机构：
Department of Computer Science, University of Kufa, P.O. Box 21, Kufa, Najaf, Iraq
期刊：
Proceedings of the World Congress on Intelligent Control and Automation (WCICA),2010年:724-728
通讯作者：
Ma, Max Ying
作者机构：
[Ma, Max Ying; He, X-L; Dong, C-L; Chen, Z-Z] Huazhong Normal Univ, Dept Math & Stat, Wuhan, Hubei, Peoples R China.
通讯机构：
[Ma, Max Ying] H;Huazhong Normal Univ, Dept Math & Stat, Wuhan, Hubei, Peoples R China.
会议名称：
2010 8th World Congress on Intelligent Control and Automation
会议时间：
July 2010
会议地点：
Jinan, China
会议主办单位：
[Ma, Max Ying;He, X-L;Chen, Z-Z;Dong, C-L] Huazhong Normal Univ, Dept Math & Stat, Wuhan, Hubei, Peoples R China.
会议论文集名称：
2010 8th World Congress on Intelligent Control and Automation
关键词：
Quaternion;Image information entropy;Image blocking;singular value decomposition;Isomap;region of interest
摘要：
This paper studied a new method of ROI extraction based on quaternion matrix. Firstly, the image is cut into sub-blocks, and the Candidate region is gained by image entropy. Then the greatest expression of sub-blocks is obtained by svd of quaternion matrixs, and the relationship between the image blocks is found according to the definition of quaternion inner product. At last, dimensionality reduction of incidence matrix is carried out with Isomap, and in this process the ROI is extracted. The results of experiments confirm the feasibility and efficiency of this method.
期刊：
Proceedings of the 2010 2nd International Conference on Future Computer and Communication, ICFCC 2010,2010年1:V1477-V1480
通讯作者：
Dong, C.-L.(cldong@mail.ccnu.edu.cn)
作者机构：
[Zhang, Zhen-Zhen; Dong, Cai-Lin] School of Mathematics and Statistics, Huazhong Normal University, Wuhan, China;[Yu, Ying] Department of Computer Science, Huazhong Normal University, Wuhan, China
期刊：
Proceedings of the World Congress on Intelligent Control and Automation (WCICA),2008年:7355-7361
通讯作者：
Chen, ZZ
作者机构：
[Chen, Zengzhao; He, Xiuling; Dong, Cailin] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Liu, Chungui] Wuhan R&D Ctr China Construct Bank, Wuhan 430014, Peoples R China.;[Yang, Yang] Beijing Univ Sci & Technol, Beijing 100083, Peoples R China.
通讯机构：
[Chen, ZZ ] ;Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.
会议名称：
2008 7th World Congress on Intelligent Control and Automation
会议时间：
June 2008
会议地点：
Chongqing
会议主办单位：
(1) Mathematics and Statistics School of Central China, Normal University, Wuhan 430079, China; (2) Wuhan R and D Center, China Construction Bank, Wuhan 430014, China; (3) University of Science and Technology Beijing, Beijing 100083, China
会议论文集名称：
2008 7th World Congress on Intelligent Control and Automation
关键词：
support vector machine;model parameter optimization;Chinese character;recognition
期刊：
Proceedings of the World Congress on Intelligent Control and Automation (WCICA),2008年:9286-9291
通讯作者：
He, Xiuling
作者机构：
[He, Xiuling; Chen, Zengzhao; Dong, Cailin; Yu, Ying] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Yang, Yang] Univ Sci & Technol Beijing, Beijing 100083, Peoples R China.;[Yu, Ying] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[He, Xiuling] C;Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.
会议名称：
2008 7th World Congress on Intelligent Control and Automation
会议时间：
June 2008
会议地点：
Chongqing, China
会议主办单位：
[He, Xiuling;Chen, Zengzhao;Yu, Ying;Dong, Cailin] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.^[Yang, Yang] Univ Sci & Technol Beijing, Beijing 100083, Peoples R China.^[Yu, Ying] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
会议论文集名称：
2008 7th World Congress on Intelligent Control and Automation
关键词：
identification of character type;manifold learning;Locally Linear Embedding(LLE);parameter estimation
摘要：
The identification of language and character type has been an active area of research after recognition of machine printed text. Research on identification of handwritten text and printed text is seldom conducted. But it is common used in recognition of form. For character type identification, manifold learning algorithm locally linear embedding (LLE) is imported. A generalizing method and a parameters estimation method are proposed. Experiments in identification printed/handwritten Chinese characters and digits show that its performance is higher than support vector machine (SVM) classification. The combination of dimensionality reduction of LLE and linear discriminant analysis (LDA) classification achieves a similar accurate rate as combination of LLE and SVM classification but runs much faster than it.
