期刊：
Journal of Computational and Theoretical Nanoscience,2015年12(10):3658-3661 ISSN：1546-1955
通讯作者：
Peng, Yingying
作者机构：
[Peng, Yingying; Li, Man] College of Management and Information Engineering, Hunan University of Chinese Medicine, Changsha, Hunan, China;[Li, Kenli; Peng, Yingying] College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
通讯机构：
College of Management and Information Engineering, Hunan University of Chinese Medicine, Changsha, Hunan, China
关键词：
Cluster;Data Mining;Improved K-Means
摘要：
K-Means algorithm has been researched adequately in recent years. Clustering result of traditional K-Means algorithm is affected by the choice of initial point and noise. In addition to, traditional K-Means algorithm only favors clusters with spherical shapes and similar sizes. A novel K-Means algorithm combining K-Means algorithm and KNN algorithm called KK-Means is proposed to solve these weaknesses in this paper. Experimental result shows that KK-Means algorithm has better performance more than traditional K-Means algorithm.
作者：
An improved dynamic framed slotted aloha anti-collision algorithm based on estimation method for RFID systems
作者机构：
湖南大学
会议名称：
IEEE International Conference on RFID (RFID)
会议时间：
2015-04-15到2015-04-17
会议地点：
San Diego, CA
会议主办单位：
Institute of Electrical and Electronics Engineers Inc.
关键词：
RFID; anti-collision algorithm; ALOHA
摘要：
In order to solve the collision problems between
multiple RFID tags and improve the identification efficiency, this
work presents an improved dynamic framed slotted ALOHA
Anti-collision algorithm for RFID systems. Based on the time slot
distribution information, the algorithm estimates the average
number of tags in each collision slot and then dynamically sets
the frame size for unrecognized tags due to the collision between
multiple tags. We theoretically analyzed the time complexity of
the proposed algorithm. Simulation results demonstrate that our
proposed algorithm outperforms other existing algorithms in the
literature in terms of the identification efficiency and the total
number of time slots.
摘要：
Clustering high-dimensional data is challenging for traditional clustering methods. Spectral clustering is one of the most popular methods to cluster high-dimensional data, in which the similarity matrix plays an important role. Recently, sparse representation coefficients have been proposed to construct the similarity matrix via the cosine similarity between each pair of coefficient vectors for spectral clustering and showed promising results. However, the sparse representation emphasizes too much on the role of ‘1-norm sparsity and ignores the role of collaborative representation, which makes its computational cost very high. In this paper, we propose a spectral clustering method based on the similarity matrix which is constructed based on the collaborative representation coefficient vectors. Extensive experiments show that the proposed method has a strong competitiveness both in terms of computational cost and clustering performance.
摘要：
For low efficient of key management, most cloud centers lack fine-grained attribute revocation, which only support revocation of the keys. This paper proposes a novel access policy which supporting fine-grained attributes revocation based on CP-ABE. In this paper, we firstly redesign the key manage policy. The generation and distribubition of the user's private keys are completed by the collaboration of the data owner and attribute authority and it has reduced the cost of the key management. Secondly we embed a random parameter EDx when publish the master key. Through the embeddoor, this scheme can control the access authority of users for who have lost the authority to acess the cloud data. Then, we can gurantee the security of keys based on the designed mechanism of binary tree. Our approach can achieve high performance and can also deal with fine-grained attribute revocation. Finally, the security of this policy has been proved under the standard model.
摘要：
Motivation: Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data.
Results: In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l(2)-norm or l(1)-norm of the coding residual.
Conclusions: Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.
作者机构：
[彭东明; 刘艳飞; 王晓红; 张航] School of Chemistry and Chemical Engineering, Central South University, Changsha, China;[彭东明] School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China
通讯机构：
School of Chemistry and Chemical Engineering, Central South University, Changsha, China
作者机构：
[黎继烈; 钟海雁; 李忠海; 丁丽霞] Key Laboratory of Non-wood Forest Nurturing and protection of Ministry of Education, Central South University of Forestry and Technology, Changsha 410004, China;[崔培梧] College of Pharmaceutical Science, Hunan University of Chinese Medicine, Changsha 410208, China;[胡铁] Guangzhou Maritime Institute, Guangzhou 510725, China
通讯机构：
Key Laboratory of Non-wood Forest Nurturing and protection of Ministry of Education, Central South University of Forestry and Technology, China
作者机构：
[Xie Xia; Dai Ling; Liu Heng-yan; Liu Xiang-qian] Hunan Univ Chinese Med, Hunan Key Lab Tradit Chinese Med Modernizat, Sch Pharm, Changsha 410208, Hunan, Peoples R China.
通讯机构：
[Liu Xiang-qian] H;Hunan Univ Chinese Med, Hunan Key Lab Tradit Chinese Med Modernizat, Sch Pharm, Changsha 410208, Hunan, Peoples R China.
关键词：
Acanthopanax gracilistylus W. W. Smith (AGS);acankoreanogenin;central composite design-response surface methodology;extraction in combination hydrolysis in situ
作者机构：
[杜方麓; 欧阳文] School of Pharmacy, Hunan University of Chinese Medicine, Changsha , China;[曹庸; 陈雪香] College of Food Science, South China Agricultural University, Guangzhou , China
通讯机构：
College of Food Science, South China Agricultural University, Guangzhou, China
作者：
A method of on-road vehicle detection based on comprehensive feature cascade of classifier
作者机构：
湖南大学
会议名称：
International Conference on Information Technology and Computer Application Engineering (ITCAE)
会议时间：
2013-08-27到2013-08-28
会议地点：
Hong Kong, PEOPLES R CHINA
会议主办单位：
Int Frontiers Sci & Technol Res Assoc; Hong Kong Control Engn & Informat Sci Res Assoc
关键词：
Vehicle Detection;BRIEF;Haar-like;Adaboost
摘要：
To detecting on-road vehicles rapidly and efficiently, we propose a robust method to improve the accuracy of on-road vehicle detection rate and reduce false alarm rate. Firstly, we have enhanced the feature expressive force by combining Haar-like feature with BRIEF feature. Some improvements have been done for achieving the robustness under the lighting and road conditions changes. Secondly, we have improved the performance of weak classier based on Gentle Adaboost algorithm. Experimental results show that the detection rate increased by 2.6% compared with the traditional cascade structure of classifier, and the false alarm rate reduced in some degrees.
作者机构：
[Zeng, Weilan; Dang, Pan; Zhang, Xiaoyun; Liang, Yun] Hunan Normal Univ, Natl & Local Joint Engn Lab New Petrochem Mat & F, Key Lab Chem Biol & Tradit Chinese Med Res, Minist Educ, Changsha 410081, Hunan, Peoples R China.;[Peng, Caiyun] Hunan Univ Chinese Med, Sch Pharmaceut Sci, Changsha 410208, Hunan, Peoples R China.
通讯机构：
[Liang, Yun] H;Hunan Normal Univ, Natl & Local Joint Engn Lab New Petrochem Mat & F, Key Lab Chem Biol & Tradit Chinese Med Res, Minist Educ, Changsha 410081, Hunan, Peoples R China.
摘要：
An efficient copper-catalyzed method for the synthesis of a variety of 2-aminobenzothiazoles has been developed. The reaction proceeded from carbodiimide and sodium hydrosulfide via a tandem reaction in the presence of copper(II) trifluoromethanesulfonate to afford the corresponding 2-aminobenzothiazole derivatives in good to perfect yields.
