作者机构：
[Zhang, Cong; Sun, Changqi] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Xiong, Naixue] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.
通讯机构：
[Zhang, Cong] W;[Xiong, Naixue] N;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.
摘要：
Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, while neglecting the parameter estimation of variogram nested model. Most recently, the evolutionary algorithms(EA), including genetic algorithm(GA), are exploited to calculate the parameters of variogram model, which can obtain a more accurate solution. These methods have some hyper-parameters to set and suffer from the well-recognized premature convergence and slow global convergence problem of EA. In this paper, a double elite co-evolutionary genetic algorithm(DECGA) and deep reinforcement learning(dueling DQN) was introduced to estimate the parameters of variogram single or nested models so as to achieve better generalization performance. The DECGA can get the global optimal solution faster than GA with the help of dueling DQN, which can set the hyper-parameters according to the state of DECGA. To verify the effectiveness of the proposed method(DDQNGA), we conduct experiments on the agricultural heavy metal database. Experimental results demonstrate that our method can obtain parameter estimation more accurately. The method proposed in this paper have a certain practical value in the field of geostatistics and environmental engineering.
作者机构：
[张帆; 张聪] School of Mathematics &, Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China;[徐明迪] Wuhan Digital and Engineering Institute, Wuhan, 430205, China;[陈伟] School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China;[胡方宁; 张帆] School of Electrical and Computer Engineering, Jacobs University Bremen, Bremen, 28759, Germany
通讯机构：
Wuhan Digital and Engineering Institute, Wuhan, China
作者机构：
[张帆; Chao, Han-Chieh; 张聪] School of Mathematics &, Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China;[刘小丽] College of Cyber Security, Jinan University, Guangzhou, 510632, China;[刘小丽] College of Information Science and Technology, Jinan University, Guangzhou, 510632, China;[Chao, Han-Chieh] Department of Computer Science and Information Engineering, Ilan University, Ilan, 02415271, Taiwan;[Chao, Han-Chieh] Department log Electrical Engineering, Dong Hwa University, Hwalian, 08153719, Taiwan
摘要：
In the era of big data, the volumes of data are in increasingly rapid growth in social networks. Social networks are a theoretical construct, which is useful in the social sciences to study relationships and interactions between individuals, group, organizations. Massive data processing is essential for providing social network services. In this paper, we focus on the extraction of the implicit aspect and opinion words in social networks. The Latent Dirichlet Allocation (LDA) model is a generative probabilistic model to automatically extract implicit topic in the document set, which has been widely used in natural language processing, text mining and text categorization. However, a large number of non-taxonomy high-frequency content words in the Chinese patent documents will affect the implicit topic generation, and for the more, affect Chinese patent classification. The study finds that the probability distribution of the words in the expert database has an impact on the extraction of the feature words for patent document. This paper proposes a weight-LDA model for the problem of the LDA topic model in Chinese patent classification. The weight-LDA model, which combines the probability distribution of feature words in the expert database with Gibbs sampling, reduces the impact of nontaxonomy high-frequency content words on the distribution of topic and enhances that of low-frequency content words with strong classification effects on the distribution of topic. Six different types of patent data sets extracted from State Intellectual Property Office of the P.R.C are tested. The average F value of the weight-LDA model is 6% higher than that of the traditional LDA model. In addition, the weight-LDA model is compared with wordfrequency-based feature selection methods such as the TFIDF algorithm, and the average F value of the weight-LDA model is 11.4% higher than that of the TF-IDF algorithm. Through the analysis of the experimental results, the weight-LDA for the Chinese patent has better classification effects.<br/>
会议名称：
9th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision
会议时间：
OCT 31-NOV 01, 2015
会议地点：
Enshi, PEOPLES R CHINA
会议主办单位：
[Liu, Renfeng;Zhang, Cong] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.^[Tian, Jinwen] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China.
会议论文集名称：
Proceedings of SPIE
关键词：
Point matching;non-parametric;non-rigid;outlier;regularization
摘要：
Establishing reliable feature correspondence between two images is a fundamental problem in vision analysis and it is a critical prerequisite in a wide range of applications including structure-from-motion, 3D reconstruction, tracking, image retrieval, registration, and object recognition. The feature could be point, line, curve or surface, among which the point feature is primary and is the foundation of all features. Numerous techniques related to point matching have been proposed within a rich and extensive literature, which are typically studied under rigid/affine or non-rigid motion, corresponding to parametric and non-parametric models for the underlying image relations. In this paper, we provide a review of our previous work on point matching, focusing on nonparametric models. We also make an experimental comparison of the introduced methods, and discuss their advantages and disadvantages as well.
作者机构：
[张帆] College of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China;[杨捷] Software School, South China University of Technology, Guangzhou, 510006, China;[徐明迪] System Software Department, Wuhan Digital Engineering Institute, Wuhan, 430072, China;[张聪] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China
通讯机构：
Software School, South China University of Technology, Guangzhou, China
期刊：
Lecture Notes in Computer Science,2014年8326 LNCS(PART 2):353-360 ISSN：0302-9743
作者机构：
[Wang, Heng; Zhang, Cong] School of Mathematic and Computer Science, Wuhan Polytechnic University, Wuhan, China;[Tu, Weiping; Wang, Xiaochen; Hu, Ruimin] National Engineering Research Center for Multimedia Software, Wuhan University, China
会议名称：
International Conference on Multimedia Modeling
作者机构：
[王松; 涂卫平; 胡瑞敏] National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China;[王恒; 张聪] ISchool of Mathematic &, Computer Science, Wuhan Polytechnic University, Wuhan, China
通讯机构：
National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China
关键词：
双耳时间差变化感知阈限;时间差;频率
摘要：
为探索双耳时间差(Interaural Time Difference, ITD)的感知机理,研究ITD恰可感知差异(Just Notice Difference,JND)与时间差和频率的关系。依据人耳对ITD的敏感程度的定性分析,非均匀地选取7个离散的ITD测试值,按照临界频带的划分方法将低频段划分为12个频带进行测试;采用1 up/2 down和2AFC心理学测试方法,同时采用窄带的高斯白噪声作为测试序列以避免相位混淆。测试结果表明:随频率的变化ITD的JND变化较为显著,在500 Hz左右出现极小值,两端较大;随ITD的增大,ITD的JND也相应增大。实验所得数据及结论可为多声道音频的高效压缩提供基础数据和理论支撑。
作者机构：
[张帆; 张聪] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China;[徐明迪] System Software Department, Wuhan Digital Engineering Institute, Wuhan, 430072, China;[陈伟] School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China;[张帆] College of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
通讯机构：
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
