作者机构：
[Xiao-yang Zhou] Department of Mathematics, Huazhong University of Science and Technology, Wuhan 430074, China;[Gui-ping Liao; Fang Wang] Agricultural Information Institute, Hunan Agricultural University, Changsha 410128, China;[Fang Wang; Wen Shi] Science College, Hunan Agricultural University, Changsha 410128, China
关键词：
Cross correlations - Detrended cross-correlation analysis - Load-time series - Long-term correlations - Multi fractals - Multifractal theory - Operating data - Power markets
摘要：
Recently, computational Grids have proven to be a good solution for processing large-scale, computation intensive problems. However, the heterogeneity, dynamics of resources and diversity of applications requirements have always been important factors affecting their performance. In response to these challenges, this work first builds a Grid job scheduling architecture that can dynamically monitor Grid computing center resources and make corresponding scheduling decisions. Second, a Grid job model is proposed to describe the application requirements. Third, this paper studies the characteristics of commercial interconnection networks used in Grids and forecast job transmission time. Fourth, this paper proposes an application-aware job scheduling mechanism (AJSM) that includes periodic scheduling flow and a heuristic application-aware deadline constraint job scheduling algorithm. The rigorous performance evaluation results clearly demonstrate that the proposed application-aware job scheduling mechanism can successful schedule more Grid jobs than the existing algorithms. For successful scheduled jobs, our proposed AJSM method is the best algorithm for job average processing time and makespan.
摘要：
BACKGROUND: High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery. RESULTS: In this work we present the CMOPSOB (Crowding distance based Multi-objective Particle Swarm Optimization Biclustering), a novel clustering approach for microarray datasets to cluster genes and conditions highly related in sub-portions of the microarray data. The objective of biclustering is to find sub-matrices, i.e. maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a subset of conditions. Since these objectives are mutually conflicting, they become suitable candidates for multi-objective modelling. Our approach CMOPSOB is based on a heuristic search technique, multi-objective particle swarm optimization, which simulates the movements of a flock of birds which aim to find food. In the meantime, the nearest neighbour search strategies based on crowding distance and -dominance can rapidly converge to the Pareto front and guarantee diversity of solutions. We compare the potential of this methodology with other biclustering algorithms by analyzing two common and public datasets of gene expression profiles. In all cases our method can find localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. CONCLUSION: The proposed CMOPSOB algorithm is successfully applied to biclustering of microarray dataset. It achieves a good diversity in the obtained Pareto front, and rapid convergence. Therefore, it is a useful tool to analyze large microarray datasets.
作者机构：
[Hongyan Zhang; Zhijun Dai; Zheming Yuan] Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha, 410128, China;[Hongyan Zhang; Zhijun Dai; Zheming Yuan] College of Bio-safety Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Hongyan Zhang] College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Haiyan Wang] Department of Statistics, Kansas State University, Manhattan, KS, 66506, United States;[Ming-shun Chen] USDA-ARS and Department of Entomology, Kansas State University, Manhattan, KS, 66506, United States
通讯机构：
[Wang, Haiyan] Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA.
关键词：
Binary matrix - Cancer classification - Cancer tissues - Classification accuracy - Computationally efficient - Feature space - Gene Expression Data - Gene expression datasets - Gene interactions - Gene selection - Human cancer - Informative genes - Joint effect - Leave-one-out - Multiple classifiers - Over fitting problem - Search scheme - Search spaces - Wrapper methods
作者机构：
[Ding, Xiangling; Song, Kehui; Deng, Xiaopeng] Department of Physics and Information Engineering, Huaihua University, Huaihua 418008, China;[Chen, Guangyi] College of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
期刊：
IEEE Transactions on Nanobioscience,2012年11(4):386-393 ISSN：1536-1241
通讯作者：
Wang, J.(jxwang@mail.csu.edu.cn)
作者机构：
[Ren, Jun] College of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China;[Ren, Jun; Li, Min; Wang, Jianxin] School of Information Science and Engineering, Central SouthUniversity, Changsha, 410083, China;[Wu, Fang-Xiang] Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
通讯机构：
[Wang, Jianxin] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.
摘要：
A novel method, which we called the analogous multifractal cross-correlation analysis, is proposed in this paper to study the multifractal behavior in the power-law cross-correlation between price and load in California electricity market. In addition, a statistic rho(AMF-XA), which we call the analogous multifractal cross-correlation coefficient, is defined to test whether the cross-correlation between two given signals is genuine or not. Our analysis finds that both the price and load time series in California electricity market express multifractal nature. While, as indicated by the rho(AMF-XA) statistical test, there is a huge difference in the cross-correlation behavior between the years 1999 and 2000 in California electricity markets. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4793355]
摘要：
A novel Grey forecasting model for predicting S. sclerotiorum (Lib) de Bary disease on winter rapeseed (B. nopus) is built based on Grey GM (1,1) model. The residual error test and the posterror test methods were used for calibration of the model. Different from other conventional forecasting methods, the GM (1,1)-based Grey calamity prediction forecasts a prediction Delta T to infer the probable year of Sclerotinia disease outbreaks according to the origin, and then uses the result to recommend spraying a field or not in order to avoid unnecessary fungicide application. Based on practical experiments in Hunan province, the threshold (integral) of the disease rate at the time when winter rapeseed begins to flower is defined as 5, and (f < 5) is called a down-calamity. The Grey forecasting model was tested at the 7 stations in 2004 and 2005 and predicted the probable year of Sclerotinia disease outbreaks and the need for fungicide application with the first-class grade and high accuracy.
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2005年3759:647-656 ISSN：0302-9743
通讯作者：
Yu, F.(yufei@hunau.net)
作者机构：
[Dai, Xiao-Peng; Huang, Hunag; Yu, Fei] Institute of Computer and Information Engineering, Hunan Agricultural University, Changsha 410128, Hunan, China;[Xu, Cheng] College of Computer and Communication, Hunan University, Changsha 410082, Hunan, China;[Zhu, Miao-Liang; Yu, Fei] Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, Zhejiang, China
摘要：
The problem in network security presently is how to get real-time intrusion detection and alert. In the paper, we design Intrusion Detection System architecture and arithmetic again, meanwhile put forward an improved pattern matching arithmetic based on protocol analysis and theorization machine on Frete. New architecture can use network processor to collect and analyze data in network bottom, which enhance the speed and efficiency in Detection System.
作者机构：
[Hu, Xiaohua] College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, United States;[Li, Zhoujun; Chen, Yiming; Liu, Junwan] Computer School, National University of Defence and Technology, Changsha, Hunan, China;[Li, Zhoujun] Computer School, BeiHang University, BeiJing, China;[Chen, Yiming] School of Information Science and Technology, Hunan Agricultural University, Changsha, Hunan, China
会议名称：
6th International Conference on Advanced Data Mining and Applications (ADMA)
会议论文集名称：
Lecture Notes in Engineering and Computer Science
关键词：
Channel allocation mechanism - Distributed coordination functions - IEEE 802.11s - Model and simulation - OPNET - Performance analysis - Point coordination functions - WLAN
摘要：
The paper proposed modeling and simulation of distributed WLAN in OPNET. This model comprises of network model, node model and process model. Network model is the basic topology structure, node model describes the inside function module of all kinds of communication nodes, process model is used to make certain the management manner and management process of data. Moreover, we compare two primary channel allocation mechanisms of MAC level in IEEE 802.11 WLAN, Distributed Coordination Function (DCF) and Point Coordination Function (PCF).
作者：
Ren, Jun;Wang, Jianxin;Chen, Jianâer;Li, Min;Chen, Gang
作者机构：
[er; Ren, Jun; Wang, Jianxin; Li, Min; Chen, Gang] School of Information Science and Engineering, Central South University, Changsha 410083, China;[Ren, Jun] College of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
会议名称：
6th International Symposium on Bioinformatics Research and Applications
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2006年3842:869-876 ISSN：0302-9743
通讯作者：
Yu, F
作者机构：
[Shen, Y; Dai, XP; Yu, F; Huang, H] School of Computer and Information Engineering, Hunan Agricultural University, Changsha, 410128, China;[Xu, C] College of Computer and Communication, Hunan University, Changsha, 410082, China;[Yu, F] State Key Laboratory of Information Security, Graduate School, Chinese Academy of Sciences, Beijing, 100049, China
摘要：
Although the current E-Learning systems have many Merits, Many of them only treat advanced information technology as simple communication tools, and release some learning contents and exercises in the network. In this paper, a one-class-in one network for emotion recognition system in E-learning is proposed and implemented in the paper. Using a large database of phoneme-balanced Chinese words read by speakers consciously trying to portray an emotion, we trained and tested this module. We achieved a recognition rate of approximately 55%. The results obtained in this study demonstrate that emotion recognition in speech is feasible, and that neural networks are well suited for this task.
作者机构：
[Liao, Gui-ping; Li, Jin-wei] Agricultural Information Institute, Hunan Agricultural University, Changsha, 410128 China;[Wang, Fang] College of Science, Hunan Agricultural University, Changsha, 410128 China;[Liao, Deng-wen] Forestry Department of Hunan Province, Quality Testing and Inspection Centre of Forest Products, Changsha, 410007 China
摘要：
BACKGROUND: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I 0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species. RESULTS: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species. CONCLUSIONS: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.
