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3rd International Conference on Cyberspace Technology, CCT 2015

作者： Pan, Qingfeng Zheng, Xianghan College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China

ISBN: (纸本)9781785610899

Multi-constrained optimal routing problem and disjoint path selection are NP-hard problem. The conventional single-path and single constraint routing can not meet people's needs. The reliability of network data transmission and packet loss rate can be decreased when the route or the transmission fails. To solve the problems, we need to study the routing path selection strategies. This paper optimizes the routing path that meets the QoS constraints under the SDN pre planning routing, and proposes a routing mechanism that can solve problems in polynomial time. Firstly, the paper combined with Clustering and Gauss algorithm to simulate the network simulation, obtain the initial experimental data. It can reflect the network status more accurately. Secondly, simplify the multi-objectives based on the AHP, It reduces the complexity of the problem and meets QoS requirements. Finally, select the disjoint communication paths and backup paths with the heuristic algorithms in order to balance the load of network and improve the transmission reliability. The selection mechanism can improve the efficiency of data transmission, and the survival of network communication.

关键词： Heuristic algorithms

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Runtime model based approach to IoT application development

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Frontiers of Computer science 2015年第4期9卷 540-553页

作者： Xing CHEN College of Mathematics and Computer Science Fuzhou University Fuzhou 350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou 350116 China Key Laboratory of High Confidence Software Technologies (Ministry of Education) Beijing 100871 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

The internet of things （loT） attracts great interest in many application domains concerned with monitoring and ：ontrol of physical phenomena. However, application devel- opment is still one of the main hurdles to a wide adoption of IoT technology. Application development is done at a low level, very close to the operating system and requires pro- grammers to focus on low-level system issues. The under- lying APIs can be very complicated and the amount of data collected can be huge. This can be very hard to deal with as a developer. In this paper, we present a runtime model based approach to IoT application development. First, the manage- ability of sensor devices is abstracted as runtime models that are automatically connected with the corresponding systems. Second, a customized model is constructed according to a personalized application scenario and the synchronization be- tween the customized model and sensor device runtime mod- els is ensured through model transformation. Thus, all the application logic can be carried out by executing programs on the customized model. An experiment on a real-world ap- plication scenario demonstrates the feasibility, effectiveness, and benefits of the new approach to IoT application develop- ment.

关键词： IoT application development software architec-ture models at runtime

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Semantic automatic image annotation based on Non-Abstract Visual Keywords

Semantic automatic image annotation based on Non-Abstract Vi...

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2014 International Conference on information Technology and Application, ITA2014

作者： Ke, Xiao Chen, Guolong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9781138026773

Automatic image annotation is a significant and challenging problem in pattern recognition and computer vision. Existing models did not describe the visual representations of corresponding keywords, which would lead to appearing plenty of irrelevant annotations in final annotation results. These annotations did not relate to any part of images considering visual contents. We propose a new automatic image annotation model (NAVK) based on relevant visual keywords to overcome above problems. Our model focuses on non-abstract words. First, we establish visual keyword seeds of each non-abstract word, and then a new method is proposed to extract visual keyword collections by using corresponding seeds. Second, we propose adaptive parameter method and fast solution algorithm to determine similarity thresholds of each keyword. Finally, the combinations of above methods are used to improve annotation performance. Experimental results verify the effectiveness of proposed image annotation model. © 2015 Taylor & Francis Group, London.

关键词： Image annotation

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Real-time task scheduling algorithm for cloud computing based on particle swarm optimization 2nd

Real-time task scheduling algorithm for cloud computing base...

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2nd International Conference on Cloud computing and Big Data, CloudCom-Asia 2015

作者： Chen, Huangning Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9783319284293

As a new computing paradigm, cloud computing is receiving considerable attention in both industry and academia. Task scheduling plays an important role in large-scale distributed systems. However, most previous work only consider cost or makespan as optimized objective for cloud computing. In this paper, we propose a soft real-time task scheduling algorithm based on particle swarm optimization approach for cloud computing. The optimized objectives include not only cost and makespan, but also deadline missing ratio and load balancing degree. In addition, to improve resource utilization and maximize the profit of cloud service provider, a utility function is employed to allocate tasks to machines with high performance. Simulation results show the proposed algorithm can effectively minimize deadline missing ratio, maximize the profit of cloud service provider and achieve better load balancing com- pared with baseline algorithms. © Springer International Publishing Switzerland 2015.

关键词： Cloud computing

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A novel model for wind power forecasting based on Markov residual correction 6

A novel model for wind power forecasting based on Markov res...

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2015 6th International Renewable Energy Congress, IREC 2015

作者： Li, Lijuan Liu, Hongliang Wu, Jun Bo, Hai Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Xiangtan China School of Mathematics and Computational Science Xiangtan University Xiangtan China College of Information Engineering Xiangtan University Xiangtan China

ISBN: (纸本)9781479979479

An accurate wind power forecasting model has great significance in wind farm operation and electric power system dispatching and operation. An auto regressive integrated moving average (ARIMA) time series model with Markov residual correction is proposed to forecast the wind power in this paper. After establishing ARIMA model, random residual sequence with Markov property can be proved through chi-square statistics. The residual correction model based on Markov chain is then established. The prediction results of wind power of two wind turbines and the wind farm are achieved. The results with the assessment of accuracy rate and qualification rate show that the proposed model has excellent performances and precision. Compared with time series and artificial neural network model, the accuracy is improved by 6-10%, and qualification rate is improved by 2-7%. The proposed method implements more simply than some combined models, which has better practical value. © 2015 IEEE.

关键词： Time series

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Regression-based execution time prediction in hadoop environment

Regression-based execution time prediction in hadoop environ...

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International Conference on information Technology and Computer Application Engineering, ITCAE 2014

作者： Wu, Xiao Dong Zeng, Yu Zhu Zhao, C.T. Faculty of Mathematics and Computer Science Quanzhou Normal University China Fujian Provincial Key Laboratory of Data Intensive Computing China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University China

ISBN: (纸本)9781138027176

Hadoop MapReduce has been proven an effective computing model to deal with big data for the last few years. However, one technical challenge facing this framework is how to predict the execution time of an individual job. In this paper, we propose a regression-based method to predict the execution times of Hadoop jobs. The proposed prediction method consists of three phases: learning data sampling, regression analysis and making prediction. Experimental results show that the predicted execution time obtained by our method is close to the actual execution time. © 2015 Taylor & Francis Group, London.

关键词： Regression analysis

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Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

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Journal of Computer science & Technology 2015年第4期30卷 859-873页

作者：周竞朱山风黄晓地张彦春 School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai 200433 China School of Computing and Mathematics Charles Sturt University Albury NSW 2640 Australia School of Engineering and Science Victoria University Melbourne Victoria 8001 Australia Shanghai Key Laboratory of Data Science Fudan University Shanghai 201203 China

Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping （DTW） based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.

关键词： time series analysis clustering dynamic programming information search and retrieval

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The analysis of big Data application mode and security risk

The analysis of big Data application mode and security risk

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International Conference on information sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： ZENG Tai-sheng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9789462520677

In the current social background,Existing facilities and tools already can not meet the needs of big data in expanding and analysis ***'s data storage and analysis work is achieved under cloud conditions and Hadoop were set *** the conditions for cloud computing,cloud computing applications who have remote data files were not authorized to read its contents,which results in unauthorized manipulation,and it will produce a lot of security risks for large *** this paper,according to the cloud of different modes,Hadoop different stages of the operation,subject to the threat of non- confidence and security to steal big data generated to analyze a variety of privacy,with threat model as an example,it explores ways to address security threats.

关键词： big data security analysis Hadoop

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A nonlocal interferometric phase filtering strategy for practical InSAR system

A nonlocal interferometric phase filtering strategy for prac...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xue Lin Dongsheng Fang Fangfang Li Ke Wang Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China School of Information Science and Engineering University of Jinan Jinan China Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Information Science and Engineering Henan University of Technology Zhengzhou China

Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the nonlocal methods is the computational burden. This paper proposed a nonlocal phase filtering strategy for the practical InSAR system, which combine the nonlocal algorithm with the traditional method to improve the efficiency.

关键词： Filtering Coherence Noise reduction Frequency estimation Estimation Algorithm design and analysis Surfaces

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Proteomic identification of intact glycopeptides

Proteomic identification of intact glycopeptides

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2016 International Symposium on Analytical Chemistry Frontiers & China-US Analytical Chemistry Workshop

作者： Ming-Qi Liu Wen-Feng Zeng Pan Fang Yang Zhang Wei-qian Cao Jian-Qiang Wu Rui-Xiang Sun Chao Peng Catherine C.L.Wong Si-Min He Peng-Yuan Yang Department Chemistry and Institutes of Biomedical Sciences Fudan University Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS and University of Chinese Academy of Sciences National Center for Protein Science (Shanghai) Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences CAS

We have developed a workflow that robustly identifies intact glycopeptides at a proteome scale using stepped-energy mass-spectrometry. This workflow has decoded as a computational strategy called as pGlyco+, a dedicated search engine for glycopeptide analysis. Our high-throughput method has enable us to interpret a total of 5,564 distinct site-specific N-glycoforms, on 1,478 glycosylation sites of relative 809 glycoproteins from five mouse tissues, and in 20 hours of mass-spectrometry analysis. Strict quality control(1% false discovery rate) was performed not only for the peptide matches but novel for glycan ones as well. We have also compared our search engine with a routinely used glycoproteomic software and believed that our method is a rather robust tool for site-specific glycosylation study likely. To demonstrate the importance of comprehensive quality control for both the glycan and peptide matches, we have conducted a fair performance comparison between pGlyco+ and Byonic, a routinely used glycoproteomic software: exactly same MS/MS data, and parameters such as tolerance, databases of protein and glycan were used. Under 1% FDR, pGlyco+ identified 23,086 glycopeptide spectra, while Byonic only identified 8,866 glycopeptide spectra. The corresponding number of identified distinct glycopeptides were 5,430 for pGlyco+ and 2,206 for Byoinc. To reach the same sensitivity of pGlyco+(23,086 identified spectra under 1% FDR), Byonic would have unacceptably 30% FDR using the same criteria.

关键词： Glycopeptide Mass Spectrometry pGlyco+

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