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Cluster based hybrid key management scheme for ad hoc networks

ICIC Express Letters, Part B: Applications 2016年第6期7卷 1329-1334页

作者： Zhang, Jing Xu, Li Tang, Rongmei Zhang, Shunmiao School of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China School of Mathematics and Computer Science Fujian Normal University Fuzhou University Town Minhou Fuzhou350118 China

Secure communication is part of the most important aspects for ad hoc networks. One cluster based Hybrid key Management Scheme is proposed in this paper for ad hoc networks. The clusters are constructed by the R-hop connected dominating sets based Partition Algorithm, which is only with the knowledge of local connectivity information. Furthermore, Hybrid key Management Scheme can be seen as an integration of both the asymmetric key and symmetric key cryptography. The asymmetric one is based on the elliptic curve cryptosystem, which is an asymmetric one. However, inter cluster key management is based on symmetric key encryption. Experimental results show this scheme is reliable and it is able to achieve higher success of landing ratio than other comparable algorithms. © 2016 ICIC International.

关键词： Ad hoc networks

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Correction to: Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

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Critical care (London, England) 2022年第1期26卷 115页

作者： Lu Ke Jiajia Lin Gordon S Doig Arthur R H van Zanten Yang Wang Juan Xing Zhongheng Zhang Tao Chen Lixin Zhou Dongpo Jiang Qindong Shi Jiandong Lin Jun Liu Aibin Cheng Yafeng Liang Peiyang Gao Junli Sun Wenming Liu Zhenyu Yang Rumin Zhang Wei Xing An Zhang Zhigang Zhou Tingfa Zhou Yang Liu Fei Tong Qiuhui Wang Aijun Pan Xiaobo Huang Chuming Fan Weihua Lu Dongwu Shi Lei Wang Wei Li Liming Gu Yingguang Xie Rongqing Sun Feng Guo Lin Han Lihua Zhou Xiangde Zheng Feng Shan Jianbo Liu Yuhang Ai Yan Qu Liandi Li Hailing Li Zhiguo Pan Donglin Xu Zhiqiang Zou Yan Gao Chunli Yang Qiuye Kou Xijing Zhang Jinglan Wu Chuanyun Qian Weixing Zhang Minjie Zhang Yuan Zong Bingyu Qin Fusen Zhang Zhe Zhai Yun Sun Ping Chang Bo Yu Min Yu Shiying Yuan Yijun Deng Liyun Zhao Bin Zang Yuanfei Li Fachun Zhou Xiaomei Chen Min Shao Weidong Wu Ming Wu Zhaohui Zhang Yimin Li Qiang Guo Zhiyong Wang Yuanqi Gong Yunlin Song Kejian Qian Yongjian Feng Baocai Fu Xueyan Liu Zhiping Li Chuanyong Gong Cheng Sun Jian Yu Zhongzhi Tang Linxi Huang Biao Ma Zhijie He Qingshan Zhou Rongguo Yu Zhihui Tong Weiqin Li Department of Critical Care Medicine Jinling Hospital Medical School of Nanjing University No. 305 Zhongshan East Road Nanjing 210000 Jiangsu Province China. National Institute of Healthcare Data Science Nanjing University Nanjing China. Northern Clinical School Royal North Shore Hospital University of Sydney Sydney Australia. Department of Intensive Care Medicine Gelderse Vallei Hospital Willy Brandtlaan 10 6716 RP Ede The Netherlands. Department of Medical Research and Biometrics Center State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Benq Medical Center Nanjing China. Department of Emergency Medicine Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China. Tropical Clinical Trials Unit Department of Clinical Sciences Liverpool School of Tropical Medicine Liverpool L3 5QA UK. First People's Hospital of Foshan Foshan China. Daping Hospital Army Medical University Chongqing China. First Affiliated Hospital of Xi'an Jiao Tong University Xi'an China. First Affiliated Hospital of Fujian Medical University Fuzhou China. Suzhou Municipal Hospital Suzhou China. North China University of Science and Technology Affiliated Hospital Tangshan China. Qindao University Medical College Affiliated Yantai Yuhuangding Hospital Yantai China. Chengdu University of Traditional Chinese Medicine Affiliated Hospital Chengdu China. Luoyang Central Hospital Affiliated to Zhengzhou University Luoyang China. Changzhou No. 2 People's Hospital Affiliated to Nanjing Medical University Changzhou China. The Second Affiliated Hospital of Harbin Medical University Harbin China. Zibo Central Hospital Zibo China. Department of Intensive Care Medicine The Third Xiangya Hospital of Central South University Changsha China. The Second Affiliated Hospital of Chongqing Medical University Chongqing China. First People's Hospita

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Which year is the hottest or coldest from 2001 to 2012 based on remote sensing data

Which year is the hottest or coldest from 2001 to 2012 based...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： K. B. Mao Y. Ma Z. Y. Zuo F. Wang Y. Q. Jiao X. Y. Shen Q. Liu National Hulunber Grassland Ecosystem Observation and Research Station Chinese Academy of Agricultural Sciences Beijing China Chinese Academy of Agricultural Big Data and Nutrition Hong Kong China State Key Laboratory of Remote Sensing Science Beijing Normal University Beijing China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and Technology Xiangtan China College of Science of China University of Petroleum Beijing China Hydrometeorology and Remote Sensing Laboratory University of Oklahoma Norman Oklahoma

ISBN: (纸本)9781509033331

Increasing extreme weather events has made people confused with which year is the hottest or the coldest in recent years. To answer this question, the region scale and time must be considered. In this study, we calculate global surface temperature by using four surface temperature products at day and night retrieved from MODIS data. The global mean surface temperature is about 14.56° from 2001 to 2012, and the hottest and coldest surface temperatures of the global earth are in 2005 and 2008 in the recent twelve years. The hottest and coldest surface temperatures for the global land are in 2005 and 2001, and in 2010 and 2008 for Ocean. The hottest and coldest surface temperatures for Northern and Southern Hemisphere, Eastern and Western hemisphere, seven continents, and four Ocean are also obtained. The analysis indicates that the change is different in different region. We propose to build a spatial weather-climate model based on gravitational-magmatic change with the solar and the galactic system as its centre to analyze the long-term temperature changes.

关键词： Ocean temperature Sea surface Land surface temperature Temperature distribution Temperature measurement Meteorology

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Ramp Loss Linear Programming Nonparallel Support Vector Machine

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Procedia Computer science 2016年 80卷 1745-1754页

作者： Dalian Liu Dandan Chen Yong Shi Yingjie Tian School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Basic Course Teaching Beijing Union University Beijing 100101 China College of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Motivated by the fact that the l 1 -penalty is piecewise linear, we proposed a ramp loss linear programming nonparallel support vector machine (ramp-LPNPSVM), in which the l 1 -penalty is applied for the RNPSVM, for binary classification. Since the ramp loss has the piecewise linearity as well, ramp- LPNPSVM is a piecewise linear minimization problem and a local minimum can be effectively found by the Concave Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm. Moreover, the l 1 -penalty can enhance the sparsity.

关键词： support vector machine nonparallel CCCP linear programming ramp loss

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A hierarchical and dynamic large-scale railway-track modeling solution for high-speed train real-time visual simulation

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2705-2711页

作者： Liu, Shijian Pan, Jeng-Shyang Zou, Zheng Zou, Beiji Liao, Shenghui Shcool of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Key Laboratory of Big Data Mining and Applications of Fujian Province Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China The Key Laboratory for Automotive Electronics and Electric Drive of Fujian Province Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Shcool of Information Science and Engineering Central South University No. 932 South Lushan Road Changsha410083 China

Large-scale railway-track modeling is a key and challenging problem for real- time visual simulation of the high-speed train. The railway-track can generally be described by its central line, whose length and shape vary dramatically between different ones. Therefore, modeling of a large-scale specified railway-track is not a trivial work. A new hierarchical and dynamic modeling framework is proposed to deal with the challenges. Unlike existing top-down approach, this bottom-up solution treats the entire railway-track as the combination of multiple repetitive regularity patterns, namely the elements referred by us. It is an optimized strategy for large model decomposition and is superior in modeling local details. Experiments, including the usage of the proposed method for track assembling and its applications to high-speed train real-time visual simulation, demonstrate the effect and efficiency of our work. Though designed for railway-track modeling, this method can be easily extended to other transportation applications as well. © 2016 ISSN 2185-2766.

关键词： Railroads

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Optimizing multiple ontology alignments through hybrid evolutionary algorithm based on a new elitist strategy

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ICIC Express Letters, Part B: Applications 2016年第6期7卷 1241-1248页

作者： Jiang, Li Tsai, Pei-Wei Xue, Xingsi Computer Department Fuzhou Polytechnic No. 8 Lianrong Road University Town Minhou Fuzhou350108 China College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350118 China

In this paper, we propose a hybrid evolutionary algorithm based on a new elitist strategy to address the problem of optimizing the multiple ontology alignments simultaneously. Comparing with the conventional approach, our method is able to deal with multiple pair of ontologies at a time, and through a new elitist strategy, avoid the bias improvement caused by f-measure. Experimental results show that our approach is effective. © 2016 ICIC International.

关键词： Ontology

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Global water vapor content and vegetation change analysis based on remote sensing data

Global water vapor content and vegetation change analysis ba...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： K.B. Mao Y. Ma Z. Y. Zuo Y. Q. Jiao F. Wang Q. Liu Z. W. Sun National Hulunber Grassland Ecosystem Observation and Research Station Chinese Academy of Agricultural Sciences Beijing China Chinese Academy of Agricultural Big Data and Nutrition Hong Kong China State Key Laboratory of Remote Sensing Science Beijing Normal University Beijing China College of Science China University of Petroleum Beijing China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and Technology Xiangtan China Space Star Technology Co. Ltd. Beijing China

Water vapor in the Earth's upper atmosphere plays a crucial in the radiative balance, hydrological process, and climate change. Based on the latest MODIS data, this study probes the spatio-temporal variations of global water vapor content in the past decade. It is found that overall the global water vapor concentrations declined over 2003-2012 (b=- 0.0149, R 2 =0.797, p=0.0005). The decreasing trend over the ocean surface (b= -0.017, R 2 =0.824, p=0.0003) is more explicit than over terrestrial surface (b= -0.010, R 2 =0.612, p=0.007), more significant over the Northern Hemisphere (b= -0.0175, R 2 =0.852, p=0.0001) than over the Southern Hemisphere (b= -0.0123, R 2 =0.682, p=0.003). Vegetation cover change is critical for an understanding of the impacts and responses of vegetation to climate change. The global vegetation growth has been analysised from 2001 to 2012. After thorough analysis based on satellite data, we find evidence that the global vegetation change little, and it is increasing slightly in Northern hemisphere while it is deceasing slightly in Southern Hemisphere. For different latitudes, the vegetation is increasing 0.17% every year from 60°N to 70 °N (R 2 =0.47, P>0.013), while the vegetation is decreasing 0.11% every year from 10°N to 10°S (R 2 =0.54, P>0.004). For different continents, the vegetation in South America is decreasing 0.16% every year (R 2 =0.78, P>0.0001) and it is increasing 0.05% every year in Asia (R 2 =0.28, P>0.072) and 0.25% every year in Oceania (R 2 =0.24, P>0.1).

关键词： Vegetation mapping Vegetation MODIS Remote sensing Meteorology Market research Ocean temperature

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Road network modeling with layered abstraction for path discovery in vehicle navigation systems

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Journal of Information Hiding and Multimedia Signal Processing 2016年第4期7卷 791-801页

作者： Pan, Jeng-Shyang Wang, Chen Sung, Tien-Wen Fuzhou College of Foreign Studies and Trade No. 28 Yuhuan Road Changle Fuzhou CityFujian Province350202 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School University Town Shenzhen City518055 China College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road Fuzhou CityFujian Province350118 China

Algorithms for path planning have recently drawn renewed attention from researchers due to the advances of intelligent transportation applications. The difference on path planning between past and nowadays is the complexity of dynamic transportation network. This paper focuses on the road network modeling to speed up real time path discovery in dynamic and complex road networks. On the basis of community detection (partitioning), an agglomerative hierarchical clustering algorithm with greedy strategy is proposed in this paper. The algorithm performs a hierarchical network abstraction to make a layered road network with largest community modularity. Static and dynamic information of the real time road network conditions is concerned in the process of network abstraction. Based on the road network layering, the performance of path discovery can be improved due to the reduction of unnecessary searching. The performance of the proposed algorithm was evaluated and compared with the well-known Dijkstra and A-star algorithms. The result shows that the proposed algorithm performs better than the other two algorithms. © 2016.

关键词： Motion planning

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Precision Medicine:What Challenges Are We Facing?

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Genomics, Proteomics & Bioinformatics 2016年第5期14卷 253-261页

作者： Yu Xue Eric-Wubbo Lameijer Kai Ye Kunlin Zhang Suhua Chang Xiaoyue Wang Jianmin Wu Ge Gao Fangqing Zhao Jian Li Chunsheng Han Shuhua Xu Jingfa Xiao Xuerui Yang Xiaomin Ying Xuegong Zhang Wei-Hua Chen Yun Liu Zhang Zhang Kun Huang Jun Yu MOE Key Lahoratory of Molecular Biophysics College of Life Science and Technology and the Collaborative Innovation Center for Brain ScienceHuazhong University of Science and TechnologyWuhan 430074China School of Electronic and Information Engineering.Xi'an Jiaotong University.Xi'an 710049 China CAS Key Laboratory of Mental Health Institute of PsychologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of'Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100005China MOE Beijing Key Laboratory of Carcinogenesis and Translatiooal ResearchCenter for Cancer BioinformaticsPeking University Cancer Hospital&InstituteBeijing 100142China Center for Bioinformatics State Key Laboratory of Protein and Plant Gene ResearchSchool of Life SciencesPeking UniversityBeijing 100871China Beijing Institutes of Life Science.Chinese Academy of Sciences Beijing 100101China MOE Key Lahoratory of Developnlental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing 210096China State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of SciencesBeijing 100101China Max Planck Independent Research Group on Population Genomics CAS-MPG Partner Institute for Computational Biology(PICB)Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200031China CAS Ker Laboratory of Genome Sciences and Information Chinese Academy of Sciences.Beijing 100101China MOE Key Laboratory of Bioinformatics School of Life Sciences Center for Synthetic and Systems BiologyTsinghua UniversityBeijing 100084China Computational Omics Lahoratory Center of Computational BiologyBeijing Institute of Basic Medical SciencesBeijing 100850China Bioinformatics Division TNLIST and MOE Key Laboratory for Bioinformatics Department of AutomationTsinghua UniversityBeijing 100084China Department of Bioinformatics and Systems Biology College of LifeScience and Technol

Following the publication of the US National Research Council （N RC） report ＂ Toward PrecMon Medicine.＂ Building a Knowledge Network for Biomedical Research and a New Taxonomy of Diseases＂ in 2011 [1], several n... 详细信息

关键词： PMI Precision Medicine data We

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Nonlinear Time Series Prediction Based on the Dynamic Characteristics Clustering Neural Network

Nonlinear Time Series Prediction Based on the Dynamic Charac...

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International Conference on Intelligent System Design and Engineering Application (ISDEA)

作者： Lin Yi College of Information and Control Nanjing University of Information Science & Technology Nanjing China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing China

A novel nonlinear time series prediction method based on the dynamic clustering neural network is proposed. This method selects prediction samples which are similar with training samples according to the cluster analysis based on the dynamic characteristics of samples and then a new subset of the samples is obtained. All of the samples in this subset have the similar dynamic characteristics. By training with these samples, a model of BP neural network based on clustering is got and it is used in nonlinear time series prediction. Take the stress data of a large span bridge tower induced by strong typhoon as example. The results indicate the validity and the better prediction accuracy of this method in nonlinear time series prediction.

关键词： Neural networks Time series analysis Predictive models Training data models Stress Heuristic algorithms

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