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A novel method for road centerline extraction from high spatial resolution satellite images

Journal of Information Hiding and Multimedia Signal Processing 2016年第5期7卷 1069-1080页

作者： Xu, Rui Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No.523 Gongye Road Fuzhou350002 China College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China

An integrated method for road centerline extraction has been proposed in this paper which consists of four main steps. First, the Graph Cut algorithm using proposed similarity function is employed to segment the image, and the segmented image is thresholded to initial binary skeleton. Second, the road skeleton is refined by using a series of morphology operations and shape features. Third, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally,a regression method is performed to extract smooth and accurate road centerlines. Validation tests show that the proposed method is able to achieve a comparatively good performance in road centerline extraction from high spatial resolution satellite images. © 2016, Ubiquitous International. All Rights Reserved.

关键词： Regression analysis

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A hierarchical object-based method for suburban road extraction from remote sensing image

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2671-2676页

作者： Xu, Rui College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No. 523 Gongye Road Fuzhou350002 China

Road extraction from remote sensing image is a very popular topic. There are many methods dealing with this problem, but few of them focus on suburban roads. This paper presents a hierarchical object-based method for suburban road extraction. First, the image is preprocessed by the Normalized Difference Vegetation Index (NDVI) and the Hue-Saturation-Intensity (HSI) Index. Vegetation regions, shadows and water areas are then removed. Second, texture features are applied to classifying the preprocessed image into two categories: road and non-road. Homogeneous property is combined to remove false classification so as to generate initial road skeleton. Third, the road skeleton is refined by using a series of morphology operations and shape features. Fourth, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally, a regression method is performed to extract smooth and accurate road centerlines. The experimental results indicate that the proposed method is suitable for suburban road extraction. © 2016 ISSN 2185-2766.

关键词： Vegetation

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Application of an improved clustering algorithm in customer risk preference recognition model

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ICIC Express Letters, Part B: Applications 2016年第5期7卷 1077-1082页

作者： Xiao, Lin Xu, Shenghua Pan, Jeng-Shyang Yang, Tonghua College of Information Management Jiangxi University of Finance and Economics No. 665 West Yuping Road Nanchang330013 China College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou350108 China Key Laboratory of Big Data Mining and Applications of Fujian Province Fuzhou350108 China

The main problem of this paper is to establish a recognition model for the risk preference of brokerage individual customer. It is based on transaction data. Its core algorithm is the use of improved K-means algorithm. Its process includes the principal component analysis and clustering operation of the data. In this paper, we improve the design of automatic merging approximation class in clustering algorithm. The main basis of customer risk is the investment subject of the customer. The contribution of this research is to promote the accuracy and success rate of marketing and provide a good support for the company to make reasonable marketing decision. © 2016, ICIC International. All rights reserved.

关键词： Clustering algorithms

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Cluster based hybrid key management scheme for ad hoc networks

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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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