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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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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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The Mobile Personalized Recommendation Model Containing Implicit Intention

The Mobile Personalized Recommendation Model Containing Impl...

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2015大数据技术及应用论坛

作者： Jing Liu Jun Zhang Yan Li Shuqun He Caixue Zheng School of Computer Science and Engineering Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation School of Economic Management Hebei University of Technology

Because mobile e-commerce is limited by the mobile terminal,network environment and other factors, accurate personalized recommendations become more and more important. We establish a large data intelligence platform,aiming at the characteristics of mobile e-commerce;we put forward a personal‐ized recommendation model with implicit intention ***, create an intelligence unit with the virtual individual association set,virtual demand association set and virtual behavior associated set;Secondly,calculate the complex buying behavior prediction engine;Finally, give the predic‐tive value of complex buying *** method takes full account of factors such as hidden wishes perturbations that affect the predict of complex buying behavior, which to some extent solve a long-span composite purchasing behavior prediction. It shows that this method improves the purchasing behavior prediction accuracy effectively through experiments.

关键词： Mobile e-commerce Personalized recommendations Hidden wishes big data intelligence platform

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An accuracy comparison between the time-series and the computational intelligence models on the Taiwan-central America free trade agreements

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ICIC Express Letters, Part B: Applications 2016年第9期7卷 1925-1932页

作者： Tsai, Pei-Wei Yang, Li-Hui Zhang, Jing Xue, Xingsi Chen, Jung-Fang Chang, Jui-Fang Pan, Jeng-Shyang College of Information Science and Engineering Fujian University of Technology University Town No. 3 Xueyan Road Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology University Town No. 3 Xueyan Road Minhou Fuzhou350118 China Department of International Business National Kaohsiung University of Applied Sciences No. 415 Chien-Kong Road Kaohsiung City80778 Taiwan Department of Business Administration National Kaohsiung University of Applied Sciences No. 415 Chien-Kong Road Kaohsiung City80778 Taiwan

Starting from the mid-20th century, Taiwanese economy is extremely exportoriented due to the lack of natural resources. To ensure the growing of the economy, Taiwan government keeps actively looking for opportunities for participation in the world economic system to avoid the slowdown of multinational liberalization. As one example, Taiwan vigorously participates in FTAs with countries of Central America. International economic conditions are significantly important to international trade and the export/import volume will be affected by the foreign exchange rate. If the impact level of an incident or an event occurrence is analyzed along with the foreign exchange rate forecasting, there is a chance to maximize the trade surplus, thus boosting the economic growth. Based on the event study methodology, the selected agreements include four Taiwan signed FTAs with countries in Central America. The observation period setting is 70 days of pre-event period and 70 days of post-event period. In this paper, we use two commonly used time-series models including the GARCH and the EGARCH and a Computational Intelligence (CI) model established by the Interactive Artificial Bee Colony (IABC) method for finding the impact on the foreign exchange rate forecasting. The Mean Absolute Percentage Error (MAPE) is adopted to compare the accuracy of exchange rate prediction. The experimental results indicate that the IABC based foreign exchange rate forecasting model presents higher forecasting accuracy than the conventional time-series models and is able to resist the impact caused by the FTAs. © 2016 ICIC International.

关键词： International trade

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Utilizing directional information in evolved bat algorithm for guiding artificial agents

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ICIC Express Letters, Part B: Applications 2016年第7期7卷 1461-1466页

作者： Tsai, Pei-Wei Cai, Sijing Zhang, Jing Istanda, Vaci Pan, Jeng-Shyang College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Rd. University Town Minhou Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Rd. University Town Minhou Fuzhou350118 China Council of Indigenous Peoples Executive Yuan 14-16F. North Tower No. 439 Zhongping Rd. Xinzhuang Dist New Taipei City24220 Taiwan Innovative Information Industry Research Harbin Institute of Technology Shenzhen Graduate School Room 424 Building D University Town Shenzhen518055 China

In this paper, an idea of utilizing the directional information to guide the artificial agents in the searching process is led in to the Evolved Bat Algorithm (EBA). By properly using the directional information, the guided artificial agents can present higher searching accuracy than the agents in the conventional EBA. The advantage of our proposed method guides the artificial agent to cruise along the gradient information from the known near best solutions. It results in that the searching accuracy is increased with only a slight addition to the calculation. To test the accuracy on finding the near best solutions, 2 benchmark functions, whose global optimum are known, with different dimensional criteria are used in the experiments. The experimental results are compared with the conventional EBA under the same experimental conditions. The experimental results indicate that our proposed method improves the accuracy on finding the near best solution. According to the experimental results, our proposed Directional guided Evolved Bat Algorithm (DgEBA) is significantly improved compared with the conventional EBA. © 2016, ICIC Express Letters Office. All rights reserved.

关键词： Swarm intelligence

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