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

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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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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The monitoring of land use and land cover change of Sichuan province and Chengdu district, China

The monitoring of land use and land cover change of Sichuan ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shijie Yu Zezhong Zheng Wunian Yang Mingcang Zhu Yong He Zhenlu Yu Shengli Wang Fu Wang Jiang Li School of Resources and Environment Univ. of Electronic Science and Technology of China Chengdu Sichuan PRC State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan Univ. Wuhan Hubei PRC Ministry of Land and Resources of the China Chengdu Univ. of Technology Chengdu Sichuan PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin Guangxi PRC Land and Resources Department of Sichuan Province Chengdu Sichuan PRC Sichuan Institute of Geo-Environment Monitoring Chengdu Sichuan PRC China National Space Administration Earth Observation System and Data Center Beijing PRC CMA National Satellite Meteorological Center Beijing PRC Dep. of Electrical and Computer Engineering Old Dominion Univ. Norfolk VA USA

Land use and land cover change (LUCC) is necessary to explore the factors leading to heavy drought and rainy-flood disaster in some districts of Sichuan province. A method based RS, GIS, GPS and Google earth (GE) is presented to establish LUCC database in Sichuan province and Chengdu district. At first, LUCC is interpreted based on the new temporal images and the land use and land cover database from TM in ***, some ground objects, which could not be identified in the new temporal images, were interpreted utilizing GE with some higher spatial resolution images. Thirdly, the new interpreted LUCC was validated in the field with GPS handheld receiver. Then, LUCC of Sichuan province was updated. A comparative analysis of LUCC between in Sichuan province and in Chengdu district was conducted and the result showed: (1) a large amount of farmland in Sichuan Province was occupied from 2000 to 2005 and the area is 84 573 ha. While construction land gained obviously and the area was 35 828 ha. The dynamic degree of construction land was 111.10 0 / 00 from 2000 to 2005. The LUCC demonstrated that the economy of Sichuan province continued to develop, the cities were overspreading and the urban heat island effect was deteriorated from 2000 to 2005. (2) A large amount of farmland was also occupied in Chengdu district from 2000 to 2005, the area amounted to 12 989 ha. The farmland lost was mainly changed to construction land, amounting to 93%. And the dynamic degree was 117.41 0 / 00 from 2000 to 2005, which was bigger than that in Sichuan province.

关键词： Spatial resolution Global Positioning System Urban areas Thermal pollution Remote sensing Google databases

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Parallel knowledge acquisition algorithm for big data using MapReduce

Parallel knowledge acquisition algorithm for big data using ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781467372220

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

关键词： big data Hierarchical Rough Set Knowledge Acquisition MapReduce big data Information Extraction Decision tables

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A novel brain tumor segmentation method for multi-modality human brain MRIs

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2015年第11期10卷 115-122页

作者： Zhan, Tianming Gu, Shenghua Jiang, Lei Zhan, Yongzhao School of Computer Science and Communications Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Petrochina Southwest Oil and Gasfield Company Luzhou China

Delineating brain tumor boundaries from multi-modality magnetic resonance images (MRIs) is a crucial step in brain cancer surgical and treatment planning. In this paper, we propose a fully automatic technique for brain tumor segmentation from multi-modality human brain MRIs. We first use the intensities of different modalities in MRIs to represent the features of both normal and abnormal tissues. Then, the multiple classifier system (MCS) is applied to calculate the probabilities of brain tumor and normal brain tissue in the whole image. At last, the spatial-contextual information is proposed by constraining the classified neighbors to improve the classification accuracy. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results. © 2015 SERSC.

关键词： Magnetic resonance

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Feedback delay identification by time-dependent exponent evaluation for a chaotic semiconductor laser

Feedback delay identification by time-dependent exponent eva...

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Frontiers in Optics 2015, FIO 2015

作者： Li, Xiao-Zhou Li, Song-Sui Zhuang, Jun-Ping Gao, Jianbo Chan, Sze-Chun Department of Electronic Engineering City University of Hong Kong Hong Kong Hong Kong Institute of Complexity Science and Big Data Technology Guangxi University Nanning Guangxi China PMB Intelligence LLC SunnyvaleCA94087 United States State Key Laboratory of Millimeter Waves City University of Hong Kong Hong Kong Hong Kong

Feedback delay time of a chaotic laser is experimentally extracted by calculating the time-dependent exponent ∧. The exponent is directly obtained through phase space reconstruction and contains the information of th... 详细信息

ISBN: (纸本)9781943580033

关键词： Semiconductor lasers

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