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International Conference of Young computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Du, Zhengyang Zhang, Wenqiang Qin, Jinxian Lu, Hong Chen, Zhong Zheng, Xidian Shanghai Engineering Research Center for Video Technology and System School of Computer Science Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Electric Group CO LTD Central Academe China

ISBN: (纸本)9783662462478

With the high-speed development of digital image processing technology, machine vision technology has been widely used in automatic detection of industrial products. A large amount of products can be treated by computer instead of human in a shorter time. In the process of automatic detection, edge detection is one of the most commonly used methods. But with the increasing demand for detection precision, traditional pixel-level methods are difficult to meet the requirement, and more subpixel level methods are in the use. This paper presents a new method to detect curved edge with high precision. First, the target area ratio of pixels near the edge is computed by using one-dimensional edge detection method. Second, parabola is used to approximately represent the curved edge. And we select appropriate parameters to obtain accurate results. This method is able to detect curved edges in subpixel level, and shows its practical effectiveness in automatic measure of products with arc shape in large industrial scene. © Springer-Verlag Berlin Heidelberg 2015.

关键词： computer vision

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Protein model quality assessment by learning-to-rank

Protein model quality assessment by learning-to-rank

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IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

作者： Jing, Xiaoyang Dong, Qiwen Liu, Xuan Liu, Bin School of Computer Science Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China College of Engineering Shanghai Ocean University Shanghai201303 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen Guangdong518055 China

ISBN: (纸本)9781467367981

Protein structures are essential to understand the function. The predicted models have a broad range of the accuracy. Reliable estimates of the model quality are critical in determining the usefulness of the model to address a specific problem. In this study, a novel method has been presented to rank the models by their relative qualities. The proposed method first extracts various features from the three dimensional structures of proteins and then the learning-to-rank algorithm is used to rank the models based on their similarities with the native structures. Furthermore, a quasi single-model method is presented, which uses the top five identified models as references and ranks the other models by the average similarity with the reference models. Benchmark test is performed on a newly developed, template-based decoy generators which covers all the main structure classes of proteins. The proposed learning-to-rank method achieves an average Pearson correlation coefficient of 0.94 and a AUC value of 0.97, which consistently outperform all other well-developed methods. The quasi single-model can further improves the performance and achieve nearly perfect results with both PCC and AUC value of 0.99. The results demonstrate that the proposed method is an effective methodology for model quality assessment and provides the state-of-the-art performance. © 2015 IEEE.

关键词： Proteins

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The Fudan participation in the 2015 BioASQ Challenge: Large-scale biomedical semantic indexing and question answering 16

The Fudan participation in the 2015 BioASQ Challenge: Large-...

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16th Conference and labs of the Evaluation Forum, CLEF 2015

作者： Peng, Shengwen You, Ronghui Xie, Zhikai Wang, Beichen Zhang, Yanchun Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China Shanghai Key Lab of Data Science Fudan University Shanghai200433 China Centre for Applied Informatics College of Engineering and Science Victoria University Australia

This article describes the participation of Fudan team in the 2015 BioASQ challenge. The challenge consists of two tasks, large-scale biomedical semantic indexing (task 3a) and biomedical question answering (task 3b). In task 3a, our method, MeSHlabeler, achieved the first place in all 15 weeks of three batches. Based on 3215 annotated citations (June 6, 2015) out of all 4435 citations in the official test set (batch 1, week 2), our submission best achieved 0.6194 in at Micro F-measure. This is 0.0576(10.25%) higher than 0.5618, obtained by the official NLM solution Medical Text Indexer (MTI). Task 3b includes two phases. Given the questions raised by a team of biomedical experts from around Europe, the main task of phase A is to find relevant documents, snippets, concepts and RDF triples, while the main task of phase B is to provide exact and ideal answers. In the phase A of task 3b, our submission, fdu, achieved the first place in both document and snippet retrieval in batch 5 (June 6, 2015). Suggestions and insightful discussion.

关键词： information retrieval

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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FDUMedSearch at TREC 2015 Clinical Decision Support Track 24

FDUMedSearch at TREC 2015 Clinical Decision Support Track

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24th Text REtrieval Conference, TREC 2015

作者： You, Ronghui Zhou, Yuanjie Peng, Shengwen Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai20433 China Shanghai Children's Medical Center Medical School Shanghai Jiaotong University 1678 Dongfang Road Shanghai200127 China

This paper describes the participation of the FDUMedSearch team at TREC 2015 Clinical Decision Support track (CDS2015). Given the medical cases, the main purpose of CDS2015 is to develop effective information retrieval techniques in finding relevant documents for patient care. We used Indri as the retrieval engine, which implemented query likelihood method as the baseline. In addition, query expansion using Medical Subject Headings (MeSH), pseudo relevance feedback and classification were used to enhance the retrieval performance. We also tried to extract keywords in two different ways, automatically and manually. Experimental results show that our method achieved significant improvement over baseline methods. © 2015 24th Text REtrieval Conference, TREC 2015 - Proceedings. All Rights Reserved.

关键词： information retrieval

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Identification of hot regions in protein interfaces: Combining density clustering and neighbor residues improves the accuracy 11th

Identification of hot regions in protein interfaces: Combini...

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11th International Conference on intelligent Computing, ICIC 2015

作者： Hu, Jing Zhang, Xiaolong School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan Hubei430065 China

ISBN: (纸本)9783319221854

Discovering hot regions in protein–protein interaction is important for understanding the interactions between proteins, while because of the complexity and time-consuming of experimental methods, the computational prediction method can be very helpful to improve the efficiency to predict hot regions. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method that uses density-based incremental clustering to predict hot regions and optimizes the predicted hot regions using neighbor residues is proposed. Experimental results show that the proposed method significantly improves the prediction performance of hot regions. © Springer International Publishing Switzerland 2015.

关键词： Proteins

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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Reflection invariant local binary patterns for image texture classification

Reflection invariant local binary patterns for image texture...

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Research in Adaptive and Convergent Systems, RACS 2015

作者： Song, Enmin Pan, Ning Hung, Chih-Cheng Li, Xiang Jin, Lianghai School of Computer Science and Technology Huazhong University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan430074 China College of Biomedical Engineering South-Central University for Nationalities Wuhan430074 China Sino-US Intelligent Information Processing Lab Anyang Normal University Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9781450337380

In this study we modified the local binary pattern operator (LBP) to obtain the robust invariant texture patterns for image texture classification. The modified method will be able to calculate patterns which are invariant for translation, scaling, rotation and reflection. Therefore, the modified LBP is called R-LBP. Although many variation of LBP have been proposed, most of them cannot detect and recognize the patterns of reflection. Both clockwise and counter clockwise coding is used in the proposed R-LBP operator in order to derive a minimum code to representing the pattern. Experimental results show that the proposed method is effective in determining the invariant patterns for image texture classification. © 2015 ACM.

关键词： Local binary pattern

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Social sensing enhanced time estimation for bus service

Social sensing enhanced time estimation for bus service

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作者： Liu, Jin Li, Juan Cui, Xiaohui Niu, Xiaoguang Sun, Xiaoping Zhou, Jing State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China International School of Software Wuhan University Wuhan China Computer School Wuhan University Wuhan China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science Communication University of China China

The precise prediction of bus routes or the arrival time of buses for a traveler can enhance the quality of bus service. However, many social factors influence people's preferences for taking buses. These social factors may include heavy traffic cost, traffic congestion, poor air quality and so forth. Existing prediction techniques rarely consider social sensing when predicting the bus arrival time. Accordingly, this paper proposes a social sensing enhanced service for predicting bus routes, which integrates sensing ability and social networks to understand and measure the influence between social events and vehicle velocity. We focus on the analysis of two different attributions: PT service quality attributions PEAs and road condition attributions PRCAs. Both of them synthesize the social sensing in their evaluation of bus routes. PEA represents individual preferences and PRCA represents physical factors that significantly influence vehicle velocity. Bus relevant social events were further categorized into PEA events or PRCA events. PEAs of buses were scored according to the tendency of bus conditions reflected in social events. Furthermore, an artificial neural network prediction model is established to estimate the bus travel time. © 2015 John Wiley & Sons, Ltd.

关键词： Buses

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