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Rotation Estimation for Mobile Robot Based on Single-axis Gyroscope and Monocular Camera

International Journal of Automation and computing 2012年第3期9卷 292-298页

作者： Yang, Ke-Hu Yu, Wen-Sheng Ji, Xiao-Qiang School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing 100083 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai 200062 China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.

关键词： Rotation matrix estimation quaternion extended Kalman filter (EKF) monocular camera gyroscope.

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CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

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Protein & Cell 2012年第2期3卷 148-152页

作者： Guoguang Zhao Dechao Bu Changning Liu Jing Li Jian Yang Zhiyong Liu Yi Zhao Runsheng Chen Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular Virology and Genetic Engineering National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100176China Graduate School of the Chinese Academy of Sciences Beijing 100190China

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.

关键词： CloudLCA metagenome analysis cloud computing

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Towards Collecting Commonsense Knowledge with Hint Questions

Towards Collecting Commonsense Knowledge with Hint Questions

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第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Liangjun Zang Cong Cao Cungen Cao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca

Accumulating commonsense knowledge (CK) has proven very useful for many natural language processing *** far the most reliable way of acquisition is still relying on knowledge contributors to offer ***,knowledge contributors often fail to think of much CK because this knowledge is usually taken for granted by *** this paper,we explore the issue of reminding knowledge contributors by means of hint ***,we investigate what kinds of CK are easily ignored,analyze its reason and suggest possible *** hope to sketch sources of the research problem and suggest possible ways forward.

关键词： Ontologies History Humans Cognition Computers Roads Reliability

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Improving transfer learning by introspective reasoner 1

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7th IFIP International Conference on intelligent information Processing, IIP 2012

作者： Shi, Zhongzhi Zhang, Bo Zhuang, Fuzhen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate School Chinese Academy of Sciences 100039 Beijing China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Traditional learning techniques have the assumption that training and test data are drawn from the same data distribution, and thus they are not suitable for dealing with the situation where new unlabeled data are obtained from fast evolving, related but different information sources. This leads to the cross-domain learning problem which targets on adapting the knowledge learned from one or more source domains to target domains. Transfer learning has made a great progress, and a lot of approaches and algorithms are presented. But negative transfer learning will cause trouble in the problem solving, which is difficult to avoid. In this paper we have proposed an introspective reasoner to overcome the negative transfer learning. Introspective learning exploits explicit representations of its own organization and desired behavior to determine when, what, and how to learn in order to improve its own reasoning. According to the transfer learning process we will present the architecture of introspective reasoner for transductive transfer learning. © 2012 IFIP International Federation for information Processing.

关键词： Learning systems

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Parallel max-min ant system using mapreduce

Parallel max-min ant system using mapreduce

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3rd International Conference on Swarm Intelligence, ICSI 2012

作者： Tan, Qing He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate University of Chinese Academy of Sciences 100049 Beijing China

ISBN: (纸本)9783642309755

Ant colony optimization algorithms have been successfully applied to solve many problems. However, in some large scale optimization problems involving large amounts of data, the optimization process may take hours or even days to get an excellent solution. Developing parallel optimization algorithms is a common way to tackle with this issue. In this paper, we present a MapReduce Max-Min Ant System (MRMMAS), a MMAS implementation based on the MapReduce parallel programming model. We describe MapReduce and show how MMAS can be naturally adapted and expressed in this model, without explicitly addressing any of the details of parallelization. We present benchmark travelling salesman problems for evaluating MRMMAS. The experimental results demonstrate that the proposed algorithm can scale well and outperform the traditional MMAS with similar running times. © 2012 Springer-Verlag.

关键词： MapReduce

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Support vector machine with mixture of kernels for image classification 1

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7th IFIP International Conference on intelligent information Processing, IIP 2012

作者： Tian, Dongping Zhao, Xiaofei Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Image classification is a challenging problem in computer vision. Its performance heavily depends on image features extracted and classifiers to be constructed. In this paper, we present a new support vector machine with mixture of kernels (SVM-MK) for image classification. On the one hand, the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible. SVM-MK, on the other hand, is constructed to shoot for better classification performance. Experimental results on the Berg dataset show that the proposed image feature representation method together with the constructed image classifier, SVMMK, can achieve higher classification accuracy than conventional SVM with any single kernels as well as compare favorably with several state-of-the-art approaches. © 2012 IFIP International Federation for information Processing.

关键词： Image classification

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Properties and axiomatization of fuzzy rough sets based on fuzzy covering

Properties and axiomatization of fuzzy rough sets based on f...

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Wu, Ming-Fen Han, Hao-Han Si, Yan-Fei School of Computer Science Wuyi University Guangdong 529020 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467314855

Some expanded fuzzy rough sets models have been investigated to handle fuzzy databases with uncertain, imprecise and incomplete real-valued information. In this paper, we make further research on fuzzy rough sets models in fuzzy environment, and we generalize rough fuzzy sets model based on a covering to fuzzy rough sets model based on a fuzzy covering. The lower and upper approximations of fuzzy subsets are defined based on a fuzzy covering, and basic properties are investigated. Then, the axiom definition of the lower approximation operator is given. It is shown that the rough fuzzy sets model based on a covering is a special instance of the fuzzy rough sets model based on a fuzzy covering. © 2012 IEEE.

关键词： Fuzzy sets

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Left-to-right tree-to-string decoding with prediction

Left-to-right tree-to-string decoding with prediction

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2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012

作者： Feng, Yang Liu, Yang Liu, Qun Cohn, Trevor Department of Computer Science University of Sheffield Sheffield United Kingdom State Key Laboratory on Intelligent Technology and Systems Department of Computer Sci. and Tech. Tsinghua University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781937284435

Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying the language model evaluation. This paper presents a novel left to right decoding algorithm for tree-to-string translation, using a bottom-up parsing strategy and dynamic future cost estimation for each partial translation. Our method outperforms previously published tree-to-string decoders, including a competing left-to-right method. © 2012 Association for Computational Linguistics.

关键词： Context free grammars

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Quad-tuple PLSA: Incorporating entity and its rating in aspect identification

Quad-tuple PLSA: Incorporating entity and its rating in aspe...

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16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012

作者： Luo, Wenjuan Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)9783642302169

With the opinion explosion on Web, there are growing research interests in opinion mining. In this study we focus on an important problem in opinion mining - Aspect Identification (AI), which aims to extract aspect terms in entity reviews. Previous PLSA based AI methods exploit the 2-tuples (e.g. the co-occurrence of head and modifier), where each latent topic corresponds to an aspect. Here, we notice that each review is also accompanied by an entity and its overall rating, resulting in quad-tuples joined with the previously mentioned 2-tuples. Believing that the quad-tuples contain more co-occurrence information and thus provide more ability in differentiating topics, we propose a model of Quad-tuple PLSA, which incorporates two more items - entity and its rating, into topic modeling for more accurate aspect identification. The experiments on different numbers of hotel and restaurant reviews show the consistent and significant improvements of the proposed model compared to the 2-tuple PLSA based methods. © 2012 Springer-Verlag.

关键词： Sentiment analysis

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microRNA的功能(英文)

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生物化学与生物物理进展 2012年第10期39卷 979-980页

作者：罗海涛赵屹 Bioinformatics Research Group Advanced Computing Research LaboratoryKey Laboratory of Intelligent Information ProcessingInstitute of Computing TechnologyChinese Academy of Sciences Research Center of Bioinformatics Kunshan RNAi Institute

MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of biological functions such as regulation of innate and adaptive immunity,cell differentiation and development as well as

关键词：肾癌 IL-8 上皮细胞-间质细胞转化蛋白激酶C

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