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A comparative study on artificial neural networks for environmental quality assessment

Computer Modelling and New Technologies 2014年第7期18卷 41-47页

作者： Liu, Yijun He, Sheng Wang, Yao Wang, Xiumei Key laboratory of cloud computing and intelligent information processing of Changzhou City Jiangsu University of Technology China

The aim of this study is to use neural network tools as an environmental decision support in assessing environmental quality. A three-layer feedforward neural network using three learning approaches of BP, LM and GA-BP has been applied in non-linear modelling for the problem of environmental quality assessment. The case study shows that the well designed and trained neural networks are effective and form a useful tool for the prediction of environmental quality. Furthermore, the LM network has the fastest convergence speed and the GA-BP network outperforms the other two networks in both predictive and final classification accuracies of environmental quality.

关键词： Neural network models

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Sparse posterior probability support vector machines

Sparse posterior probability support vector machines

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： Dongli Wang Yan Zhou Key Laboratory of Intelligent Computing Information Processing of MOE Xiangtan China College of Information Engineering Xiangtan University Xiangtan China

Posterior probability support vector machines (PPSVMs) are proved to have good generalization performance and robustness against outliers. However, the disadvantage of a PPSVM is lack of sparseness of solution, i.e., the number of support vectors is still too large. This results in high computational burden and decision time. In this paper, we present two approaches to obtain sparse PPSVMs, which are expected to combine benefits of both PPSVMs and sparse classifiers. The first approach sparsifies the PPSVMs by adding l 1 norm penalties on the dual cost function of soft margin PPSVMs. The second one handles a mixed l 1 -l 2 multi-objective optimization by interior-point algorithm. Simulation results show that both approaches have good generalization performance, good robustness against outliers, and high efficiency on decision evaluation.

关键词： Support vector machines Optimization Accuracy Robustness Kernel Probability Training

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A Bayesian Compressed Sensing Approach to Robust Object Localization in Wireless Sensor Networks

A Bayesian Compressed Sensing Approach to Robust Object Loca...

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IEEE International Conference on Mobile Services (MS)

作者： Dongli Wang Yan Zhou Yanhua Wei College of Information Engineering Xiangtan University Xiangtan China Key Laboratory of Intelligent Computing & Information Processing of MOE Xiangtan China

The recent growing interest for location-based services (LBSs) has created a demand on more accurate and robust object localization approaches. In this paper, the Bayesian compressed sensing (BCS) is employed to localize a single or multiple objects in a wireless sensor network (WSN). This is motivated by the advantages of BCS such as closer to l 0 -norm, and better performance of reconstruction in case of noisy measurements. Due to the spatial sparsity of number of girds containing an object (comparing with the total number of grids in the region of interest), the localization problem can be transferred into recovering a spare index vector, which is reformulated as a Bayesian estimation problem according to the BCS theory. The proposed method is relieved from the requirement on accurate prior position knowledge of beacon nodes. Besides, by building location fingerprinting based on both line-of-sight (LOS) and non-line-of-sight (NLOS) measurements, the proposed method is robust and applicable to mixed LOS/NLOS environment. Finally, simulation examples are included to demonstrate the superiority of the proposed method.

关键词： Bayes methods Vectors Wireless sensor networks Noise measurement Compressed sensing Robustness Indexes

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Identity Recognition of Multi-Haptic Pressure Feature based on Sparse Representation

Identity Recognition of Multi-Haptic Pressure Feature based ...

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The 6th International Conference on Computer Research and Development

作者： Yan Zhang Dong Liang Ming Zhu Wenxia Bao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Physics and Materials ScienceAnhui University School of Electronics and Information Engineering Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education

Herein,a new identity recognition method of multi-haptic pressure feature based on sparse representation was *** to the common dynamic features,the regional feature and the ratio of length *** of external bounding rectangle(extracted by using the least area method) were *** subset of dynamic feature was optimized by correlation criterion,the sparse representation of haptic pressure was obtained according to the sparse basis(i.e.,wavelet basis),and the sparse feature vector was calculated by the Topelitz measurement *** that,the haptic pressure feature set was created by combining dynamic feature subset and sparse feature subset ***,Support Vector Machine(SVM) classifier identified more than two objects following the one to many rule and output the identification result according to the rule of majority voting,and the stability of features is studied by calculating the intraclass correlation coefficient(ICC) and coefficient of variation(C.V).Overall,the improved acuracy of identity recognition demonstrating the effectiveness and stability of the multihaptic pressure feature.

关键词： dynamic feature sparse representation optimization correlation criterion recognition

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Bio-inspired visual attention model and saliency guided object segmentation 7

Bio-inspired visual attention model and saliency guided obje...

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7th International Conference on Genetic and Evolutionary computing, ICGEC 2013

作者： Duan, Lijuan Gu, Jili Yang, Zhen Miao, Jun Ma, Wei Wu, Chunpeng College of Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Fujitsu Research and Development Center Co. Ltd Beijing China

ISBN: (纸本)9783319017808

In this paper, we present a saliency guided image object segment method. We suppose that saliency maps can indicate informative regions, and filter out background in images. To produce perceptual satisfactory salient objects, we use our bio-inspired saliency measure which integrating three factors: dissimilarity, spatial distance and central bias to compute saliency map. Then the saliency map is used as the importance map in the salient object segment method. Experimental results demonstrate that our method outperforms previous saliency detection method, yielding higher precision (0.7669) and better recall rates (0.825), F-Measure (0.7545), when evaluated using one of the largest publicly available data sets. © Springer International Publishing Switzerland 2014.

关键词： Object detection

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A secure broadcasting method based on the generalized aryabhata remainder theorem

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Journal of information Hiding and Multimedia Signal processing 2014年第4期5卷 709-718页

作者： Liu, Yanjun Chang, Chin-Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University No.111 Jiulong Rd Hefei230601 China Department of Computer Science and Information Engineering Asia University No.500 Lioufeng Rd Wufeng Taichung413 Taiwan Department of Information Engineering and Computer Science Feng Chia University No.100 Wenhwa Rd Seatwen Taichung407 Taiwan

With the prevalence of group communications, how to implement secure broadcasting among group members has become one of the most important issues. Broadcasting is a point-to-multipoint communication, and secure broadcasting should ensure that only authorized group members can obtain the correct content of the message that is broadcast by a sender. In this paper, we propose a secure broadcasting method based on the generalized Aryabhata remainder theorem (GART). By the use of the RSA public-key scheme, the proposed scheme allows the sender to send one copy of an encrypted message to all group members, but only the authorized members can recover the plaintext. Analysis showed that our scheme is secure and efficient. © 2014.

关键词： Cryptography

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Quantitative identification of yellow rust, powdery mildew and fertilizer-water stress in winter wheat using In-Situ hyperspectral data

Quantitative identification of yellow rust, powdery mildew a...

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作者： Guan, Qingsong Huang, Wenjiang Zhao, Jinling Liu, Liangyun Liang, Dong Huang, Linsheng Wang, Li Yang, Guijun Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China Beijing Agriculture Information Technology Research Center Beijing China

As the progressive effects of global warming, the yield loss caused by diseases and pests are increasing in winter wheat. It is necessary to distinguish different diseases for guiding variable rate spraying in wheat. Nevertheless, it is very difficult to quantitatively identify different diseases and fertilizer-water stress by specific sensitive bands selected from multi spectral data over a large area. Conversely, hyper spectral data contain more information, and provide the potential for quantitative identification of different stresses. This study focused on identification and distinction of yellow rust, powdery mildew and fertilizer-water stress by canopy spectral reflectance. Fifteen commonly used vegetation indices were selected, and independent t-test was done to get sensitivity index for each stress. Finally, a combination index was optimally selected to distinguish the three stresses. The results showed that the integrative index (NDVI-PhRI) combining normalized difference vegetation index (NDVI) and physiological reflectance index (PhRI) could be used to identify powdery mildew and yellow rust (PM-YR). A 2-dimensional spatial coordinate was established based on the NDVI and PhRI derived from hyper spectral data, then the different stress data were displayed in the spatial coordinate and the classification boundary could be used to identify the powdery mildew and yellow rust stress. Similarly, yellow rust and fertilizer-water stress (YR-n0w0) can be distinguished by the combination index (MSR-PhRI) derived from modified simple ratio (MSR) and physiological reflectance index (PhRI);and the combination index (NRI-RVSI) derived from nitrogen reflectance index (NRI) and red-edge vegetation stress index (RVSI) was accurate to identify powdery mildew and fertilizer-water stress (PM-n0w0). For the PM-YR, YR-n0w0 and PM-n0w0 models, their verification accuracies were 83.3%, 88%, 88.75%, and the kappa accuracies were 63.41%, 74.79%, 71.43%, respectively. It indicate

关键词： Physiology

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Semi-paired probabilistic canonical correlation analysis 8

Semi-paired probabilistic canonical correlation analysis

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8th IFIP International Conference on intelligent information processing, IIP 2014

作者： Zhang, Bo Hao, Jie Ma, Gang Yue, Jinpeng Shi, Zhongzhi Chinese Academy of Sciences Institute of Computing Technology The Key Laboratory of Intelligent Information Processing Beijing China University of Chinese Academy of Sciences Beijing China Xuzhou Medical College School of Medicine Information Xuzhou China China University of Mining and Technology School of Computer Science and Technology Xuzhou China

ISBN: (纸本)9783662449790

CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with artificially generated samples demonstrate the effectiveness of the proposed method. © IFIP International Federation for information processing 2014.

关键词： Correlation methods

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Modeling term translation for document-informed machine translation

Modeling term translation for document-informed machine tran...

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2014 Conference on Empirical Methods in Natural Language processing, EMNLP 2014

作者： Meng, Fandong Xiong, Deyi Jiang, Wenbin Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Computer Science and Technology Soochow University China Centre for Next Generation Localisation Dublin City University Ireland

ISBN: (纸本)9781937284961

Term translation is of great importance for statistical machine translation (SMT), especially document-informed SMT. In this paper, we investigate three issues of term translation in the context of documentinformed SMT and propose three corresponding models: (a) a term translation disambiguation model which selects desirable translations for terms in the source language with domain information, (b) a term translation consistency model that encourages consistent translations for terms with a high strength of translation consistency throughout a document, and (c) a term bracketing model that rewards translation hypotheses where bracketable source terms are translated as a whole unit. We integrate the three models into hierarchical phrase-based SMT and evaluate their effectiveness on NIST Chinese- English translation tasks with large-scale training data. Experiment results show that all three models can achieve significant improvements over the baseline. Additionally, we can obtain a further improvement when combining the three models. © 2014 Association for Computational Linguistics.

关键词： Machine translation

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A Hierarchical Multi-label Propagation Algorithm for Overlapping Community Discovery in Social Networks

A Hierarchical Multi-label Propagation Algorithm for Overlap...

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2014 11th Web information System and Application Conference(第十一届全国web信息系统及其应用学术会议)

作者： Song Shi Yuzhong Chen Mingyue Fang Wanhua Li Shining College of Mathematics and Computer Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University FuZhou China

ISBN: (纸本)9781479957286

Multi-label propagation algorithms (MLPAs) have nearly linear time complexity, but the accuracy and stability still need to be improved when applied to overlapping community *** from the idea that boundary nodes are more probable to appear in the overlapping regions of different communities, a Hierarchical Multi-label Propagation Algorithm (HMPA) based on node hierarchy and label propagation gain for overlapping community discovery in social networks is proposed in this paper, HMPA consists of three ***, HMPA utilizes LPAm to unfold initial non-overlapping ***, a PageRank-like method is proposed to mark the hierarchy of each node according to the initial partition of the first ***, multi-label propagation algorithm considering label propagation gain between nodes, which is calculated based on node hierarchy, is introduced to refine overlapping *** results on both the synthetic and real world networks show that the proposed algorithm can effectively solve the problems of traditional multi-label propagation algorithms in terms of accuracy and stability.

关键词： overlapping community detection multi-label propagation node hierarchy propagation gain

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