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jimyang2008木虫 (正式写手)
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[求助]
有条件的帮忙查询一下论文SCI收录情况已有2人参与
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各位虫友： 我现在有两篇论文发了快2个月了，不知道是否被sci收录。由于单位没条件查询 谁能帮忙查询一下论文SCI收录号，并将收录页面以网页截图或pdf形式发送到邮箱： jimyang2008@163.com. 先谢谢大家了！ （1） 期刊：computational intelligence and neuroscience 论文名：Deep Convolutional Extreme Learning Machine and its application in Handwritten Digit Classification 第一作者：Shan Pang （2） 期刊：International Journal of Aerospace Engineering 论文名：Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure 第一作者：Shan Pang |
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2楼2016-10-17 19:51:53
小凡下山
铁杆木虫 (著名写手)
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★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★
感谢参与，应助指数 +1
jimyang2008(paperhunter代发): 金币+15, 鼓励交流 2016-10-17 20:10:33
感谢参与，应助指数 +1
jimyang2008(paperhunter代发): 金币+15, 鼓励交流 2016-10-17 20:10:33
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Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification 作者  ang, S (Pang, Shan)[ 1 ] ; Yang, XY (Yang, Xinyi)[ 2 ]COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 文献号: 3049632 DOI: 10.1155/2016/3049632 出版年: 2016 查看期刊信息 摘要 In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. 关键词 KeyWords Plus:IMAGE RECOGNITION; BELIEF NETWORKS; NEURAL-NETWORKS; EFFICIENT 作者信息 通讯作者地址: Pang, S (通讯作者) 显示增强组织信息的名称 Ludong Univ, Coll Elect & Informat Engn, Yantai 264025, Peoples R China. 地址: 显示增强组织信息的名称 [ 1 ] Ludong Univ, Coll Elect & Informat Engn, Yantai 264025, Peoples R China [ 2 ] Naval Aeronaut & Astronaut Univ, Dept Aircraft Engn, Yantai 264001, Peoples R China 电子邮件地址:pangshanpp@163.com 出版商 HINDAWI PUBLISHING CORP, 315 MADISON AVE 3RD FLR, STE 3070, NEW YORK, NY 10017 USA 类别 / 分类 研究方向:Mathematical & Computational Biology; Neurosciences & Neurology Web of Science 类别:Mathematical & Computational Biology; Neurosciences 文献信息 文献类型:Article 语种:English 入藏号: WOS:000382046500001 ISSN: 1687-5265 eISSN: 1687-5273 期刊信息 Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: DU2MZ Web of Science 核心合集中的 "引用的参考文献": 31 Web of Science 核心合集中的 "被引频次": 0 |
3楼2016-10-17 19:52:19
小凡下山
铁杆木虫 (著名写手)
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【答案】应助回帖
★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ ★
jimyang2008: 金币+15, ★★★★★最佳答案, 谢谢小凡下山，已经下载了pdf 2016-10-18 09:51:32
jimyang2008: 金币+15, ★★★★★最佳答案, 谢谢小凡下山，已经下载了pdf 2016-10-18 09:51:32
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Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure 作者 ang, S (Pang, Shan)[ 1 ] ; Yang, XY (Yang, Xinyi)[ 2 ] ; Zhang, XF (Zhang, Xiaofeng)[ 1 ]INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING 文献号: 1329561 DOI: 10.1155/2016/1329561 出版年: 2016 查看期刊信息 摘要 A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM) was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool. 关键词 KeyWords Plus  EEP BELIEF NETWORKS; NEURAL-NETWORKS作者信息 通讯作者地址: Pang, S (通讯作者) 显示增强组织信息的名称 Ludong Univ, Coll Informat & Elect Engn, Yantai 264025, Peoples R China. 地址: 显示增强组织信息的名称 [ 1 ] Ludong Univ, Coll Informat & Elect Engn, Yantai 264025, Peoples R China [ 2 ] Naval Aeronaut & Astronaut Univ, Dept Aircraft Engn, Yantai 264001, Peoples R China 电子邮件地址:pangshanpp@163.com 出版商 HINDAWI PUBLISHING CORP, 315 MADISON AVE 3RD FLR, STE 3070, NEW YORK, NY 10017 USA 类别 / 分类 研究方向:Engineering Web of Science 类别:Engineering, Aerospace 文献信息 文献类型:Article 语种:English 入藏号: WOS:000382664100001 ISSN: 1687-5966 eISSN: 1687-5974 期刊信息 Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: DV1EX Web of Science 核心合集中的 "引用的参考文献": 26 Web of Science 核心合集中的 "被引频次": 0 |
4楼2016-10-17 19:55:42
小凡下山
铁杆木虫 (著名写手)
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【答案】应助回帖
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5楼2016-10-17 20:05:58
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