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Your search keyword '"Ren, Mifeng"' showing total 5 results
Start Over Author "Ren, Mifeng" Publisher springer singapore
5 results on '"Ren, Mifeng"'

2. Filter-Based Fault Diagnosis of Heat Exchangers

Author

Zhang, Jianhua, Li, Hongrui, Fan, Penghao, Hou, Guolian, Ren, Mifeng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fei, Minrui, editor, Li, Kang, editor, Yang, Zhile, editor, Niu, Qun, editor, and Li, Xin, editor

Published
2020
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3. Multi-scale Local Receptive Field Based Online Sequential Extreme Learning Machine for Material Classification

Author

Xu, Xinying, Fang, Jing, Li, Qi, Xie, Gang, Xie, Jun, Ren, Mifeng, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Fuchun, editor, Liu, Huaping, editor, and Hu, Dewen, editor

Published
2019
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5. Multi-scale Local Receptive Field Based Online Sequential Extreme Learning Machine for Material Classification

Author

Fang Jing, Ren Mifeng, Xinying Xu, Gang Xie, Jun Xie, and Li Qi

Subjects
Receptive field, Generalization, business.industry, Computer science, Industrial production, Pattern recognition, Artificial intelligence, Texture (music), Object (computer science), Scale (map), business, MNIST database, Extreme learning machine
Abstract

Surface material classification has attracted a lot of attention from the academic and industrial communities. The surface material classification methods are for static object material data. However, in real industrial production, data cannot be generated overnight. It is generated continuously. In this work, we propose an algorithm named Multi-Scale Local Receptive Field Based Online Sequential Extreme Learning Machine (MSLRF-OSELM) for material classification, which not only can make dynamic training of networks by using data that are generated online of material images, but also can extract highly representative features from complex texture by multi-scale local receptive field. We conduct experiments on the public texture ALOT dataset and MNIST dataset. Experimental results verify the effectiveness of our algorithm and has good generalization performance.

Published
2019

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