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Electrical Engineering and Automation

Electrical Engineering and Automation (Biquarterly) is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of electrical theory and automation on the combination of electrical theory and modern industrial technology. The main focus of the journal is the academic papers and comments of latest power electronics theoretical and technical research improvement in the fields of nature science, engin... [More] Electrical Engineering and Automation (Biquarterly) is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of electrical theory and automation on the combination of electrical theory and modern industrial technology. The main focus of the journal is the academic papers and comments of latest power electronics theoretical and technical research improvement in the fields of nature science, engineering technology, economy and science, report of latest research result, aiming at providing a good communication platform to transfer, share and discuss the theoretical and technical development of electrical theory development for professionals, scholars and researchers in this field, reflecting the academic front level, promote academic change and foster the rapid expansion of electrical theory and automation application technology.

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ISSN Print:2326-876X

ISSN Online:2326-8778

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Paper Infomation

Profiled Fiber Image Recognition Based on Deep Convolutional Neural Network

Full Text(PDF, 1356KB)

Author: Shuolei Sun, Huan Wu, Zheng Tie

Abstract: In recent years, deep convolutional neural network (DCNN), a deep learning method, has been widely used in the field of image recognition. It not only significantly improves the recognition accuracy, but also can automatically learn and extract features layer by layer to avoid complex feature extraction process of the traditional recognition algorithm. This paper firstly introduces the DCNN for the profiled image recognition, and designs a fiber image recognition method based on a proposed DCNN. The classification results of five types of profiled fibers show that the average recognition rate is 94.4% and significant improvements are achieved in recognition accuracy as compared with SVM classifier.

Keywords: Deep Learning; Deep Convolutional Neural Network; SVM; Profiled Fiber; Fiber Recognition

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