A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB

Fusaomi Nagata, Kenta Tokuno, Hiroaki Ochi, Akimasa Otsuka, Takeshi Ikeda, Keigo Watanabe, Maki K. Habib

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper presents a user-friendly design application development environment based on MATLAB that facilitates two applications using convolutional neural networks (CNNs) and support vector machines (SVMs). Firstly, an application of deep CNN (DCNN) for visual inspection is developed and is trained using a large number of images to inspect undesirable defects such as crack, burr, protrusion, chipping, spot and fracture phenomena which appear in the manufacturing process of resin molded articles. The DCNN is named sssNet. Then, two kinds of SVMs are respectively incorporated with two trained DCNNs, i.e., our designed sssNet and well-known AlexNet, to classify test images with high recognition rate into accept as OK or reject as NG categories, in which compressed features obtained from the DCNNs are used as inputs for the SVMs. The usability and operability of the developed design and training application for DCNNs and SVMs are demonstrated and evaluated through training and classification experiments.

Original languageEnglish
Title of host publicationLecture Notes in Mechanical Engineering
PublisherPleiades Publishing
Pages27-33
Number of pages7
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Fingerprint

MATLAB
Support vector machines
Neural networks
Resins
Inspection
Cracks
Defects
Experiments

Keywords

  • Convolutional neural network (CNN)
  • Design tool for CNN and SVM
  • MATLAB
  • Support vector machine (SVM)
  • Visual inspection

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

Cite this

Nagata, F., Tokuno, K., Ochi, H., Otsuka, A., Ikeda, T., Watanabe, K., & Habib, M. K. (2020). A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB. In Lecture Notes in Mechanical Engineering (pp. 27-33). (Lecture Notes in Mechanical Engineering). Pleiades Publishing. https://doi.org/10.1007/978-981-13-8323-6_3

A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB. / Nagata, Fusaomi; Tokuno, Kenta; Ochi, Hiroaki; Otsuka, Akimasa; Ikeda, Takeshi; Watanabe, Keigo; Habib, Maki K.

Lecture Notes in Mechanical Engineering. Pleiades Publishing, 2020. p. 27-33 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceedingChapter

Nagata, F, Tokuno, K, Ochi, H, Otsuka, A, Ikeda, T, Watanabe, K & Habib, MK 2020, A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB. in Lecture Notes in Mechanical Engineering. Lecture Notes in Mechanical Engineering, Pleiades Publishing, pp. 27-33. https://doi.org/10.1007/978-981-13-8323-6_3
Nagata F, Tokuno K, Ochi H, Otsuka A, Ikeda T, Watanabe K et al. A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB. In Lecture Notes in Mechanical Engineering. Pleiades Publishing. 2020. p. 27-33. (Lecture Notes in Mechanical Engineering). https://doi.org/10.1007/978-981-13-8323-6_3
Nagata, Fusaomi ; Tokuno, Kenta ; Ochi, Hiroaki ; Otsuka, Akimasa ; Ikeda, Takeshi ; Watanabe, Keigo ; Habib, Maki K. / A Design and Training Application for Deep Convolutional Neural Networks and Support Vector Machines Developed on MATLAB. Lecture Notes in Mechanical Engineering. Pleiades Publishing, 2020. pp. 27-33 (Lecture Notes in Mechanical Engineering).
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