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Transactions of the Institute of Systems, Control and Information Engineers Vol. 34 (2021), No. 1

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  1. Vol. 37 (2024)

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  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 34 (2021), No. 1

Defect Classification on Automobile Tire Inner Surfaces with Functional Classifiers

Hirotaro Tada, Akihiko Sugiura

pp. 1-10

DOI:

10.5687/iscie.34.1

Abstract

In this paper, we present a method that functionally combines a convolutional neural network (CNN) and a support vector machine (SVM) to classify defects occurring on the inner surface of an automobile tire. Because such defects are usually small, the image requires high resolution to show the shape change of the defect in the image. Dividing one image into multiple images of smaller regions increases the number of images, which limits the applicable machine learning methods. For this reason, CNN is applied to the divided images of the whole tire, while SVM is applied to the divided images within the range delimited by CNN. Experimental results demonstrate that the defect detection rate of the proposed method is 100%, with an area rate of over-detection of 0.040% in the inspection range of a non-defective tire, demonstrating that the method is effective for reducing over-detection errors while maintaining defect detection accuracy.

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Defect Classification on Automobile Tire Inner Surfaces with Functional Classifiers

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Ris

Gradient Descent Learning for Hyperbolic Hopfield Associative Memory

Masayuki Tsuji, Teijiro Isokawa, Masaki Kobayashi, Nobuyuki Matsui, Naotake Kamiura

pp. 11-22

DOI:

10.5687/iscie.34.11

Abstract

This paper proposes a scheme for embedding patterns onto the Hyperbolic-valued Hopfield Neural Networks (HHNNs). This scheme is based on gradient descent learning (GDL), in which the connection weights among neurons are gradually modified by iterative applications of patterns to be embedded. The performances of the proposed scheme are evaluated though several types of numerical experiments, as compared to projection rule (PR) for HHNNs. Experimental results show that pattern embedding by the proposed GDL is still possible for large number of patterns, in which the embedding by PR often fails. It is also shown that the proposed GDL can be improved, in terms both of stability of embedded patterns and of computational costs, by configuring the initial connection weights by PR and then by modifying the connection weights by GDL.

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Gradient Descent Learning for Hyperbolic Hopfield Associative Memory

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Development of Automatic Verification for GUI System using Image Feature

Masatoshi Arai, Kazuhito Ito

pp. 23-25

DOI:

10.5687/iscie.34.23

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Development of Automatic Verification for GUI System using Image Feature

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Acquisition of Location Information in the Permanent Warehouse

Akira Yonekawa, Hidetoshi Morokuma, Kazumi Yamamoto, Keisuke Butsumoto

pp. 26-28

DOI:

10.5687/iscie.34.26

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Acquisition of Location Information in the Permanent Warehouse

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