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Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine
المؤلفون المشاركون
Li, Qiong
Zhao, Tingting
Zhang, Lingchao
Sun, Wenhui
Zhao, Xi
المصدر
Journal of Electrical and Computer Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-03-21
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
The morphology of wear particles reflects the complex properties of wear processes involved in particle formation.
Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations.
This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap.
With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them.
However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily.
Recently, extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms.
In this paper, we propose to employ ELM for ferrography wear particles image recognition.
We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier.
Therefore, the novel ferrography wear particle classifier is founded based on ELM.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Qiong& Zhao, Tingting& Zhang, Lingchao& Sun, Wenhui& Zhao, Xi. 2017. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine. Journal of Electrical and Computer Engineering،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1175262
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Qiong…[et al.]. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine. Journal of Electrical and Computer Engineering No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1175262
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Qiong& Zhao, Tingting& Zhang, Lingchao& Sun, Wenhui& Zhao, Xi. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine. Journal of Electrical and Computer Engineering. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1175262
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175262
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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