Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations
المؤلفون المشاركون
Sun, Yunlong
Li, Hui
Zhu, Chuchu
Xu, Ou
Gholam Hosseini, Hamid
Luo, Dehan
المصدر
Journal of Electrical and Computer Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-03-01
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Gas sensors have been widely reported for industrial gas detection and monitoring.
However, the rapid detection and identification of industrial gases are still a challenge.
In this work, we measure four typical industrial gases including CO2, CH4, NH3, and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations.
To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data.
Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases.
We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction.
The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms.
In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data.
It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sun, Yunlong& Luo, Dehan& Li, Hui& Zhu, Chuchu& Xu, Ou& Gholam Hosseini, Hamid. 2018. Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations. Journal of Electrical and Computer Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1184349
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sun, Yunlong…[et al.]. Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations. Journal of Electrical and Computer Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1184349
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sun, Yunlong& Luo, Dehan& Li, Hui& Zhu, Chuchu& Xu, Ou& Gholam Hosseini, Hamid. Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations. Journal of Electrical and Computer Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1184349
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1184349
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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