Deep Learning Methods for Underwater Target Feature Extraction and Recognition
المؤلفون المشاركون
Hu, Gang
Wang, Kejun
Peng, Yuan
Qiu, Mengran
Shi, Jianfei
Kang, Baolin
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-03-27
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing.
Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction.
In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed.
An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network.
An underwater target recognition classifier is based on extreme learning machine.
Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage.
Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers.
Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification.
Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hu, Gang& Wang, Kejun& Peng, Yuan& Qiu, Mengran& Shi, Jianfei& Kang, Baolin. 2018. Deep Learning Methods for Underwater Target Feature Extraction and Recognition. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130579
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hu, Gang…[et al.]. Deep Learning Methods for Underwater Target Feature Extraction and Recognition. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130579
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hu, Gang& Wang, Kejun& Peng, Yuan& Qiu, Mengran& Shi, Jianfei& Kang, Baolin. Deep Learning Methods for Underwater Target Feature Extraction and Recognition. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130579
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130579
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