An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms
المؤلفون المشاركون
Adege, Abebe Belay
Lin, Hsin-Piao
Tarekegn, Getaneh Berie
Munaye, Yirga Yayeh
Yen, Lei
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-12-16
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Indoor and outdoor positioning lets to offer universal location services in industry and academia.
Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively.
However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects.
GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion.
In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments.
In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments.
Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents.
Thirdly, we construct a model for positioning through the integration of SVM and DNN.
Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values.
Fifthly, we project the online data through an LDA-based projected vector.
Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN.
The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework.
The proposed method provides accurate and scalable positioning services in different scenarios.
The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Adege, Abebe Belay& Lin, Hsin-Piao& Tarekegn, Getaneh Berie& Munaye, Yirga Yayeh& Yen, Lei. 2018. An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms. Journal of Sensors،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1200682
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Adege, Abebe Belay…[et al.]. An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms. Journal of Sensors No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1200682
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Adege, Abebe Belay& Lin, Hsin-Piao& Tarekegn, Getaneh Berie& Munaye, Yirga Yayeh& Yen, Lei. An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1200682
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1200682
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