التفاصيل البيبلوغرافية
| العنوان: |
Lightweight hybrid CNN-ELM model for multi-building and multi-floor classification |
| المؤلفون: |
Quezada-Gaibor, Darwin, Torres-Sospedra, Joaquín, Nurmi, Jari, Koucheryavy, Yevgeni, Huerta, Joaquin |
| المساهمون: |
Universidade do Minho |
| بيانات النشر: |
IEEE, 2022. |
| سنة النشر: |
2022 |
| مصطلحات موضوعية: |
Indoor Localisation, Wi-Fi fingerprinting, deep learning, extreme learning machine, Ciências Naturais::Ciências da Computação e da Informação, Science & Technology |
| الوصف: |
Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1%). |
| الوصف (مترجم): |
The authors gratefully acknowledge funding from European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowska Curie grant agreements No. 813278 (A-WEAR: A network for dynamic wearable applications with privacy constraints, http://www.a-wear.euTest/) and No. 101023072 (ORIENTATE: Low-cost Reliable Indoor Positioning in Smart Factories, http://orientate.dsi.uminho.ptTest). |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
Quezada-Gaibor, D., Torres-Sospedra, J., Nurmi, J., Koucheryavy, Y., & Huerta, J. (2022, June 7). Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification. 2022 International Conference on Localization and GNSS (ICL-GNSS). IEEE. http://doi.org/10.1109/icl-gnss54081.2022.9797021Test; 9781665405751; 2325-0747; https://ieeexplore.ieee.org/document/9797021Test |
| DOI: |
10.1109/ICL-GNSS54081.2022.9797021 |
| الإتاحة: |
https://hdl.handle.net/1822/82039Test |
| حقوق: |
open access |
| رقم الانضمام: |
rcaap.com.repositorium.repositorium.sdum.uminho.pt.1822.82039 |
| قاعدة البيانات: |
RCAAP |
