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A preliminary Design of Smartphone-Based Earthquake Early Warning System via Deep Learning

Yıl 2021, Sayı: 25, 23 - 27, 31.08.2021
https://doi.org/10.31590/ejosat.891896

Öz

Kaynakça

  • Allen R.M., Kong, Q., Martin-Short, R. (2020). The MyShake Platform: A Global Vision for Earthquake Early Warning. Pure Appl Geophys. doi: 10.1007/s00024-019-02337-7
  • Allen, R. M., Gasparini, P., Kamigaichi, O., & Bose, M. (2009). The status of earthquake early warning around the world: An introductory overview, Seismological Research Letters, 80(5), 682-693.
  • Chen, Z., Zou, H., Jiang, H., vd. (2015). Fusion of WiFi, smartphone sensors and landmarks using the kalman filter for indoor localization, Sensors (Switzerland). doi: 10.3390/s150100715
  • del Rosario, MB., Redmond, SJ., Lovell, NH. (2015). Tracking the evolution of smartphone sensing for monitoring human movement, Sensors (Switzerland).
  • Gang, H-S., Pyun, J-Y. (2019). A Smartphone Indoor Positioning System Using Hybrid Localization Technology, Energies, 12(19), 3702.
  • Hassan, MM., Uddin, M.Z., Mohamed, A., Almogren, A. (2018). A robust human activity recognition system using smartphone sensors and deep learning, Futur Gener Comput Syst. doi: 10.1016/j.future.2017.11.029.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Horvath, Z., Jenak, I., Wu, T., Xuan, C. (2016). Sensitivity of sensors built in Smartphones. In: Harmony Search Algorithm, Springer, 305–313.
  • Kong, Q., Allen, R. M., Schreier, L., & Kwon, Y. W. (2016a). MyShake: A smartphone seismic network for earthquake early warning and beyond. Science advances, 2(2), e1501055.
  • Kong, Q., Allen, R. M., & Schreier, L. (2016b). MyShake: Initial observations from a global smartphone seismic network. Geophysical Research Letters, 43(18), 9588-9594.
  • Lee, S., Suh, J., Choi, Y. (2018). Review of smartphone applications for geoscience: current status, limitations, and future perspectives, Earth Sci. Informatics.
  • Lima, W.S., Souto, E., El-Khatib. K., vd. (2019). Human activity recognition using inertial sensors in a smartphone: An overview, Sensors (Switzerland). doi: 10.3390/s19143213.
  • Lipton, Z.C., Berkowitz, J., Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning, arXiv preprint arXiv:1506.00019.
  • Liu, J., Chen, R., Chen, Y., vd. (2012a). iParking: An intelligent indoor location-based smartphone parking service, Sensors (Switzerland). doi: 10.3390/s121114612.
  • Liu, J., Chen, R., Pei, L., vd. (2012b). A hybrid smartphone indoor positioning solution for mobile LBS, Sensors (Switzerland). doi: 10.3390/s121217208.
  • Majumder, S., Deen, M.J. (2019). Smartphone sensors for health monitoring and diagnosis, Sensors (Switzerland).
  • Manos, A., Klein, I., Hazan, T. (2019). Gravity-based methods for heading computation in pedestrian dead reckoning, Sensors (Switzerland). doi: 10.3390/s19051170.
  • Real Ehrlich, C., Blankenbach, J. (2019). Indoor localization for pedestrians with real-time capability using multi-sensor smartphones, Geo-Spatial Inf Sci. doi: 10.1080/10095020.2019.1613778
  • Retscher, G. (2019). Indoor Altitude Determination Using MEMS-based Sensors in Smartphones, In Proceedings of the ION 2019 Pacific PNT Meeting (pp. 615-627).
  • Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
  • Voicu, R.A., Dobre, C., Bajenaru, L., Ciobanu RI (2019) Human physical activity recognition using smartphone sensors. Sensors (Switzerland). doi: 10.3390/s19030458.
  • Zhuang, Y., Yang, J., Li, Y., vd. (2016). Smartphone-based indoor localization with bluetooth low energy beacons, Sensors (Switzerland). doi: 10.3390/s16050596.

Akıllı Telefonda Derin Öğrenme ile Deprem Erken Uyarı Sistemi Tasarımı

Yıl 2021, Sayı: 25, 23 - 27, 31.08.2021
https://doi.org/10.31590/ejosat.891896

Öz

Ülkemiz gibi deprem kuşağında olan bir coğrafya için deprem araştırmaları ve olası erken uyarı sistemlerine dair olan yeni yaklaşımlar son zamanlarda meydana gelen depremleri de göz önünde bulunduracak olursak (ör. İzmir, 2020) artan bir önem ve ihtiyaç teşkil etmektedir. Özellikle uyku halinde iken yakalanılan depremler bilindiği üzere çok daha vahim sonuçlar doğurmaktadır. Bu çalışmada, mevcut çalışmalardan farklı olarak, ilk tasarımını yaptığımız deprem erken uyarı sistemi yaklaşımı uyku halinde iken, olası bir depremi, içinde bulunan sensörler aracılığı ile ivmeölçer’e dönüştürülen akıllı telefonlar sayesinde, ReQuakenition ismini verdiğimiz bir telefon uygulaması arayüzü ile acil durumlarda haber vermeyi amaçlamaktadır. Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) web sayfasından indirilen gerçek deprem verilerinden yararlanarak Uzun kısa süreli belleğe sahip (Long-Short Term Memory: LSTM) tekrarlayan sinir ağı mimarisi (Recurrent Neural Network: RNN) derin öğrenme algoritmaları ile eğitilen verilerden elde edilen sonuçlarda %82’nin üzerinde duyarlılık gözlemlenmiştir. Elde edilen bu ilk sonuçlar, son derece yaygın olarak kullanılan akıllı telefonların, deprem erken uyarı sistemlerinde kullanılmak üzere, jeodezik ve sismik ağların yanı sıra çok daha yoğun ve homojen bir ivmeölçer ağı gibi çalışabilmesi adına ümit vericidir.

Kaynakça

  • Allen R.M., Kong, Q., Martin-Short, R. (2020). The MyShake Platform: A Global Vision for Earthquake Early Warning. Pure Appl Geophys. doi: 10.1007/s00024-019-02337-7
  • Allen, R. M., Gasparini, P., Kamigaichi, O., & Bose, M. (2009). The status of earthquake early warning around the world: An introductory overview, Seismological Research Letters, 80(5), 682-693.
  • Chen, Z., Zou, H., Jiang, H., vd. (2015). Fusion of WiFi, smartphone sensors and landmarks using the kalman filter for indoor localization, Sensors (Switzerland). doi: 10.3390/s150100715
  • del Rosario, MB., Redmond, SJ., Lovell, NH. (2015). Tracking the evolution of smartphone sensing for monitoring human movement, Sensors (Switzerland).
  • Gang, H-S., Pyun, J-Y. (2019). A Smartphone Indoor Positioning System Using Hybrid Localization Technology, Energies, 12(19), 3702.
  • Hassan, MM., Uddin, M.Z., Mohamed, A., Almogren, A. (2018). A robust human activity recognition system using smartphone sensors and deep learning, Futur Gener Comput Syst. doi: 10.1016/j.future.2017.11.029.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Horvath, Z., Jenak, I., Wu, T., Xuan, C. (2016). Sensitivity of sensors built in Smartphones. In: Harmony Search Algorithm, Springer, 305–313.
  • Kong, Q., Allen, R. M., Schreier, L., & Kwon, Y. W. (2016a). MyShake: A smartphone seismic network for earthquake early warning and beyond. Science advances, 2(2), e1501055.
  • Kong, Q., Allen, R. M., & Schreier, L. (2016b). MyShake: Initial observations from a global smartphone seismic network. Geophysical Research Letters, 43(18), 9588-9594.
  • Lee, S., Suh, J., Choi, Y. (2018). Review of smartphone applications for geoscience: current status, limitations, and future perspectives, Earth Sci. Informatics.
  • Lima, W.S., Souto, E., El-Khatib. K., vd. (2019). Human activity recognition using inertial sensors in a smartphone: An overview, Sensors (Switzerland). doi: 10.3390/s19143213.
  • Lipton, Z.C., Berkowitz, J., Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning, arXiv preprint arXiv:1506.00019.
  • Liu, J., Chen, R., Chen, Y., vd. (2012a). iParking: An intelligent indoor location-based smartphone parking service, Sensors (Switzerland). doi: 10.3390/s121114612.
  • Liu, J., Chen, R., Pei, L., vd. (2012b). A hybrid smartphone indoor positioning solution for mobile LBS, Sensors (Switzerland). doi: 10.3390/s121217208.
  • Majumder, S., Deen, M.J. (2019). Smartphone sensors for health monitoring and diagnosis, Sensors (Switzerland).
  • Manos, A., Klein, I., Hazan, T. (2019). Gravity-based methods for heading computation in pedestrian dead reckoning, Sensors (Switzerland). doi: 10.3390/s19051170.
  • Real Ehrlich, C., Blankenbach, J. (2019). Indoor localization for pedestrians with real-time capability using multi-sensor smartphones, Geo-Spatial Inf Sci. doi: 10.1080/10095020.2019.1613778
  • Retscher, G. (2019). Indoor Altitude Determination Using MEMS-based Sensors in Smartphones, In Proceedings of the ION 2019 Pacific PNT Meeting (pp. 615-627).
  • Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
  • Voicu, R.A., Dobre, C., Bajenaru, L., Ciobanu RI (2019) Human physical activity recognition using smartphone sensors. Sensors (Switzerland). doi: 10.3390/s19030458.
  • Zhuang, Y., Yang, J., Li, Y., vd. (2016). Smartphone-based indoor localization with bluetooth low energy beacons, Sensors (Switzerland). doi: 10.3390/s16050596.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Gonca Okay Ahi 0000-0001-7235-1502

Baran Canpolat 0000-0002-8879-6394

Yayımlanma Tarihi 31 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 25

Kaynak Göster

APA Okay Ahi, G., & Canpolat, B. (2021). Akıllı Telefonda Derin Öğrenme ile Deprem Erken Uyarı Sistemi Tasarımı. Avrupa Bilim Ve Teknoloji Dergisi(25), 23-27. https://doi.org/10.31590/ejosat.891896