SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model
Yazarlar (5)
Doç. Dr. Ümit Atila Gazi Üniversitesi, Türkiye
Doç. Dr. Yasin ORTAKCI Karabük Üniversitesi, Türkiye
Doç. Dr. Kasım ÖZACAR Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Emrullah DEMİRAL Karabük Üniversitesi, Türkiye
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | ISPRS International Journal of Geo Information (Q2) | ||
| Dergi ISSN | 2220-9964 Dergi Bilgileri (2018) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2018 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 16-06-2018 |
| Cilt / Sayı / Sayfa | 7 / 6 / 223–241 | DOI | 10.3390/ijgi7060223 |
| Makale Linki | https://doi.org/10.3390/ijgi7060223 | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees’ individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route. |
| Anahtar Kelimeler |
| Artificial neural network | Fire evacuation | Indoor navigation | Intelligent routing | Mobile | Spatial data |
| Atıf Sayıları | |
| Web of Science | 33 |
| Scopus | 45 |
| Google Scholar | 64 |