Research Article
BibTex RIS Cite

Edge ai technology in the defense industry via reinforcement learning in simulation environments

Year 2023, Volume: 13 Issue: 3, 718 - 732, 15.07.2023

Arif Furkan Mendi

https://doi.org/10.17714/gumusfenbil.1266035

Abstract

Edge artificial intelligence (Edge AI) technology helps to avoid vulnerabilities while benefiting from the advantages of cloud technologies, which are widely used today, especially with artificial intelligence and big data. In the case of transferring systems to the cloud, and needs such as protection of sensitive information and high bandwidth emerges in cloud approaches. Edge AI, which provides solutions to issues such as the security of sensitive data and reducing system traffic, while meeting the needs in this field, can offer a new perspective, especially to projects in the military field, when used with digital twin and autonomous system technologies. In this study, we evaluated the “Forces in virtual environment machine learning (FIVE-ML)” simulation system technically in which we use edge AI technology, analyzed the results obtained with the use of this technology. It has been determined that the current work is at the 2nd level in Edge AI levelling system, also there is a 54% performance (in terms of time with accuracy) increase with edge AI. Besides, the accuracy of hitting the target in simulation system is also increased, with the rate of 34%.

Keywords

Aerospace simulation Artificial intelligence Digital twin Edge ai Edge computing

References

  • 360 Research Reports. (2019). Global Edge AI Software Market Growth (Status and Outlook) 2019-2024. https://www.360researchreports.com/global-edge-ai-software-market-14355998
  • Ahmed, E., Rehmani, M. H., & Bonnet, P. (2017). Editorial to a Special Section on Information Fusion in Internet of Things. Information Systems, 69, 194. https://doi.org/10.1016/j.is.2017.06.004
  • Anciaux, N., Bonnet, P., Bouganim, L., Nguyen, B., Pucheral, P., Sandu Popa, I., & Scerri, G. (2019). Personal Data Management Systems: The security and functionality standpoint. Information Systems, 80, 13–35. https://doi.org/10.1016/j.is.2018.09.002
  • Arisoy, E. B., Ren, G., Ulu, E., Ulu, N. G., & Musuvathy, S. (2016). A data-driven approach to predict hand positions for two-hand grasps of industrial objects. Proceedings of the ASME Design Engineering Technical Conference, 1A-2016(August). https://doi.org/10.1115/DETC2016-60095
  • Atos. (2020, November 23). Atos named by Gartner as an Edge AI Technology Innovator for 2020 - Atos. Atos. https://atos.net/en/2020/press-release_2020_11_23/atos-gartner-edge
  • Bernard Marr. (2017, March). What Is Digital Twin Technology - And Why Is It So Important? Forbes. https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/?sh=66775e592e2a
  • Chang, W. J., Chen, L. B., & Su, K. Y. (2019). Deepcrash: A Deep Learning-Based Internet of Vehicles System for Head-on and Single-Vehicle Accident Detection with Emergency Notification. IEEE Access, 7, 148163–148175. https://doi.org/10.1109/ACCESS.2019.2946468
  • Dursun, M., & Çuhasdar, İ. (2018). Video Streaming with Raspberry Pi 3, Picamera and https Implementation for Unmanned Aerial Vehicle (UAV) Data Link. Bilişim Teknolojileri Dergisi, 23–28. https://doi.org/10.17671/gazibtd.307294
  • Eurotech. (2021). Edge AI: Enabling Deep Learning and Machine Learning with High Performance Edge Computers. Eurotech. https://www.eurotech.com/en/page/edge-ai
  • Grand View Research. (2021, May). Edge Computing Market Size, Share & Trends Analysis Report By Component (Hardware, Software, Services, Edge-managed Platforms), By Application, By Industry Vertical, By Region, And Segment Forecasts, 2021 - 2028. Grandviewresearch. https://www.grandviewresearch.com/industry-analysis/edge-computing-market
  • Hung, N. Q. V., Weidlich, M., Tam, N. T., Miklós, Z., Aberer, K., Gal, A., & Stantic, B. (2019). Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models. Information Systems, 83, 166–180. https://doi.org/10.1016/j.is.2019.04.002
  • Karaarslan, A. (2019). Bilgisayar Sistemlerinde SEPIC Dönüştürücü Uygulaması ve Benzetim Çalışması. Bilişim Teknolojileri Dergisi, 111–117. https://doi.org/10.17671/gazibtd.540127
  • Koohestani, A., Abdar, M., Khosravi, A., Nahavandi, S., & Koohestani, M. (2019). Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals. IEEE Access, 7, 98971–98992. https://doi.org/10.1109/ACCESS.2019.2926444
  • Lechtenbörger, J., & Vossen, G. (2003). Multidimensional normal forms for data warehouse design. Information Systems, 28(5), 415–434. https://doi.org/10.1016/S0306-4379(02)00024-8
  • Lee, Y. L., Tsung, P. K., & Wu, M. (2018). Techology trend of edge AI. 2018 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2018, 1–2. https://doi.org/10.1109/VLSI-DAT.2018.8373244
  • Navrat, P., Manolopoulos, Y., & Vossen, G. (2004). Special issue on ADBIS 2002: Advances in databases and information systems. Information Systems, 29(6), 437–438. https://doi.org/10.1016/j.is.2003.10.003
  • Nguyen, N. D., Nguyen, T., & Nahavandi, S. (2017). System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey. IEEE Access, 5, 27091–27102. https://doi.org/10.1109/ACCESS.2017.2777827
  • Nguyen, T. (2023). Emerging Technologies on the 2023 Gartner Impact Radar. Gartner. https://www.gartner.com/en/articles/4-emerging-technologies-you-need-to-know-about
  • Nvidia. (2021). AIMobile Edge AI Solution Powered by NVIDIA. Nvidia. https://www.aimobile.com.tw/EdgeAI_Products.html
  • Orrin, S., & Chehreh, C. (2020, October 20). How Edge Computing and Hybrid Cloud Are Shifting the IT Paradigm - Nextgov. Nextgov. https://www.nextgov.com/ideas/2020/11/how-edge-computing-and-hybrid-cloud-are-shifting-it-paradigm/170238/
  • Paajanen, S. (2020, April 28). What is Edge Analytics? Advian. https://www.advian.fi/en/blog/what-is-edge-analytics
  • Satyanarayanan, M., & Davies, N. (2019). Augmenting Cognition Through Edge Computing. Computer, 52(7), 37–46. https://doi.org/10.1109/MC.2019.2911878
  • Senderovich, A., Weidlich, M., & Gal, A. (2019). Context-aware temporal network representation of event logs: Model and methods for process performance analysis. Information Systems, 84, 240–254. https://doi.org/10.1016/j.is.2019.04.004
  • Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48(January), 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006
  • Van Den Bussche, J., Vansummeren, S., & Vossen, G. (2005). Towards practical meta-querying. Information Systems, 30(4), 317–332. https://doi.org/10.1016/j.is.2004.04.001
  • Wang, P., Ye, F., Chen, X., & Qian, Y. (2018). Datanet: Deep learning based encrypted network traffic classification in SDN home gateway. IEEE Access, 6, 55380–55391. https://doi.org/10.1109/ACCESS.2018.2872430
  • Watkinson, N., Zaitsev, F., Shivam, A., Demirev, M., Heddes, M., Givargis, T., Nicolau, A., & Veidenbaum, A. (2021). EdgeAvatar: An Edge Computing System for Building Virtual Beings. Electronics, 10(3), 229. https://doi.org/10.3390/ELECTRONICS10030229
  • Xilinx. (2021). Edge AI. Xilinx. https://www.xilinx.com/applications/industrial/analytics-machine-learning.html
  • Yılmaz, S. (2022). Development stages of a semi-autonomous underwater vehicle experiment platform. International Journal of Advanced Robotic Systems, 19(3), 1–21. https://doi.org/10.1177/17298806221103710
  • Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE. https://doi.org/10.1109/JPROC.2019.2918951

Simülasyon ortamlarında pekiştirmeli öğrenme yoluyla savunma sanayinde uç yapay zeka teknolojisi

Year 2023, Volume: 13 Issue: 3, 718 - 732, 15.07.2023

Arif Furkan Mendi

https://doi.org/10.17714/gumusfenbil.1266035

Abstract

Edge AI teknolojisi, günümüzde özellikle yapay zekâ ve büyük veri ile yaygın olarak kullanılan bulut teknolojilerinin avantajlarından yararlanırken güvenlik açıklarının önlenmesine de yardımcı olmaktadır. Sistemlerin buluta taşınması durumunda, hassas bilgilerin korunması ve yüksek bant genişliği gibi ihtiyaçlar ortaya çıkmaktadır. Bu alandaki ihtiyaçları karşılarken hassas verilerin güvenliği ve sistem trafiğinin azaltılması gibi konulara çözüm sunan Edge AI, dijital ikiz ve otonom sistem ile birlikte kullanıldığında özellikle askeri alandaki projelere yeni bir bakış açısı sunabilmektedir. Bu çalışmada edge AI teknolojisini simülasyon sistemlerinde kullanımı teknik olarak ele aldık. Bu teknolojinin kullanımı ile elde edilen sonuçları analiz ettik. Edge AI seviyelendirme sisteminde mevcut çalışmanın 2. seviyede olduğu tespit edilmiş, Edge AI kullanımı sayesinde %54 performans artışı elde edilmiştir. Ayrıca simülasyon sisteminde hedefi vurma isabet oranı da %34 oranında artırılmıştır.

Keywords

Havacılık simülasyonu Yapay zekâ Dijital ikiz Uç yapay zeka Uç hesaplama

References

  • 360 Research Reports. (2019). Global Edge AI Software Market Growth (Status and Outlook) 2019-2024. https://www.360researchreports.com/global-edge-ai-software-market-14355998
  • Ahmed, E., Rehmani, M. H., & Bonnet, P. (2017). Editorial to a Special Section on Information Fusion in Internet of Things. Information Systems, 69, 194. https://doi.org/10.1016/j.is.2017.06.004
  • Anciaux, N., Bonnet, P., Bouganim, L., Nguyen, B., Pucheral, P., Sandu Popa, I., & Scerri, G. (2019). Personal Data Management Systems: The security and functionality standpoint. Information Systems, 80, 13–35. https://doi.org/10.1016/j.is.2018.09.002
  • Arisoy, E. B., Ren, G., Ulu, E., Ulu, N. G., & Musuvathy, S. (2016). A data-driven approach to predict hand positions for two-hand grasps of industrial objects. Proceedings of the ASME Design Engineering Technical Conference, 1A-2016(August). https://doi.org/10.1115/DETC2016-60095
  • Atos. (2020, November 23). Atos named by Gartner as an Edge AI Technology Innovator for 2020 - Atos. Atos. https://atos.net/en/2020/press-release_2020_11_23/atos-gartner-edge
  • Bernard Marr. (2017, March). What Is Digital Twin Technology - And Why Is It So Important? Forbes. https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/?sh=66775e592e2a
  • Chang, W. J., Chen, L. B., & Su, K. Y. (2019). Deepcrash: A Deep Learning-Based Internet of Vehicles System for Head-on and Single-Vehicle Accident Detection with Emergency Notification. IEEE Access, 7, 148163–148175. https://doi.org/10.1109/ACCESS.2019.2946468
  • Dursun, M., & Çuhasdar, İ. (2018). Video Streaming with Raspberry Pi 3, Picamera and https Implementation for Unmanned Aerial Vehicle (UAV) Data Link. Bilişim Teknolojileri Dergisi, 23–28. https://doi.org/10.17671/gazibtd.307294
  • Eurotech. (2021). Edge AI: Enabling Deep Learning and Machine Learning with High Performance Edge Computers. Eurotech. https://www.eurotech.com/en/page/edge-ai
  • Grand View Research. (2021, May). Edge Computing Market Size, Share & Trends Analysis Report By Component (Hardware, Software, Services, Edge-managed Platforms), By Application, By Industry Vertical, By Region, And Segment Forecasts, 2021 - 2028. Grandviewresearch. https://www.grandviewresearch.com/industry-analysis/edge-computing-market
  • Hung, N. Q. V., Weidlich, M., Tam, N. T., Miklós, Z., Aberer, K., Gal, A., & Stantic, B. (2019). Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models. Information Systems, 83, 166–180. https://doi.org/10.1016/j.is.2019.04.002
  • Karaarslan, A. (2019). Bilgisayar Sistemlerinde SEPIC Dönüştürücü Uygulaması ve Benzetim Çalışması. Bilişim Teknolojileri Dergisi, 111–117. https://doi.org/10.17671/gazibtd.540127
  • Koohestani, A., Abdar, M., Khosravi, A., Nahavandi, S., & Koohestani, M. (2019). Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals. IEEE Access, 7, 98971–98992. https://doi.org/10.1109/ACCESS.2019.2926444
  • Lechtenbörger, J., & Vossen, G. (2003). Multidimensional normal forms for data warehouse design. Information Systems, 28(5), 415–434. https://doi.org/10.1016/S0306-4379(02)00024-8
  • Lee, Y. L., Tsung, P. K., & Wu, M. (2018). Techology trend of edge AI. 2018 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2018, 1–2. https://doi.org/10.1109/VLSI-DAT.2018.8373244
  • Navrat, P., Manolopoulos, Y., & Vossen, G. (2004). Special issue on ADBIS 2002: Advances in databases and information systems. Information Systems, 29(6), 437–438. https://doi.org/10.1016/j.is.2003.10.003
  • Nguyen, N. D., Nguyen, T., & Nahavandi, S. (2017). System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey. IEEE Access, 5, 27091–27102. https://doi.org/10.1109/ACCESS.2017.2777827
  • Nguyen, T. (2023). Emerging Technologies on the 2023 Gartner Impact Radar. Gartner. https://www.gartner.com/en/articles/4-emerging-technologies-you-need-to-know-about
  • Nvidia. (2021). AIMobile Edge AI Solution Powered by NVIDIA. Nvidia. https://www.aimobile.com.tw/EdgeAI_Products.html
  • Orrin, S., & Chehreh, C. (2020, October 20). How Edge Computing and Hybrid Cloud Are Shifting the IT Paradigm - Nextgov. Nextgov. https://www.nextgov.com/ideas/2020/11/how-edge-computing-and-hybrid-cloud-are-shifting-it-paradigm/170238/
  • Paajanen, S. (2020, April 28). What is Edge Analytics? Advian. https://www.advian.fi/en/blog/what-is-edge-analytics
  • Satyanarayanan, M., & Davies, N. (2019). Augmenting Cognition Through Edge Computing. Computer, 52(7), 37–46. https://doi.org/10.1109/MC.2019.2911878
  • Senderovich, A., Weidlich, M., & Gal, A. (2019). Context-aware temporal network representation of event logs: Model and methods for process performance analysis. Information Systems, 84, 240–254. https://doi.org/10.1016/j.is.2019.04.004
  • Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48(January), 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006
  • Van Den Bussche, J., Vansummeren, S., & Vossen, G. (2005). Towards practical meta-querying. Information Systems, 30(4), 317–332. https://doi.org/10.1016/j.is.2004.04.001
  • Wang, P., Ye, F., Chen, X., & Qian, Y. (2018). Datanet: Deep learning based encrypted network traffic classification in SDN home gateway. IEEE Access, 6, 55380–55391. https://doi.org/10.1109/ACCESS.2018.2872430
  • Watkinson, N., Zaitsev, F., Shivam, A., Demirev, M., Heddes, M., Givargis, T., Nicolau, A., & Veidenbaum, A. (2021). EdgeAvatar: An Edge Computing System for Building Virtual Beings. Electronics, 10(3), 229. https://doi.org/10.3390/ELECTRONICS10030229
  • Xilinx. (2021). Edge AI. Xilinx. https://www.xilinx.com/applications/industrial/analytics-machine-learning.html
  • Yılmaz, S. (2022). Development stages of a semi-autonomous underwater vehicle experiment platform. International Journal of Advanced Robotic Systems, 19(3), 1–21. https://doi.org/10.1177/17298806221103710
  • Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE. https://doi.org/10.1109/JPROC.2019.2918951
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Arif Furkan Mendi 0000-0002-0750-4012

Publication Date July 15, 2023
Submission Date March 15, 2023
Acceptance Date June 19, 2023
Published in Issue Year 2023 Volume: 13 Issue: 3

Cite

APA Mendi, A. F. (2023). Edge ai technology in the defense industry via reinforcement learning in simulation environments. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(3), 718-732. https://doi.org/10.17714/gumusfenbil.1266035
 Download Cover Image