Skip to main content
SpringerLink
Log in

Evaluation of Vehicles’ Best Route in Virtual Tactical Teaching Environment Based on Machine-Learning Techniques

  • Conference paper
  • First Online:
Advances in Usability, User Experience, Wearable and Assistive Technology (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1217))

Included in the following conference series:

  • 2710 Accesses

Abstract

Machine learning techniques to assess teaching military tactics when the route traveled by the vehicle, in order to sum up the program operation of the route, can be a more objective measure. At the same time, a 3D virtual battlefield and information visualization are combined to assist the determination of decisions and judgment of tactical actions. This technology helps military personnel to maintain the training model deduced by war and at the same time enhances the concept of situation awareness. In the semi-structured interview, we can see that the tactical actions and thinking methods learned base on machine learning to virtual applications are more realistic and interesting and can help them understand and analyze for battlefield situations. Besides, the study also discussed the significance of artificial intelligence and virtual environments for military education tactics and suggestions for further research

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wahab, N., Hasbullah, N., Ramli, S., Zainuddin, N.Z.: Verification of a battlefield visualization framework in military decision making using holograms (3D) and multi-touch technology. In: ICICTM 2016 - Proceedings of the 1st International Conference on Information Communication Technologies, pp. 23–26 (2016)

    Google Scholar 

  2. Fan, Z., Liu, C., Cai, D., Yue, S.: Research on black spot identification of safety in urban traffic accidents based on machine learning method. Saf. Sci. 118, 607–616 (2019)

    Article  Google Scholar 

  3. Oskoei, M.A., Hu, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng. 55, 1956–1965 (2008)

    Article  Google Scholar 

  4. Mao, C.C., Chen, F.Y.: Augmented reality and 3-D visualization effects to enhance battlefield situational awareness. In: Advances in Intelligent Systems and Computing, pp. 303–309 (2019)

    Google Scholar 

  5. Huber, B., Sippl, C., German, R.: Augmented, Virtual and Mixed Reality Simulation A Validated Failure Behavior Model for Driver

    Google Scholar 

  6. Merkert, J., Mueller, M., Hubl, M.: A survey of the application of machine learning in decision support systems. In: 23rd European Conference on Information Systems, ECIS 2015 (2015)

    Google Scholar 

  7. Chan, S.H., Song, Q., Sarker, S., Plumlee, R.D.: Decision support system (DSS) use and decision performance: DSS motivation and its antecedents. Inf. Manag. 54, 934–947 (2017)

    Article  Google Scholar 

  8. Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inform. (2015). https://doi.org/10.1109/TII.2014.2349359

    Article  Google Scholar 

  9. Razavi-Far, R., Farajzadeh-Zanjani, M., Saif, M.: An integrated class-imbalanced learning scheme for diagnosing bearing defects in induction motors. IEEE Trans. Ind. Inform. 13, 2758–2769 (2017)

    Article  Google Scholar 

  10. Romeo, L., Loncarski, J., Paolanti, M., Bocchini, G., Mancini, A., Frontoni, E.: Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0. Expert Syst. Appl. 140 (2020)

    Google Scholar 

  11. Moral-García, S., Castellano, J.G., Mantas, C.J., Montella, A., Abellán, J.: Decision tree ensemble method for analyzing traffic accidents of novice drivers in urban areas. Entropy 21, 1–15 (2019)

    Article  MathSciNet  Google Scholar 

  12. Podgorelec, V., Zorman, M.: Decision tree learning. In: Encyclopedia of Complexity and Systems Science, pp. 1–28 (2015)

    Google Scholar 

  13. George, W., Casey, J.: FM 5-0: The Operations Process (2010)

    Google Scholar 

  14. United States Marine Corps: The Military Decision-Making Process (2003)

    Google Scholar 

  15. Liu, Y., Chen, X., Wang, Z., Wang, Z.J., Ward, R.K., Wang, X.: Deep learning for pixel-level image fusion: recent advances and future prospects. Inf. Fusion 42, 158–173 (2018). https://doi.org/10.1016/j.inffus.2017.10.007

    Article  Google Scholar 

  16. Kahn, S.: Reducing the gap between Augmented Reality and 3D modeling with real-time depth imaging. Virtual Real. 17, 111–123 (2013)

    Article  Google Scholar 

  17. Mao, C.C., Sun, C.C., Chen, C.H.: Evaluate learner’s acceptance of augmented reality based military decision making process training system. In: Proceedings of the 2017 5th International Conference on Information and Education Technology, pp. 73–77. ACM (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fu Hsing Kang College, NDU, Taipei City, 112, Taiwan (R.O.C.)

    Chia-Chi Mao

  2. Yuan Ze University, NDU, Taoyuan City, 320, Taiwan (R.O.C.)

    Shuo-Ting Hung

Authors
  1. Chia-Chi Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuo-Ting Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chia-Chi Mao .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

  2. Catholic University of Pernambuco, Boa Vista, Recife, Brazil

    Christianne Falcão

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mao, CC., Hung, ST. (2020). Evaluation of Vehicles’ Best Route in Virtual Tactical Teaching Environment Based on Machine-Learning Techniques. In: Ahram, T., Falcão, C. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1217. Springer, Cham. https://doi.org/10.1007/978-3-030-51828-8_94

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-51828-8_94

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51827-1

  • Online ISBN: 978-3-030-51828-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation