참고문헌 (30)

1. Calabrese, J., Esponda, S., and Pesado, P.M., Framework？for Data Quality Evaluation Based on ISO/IEC 25012？and ISO/IEC 25024, VIII Conference on Cloud？Computing, Big Data & Emerging Topics (Modalidad？virtual, 8 al 10 de septiembre de 2020), 2020. 

2. Chang, W., ISO/IEC JTC 1/SC 42 (AI)/WG 2 (data)？data quality for analytics and machine learning (ML),？2022, Information Technology Laboratory. 

3. Cho, K.T., Lee, S.Y., Lee, H.M., Kim, S.H., and Jeong,？H.M., Enhancing the Efficiency and Reliability for M&S？based Test and Evaluation System Development, Journal？of the Korea society for Simulation, 2012 21.1 pp. 89-96. 

4. Cho, Q., Han, M., Ryu, H., Lee, J., and Kim, S., A？Study on Development of Test and Evaluation Methods？for AI-based Image Surveillance Systems in Defense,？Journal of Applied Reliability, 2023 Vol. 23, No. 1,？pp. 97-104. 

   상세보기

5. Choi, M., Choi, E., Song, Y., Kim, J., Park, S.T., Kwon,？O., and Cho, N., Simulation Based Reinforcement？Learning for the Intelligence Behavior of Autonomous？Weapon System, Journal of the Korea Society For？Simulation, 2023, Vol. 32, No. 2, pp. 91-111. 

6. Clegg, A., Wijmans, E., Lee, S., Savva, M., Chernova,？S., and Batra, D., Sim2real Predictivity: Does Evaluation？in Simulation Predict Real-World Performance?, IEEE？Robotics and Automation Letters, 2020, Vol.5, No. 4.？pp. 6670-6677. 

   상세보기

7. Geron, A., Hands-On Machine Learning with ScikitLearn, Keras, and TensorFlow, H. S. Park Ed., Republic？of Korea: Hanbit Publishing Network, 2020, pp. 61. 

8. https://www.etnews.com/20231012000315. 

9. https://www.iso.org/standard/35749.html. 

10. ISO/IEC JTC 1/SC 42/WG2, >, 2022. 

11. ISO/IEC JTC 1/SC 42/WG2, >, 2022. 

12. ISO/IEC JTC 1/SC 42/WG2, >,？2022. 

13. ISO/IEC JTC 1/SC 42/WG2, >, 2022. 

14. ISO/IEC JTC 1/SC 42/WG2, >, 2022. 

15. ISO/IEC JTC 1/SC 7, >, 2015. 

16. Kim, B.-S. and Yun, K., A study on the test evaluation？of automated-vehicles based on artificial intelligence,？Spring Conf, of KSAE, 2019, pp. 722-722. 

17. Kim, B.S. and Yun, K., A Study on the Test Evaluation？of Automated-Vehicles Based on Artificial Intelligence,？The Korean Society of Automotive Engineers, Annual？Spring Conference, 2019, pp. 722-722. 

18. Kim, M.Y. and Noh, S.C., Acquisition Process of Military？Weapon System Using Artificial Intelligence, Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 2022. 

19. Kohavi, R., A study of cross-validation and bootstrap？for accuracy estimation and model selection, in Proc.？14th Int. Joint Conf. on Artif. Intell., 1995, pp. 1137-1143. 

20. Kurrek, P., Jocas, M., Zoghlami, F., Stoelen, M., Salehi,？V., Ai motion control-a generic approach to develop？control policies for robotic manipulation tasks, Proceedings of the Design Society: International Conference？on Engineering Design, 2019, Vol. 1, No. 1. Cambridge？University Press. 

21. Lee, Y., Jeon, I., and Kim, S., A Study on the Development？of Artificial Intelligence Weapon System Test and？Evaluation Method: Focusing on the Performance？Evaluation of the Classification Model, Reliability？Applied Research, 2022, Vol. 22, No. 1, pp. 1-9. 

    상세보기

22. Ministry of Science and ICT, Korea Information and？Communication Technology Association, Trustworthy？Artificial Intelligence Development Guide (Plan), 2023. 

23. National Information Society Agency (Vol 3.0), Republic？of Korea: Guide for Constructing Artificial Intelligence？Training Data, 2023. 

24. Park, J.H., A Study on the V&V Process of M&S for？the Test and Evaluation, Journal of the Korea？Academia-Industrial Cooperation Society, 2019, Vol. 20,？No. 9, pp. 397-404. 

25. Porter, D.J. and Dennis, J.W., Test & evaluation of ai-enabled and autonomous systems: A literature review,？Institute for Defense Analyses, 2020. 

26. Priestley, M., O'Donnell, F., and Simperl, E., A survey？of data quality requirements that matter in ML development pipelines, ACM Journal of Data and Information？Quality, 2023, Vol. 15, No. 2, pp. 1-39. 

    상세보기

27. Rangineni, S., An Analysis of Data Quality Requirements？for Machine Learning Development Pipelines Frameworks, International Journal of Computer Trends and？Technology, 2023, Vol. 71, No. 9, pp. 16-27. 

    

28. Schnelle, S. and Favaro, F. M., ADS Standardization？Landscape: Making Sense of its Status and of the？Associated Research Questions, arXiv preprint arXiv,？2023, 2306.17682. 

29. Shin, J.H., Data quality verification method for artificial？intelligence learning, Journal of Electronic Engineering,？2021, Vol. 48, No. 7, pp. 28-34. 

30. Torchiano, M., Vetro, A., and Iuliano, F., Preserving？the benefits of Open Government Data by measuring？and improving their quality: An Empirical Study, IEEE？41st Annual Computer Software and Applications？Conference (COMPSAC), 2017, Vol. 1. IEEE. 

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