Evaluating the Contribution of Human Factor to Performance Characteristics of Complex Technical Systems

Estimating the infl uence of human factor on the activity of operators of complex technical systems is an important problem for condition monitoring, personnel training and diagnostics. Presented are both an overview and mutual comparisons of the approaches which are useful to reveal the effect of human factor and have already shown their performances in practical applications. Under consideration are: the structural equation modeling, the Bayesian estimations for probabilistic models represented by Markov random processes, the multivariate statistical techniques including the discriminant and cluster analysis as well as wavelet transforms.

Estimating the influence of human factor on the activity of operators of complex technical systems is an important problem for condition monitoring personnel training and diagnostics. Presented are the approaches which are useful to reveal the effect of human factor and have already shown their performances in practical applications.

1. Baranov S.N., Kuravsky L.S., Baranov N.I. Studying infl uence of maneuvering loads occurrences and climatic conditions of basing on aircraft damage accumulation rate with the aid of trained structures. In: Proc. 5th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Edinburgh, Scotland, United Kingdom, July 2008.
2. Bastani V., Marcenaro L., Regazzoni C. Unsupervised trajectory pattern classifi cation using hierarchical Dirichlet Process Mixture hidden Markov model. 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) / IEEE. 2014. Pp. 1–6.
3. Bishop Y.M.M., Fienberg S.E., Holland P.W. Discrete multivariate analysis: Theory and practice. (Cambridge, MA, M I T Press, 1975.)
4. Bollen K.A. Structural equations with latent variables. (New York, John Wiley, 1989.)
5. Borg, I., Groenen, P. J. F. Modern Multidimensional Scaling Theory and Applications. – Springer, 2005. – P.140.
6. Cramer H. Mathematical Methods of Statistics. Princeton: Princeton University Press. 1999. – 575 pp.
7. Eerland W.J., Box S. Trajectory Clustering, Modelling and Selection with the focus on Airspace Protection. AIAA Infotech@ Aerospace. _ AIAA, 2016. Pp. 1–14.
8. Enriquez M. Identifying temporally persistent fl ows in the terminal airspace via spectral clustering. Tenth USA/Europe Air Traffi c Management Research and Development Seminar (ATM2013). Federal Aviation Administration (FAA) and EUROCONTROL. Chicago, IL, USA: 2013. June 10-13.
9. Enriquez M., Kurcz C. A Simple and Robust Flow Detection Algorithm Based on Spectral Clustering. International Conference on Research in Air Transportation (ICRAT). Federal Aviation Administration (FAA) and EUROCONTROL. – Berkeley, CA, USA: 2012. May 22-25.
10. Gaffney S., Smyth P. Joint probabilistic curve clustering and alignment. In Advances in Neural Information Processing Systems. Vol. 17. Cambridge, MA: MIT Press, 2005. Pp. 473–480.
11. Gaffney S., Smyth P. Trajectory clustering with mixtures of regression models. Proceedings of the fi fth ACM SIGKDD international conference on Knowledge discovery and data mining. 1999. Pp. 63–72.
12. Gariel M., Srivastava A., Feron E. Trajectory clustering and an application to airspace monitoring. IEEE Transactions on Intelligent Transportation Systems. 2011. Vol. 12, no. 4. Pp. 1511–1524.
13. Goldstein H. Multilevel statistical models. (3rd ed., London, Arnold, 2003.)
14. Grevtsov N. Synthesis of control algorithms for aircraft trajectories in time optimal climb and descent. Journal of Computer and Systems Sciences International. 2008. Vol. 47, no. 1. Pp. 129–138.
15. Hung C., Peng W., Lee W. Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. The VLDB Journal – The International Journal on Very Large Data Bases. 2015. Vol. 24, no. 2. Pp. 169–192.
16. Jöreskog K.G. Estimation and testing of simplex models. British Journal of Mathematical and Statistical Psychology, Vol. 23, pp.121-145, 1970.
17. Krasilshchikov M.N., Evdokimenkov V.N., Bazlev D.A. Individually adapted airborne systems for monitoring the aircraft technical condition and supporting the pilot control actions. – Moscow, MAI Publishing House, 2011. – 440 pp (in Russian).
18. Kuchar J. K., Yang L. C. A review of confl ict detection and resolution modeling methods. IEEE Transactions on Intelligent Transportation Systems. 2000. Vol. 1, no. 4. Pp. 179–189.
19. Kuravsky L.S., Artemenkov S.L., Yuriev G.A., Grigorenko E.L. New approach to computer-based adaptive testing. – Experimental Psychology. 2017. Vol. 10. No. 3. Pp. 33–45. doi:10.17759/exppsy.2017100303.
20. Kuravsky L.S., Baranov S.N. Development of the wavelet-based confi rmatory factor analysis for monitoring of system factors. – In: Proc. 5th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Edinburgh, United Kingdom, July 2008, pp.818-834.
21. Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Panfi lova A.S. , Yuriev G.A. Mathematical aspects of the adaptive simulator concept – Psychological Science and Education. 2016. Vol. 21. No. 2. Pp. 84–95. doi: 10.17759/pse.2016210210 (in Russian).
22. Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Panfi lova A.S., Yuryev G.A., Dumin P.N. A Probabilistic Model of Adaptive Training. – Applied Mathematical Sciences, Vol. 10, 2016, no. 48, 2369 – 2380, http://dx.doi.org/10.12988/ams.2016.65168.
23. Kuravsky L.S., Marmalyuk P.A., Yurev G.A. Diagnostics of Professional Skills Based on Probability Distributions of Oculomotor Activity. – RFBR Journal, No. 3 (91), 2016, pp.72-82 (Supplement to “Information Bulletin of RFBR” No. 24, in Russian).
24. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A. and Dumin P.N. A Numerical Technique for the Identifi cation of Discrete-State Continuous-Time Markov Models – Applied Mathematical Sciences. Vol. 9, 2015, No. 8, pp. 379–391. URL: http://dx.doi.org/10.12988/ams. 2015.410882.
25. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Belyaeva O.B. and Prokopieva O.Yu. Mathematical Foundations of Flight Crew Diagnostics Based on Videooculography Data. – Applied Mathematical Sciences, Vol. 10, 2016, no. 30, 1449–1466, http://dx.doi.org/10.12988/ams.2016.6122.
26. Kuravsky L.S., Marmalyuk P.A., Yuryev G.A., Dumin P.N., Panfi lova A.S. Probabilistic modeling of CM operator activity on the base of the Rasch model. – In: Proc. 12th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Oxford, UK, June 2015.
27. Kuravsky L.S., Yuriev G.A. Probabilistic method of fi ltering artefacts in adaptive testing. – Experimental Psychology, Vol.5, No. 1, 2012, pp. 119-131 (in Russian).
28. Kuravsky L.S., Yuryev G.A. Certifi cate of state registration of the computer program №2018660358 Intelligent System for Flight Analysis v1.0 (ISFA#1.0). – Application №2018617617; declared 18 July 2018; registered 22 August 2018. – (ROSPATENT).
29. Kuravsky L.S., Yuryev G.A. Detecting abnormal activities of operators of complex technical systems and their causes basing on wavelet representations. – International Journal of Civil Engineering and Technology (IJCIET). 10(2), 2019, pp. 724–742. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=2.
30. Kuravsky L.S., Yuryev G.A. On the approaches to assessing the skills of operators of complex technical systems. – In: Proc. 15th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Nottingham, UK, September 2018. – 25 pp.
31. Kuravsky L.S., Yuriev G.A., Dumin P.N.. Estimating the infl uence of human factor on the activity of operators of complex technical systems in civil engineering with the aid of adaptive diagnostics, International Journal of Civil Engineering and Technology (IJCIET). 10(2), 2019, pp. 1930-1941. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=02.
32. Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I. New approaches for assessing the activities of operators of complex technical systems. Eksperimental’naya psikhologiya = Experimental psychology (Russia), 2019, vol. 12, no. 4, pp. 27–49. doi:10.17759/exppsy.2019120403.
33. Lawley D.N., Maxwell A.E. Factor analysis as a statistical method. (London, Butterworths, 1971, 2nd ed., 153 pp).
34. Laxhammar R., Falkman G. Online learning and sequential anomaly detection in trajectories. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014. Vol. 36, no. 6. Pp. 1158–1173.
35. Li Z., et al. Incremental clustering for trajectories. Database Systems for Advanced Applications. Lecture Notes in Computer Science. 2010. Vol. 5982. Pp. 32–46.
36. Liu H., Li J. Unsupervised multi-target trajectory detection, learning and analysis in complicated environments. 2012 21st International Conference on Pattern Recognition (ICPR) / IEEE. 2012. Pp. 3716–3720.
37. Loehlin J.C. Latent variable models: An introduction to factor, path, and structural analysis. (Hillsdale, NJ, Erlbaum, 1987.)
38. Markov models in the diagnostics and prediction problems: Textbook. /Edited by L.S. Kuravsky. – 2nd Edition, Enlarged. – Moscow: MSUPE Edition, 2017. – 203 pp. (in Russian).
39. Neal P.G. Multiresolution Analysis for Adaptive Refi nement of Multiphase Flow Computations. – University of Iowa, 2010. –116 pp.
40. René Vidal, Yi Ma, Shankar Sastry. Generalized Principal Component Analysis. – Springer-Verlag: New York, 2016.URL: http://www.springer.com/ us/book/9780387878102
41. Rintoul M., Wilson A. Trajectory analysis via a geometric feature space approach. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2015.
42. Trevor F. Cox, M.A.A. Cox. Multidimensional Scaling, Second Edition. – Chapman & Hall/CRC, 2001. – P. 299.
43. Wei J., et al. Design and Evaluation of a Dynamic Sectorization Algorithm for Terminal Airspace. Journal of Guidance, Control, and Dynamics. 2014. Vol. 37, no. 5. Pp. 1539–1555.
44. Wilson A., Rintoul M., Valicka C. Exploratory Trajectory Clustering with Distance Geometry. International Conference on Augmented Cognition. – Springer. 2016. Pp. 263–274.
45. Xiangyu Kong, Changhua Hu, Zhansheng Duan. Principal Component Analysis Networks and Algorithms. – Springer, 2017. URL: http://www.springer.com/us/book/9789811029134.