ENGINEERING MANAGEMENT THEORIES AND METHODOLOGIES |
|
|
|
Neural Operation Management: A New Avenue for Productive and Military Operations |
Qing-guo Ma() |
Department of Management Science and Engineering, Zhejiang University, Hangzhou 310027, China |
|
|
Abstract An important effect of technological progress is the increasing replacement of manual labor by mental labor in productive and military operations. The variation of the operator’s capabilities in cognition, judgment and decision-making has drawn much attention from operation management researchers. Monitoring and evaluation of these capabilities is especially significant in conditions such as long-time operation, operation with special properties and operation under special circumstances. The military power and economic power are both the key concerns for a nation. The military power depends not only on the weapon system, but also the operators’ capabilities of manipulating the system. Similarly, the economic power is not only dependent on advanced machine system, but also the operational capability of the operators. Thus it has become a hot field of research and practice to monitor and assess the operator’s physiological and psychological states online based on neural measurement technology, and then to give real time intervention, so as to reduce the occurrence of accidents and increase the operation performance.
|
Keywords
operation management
productive operation
military operation
neural operation management
neuromanagement
|
Corresponding Author(s):
Qing-guo Ma
|
Issue Date: 04 February 2015
|
|
1 |
Ayaz, H., Shewokis, P. A., Bunce, S., Izzetoglu, k., Willems, B., & Onaral, B. (2012). Optical brain monitoring for operator training and mental workload assessment. Neuroimage, 59(1), 36-47
https://doi.org/10.1016/j.neuroimage.2011.06.023
|
2 |
Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. Journal of Economic Literature, 9-64
https://doi.org/10.1257/0022051053737843
|
3 |
Dussault, C., Jouanin, J. C., Philippe, M., & Cuezennec, C.Y. (2005). EEG and ECG changes during simulator operation reflct mental workload and vigilance. Aviation, Space, and Environmental Medicine, 76(4), 344-351.
|
4 |
Gopalsamy, C., Park, S., Rajamanickam, R., & Jayaraman, S. (1999). The Wearable Motherboard?: The first generation of adaptive and responsive textile structures (ARTS) for medical applications. Virtual Reality, 4(3), 152-168
https://doi.org/10.1007/BF01418152
|
5 |
Hoyt, R. W., Reifman, J., Coster, T. S., & Buller, M.J. (2002). Combat medical informatics: Present and future. In Proceedings of the AMIA Symposium. American Medical Informatics Association, 335.
|
6 |
Ma, Q. G. (2012). The framework of engineering management and neuro-engineering management. Science &Technology Progress and Policy, 29(18), 9-12.
|
7 |
Ma, Q., & Wang, X. (2006). Cognitive neuroscience, neuroeconomics and neuromanagement. Management World, 10, 139-149.
|
8 |
Ma, Q., Fu, H., & Bian, J. (2012). Neuro-industrial engineering: the new stage of industrial engineering. Management World, 6, 163-168, 179.
|
9 |
Ma, Q., Shang, Q., Fu, H., Chen, F. (2012). Mental workload analysis during the production process: EEG and GSR activity. Applied Mechanics and Materials, 220, 193-197.
|
10 |
Ma, Q., Sun, X., Fu, H., Zhao, D., & Guo, J. (2013). Manufacturing process design based on mental and physical workload analysis. Applied Mechanics and Materials, 345, 482-485
https://doi.org/10.4028/www.scientific.net/AMM.345.482
|
11 |
Ma, Q., Jin, J., & Wang, L. (2010). The neural process of hazard perception and evaluation for warning signal words: Evidence from event-related potentials. Neuroscience Letters, 483(3), 206-210
https://doi.org/10.1016/j.neulet.2010.08.009
|
12 |
Parasuraman, R., & Rizzo, M. (2008). Neuroergonomics: The Brain at Work. Oxford University Press, Inc.
|
13 |
Parasuraman, R., & Wilson, G. F. (2008). Putting the brain to work: Neuroergonomics past, present, and future. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 468-474
https://doi.org/10.1518/001872008X288349
|
14 |
Park, B., Kim, J. I., Lee, D., Jeong, S.O., & Park, H.J. (2012). Are brain networks stable during a 24-hour period. Neuroimage, 59(1), 456-466
https://doi.org/10.1016/j.neuroimage.2011.07.049
|
15 |
Smith, M. E., Gevins, A., Brown, H., Kamik, A., & Du, R. (2001). Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(3), 366-380
https://doi.org/10.1518/001872001775898287
|
16 |
John St., M., Kobus, D. A., Morrison, J. G., & Schmorrow, D. (2004). Overview of the DARPA augmented cognition technical integration experiment. International Journal of Human-Computer Interaction, 17(2), 131-149
https://doi.org/10.1207/s15327590ijhc1702_2
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|