1 |
J.W. Hines, D.J. Wrest, R.E. Uhrig, Signal validation using an adaptive neural fuzzy inference system, Nucl. Technol. 119 (1997) 181-193.
DOI
|
2 |
M.G. Na, A neuro-fuzzy inference system for sensor failure detection using wavelet denoising, PCA and SPRT, Journal of the Korean Nucl. Soc. 33 (2001) 483-497.
|
3 |
J. Garvey, D. Garvey, R. Seibert, J.W. Hines, Validation of online monitoring techniques to nuclear plant data, Nucl. Eng. Technol. 39 (2007) 149-158.
DOI
|
4 |
E.B. Bartlett, R.E. Uhrig, Nuclear power plant diagnostics using an artificial neural network, Nucl. Technol. 97 (1992) 272-281.
DOI
|
5 |
M. Marseguerra, E. Zio, Fault diagnosis via neural networks: The Boltzmann machine, Nucl. Sci. Technol. 117 (1994) 194-200.
|
6 |
Y.G. No, J.H. Kim, M.G. Na, D.H. Lim, K.I. Ahn, Monitoring severe accidents using AI techniques, Nucl. Eng. Technol. 44 (2012) 393-404.
DOI
|
7 |
A. Gofuku, H. Yoshikawa, S. Hayashi, K. Shimizu, J. Wakabayashi, Diagnostic techniques of a small-break loss-of-coolant accident at a pressurized water reactor plant, Nucl. Technol. 81 (1988) 313-332.
DOI
|
8 |
M.G. Na, S.M. Lee, S.H. Shin, D.W. Jung, S.P. Kim, J.H. Jeong, B.C. Lee, Prediction of major transient scenarios for severe accidents of nuclear power plants, IEEE Trans. Nucl. Sci. 51 (2004) 313-321.
DOI
|
9 |
Y. Bartal, J. Lin, R.E. Uhrig, Nuclear power plant transient diagnostics using artificial neural networks that allow don't-know classifications, Nucl. Technol. 110 (1995) 436-449.
DOI
|
10 |
S.H. Park, J.H. Kim, K.H. Yoo, M.G. Na, Smart sensing of the RPV water level in NPP severe accidents using a GMDH algorithm, IEEE Trans. Nucl. Sci. 61 (2014) 931-938.
DOI
|
11 |
S.H. Park, D.S. Kim, J.H. Kim, M.G. Na, Prediction of the reactor vessel water level using fuzzy neural networks in severe accident circumstances of NPPs, Nucl. Eng. Technol. 46 (2014) 373-380.
DOI
|
12 |
M.G. Na, H.Y. Yang, D.H. Lim, A soft-sensing model for feedwater flow rate using fuzzy support vector regression, Nucl. Eng. Technol. 40 (2008) 69-76.
DOI
|
13 |
J.S. Roger Jang, C.T.Sun, Functionalequivalencebetweenradial basis function networks and fuzzy inference systems, Inst. Electr. Electron. Eng. Trans. Neural Netw. 4 (1993) 156-159.
|
14 |
MAAP4 Modular Accident Analysis Program for LWR Power Plants User's Manual., Electric Power Research Institute, Palo Alto (1994-2005)
|
15 |
T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, Inst. Electr. Electron. Eng. Trans. Syst. Man. Cybern. SMC-15 (1985) 116-132.
|
16 |
E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man Mach. Stud. 7 (1975) 1-13.
DOI
|
17 |
S.H. Lee, Y.G. No, M.G. Na, K.I. Ahn, S.Y. Park, Diagnostics of loss of coolant accidents using SVC and GMDH models, IEEE Trans. Nucl. Sci. 58 (2011) 267-276.
DOI
|
18 |
S.W. Cheon, S.H. Chang, Application of neural networks to a connectionist expert system for transient identification in nuclear power plants, Nucl. Technol. 102 (1993) 177-191.
DOI
|
19 |
M.G. Na, W.S. Park, D.H. Lim, Detection and diagnostics of loss of coolant accidents using support vector machines, IEEE Trans. Nucl. Sci. 55 (2008) 628-636.
DOI
|
20 |
M.G. Na, S.H. Shin, D.W. Jung, S.P. Kim, J.H. Jeong, B.C. Lee, Estimation of break location and size for loss of coolant accidents using neural networks, Nucl. Eng. Design 232 (2004) 289-300.
DOI
|