Full Name
Szu Hui Ng
Variants
Ng, S.-H.
Ng, Szuhui
Ng, S.H.
 
 
 
Email
isensh@nus.edu.sg
 
 

Publications

Refined By:
Department:  INDUSTRIAL SYSTEMS ENGINEERING AND MANAGEMENT
Type:  Article
Date Issued:  [2000 TO 2009]

Results 1-14 of 14 (Search time: 0.005 seconds).

Issue DateTitleAuthor(s)
1Dec-2004A model for correlated failures in N-version programmingDai, Y.S.; Xie, M. ; Poh, K.L. ; Ng, S.H. 
2Apr-2006A statistical investigation and optimization of an industrial radiography inspection process for aero-engine componentsWong, W.K.; Ng, S.H. ; Xu, K. 
3Jun-2007A study of the modeling and analysis of software fault-detection and fault-correction processesXie, M. ; Hu, Q.P.; Wu, Y.P.; Ng, S.H. 
4Dec-2004Design of follow-up experiments for improving model discrimination and parameter estimationNg, S.H. ; Chick, S.E.
52009Estimation of the mean and variance response surfaces when the means and variances of the noise variables are unknownTan, M.H.Y.; Ng, S.H. 
6Dec-2007Modeling and analysis of software fault detection and correction process by considering time dependencyWu, Y.P.; Hu, Q.P.; Xie, M. ; Ng, S.H. 
72008On the trend of remaining software defect estimationBai, C.-G.; Cai, K.-Y.; Hu, Q.-P.; Ng, S.-H. 
8Jan-2007Optimization of multiple response surfaces with secondary constraints for improving a radiography inspection processNg, S.H. ; Xu, K. ; Wong, W.K.
92006Reducing parameter uncertainty for stochastic systemsNg, S.H. ; Chick, S.E.
1016-Nov-2008Reliability analysis and optimization of weighted voting systems with continuous states inputLong, Q.; Xie, M. ; Ng, S.H. ; Levitin, G.
11Mar-2007Robust recurrent neural network modeling for software fault detection and correction predictionHu, Q.P.; Xie, M. ; Ng, S.H. ; Levitin, G.
121-Feb-2005Software failure prediction based on a Markov Bayesian network modelBai, C.G.; Hu, Q.P.; Xie, M. ; Ng, S.H. 
132007Software reliability predictions using artificial neural networksHu, Q.P.; Xie, M. ; Ng, S.H. 
14Nov-2007Uncertainty analysis in software reliability modeling by Bayesian approach with maximum-entropy principleDai, Y.-S.; Xie, M. ; Long, Q.; Ng, S.-H.