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  1. 1
    Fuzzy-based multi-agent approach for reliability assessment and improvement of power system protection

    Fuzzy-based multi-agent approach for reliability assessment and improvement of power system protection by Nadheer Abdulridha, Shalash

    Published 2015
    “…Meanwhile, the simulation results for second model shown that the failure rate decreased to approximately 40% for over current and earth fault relays. …”
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  2. 2
    Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates

    Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates by Yusuf, Madaki Umar

    Published 2017
    “…The comparison was done between the BKBX, KBX and BWB models to validate the usefulness of the modified distributions. …”
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  3. 3
    Time-Uncertainty Analysis by Using Simulation in Project Scheduling Networks

    Time-Uncertainty Analysis by Using Simulation in Project Scheduling Networks by Mohammed Omer, Anwar

    Published 1999
    “…The merge event bias as one of the essential problems associated with PERT is discussed, along with models and approaches developed by other researchers, namely, Probabilistic Network Evaluation Technique (PNET algorithm), Modified PNET, Back-Forward Uncertainly Estimation procedure (BFUE) and concept based on the robust reliability idea. …”
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  4. 4
    Characterization of dumping soil and settlement prediction using Monte Carlo approach

    Characterization of dumping soil and settlement prediction using Monte Carlo approach by Mohd Pauzi, Nur Irfah

    Published 2013
    “…These five models are simulated using Monte Carlo approaches. …”
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  5. 5
    Application of parallel ensemble Monte Carlo technique in charge dynamics simulation

    Application of parallel ensemble Monte Carlo technique in charge dynamics simulation by Umar, Roslan, -, Othman, -, A.P., -, Gopir

    Published 2008
    “…This is done using a computer cluster network in a master-slave model. This paper report the development of a LINUX cluster for the purpose of implementing parallel ensemble Monte Carlo modelling for solid states device. …”
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  6. 6
    Monte Carlo simulation of micelle formation in mixed surfactants and palm-kernel oil esters-based nanoemulsion

    Monte Carlo simulation of micelle formation in mixed surfactants and palm-kernel oil esters-based nanoemulsion by Yahya, Ruzanna

    Published 2014
    “…The physical properties of the mixed surfactants and nanoemulsions formulation were studied using the Metropolis Monte Carlo (MMC) algorithm while grand canonical Monte Carlo (GCMC) simulation was applied to investigate the displacements of critical micelle concentration (CMC) for both systems. …”
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  7. 7
    Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH

    Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH by Norhafidzah, Mohd Saad

    Published 2021
    “…For the load modelling studies, the Monte Carlo simulation is performed in Gaussian PDF to develop a probability model of various types of loads. …”
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  8. 8
    Application of parallel ensemble Monte Carlo technique in charge dynamics simulation

    Application of parallel ensemble Monte Carlo technique in charge dynamics simulation by A. P. Othman, R. Umar, G. Gopir

    Published 2008
    “…This is done using a computer cluster network in a master-slave model. In this paper we report the development of a LINUX cluster for the purpose of implementing parallel ensemble Monte Carlo modelling for solid states device. …”
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  9. 9
    Simulation of single electron transistor (SET) circuits using Monte Carlo method

    Simulation of single electron transistor (SET) circuits using Monte Carlo method by Ahmad, Syabani

    Published 2007
    “…The simulator that is used for this project is MOSES version 1.2 (Monte Carlo Single Electron Transistor Simulator) which has been developed by Ruby Chen in Year 1997. …”
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  10. 10
    Markov chain Monte Carlo convergence diagnostics for Gumbel model

    Markov chain Monte Carlo convergence diagnostics for Gumbel model by Mohd Amin, Nor Azrita, Adam, Mohd. Bakri

    Published 2016
    “…Markov chain Monte Carlo (MCMC) has been widely used in Bayesian analysis for the analysis of complex statistical models. …”
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  11. 11
    Cash-flow analysis of a wind turbine operator

    Cash-flow analysis of a wind turbine operator by Muhamad Razali N.M., Hashim A.H.

    Published 2023
    Conference Paper
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  12. 12
    Simple algorithm for estimating the effects of reinforcement loadings and arrangements on composite properties

    Simple algorithm for estimating the effects of reinforcement loadings and arrangements on composite properties by Wan Ibrahim, W. M. A, Yeoh, C. K., Teh, P.L., Khairel Rafezi, Ahmad, Ruhiyuddin , Mohd Zaki

    Published 2010
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  13. 13
    SURE-Autometrics algorithm for model selection in multiple equations

    SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

    Published 2016
    “…Its performance is assessed using Monte Carlo simulation experiments based on five specification models, three strengths of correlation disturbances and two sample sizes. …”
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  14. 14
    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm

    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

    Published 2018
    “…The performance of the algorithm is tested by using simulated data. The test results show that the algorithm can estimate the order and coefficients of the autoregressive model very well. …”
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  15. 15
    Slice sampler algorithm for generalized pareto distribution

    Slice sampler algorithm for generalized pareto distribution by Rostami, Mohammad, Adam, Mohd Bakri, Yahya, Mohamed Hisham, Ibrahim, Noor Akma

    Published 2018
    “…In this paper, we developed the slice sampler algorithm for the generalized Pareto distribution (GPD) model. …”
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  16. 16
    Bayesian inference for the bivariate extreme model

    Bayesian inference for the bivariate extreme model by Mohd Amin, Nor Azrita, Adam, Mohd Bakri

    Published 2016
    “…Using simulation study, the capability of MTM algorithm to analyze the posterior distribution is implement. …”
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    Conference or Workshop Item
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  17. 17
    Parameter-driven count time series models / Nawwal Ahmad Bukhari

    Parameter-driven count time series models / Nawwal Ahmad Bukhari by Nawwal , Ahmad Bukhari

    Published 2018
    “…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
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  18. 18
    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm

    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

    Published 2021
    “…This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. …”
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  19. 19
    Active control of high frequency vibration in uncertain structures

    Active control of high frequency vibration in uncertain structures by Abdul Muthalif, Asan Gani, Langley, Robin S

    Published 2006
    “…The aim of this paper is to numerically investigate the robustness of LQR and LQG algorithms by designing the controller for a nominal system, and then assessing (via Monte Carlo simulation) the effects of uncertainties in the system. …”
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    Proceeding Paper
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  20. 20
    Numerical Analysis of structural batteries response with the presence of uncertainty

    Numerical Analysis of structural batteries response with the presence of uncertainty by Syahiir Kamil, Ahmad Kamal Ariffin, Abdul Hadi Azman, Mohamad Syazwan Zafwan Mohamad Suffian

    Published 2023
    “…Simulation results between the Interval Monte Carlo and Deterministic are compared to evaluate the significance of the uncertainty factor influences. …”
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