S.Ilangovan^1,*, A. Vincent Antony Kumar²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 261-268, 2020, DOI:10.31209/2019.100000154

Abstract In this work, a Novel Feature selection framework called SU embedded PSO Feature Selector has been proposed (SU-PSO) towards the selection of optimal feature subset for the improvement of detection performance of classifiers. The feature space ranking is done through the Symmetrical Uncertainty method. Further, memetic operators of PSO include features and remove features are used to choose relevant features and the best of best features are selected using PSO. The proposed feature selector efficiently removes not only irrelevant but also redundant features. Performance metric such as classification accuracy, subset of features selected and running time are used for comparison. More >

Tairan Hu¹, Tianyang Zhou¹, Yichao Zang^{1, *}, Qingxian Wang¹, Hang Li²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1795-1807, 2020, DOI:10.32604/cmc.2020.011071

Abstract With serious cybersecurity situations and frequent network attacks, the demands for automated pentests continue to increase, and the key issue lies in attack planning. Considering the limited viewpoint of the attacker, attack planning under uncertainty is more suitable and practical for pentesting than is the traditional planning approach, but it also poses some challenges. To address the efficiency problem in uncertainty planning, we propose the APU-D* Lite algorithm in this paper. First, the pentest framework is mapped to the planning problem with the Planning Domain Definition Language (PDDL). Next, we develop the pentest information graph to organize network information and… More >

Gunay Sadikoglu

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 173-178, 2018, DOI:10.1080/10798587.2017.1327159

Abstract Consumer behaviour has always been of a great interest in marketing research. The consumer buying behaviour has become an integral part of strategic market planning and includes mental, emotional and physical activities. The consumer behaviour and decision-making process are usually subject to uncertainties related to influences of socio-cultural, psychological and personal factors. In this paper, the Z-number concept is applied for handling uncertainties in analysing the consumer buying behaviour. More >

Nan Wu^1,#, Kuan Lu^1,2,#,*, Yulin Jin^2,3,*, Haopeng Zhang¹, Yushu Chen²

Sound & Vibration, Vol.54, No.2, pp. 113-125, 2020, DOI:10.32604/sv.2020.09783

Abstract Two different order reduction methods of the deterministic and stochastic systems are discussed in this paper. First, the transient proper orthogonal decomposition (T-POD) method is introduced based on the high-dimensional nonlinear dynamic system. The optimal order reduction conditions of the T-POD method are provided by analyzing the rotor-bearing system with pedestal looseness fault at both ends. The efficiency of the T-POD method is verified via comparing with the results of the original system. Second, the polynomial dimensional decomposition (PDD) method is applied to the 2 DOFs spring system considering the uncertain stiffness to study the amplitude-frequency response. The numerical results… More >

Fangyi Li^1,2, Zhen Luo³, Jianhua Rong¹, Lin Hu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.95, No.6, pp. 453-482, 2013, DOI:10.3970/cmes.2013.095.453

Abstract This paper aims to propose a non-probabilistic reliability-based multiobjective optimization method for structures with uncertain-but-bounded parameters. A combination of the interval and ellipsoid convex models is used to account for the different groups of uncertain parameters, in which the interval model accounts for uncorrelated parameters, while the ellipsoid model is applied to correlated parameters. The design is then formulated as a nested double-loop optimization problem. A multi-objective genetic algorithm is used in the out loop optimization to optimize the design vector for evaluating the objectives, and the Sequential Quadratic Programming (SQP) algorithm is applied in the inner loop to evaluate… More >

P. Gayathri¹, R. Ganguli^1,2

Structural Durability & Health Monitoring, Vol.7, No.1&2, pp. 119-138, 2011, DOI:10.3970/sdhm.2011.007.119

Abstract The effect of random variation in composite material properties on the reliability of structural damage detection is addressed in this paper. A composite plate is considered as the structure and a finite element model is used for the simulation. Damage growth due to cyclic loading is addressed. Matrix crack induced delamination is emphasized in this paper. Thresholds for the damage accumulation are found using finite element simulations so that the structure can be subjected to inspections and removed from service safely. Uncertainty effects of composite material properties on the response of the structure are quantified using Monte Carlo simulations. Vibration… More >

Nengguang Liu, Wei Gao, Chongmin Song, Nong Zhang

The International Conference on Computational & Experimental Engineering and Sciences, Vol.20, No.3, pp. 83-84, 2011, DOI:10.3970/icces.2011.020.083

Abstract This paper presents the probabilistic dynamic analysis of vehicle-bridge interaction systems. The bridge's and vehicle's parameters are considered as random variables as well as the road surface roughness is modeled as random process. A quarter-car model is used to represent a moving vehicle and the bridge is treated as an Euler-Bernoulli beam. From the equation of motion for the vehicle-bridge coupling system, the expressions for mean value and standard deviation of bridge response are developed by using the random variable's functional moment method. The effects of the individual system parameters and the road surface roughness on the bridge response are… More >

Yang Cheng, Bin Jia, Ming Xin

The International Conference on Computational & Experimental Engineering and Sciences, Vol.16, No.2, pp. 33-34, 2011, DOI:10.3970/icces.2011.016.033

Abstract A sparse grid approach to orbit uncertainty propagation is presented. Efficient and accurate uncertainty propagation methods for nonlinear dynamic systems have been of enormous interest to space object tracking. Recent methods include those based on the time evolution of the probability density function, the statistical moments, the random samples, or a sum of Gaussian components. The idea of the sparse grid method for orbit uncertainty propagation is to represent the initial uncertainty by a sparse grid, propagate the sparse grid points individually through the nonlinear orbit dynamics, and compute the statistical moments from the propagated sparse grid points. The Smolyak… More >

Jieping Liu¹, Lingxin Zhang^1,*, Haohao Zhang², Tao Liu¹

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 123-142, 2019, DOI:10.32604/sdhm.2019.04486

Abstract As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed that, with different designs at… More >

B. Amaziane¹, A. Naji², D. Ouazar³, A. H.-D. Cheng⁴

CMC-Computers, Materials & Continua, Vol.2, No.2, pp. 85-96, 2005, DOI:10.3970/cmc.2005.002.085

Abstract In this paper the optimization of groundwater pumping in coastal aquifers under the threat of saltwater intrusion is investigated. The aquifer is inhomogeneous and contains several hydraulic conductivities zones. The aquifer data such as the hydraulic conductivities are uncertain, but with their expected mean and standard deviation values given. A stochastic boundary element method based on the perturbation technique is employed as the simulation tool. The stochastic optimization is handled by the chance-constrained programming. Genetic algorithm is selected as the optimization tool. Numerical examples of deterministic and stochastic problems are provided to demonstrate the feasibility of the proposed schemes. More >