• Journal of applied mathematics & informatics
• /
• v.28 no.5_6
• /
• pp.1227-1237
• /
• 2010

In this paper, a Klein-Gordon equation is solved by using the homotopy analysis method (HAM), homotopy perturbation method (HPM) and modified homotopy perturbation method (MHPM). The approximation solution of this equation is calculated in the form of series which its components are computed easily. The uniqueness of the solution and the convergence of the proposed methods are proved. The accuracy of these methods are compared by solving an example.

• Journal of Information Processing Systems
• /
• v.20 no.4
• /
• pp.524-534
• /
• 2024

The release of relational data containing personal sensitive information poses a significant risk of privacy breaches. To preserve privacy while publishing such data, it is important to implement techniques that ensure protection of sensitive information. One popular technique used for this purpose is data perturbation, which is popularly used for privacy-preserving data release due to its simplicity and efficiency. However, the data perturbation has some limitations that prevent its practical application. As such, it is necessary to propose alternative solutions to overcome these limitations. In this study, we propose a novel approach to preserve privacy in the release of relational data containing personal sensitive information. This approach addresses an intuitive, syntactic privacy criterion for data perturbation and two perturbation methods for relational data release. Through experiments with synthetic and real data, we evaluate the performance of our methods.

• Communications for Statistical Applications and Methods
• /
• v.7 no.3
• /
• pp.871-884
• /
• 2000

Using simulation, we compare the perturbation analysis estimate and the forward difference estimate for the first and second derivatives of performance measures in a Markov renewal process. We find the perturbation analysis estimate has much les mean squared error than the traditional forward difference estimate.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.81-88
• /
• 2023

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

• Management Science and Financial Engineering
• /
• v.21 no.2
• /
• pp.25-30
• /
• 2015

This short article compares different solution methods for a basic RBC model (Hansen, 1985). We solve and simulate the model using two main algorithms: the methods of perturbation and projection, respectively. One novelty is that we offer a type of the hybrid method: we compute easily a second-order approximation to decision rules and use that approximation as an initial guess for finding Chebyshev polynomials. We also find that the second-order perturbation method is most competitive in terms of accuracy for standard RBC model.

• Steel and Composite Structures
• /
• v.8 no.2
• /
• pp.129-144
• /
• 2008

This paper demonstrates an application of the perturbation based stochastic finite element method (SFEM) for predicting the performance of structural systems made of composite sections with random material properties. The composite member consists of materials in contact each of which can surround a finite number of inclusions. The perturbation based stochastic finite element analysis can provide probabilistic behavior of a structure, only the first two moments of random variables need to be known, and should therefore be suitable as an alternative to Monte Carlo simulation (MCS) for realizing structural analysis. A summary of stiffness matrix formulation of composite systems and perturbation based stochastic finite element dynamic analysis formulation of structural systems made of composite sections is given. Two numerical examples are presented to illustrate the method. During stochastic analysis, displacements and sectional forces of composite systems are obtained from perturbation and Monte Carlo methods by changing elastic modulus as random variable. The results imply that perturbation based SFEM method gives close results to MCS method and it can be used instead of MCS method, especially, if computational cost is taken into consideration.

• Journal of applied mathematics & informatics
• /
• v.31 no.1_2
• /
• pp.165-177
• /
• 2013

In this paper, we apply Homotopy perturbation transform method (HPTM) for solving singular fourth order parabolic partial differential equations with variable coefficients. This method is the combination of the Laplace transform method and Homotopy perturbation method. The nonlinear terms can be easily handled by the use of He's polynomials. The aim of using the Laplace transform is to overcome the deficiency that is mainly caused by unsatisfied conditions in other semi-analytical methods such as Homotopy perturbation method (HPM), Variational iteration method (VIM) and Adomain Decomposition method (ADM). The proposed scheme finds the solutions without any discretization or restrictive assumptions and avoids the round-off errors. The comparison shows a precise agreement between the results and introduces this method as an applicable one which it needs fewer computations and is much easier and more convenient than others, so it can be widely used in engineering too.

• Proceedings of the KSME Conference
• /
• 2004.11a
• /
• pp.782-787
• /
• 2004

In the conventional sliding mode control technique, a priori knowledge of the bound of external disturbances or/and parameter uncertainties is required to assure control robustness. This, however, may not be easy to obtain in practical situation. This work presents a novel methodology, a sliding mode controller with perturbation estimator, which offers a robust control performance without a priori knowledge about the perturbations (disturbances and parameter uncertainties). The proposed technique is featured by an integrated average value of the imposed perturbation over a certain sampling period. This work also proposes two effective actuating methods of the perturbation estimator: on-off condition and filtering condition. In order to demonstrate the effectiveness of the proposed methodology, a two-link robotic system is adopted and its position control performance is evaluated. In addition, a comparative work between the conventional technique and the proposed one is undertaken.

• Journal of applied mathematics & informatics
• /
• v.30 no.5_6
• /
• pp.981-992
• /
• 2012

Integro-differential equations arise in modeling various physical and engineering problems. Several numerical and analytical methods have been developed to solving such equations. We introduce the NHPM for solving nonlinear integro-differential equations. Several examples for solving integro-differential equations are presented to illustrate the efficiency of the proposed NHPM.

• Communications for Statistical Applications and Methods
• /
• v.13 no.2
• /
• pp.267-272
• /
• 2006

A procedure for selecting regressors in the linear regression model is suggested using local influence approach. Under an appropriate perturbation scheme, the effect of perturbation of regressors on the profile log-likelihood displacement is assessed for variable selection. A numerical example is provided for illustration.