Reza Ghaderi - Academia.edu (original) (raw)
Papers by Reza Ghaderi
Nonlinear Dynamics, 2016
This manuscript presents two systematic design procedures, to tune parameters of a fractional com... more This manuscript presents two systematic design procedures, to tune parameters of a fractional complex-order PI (FCO-PI) controller in the form of \mathrm{PI}^{a+ib}$$PIa+ib. The \mathrm{PI}^{a+ib}$$PIa+ib controller uses extra parameter(s) than the conventional fractional- and/or integer-order PI controllers. Therefore, more specifications can be achieved. These are investigated in two different approaches through comparative studies. The proposed design procedures are based on realizing some frequency domain restrictions. These are eventually stated in terms of M_{s}$$Ms and M_{p}$$Mp constraints, developing integral gain optimization tuning method. In this method, optimized amount of parameters are assessed based on minimizing the integral error indices with a constraint on the maximum sensitivity functions. In this aim, tuning of parameters of fractional complex-order controller via M constraint integral gain optimization (FC-MIGO) algorithm is innovatively defined and then the so-called FC-MIGO rule is proposed by applying FC-MIGO algorithm on a test batch. Comprehensive simulations illustrate how systematic and significant the proposed algorithms are. Capability of the design procedure will be also investigated on a PEM fuel cell as a case study.
2011 7th Iranian Conference on Machine Vision and Image Processing, 2011
Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissu... more Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance image (MRI) is a complicated concern. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer Theory (DST) for information fusion. In the proposed method, Fuzzy C-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted to basic belief structures. The salient aspect of this work is the interpretation of each FCM outputs to the belief structures ...
2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
Abstract Due to intensity non-uniformity (INU) and noise brain magnetic resonance image (MRI) seg... more Abstract Due to intensity non-uniformity (INU) and noise brain magnetic resonance image (MRI) segmentation is a complicated concern. Many methods have been presented to overcome brain MRI segmentation. Among these methods, using fuzzy c-means (FCM) is introduced as an effective strategy. Spatial information cannot be considered at a standard FCM. Therefore, many methods have been presented to optimize standard FCM with optimization of objective function. In this research work, a novel method has been ...
Journal of Zhejiang University SCIENCE C, 2012
Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissu... more Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method, fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure ...
Journal of Applied Mathematics and Computing, 2008
ABSTRACT The main goal of this study is to present a technique to find a state response of multiv... more ABSTRACT The main goal of this study is to present a technique to find a state response of multivariable time varying systems. In this paper a novel Homotopy Perturbation based Method (HPM) will be presented to find a dynamic response of time varying system. According to this method, the linear part of the described system is partitioned into two time varying and invariant subsections. Time invariant part analytically constructs the state transition matrix. This matrix is a core of the rest of time varying differential equation without any further changes in a sequence order. The main advantage of this method is only the necessity to solve the time invariant part of the state transition matrix. Simulation results verify the significance of the proposed analytic and asymptotic method.
Information Sciences, 2013
Abstract Brain magnetic resonance imaging (MRI) segmentation is a challenging task due to the com... more Abstract Brain magnetic resonance imaging (MRI) segmentation is a challenging task due to the complex anatomical structure of brain tissues as well as intensity non-uniformity, partial volume effects and noise. Segmentation methods based on fuzzy approaches have been developed to overcome the uncertainty caused by these effects. In this study, a novel combination of Fuzzy Inference System and Dempster-Shafer Theory is applied to brain MRI for the purpose of segmentation where the pixel intensity and the spatial information are ...
Abstract and Applied Analysis, 2011
The European Physical Journal Applied Physics
Piezoelectric microcantilevers (MCs) have extensive applications in microelectromechanical system... more Piezoelectric microcantilevers (MCs) have extensive applications in microelectromechanical systems. One of the applications of piezoelectric MCs is in self-sensing sensors. These sensors are highly popular due to their high accuracy, quick response, and environmental compatibility. Since the output current of piezoelectric layer is used as the sensing parameter in piezoelectric MCs, sensor optimization requires the maximum output current for each specific vibration. This paper uses dynamic piezoelectric MC analysis in different operating environments (air and liquid) to determine the factors influencing the output current of a piezoelectric layer. To obtain the differential equation of vibration, the hydrodynamic force applied to the piezoelectric MC by using the sphere string model. The equation was obtained via the Euler-Bernoulli beam theory and the Lagrange equation. The differential equation of the movement would yield both the MC deformation and the piezoelectric layer current...
International Journal of Intelligent Systems Technologies and Applications, 2016
ABSTRACT In this work, we propose a new approach to improve the performance of speech enhancement... more ABSTRACT In this work, we propose a new approach to improve the performance of speech enhancement technique based on partial differential equations. As we know, the real-world noise is highly random in nature. So we try for reduction of white Gaussian noise. The proposed method was evaluated on several speakers. The subjective and objective results show that the new method highly improves speech enhancement. Comparisons of several methods are reported.
Journal of Contemporary Brachytherapy, 2016
Expert Systems With Applications, May 1, 2011
A new approach for classification of circular knitted fabric defect is proposed which is based on... more A new approach for classification of circular knitted fabric defect is proposed which is based on accepting uncertainty in labels of the learning data. In the basic classification methodologies it is assumed that correct labels are assigned to samples and these ...
Informatica
Text Automatic identification of digital signal types is of interest for both civil and military ... more Text Automatic identification of digital signal types is of interest for both civil and military applications. This paper presents an efficient signal type identifier that includes a variety of digital signals. In this method, a combination of higher order moments (HOM) and higher order cumulants (HOC) are used as the features. A multi-layer perceptron neural network with SASS learning algorithm is proposed to determine the membership of the received signal. We have used swarm intelligence (SI) for feature selection in order to reduce the complexity of the classifier. Simulations results show that the proposed method has high performance for identification of different kinds of digital signal even at very low SNRs. This high efficiency is achieved with only seven features, which have been selected using particle swarm optimizer. Povzetek: Opisana je metoda za identifikacijo digitalnih signalov.
This paper presents an automatic design of fuzzy rule-based classification systems which play an ... more This paper presents an automatic design of fuzzy rule-based classification systems which play an important role in dealing with uncertainty and vagueness inherent in multi-dimensional pattern classification problems. The performance and interpretability are two main factors which have to be considered while designing a fuzzy classification system. Finding an optimal rule set in terms of conciseness and comprehensibility is a key to build fuzzy classification systems. In this paper, a fuzzy artificial bee colony (ABC) approach is developed to generate fuzzy classification rule set with appropriate number of rules in order to maximize the number of correctly classified patterns. In order to illustrate the efficiency of the proposed method, it is applied to some well-known data sets plus a collection of data contained in 2D images captured by a video camera to identify the class of moving objects in a traffic scene. The simulation results are also compared with other methods.
Fuzzy classification systems play an important role in dealing with uncertainty and vagueness inh... more Fuzzy classification systems play an important role in dealing with uncertainty and vagueness inherent in multi-dimensional pattern classification problems. Finding an optimal fuzzy rule set is a milestone in order for fuzzy classification systems to be built. In this paper, a fuzzy Genetic Algorithm (GA) is developed to generate fuzzy classification rules with appropriate number of rules in order to maximize the number of correctly classified patterns. The proposed algorithm is applied to a collection of data contained in 2D images captured by a video camera to identify the class of moving objects in a traffic scene. The simulation results are compared with other methods in order to illustrate the efficiency of the proposed method.
International Journal of Engineering, 2013
Nonlinear Dynamics, 2016
This manuscript presents two systematic design procedures, to tune parameters of a fractional com... more This manuscript presents two systematic design procedures, to tune parameters of a fractional complex-order PI (FCO-PI) controller in the form of \mathrm{PI}^{a+ib}$$PIa+ib. The \mathrm{PI}^{a+ib}$$PIa+ib controller uses extra parameter(s) than the conventional fractional- and/or integer-order PI controllers. Therefore, more specifications can be achieved. These are investigated in two different approaches through comparative studies. The proposed design procedures are based on realizing some frequency domain restrictions. These are eventually stated in terms of M_{s}$$Ms and M_{p}$$Mp constraints, developing integral gain optimization tuning method. In this method, optimized amount of parameters are assessed based on minimizing the integral error indices with a constraint on the maximum sensitivity functions. In this aim, tuning of parameters of fractional complex-order controller via M constraint integral gain optimization (FC-MIGO) algorithm is innovatively defined and then the so-called FC-MIGO rule is proposed by applying FC-MIGO algorithm on a test batch. Comprehensive simulations illustrate how systematic and significant the proposed algorithms are. Capability of the design procedure will be also investigated on a PEM fuel cell as a case study.
2011 7th Iranian Conference on Machine Vision and Image Processing, 2011
Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissu... more Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance image (MRI) is a complicated concern. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer Theory (DST) for information fusion. In the proposed method, Fuzzy C-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted to basic belief structures. The salient aspect of this work is the interpretation of each FCM outputs to the belief structures ...
2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
Abstract Due to intensity non-uniformity (INU) and noise brain magnetic resonance image (MRI) seg... more Abstract Due to intensity non-uniformity (INU) and noise brain magnetic resonance image (MRI) segmentation is a complicated concern. Many methods have been presented to overcome brain MRI segmentation. Among these methods, using fuzzy c-means (FCM) is introduced as an effective strategy. Spatial information cannot be considered at a standard FCM. Therefore, many methods have been presented to optimize standard FCM with optimization of objective function. In this research work, a novel method has been ...
Journal of Zhejiang University SCIENCE C, 2012
Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissu... more Abstract As a result of noise and intensity non-uniformity, automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task. In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion. In the proposed method, fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures. The salient aspect of this paper is the interpretation of each FCM output as a belief structure ...
Journal of Applied Mathematics and Computing, 2008
ABSTRACT The main goal of this study is to present a technique to find a state response of multiv... more ABSTRACT The main goal of this study is to present a technique to find a state response of multivariable time varying systems. In this paper a novel Homotopy Perturbation based Method (HPM) will be presented to find a dynamic response of time varying system. According to this method, the linear part of the described system is partitioned into two time varying and invariant subsections. Time invariant part analytically constructs the state transition matrix. This matrix is a core of the rest of time varying differential equation without any further changes in a sequence order. The main advantage of this method is only the necessity to solve the time invariant part of the state transition matrix. Simulation results verify the significance of the proposed analytic and asymptotic method.
Information Sciences, 2013
Abstract Brain magnetic resonance imaging (MRI) segmentation is a challenging task due to the com... more Abstract Brain magnetic resonance imaging (MRI) segmentation is a challenging task due to the complex anatomical structure of brain tissues as well as intensity non-uniformity, partial volume effects and noise. Segmentation methods based on fuzzy approaches have been developed to overcome the uncertainty caused by these effects. In this study, a novel combination of Fuzzy Inference System and Dempster-Shafer Theory is applied to brain MRI for the purpose of segmentation where the pixel intensity and the spatial information are ...
Abstract and Applied Analysis, 2011
The European Physical Journal Applied Physics
Piezoelectric microcantilevers (MCs) have extensive applications in microelectromechanical system... more Piezoelectric microcantilevers (MCs) have extensive applications in microelectromechanical systems. One of the applications of piezoelectric MCs is in self-sensing sensors. These sensors are highly popular due to their high accuracy, quick response, and environmental compatibility. Since the output current of piezoelectric layer is used as the sensing parameter in piezoelectric MCs, sensor optimization requires the maximum output current for each specific vibration. This paper uses dynamic piezoelectric MC analysis in different operating environments (air and liquid) to determine the factors influencing the output current of a piezoelectric layer. To obtain the differential equation of vibration, the hydrodynamic force applied to the piezoelectric MC by using the sphere string model. The equation was obtained via the Euler-Bernoulli beam theory and the Lagrange equation. The differential equation of the movement would yield both the MC deformation and the piezoelectric layer current...
International Journal of Intelligent Systems Technologies and Applications, 2016
ABSTRACT In this work, we propose a new approach to improve the performance of speech enhancement... more ABSTRACT In this work, we propose a new approach to improve the performance of speech enhancement technique based on partial differential equations. As we know, the real-world noise is highly random in nature. So we try for reduction of white Gaussian noise. The proposed method was evaluated on several speakers. The subjective and objective results show that the new method highly improves speech enhancement. Comparisons of several methods are reported.
Journal of Contemporary Brachytherapy, 2016
Expert Systems With Applications, May 1, 2011
A new approach for classification of circular knitted fabric defect is proposed which is based on... more A new approach for classification of circular knitted fabric defect is proposed which is based on accepting uncertainty in labels of the learning data. In the basic classification methodologies it is assumed that correct labels are assigned to samples and these ...
Informatica
Text Automatic identification of digital signal types is of interest for both civil and military ... more Text Automatic identification of digital signal types is of interest for both civil and military applications. This paper presents an efficient signal type identifier that includes a variety of digital signals. In this method, a combination of higher order moments (HOM) and higher order cumulants (HOC) are used as the features. A multi-layer perceptron neural network with SASS learning algorithm is proposed to determine the membership of the received signal. We have used swarm intelligence (SI) for feature selection in order to reduce the complexity of the classifier. Simulations results show that the proposed method has high performance for identification of different kinds of digital signal even at very low SNRs. This high efficiency is achieved with only seven features, which have been selected using particle swarm optimizer. Povzetek: Opisana je metoda za identifikacijo digitalnih signalov.
This paper presents an automatic design of fuzzy rule-based classification systems which play an ... more This paper presents an automatic design of fuzzy rule-based classification systems which play an important role in dealing with uncertainty and vagueness inherent in multi-dimensional pattern classification problems. The performance and interpretability are two main factors which have to be considered while designing a fuzzy classification system. Finding an optimal rule set in terms of conciseness and comprehensibility is a key to build fuzzy classification systems. In this paper, a fuzzy artificial bee colony (ABC) approach is developed to generate fuzzy classification rule set with appropriate number of rules in order to maximize the number of correctly classified patterns. In order to illustrate the efficiency of the proposed method, it is applied to some well-known data sets plus a collection of data contained in 2D images captured by a video camera to identify the class of moving objects in a traffic scene. The simulation results are also compared with other methods.
Fuzzy classification systems play an important role in dealing with uncertainty and vagueness inh... more Fuzzy classification systems play an important role in dealing with uncertainty and vagueness inherent in multi-dimensional pattern classification problems. Finding an optimal fuzzy rule set is a milestone in order for fuzzy classification systems to be built. In this paper, a fuzzy Genetic Algorithm (GA) is developed to generate fuzzy classification rules with appropriate number of rules in order to maximize the number of correctly classified patterns. The proposed algorithm is applied to a collection of data contained in 2D images captured by a video camera to identify the class of moving objects in a traffic scene. The simulation results are compared with other methods in order to illustrate the efficiency of the proposed method.
International Journal of Engineering, 2013