Papers by Moussavi S Zeinolabedin
Advances in Computer Science : an International Journal, Mar 31, 2015
Maximum power point tracking is considered to be an efficiency improving method applicable to the... more Maximum power point tracking is considered to be an efficiency improving method applicable to the solar arrays. In this paper, Fuzzy-Big Bang-Big Crunch algorithm is applied in tracking of maximum power point. Simulation has been done in both MATLAB software where comparison made between Fuzzy-Big Bang and Fuzzy algorithm-alone. It has shown that in various temperatures and irradiations, optimized duty cycle achieved by proposed method is closer than fuzzy logic method to the values previously expressed in the referred source. Using those calculated values, the maximum error in Fuzzy-Big Bang method is only 0.0028 where fuzzy logic shows 0.0088. That is, the maximum error is reduced by 6.67% also efficiency of the system with Fuzzy-Big Bang algorithm is 5% improved in comparison with the Fuzzy method. The tracking system will quickly adapt itself even in rapidly environmental changes.
International Journal of Control and Automation
Intelligent control that can be used learning ability and human experiences, is widely used in in... more Intelligent control that can be used learning ability and human experiences, is widely used in industrial application such as motors speed control. This paper presents Proportional-Integral-Derivative Controller (PID controller) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for Permanent Magnet Direct Current (PMDC) motor. ANFIS provides combination of artificial neural network and fuzzy inference systems therefore ANFIS uses advantages of them simultaneously. The proposed PID controller Coefficients are determined by ANFIS. The proposed controller based system is compared with Internal Model Control (IMC) PID controller based system. The Comparison shows that proposed controller improves characteristics in different conditions such as no load, increasing reference speed, applied load and noisy load. Proposed controller based system can improve system performance by smaller fuzzy rule set.
Artificial Intelligence and Applications, Aug 1, 2014
To provide an analytical classification on a signal database of power plants pipelines defects, P... more To provide an analytical classification on a signal database of power plants pipelines defects, PCA (Principal Component Analysis) is applied to database of power plants pipeline defects. The network receives in input a matrix of defects that are derived from a simulator formula that will be explained in follow. The aim of this research approach is the audit ability for safe or non safe material in pipelines and provides a binary output for indicating whether defect is recognized or not. The network proposed is MLP (multilayer perceptron) with strictly local connections. The first layer performs local linear operations, while the second has a non linear functionality. Result shown that this procedure could be used as an appropriate solution for pipelines defect detections.
Journal of Technology Innovations in Renewable Energy, 2014
The brushless doubly fed induction generator (BDFIG) has the potential to be employed as a variab... more The brushless doubly fed induction generator (BDFIG) has the potential to be employed as a variable speed wind turbine generator. Owing to brushless configuration of this generator, its reliability is higher than DFIG. Most of the grid faults are unsymmetrical. Hence, this paper analyzes dynamic behavior of BDFIG under symmetrical and unsymmetrical faults and presents dynamic models for both fault types. In order to validate the results of analysis, simulations have been carried out using MATLAB/Simulink software. Then, the control winding (CW) current is compared under symmetrical and unsymmetrical faults.
There are pivotal reasons for PMDC application in industry. Various control structures can be imp... more There are pivotal reasons for PMDC application in industry. Various control structures can be implemented to achieve speed optimization. The paper take into account control structure based on combination of MRAC (Model Reference Adaptive Control) and ANFIS (Adaptive Neuro-Fuzzy Inference System) for PMDC (Permanent Magnet Direct Current) motor to use advantages of both structures simultaneously. The error signal reduction in MRAC system is modified by means of ANFIS controller. The reason behind is due to smaller rule base for covering any possible situation occurring in real motor applications. Comparison of the above named combination of structures with the previous ones by means of simulation in MATLAB, Simulink proved notable improvement behavioural characteristics of PMDC motor.
Non-Destructive Testing
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component A... more In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason models were developed to determine surface-breaking defects along the applied field when using the magnetic flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Abstract
This paper present Proportional-Integral-Derivative Controller (PID controller) based on... more Abstract
This paper present Proportional-Integral-Derivative Controller (PID controller) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for Permanent Magnet Direct Current (PMDC) motor. ANFIS provides combination of artificial neural network and fuzzy inference systems therefore ANFIS uses advantages of them also ANFIS mostly overcomes disadvantages of them. Coefficients of proposed PID controller are determined by means of ANFIS. Simulation results of proposed controller are compared with Internal Model Control (IMC) PID. Comparison shows that proposed controller improves performance criteria in different conditions such as no load, increasing reference speed, applied load and noisy load. Proposed controller can improve performance of system by means of smaller fuzzy rule set.
Abstract There are pivotal reasons for PMDC application in industry. Various control structures c... more Abstract There are pivotal reasons for PMDC application in industry. Various control structures can be implemented to achieve speed optimization. The paper take into account control structure based on combination of MRAC (Model Reference Adaptive Control) and ANFIS (Adaptive Neuro-Fuzzy Inference System) for PMDC (Permanent Magnet Direct Current) motor to use advantages of both structures simultaneously. The error signal reduction in MRAC system is modified by means of ANFIS controller. The reason behind is due to smaller rule base for covering any possible situation occurring in real motor applications. Comparison of the above named combination of structures with the previous ones by means of simulation in MATLAB, Simulink proved notable improvement behavioural characteristics of PMDC motor.
Abstract
In this paper an efficient defect detection algorithm that is based on PCA (Principal Co... more Abstract
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and
MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from
the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The
fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only
few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive
Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason
models were developed to determine surface-breaking defects along the applied field when using the magnetic
flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from
simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of
defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using
PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Keywords: Magnetic flux leakage (MFL), Non destructive Testing (NDT), Principal Component Analysis
(PCA), Multilayer Perceptron (MLP).
Abstract
In this paper an efficient defect detection algorithm that is based on PCA (Principal Co... more Abstract
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and
MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from
the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The
fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only
few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive
Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason
models were developed to determine surface-breaking defects along the applied field when using the magnetic
flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from
simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of
defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using
PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Keywords: Magnetic flux leakage (MFL),
Abstract:
To provide an analytical classification on a signal database of power plants pipelines ... more Abstract:
To provide an analytical classification on a signal database of power plants pipelines defects,
PCA (Principal Component Analysis) is applied to database of power plants pipeline defects.
The network receives in input a matrix of defects that are derived from a simulator formula
that will be explained in follow. The aim of this research approach is the audit ability for safe
or non safe material in pipelines and provides a binary output for indicating whether defect
is recognized or not. The network proposed is MLP (multilayer perceptron) with strictly local
connections. The first layer performs local linear operations, while the second has a non linear
functionality. Result shown that this procedure could be used as an appropriate solution for pipelines defect detection.
Journal of Convergence Information Technology, 2010
Artificial Neural Networks(ANNS) have top level of capability to progress the estimation of crack... more Artificial Neural Networks(ANNS) have top level of capability to progress the estimation of cracks in metal tubes. The aim of this paper is to propose an algorithm to identify modeled cracks by magnetic flux leakage inspection in Non Destructive Testing (NDT) [1, 2, 3, 4, 5, and 6]. The analysis is carried out with a simulated database of signals in which the depth of the crack, its width, shape, And geometric dimension of the detection process, is allowed to change. The simulated signal is input to the network, after a reduction process in which the main features of the signal are extracted. Feature extractors are used in pattern recognition area due to their advantages in representing data. With this approach classifier's job became easier and more effective. The main goal of the feature extractor is to reflect the characteristics of an object in a given dataset. In this way feature extractor simplify the amount of resources required to describe a large dataset accurately. This paper presents the results of employing different kinds of feature extraction functions and classification and provides compression between them. As the output of ANN, we shall justify if any care in meta lto indicate whether the input signal is crack or not. The analysis based on the neural network and feature extractor functions is shown to be quite top probability of detection.
of undetectable defects. Among various methods, Non Destructive Testing (NDT) techniques are the ... more of undetectable defects. Among various methods, Non Destructive Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. Models were developed to determine surface-breaking defects along the applied field when using the magnetic flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of defects. Permeability variations were neglected by employing a flux density close to sample saturation. Three different defect geometries were experimentally investigated and the validity of the analytical model was verified. Different Feature extractor functions are applied in this paper to yield fast decision and more accurate. Indeed more accuracy is because of decision on different features that yields by employing two kinds of feature extractors, PCA and DCT. In our previous works, we applied BELBIC (Brain Emotional Learning Based Intelligent Controller) controller on the extracted features and observed that the results were more accurate in some cases. Linear Discriminate Analysis (LDA) is another helpful instrument that is employed precise decision. But for this paper, we decided to apply LDA and BELBIC serially and observe the results. This method was so useful and more precise results were provided. All feature extractions LDAs, Multilayer perceptron (MLP,) and BELBIC are methods for identifying erosion defects are described and employed in this paper. Great accuracy rate in compare between results of related approaches suggests that this Method can be used as an algorithm of MFL data interpretation technique .
Journal of Convergence Information Technology, 2008
... Sh. B. Shokouhi for his golden comments on this contribution. and also my M.Sc. Guidance, Pro... more ... Sh. B. Shokouhi for his golden comments on this contribution. and also my M.Sc. Guidance, Prof. Ali Sadr for his invaluable attempts on my life. 8. References ... 262-269, 2007. [17] Saeedreza Ehteram, Seyed zeinolabedin Mousavi, Ali Sadr, Ali Akbar Jalali "Quantum Electronics ...
In combining classifiers, effort is made to achieve higher accuracy in comparison with the base c... more In combining classifiers, effort is made to achieve higher accuracy in comparison with the base classifiers that form the ensemble. In this paper, we make modifications to the conventional decision template, DT, method, so that its classification performance is improved in experiments with Satimage, Image Segmentation and Soybean datasets. In our modified version, DT, an elegant strategy in classifier fusion, is used in the first stage of classification task, and in the second stage, the most misclassified classes are directed to a classifier that is specifically devoted to those classes. To identify the most misclassified classes, the confusion matrix of the output of the decision template stage is considered. Experimental results demonstrate the improved performance of the modified version by a 3% increase in the recognition rate for Satimage dataset in comparison with previously published results on Satimage dataset, a 10.57% increase in the recognition rate for Image Segmentation and 4.88% for Soybean dataset, in comparison with the conventional method.
Direct current (DC) motors have been widely used in many industrial applications such as electric... more Direct current (DC) motors have been widely used in many industrial applications such as electric vehicles, steel rolling mills, electric cranes, and robotic manipulators due to precise, wide, simple, and continuous control characteristics. A novel PID controller based on Internal Model Control (IMC) and Linear Quadratic Regulator (LQR) for DC motor is proposed in this paper. The strategy proposed is compared with IMC-PID controller and LQR controller. The simulation shows that proposed strategy achieved shorter settling time and rise time also improvement in overshoot and final value has resulted.
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Papers by Moussavi S Zeinolabedin
This paper present Proportional-Integral-Derivative Controller (PID controller) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for Permanent Magnet Direct Current (PMDC) motor. ANFIS provides combination of artificial neural network and fuzzy inference systems therefore ANFIS uses advantages of them also ANFIS mostly overcomes disadvantages of them. Coefficients of proposed PID controller are determined by means of ANFIS. Simulation results of proposed controller are compared with Internal Model Control (IMC) PID. Comparison shows that proposed controller improves performance criteria in different conditions such as no load, increasing reference speed, applied load and noisy load. Proposed controller can improve performance of system by means of smaller fuzzy rule set.
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and
MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from
the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The
fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only
few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive
Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason
models were developed to determine surface-breaking defects along the applied field when using the magnetic
flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from
simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of
defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using
PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Keywords: Magnetic flux leakage (MFL), Non destructive Testing (NDT), Principal Component Analysis
(PCA), Multilayer Perceptron (MLP).
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and
MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from
the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The
fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only
few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive
Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason
models were developed to determine surface-breaking defects along the applied field when using the magnetic
flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from
simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of
defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using
PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Keywords: Magnetic flux leakage (MFL),
To provide an analytical classification on a signal database of power plants pipelines defects,
PCA (Principal Component Analysis) is applied to database of power plants pipeline defects.
The network receives in input a matrix of defects that are derived from a simulator formula
that will be explained in follow. The aim of this research approach is the audit ability for safe
or non safe material in pipelines and provides a binary output for indicating whether defect
is recognized or not. The network proposed is MLP (multilayer perceptron) with strictly local
connections. The first layer performs local linear operations, while the second has a non linear
functionality. Result shown that this procedure could be used as an appropriate solution for pipelines defect detection.
This paper present Proportional-Integral-Derivative Controller (PID controller) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for Permanent Magnet Direct Current (PMDC) motor. ANFIS provides combination of artificial neural network and fuzzy inference systems therefore ANFIS uses advantages of them also ANFIS mostly overcomes disadvantages of them. Coefficients of proposed PID controller are determined by means of ANFIS. Simulation results of proposed controller are compared with Internal Model Control (IMC) PID. Comparison shows that proposed controller improves performance criteria in different conditions such as no load, increasing reference speed, applied load and noisy load. Proposed controller can improve performance of system by means of smaller fuzzy rule set.
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and
MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from
the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The
fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only
few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive
Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason
models were developed to determine surface-breaking defects along the applied field when using the magnetic
flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from
simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of
defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using
PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Keywords: Magnetic flux leakage (MFL), Non destructive Testing (NDT), Principal Component Analysis
(PCA), Multilayer Perceptron (MLP).
In this paper an efficient defect detection algorithm that is based on PCA (Principal Component Analysis) and
MLP (Multilayer perceptron) is described. The method consists of two steps: primarily we project defects from
the original vector space to an eigen subspace via PCA; secondly we apply MLP to obtain a linear classifier. The
fundamental idea of combining PCA and MLP is to improve the generalization capability of MLP when only
few samples per class are available. Among various methods of collecting details of pipelines, Non Destructive
Testing (NDT) techniques are the most useful methods due to their efficiency and low cost. For this reason
models were developed to determine surface-breaking defects along the applied field when using the magnetic
flux leakage (MFL) non-destructive technique. The theoretical model fits the experimental MFL results from
simulated defects. For MFL sensors, the normal magnetic leakage field is subsequently used for evaluation of
defects. With regards to experimental results that is explained at the rest of this paper, The hybrid classifier using
PCA and multiple MLPs provide a useful framework for the task of defect detection as well.
Keywords: Magnetic flux leakage (MFL),
To provide an analytical classification on a signal database of power plants pipelines defects,
PCA (Principal Component Analysis) is applied to database of power plants pipeline defects.
The network receives in input a matrix of defects that are derived from a simulator formula
that will be explained in follow. The aim of this research approach is the audit ability for safe
or non safe material in pipelines and provides a binary output for indicating whether defect
is recognized or not. The network proposed is MLP (multilayer perceptron) with strictly local
connections. The first layer performs local linear operations, while the second has a non linear
functionality. Result shown that this procedure could be used as an appropriate solution for pipelines defect detection.