Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICL...IJITCA Journal
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model . Our proposed approach controls
deformation of target's model. If deformation of target's model is larger than a predetermined threshold,then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF) . DDPF
approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target' s model. However, DDPF approach updates target's model when the rotation or
scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficiently
and accurately.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
POSTERIOR RESOLUTION AND STRUCTURAL MODIFICATION FOR PARAMETER DETERMINATION ...IJCI JOURNAL
When only a few lower modes data are available to evaluate a large number of unknown parameters, it is
difficult to acquire information about all unknown parameters. The challenge in this kind of updation
problem is first to get confidence about the parameters that are evaluated correctly using the available
data and second to get information about the remaining parameters. In this work, the first issue is resolved
employing the sensitivity of the modal data used for updation. Once it is fixed that which parameters are
evaluated satisfactorily using the available modal data the remaining parameters are evaluated employing
modal data of a virtual structure. This virtual structure is created by adding or removing some known
stiffness to or from some of the stories of the original structure. A 12-story shear building is considered for
the numerical illustration of the approach. Results of the study show that the present approach is an
effective tool in system identification problem when only a few data is available for updation.
Iterative Determinant Method for Solving Eigenvalue Problemsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural...IJECEIAES
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICL...IJITCA Journal
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model . Our proposed approach controls
deformation of target's model. If deformation of target's model is larger than a predetermined threshold,then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF) . DDPF
approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target' s model. However, DDPF approach updates target's model when the rotation or
scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficiently
and accurately.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
POSTERIOR RESOLUTION AND STRUCTURAL MODIFICATION FOR PARAMETER DETERMINATION ...IJCI JOURNAL
When only a few lower modes data are available to evaluate a large number of unknown parameters, it is
difficult to acquire information about all unknown parameters. The challenge in this kind of updation
problem is first to get confidence about the parameters that are evaluated correctly using the available
data and second to get information about the remaining parameters. In this work, the first issue is resolved
employing the sensitivity of the modal data used for updation. Once it is fixed that which parameters are
evaluated satisfactorily using the available modal data the remaining parameters are evaluated employing
modal data of a virtual structure. This virtual structure is created by adding or removing some known
stiffness to or from some of the stories of the original structure. A 12-story shear building is considered for
the numerical illustration of the approach. Results of the study show that the present approach is an
effective tool in system identification problem when only a few data is available for updation.
Iterative Determinant Method for Solving Eigenvalue Problemsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural...IJECEIAES
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
FUZZY IMAGE SEGMENTATION USING VALIDITY INDEXES CORRELATIONijcsit
This paper introduces an algorithm for image segmentation using a clustering technique; the technique is
based on the fuzzy c means algorithm (FCM) that is executed iteratively with different number of clusters.
Furthermore, simultaneously five validity indexes are calculated and their information is correlated to
determine the optimal number of clusters in order to segment an image, results and simulations are shown
in the paper.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Particle Swarm Optimization for Nano-Particles Extraction from Supporting Mat...CSCJournals
Metallic and non-metallic nano-particles have attracted much interest concerning their wide applications. Transmission electron microscopy (TEM) is the state of the art method to characterize a nano-particle with respect to size, morphology, structure, or composition. This paper presents an efficient evolutionary computational method, particle swarm optimization (PSO), for automatic segmentation of nano-particles. A threshold-based segmentation technique is applied, where image entropy is attacked as a minimization problem to specify local and global thresholds. We are concerned with reducing wrong characterization of nano-particles due to concentration of liquid solutions or supporting material within the acquired image. The obtained results are compared with manual techniques and with previous researches in this area.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Framework for Automated Association Mining Over Multiple Databasesertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-framework-for-automated-association-mining-over-multiple-databases/
Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Ve...ijaia
In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is
presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to
represent for each user’s signature model based on the prior information collected. Different from the early
works, in this paper, we use the Bayes Ying Yang machine combined with the harmony function to achieve
Automatic Model Selection(AMS) during the parameter learning for the GMMs, so that a better
approximation of the user model is assured. Experiments on a database from the First International
Signature Verification Competition(SVC 2004) confirm that this combined algorithm yields quite a
satisfactory result.
DATA MINING ATTRIBUTE SELECTION APPROACH FOR DROUGHT MODELLING: A CASE STUDY ...IJDKP
ABSTRACT
The objectives of this paper were to 1) develop an empirical method for selecting relevant attributes for modelling drought and 2) select the most relevant attribute for drought modelling and predictions in the Greater Horn of Africa (GHA). Twenty four attributes from different domain areas were used for this experimental analysis. Two attribute selection algorithms were used for the current study: Principal Component Analysis (PCA) and correlation-based attribute selection (CAS). Using the PCA and CAS algorithms, the 24 attributes were ranked by their merit value. Accordingly, 15 attributes were selected for modelling drought in GHA. The average merit values for the selected attributes ranged from 0.5 to 0.9. The methodology developed here helps to avoid the uncertainty of domain experts’ attribute selection
challenges, which are unsystematic and dominated by somewhat arbitrary trial. Future research may evaluate the developed methodology using relevant classification techniques and quantify the actual information gain from the developed approach.
Reconstruction of Time Series using Optimal Ordering of ICA Componentsrahulmonikasharma
We investigate the application of Independent Component Analysis (ICA) and the process of optimal ordering of independent components in reconstructing time series generated by mixed independent sources. We use a modified fast neural learning ICA algorithm with a non-linearity dependent on the statistical properties of the observed time series to obtain independent components (IC’s). Experimental results are presented on the reconstruction of both artificial time series and actual time series of currency exchange rates using different error measures. The area of the error profile is introduced as a minimizing parameter to obtain optimal ordered lists of IC’s for the different series. We compare different error measures and different algorithms for determining optimal ordering lists. Our results support the use of an Euclidean error measure for evaluating reconstruction errors and are in favor of a method for obtaining optimal ordering lists based on minimizing the error profile between contributions of independent components in the lists and the observed time series. For the majority of the series considered, we find that quite acceptable reconstructions can be obtained with only the first few dominant IC’s in the lists.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
PARTICLE SWARM OPTIMIZATION FOR MULTIDIMENSIONAL CLUSTERING OF NATURAL LANGUA...IAEME Publication
Consider a non-linear dimensionality reduction method which takes into account
the discriminating power of the solution found for given values of the categorical
variable associated with each observation. Stochastic optimization method known as
the "Particle swarm optimization" is proposed to found characteristics that ensure the
best separation of observations in terms of a given quality functional. The basis for
evaluating the quality of the solution lies in the purity of the clusters obtained with the
k-means method, or with using self-organizing Kohonen feature maps.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICL...IJITCA Journal
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model. Our proposed approach controls deformation of target's model. If deformation of target's model is larger than a predetermined threshold, then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF). DDPF approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target's model. However, DDPF approach updates target's model when the rotation or scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficientlyand accurately.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
FUZZY IMAGE SEGMENTATION USING VALIDITY INDEXES CORRELATIONijcsit
This paper introduces an algorithm for image segmentation using a clustering technique; the technique is
based on the fuzzy c means algorithm (FCM) that is executed iteratively with different number of clusters.
Furthermore, simultaneously five validity indexes are calculated and their information is correlated to
determine the optimal number of clusters in order to segment an image, results and simulations are shown
in the paper.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Particle Swarm Optimization for Nano-Particles Extraction from Supporting Mat...CSCJournals
Metallic and non-metallic nano-particles have attracted much interest concerning their wide applications. Transmission electron microscopy (TEM) is the state of the art method to characterize a nano-particle with respect to size, morphology, structure, or composition. This paper presents an efficient evolutionary computational method, particle swarm optimization (PSO), for automatic segmentation of nano-particles. A threshold-based segmentation technique is applied, where image entropy is attacked as a minimization problem to specify local and global thresholds. We are concerned with reducing wrong characterization of nano-particles due to concentration of liquid solutions or supporting material within the acquired image. The obtained results are compared with manual techniques and with previous researches in this area.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Framework for Automated Association Mining Over Multiple Databasesertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-framework-for-automated-association-mining-over-multiple-databases/
Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
The Application Of Bayes Ying-Yang Harmony Based Gmms In On-Line Signature Ve...ijaia
In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is
presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to
represent for each user’s signature model based on the prior information collected. Different from the early
works, in this paper, we use the Bayes Ying Yang machine combined with the harmony function to achieve
Automatic Model Selection(AMS) during the parameter learning for the GMMs, so that a better
approximation of the user model is assured. Experiments on a database from the First International
Signature Verification Competition(SVC 2004) confirm that this combined algorithm yields quite a
satisfactory result.
DATA MINING ATTRIBUTE SELECTION APPROACH FOR DROUGHT MODELLING: A CASE STUDY ...IJDKP
ABSTRACT
The objectives of this paper were to 1) develop an empirical method for selecting relevant attributes for modelling drought and 2) select the most relevant attribute for drought modelling and predictions in the Greater Horn of Africa (GHA). Twenty four attributes from different domain areas were used for this experimental analysis. Two attribute selection algorithms were used for the current study: Principal Component Analysis (PCA) and correlation-based attribute selection (CAS). Using the PCA and CAS algorithms, the 24 attributes were ranked by their merit value. Accordingly, 15 attributes were selected for modelling drought in GHA. The average merit values for the selected attributes ranged from 0.5 to 0.9. The methodology developed here helps to avoid the uncertainty of domain experts’ attribute selection
challenges, which are unsystematic and dominated by somewhat arbitrary trial. Future research may evaluate the developed methodology using relevant classification techniques and quantify the actual information gain from the developed approach.
Reconstruction of Time Series using Optimal Ordering of ICA Componentsrahulmonikasharma
We investigate the application of Independent Component Analysis (ICA) and the process of optimal ordering of independent components in reconstructing time series generated by mixed independent sources. We use a modified fast neural learning ICA algorithm with a non-linearity dependent on the statistical properties of the observed time series to obtain independent components (IC’s). Experimental results are presented on the reconstruction of both artificial time series and actual time series of currency exchange rates using different error measures. The area of the error profile is introduced as a minimizing parameter to obtain optimal ordered lists of IC’s for the different series. We compare different error measures and different algorithms for determining optimal ordering lists. Our results support the use of an Euclidean error measure for evaluating reconstruction errors and are in favor of a method for obtaining optimal ordering lists based on minimizing the error profile between contributions of independent components in the lists and the observed time series. For the majority of the series considered, we find that quite acceptable reconstructions can be obtained with only the first few dominant IC’s in the lists.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
PARTICLE SWARM OPTIMIZATION FOR MULTIDIMENSIONAL CLUSTERING OF NATURAL LANGUA...IAEME Publication
Consider a non-linear dimensionality reduction method which takes into account
the discriminating power of the solution found for given values of the categorical
variable associated with each observation. Stochastic optimization method known as
the "Particle swarm optimization" is proposed to found characteristics that ensure the
best separation of observations in terms of a given quality functional. The basis for
evaluating the quality of the solution lies in the purity of the clusters obtained with the
k-means method, or with using self-organizing Kohonen feature maps.
APPLYING DYNAMIC MODEL FOR MULTIPLE MANOEUVRING TARGET TRACKING USING PARTICL...IJITCA Journal
In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model. Our proposed approach controls deformation of target's model. If deformation of target's model is larger than a predetermined threshold, then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF). DDPF approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target's model. However, DDPF approach updates target's model when the rotation or scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficientlyand accurately.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Increasing electrical grid stability classification performance using ensemb...IJECEIAES
The increasing demand for electricity every year makes the electricity infrastructure approach the maximum threshold value, thus affecting the stability of the electricity network. The decentralized smart grid control (DSGC) system has succeeded in maintaining the stability of the electricity network with various assumptions. The data mining approach on the DSGC system shows that the decision tree algorithm provides new knowledge, however, its performance is not yet optimal. This paper poses an ensemble bagging algorithm to reinforce the performance of decision trees C4.5 and classification and regression trees (CART). To evaluate the classification performance, 10-fold cross-validation was used on the grid data. The results showed that the ensemble bagging algorithm succeeded in increasing the performance of both methods in terms of accuracy by 5.6% for C4.5 and 5.3% for CART.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Intelligent fault diagnosis for power distribution systemcomparative studiesnooriasukmaningtyas
Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-toground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double lineto-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
SENSITIVITY ANALYSIS IN A LIDARCAMERA CALIBRATIONcscpconf
In this paper, variability analysis was performed on the model calibration methodology between
a multi-camera system and a LiDAR laser sensor (Light Detection and Ranging). Both sensors
are used to digitize urban environments. A practical and complete methodology is presented to
predict the error propagation inside the LiDAR-camera calibration. We perform a sensitivity
analysis in a local and global way. The local approach analyses the output variance with
respect to the input, only one parameter is varied at once. In the global sensitivity approach, all
parameters are varied simultaneously and sensitivity indexes are calculated on the total
variation range of the input parameters. We quantify the uncertainty behaviour in the intrinsic
camera parameters and the relationship between the noisy data of both sensors and their
calibration. We calculated the sensitivity indexes by two techniques, Sobol and FAST (Fourier
amplitude sensitivity test). Statistics of the sensitivity analysis are displayed for each sensor, the
sensitivity ratio in laser-camera calibration data
Sensitivity analysis in a lidar camera calibrationcsandit
In this paper, variability analysis was performed o
n the model calibration methodology between
a multi-camera system and a LiDAR laser sensor (Lig
ht Detection and Ranging). Both sensors
are used to digitize urban environments. A practica
l and complete methodology is presented to
predict the error propagation inside the LiDAR-came
ra calibration. We perform a sensitivity
analysis in a local and global way. The local appro
ach analyses the output variance with
respect to the input, only one parameter is varied
at once. In the global sensitivity approach, all
parameters are varied simultaneously and sensitivit
y indexes are calculated on the total
variation range of the input parameters. We quantif
y the uncertainty behaviour in the intrinsic
camera parameters and the relationship between the
noisy data of both sensors and their
calibration. We calculated the sensitivity indexes
by two techniques, Sobol and FAST (Fourier
amplitude sensitivity test). Statistics of the sens
itivity analysis are displayed for each sensor, the
sensitivity ratio in laser-camera calibration data
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
BPSO&1-NN algorithm-based variable selection for power system stability identification
1. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-6, Issue-9, Sep-2020]
https://dx.doi.org/10.22161/ijaems.69.3 ISSN: 2454-1311
www.ijaems.com Page | 432
BPSO&1-NN algorithm-based variable selection
for power system stability identification
N. N. Au, V. V. Cuong, T. T. Giang, L. T. Nghia, T. V. Hien
Faculty of Electrical and Electronics Engineering, HCMC University of Technology and Education, Ho Chi Minh City, Viet Nam
Abstract— Due to the very high nonlinearity of the power system, traditional analytical methods take a lot
of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability
helps the control system to make timely decisions become the key factor to ensure stable operation of the
power system. Power system stability identification encounters large data set size problem. The need is to
select representative variables as input variables for the identifier. This paper proposes to apply wrapper
method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines
with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on
IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high
accuracy.
Keywords— Identification, Power system, BPSO, Variable selection, K-NN.
I. INTRODUCTION
Modern power systems suffer from operating pressure
very close to a stable boundary limit, while power systems
are always faced with abnormal agitation that can easily
cause instability (Makarov, Reshetov, Stroev, & Voropai,
2005). Due to the very high nonlinearity of the power
system, traditional analytical methods take a lot of time to
solve, causing delay in decision-making. Therefore,
quickly detecting power system instability helps the
control system to make timely decisions become the key
factor to ensure stable operation of the power system.
In recent years, the identification method has been applied
as an alternative to solving difficult problems that
traditional methods of analysis cannot solve in terms of
computational (Dong, Rui, & Xu, 2012; Mitra, Benidris,
Nguyen, & Deb, 2017; Oliveira, Vieira, Bezerra, Martins,
& Rodrigues, 2017; Zhang, Xu, Dong, Meng, & Xu,
2011). By learning the database, the nonlinear input/output
relationship between the electrical system operating
parameters and stability can be quickly calculated.
However, if the identifier works fast, the number of input
variables must be characteristic variables. Therefore, the
selected variables must be characteristic, eliminate
redundant variables and cause noise.
The selection of variables of previously published works
mainly applies the filter method that has to go through
many stages (Dong et al., 2012; Kalyani & Swarup, 2013;
Swarup, 2008). With this method, the first is based on the
statistical criteria to filter out the good variables first, then
ask the identifier to evaluate the accuracy to find the
appropriate set of variables. This article recommends a
wrapper method to select variables. In which, BPSO
(Binary Particle Swarm Optimization) algorithm combined
with K-NN (K=1) identifier to perform the process of
searching for good set of variables. It is named BPSO&1-
NN. This helps to reduce the step of finding the variable
set compared to the filter method and improves the
automation of the process of finding the variable set. Test
results on IEEE 39-bus diagram show that the proposed
method achieves the goal of variable reduction with high
accuracy.
II. VARIABLE SELECTION
In the design stages of a stable diagnostic model of the
power system using the identification method, the
selection of characteristic variables is very important
because it directly affects the diagnostic accuracy of the
model. This also helps to reduce the number of
measurement sensors, reducing computation time for the
model. This step defines a specific set of variables that
represent the database for training. These initial
characteristic variables are the input variable representing
the operating parameters of the power system and covering
the operating status of the power system. The
characteristic variable of the power system in transient
mode or dynamic mode is the change in generator
2. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-6, Issue-9, Sep-2020]
https://dx.doi.org/10.22161/ijaems.69.3 ISSN: 2454-1311
www.ijaems.com Page | 433
capacity, change of load capacity, change of power on
transmission lines, voltage drop at nodes, ... right at the
time of the incident. The output variable is labeled binary.
‘0’ is unstable and ‘1’ is stable.
Filter method:
The Filter method applies the search algorithm with the
statistical standard function such as Fisher, Divergence, ...
to guide the search strategy for the best set of variables to
achieve the target value (J) (Dong et al., 2012; Kalyani &
Swarup, 2013; Swarup, 2008). The process of searching
for the set of variables by the filter method is presented as
Figure 1. This method shows that the result is a set of
variables meeting statistical standards but does not show
the identification accuracy and the number of necessary
variables. Therefore, the filter method needs to add an
identification accuracy assessment to select the last set of
variables.
Fig. 1: Diagram of Filter method
Wrapper method:
Fig. 2: Diagram of Wrapper method
The Wrapper method is presented as in Figure 2. However,
this method differs from the Filter method in that the target
function considering the identity accuracy. Thus, this
method results in a set of variables to be selected with a
specific identification accuracy.
The fitness function for selecting variables is presented in
formulas (1), (2), and (3).
ErrsF
Fitness
TotalF sF
=
−
(1)
(%) (%)
AccSample
AccsF
TotalSample
=
(2)
(%) 1ErrsF AccsF= − (3)
where:
Finess is the objective function.
AccsF is the identification accuracy of the selected set
of variables.
AccSample is the number of the correct identifier.
TotalSample is the total number of samples.
TotalF: the total number of variables in the data set.
sF is the number of selected variables.
ErrsF is the identification error of the selected variable.
III. BPSO ALGORITHM IN VARIABLE
SELECTION
PSO (Particle Swarm Optimization) is the optimal search
algorithm proposed by Kennedy and Eberhart (Kennedy &
Eberhart, 1995). In the PSO, each candidate answer of the
problem is encoded as an instance that moves through the
search space. The whole herd seeks the optimal solution by
updating the position of each individual based on their
own experience and on neighboring individuals.
Generally, the vector xi = (xi1, xi2, ..., xiD) used in the PSO
represents the position of the i th instance. The vector vi =
(vi1, vi2,…, viD) is used in the PSO to represent the velocity
of the i th instance. D is the size of the search space.
During the search, the best position for each previous
individual was recorded as pbest. The best location of the
herd is the gbest. The herd was randomly generated from
the population. Finding the best solution by updating the
velocity and position of each individual according to
equations (4) and (5).
1 1t t t
id id idx x v+ +
= +
(4)
1
1 1
2 2
* *(p )
*(p )
t t t
id id i id id
t
i gd id
v w v c r x
c r x
+
= + − +
+ −
(5)
Where: t is the t th iteration of the search process. d is the
size in the search space, dD. c1, c2 are acceleration
constants. r1i, r2i are random values, valid in the range
[0,1]. pid and pgd represent pbest and gbest particles of size
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d. w is the inertial weight. is the velocity, limited to the
maximum velocity vmax,
t
idv [-vmax, vmax].
The original PSO algorithm applied to the problem of
continuity. Kennedy and Eberhart developed the BPSO
algorithm for the discrete problem (Kennedy & Eberhart,
1997). The velocity in BPSO represents the element that
can take the value 1. Equation (4) is still used to update the
velocity while xid, pid get the value 0 or 1. The function
sigmoid s(vid) is used to convert the value of vid into a
range of values (0,1). The BPSO updates each instance's
position using equations (6) and (7).
1,if () ( )
0,
id
id
rand s v
x
otherwise
=
(6)
1
( )
1 id
id v
s v
e−
=
+
(7)
The function rand() is a random function whose value is
distributed in (0,1).
BPSO
Begin
Data set, kfold; D: dimensionality of search space
N : the population size; T : maximum iterations;
c1, c2, vmax, w
Randomly initialise the position and velocity of each
particle;
while t ≤T
evaluate fitness of each particle according to
Equation (1);
for i=1 to N
update the pbest of particle i;
update the gbest of particle i;
end
for i=1 to N
for d=1 to D
update the velocity of particle i according to
Equation (4);
update the position of particle i according to
Equations (6) and (7);
end
end
end
calculate the classification error of the selected feature
subset;
return the position of gbest (the selected feature
subset);
return the classification error of the selected feature
subset;
end
IV. RESULTS
Features and samples:
The study was tested on the IEEE 39-bus scheme. It
includes 39 buses, 19 loads, 10 generators. The diagram
IEEE 39-bus scheme is shown as Fig. 2. It was used in
many published works. The off-line simulation was
implemented to collect data for training. Load levels are
(20,30,…,120)% normal load. The setting fault clearing
time (FCT) is 50ms (Glover, Sarma, & Overbye, 2012). In
this paper, all kinds of faults such as single phase to
ground, double phase to ground, three phases to ground
and phase-to-phase short-circuit are considered. Faults are
tested in any buses and in each of 5% distances of long
transmission lines of the test systems. For each of the
considered load samples, the generator samples have been
got accordingly by running optimal power flow (OPF) tool
of Power-World software.
The input and output feature are
x{delVbus,delPLoad,delPflow} and y{1,0}. Total of input
features is 104, x{104(39+19+46)}. The number of output
feature is one, y{1,0}. From simulating results, there are
1167 samples that include 834 S samples and 783 U
samples. The K-NN (K=1) is used as a classifier for
evaluating accurate classification because of its simplicity.
Assessment of classification error is a cross-assessment
method. It is a 10-fold cross-validation.
Fig. 2: The IEEE 39-bus diagram
Feature selection results:
The BPSO&1-NN algorithm works with different N
values, namely 10, 20, 30, 40, and 50. The values of w are
4. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-6, Issue-9, Sep-2020]
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0.4, 0.5, 0.6, 0.7, 0.8, and 0.9. The number of iterations is
100 for the program executions, T=100. The values of c1
and c2 are selected unchanged during program execution,
c1=2, c2=2. The program is also executed on Matlab 2018a
software. Identification tool, 1-NN, is supported from this
software.
The convergence characteristics of BPSO&1-NN variable
selection algorithm are shown as Fig. 3 to Fig. 8. The
results of the selected number of variables, the value of the
best fitness function and the test error are presented in
Table 1.
Table 1. Best results during the implementation of BPSO
program with w = 0.9, N = 50, the number of variables in
the set of variables is 23, the identification error is 5.07%.
Thus, the algorithm BPSO&1-1NN has reduced the
number of variables from 104 to 23 variables. The number
of variables is reduced by 78% while test accuracy reaches
94.93%. This is the accepted result in previously published
(Abdelaziz & El-Dessouki, 2013; Amjady & Majedi,
2007; Dong et al., 2012; Haidar, Mustafa, Ibrahim, &
Ahmed, 2011; Kalyani & Swarup, 2013; Karami &
Esmaili, 2013).
Table 1. Results of BPSO algorithm
w N f Best Fitness ErrsF(%)
0.4 10 32 7.3868e-04 5,07
20 32 7.2150e-04 5,07
30 33 7.3166e-04 5,01
40 30 6.7693e-04 5,13
50 37 7.1073e-04 5,01
0.5 10 33 7.1424e-04 5,19
20 32 6.9573e-04 5,26
30 33 7.0553e-04 5,07
40 34 7.2445e-04 5,07
50 31 7.3703e-04 5,26
0.6 10 33 7.2295e-04 4,95
20 32 7.2150e-04 5,19
30 36 7.3666e-04 5,01
40 33 6.5327e-04 4,89
50 36 6.9119e-04 5,13
0.7 10 31 7.0315e-04 5,13
20 35 7.1702e-04 5,26
30 30 6.9364e-04 5,26
40 29 7.0089e-04 5,13
50 26 6.6600e-04 4,95
0.8 10 32 6.6138e-04 5,07
20 30 6.6021e-04 4,89
30 33 6.5327e-04 5,26
40 30 6.6857e-04 5,07
50 27 6.1843e-04 5,26
0.9 10 32 6.6996e-04 5,44
20 27 6.2646e-04 5,38
30 27 6.1040e-04 5,13
40 23 5.9552e-04 5,13
50 23 6.1079e-04 5,07
Fig. 3: Convergence characteristics of BPSO&1-NN,
w=0,4
Fig. 4: Convergence characteristics of BPSO&1-NN,
w=0,5
Fig. 5: Convergence characteristics of BPSO&1-NN,
w=0,6
5. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-6, Issue-9, Sep-2020]
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Fig. 6: Convergence characteristics of BPSO&1-NN,
w=0,7
Fig. 7: Convergence characteristics of BPSO&1-NN,
w=0,8
Fig. 8: Convergence characteristics of BPSO&1-NN,
w=0,9
V. CONCLUSION
The paper successfully applied BPSO algorithm combined
with 1-NN identifier in the variable selection approach to
the problem of identifying stability of power system. Test
results on IEEE-39 bus diagram showed that the number of
variables decreased significantly, but the test accuracy still
met expectations. This achieved result will be the driving
force for the research and application of evolutionary
algorithms to select variables for the problem of
identifying stability of power systems.
ACKNOWLEDGEMENTS
This work belongs to the project grant No: T2020-26TĐ
funded by Ho Chi Minh City University of Technology
and Education, Vietnam.
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