Search results for: Discreet%20Wavelet%20Transform

Authors: Kaziyev G. Z., Nabiyeva G. S., Kalizhanova A.U.

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We consider herein a concise view of discreet programming models and methods. There has been conducted the models and methods analysis. On the basis of discreet programming models there has been elaborated and offered a new class of problems, i.e. block-symmetry models and methods of applied tasks statements and solutions.

Keywords: Discreet programming, block-symmetry, analysis methods, information systems development.

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Authors: Janis Šliseris, Karlis Rocens

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Optimization of rational geometrical and mechanical parameters of panel with curved plywood ribs is considered in this paper. The panel consists of cylindrical plywood ribs manufactured from Finish plywood, upper and bottom plywood flange, stiffness diaphragms. Panel is filled with foam. Minimal ratio of structure self weight and load that could be applied to structure is considered as rationality criteria. Optimization is done, by using classical beam theory without nonlinearities. Optimization of discreet design variables is done by Genetic algorithm.

Keywords: Curved plywood ribs, genetic algorithm, rationalparameters of ribbed panel, structure optimization.

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Authors: Ibtihel Nouira, Asma Ben Abdallah, Ibtissem Kouaja, Mohamed Hèdi Bedoui

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The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In addition, this paper will include two main R peak detection methods by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on Dyadic Wavelet Transform DyWT.

Keywords: Derived calculation methods, Electrocardiogram, R peaks, Wavelet Transform.

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Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan

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This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed by pruning of the data set. The refined data set is then processed through various Rough Set operators which enable discovery of parameter relationships and interdependencies. The discovered knowledge is automatically transformed into a rule base expressed in Fuzzy terms. Two exemplary cancer repository datasets (for Breast and Lung Cancer) have been used to test and implement the proposed framework.

Keywords: Knowledge Extraction, Fuzzy Sets, Rough Sets, Neuro–Fuzzy Systems, Databases

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Authors: Mohamad Mahdavi

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Most of the real queuing systems include special properties and constraints, which can not be analyzed directly by using the results of solved classical queuing models. Lack of Markov chains features, unexponential patterns and service constraints, are the mentioned conditions. This paper represents an applied general algorithm for analysis and optimizing the queuing systems. The algorithm stages are described through a real case study. It is consisted of an almost completed non-Markov system with limited number of customers and capacities as well as lots of common exception of real queuing networks. Simulation is used for optimizing this system. So introduced stages over the following article include primary modeling, determining queuing system kinds, index defining, statistical analysis and goodness of fit test, validation of model and optimizing methods of system with simulation.

Keywords: Estimation, queuing system, simulation model, probability distribution, non-Markov chain.

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Authors: Lenka Ocenasova, Bartosz Gapinski, Robert Cep, Linda Gregova, Branimir Barisic, Jana Novakova, Lenka Petrkovska

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Today technological process makes possible surface control of producing parts which is needful for product quality guarantee. Geometrical structure of part surface includes form, proportion, accuracy to shape, accuracy to size, alignment and surface topography (roughness, waviness, etc.). All these parameters are dependence at technology, production machine parameters, material properties, but also at human, etc. Every parameters approves at total part accuracy, it is means at accuracy to shape. One of the most important accuracy to shape element is roundness. This paper will be deals by comparison of roughness deviations at coordination measuring machines and at special single purpose machines. Will describing measuring by discreet method (discontinuous) and scanning method (continuous) at coordination measuring machines and confrontation with reference method using at single purpose machines.

Keywords: Coordinating Measuring Machines (CMM), Measuring Strategy, Roughness Deviation, Accuracy.

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Authors: Commander Sunil Tyagi

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Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standard deviation, skewness, kurtosis etc. of the time-domain vibration signal segments along with peaks of the signal and peak of power spectral density (PSD) are used as features to input the ANN and SVM classifier. The effect of preprocessing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied. It is shown from the experimental results that the performance of SVM classifier in identification of bearing condition is better then ANN and pre-processing of vibration signal by DWT enhances the effectiveness of both ANN and SVM classifier

Keywords: ANN, Artificial Intelligence, Fault Diagnosis, Pattern Recognition, Rolling Element Bearing, SVM. Wavelet Transform

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Authors: Mamoun Sqali, Mohamed Wassim Trojet

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The requirements analysis, modeling, and simulation have consistently been one of the main challenges during the development of complex systems. The scenarios and the state machines are two successful models to describe the behavior of an interactive system. The scenarios represent examples of system execution in the form of sequences of messages exchanged between objects and are a partial view of the system. In contrast, state machines can represent the overall system behavior. The automation of processing scenarios in the state machines provide some answers to various problems such as system behavior validation and scenarios consistency checking. In this paper, we propose a method for translating scenarios in state machines represented by Discreet EVent Specification and procedure to detect implied scenarios. Each induced DEVS model represents the behavior of an object of the system. The global system behavior is described by coupling the atomic DEVS models and validated through simulation. We improve the validation process with integrating formal methods to eliminate logical inconsistencies in the global model. For that end, we use the Z notation.

Keywords: Scenarios, DEVS, synthesis, validation and verification, simulation, formal verification, z notation.

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Authors: K. G. Firouzjah, A. Sheikholeslami

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The perfect operation of common Active Filters is depended on accuracy of identification system distortion. Also, using a suitable method in current injection and reactive power compensation, leads to increased filter performance. Due to this fact, this paper presents a method based on predictive current control theory in shunt active filter applications. The harmonics of the load current is identified by using o–d–q reference frame on load current and eliminating the DC part of d–q components. Then, the rest of these components deliver to predictive current controller as a Threephase reference current by using Park inverse transformation. System is modeled in discreet time domain. The proposed method has been tested using MATLAB model for a nonlinear load (with Total Harmonic Distortion=20%). The simulation results indicate that the proposed filter leads to flowing a sinusoidal current (THD=0.15%) through the source. In addition, the results show that the filter tracks the reference current accurately.

Keywords: Active filter, predictive current control, low pass filter, harmonic omitting, o–d–q reference frame.

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Authors: Mohammed Rziza, Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali, Driss Aboutajdine

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In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA, and independent component Analysis (ICA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet-LDA algorithm. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Keywords: Curvelet, Linear Discriminant Analysis (LDA) , Contourlet, Discreet Wavelet Transform, DWT, Block-based analysis, face recognition (FR).

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