Search results for: Bayesian approach Kalman filtering approach

Authors: Tamara Nestorović, Jean Lefèvre, Stefan Ringwelski, Ulrich Gabbert

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Two approaches for model development of a smart acoustic box are suggested in this paper: the finite element (FE) approach and the subspace identification. Both approaches result in a state-space model, which can be used for obtaining the frequency responses and for the controller design. In order to validate the developed FE model and to perform the subspace identification, an experimental set-up with the acoustic box and dSPACE system was used. Experimentally obtained frequency responses show good agreement with the frequency responses obtained from the FE model and from the identified model.

Keywords: Acoustic box, experimental verification, finite element model, subspace identification.

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Authors: Krishnamoorthi Makkithaya, N. V. Subba Reddy, U. Dinesh Acharya

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As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents our work to test and improve the performance of a new class of decision tree c-fuzzy decision tree to detect intrusion. The work also includes identifying best candidate feature sub set to build the efficient c-fuzzy decision tree based Intrusion Detection System (IDS). We investigated the usefulness of c-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Empirical results indicate the usefulness of our approach in developing the efficient IDS.

Keywords: Data mining, Decision tree, Feature selection, Fuzzyc- means clustering, Intrusion detection.

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Authors: Pavlo Selyshchev

Abstract:

A theoretical approach to radiation damage evolution is developed. Stable temporal behavior taking place in solids under irradiation are examined as phenomena of self-organization in nonequilibrium systems. Experimental effects of temporal self-organization in solids under irradiation are reviewed. Their essential common properties and features are highlighted and analyzed. Dynamical model to describe development of self-oscillation of density of point defects under stationary irradiation is proposed. The emphasis is the nonlinear couplings between rate of annealing and density of defects that determine the kind and parameters of an arising self-oscillation. The field of parameters (defect generation rate and environment temperature) at which self-oscillations develop is found. Bifurcation curve and self-oscillation period near it is obtained.

Keywords: Irradiation, Point Defects, Solids, Temporal Selforganization.

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Authors: Rana Yousef, Khalil el Hindi

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The behavior of Radial Basis Function (RBF) Networks greatly depends on how the center points of the basis functions are selected. In this work we investigate the use of instance reduction techniques, originally developed to reduce the storage requirements of instance based learners, for this purpose. Five Instance-Based Reduction Techniques were used to determine the set of center points, and RBF networks were trained using these sets of centers. The performance of the RBF networks is studied in terms of classification accuracy and training time. The results obtained were compared with two Radial Basis Function Networks: RBF networks that use all instances of the training set as center points (RBF-ALL) and Probabilistic Neural Networks (PNN). The former achieves high classification accuracies and the latter requires smaller training time. Results showed that RBF networks trained using sets of centers located by noise-filtering techniques (ALLKNN and ENN) rather than pure reduction techniques produce the best results in terms of classification accuracy. The results show that these networks require smaller training time than that of RBF-ALL and higher classification accuracy than that of PNN. Thus, using ALLKNN and ENN to select center points gives better combination of classification accuracy and training time. Our experiments also show that using the reduced sets to train the networks is beneficial especially in the presence of noise in the original training sets.

Keywords: Radial basis function networks, Instance-based reduction, PNN.

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Authors: José M. Merigó, Anna M. Gil-Lafuente

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We study different types of aggregation operators such as the ordered weighted averaging (OWA) operator and the generalized OWA (GOWA) operator. We analyze the use of OWA operators in the Minkowski distance. We will call these new distance aggregation operator the Minkowski ordered weighted averaging distance (MOWAD) operator. We give a general overview of this type of generalization and study some of their main properties. We also analyze a wide range of particular cases found in this generalization such as the ordered weighted averaging distance (OWAD) operator, the Euclidean ordered weighted averaging distance (EOWAD) operator, the normalized Minkowski distance, etc. Finally, we give an illustrative example of the new approach where we can see the different results obtained by using different aggregation operators.

Keywords: Aggregation operators, Minkowski distance, OWA operators, Selection of strategies.

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Authors: N. Sefiani, L. Chraibi, K. Reklaoui, Y. Sefiani

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Currently, business environment is characterized by pressure caused by stiff competition, constant changes (e.g., product/ technological innovations, decreasing product lifecycles, and product proliferation), and a high level of market uncertainty band unpredictability. The agility of the Supply Chain Management (SCM) is clearly identified as a key factor for success and a strategic essential lever. This paper explores the impact of deploying competence management and Supply Chain Operations Reference (SCOR) model on firm performance. Our approach is based on a systemic view by considering the SCOR reference model as the heart of competence management system.

Keywords: Competence, competence management, performance, SCOR model and agility.

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Authors: C. E. Nugraheni, L. Abednego

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This paper introduces a framework based on the collaboration of multi agent and hyper-heuristics to find a solution of the real single machine production problem. There are many techniques used to solve this problem. Each of it has its own advantages and disadvantages. By the collaboration of multi agent system and hyper-heuristics, we can get more optimal solution. The hyper-heuristics approach operates on a search space of heuristics rather than directly on a search space of solutions. The proposed framework consists of some agents, i.e. problem agent, trainer agent, algorithm agent (GPHH, GAHH, and SAHH), optimizer agent, and solver agent. Some low level heuristics used in this paper are MRT, SPT, LPT, EDD, LDD, and MON

Keywords: Hyper-heuristics, multi-agent systems, scheduling problem.

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Authors: C. Lodato, S. Lopes

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The image segmentation method described in this paper has been developed as a pre-processing stage to be used in methodologies and tools for video/image indexing and retrieval by content. This method solves the problem of whole objects extraction from background and it produces images of single complete objects from videos or photos. The extracted images are used for calculating the object visual features necessary for both indexing and retrieval processes. The segmentation algorithm is based on the cooperation among an optical flow evaluation method, edge detection and region growing procedures. The optical flow estimator belongs to the class of differential methods. It permits to detect motions ranging from a fraction of a pixel to a few pixels per frame, achieving good results in presence of noise without the need of a filtering pre-processing stage and includes a specialised model for moving object detection. The first task of the presented method exploits the cues from motion analysis for moving areas detection. Objects and background are then refined using respectively edge detection and seeded region growing procedures. All the tasks are iteratively performed until objects and background are completely resolved. The method has been applied to a variety of indoor and outdoor scenes where objects of different type and shape are represented on variously textured background.

Keywords: Image Segmentation, Motion Detection, Object Extraction, Optical Flow

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Authors: Mehrnoush Shamsfard, Maryam Sadr Mousavi

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Extracting thematic (semantic) roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. In this paper we present a rule-based approach to extract semantic roles from Persian sentences. The system exploits a twophase architecture to (1) identify the arguments and (2) label them for each predicate. For the first phase we developed a rule based shallow parser to chunk Persian sentences and for the second phase we developed a knowledge-based system to assign 16 selected thematic roles to the chunks. The experimental results of testing each phase are shown at the end of the paper.

Keywords: Natural Language Processing, Semantic RoleLabeling, Shallow parsing, Thematic Roles.

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Authors: L. Zehtaban, D. Roller

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Apart from geometry, functionality is one of the most significant hallmarks of a product. The functionality of a product can be considered as the fundamental justification for a product existence. Therefore a functional analysis including a complete and reliable descriptor has a high potential to improve product development process in various fields especially in knowledge-based design. One of the important applications of the functional analysis and indexing is in retrieval and design reuse concept. More than 75% of design activity for a new product development contains reusing earlier and existing design know-how. Thus, analysis and categorization of product functions concluded by functional indexing, influences directly in design optimization. This paper elucidates and evaluates major classes for functional analysis by discussing their major methods. Moreover it is finalized by presenting a noble hybrid approach for functional analysis.

Keywords: Functional analysis, design reuse, functionalindexing and representation.

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Authors: Ho Yuan-Hong, Hunag Chiung-Ju

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This study conducts simulation analyses to find the optimal debt ceiling of Taiwan, while factoring in welfare maximization under a dynamic stochastic general equilibrium framework. The simulation is based on Taiwan's 2001 to 2011 economic data and shows that welfare is maximized at a debt/GDP ratio of 0.2, increases in the debt/GDP ratio leads to increases in both tax and interest rates and decreases in the consumption ratio and working hours. The study results indicate that the optimal debt ceiling of Taiwan is 20% of GDP, where if the debt/GDP ratio is greater than 40%, the welfare will be negative and result in welfare loss.

Keywords: Debt sustainability, optimal debt ceiling, dynamic stochastic general equilibrium, welfare maximization.

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Authors: Mohsen Shirani, Micaela Demichela

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During the last decades, a number of food crises such as Bovine Spongiform Encephalopathy (BSE), Mad-Cow disease, Dioxin in chicken food, Food-and-Mouth Disease (FMD), have certainly inflected the reliability of the food industry. Consequently, the trend in applying different scientific methods of risk assessment in food safety has obtained more attentions in the academic and practice. However, lack of practical approach considering entire food supply chain is tangible in the academic literature. In this regard, this paper aims to apply risk assessment tool (FMEA) with integration of Human Factor along the entire supply chain of food production and test the method in a case study of Diary production, and analyze its results.

Keywords: Food Risk Assessment, FMEA, Human Factor.

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Authors: Patrick Maier, Marcus Tönnis, Gudrun Klinker

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Spatial understanding and the understanding of dynamic change in the spatial structure of molecules during a reaction is essential for designing new molecules. Knowing the physical processes in the reactions helps to speed up the designing process. To support the designer with the correct representation of the designed molecule as well as showing the dynamic behavior of the whole reacting system is the goal of our application. Our system shows the spatial deformation of the molecules at every time interval by minimizing the energy level of the molecules. The position and orientation of the molecules can be intuitively controlled by manipulating objects of the real world using Augmented Reality techniques. Our approach has the potential to speed up the design of new molecules and help students to understand the chemical processes better.

Keywords: Augmented Augmented Chemical Reactions, Augmented Reality, chemistry, education.

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Authors: Sharjeel Aslam, Vassil Vassilev, Karim Ouazzane

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Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.

Keywords: Distributed ontologies, parallel querying, semantic indexing, shared vocabulary, SPARQL.

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Authors: Xiaowei Zhang, Min Xu, Saeid Habibi, Fengjun Yan, Ryan Ahmed

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Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: Lithium-Ion batteries, genetic algorithm optimization, battery aging test, and parameter identification.

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Authors: Neveen I. Ghali, Nahed El-Dessouki, Mervat A. N., Lamiaa Bakrawi

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In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm.

Keywords: Particle swarm optimization, data clustering, exponential PSO.

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Authors: Yu Wang

Abstract:

Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided.

Keywords: Online opinion leaders, user attributes analysis, text mining analysis, network structure analysis.

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Authors: S. Dorbani, M. Badaoui, D. Benouar

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The goal of the paper is to highlight the effect of the building design and epicentral distance on the storey lateral displacements, for several reinforced concrete buildings (6, 9 and 12 stories). These structures are subjected to seismic accelerations from the Boumerdes earthquake (Algeria, May 21st, Mw = 6.8). Using the response spectrum method (modal spectral approach), the analysis is performed in both longitudinal and transverse directions. The building design is expressed through the fundamental period and epicentral distance is used to represent the earthquake effect variation on storey lateral displacements and interstory drift for the considered buildings.

Keywords: Epicentral distance, interstory drift, lateral displacement, natural period, reinforced concrete buildings.

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Authors: Farag Azzedin, Ahmad Ridha, Ali Rizvi

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Trust management is one of the drawbacks in Peer-to-Peer (P2P) system. Lack of centralized control makes it difficult to control the behavior of the peers. Reputation system is one approach to provide trust assessment in P2P system. In this paper, we use fuzzy logic to model trust in a P2P environment. Our trust model combines first-hand (direct experience) and second-hand (reputation)information to allow peers to represent and reason with uncertainty regarding other peers' trustworthiness. Fuzzy logic can help in handling the imprecise nature and uncertainty of trust. Linguistic labels are used to enable peers assign a trust level intuitively. Our fuzzy trust model is flexible such that inference rules are used to weight first-hand and second-hand accordingly.

Keywords: P2P Systems; Trust, Reputation, Fuzzy Logic.

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Authors: Yerbol Sapargaliyev, Tatiana Kalganova

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Evolvable Hardware (EHW) has been regarded as adaptive system acquired by wide application market. Consumer market of any good requires diversity to satisfy consumers- preferences. Adaptation of EHW is a key technology that could provide individual approach to every particular user. This situation raises a question: how to set target for evolutionary algorithm? The existing techniques do not allow consumer to influence evolutionary process. Only designer at the moment is capable to influence the evolution. The proposed consumer-triggered evolution overcomes this problem by introducing new features to EHW that help adaptive system to obtain targets during consumer stage. Classification of EHW is given according to responsiveness, imitation of human behavior and target circuit response. Home intelligent water heating system is considered as an example.

Keywords: Actuators, consumer-triggered evolution, evolvable hardware, sensors.

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Authors: H. Abderrezek, M. N. Harmas

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DC-DC converters are widely used as reliable power source for many industrial and military applications, computers and electronic devices. Several control methods were developed for DC-DC converters control mostly with asymptotic convergence. Synergetic control (SC) is a proven robust control approach and will be used here in a so called terminal scheme to achieve finite time convergence. Lyapounov synthesis is adopted to assure controlled system stability. Furthermore particle swarm optimization (PSO) algorithm, based on an integral time absolute of error (ITAE) criterion will be used to optimize controller parameters. Simulation of terminal synergetic control of a DC-DC converter is carried out for different operating conditions and results are compared to classic synergetic control performance, that which demonstrate the effectiveness and feasibility of the proposed control method.

Keywords: DC-DC converter, PSO, finite time, terminal, synergetic control.

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Authors: Jaechun No

Abstract:

As data to be stored in storage subsystems tremendously increases, data protection techniques have become more important than ever, to provide data availability and reliability. In this paper, we present the file system-based data protection (WOWSnap) that has been implemented using WORM (Write-Once-Read-Many) scheme. In the WOWSnap, once WORM files have been created, only the privileged read requests to them are allowed to protect data against any intentional/accidental intrusions. Furthermore, all WORM files are related to their protection cycle that is a time period during which WORM files should securely be protected. Once their protection cycle is expired, the WORM files are automatically moved to the general-purpose data section without any user interference. This prevents the WORM data section from being consumed by unnecessary files. We evaluated the performance of WOWSnap on Linux cluster.

Keywords: Data protection, Protection cycle, WORM

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Authors: Firas Gerges, Frank Y. Shih

Abstract:

Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: Deep learning, skin cancer, image processing, melanoma.

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Authors: Norwati Mustapha, Manijeh Jalali, Abolghasem Bozorgniya, Mehrdad Jalali

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Web usage mining algorithms have been widely utilized for modeling user web navigation behavior. In this study we advance a model for mining of user-s navigation pattern. The model makes user model based on expectation-maximization (EM) algorithm.An EM algorithm is used in statistics for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. The experimental results represent that by decreasing the number of clusters, the log likelihood converges toward lower values and probability of the largest cluster will be decreased while the number of the clusters increases in each treatment.

Keywords: Web Usage Mining, Expectation maximization, navigation pattern mining.

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Authors: Alexandra Oliveros, Miguel Sotaquirá

Abstract:

DNA microarray technology is widely used by geneticists to diagnose or treat diseases through gene expression. This technology is based on the hybridization of a tissue-s DNA sequence into a substrate and the further analysis of the image formed by the thousands of genes in the DNA as green, red or yellow spots. The process of DNA microarray image analysis involves finding the location of the spots and the quantification of the expression level of these. In this paper, a tool to perform DNA microarray image analysis is presented, including a spot addressing method based on the image projections, the spot segmentation through contour based segmentation and the extraction of relevant information due to gene expression.

Keywords: Contour segmentation, DNA microarrays, edge detection, image processing, segmentation, spot addressing.

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Authors: A. V. Varfolomeeva, N. S. Tkachenko, A. G. Tishchenko

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In this study, we addressed the problem of weak validity, implausible results, and inaccurate reporting in psychological research on different species. The theoretical basis of the study was the systems-evolutionary approach (SEA). We assumed that the root of the problem is the values and attitudes of the researchers (in particular anthropomorphism and anthropocentrism). The first aim of the study was the formulation of a research design that avoids this problem. Based on a literature review, we concluded that such design, amongst other things, should include methodics with playful components. The second aim was to conduct a series of studies on the differences in the formation of instrumental skill in rats raised and housed in different environments. As a result, we revealed that there are contradictions between some of the statements of SEA, so that it is not possible to choose one of the alternative hypotheses. We suggested that in order to get out of this problem, it is necessary to modify these provisions by aligning them with the attitude of multicentrism.

Keywords: epistemological attitudes, experimental design, validity, psychological structure, learning

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Authors: M. Hemam, M. Djezzar, T. Djouad

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We are interested in the problem of building an ontology in a heterogeneous organization, by taking into account different viewpoints and different terminologies of communities in the organization. Such ontology, that we call multi-viewpoint ontology, confers to the same universe of discourse, several partial descriptions, where each one is relative to a particular viewpoint. In addition, these partial descriptions share at global level, ontological elements constituent a consensus between the various viewpoints. In order to provide response elements to this problem we define a multi-viewpoints knowledge model based on viewpoint and ontology notions. The multi-viewpoints knowledge model is used to formalize the multi-viewpoints ontology in description logics language.

Keywords: Description logic, knowledge engineering, ontology, viewpoint.

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Authors: Masoud Hekmatpanah, Mohammad Sadroddin, Saeid Shahbaz, Farhad Mokhtari, Farahnaz Fadavinia

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The six sigma method is a project-driven management approach to improve the organization-s products, services, and processes by continually reducing defects in the organization. Understanding the key features, obstacles, and shortcomings of the six sigma method allows organizations to better support their strategic directions, and increasing needs for coaching, mentoring, and training. It also provides opportunities to better implement six sigma projects. The purpose of this paper is the survey of six sigma process and its impact on the organizational productivity. So I have studied key concepts , problem solving process of six sigmaas well as the survey of important fields such as: DMAIC, six sigma and productivity applied programme, and other advantages of six sigma. In the end of this paper, present research conclusions. (direct and positive relation between six sigma and productivity)

Keywords: Six sigma, project management, quality, theory, productivity.

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Authors: Ahmad Rabanimotlagh

Abstract:

In this paper an ant colony optimization algorithm is developed to solve the permutation flow shop scheduling problem. In the permutation flow shop scheduling problem which has been vastly studied in the literature, there are a set of m machines and a set of n jobs. All the jobs are processed on all the machines and the sequence of jobs being processed is the same on all the machines. Here this problem is optimized considering two criteria, makespan and total flow time. Then the results are compared with the ones obtained by previously developed algorithms. Finally it is visible that our proposed approach performs best among all other algorithms in the literature.

Keywords: Scheduling, Flow shop, Ant colony optimization, Makespan, Flow time

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Authors: M. Mordjaoui, B. Boudjema, M. Chabane, R. Daira

Abstract:

In determining the electromagnetic properties of magnetic materials, hysteresis modeling is of high importance. Many models are available to investigate those characteristics but they tend to be complex and difficult to implement. In this paper a new qualitative hysteresis model for ferromagnetic core is presented, based on the function approximation capabilities of adaptive neuro fuzzy inference system (ANFIS). The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach can restored the hysteresis curve with a little RMS error. The model accuracy is good and can be easily adapted to the requirements of the application by extending or reducing the network training set and thus the required amount of measurement data.

Keywords: ANFIS modeling technique, magnetic hysteresis, Jiles-Atherton model, ferromagnetic core.

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