Search results for: RSA algorithm.
804 Analysis of Diverse Cluster Ensemble Techniques
Authors: S. Sarumathi, N. Shanthi, P. Ranjetha
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Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.Keywords: Cluster Ensemble, Consensus Function, CSPA, Diversity, HGPA, MCLA.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1820803 A Survey: Clustering Ensembles Techniques
Authors: Reza Ghaemi , Md. Nasir Sulaiman , Hamidah Ibrahim , Norwati Mustapha
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The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.Keywords: Clustering Ensembles, Combinational Algorithm, Consensus Function, Unsupervised Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3416802 A Generic Approach to Achieve Optimal Server Consolidation by Using Existing Servers in Virtualized Data Center
Authors: Siyuan Jing, Kun She
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Virtualization-based server consolidation has been proven to be an ideal technique to solve the server sprawl problem by consolidating multiple virtualized servers onto a few physical servers leading to improved resource utilization and return on investment. In this paper, we solve this problem by using existing servers, which are heterogeneous and diversely preferred by IT managers. Five practical consolidation rules are introduced, and a decision model is proposed to optimally allocate source services to physical target servers while maximizing the average resource utilization and preference value. Our model can be regarded as a multi-objective multi-dimension bin-packing (MOMDBP) problem with constraints, which is strongly NP-hard. An improved grouping generic algorithm (GGA) is introduced for the problem. Extensive simulations were performed and the results are given.Keywords: GGA-based Heuristics, Preference, Real-worldConstraints, Resource Utilization, Server Consolidation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1629801 Novel Direct Flux and Torque Control of Optimally Designed 6 Phase Reluctance Machine with Special Current Waveform
Authors: E T. Rakgati, E. Matlotse
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In this paper the principle, basic torque theory and design optimisation of a six-phase reluctance dc machine are considered. A trapezoidal phase current waveform for the machine drive is proposed and evaluated to minimise ripple torque. Low cost normal laminated salient-pole rotors with and without slits and chamfered poles are investigated. The six-phase machine is optimised in multi-dimensions by linking the finite-element analysis method directly with an optimisation algorithm; the objective function is to maximise the torque per copper losses of the machine. The armature reaction effect is investigated in detail and found to be severe. The measured and calculated torque performances of a 35 kW optimum designed six-phase reluctance dc machine drive are presented.
Keywords: Reluctance dc machine, current waveform, design optimisation, finite element analysis, armature reaction effect.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1716800 A Distributed Approach to Extract High Utility Itemsets from XML Data
Authors: S. Kannimuthu, K. Premalatha
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This paper investigates a new data mining capability that entails mining of High Utility Itemsets (HUI) in a distributed environment. Existing research in data mining deals with only presence or absence of an items and do not consider the semantic measures like weight or cost of the items. Thus, HUI mining algorithm has evolved. HUI mining is the one kind of utility mining concept, aims to identify itemsets whose utility satisfies a given threshold. Although, the approach of mining HUIs in a distributed environment and mining of the same from XML data have not explored yet. In this work, a novel approach is proposed to mine HUIs from the XML based data in a distributed environment. This work utilizes Service Oriented Computing (SOC) paradigm which provides Knowledge as a Service (KaaS). The interesting patterns are provided via the web services with the help of knowledge server to answer the queries of the consumers. The performance of the approach is evaluated on various databases using execution time and memory consumption.
Keywords: Data mining, Knowledge as a Service, service oriented computing, utility mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2442799 A Combination of Similarity Ranking and Time for Social Research Paper Searching
Authors: P. Jomsri
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Nowadays social media are important tools for web resource discovery. The performance and capabilities of web searches are vital, especially search results from social research paper bookmarking. This paper proposes a new algorithm for ranking method that is a combination of similarity ranking with paper posted time or CSTRank. The paper posted time is static ranking for improving search results. For this particular study, the paper posted time is combined with similarity ranking to produce a better ranking than other methods such as similarity ranking or SimRank. The retrieval performance of combination rankings is evaluated using mean values of NDCG. The evaluation in the experiments implies that the chosen CSTRank ranking by using weight score at ratio 90:10 can improve the efficiency of research paper searching on social bookmarking websites.Keywords: combination ranking, information retrieval, time, similarity ranking, static ranking, weight score
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1648798 A New Technique for Solar Activity Forecasting Using Recurrent Elman Networks
Authors: Salvatore Marra, Francesco C. Morabito
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In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Keywords: Elman neural networks, sunspot, solar activity, time series prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1844797 MIMO Broadcast Scheduling for Weighted Sum-rate Maximization
Authors: Swadhin Kumar Mishra, Sidhartha Panda, C. Ardil
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Multiple-Input-Multiple-Output (MIMO) is one of the most important communication techniques that allow wireless systems to achieve higher data rate. To overcome the practical difficulties in implementing Dirty Paper Coding (DPC), various suboptimal MIMO Broadcast (MIMO-BC) scheduling algorithms are employed which choose the best set of users among all the users. In this paper we discuss such a sub-optimal MIMO-BC scheduling algorithm which employs antenna selection at the receiver side. The channels for the users considered here are not Identical and Independent Distributed (IID) so that users at the receiver side do not get equal opportunity for communication. So we introduce a method of applying weights to channels of the users which are not IID in such a way that each of the users gets equal opportunity for communication. The effect of weights on overall sum-rate achieved by the system has been investigated and presented.
Keywords: Antenna selection, Identical and Independent Distributed (IID), Sum-rate capacity, Weighted sum rate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1578796 Portfolio Management: A Fuzzy Set Based Approach to Monitoring Size to Maximize Return and Minimize Risk
Authors: Margaret F. Shipley
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Fuzzy logic can be used when knowledge is incomplete or when ambiguity of data exists. The purpose of this paper is to propose a proactive fuzzy set- based model for reacting to the risk inherent in investment activities relative to a complete view of portfolio management. Fuzzy rules are given where, depending on the antecedents, the portfolio size may be slightly or significantly decreased or increased. The decision maker considers acceptable bounds on the proportion of acceptable risk and return. The Fuzzy Controller model allows learning to be achieved as 1) the firing strength of each rule is measured, 2) fuzzy output allows rules to be updated, and 3) new actions are recommended as the system continues to loop. An extension is given to the fuzzy controller that evaluates potential financial loss before adjusting the portfolio. An application is presented that illustrates the algorithm and extension developed in the paper.Keywords: Portfolio Management, Financial Market Monitoring, Fuzzy Controller, Fuzzy Logic,
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1834795 Avoiding Catastrophic Forgetting by a Dual-Network Memory Model Using a Chaotic Neural Network
Authors: Motonobu Hattori
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In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.
Keywords: catastrophic forgetting, chaotic neural network, complementary learning systems, dual-network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2078794 Using a Semantic Self-Organising Web Page-Ranking Mechanism for Public Administration and Education
Authors: Marios Poulos, Sozon Papavlasopoulos, V. S. Belesiotis
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In the proposed method for Web page-ranking, a novel theoretic model is introduced and tested by examples of order relationships among IP addresses. Ranking is induced using a convexity feature, which is learned according to these examples using a self-organizing procedure. We consider the problem of selforganizing learning from IP data to be represented by a semi-random convex polygon procedure, in which the vertices correspond to IP addresses. Based on recent developments in our regularization theory for convex polygons and corresponding Euclidean distance based methods for classification, we develop an algorithmic framework for learning ranking functions based on a Computational Geometric Theory. We show that our algorithm is generic, and present experimental results explaining the potential of our approach. In addition, we explain the generality of our approach by showing its possible use as a visualization tool for data obtained from diverse domains, such as Public Administration and Education.Keywords: Computational Geometry, Education, e-Governance, Semantic Web.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1741793 Neural Networks for Short Term Wind Speed Prediction
Authors: K. Sreelakshmi, P. Ramakanthkumar
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Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. The values of these parameters are obtained from a nearest weather station and are used to train various forms of neural networks. The trained model of neural networks is validated using a similar set of data. The model is then used to predict the wind speed, using the same meteorological information. This paper reports an Artificial Neural Network model for short term wind speed prediction, which uses back propagation algorithm.Keywords: Short term wind speed prediction, Neural networks, Back propagation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3048792 Finite Element Modeling of Rotating Mixing of Toothpaste
Authors: Inamullah Bhatti, Ahsanullah Baloch, Khadija Qureshi
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The objective of this research is to examine the shear thinning behaviour of mixing flow of non-Newtonian fluid like toothpaste in the dissolution container with rotating stirrer. The problem under investigation is related to the chemical industry. Mixing of fluid is performed in a cylindrical container with rotating stirrer, where stirrer is eccentrically placed on the lid of the container. For the simulation purpose the associated motion of the fluid is considered as revolving of the container, with stick stirrer. For numerical prediction, a time-stepping finite element algorithm in a cylindrical polar coordinate system is adopted based on semi-implicit Taylor-Galerkin/pressure-correction scheme. Numerical solutions are obtained for non-Newtonian fluids employing power law model. Variations with power law index have been analysed, with respect to the flow structure and pressure drop.
Keywords: finite element simulation, mixing fluid, rheology, rotating flow, toothpaste
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2246791 Error Correction Method for 2D Ultra-Wideband Indoor Wireless Positioning System Using Logarithmic Error Model
Authors: Phornpat Chewasoonthorn, Surat Kwanmuang
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Indoor positioning technologies have been evolved rapidly. They augment the Global Positioning System (GPS) which requires line-of-sight to the sky to track the location of people or objects. In this study, we developed an error correction method for an indoor real-time location system (RTLS) based on an ultra-wideband (UWB) sensor from Decawave. Multiple stationary nodes (anchor) were installed throughout the workspace. The distance between stationary and moving nodes (tag) can be measured using a two-way-ranging (TWR) scheme. The result has shown that the uncorrected ranging error from the sensor system can be as large as 1 m. To reduce ranging error and thus increase positioning accuracy, we present an online correction algorithm using the Kalman filter. The results from experiments have shown that the system can reduce ranging error down to 5 cm.
Keywords: Indoor positioning, ultra-wideband, error correction, Kalman filter.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 516790 A Real Time Collision Avoidance Algorithm for Mobile Robot based on Elastic Force
Authors: Kyung Hyun, Choi, Minh Ngoc, Nong, M. Asif Ali, Rehmani
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This present paper proposes the modified Elastic Strip method for mobile robot to avoid obstacles with a real time system in an uncertain environment. The method deals with the problem of robot in driving from an initial position to a target position based on elastic force and potential field force. To avoid the obstacles, the robot has to modify the trajectory based on signal received from the sensor system in the sampling times. It was evident that with the combination of Modification Elastic strip and Pseudomedian filter to process the nonlinear data from sensor uncertainties in the data received from the sensor system can be reduced. The simulations and experiments of these methods were carried out.Keywords: Collision avoidance, Avoidance obstacle, Elastic Strip, Real time collision avoidance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1985789 The Decentralized Nonlinear Controller of Robot Manipulator with External Load Compensation
Authors: Sun Lim, Il-Kyun Jung
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This paper describes a newly designed decentralized nonlinear control strategy to control a robot manipulator. Based on the concept of the nonlinear state feedback theory and decentralized concept is developed to improve the drawbacks in previous works concerned with complicate intelligent control and low cost effective sensor. The control methodology is derived in the sense of Lyapunov theorem so that the stability of the control system is guaranteed. The decentralized algorithm does not require other joint angle and velocity information. Individual Joint controller is implemented using a digital processor with nearly actuator to make it possible to achieve good dynamics and modular. Computer simulation result has been conducted to validate the effectiveness of the proposed control scheme under the occurrence of possible uncertainties and different reference trajectories. The merit of the proposed control system is indicated in comparison with a classical control system.Keywords: Robot manipulator control, nonlinear controller, Lyapunov based stability, Interconnection compensation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1614788 Enhanced Approaches to Rectify the Noise, Illumination and Shadow Artifacts
Authors: M. Sankari, C. Meena
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Enhancing the quality of two dimensional signals is one of the most important factors in the fields of video surveillance and computer vision. Usually in real-life video surveillance, false detection occurs due to the presence of random noise, illumination and shadow artifacts. The detection methods based on background subtraction faces several problems in accurately detecting objects in realistic environments: In this paper, we propose a noise removal algorithm using neighborhood comparison method with thresholding. The illumination variations correction is done in the detected foreground objects by using an amalgamation of techniques like homomorphic decomposition, curvelet transformation and gamma adjustment operator. Shadow is removed using chromaticity estimator with local relation estimator. Results are compared with the existing methods and prove as high robustness in the video surveillance.
Keywords: Chromaticity Estimator, Curvelet Transformation, Denoising, Gamma correction, Homomorphic, Neighborhood Assessment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1946787 Prediction of Phenolic Compound Migration Process through Soil Media using Artificial Neural Network Approach
Authors: Supriya Pal, Kalyan Adhikari, Somnath Mukherjee, Sudipta Ghosh
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This study presents the application of artificial neural network for modeling the phenolic compound migration through vertical soil column. A three layered feed forward neural network with back propagation training algorithm was developed using forty eight experimental data sets obtained from laboratory fixed bed vertical column tests. The input parameters used in the model were the influent concentration of phenol(mg/L) on the top end of the soil column, depth of the soil column (cm), elapsed time after phenol injection (hr), percentage of clay (%), percentage of silt (%) in soils. The output of the ANN was the effluent phenol concentration (mg/L) from the bottom end of the soil columns. The ANN predicted results were compared with the experimental results of the laboratory tests and the accuracy of the ANN model was evaluated.Keywords: Modeling, Neural Networks, Phenol, Soil media
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2127786 Dynamic Fast Tracing and Smoothing Technique for Geiger-Muller Dosimeter
Authors: M. Ebrahimi Shohani, S. M. Taheri, S. M. Golgoun
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Environmental radiation dosimeter is a kind of detector that measures the dose of the radiation area. Dosimeter registers the radiation and converts it to the dose according to the calibration parameters. The limit of a dose is different at each radiation area and this limit should be notified and reported to the user and health physics department. The stochastic nature of radiation is the reason for the fluctuation of any gamma detector dosimetry. In this research we investigated Geiger-Muller type of dosimeter and tried to improve the dose measurement. Geiger-Muller dosimeter is a counter that converts registered radiation to the dose. Therefore, for better data analysis, it is necessary to apply an algorithm to smooth statistical variations of registered radiation. We proposed a method to smooth these fluctuations much more and also proposed a dynamic way to trace rapid changes of radiations. Results show that our method is fast and reliable method in comparison the traditional method.
Keywords: Geiger-Muller, radiation detection, smoothing algorithms, dosimeter, dose calculation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 419785 An Optimized Virtual Scheme for Reducing Collisions in MAC Layer
Authors: M. Sivakumar, S. Saravanan
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The main function of Medium Access Control (MAC) is to share the channel efficiently between all nodes. In the real-time scenario, there will be certain amount of wastage in bandwidth due to back-off periods. More bandwidth will be wasted in idle state if the back-off period is very high and collision may occur if the back-off period is small. So, an optimization is needed for this problem. The main objective of the work is to reduce delay due to back-off period thereby reducing collision and increasing throughput. Here a method, called the virtual back-off algorithm (VBA) is used to optimize the back-off period and thereby it increases throughput and reduces collisions. The main idea is to optimize the number of transmission for every node. A counter is introduced at each node to implement this idea. Here counter value represents the sequence number. VBA is classified into two types VBA with counter sharing (VBA-CS) and VBA with no counter sharing (VBA-NCS). These two classifications of VBA are compared for various parameters. Simulation is done in NS-2 environment. The results obtained are found to be promising.
Keywords: VBA, sequence number, counter, back-off period.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1375784 Nonlinear Model Predictive Control of Water Quality in Drinking Water Distribution Systems with DBPs Objectives
Authors: Mingyu Xie, Mietek Brdys
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The paper develops a Non-Linear Model Predictive Control (NMPC) of water quality in Drinking Water Distribution Systems (DWDS) based on the advanced non-linear quality dynamics model including disinfections by-products (DBPs). A special attention is paid to the analysis of an impact of the flow trajectories prescribed by an upper control level of the recently developed two-time scale architecture of an integrated quality and quantity control in DWDS. The new quality controller is to operate within this architecture in the fast time scale as the lower level quality controller. The controller performance is validated by a comprehensive simulation study based on an example case study DWDS.Keywords: Model predictive control, hierarchical control structure, genetic algorithm, water quality with DBPs objectives.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2467783 Smart Grid Simulator
Authors: Andrei Ursachi, Dorin Bordeasu
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The Smart Grid Simulator is a computer software based on advance algorithms which has as the main purpose to lower the energy bill in the most optimized price efficient way as possible for private households, companies or energy providers. It combines the energy provided by a number of solar modules and wind turbines with the consumption of one household or a cluster of nearby households and information regarding weather conditions and energy prices in order to predict the amount of energy that can be produced by renewable energy sources and the amount of energy that will be bought from the distributor for the following day. The user of the system will not only be able to minimize his expenditures on energy factures, but also he will be informed about his hourly consumption, electricity prices fluctuation and money spent for energy bought as well as how much money he saved each day and since he installed the system. The paper outlines the algorithm that supports the Smart Grid Simulator idea and presents preliminary test results that supports the discussion and implementation of the system.
Keywords: Applied Science, Renewable energy sources, Smart Grid, Sustainable energy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3075782 Selective Forwarding Attack and Its Detection Algorithms: A Review
Authors: Sushil Sarwa, Rajeev Kumar
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The wireless mesh networks (WMNs) are emerging technology in wireless networking as they can serve large scale high speed internet access. Due to its wireless multi-hop feature, wireless mesh network is prone to suffer from many attacks, such as denial of service attack (DoS). We consider a special case of DoS attack which is selective forwarding attack (a.k.a. gray hole attack). In such attack, a misbehaving mesh router selectively drops the packets it receives rom its predecessor mesh router. It is very hard to detect that packet loss is due to medium access collision, bad channel quality or because of selective forwarding attack. In this paper, we present a review of detection algorithms of selective forwarding attack and discuss their advantage & disadvantage. Finally we conclude this paper with open research issues and challenges.
Keywords: CAD algorithm, CHEMAS, selective forwarding attack, watchdog & pathrater, wireless mesh network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2782781 Design Application Procedures of 15 Storied 3D Reinforced Concrete Shear Wall-Frame Structure
Authors: H. Nikzad, S. Yoshitomi
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This paper presents the design application and reinforcement detailing of 15 storied reinforced concrete shear wall-frame structure based on linear static analysis. Databases are generated for section sizes based on automated structural optimization method utilizing Active-set Algorithm in MATLAB platform. The design constraints of allowable section sizes, capacity criteria and seismic provisions for static loads, combination of gravity and lateral loads are checked and determined based on ASCE 7-10 documents and ACI 318-14 design provision. The result of this study illustrates the efficiency of proposed method, and is expected to provide a useful reference in designing of RC shear wall-frame structures.
Keywords: Structural optimization, linear static analysis, ETABS, MATLAB, RC shear wall-frame structures.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 928780 Modelling of Multi-Agent Systems for the Scheduling of Multi-EV Charging from Power Limited Sources
Authors: Manan’Iarivo Rasolonjanahary, Chris Bingham, Nigel Schofield, Masoud Bazargan
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This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters.
Keywords: Model predictive control, non-scheduled, power limited sources, scheduled and stop-start battery charging.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 490779 Performance Analysis of a Series of Adaptive Filters in Non-Stationary Environment for Noise Cancelling Setup
Authors: Anam Rafique, Syed Sohail Ahmed
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One of the essential components of much of DSP application is noise cancellation. Changes in real time signals are quite rapid and swift. In noise cancellation, a reference signal which is an approximation of noise signal (that corrupts the original information signal) is obtained and then subtracted from the noise bearing signal to obtain a noise free signal. This approximation of noise signal is obtained through adaptive filters which are self adjusting. As the changes in real time signals are abrupt, this needs adaptive algorithm that converges fast and is stable. Least mean square (LMS) and normalized LMS (NLMS) are two widely used algorithms because of their plainness in calculations and implementation. But their convergence rates are small. Adaptive averaging filters (AFA) are also used because they have high convergence, but they are less stable. This paper provides the comparative study of LMS and Normalized NLMS, AFA and new enhanced average adaptive (Average NLMS-ANLMS) filters for noise cancelling application using speech signals.Keywords: AFA, ANLMS, LMS, NLMS.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1924778 Concept Abduction in Description Logics with Cardinality Restrictions
Authors: Viet-Hoang Vu, Nhan Le-Thanh
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Recently the usefulness of Concept Abduction, a novel non-monotonic inference service for Description Logics (DLs), has been argued in the context of ontology-based applications such as semantic matchmaking and resource retrieval. Based on tableau calculus, a method has been proposed to realize this reasoning task in ALN, a description logic that supports simple cardinality restrictions as well as other basic constructors. However, in many ontology-based systems, the representation of ontology would require expressive formalisms for capturing domain-specific constraints, this language is not sufficient. In order to increase the applicability of the abductive reasoning method in such contexts, we would like to present in the scope of this paper an extension of the tableaux-based algorithm for dealing with concepts represented inALCQ, the description logic that extends ALN with full concept negation and quantified number restrictions.
Keywords: Abductive reasoning, description logics, semantic matchmaking, non-monotonic inference, tableaux-based method.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1546777 Genetic-based Anomaly Detection in Logs of Process Aware Systems
Authors: Hanieh Jalali, Ahmad Baraani
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Nowaday-s, many organizations use systems that support business process as a whole or partially. However, in some application domains, like software development and health care processes, a normative Process Aware System (PAS) is not suitable, because a flexible support is needed to respond rapidly to new process models. On the other hand, a flexible Process Aware System may be vulnerable to undesirable and fraudulent executions, which imposes a tradeoff between flexibility and security. In order to make this tradeoff available, a genetic-based anomaly detection model for logs of Process Aware Systems is presented in this paper. The detection of an anomalous trace is based on discovering an appropriate process model by using genetic process mining and detecting traces that do not fit the appropriate model as anomalous trace; therefore, when used in PAS, this model is an automated solution that can support coexistence of flexibility and security.Keywords: Anomaly Detection, Genetic Algorithm, ProcessAware Systems, Process Mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1905776 Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier
Authors: I. Omerhodzic, S. Avdakovic, A. Nuhanovic, K. Dizdarevic
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In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.
Keywords: Epilepsy, EEG, Wavelet transform, Energydistribution, Neural Network, Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1959775 Five-axis Strip Machining with Barrel Cutter Based On Tolerance Constraint for Sculptured Surfaces
Authors: YaoAn Lu, QingZhen Bi, BaoRui Du, ShuLin Chen, LiMin Zhu, Kai Huang
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Taking the design tolerance into account, this paper presents a novel efficient approach to generate iso-scallop tool path for five-axis strip machining with a barrel cutter. The cutter location is first determined on the scallop surface instead of the design surface, and then the cutter is adjusted to locate the optimal tool position based on the differential rotation of the tool axis and satisfies the design tolerance simultaneously. The machining strip width and error are calculated with the aid of the grazing curve of the cutter. Based on the proposed tool positioning algorithm, the tool paths are generated by keeping the scallop height formed by adjacent tool paths constant. An example is conducted to confirm the validity of the proposed method.
Keywords: Strip machining, barrel cutter, iso-scallop tool path, sculptured surfaces, differential motion.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2517