Search results for: network properties
5083 Identification of Nonlinear Systems Using Radial Basis Function Neural Network
Authors: C. Pislaru, A. Shebani
Abstract:
This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.
Keywords: System identification, Nonlinear system, Neural networks, RBF neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28645082 Prioritizing Service Quality Dimensions: A Neural Network Approach
Authors: A. Golmohammadi, B. Jahandideh
Abstract:
One of the determinants of a firm-s prosperity is the customers- perceived service quality and satisfaction. While service quality is wide in scope, and consists of various dimensions, there may be differences in the relative importance of these dimensions in affecting customers- overall satisfaction of service quality. Identifying the relative rank of different dimensions of service quality is very important in that it can help managers to find out which service dimensions have a greater effect on customers- overall satisfaction. Such an insight will consequently lead to more effective resource allocation which will finally end in higher levels of customer satisfaction. This issue – despite its criticality- has not received enough attention so far. Therefore, using a sample of 240 bank customers in Iran, an artificial neural network is developed to address this gap in the literature. As customers- evaluation of service quality is a subjective process, artificial neural networks –as a brain metaphor- may appear to have a potentiality to model such a complicated process. Proposing a neural network which is able to predict the customers- overall satisfaction of service quality with a promising level of accuracy is the first contribution of this study. In addition, prioritizing the service quality dimensions in affecting customers- overall satisfaction –by using sensitivity analysis of neural network- is the second important finding of this paper.Keywords: service quality, customer satisfaction, relative importance, artificial neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16455081 Study on the Optimization of Completely Batch Water-using Network with Multiple Contaminants Considering Flow Change
Authors: Jian Du, Shui Hong Hong, Lu Meng, Qing Wei Meng
Abstract:
This work addresses the problem of optimizing completely batch water-using network with multiple contaminants where the flow change caused by mass transfer is taken into consideration for the first time. A mathematical technique for optimizing water-using network is proposed based on source-tank-sink superstructure. The task is to obtain the freshwater usage, recycle assignments among water-using units, wastewater discharge and a steady water-using network configuration by following steps. Firstly, operating sequences of water-using units are determined by time constraints. Next, superstructure is simplified by eliminating the reuse and recycle from water-using units with maximum concentration of key contaminants. Then, the non-linear programming model is solved by GAMS (General Algebra Model System) for minimum freshwater usage, maximum water recycle and minimum wastewater discharge. Finally, numbers of operating periods are calculated to acquire the steady network configuration. A case study is solved to illustrate the applicability of the proposed approach.Keywords: Completely batch process, flow change, multiple contaminants, water-using network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14525080 Community Detection-based Analysis of the Human Interactome Network
Authors: Razvan Bocu, Sabin Tabirca
Abstract:
The study of proteomics reached unexpected levels of interest, as a direct consequence of its discovered influence over some complex biological phenomena, such as problematic diseases like cancer. This paper presents a new technique that allows for an accurate analysis of the human interactome network. It is basically a two-step analysis process that involves, at first, the detection of each protein-s absolute importance through the betweenness centrality computation. Then, the second step determines the functionallyrelated communities of proteins. For this purpose, we use a community detection technique that is based on the edge betweenness calculation. The new technique was thoroughly tested on real biological data and the results prove some interesting properties of those proteins that are involved in the carcinogenesis process. Apart from its experimental usefulness, the novel technique is also computationally effective in terms of execution times. Based on the analysis- results, some topological features of cancer mutated proteins are presented and a possible optimization solution for cancer drugs design is suggested.Keywords: Betweenness centrality, interactome networks, proteinprotein interactions, protein communities, cancer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12955079 Lithium Oxide Effect on the Thermal and Physical Properties of the Ternary System Glasses (Li2O3-B2O3-Al2O3)
Authors: D. Aboutaleb, B. Safi
Abstract:
The borate glasses are known by their structural characterized by existence of unit’s structural composed by triangles and tetrahedrons boron in different configurations depending on the percentage of B2O3 in the glass chemical composition. In this paper, effect of lithium oxide addition on the thermal and physical properties of an alumina borate glass, was investigated. It was found that the boron abnormality has a significant effect in the change of glass properties according to the addition rate of lithium oxide.
Keywords: Borate glasses, triangles and tetrahedrons boron, Lithium oxide, Boron anomaly, thermal properties, physical properties.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29265078 Analysis of Combined Use of NN and MFCC for Speech Recognition
Authors: Safdar Tanweer, Abdul Mobin, Afshar Alam
Abstract:
The performance and analysis of speech recognition system is illustrated in this paper. An approach to recognize the English word corresponding to digit (0-9) spoken by 2 different speakers is captured in noise free environment. For feature extraction, speech Mel frequency cepstral coefficients (MFCC) has been used which gives a set of feature vectors from recorded speech samples. Neural network model is used to enhance the recognition performance. Feed forward neural network with back propagation algorithm model is used. However other speech recognition techniques such as HMM, DTW exist. All experiments are carried out on Matlab.
Keywords: Speech Recognition, MFCC, Neural Network, classifier.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32705077 Extremal Properties of Generalized Class of Close-to-convex Functions
Authors: Norlyda Mohamed, Daud Mohamad, Shaharuddin Cik Soh
Abstract:
Let Gα ,β (γ ,δ ) denote the class of function f (z), f (0) = f ′(0)−1= 0 which satisfied e δ {αf ′(z)+ βzf ′′(z)}> γ i Re in the open unit disk D = {z ∈ı : z < 1} for some α ∈ı (α ≠ 0) , β ∈ı and γ ∈ı (0 ≤γ <α ) where δ ≤ π and α cosδ −γ > 0 . In this paper, we determine some extremal properties including distortion theorem and argument of f ′( z ) .Keywords: Argument of f ′(z) , Carathéodory Function, Closeto- convex Function, Distortion Theorem, Extremal Properties
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13575076 Integer Programming Model for the Network Design Problem with Facility Dependent Shortest Path Routing
Authors: Taehan Lee
Abstract:
We consider a network design problem which has shortest routing restriction based on the values determined by the installed facilities on each arc. In conventional multicommodity network design problem, a commodity can be routed through any possible path when the capacity is available. But, we consider a problem in which the commodity between two nodes must be routed on a path which has shortest metric value and the link metric value is determined by the installed facilities on the link. By this routing restriction, the problem has a distinct characteristic. We present an integer programming formulation containing the primal-dual optimality conditions to the shortest path routing. We give some computational results for the model.Keywords: Integer programming, multicommodity network design, routing, shortest path.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10575075 Trust Building Mechanisms for Electronic Business Networks and Their Relation to eSkills
Authors: Radoslav Delina, Michal Tkáč
Abstract:
Globalization, supported by information and communication technologies, changes the rules of competitiveness and increases the significance of information, knowledge and network cooperation. In line with this trend, the need for efficient trust-building tools has emerged. The absence of trust building mechanisms and strategies was identified within several studies. Through trust development, participation on e-business network and usage of network services will increase and provide to SMEs new economic benefits. This work is focused on effective trust building strategies development for electronic business network platforms. Based on trust building mechanism identification, the questionnairebased analysis of its significance and minimum level of requirements was conducted. In the paper, we are confirming the trust dependency on e-Skills which play crucial role in higher level of trust into the more sophisticated and complex trust building ICT solutions.Keywords: Correlation analysis, decision trees, e-marketplace, trust building
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19275074 Prediction of Compressive Strength Using Artificial Neural Network
Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal
Abstract:
Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.
Keywords: Rebound, ultra-sonic pulse, penetration, ANN, NDT, regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 43865073 A Black-Box Approach in Modeling Valve Stiction
Abstract:
Several valve stiction models have been proposed in the literature to help understand and study the behavior of sticky valves. In this paper, an alternative black-box modeling approach based on Neural Network (NN) is presented. It is shown that with proper network type and optimum model structures, the performance of the developed NN stiction model is comparable to other established method. The resulting NN model is also tested for its robustness against the uncertainty in the stiction parameter values. Predictive mode operation also shows excellent performance of the proposed model for multi-steps ahead prediction.
Keywords: Control valve stiction, neural network, modeling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16085072 Tree Based Data Aggregation to Resolve Funneling Effect in Wireless Sensor Network
Authors: G. Rajesh, B. Vinayaga Sundaram, C. Aarthi
Abstract:
In wireless sensor network, sensor node transmits the sensed data to the sink node in multi-hop communication periodically. This high traffic induces congestion at the node which is present one-hop distance to the sink node. The packet transmission and reception rate of these nodes should be very high, when compared to other sensor nodes in the network. Therefore, the energy consumption of that node is very high and this effect is known as the “funneling effect”. The tree based-data aggregation technique (TBDA) is used to reduce the energy consumption of the node. The throughput of the overall performance shows a considerable decrease in the number of packet transmissions to the sink node. The proposed scheme, TBDA, avoids the funneling effect and extends the lifetime of the wireless sensor network. The average case time complexity for inserting the node in the tree is O(n log n) and for the worst case time complexity is O(n2).Keywords: Data Aggregation, Funneling Effect, Traffic Congestion, Wireless Sensor Network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13165071 Criticality Assessment of Failures in Multipoint Communication Networks
Authors: Myriam Noureddine, Rachid Noureddine
Abstract:
Following the current economic challenges and competition, all systems, whatever their field, must be efficient and operational during their activity. In this context, it is imperative to anticipate, identify, eliminate and estimate the failures of systems, which may lead to an interruption of their function. This need requires the management of possible risks, through an assessment of the failures criticality following a dependability approach. On the other hand, at the time of new information technologies and considering the networks field evolution, the data transmission has evolved towards a multipoint communication, which can simultaneously transmit information from a sender to multiple receivers. This article proposes the failures criticality assessment of a multipoint communication network, integrates a database of network failures and their quantifications. The proposed approach is validated on a case study and the final result allows having the criticality matrix associated with failures on the considered network, giving the identification of acceptable risks.
Keywords: Dependability, failure, multipoint network, criticality matrix.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16055070 Applying Complex Network Theory to Software Structure Analysis
Authors: Weifeng Pan
Abstract:
Complex networks have been intensively studied across many fields, especially in Internet technology, biological engineering, and nonlinear science. Software is built up out of many interacting components at various levels of granularity, such as functions, classes, and packages, representing another important class of complex networks. It can also be studied using complex network theory. Over the last decade, many papers on the interdisciplinary research between software engineering and complex networks have been published. It provides a different dimension to our understanding of software and also is very useful for the design and development of software systems. This paper will explore how to use the complex network theory to analyze software structure, and briefly review the main advances in corresponding aspects.Keywords: Metrics, measurement, complex networks, software.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25985069 Prediction of Rubberised Concrete Strength by Using Artificial Neural Networks
Authors: A. M. N. El-Khoja, A. F. Ashour, J. Abdalhmid, X. Dai, A. Khan
Abstract:
In recent years, waste tyre problem is considered as one of the most crucial environmental pollution problems facing the world. Thus, reusing waste rubber crumb from recycled tyres to develop highly damping concrete is technically feasible and a viable alternative to landfill or incineration. The utilization of waste rubber in concrete generally enhances the ductility, toughness, thermal insulation, and impact resistance. However, the mechanical properties decrease with the amount of rubber used in concrete. The aim of this paper is to develop artificial neural network (ANN) models to predict the compressive strength of rubberised concrete (RuC). A trained and tested ANN was developed using a comprehensive database collected from different sources in the literature. The ANN model developed used 5 input parameters that include: coarse aggregate (CA), fine aggregate (FA), w/c ratio, fine rubber (Fr), and coarse rubber (Cr), whereas the ANN outputs were the corresponding compressive strengths. A parametric study was also conducted to study the trend of various RuC constituents on the compressive strength of RuC.Keywords: Rubberized concrete, compressive strength, artificial neural network, prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9095068 E-Learning Network Support Services: A Comparative Case Study of Australian and United States Universities
Authors: Sayed Hadi Sadeghi
Abstract:
This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.
Keywords: Support services, e-network practice, Australian universities, United States universities.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9905067 Maximum Induced Subgraph of an Augmented Cube
Authors: Meng-Jou Chien, Jheng-Cheng Chen, Chang-Hsiung Tsai
Abstract:
Let maxζG(m) denote the maximum number of edges in a subgraph of graph G induced by m nodes. The n-dimensional augmented cube, denoted as AQn, a variation of the hypercube, possesses some properties superior to those of the hypercube. We study the cases when G is the augmented cube AQn.
Keywords: Interconnection network, Augmented cube, Induced subgraph, Bisection width.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15485066 Continuous Functions Modeling with Artificial Neural Network: An Improvement Technique to Feed the Input-Output Mapping
Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa
Abstract:
The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.
Keywords: Neural network computing, information processing, input-output mapping, training time, computers with high memory.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13245065 Security Threats on Wireless Sensor Network Protocols
Authors: H. Gorine, M. Ramadan Elmezughi
Abstract:
In this paper, we investigate security issues and challenges facing researchers in wireless sensor networks and countermeasures to resolve them. The broadcast nature of wireless communication makes Wireless Sensor Networks prone to various attacks. Due to resources limitation constraint in terms of limited energy, computation power and memory, security in wireless sensor networks creates different challenges than wired network security. We will discuss several attempts at addressing the issues of security in wireless sensor networks in an attempt to encourage more research into this area.Keywords: Malicious nodes, network security, soft encryption, threats, wireless sensor networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18765064 Determination of an Efficient Differentiation Pathway of Stem Cells Employing Predictory Neural Network Model
Authors: Mughal Yar M, Israr Ul Haq, Bushra Noman
Abstract:
The stem cells have ability to differentiated themselves through mitotic cell division and various range of specialized cell types. Cellular differentiation is a way by which few specialized cell develops into more specialized.This paper studies the fundamental problem of computational schema for an artificial neural network based on chemical, physical and biological variables of state. By doing this type of study system could be model for a viable propagation of various economically important stem cells differentiation. This paper proposes various differentiation outcomes of artificial neural network into variety of potential specialized cells on implementing MATLAB version 2009. A feed-forward back propagation kind of network was created to input vector (five input elements) with single hidden layer and one output unit in output layer. The efficiency of neural network was done by the assessment of results achieved from this study with that of experimental data input and chosen target data. The propose solution for the efficiency of artificial neural network assessed by the comparatative analysis of “Mean Square Error" at zero epochs. There are different variables of data in order to test the targeted results.Keywords: Computational shcmin, meiosis, mitosis, neuralnetwork, Stem cell SOM;
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15095063 Flexural Properties of Halloysite Nanotubes-Polyester Nanocomposites Exposed to Aggressive Environment
Authors: Mohd Shahneel Saharudin, Jiacheng Wei, Islam Shyha, Fawad Inam
Abstract:
This study aimed to investigate the effect of aggressive environment on the flexural properties of halloysite nanotubes-polyester nanocomposites. Results showed that the addition of halloysite nanotubes into polyester matrix was found to improve flexural properties of the nanocomposites in dry condition and after water-methanol exposure. Significant increase in surface roughness was also observed and measured by Alicona Infinite Focus optical microscope.
Keywords: Halloysite nanotubes, polymer degradation, flexural properties, surface roughness.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9745062 Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh
Authors: S. M. Anowarul Haque, Md. Asiful Islam
Abstract:
Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.Keywords: Load forecasting, artificial neural network, particle swarm optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6885061 A Review on Soft Computing Technique in Intrusion Detection System
Authors: Noor Suhana Sulaiman, Rohani Abu Bakar, Norrozila Sulaiman
Abstract:
Intrusion Detection System is significant in network security. It detects and identifies intrusion behavior or intrusion attempts in a computer system by monitoring and analyzing the network packets in real time. In the recent year, intelligent algorithms applied in the intrusion detection system (IDS) have been an increasing concern with the rapid growth of the network security. IDS data deals with a huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. Since the amount of audit data that an IDS needs to examine is very large even for a small network, classification by hand is impossible. Hence, the primary objective of this review is to review the techniques prior to classification process suit to IDS data.Keywords: Intrusion Detection System, security, soft computing, classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18665060 Two States Mapping Based Neural Network Model for Decreasing of Prediction Residual Error
Authors: Insung Jung, lockjo Koo, Gi-Nam Wang
Abstract:
The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.
Keywords: Neural network, U-healthcare, prediction, timeseries, computer aided prediction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19855059 Topology Influence on TCP Congestion Control Performance in Multi-hop Ad Hoc Wireless
Authors: Haniza N., Md Khambari, M. N, Shahrin S., Adib M.Monzer Habbal, Suhaidi Hassan
Abstract:
Wireless ad hoc nodes are freely and dynamically self-organize in communicating with others. Each node can act as host or router. However it actually depends on the capability of nodes in terms of its current power level, signal strength, number of hops, routing protocol, interference and others. In this research, a study was conducted to observe the effect of hops count over different network topologies that contribute to TCP Congestion Control performance degradation. To achieve this objective, a simulation using NS-2 with different topologies have been evaluated. The comparative analysis has been discussed based on standard observation metrics: throughput, delay and packet loss ratio. As a result, there is a relationship between types of topology and hops counts towards the performance of ad hoc network. In future, the extension study will be carried out to investigate the effect of different error rate and background traffic over same topologies.Keywords: NS-2, network topology, network performance, multi-hops
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15745058 Remaining Useful Life Prediction Using Elliptical Basis Function Network and Markov Chain
Authors: Yi Yu, Lin Ma, Yong Sun, Yuantong Gu
Abstract:
This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.Keywords: Elliptical Basis Function Network, Markov Chain, Missing Covariates, Remaining Useful Life
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16645057 Location Based Clustering in Wireless Sensor Networks
Authors: Ashok Kumar, Narottam Chand, Vinod Kumar
Abstract:
Due to the limited energy resources, energy efficient operation of sensor node is a key issue in wireless sensor networks. Clustering is an effective method to prolong the lifetime of energy constrained wireless sensor network. However, clustering in wireless sensor network faces several challenges such as selection of an optimal group of sensor nodes as cluster, optimum selection of cluster head, energy balanced optimal strategy for rotating the role of cluster head in a cluster, maintaining intra and inter cluster connectivity and optimal data routing in the network. In this paper, we propose a protocol supporting an energy efficient clustering, cluster head selection/rotation and data routing method to prolong the lifetime of sensor network. Simulation results demonstrate that the proposed protocol prolongs network lifetime due to the use of efficient clustering, cluster head selection/rotation and data routing.
Keywords: Wireless sensor networks, clustering, energy efficient, localization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26855056 Effect of Clustering on Energy Efficiency and Network Lifetime in Wireless Sensor Networks
Authors: Prakash G L, Chaitra K Meti, Poojitha K, Divya R.K.
Abstract:
Wireless Sensor Network is Multi hop Self-configuring Wireless Network consisting of sensor nodes. The deployment of wireless sensor networks in many application areas, e.g., aggregation services, requires self-organization of the network nodes into clusters. Efficient way to enhance the lifetime of the system is to partition the network into distinct clusters with a high energy node as cluster head. The different methods of node clustering techniques have appeared in the literature, and roughly fall into two families; those based on the construction of a dominating set and those which are based solely on energy considerations. Energy optimized cluster formation for a set of randomly scattered wireless sensors is presented. Sensors within a cluster are expected to be communicating with cluster head only. The energy constraint and limited computing resources of the sensor nodes present the major challenges in gathering the data. In this paper we propose a framework to study how partially correlated data affect the performance of clustering algorithms. The total energy consumption and network lifetime can be analyzed by combining random geometry techniques and rate distortion theory. We also present the relation between compression distortion and data correlation.Keywords: Clusters, multi hop, random geometry, rate distortion.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16385055 Intelligent Earthquake Prediction System Based On Neural Network
Authors: Emad Amar, Tawfik Khattab, Fatma Zada
Abstract:
Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information about Earthquake Existed throughout history & the Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of the object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.
Keywords: BP neural network, Prediction, RBF neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32205054 Micro Particles Effect on Mechanical and Thermal Properties of Ceramic Composites - A Review
Authors: S. I. Durowaye, O. P. Gbenebor, B. O. Bolasodun, I. O. Rufai, V. O. Durowaye
Abstract:
Particles are the most common and cheapest reinforcement producing discontinuous reinforced composites with isotropic properties. Conventional fabrication methods can be used to produce a wide range of product forms, making them relatively inexpensive. Optimising composite development must include consideration of all the fundamental aspect of particles including their size, shape, volume fraction, distribution and mechanical properties. Research has shown that the challenges of low fracture toughness, poor crack growth resistance and low thermal stability can be overcome by reinforcement with particles. The unique properties exhibited by micro particles reinforced ceramic composites have made them to be highly attractive in a vast array of applications.
Keywords: Ceramic composites, Mechanical properties, Microparticles, Thermal stability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2015