Search results for: network behavior

Authors: S. M. Ali, N. R. Dhar

Abstract:

Tool wear and surface roughness prediction plays a significant role in machining industry for proper planning and control of machining parameters and optimization of cutting conditions. This paper deals with developing an artificial neural network (ANN) model as a function of cutting parameters in turning steel under minimum quantity lubrication (MQL). A feed-forward backpropagation network with twenty five hidden neurons has been selected as the optimum network. The co-efficient of determination (R2) between model predictions and experimental values are 0.9915, 0.9906, 0.9761 and 0.9627 in terms of VB, VM, VS and Ra respectively. The results imply that the model can be used easily to forecast tool wear and surface roughness in response to cutting parameters.

Keywords: ANN, MQL, Surface Roughness, Tool Wear.

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Authors: Charawee Butbumrung

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This paper aimed to study the factors that relate to working behavior of employees at Pakkred Municipality, Nonthaburi Province. A questionnaire was utilized as the tool in collecting information. Descriptive statistics included frequency, percentage, mean and standard deviation. Independent- sample t- test, analysis of variance and Pearson Correlation were also used. The findings of this research revealed that the majority of the respondents were female, between 25- 35 years old, married, with a Bachelor degree. The average monthly salary of respondents was between 8,001- 12,000 Baht, and having about 4-7 years of working experience. Regarding the overall working motivation factors, the findings showed that interrelationship, respect, and acceptance were ranked as highly important factors, whereas motivation, remunerations & welfare, career growth, and working conditions were ranked as moderately important factors. Also, overall working behavior was ranked as high. The hypotheses testing revealed that different genders had a different working behavior and had a different way of working as a team, which was significant at the 0.05 confidence level, Moreover, there was a difference among employees with different monthly salary in working behavior, problem- solving and decision making, which all were significant at the 0.05 confidence level. Employees with different years of working experience were found to have work working behavior both individual and as a team at the statistical significance level of 0.01 and 0.05. The result of testing the relationship between motivation in overall working revealed that interrelationship, respect and acceptance from others, career growth, and working conditions related to working behavior at a moderate level, while motivation in performing duties and remunerations and welfares related to working behavior towards the same direction at a low level, with a statistical significance of 0.01.

Keywords: Employees of Pakkred Municipality, Factors, Nonthaburi Province, Working Behavior.

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Authors: Amir Sepasi Zahmati, Bahman Abolhassani, Ali Asghar Beheshti Shirazi, Ali Shojaee Bakhtiari

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A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.

Keywords: Clustering methods, energy efficiency, routingprotocol, wireless sensor networks.

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Authors: André Trudel, Haiyi Zhang

Abstract:

Over the years, many implementations have been proposed for solving IA networks. These implementations are concerned with finding a solution efficiently. The primary goal of our implementation is simplicity and ease of use. We present an IA network implementation based on finite domain non-binary CSPs, and constraint logic programming. The implementation has a GUI which permits the drawing of arbitrary IA networks. We then show how the implementation can be extended to find all the solutions to an IA network. One application of finding all the solutions, is solving probabilistic IA networks.

Keywords: Constraint logic programming, CSP, logic, temporalreasoning.

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Authors: Kahil Amar, Nekmouche Aghiles, Titouche Billal, Hamizi Mohand, Hannachi Naceur Eddine

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The ductility is an important parameter in the nonlinear behavior of portal structures reinforced concrete. It may be explained by the ability of the structure to deform in the plastic range, or the geometric characteristics in the map may influence the overall ductility. Our study is based on the influence of geometric slenderness (Lx / Ly) on the overall ductility of these structures, a study is made on a structure has 05 floors with varying the column section of 900 cm², 1600 cm² and 1225 cm². A slight variation in global ductility is noticed as (Lx/Ly) varies; however, column sections can control satisfactorily the plastic behavior of buildings.

Keywords: Ductility, nonlinear behavior, pushover analysis, geometric slenderness, structural behavior.

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Authors: Muhamad Abduh, Jarita Duasa, Mohd. Azmi Omar

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Unlike its conventional counterpart, Islamic principles forbid Islamic banks to take any interest-related income and thus makes deposits from depositors as an important source of fund for its operational and financing. Consequently, the risk of deposit withdrawal by depositors is an important aspect that should be wellmanaged in Islamic banking. This paper aims to investigate factors that influence depositors- withdrawal behavior in Islamic banks, particularly in Malaysia, using the framework of theory of reasoned action. A total of 368 respondents from Klang valley are involved in the analysis. The paper finds that all the constructs variable i.e. normative beliefs, subjective norms, behavioral beliefs, and attitude towards behavior are perceived to be distinct by the respondents. In addition, the structural equation model is able to verify the structural relationships between subjective norms, attitude towards behavior and behavioral intention. Subjective norms gives more influence to depositors- decision on deposit withdrawal compared to attitude towards behavior.

Keywords: Islamic bank, structural equation model, theory of reasoned action, withdrawal behavior

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Authors: Tanida Julvanichpong

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Exercise has been regarded as a necessary and important aspect to enhance physical performance and psychology health. Body weight statistics of students in junior high school students in Chonburi Province beyond a standard risk of obesity. Promoting exercise among Junior high school students in Chonburi Province, essential knowledge concerning factors influencing exercise is needed. Therefore, this study aims to (1) determine the levels of perceived exercise behavior, exercise behavior in the past, perceived barriers to exercise, perceived benefits of exercise, perceived self-efficacy to exercise, feelings associated with exercise behavior, influence of the family to exercise, influence of friends to exercise, and the perceived influence of the environment on exercise. (2) examine the predicting ability of each of the above factors while including personal factors (sex, educational level) for exercise behavior. Pender’s Health Promotion Model was used as a guide for the study. Sample included 652 students in junior high schools, Chonburi Provience. The samples were selected by Multi-Stage Random Sampling. Data Collection has been done by using self-administered questionnaires. Data were analyzed using descriptive statistics, Pearson’s product moment correlation coefficient, Eta, and stepwise multiple regression analysis. The research results showed that: 1. Perceived benefits of exercise, influence of teacher, influence of environmental, feelings associated with exercise behavior were at a high level. Influence of the family to exercise, exercise behavior, exercise behavior in the past, perceived self-efficacy to exercise and influence of friends were at a moderate level. Perceived barriers to exercise were at a low level. 2. Exercise behavior was positively significant related to perceived benefits of exercise, influence of the family to exercise, exercise behavior in the past, perceived self-efficacy to exercise, influence of friends, influence of teacher, influence of environmental and feelings associated with exercise behavior (p < .01, respectively) and was negatively significant related to educational level and perceived barriers to exercise (p < .01, respectively). Exercise behavior was significant related to sex (Eta = 0.243, p=.000). 3. Exercise behavior in the past, influence of the family to exercise significantly contributed 60.10 percent of the variance to the prediction of exercise behavior in male students (p < .01). Exercise behavior in the past, perceived self-efficacy to exercise, perceived barriers to exercise, and educational level significantly contributed 52.60 percent of the variance to the prediction of exercise behavior in female students (p < .01).

Keywords: Predictive factors, exercise behaviors, junior high school.

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Authors: Arezoo Sadrinezhad, Kallol Sett, S. I. Hariharan

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In this paper, a probabilistic framework based on Fokker-Planck-Kolmogorov (FPK) approach has been applied to simulate triaxial cyclic constitutive behavior of uncertain soils. The framework builds upon previous work of the writers, and it has been extended for cyclic probabilistic simulation of triaxial undrained behavior of soils. von Mises elastic-perfectly plastic material model is considered. It is shown that by using probabilistic framework, some of the most important aspects of soil behavior under cyclic loading can be captured even with a simple elastic-perfectly plastic constitutive model.

Keywords: Elasto-plasticity, uncertainty, soils, Fokker-Planck equation, Fourier Spectral method, Finite Difference method.

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Authors: Chaitanya Sharma, Vikas Upadhyay, A. Tripathi

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Friction stir welding and tungsten inert gas welding techniques were employed to weld armor grade aluminum alloy to investigate the effect of welding processes on tensile behavior of weld joints. Tensile tests, Vicker microhardness tests and optical microscopy were performed on developed weld joints and base metal. Welding process influenced tensile behavior and microstructure of weld joints. Friction stir welded joints showed tensile behavior better than tungsten inert gas weld joints.

Keywords: Friction stir welding, microstructure, tensile properties and fracture locations.

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Authors: C. Pislaru, A. Shebani

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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.

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Authors: A. Golmohammadi, B. Jahandideh

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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.

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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.

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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.

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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.

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Authors: Radoslav Delina, Michal Tkáč

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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

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Authors: G. Rajesh, B. Vinayaga Sundaram, C. Aarthi

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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.

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Authors: Myriam Noureddine, Rachid Noureddine

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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.

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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.

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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.

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Authors: Xun Liang

Abstract:

In this paper, we probe into the traffic assignment problem by the chromosome-learning-based path finding method in simulation, which is to model the driver' behavior in the with-in-a-day process. By simply making a combination and a change of the traffic route chromosomes, the driver at the intersection chooses his next route. The various crossover and mutation rules are proposed with extensive examples.

Keywords: Chromosome learning, crossover, mutation, traffic path finding.

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Authors: O. Hassanein, Sreenatha G. Anavatti, Tapabrata Ray

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The dynamics of the Autonomous Underwater Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate accurately because of the variations of these coefficients with different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line adaptive fuzzy model and adaptive neural fuzzy network (ANFN) model techniques to overcome the uncertain external disturbance and the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according to the back propagation algorithm based upon the error between the identified model and the actual output of the plant. The proposed ANFN model adopts a functional link neural network (FLNN) as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN model is a nonlinear combination of input variables. Fuzzy control system is applied to guide and control the AUV using both adaptive models and mathematical model. Simulation results show the superiority of the proposed adaptive neural fuzzy network (ANFN) model in tracking of the behavior of the AUV accurately even in the presence of noise and disturbance.

Keywords: AUV, AUV dynamic model, fuzzy control, fuzzy modelling, adaptive fuzzy control, back propagation, system identification, neural fuzzy model, FLNN.

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Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

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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.

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Authors: H. Gorine, M. Ramadan Elmezughi

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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.

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Authors: Mughal Yar M, Israr Ul Haq, Bushra Noman

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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;

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Authors: A. H. Akhaveissy

Abstract:

Nonlinear finite element method with eight noded isoparametric quadrilateral element is used for prediction of loaddeformation behavior including bearing capacity of foundations. Modified generalized plasticity model with non-associated flow rule is applied for analysis of soil-footing system. Also Von Mises and Tresca criterions are used for simulation of soil behavior. Modified generalized plasticity model is able to simulate load-deformation including softening behavior. Localization phenomena are considered by different meshes. Localization phenomena have not been seen in the examples. Predictions by modified generalized plasticity model show good agreement with laboratory data and theoretical prediction in comparison the other models.

Keywords: Localization phenomena, Generalized plasticity, Non-associated Flow Rule

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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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.

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Authors: Insung Jung, lockjo Koo, Gi-Nam Wang

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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.

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Authors: Haniza N., Md Khambari, M. N, Shahrin S., Adib M.Monzer Habbal, Suhaidi Hassan

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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

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, Apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: Behavior, data mining technique, Apriori algorithm.

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Authors: Yi Yu, Lin Ma, Yong Sun, Yuantong Gu

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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

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