Search results for: cycle composition networks

Authors: Amirhossein Chambari, Seyed Habib Rahmaty, Vahid Hajipour, Aida Karimi

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This paper proposes a bi-objective model for the facility location problem under a congestion system. The idea of the model is motivated by applications of locating servers in bank automated teller machines (ATMS), communication networks, and so on. This model can be specifically considered for situations in which fixed service facilities are congested by stochastic demand within queueing framework. We formulate this model with two perspectives simultaneously: (i) customers and (ii) service provider. The objectives of the model are to minimize (i) the total expected travelling and waiting time and (ii) the average facility idle-time. This model represents a mixed-integer nonlinear programming problem which belongs to the class of NP-hard problems. In addition, to solve the model, two metaheuristic algorithms including nondominated sorting genetic algorithms (NSGA-II) and non-dominated ranking genetic algorithms (NRGA) are proposed. Besides, to evaluate the performance of the two algorithms some numerical examples are produced and analyzed with some metrics to determine which algorithm works better.

Keywords: Queuing, Location, Bi-objective, NSGA-II, NRGA

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Authors: Omima E. Fadlallah, Abdullahi H. El Tinay, Elfadil E. Babiker

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Sorghum flour was supplemented with 15 and 30% chickpea flour. Sorghum flour and the supplement were fermented at 35 oC for 0, 8, 16, and 24 h. Changes in pH, titrable acidity, total soluble solids, protein content, in vitro protein digestibility and amino acid composition were investigated during fermentation and/or after supplementation of sorghum flour with chickpea. The pH of the fermenting material decreased sharply with a concomitant increase in the titrable acidity. The total soluble solids remained unchanged with progressive fermentation time. The protein content of sorghum cultivar was found to be 9.27 and that of chickpea was 22.47%. The protein content of sorghum cultivar after supplementation with15 and 30% chickpea was significantly (P ≤ 0.05) increased to 11.78 and 14.55%, respectively. The protein digestibility also increased after fermentation from 13.35 to 30.59 and 40.56% for the supplements, respectively. Further increment in protein content and digestibility was observed when supplemented and unsupplemented samples were fermented for different periods of time. Cooking of fermented samples was found to increase the protein content slightly and decreased digestibility for both supplements. Amino acid content of fermented and fermented and cooked supplements was determined. Supplementation was found to increase the lysine and therionine content. Cooking following fermentation decreased lysine, isoleucine, valine and sulfur containg amino acids.

Keywords: Amino acid, Chickpea, Cooking, Fermentation, protein, Sorghum.

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Authors: Azadeh Golshan, Craig Evans, Phillip Geary, Abigail Morrow, Zoe Rogers, Marcel Maeder

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22 physicochemical variables have been determined in water samples collected weekly from January to December in 2013 from three sampling stations located within a major drinking water reservoir. Classical Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis was used to investigate the environmental factors associated with the physico-chemical variability of the water samples at each of the sampling stations. Matrix augmentation MCR-ALS (MA-MCR-ALS) was also applied, and the two sets of results were compared for interpretative clarity. Links between these factors, reservoir inflows and catchment land-uses were investigated and interpreted in relation to chemical composition of the water and their resolved geographical distribution profiles. The results suggested that the major factors affecting reservoir water quality were those associated with agricultural runoff, with evidence of influence on algal photosynthesis within the water column. Water quality variability within the reservoir was also found to be strongly linked to physical parameters such as water temperature and the occurrence of thermal stratification. The two methods applied (MCR-ALS and MA-MCR-ALS) led to similar conclusions; however, MA-MCR-ALS appeared to provide results more amenable to interpretation of temporal and geological variation than those obtained through classical MCR-ALS.

Keywords: Catchment management, drinking water reservoir, multivariate curve resolution alternating least squares, thermal stratification, water quality.

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Authors: R. Shivarudraswamy, D. N. Gaonkar

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The continued interest in the use of distributed generation in recent years is leading to the growth in number of distributed generators connected to distribution networks. Steady state voltage rise resulting from the connection of these generators can be a major obstacle to their connection at lower voltage levels. The present electric distribution network is designed to keep the customer voltage within tolerance limit. This may require a reduction in connectable generation capacity, under utilization of appropriate generation sites. Thus distribution network operators need a proper voltage regulation method to allow the significant integration of distributed generation systems to existing network. In this work a voltage rise problem in a typical distribution system has been studied. A method for voltage regulation of distribution system with multiple DG system by coordinated operation distributed generator, capacitor and OLTC has been developed. A sensitivity based analysis has been carried out to determine the priority for individual generators in multiple DG environment. The effectiveness of the developed method has been evaluated under various cases through simulation results.

Keywords: Distributed generation, voltage control, sensitivity factor.

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Authors: Jagmohan Datt, Uma Batra

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Present investigations involve a systematic study on the machinability of austempered ductile irons (ADI) developed from four commercially viable ductile irons alloyed with different contents of 0, 0.1, 0.3 and 0.6 wt.% of Ni. The influence of Ni content, amount of retained austenite and hardness of ADI on machining behavior has been conducted systematically. Austempering heat treatment was carried out for 120 minutes at four temperatures- 270oC, 320oC, 370oC or 420oC, after austenitization at 900oC for 120 min. Milling tests were performed and machinability index, cutting forces and surface roughness measurements were used to evaluate the machinability. Higher cutting forces, lower machinability index and the poorer surface roughness of the samples austempered at lower temperatures indicated that austempering at higher temperatures resulted in better machinability. The machinability of samples austempered at 420oC, which contained higher fractions of retained austenite, was superior to that of samples austempered at lower temperatures, indicating that hardness is an important factor in assessing machinability in addition to high carbon austenite content. The ADI with 0.6% Ni, austempered at 420°C for 120 minutes, demonstrated best machinability.

Keywords: Austempering, machinability, machining index, cutting force, surface finish.

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Authors: Sh. Derakhshan, A. Mostafavi

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Nowadays, the challenge in hydraulic turbine design is the multi-objective design of turbine runner to reach higher efficiency. The hydraulic performance of a turbine is strictly depends on runner blades shape. The present paper focuses on the application of the multi-objective optimization algorithm to the design of a small Francis turbine runner. The optimization exercise focuses on the efficiency improvement at the best efficiency operating point (BEP) of the GAMM Francis turbine. A global optimization method based on artificial neural networks (ANN) and genetic algorithms (GA) coupled by 3D Navier-Stokes flow solver has been used to improve the performance of an initial geometry of a Francis runner. The results show the good ability of optimization algorithm and the final geometry has better efficiency with initial geometry. The goal was to optimize the geometry of the blades of GAMM turbine runner which leads to maximum total efficiency by changing the design parameters of camber line in at least 5 sections of a blade. The efficiency of the optimized geometry is improved from 90.7% to 92.5%. Finally, design parameters and the way of selection have been considered and discussed.

Keywords: Francis Turbine, Runner, Optimization, CFD

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Authors: M. Balfaqih, S. Haseeb, M. H. Mazlan, S. N. Hasnan, O. Mahmoud, A. Hashim

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Wireless LAN technologies have picked up momentum in the recent years due to their ease of deployment, cost and availability. The era of wireless LAN has also given rise to unique applications like VOIP, IPTV and unified messaging. However, these real-time applications are very sensitive to network and handoff latencies. To successfully support these applications, seamless roaming during the movement of mobile station has become crucial. Nowadays, centralized architecture models support roaming in WLANs. They have the ability to manage, control and troubleshoot large scale WLAN deployments. This model is managed by Control and Provision of Wireless Access Point protocol (CAPWAP). This paper covers the CAPWAP architectural solution along with its proposals that have emerged. Based on the literature survey conducted in this paper, we found that the proposed algorithms to reduce roaming latency in CAPWAP architecture do not support seamless roaming. Additionally, they are not sufficient during the initial period of the network. This paper also suggests important design consideration for mobility support in future centralized IEEE 802.11 networks.

Keywords: 802.11, centralized Architecture, CAPWAP, Roaming.

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Authors: A. Ghaffari, A. Meghdari, D. Naderi, S. Eslami

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In this paper an algorithm based on the adaptive neuro-fuzzy controller is provided to enhance the tipover stability of mobile manipulators when they are subjected to predefined trajectories for the end-effector and the vehicle. The controller creates proper configurations for the manipulator to prevent the robot from being overturned. The optimal configuration and thus the most favorable control are obtained through soft computing approaches including a combination of genetic algorithm, neural networks, and fuzzy logic. The proposed algorithm, in this paper, is that a look-up table is designed by employing the obtained values from the genetic algorithm in order to minimize the performance index and by using this data base, rule bases are designed for the ANFIS controller and will be exerted on the actuators to enhance the tipover stability of the mobile manipulator. A numerical example is presented to demonstrate the effectiveness of the proposed algorithm.

Keywords: Mobile Manipulator, Tipover Stability Enhancement, Adaptive Neuro-Fuzzy Inference Controller System, Soft Computing.

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Authors: Behnam Mehrkian, Arash Bahar, Ali Chaibakhsh

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Magneto-rheological (MR) fluid damper is a semiactive control device that has recently received more attention by the vibration control community. But inherent hysteretic and highly nonlinear dynamics of MR fluid damper is one of the challenging aspects to employ its unique characteristics. The combination of artificial neural network (ANN) and fuzzy logic system (FLS) have been used to imitate more precisely the behavior of this device. However, the derivative-based nature of adaptive networks causes some deficiencies. Therefore, in this paper, a novel approach that employ genetic algorithm, as a free-derivative algorithm, to enhance the capability of fuzzy systems, is proposed. The proposed method used to model MR damper. The results will be compared with adaptive neuro-fuzzy inference system (ANFIS) model, which is one of the well-known approaches in soft computing framework, and two best parametric models of MR damper. Data are generated based on benchmark program by applying a number of famous earthquake records.

Keywords: Benchmark program, earthquake record filtering, fuzzy logic, genetic algorithm, MR damper.

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Authors: R. Tur, A. Yardımcı

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Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.

Keywords: Fuzzy logic, decision support systems, construction industry.

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Authors: Badarch Tuyatsetseg

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This paper presents parametric probability density models for call holding times (CHTs) into emergency call center based on the actual data collected for over a week in the public Emergency Information Network (EIN) in Mongolia. When the set of chosen candidates of Gamma distribution family is fitted to the call holding time data, it is observed that the whole area in the CHT empirical histogram is underestimated due to spikes of higher probability and long tails of lower probability in the histogram. Therefore, we provide the Gaussian parametric model of a mixture of lognormal distributions with explicit analytical expressions for the modeling of CHTs of PSNs. Finally, we show that the CHTs for PSNs are fitted reasonably by a mixture of lognormal distributions via the simulation of expectation maximization algorithm. This result is significant as it expresses a useful mathematical tool in an explicit manner of a mixture of lognormal distributions.

Keywords: A mixture of lognormal distributions, modeling call holding times, public safety network.

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Authors: Jiri F. Urbanek, Jiri Barta, Oldrich Svoboda, Jiri J. Urbanek

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The organizations of European and Czech critical infrastructure have specific position, mission, characteristics and behaviour in European Union and Czech state/business environments, regarding specific requirements for regional and global security environments. They must respect policy of national security and global rules, requirements and standards in all their inherent and outer processes of supply - customer chains and networks. A controlling is generalized capability to have control over situational policy. This paper aims and purposes are to introduce the controlling as quite new necessary process attribute providing for critical infrastructure is environment the capability and profit to achieve its commitment regarding to the effectiveness of the quality management system in meeting customer/ user requirements and also the continual improvement of critical infrastructure organization’s processes overall performance and efficiency, as well as its societal security via continual planning improvement via DYVELOP modelling.

Keywords: Added Value, DYVELOP, Controlling, Environments, Process Approach.

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Authors: U. Bottigli, D.Cascio, F. Fauci, B. Golosio, R. Magro, G.L. Masala, P. Oliva, G. Raso, S.Stumbo

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Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% ± 1 of area under ROC curve obtained with the Feed Forward Neural Network.

Keywords: Neural Networks, K-Nearest Neighbours, SupportVector Machine, Computer Aided Diagnosis.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

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The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: Lambros V. Skarlas, Grigorios N. Beligiannis, Efstratios F. Georgopoulos, Adam V. Adamopoulos

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The aim of this contribution is to present a new approach in modeling the electrical activity of the human heart. A recurrent artificial neural network is being used in order to exhibit a subset of the dynamics of the electrical behavior of the human heart. The proposed model can also be used, when integrated, as a diagnostic tool of the human heart system. What makes this approach unique is the fact that every model is being developed from physiological measurements of an individual. This kind of approach is very difficult to apply successfully in many modeling problems, because of the complexity and entropy of the free variables describing the complex system. Differences between the modeled variables and the variables of an individual, measured at specific moments, can be used for diagnostic purposes. The sensor fusion used in order to optimize the utilization of biomedical sensors is another point that this paper focuses on. Sensor fusion has been known for its advantages in applications such as control and diagnostics of mechanical and chemical processes.

Keywords: Artificial Neural Networks, Diagnostic System, Health Condition Modeling Tool, Heart Diagnostics Model, Heart Electricity Model.

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Authors: G. Türkmen, Ö. Tekin, K. Kurtuluş, Y. Y. Yurtseven, M. Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: Convolutional Neural Networks, deep learning, image classification, scanning electron microscope.

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Authors: Simon E. Uguru, Victor E. Idigo, Obinna S. Oguejiofor, Naveed Nawaz

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As the deployment of the Fifth Generation (5G) mobile communication networks take shape all over the world, achieving spectral efficiency, energy efficiency, and dealing with interference are among the greatest challenges encountered so far. The aim of this study is to mitigate inter-cell interference (ICI) in a multi-cell multi-antenna system while maximizing the spectral efficiency of the system. In this study, a system model was devised that showed a miniature representation of a multi-cell multi-antenna system. Based on this system model, a convex optimization problem was formulated to maximize the spectral efficiency of the system while mitigating the ICI. This optimization problem was solved using CVX, which is a modeling system for constructing and solving discipline convex programs. The solutions to the optimization problem are sub-optimal coordinated beamformers. These coordinated beamformers direct each data to the served user equipments (UEs) in each cell without interference during downlink transmission, thereby maximizing the system-wide spectral efficiency.

Keywords: coordinated beamforming, convex optimization, inter-cell interference, multi-antenna, multi-cell, spectral efficiency

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Authors: Dib Djalel, Haddouche Ali, Chellali Benachiba

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When a lightning strike falls near an overhead power line, the intense electromagnetic field radiated by the current of the lightning return stroke coupled with power lines and there induced transient overvoltages, which can cause a back-flashover in electrical network. The indirect lightning represents a major danger owing to the fact that it is more frequent than that which results from the direct strikes. In this paper we present an analysis of the electromagnetic coupling between an external electromagnetic field generated by the lightning and an electrical overhead lines, so we give an important and original contribution: We are based on our experimental measurements which we carried in the high voltage laboratories of EPFL in Switzerland during the last trimester of 2005, on the recent works of other authors and with our mathematical improvement a new particular analytical expression of the electromagnetic field generated by the lightning return stroke was developed and presented in this paper. The results obtained by this new electromagnetic field formulation were compared with experimental results and give a reasonable approach.

Keywords: Lightning, overhead lines, electromagneticcoupling, return stroke, models, induced overvoltages.

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Authors: Masoud Mahdavi

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During an earthquake, the structure is subjected to seismic loads that cause tension in the members of the building. The use of energy dissipation elements in the structure reduces the percentage of seismic forces on the main members of the building (especially the columns). Steel plate shear wall, as one of the most widely used types of energy dissipation element, has evolved today, and regular drilling of its inner plate is one of the common cases. In the present study, using a finite element method, the shear wall of the steel plate is designed as a floor (with dimensions of 447 × 6/246 cm) with Abacus software and in three different modes on which a cyclic load has been applied. The steel shear wall has a horizontal element (beam) with a reduced beam section (RBS). The hole in the interior plate of the models is created in such a way that it has the process of increasing the area, which makes the effect of increasing the surface area of the hole on the seismic performance of the steel shear wall completely clear. In the end, it was found that with increasing the opening level in the steel shear wall (with reduced cross-section beam), total displacement and plastic strain indicators increased, structural capacity and total energy indicators decreased and the Mises Monson stress index did not change much.

Keywords: Steel plate shear wall with opening, cyclic loading, reduced cross-section beam, finite element method, Abaqus Software.

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Authors: Magdalena Naplavova, Tomas Ludik, Petr Hruza, Frantisek Bozek

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The use of IT equipment has become a part of every day. However, each device that is part of cyberspace should be secured against unauthorized use. It is very important to know the basics of these security devices, but also the basics of safe conduct their owners. This information should be part of every curriculum computer science education in primary and secondary schools. Therefore, the work focuses on the education of pupils in primary and secondary schools on the Internet. Analysis of the current state describes approaches to the education of pupils in security issues on the Internet. The paper presents a questionnaire-based survey which was carried out in the Czech Republic, whose task was to ascertain the level of opinion pupils in primary and secondary schools on the issue of communication in social networks. The research showed that awareness of socio-pathological phenomena on the Internet environment is very low. Based on the results it was proposed appropriate ways of teaching to this issue and its inclusion a proposal of curriculum for primary and secondary schools.

Keywords: Cyberspace, educational system, general awareness, information security, questionnaire, socio-pathological phenomena.

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Authors: Mohamad Reza. Mohaghegh, Majid. Malek Jafarian

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This research proposes an algorithm for the simulation of time-periodic unsteady problems via the solution unsteady Euler and Navier-Stokes equations. This algorithm which is called Time Spectral method uses a Fourier representation in time and hence solve for the periodic state directly without resolving transients (which consume most of the resources in a time-accurate scheme). Mathematical tools used here are discrete Fourier transformations. It has shown tremendous potential for reducing the computational cost compared to conventional time-accurate methods, by enforcing periodicity and using Fourier representation in time, leading to spectral accuracy. The accuracy and efficiency of this technique is verified by Euler and Navier-Stokes calculations for pitching airfoils. Because of flow turbulence nature, Baldwin-Lomax turbulence model has been used at viscous flow analysis. The results presented by the Time Spectral method are compared with experimental data. It has shown tremendous potential for reducing the computational cost compared to the conventional time-accurate methods, by enforcing periodicity and using Fourier representation in time, leading to spectral accuracy, because results verify the small number of time intervals per pitching cycle required to capture the flow physics.

Keywords: Time Spectral Method, Time-periodic unsteadyflow, Discrete Fourier transform, Pitching airfoil, Turbulence flow

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Authors: Marijana M. Ačanski, Kristian A. Pastor, Djura N. Vujić

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The identification of lipid and soluble sugar components in flour samples of different cultivars belonging to common oat species (Avena sativa L.) was performed: spring oat, winter oat and hulless oat. Fatty acids were extracted from flour samples with n-hexane, and derivatized into volatile methyl esters, using TMSH (trimethylsulfonium hydroxide in methanol). Soluble sugars were then extracted from defatted and dried samples of oat flour with 96% ethanol, and further derivatized into corresponding TMS-oximes, using hydroxylamine hydrochloride solution and BSTFA (N,O-bis-(trimethylsilyl)-trifluoroacetamide). The hexane and ethanol extracts of each oat cultivar were analyzed using GC-MS system. Lipid and simple sugar compositions are very similar in all samples of investigated cultivars. Chemometric tool was applied to numeric values of automatically integrated surface areas of detected lipid and simple sugar components in their corresponding derivatized forms. Hierarchical cluster analysis shows a very high similarity between the investigated flour samples of oat cultivars, according to the fatty acid content (0.9955). Moderate similarity was observed according to the content of soluble sugars (0.50). These preliminary results support the idea of establishing methods for oat flour authentication, and provide the means for distinguishing oat flour samples, regardless of the variety, from flour samples made of other cereal species, just by lipid and simple sugar profile analysis.

Keywords: Authentication, chemometrics, GC-MS, lipid and soluble sugar composition, oat cultivars.

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Authors: Abderrahmane Amrouche, Jean Michel Rouvaen

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In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural network (GRNN). The relative performances of the proposed model are compared to the similar recognition systems based on the Multilayer Perceptron (MLP), the Recurrent Neural Network (RNN) and the well known Discrete Hidden Markov Model (HMM-VQ) that we have achieved also. Experimental results obtained with Arabic digits have shown that the use of nonparametric density estimation with an appropriate smoothing factor (spread) improves the generalization power of the neural network. The word error rate (WER) is reduced significantly over the baseline HMM method. GRNN computation is a successful alternative to the other neural network and DHMM.

Keywords: Speech Recognition, General Regression NeuralNetwork, Hidden Markov Model, Recurrent Neural Network, ArabicDigits.

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Authors: Sheela Tiwari, R. Naresh, R. Jha

Abstract:

The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: Identification, Neural networks, Predictive control, Transient stability, UPFC.

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on timecontrolled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSO algorithm is a versatile management model for the operation of realworld water distribution system.

Keywords: JPSO, operation, optimization, water distribution system.

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: Artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic.

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Authors: Md Abdullah Al Faruque, Ram Balachandar

Abstract:

Sudden change of bed condition is frequent in open channel flow. Change of bed condition affects the turbulence characteristics in both streamwise and wall-normal direction. Understanding the turbulence intensity in open channel flow is of vital importance to the modeling of sediment transport and resuspension, bed formation, entrainment, and the exchange of energy and momentum. A comprehensive study was carried out to understand the extent of the effect of Reynolds number and bed roughness on different turbulence characteristics in an open channel flow. Four different bed conditions (impervious smooth bed, impervious continuous rough bed, pervious rough sand bed, and impervious distributed roughness) and two different Reynolds numbers were adopted for this cause. The effect of bed roughness on different turbulence characteristics is seen to be prevalent for most of the flow depth. Effect of Reynolds number on different turbulence characteristics is also evident for flow over different bed, but the extent varies on bed condition. Although the same sand grain is used to create the different rough bed conditions, the difference in turbulence characteristics is an indication that specific geometry of the roughness has an influence on turbulence characteristics. Roughness increases the contribution of the extreme turbulent events which produces very large instantaneous Reynolds shear stress and can potentially influence the sediment transport, resuspension of pollutant from bed and alter the nutrient composition, which eventually affect the sustainability of benthic organisms.

Keywords: Open channel flow, Reynolds Number, roughness, turbulence.

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Authors: W. Rowan, Y. O’Connor, L. Lynch, C. Heavin

Abstract:

Situation Awareness can offer the potential for conscious dynamic reflection. In an era of online health data sharing, it is becoming increasingly important that users of health social networks (HSNs) have the information necessary to make informed decisions as part of the registration process and in the provision of eConsent. This research aims to leverage an adapted Situation Awareness (SA) model to explore users’ decision making processes in the provision of eConsent. A HSN platform was used to investigate these behaviours. A mixed methods approach was taken. This involved the observation of registration behaviours followed by a questionnaire and focus group/s. Early results suggest that users are apt to automatically accept eConsent, and only later consider the long-term implications of sharing their personal health information. Further steps are required to continue developing knowledge and understanding of this important eConsent process. The next step in this research will be to develop a set of guidelines for the improved presentation of eConsent on the HSN platform.

Keywords: eConsent, health social network, mixed methods, situation awareness.

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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.

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Authors: Z. Mazrouei-Sebdani, A. Khoddami, H. Hadadzadeh, M. Zarrebini

Abstract:

In this research, waterglass based aerogel powder was prepared by sol–gel process and ambient pressure drying. Inspired by limited dust releasing, aerogel powder was introduced to the PET electrospinning solution in an attempt to create required bulk and surface structure for the nanofibers to improve their hydrophobic and insulation properties. The samples evaluation was carried out by measuring density, porosity, contact angle, heat transfer, FTIR, BET, and SEM. According to the results, porous silica aerogel powder was fabricated with mean pore diameter of 24 nm and contact angle of 145.9º. The results indicated the usefulness of the aerogel powder confined into nanofibers to control surface roughness for manipulating superhydrophobic nanowebs with water contact angle of 147º. It can be due to a multi-scale surface roughness which was created by nanowebs structure itself and nanofibers surface irregularity in presence of the aerogels while a layer of fluorocarbon created low surface energy. The wettability of a solid substrate is an important property that is controlled by both the chemical composition and geometry of the surface. Also, a decreasing trend in the heat transfer was observed from 22% for the nanofibers without any aerogel powder to 8% for the nanofibers with 4% aerogel powder. The development of thermal insulating materials has become increasingly more important than ever in view of the fossil energy depletion and global warming that call for more demanding energysaving practices.

Keywords: Superhydrophobicity, Insulation, Sol-gel, Surface energy, Roughness.

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