Search results for: and Wireless sensor networks.

Authors: José Neves, M. Rosário Martins, Fátima Candeias, Diana Ferreira, Sílvia Arantes, Júlio Cruz-Morais, Guida Gomes, Joaquim Macedo, António Abelha, Henrique Vicente

Abstract:

Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.

Keywords: Artificial neuronal networks, essential oils, knowledge representation and reasoning, logic programming, Schinus molle L, Schinus terebinthifolius raddi.

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Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

Abstract:

The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: Extraction and data integration, bibliometrics, scientometrics.

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Authors: S. Padmapriya

Abstract:

Vehicular Adhoc Networks (VANETs), a subset of Mobile Adhoc Networks (MANETs), refers to a set of smart vehicles used for road safety. This vehicle provides communication services among one another or with the Road Side Unit (RSU). Security is one of the most critical issues related to VANET as the information transmitted is distributed in an open access environment. As each vehicle is not a source of all messages, most of the communication depends on the information received from other vehicles. To protect VANET from malicious action, each vehicle must be able to evaluate, decide and react locally on the information received from other vehicles. Therefore, message verification is more challenging in VANET because of the security and privacy concerns of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.

Keywords: VANET, security, malicious vehicle detection, threshold value, distrust value.

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Authors: Eklas Hossain, Sheroz Khan, Ahad Ali

Abstract:

Power line communications may be used as a data communication channel in public and indoor distribution networks so that it does not require the installing of new cables. Industrial low voltage distribution network may be utilized for data transfer required by the on-line condition monitoring of electric motors. This paper presents a pilot distribution network for modeling low voltage power line as data transfer channel. The signal attenuation in communication channels in the pilot environment is presented and the analysis is done by varying the corresponding parameters for the signal attenuation.

Keywords: Data communication, indoor distribution networks, low voltage, power line.

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Authors: Ondřej Nowak

Abstract:

This paper presents an exploration into the structure of the corporate governance network and interlocking directorates in the Czech Republic. First a literature overview and a basic terminology of the network theory is presented. Further in the text, statistics and other calculations relevant to corporate governance networks are presented. For this purpose an empirical data set consisting of 2 906 joint stock companies in the Czech Republic was examined. Industries with the highest average number of interlocks per company were healthcare, and energy and utilities. There is no observable link between the financial performance of the company and the number of its interlocks. Also interlocks with financial companies are very rare.

Keywords: Corporate Governance, Interlocking Directorates, Network Theory, Czech Republic.

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Authors: Jamileh Vasheghani Farahani

Abstract:

The main purpose of this research is to show the current active faults and active tectonic of the area by three seismic networks in Tehran region: 1-Tehran Disaster Mitigation and Management Organization (TDMMO), 2-Broadband Iranian National Seismic Network Center (BIN), 3-Iranian Seismological Center (IRSC). In this study, we analyzed microearthquakes happened in Tehran city and its surroundings using the Tehran networks from 1996 to 2015. We found some active faults and trends in the region. There is a 200-year history of historical earthquakes in Tehran. Historical and instrumental seismicity show that the east of Tehran is more active than the west. The Mosha fault in the North of Tehran is one of the active faults of the central Alborz. Moreover, other major faults in the region are Kahrizak, Eyvanakey, Parchin and North Tehran faults. An important seismicity region is an intersection of the Mosha and North Tehran fault systems (Kalan village in Lavasan). This region shows a cluster of microearthquakes. According to the historical and microseismic events analyzed in this research, there is a seismic gap in SE of Tehran. The empirical relationship is used to assess the Mmax based on the rupture length. There is a probability of occurrence of a strong motion of 7.0 to 7.5 magnitudes in the region (based on the assessed capability of the major faults such as Parchin and Eyvanekey faults and historical earthquakes).

Keywords: Iran, major faults, microseismicity, Tehran.

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Authors: Pyone Lai Swe, Wanna Swe, Kyaw Myo Lin

Abstract:

Voltage stability has become an important issue to many power systems around the world due to the weak systems and long line on power system networks. In this paper, MATLAB load flow program is applied to obtain the weak points in the system combined with finding the voltage stability limit. The maximum permissible loading of a system, within the voltage stability limit, is usually determined. The methods for varying tap ratio (using tap changing transformer) and applying different values of shunt capacitor injection to improve the voltage stability within the limit are also provided.

Keywords: Load flow, Voltage stability, Tap changingtransformer, Shunt capacitor injection, Voltage stability limit

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Authors: B. Rebekka, B. Malarkodi

Abstract:

This paper describes an approach to detect the transmitted signals for 2×2 Multiple Input Multiple Output (MIMO) setup using roulette wheel based ant colony optimization technique. The results obtained are compared with classical zero forcing and least mean square techniques. The detection rates achieved using this technique are consistently larger than the one achieved using classical methods for 50 number of attempts with two different antennas transmitting the input stream from a user. This paves the path to use alternative techniques to improve the throughput achieved in advanced networks like Long Term Evolution (LTE) networks.

Keywords: MIMO, ant colony optimization, roulette wheel, soft computing, LTE.

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Authors: Shiann-Shiun Jeng, Jia-Ming Chen, Hong-Zong Lin, Chen-Wan Tsung

Abstract:

For cognitive radio networks, there is a major spectrum sensing problem, i.e. dynamic spectrum management. It is an important issue to sense and identify the spectrum holes in cognitive radio networks. The first-order derivative scheme is usually used to detect the edge of the spectrum. In this paper, a novel spectrum sensing technique for cognitive radio is presented. The proposed algorithm offers efficient edge detection. Then, simulation results show the performance of the first-order derivative scheme and the proposed scheme and depict that the proposed scheme obtains better performance than does the first-order derivative scheme.

Keywords: cognitive radio, Spectrum Sensing, wavelet, edgedetection

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Authors: Ting-Yu Hsu, Shyu-Yu Wu, Shieh-Kung Huang, Hung-Wei Chiang, Kung-Chun Lu, Pei-Yang Lin, Kuo-Liang Wen

Abstract:

Regional earthquake early warning (EEW) systems are not suitable for Taiwan, as most destructive seismic hazards arise due to in-land earthquakes. These likely cause the lead-time provided by regional EEW systems before a destructive earthquake wave arrives to become null. On the other hand, an on-site EEW system can provide more lead-time at a region closer to an epicenter, since only seismic information of the target site is required. Instead of leveraging the information of several stations, the on-site system extracts some P-wave features from the first few seconds of vertical ground acceleration of a single station and performs a prediction of the oncoming earthquake intensity at the same station according to these features. Since seismometers could be triggered by non-earthquake events such as a passing of a truck or other human activities, to reduce the likelihood of false alarms, a seismometer was installed at three different locations on the same site and the performance of the EEW system for these three sensor locations were discussed. The results show that the location on the ground of the first floor of a school building maybe a good choice, since the false alarms could be reduced and the cost for installation and maintenance is the lowest.

Keywords: Earthquake early warning, Single station approach, Seismometer location.

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Authors: Nicolás H. Beltrán, Manuel A. Duarte-Mermoud, Víctor A. Soto, Sebastián A. Salah, and Matías A. Bustos

Abstract:

Results of Chilean wine classification based on the information provided by an electronic nose are reported in this paper. The classification scheme consists of two parts; in the first stage, Principal Component Analysis is used as feature extraction method to reduce the dimensionality of the original information. Then, Radial Basis Functions Neural Networks is used as pattern recognition technique to perform the classification. The objective of this study is to classify different Cabernet Sauvignon, Merlot and Carménère wine samples from different years, valleys and vineyards of Chile.

Keywords: Feature extraction techniques, Pattern recognitiontechniques, Principal component analysis, Radial basis functionsneural networks, Wine classification.

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Authors: Muzaffar Bashir, Jürgen Kempf

Abstract:

The purpose of this paper is to present a Dynamic Time Warping technique which reduces significantly the data processing time and memory size of multi-dimensional time series sampled by the biometric smart pen device BiSP. The acquisition device is a novel ballpoint pen equipped with a diversity of sensors for monitoring the kinematics and dynamics of handwriting movement. The DTW algorithm has been applied for time series analysis of five different sensor channels providing pressure, acceleration and tilt data of the pen generated during handwriting on a paper pad. But the standard DTW has processing time and memory space problems which limit its practical use for online handwriting recognition. To face with this problem the DTW has been applied to the sum of the five sensor signals after an adequate down-sampling of the data. Preliminary results have shown that processing time and memory size could significantly be reduced without deterioration of performance in single character and word recognition. Further excellent accuracy in recognition was achieved which is mainly due to the reduced dynamic time warping RDTW technique and a novel pen device BiSP.

Keywords: Biometric character recognition, biometric person authentication, biometric smart pen BiSP, dynamic time warping DTW, online-handwriting recognition, multidimensional time series.

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Authors: Mohammad Reza Askari, Mohsen Kazemi, Ali Asghar Baziar

Abstract:

In this treatise we will study the capability of static compensator for reactive power to stabilize sheen voltage with motor loading on power networks system. We also explain the structure and main function of STATCOM and the method to control it using STATCOM transformer current to simultaneously predict after telling about the necessity of FACTS tools to compensate in power networks. Then we study topology and controlling system to stabilize voltage during start of inductive motor. The outcome of stimulat by MATLAB software supports presented controlling idea and system in the treatise.

Keywords: Power network, inductive motor, reactive power, stability of voltage, STATCOM, FACTS

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Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

Abstract:

Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: Social Network, link prediction, granular computing, Type-2 fuzzy sets.

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Authors: Takashi Shimizu, Tomoaki Hashimoto

Abstract:

A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems.

Keywords: Model predictive control, unscented Kalman filter, nonlinear systems, implicit systems.

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Authors: H. B. Mehta, Vipul M. Patel, Jyotirmay Banerjee

Abstract:

The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.

Keywords: Minichannel, Two-Phase Flow, Frictional Pressure Drop, ANN, MARD, MRD.

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Authors: Defu Zhang, Qingshan Jiang, Xin Li

Abstract:

This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.

Keywords: Data mining, neural network, stock forecasting.

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Authors: Vijayalakshmy Periaswamy S., Kevin McDaid

Abstract:

Testing accounts for the major percentage of technical contribution in the software development process. Typically, it consumes more than 50 percent of the total cost of developing a piece of software. The selection of software tests is a very important activity within this process to ensure the software reliability requirements are met. Generally tests are run to achieve maximum coverage of the software code and very little attention is given to the achieved reliability of the software. Using an existing methodology, this paper describes how to use Bayesian Belief Networks (BBNs) to select unit tests based on their contribution to the reliability of the module under consideration. In particular the work examines how the approach can enhance test-first development by assessing the quality of test suites resulting from this development methodology and providing insight into additional tests that can significantly reduce the achieved reliability. In this way the method can produce an optimal selection of inputs and the order in which the tests are executed to maximize the software reliability. To illustrate this approach, a belief network is constructed for a modern software system incorporating the expert opinion, expressed through probabilities of the relative quality of the elements of the software, and the potential effectiveness of the software tests. The steps involved in constructing the Bayesian Network are explained as is a method to allow for the test suite resulting from test-driven development.

Keywords: Software testing, Test Driven Development, Bayesian Belief Networks.

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Authors: A. Hossein Sahoolizadeh, B. Zargham Heidari, C. Hamid Dehghani

Abstract:

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Keywords: Face recognition Principal component analysis, Linear discriminant analysis, Neural networks.

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Authors: M. Sinecen, M. Makinacı

Abstract:

The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.

Keywords: Artificial neural networks, texture classification, cancer diagnosis.

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Authors: Mohamed H. S. Mohamed, Yang Xiao-zong, Liu Hong-wei, Wu Zhi-bo

Abstract:

The reliability of distributed systems and computer networks have been modeled by a probabilistic network or a graph G. Computing the residual connectedness reliability (RCR), denoted by R(G), under the node fault model is very useful, but is an NP-hard problem. Since it may need exponential time of the network size to compute the exact value of R(G), it is important to calculate its tight approximate value, especially its lower bound, at a moderate calculation time. In this paper, we propose an efficient algorithm for reliability lower bound of distributed systems with unreliable nodes. We also applied our algorithm to several typical classes of networks to evaluate the lower bounds and show the effectiveness of our algorithm.

Keywords: Distributed systems, probabilistic network, residual connectedness reliability, lower bound.

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Authors: Wajdi Bellil, Chokri Ben Amar, Adel M. Alimi

Abstract:

An application of Beta wavelet networks to synthesize pass-high and pass-low wavelet filters is investigated in this work. A Beta wavelet network is constructed using a parametric function called Beta function in order to resolve some nonlinear approximation problem. We combine the filter design theory with wavelet network approximation to synthesize perfect filter reconstruction. The order filter is given by the number of neurons in the hidden layer of the neural network. In this paper we use only the first derivative of Beta function to illustrate the proposed design procedures and exhibit its performance.

Keywords: Beta wavelets, Wavenet, multiresolution analysis, perfect filter reconstruction, salient point detect, repeatability.

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Authors: Belkacem Selma, Boumediene Selma, Samira Chouraqui, Hanifi Missoum, Tourkia Guerzou

Abstract:

Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. Earthquake prediction to prevent the loss of human lives and even property damage is an important factor; that, is why it is crucial to develop techniques for predicting this natural disaster. This study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 104 J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines have been analyzed. The results obtained show that the ANN is able to predict earthquake parameters with  high accuracy; the classification accuracy through neural networks is more than 94%, and the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: Earthquake prediction, artificial intelligence, AI, Artificial Neural Network, ANN, seismic bumps.

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Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

Abstract:

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.

Keywords: Time-series forecasting, weather modeling.

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Authors: Maruto Masserie Sardadi, Mohd Shafry bin Mohd Rahim, Zahabidin Jupri, Daut bin Daman

Abstract:

The latest Geographic Information System (GIS) technology makes it possible to administer the spatial components of daily “business object," in the corporate database, and apply suitable geographic analysis efficiently in a desktop-focused application. We can use wireless internet technology for transfer process in spatial data from server to client or vice versa. However, the problem in wireless Internet is system bottlenecks that can make the process of transferring data not efficient. The reason is large amount of spatial data. Optimization in the process of transferring and retrieving data, however, is an essential issue that must be considered. Appropriate decision to choose between R-tree and Quadtree spatial data indexing method can optimize the process. With the rapid proliferation of these databases in the past decade, extensive research has been conducted on the design of efficient data structures to enable fast spatial searching. Commercial database vendors like Oracle have also started implementing these spatial indexing to cater to the large and diverse GIS. This paper focuses on the decisions to choose R-tree and quadtree spatial indexing using Oracle spatial database in mobile GIS application. From our research condition, the result of using Quadtree and R-tree spatial data indexing method in one single spatial database can save the time until 42.5%.

Keywords: Indexing, Mobile GIS, MapViewer, Oracle SpatialDatabase.

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Authors: T. C. Manjunath, B. Bandyopadhyay

Abstract:

This paper deals with the design of a periodic output feedback controller for a flexible beam structure modeled with Timoshenko beam theory, Finite Element Method, State space methods and embedded piezoelectrics concept. The first 3 modes are considered in modeling the beam. The main objective of this work is to control the vibrations of the beam when subjected to an external force. Shear piezoelectric sensors and actuators are embedded into the top and bottom layers of a flexible aluminum beam structure, thus making it intelligent and self-adaptive. The composite beam is divided into 5 finite elements and the control actuator is placed at finite element position 1, whereas the sensor is varied from position 2 to 5, i.e., from the nearby fixed end to the free end. 4 state space SISO models are thus developed. Periodic Output Feedback (POF) Controllers are designed for the 4 SISO models of the same plant to control the flexural vibrations. The effect of placing the sensor at different locations on the beam is observed and the performance of the controller is evaluated for vibration control. Conclusions are finally drawn.

Keywords: Smart structure, Timoshenko beam theory, Periodic output feedback control, Finite Element Method, State space model, SISO, Embedded sensors and actuators, Vibration control.

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Authors: Christos E. Alexakos, Konstantinos C. Giotopoulos, Eleni J. Thermogianni, Grigorios N. Beligiannis, Spiridon D. Likothanassis

Abstract:

In this contribution an innovative platform is being presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

Keywords: Bayesian Networks, Computational Intelligence techniques, E-learning legacy systems, Service Oriented Integration, Intelligent Agents

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Authors: C. Rajan, N. Shanthi, C. Rasi Priya, K. Geetha

Abstract:

Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth, frequent topology changes caused by node mobility and power energy consumption. In order to efficiently transmit data to destinations, the applicable routing algorithms must be implemented in mobile ad-hoc networks. Thus we can increase the efficiency of the routing by satisfying the Quality of Service (QoS) parameters by developing routing algorithms for MANETs. The algorithms that are inspired by the principles of natural biological evolution and distributed collective behavior of social colonies have shown excellence in dealing with complex optimization problems and are becoming more popular. This paper presents a survey on few meta-heuristic algorithms and naturally-inspired algorithms.

Keywords: Ant colony optimization, genetic algorithm, Naturally-inspired algorithms and particle swarm optimization.

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Authors: Mahmoud M. Othman, Y. G. Hegazy, A. Y. Abdelaziz

Abstract:

This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm. 

Keywords: Distributed generation, heuristic approach, Optimization, planning.

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Authors: Dalibor Dobrilovic, Borislav Odadžic

Abstract:

In this paper is being described a possible use of virtualization technology in teaching computer networks. The virtualization can be used as a suitable tool for creating virtual network laboratories, supplementing the real laboratories and network simulation software in teaching networking concepts. It will be given a short description of characteristic projects in the area of virtualization technology usage in network simulation, network experiments and engineering education. A method for implementing laboratory has also been explained, together with possible laboratory usage and design of laboratory exercises. At the end, the laboratory test results of virtual laboratory are presented as well.

Keywords: Computer network simulation software, teaching networking concepts, virtual network laboratory, virtualization technology.

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