2012
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Optimal DG Placement for Power Loss Reduction and Improvement Voltage Profile Using Smart Methods
2
2
Distributed Generations (DGs) are utilized to supply the active and reactive power in the transmission and distribution systems. These types of power sources have many benefits such as power quality enhancement, voltage deviation reduction, power loss reduction, load shedding reduction, reliability improvement, etc. In order to reach the above benefits, the optimal placement and sizing of DG is significant. In this regard, this paper gets use of the Bacteria Foraging Algorithm (BFA) and Binary Genetic Algorithm (BGA) to investigate the DG placement with the purpose of power loss and voltage deviation reduction. The proposed method is applied on the 33bus and 69bus IEEE test systems and the optimal place and size of DGs from the power losses and voltage deviation minimization are assessed. Also, the performance of the above two algorithms are compared with each other.
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141
147


S.A
Hashemi Zadeh
Department of Electrical Engineering, Islamic Azad University, Rafsanjan, Iran,
Department of Electrical Engineering, Islamic
Iran
sa_hashemizadeh@yahoo.com


O
Zeidabadi Nejad
Department of Electrical Engineering, Islamic Azad University, Najaf Abad, Iran,
Department of Electrical Engineering, Islamic
Iran
univ_omid@yahoo.com


S
hasani
Department of Electrical Engineering, Islamic Azad University, Sirjan Science and Research Branch,
Department of Electrical Engineering, Islamic
Iran
saeed.hasani7700@yahoo.com


A.A
Gharaveisi
Assistant of Control Engineering, Department of electrical engineering, Shahid Bahonar University, Kerman, Iran,
Assistant of Control Engineering, Department
Iran
a_gharaveisi@yahoo.com


GH
Shahgholian
Associate of Electrical Engineering, Department of electrical engineering, Islamic Azad University, Najaf Abad, Iran,
Associate of Electrical Engineering, Department
Iran
shahgholian@iaun.ac.ir
Bacteria Foraging Algorithm (BFA) and Binary Genetic Algorithm (BGA)
Distributed Generation (DG)
Voltage Deviation
Distribution Systems
[Salvaderi, L.An ,“international perspective on the future of power generation and transmission worldwide: the Italian case”, IEEE Transaction on Energy Conversion, Mar 2006. ##G. Pepermans, J.Driesen, D.Haeseldonckx, R. Belmans and W.D' haeseleer, “Distrbuted generation: definition, benefits and issues”, Energy Policy, In Press, Corrected Proof, Available online 20 November, 2003. ##Khanabadi, M.; Doostizadeh, M. Esmaeilian, A. Mohseninezhad, M., “Transmission Congestion Management through Optimal Distributed Generation's Sizing and”, ##International Conference on Environment and Electrical Engineering (EEEIC), 2011. ##T. N. Shukla, S.P. Singh, K. B. Naik, “ Allocation of optimal distributed generation using GA for minimum system losses in radial distribution networks”, International Journal of Engineering, Science and Technology Vol. 2, No.3, pp. 94106,2010. ##R. K. Singh and S. K. Goswami, “Optimum Allocation of Distributed Generations Based on Nodal Pricing for Profit, Loss Reduction and Voltage Improvement Including Voltage Rice Issue”, International Journal of Electrical Power and Energy Systems, Vol. 32, No. 6, pp. 637644,2010. ##Deependra Singh, Devender Singh, and K. S. Verma, “GA based Optimal Sizing & Placement of Distributed Generation for Loss Minimization”, International Journal of Intelligent Systems and Technologieys, 2007. ##J.H. Teng, T.S. Luor and Y.H. Liu,“Strategic distributed generator placements for service reliability improvements”, IEEE Power Engineering Society Summer Meeting, Vol.2, pp.719724, July 2002. ##Hung D.Q., Mithulananthan N. and Bansal R.C., “Analytical Expressions for DG Allocation in Primary Distribution Networks”, IEEE Transactions on Energy Conversion, Vol.25, No.3, pp.814820,2010. ##Singh D., Singh D., Verma K.S., “Multiobjective optimization for DG planning with load models,” IEEE Transactions on Power Systems, Vol.24, No.1, pp.427436, 2009. ##Singh R.K. and Goswami S.K., “Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue,” Electrical Power and Energy Systems, Vol.32, pp.637–644, 2010. ##Dasan S.G.B. and Devi R.P.K., “Optimal siting and sizing of hybrid distributed generation using fuzzyEP,” International Journal of Distributed Energy Resources, Vol.6, No.2, pp.163188, 2010. ##Wichit Krueasuk, Weerakorn Ongsakul,“Optimal Placement of Distributed Generation Using Particle Swarm Optimization”, Asian Institute Of Technology Energy Field Of Study 2006. ##K.F.Man, K.S.Tang and S.Kwong,“Genetic Algorithm concepts and application”, IEEE Trans.On Industrial Electronics , Vol.43, No.5, pp.519534, Oct 1996.##]
Congestion Management in Electricity Markets Using Demand Response Programs and Series FACTS Devices
2
2
In today’s restructured environment, congestion management plays an essential role in power system operation. Different methods are presented and discussed in this respect for congestion management in shortterm and longterm intervals. It is attempted in the present paper to investigate the impact mechanism of FACTS devices and demand response programs together with generation redispatch as some facilities from transmission, consumption and generation sides on shortterm congestion management of electricity market. For this purpose, Thyristor controlled Series Capacitor (TCSC) representing series FACTS devices and Direct Load Control (DLC) program representing demand response programs in dayahead power pool market are mathematically modeled and results will be numerically studied and analyzed on the 14bus IEEE test system.
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149
159


Mohammad
Moradi
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science
Iran
moradi_mohammad63@yahoo.com


MahmoudReza
Haghifam
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Electrical and Computer Engineering,
Iran
haghifam@modares.ac.ir


Soudabe
Soleymani
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science
Iran
soodabeh_soleymani@yahoo.com
congestion management
Series FACTS Devices
Thyristor controlled Series Capacitor (TCSC)
Demand Response Programs
Direct Load Control (DLC)
[L. Jidong, Z. Zehui, Z. Li, H. Xueshan, “Evaluating Short Term Benefits of Demand Response”, IEEE 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp.12111215, 2011 ##P. Moore, P. Ashmole, “Flexible AC Transmission Systems” Pt.1, Power Engineering Journal, Dec 1995. ##S. Grebex, Cherokaoui, A.J. Germond, “Optimal Location of FACTS Devices to Enhance Power System Security”, IEEE Power Tech Conference Proceeding, Vol.3, pp.2326, Jun 2003. ##Y. Lue and A. Abur, “Improving System Static Security via Optimal Placement of Thyristor Controlled Series Capacitors (TCSC)”, IEEE Power Engineering Society Winter Meeting, Vol.2, pp.516521, 2001. ##H. Besharat, S.A. Taher, “Congestion Management by Determining Optimal Location of TCSC in Deregulated Power Systems”, Electrical Power and Energy Systems, Elsevier, pp.563568, 2008. ##Allen J. Wood and Bruce F. Wollenberg, “Power Generation, Operation & Control”, New York: Wiley, 1996. ##Federal Energy Regulatory Commission Staff, “Assessment of Demand Response and Advanced Metering”, Federal Energy Regulatory Commission, Docket AD062000, Aug 2006. ##Federal Energy Regulatory Commission Staff, “Assessment of Demand Response and Advanced Metering”, Federal Energy Regulatory Commission, Docket AD062000, Aug 2006. ##P. Jazayeri, A. Schellenberg, W.D. Rosehart, J. Doudna, “A Survey of Load Control Programs for Price and System Stability”, IEEE Transactions on Power Systems, Vol.20, No.3, pp.15041509, Aug 2005. ##A. Yousefi, E. Shayesteh, M. ParsaMoghaddam, “Enhancement of Spinning Reserve Capacity by means of Optimal Utilization of EDRP Program”, IASTED Conferences, Power and Energy Systems Conference, 2008. ##D. Kirschen, G. Strbac, “Fundamentals of Power System Economics”, John Wiley & Sons, 2004. ##H. Aalami, G.R. Yousefi, M. ParsaMoghadam, “Demand Response Model Considering EDRP and TOU Programs”, IEEE/PES Transmission and Distribution Conference & Exhibition, May 2008 ##]
Optimal Current Meter Placement for Accurate Fault Location Purpose using Dynamic Time Warping
2
2
This paper presents a fault location technique for transmission lines with minimum current measurement. This algorithm investigates proper current ratios for fault location problem based on thevenin theory in faulty power networks and calculation of short circuit currents in each branch. These current ratios are extracted regarding lowest sensitivity on thevenin impedance variations of the network structure. Proposed algorithm compares current ratios from offline calculations with corresponding values achieved from measurements with a lookup table system. Best solution based on Dynamic Time Warping (DTW) algorithm is introduced as an output (location of the fault) which includes the line and the distance. Among many current ratios to form lookup table system, the minimum number of them will be extracted by a multiobjective optimization technique using Bees Algorithm (BA). This extraction is based on lowest possible number of buses for instruments installation and required current measurements, estimation accuracy and sensitivity degree from thevenin impedances changes. Accuracy of proposed algorithm is evaluated in a widely used multimachine network of Western Systems Coordinating Council (WSCC).
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161
173


K. G.
Firouzjah
Faculty of Electrical and Computer Engineering, Babol (Noshirvani) University of Technology, Babol, Iran.
Faculty of Electrical and Computer Engineering,
Iran
kgorgani@stu.nit.ac.ir


A
Sheikholeslam
Faculty of Electrical and Computer Engineering, Babol (Noshirvani) University of Technology, Babol, Iran.
Faculty of Electrical and Computer Engineering,
Iran
Fault Location
Current Measurement placement
Dynamic Time Warping
Optimization
Bees Algorithm
[A. Sauhats, M. Danilova, "Fault location algorithms for super high voltage power transmission lines", Power Tech, in Proc IEEE, Vol. 3, pp. 3, 2003. ##Z. Galijasevic, A. Abur, "Fault location using voltage measurements", IEEE Trans. Power Delivery, Vol. 17, No. 2, pp. 441 – 445, 2002. ##P. Bastard, L. GarciaSantander, X.Le. Pivert, I. Gal, E.L. Parra, "A voltagebased fault location method for radial distribution networks", in Proc IEEE, Power System Management and Control International Conference, No. 488, pp. 216 – 221, 2002. ##C.E. de Morais Pereira, L.C. Zanetta, "Fault location in transmission lines using oneterminal postfault voltage data", IEEE Trans. Power Delivery, Vol. 19, No. 2, pp. 570 – 575, 2004. ##Li Yongli; Yi. Zhang; Ma. Zhiyu,"Fault location method based on the periodicity of the transient voltage traveling wave", TENCON, IEEE Region 10 Conference, Vol. 3, pp. ##389 – 392, 2004. ##Y.J. Xia, X.G. Yin, Z.H. Wang, J.C. Yang, X.B. Zhang, "A novel fault location scheme using voltage travelingwave of CVTs", UPEC 2004, Vol. 2, pp. 768 – 772, 2004. ##J.A. Jiang, J.Z. Yang, Y.H. Lin, C.W. Liu, J.C. Ma, "An adaptive PMU based fault detection/location technique for transmission lines, Part I: Theory and algorithms", IEEE Trans. Power Delivery, Vol. 15, pp. 486–493, 2000. ##J.A. Jiang, Y.H. Lin, J.Z. Yang, T.M. Too, C.W. Liu, "An adaptive PMU based fault detection/location technique for transmission lines, Part II: PMU implementation and performance evaluation", IEEE Trans. Power Delivery, Vol. 15, pp. 1136–1146, 2000. ##S.M. Brahma, "New faultlocation method for a single multi terminal transmission line using synchronized phasor measurements", IEEE Trans. Power Delivery, Vol. 21, No. 3, pp. 1148 – 1153, 2006. ##S.M. Brahma, A.A. Girgis, "Fault location on a transmission line using synchronized Voltage measurements", IEEE Trans. Power Delivery, Vol. 19, No. 4, pp. 1619 – 1622, 2004. ##S.M. Brahma,"Fault location scheme for a multiterminal transmission line using synchronized Voltage measurements", IEEE Trans. Power Delivery, Vol. 20, No. 2, Part 2, pp. 1325 – 1331, 2005. ##K. G. Firouzjah, A. Sheikholeslami, "Current Independent Method Based on Synchronized Voltage Measurement for Fault Location on Transmission Lines,” Simulation Modelling Practice and Theory, Vol. 17, No.4, pp. 692707, April 2009. ##M.H.J. Bollen, Understanding Power Quality previous Problems: Voltage Sags and Interruptions, New York: Wiley, 2000. ##R.F. Nuqui, and A.G. Phadke, "Phasor measurement unit placement techniques for complete and incomplete observability," IEEE Trans. Power Delivery, Vol. 20, No. 4, Oct. 2005. ##A. Ahmadi, Y. AlinejadBeromi and M. Moradi, "Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy," Expert Systems with Applications, Vol. 38, No. 6, pp. 72637269, 2011. ##R. Sodhi, S. C. Srivastava and S. N. Singh , "Optimal PMU placement method for complete topological and numerical observability of power system," Electric Power Systems Research, Vol. 80, No. 9, pp. 11541159, 2010. ##S. Chakrabarti and E. Kyriakides, "Optimal placement of phasor measurement units for power system observability," IEEE Trans. Power Systems, Vol. 23, No. 3, pp. 1433–1440, 2008. ##D.J. Won and S.I. Moon, "Optimal Number and Locations of Power Quality Monitors Considering System Topology," IEEE Trans. Power Delivery, Vol. 23, No. 1, pp. 288–295, 2008. ##M. A. Eldery, E. F. ElSaadany, M. M. A. Salama and A. Vannelia, "A Novel Power Quality Monitoring Allocation Algorithm," IEEE Trans. Power Delivery, Vol. 21, No. 2, pp. 768 – 777, 2006. ##J. Chen and A. Abur, "Placement of PMUs to Enable Bad Data Detection in State Estimation", IEEE Trans. Power System, Vol. 21, No. 4, pp. 16081615, 2006. ##B. Xu and A. Abur, "Observability analysis and measurement placement for system with PMUs," in Proc. 2004 IEEE Power Systtem Conf., Vol. 2, pp. 943–946. ##Yang X et al. "Coordinated algorithms for distributed state estimation with synchronized phasor measurements," Applied Energy, 2011, doi:10.1016/j.apenergy.2011.11.010 ##M. Hajian, A. M. Ranjbar, T. Amraee, and B. Mozafari, ##International Journal of Smart Electrical Engineering, Vol.1, No.3, Fall 2012 ISSN: 22519246 ##“Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm,” Int. J. Electric Power Energy System, Vol. 33, no. 1, pp. 28–34, Jan. 2011. ##F. Aminifar, A. Khodaei, M. FotuhiFiruzabad, M. Shahidehpour, “Contingency constrained PMU placement in power networks,” IEEE Trans. Power System, Vol. 25, No. 1, pp. 516–23, 2010. ##S. Chakrabarti, E. Kyriakides, DG. Eliades, “Placement of synchronized measurements for power system observability,” IEEE Trans. Power Delivery, Vol. 24, No. 1, pp. 12–9, 2009. ##A. Enshaee, R. A. Hooshmand, F. H. Fesharaki, “A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies,” Electric Power Systems Research, Vol. 89 pp. 110, 2012. ##K. P. Lien, C. W. Liu, C. –S. Yu, and J.A. Jiang, “Transmission network fault location observability with minimal PMU placement,” IEEE Trans.Power Del.,vol.21, no. 3, pp.11281136, Jul. 2006. ##K. Mazlumi, H. A. Askarian, S. H. Sadegi, and S. S. Geramian, “ Determination of optimal pmu placement for faultlocation observability,” in Third international conference on Electric UtilityDeregulation and Restructuring and Power Technologies, Apr. 69, 2008, pp. 19381942. ##S. S. Geramian, H. A. Askarian, K. Mazlumi, “Determination of optimal PMU placement for fault location using genetic algorithm,” ICHQP2008 13th International conference on Harmonics and Quality of power, pp. 15, 2008. ##S. P. Pokharel, S. Brahma, “Optimal PMU placement for fault location in a power system,” North American Power Symposium (NAPS), pp. 15, 2009. ##W. Pedrycz, KnowledgeBased Clustering  From Data to ##Information Granules, John Wiley & Sons, 2005. ##A. Zehtabian, H. Hassanpour, A Nondestructive Approach for Noise Reduction in Time Domain, World Applied Sciences Journal 6 (1) (2009) 5363. ##M. Muller, Information Retrieval for Music and Motion, Ch. 4, Springer, 2007. ##P. Senin, Dynamic Time Warping Algorithm Review, University of Hawaii, 2008. ##D. T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri , S. Rahim , M. Zaidi, The Bees Algorithm – A Novel Tool for Complex Optimisation Problems. Intelligent Production Machines and Systems. D.T. Pham, E.E. Eldukhri and A.J. Soroka (eds) . Cardiff University, Manufacturing Engineering Centre, Cardiff, UK. Published by Elsevier Ltd. 2006. ##Pham, D.T., Castellani, M., 2009. The Bees Algorithm – modeling foraging behaviour to solve continuous optimization problems. Proc. ImechE, Part C 223 (12), 2919–2938. ##Seeley, T.D., 1996. The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies. Harvard University Press, Cambridge, Massachusetts. ##A.A. Fahmy, Using the Bees Algorithm to select the optimal speed parameters for wind turbine generators, Journal of King Saud University  Computer and Information Sciences, Vol. 24, No. 1, pp. 17–26, January 2012. ##P. M. Anderson and A. A. Fouad, Power System Control and Stability: Galgotia publications, 1981.##]
Price Spikes Reduction with EDRP Program
2
2
With the development of deregulated power systems and increase of prices in some hours of day and increase fuel price, demand response programs were noticed more by customers. demand response consists of a series of activities that governments or utilities design to change the amount or time of electric energy consumption, to achieve better social welfare or some times for maximizing the benefits of utilities or consumers. In this paper we try to evaluate the effect of DR programs especially EDRP on Nodal Marginal Pricing spikes reduction of Restructured Power Systems while occurs events.
In order to reach to this target, EDRP program (Emergency Demand Response Program), as common demand response program, is considered. Effects of EDRP program on Nodal Marginal Pricing spikes and operation cost reduction of Restructured Power Systems are investigated using EDRP and economic load model, ACOPF Formulation and nodal marginal price evaluation techniques.
The IEEE 9 bus Test System is used to implement comparisons of impacts with and without EDRP activity on nodal marginal pricing spikes and operation cost reduction.
According to obtained results, EDRP using lead to volatility decrease in local marginal price (LMP). It can be said that solving problems such as congestion in transmission lines, power system reliability decrease and volatility decrease in local marginal price at load network peak hours, is impossible without customer interfering in power market. In other hand Consumer participation, makes the power markets more competition and enhance its performance.
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175
180


Ali
Mansouri
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Ilam, Iran
Department of Electrical Engineering, Science
Iran


Nosratollah
Mohammad Beigi
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Ilam, Iran
Department of Electrical Engineering, Science
Iran


Rahmat
Aazami
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Ilam, Iran.
Department of Electrical Engineering, Science
Iran
azami.rahmat@yahoo.com


Amin
Omidian
Iran


Ehsan
Mohamadian
Iran
Restructured Power Systems
Demand Response (DR)
Emergency demand response program (EDRP)
Nodal Marginal Pricing
ACOPF
[U. S. Department of Energy, "Energy policy Act of 2005", section 1252, February 2006. ##FERC, "Regulatory commission survey on demand response and time based rate programs/ tariffs"; August 2006, www. FERC. Gov. ##www.NYISO.com ##D. S. Kirschen, G. Strbac, P. Cumperayot, D. Mendes, "Factoring the elasticity of demand in electricity prices", IEEE ##Transaction on power systems, Vol. 15, No. 2, PP. 612617, May 2000. ##D.S. Kirschen and G. Strbac, "Fundamentals of power system economics", 2004 John Wiley & Sons. ##H. Aalami, G. R. Yousefi and M. Parsa Moghadam, “Demand Response Model Considering EDRP and TOU Programs”, IEEE/PES Transmission and Distribution Conference & Exhibition, 2008, Chicago, USA##]
Predicting the Next State of Traffic by Data Mining Classification Techniques
2
2
Traffic prediction systems can play an essential role in intelligent transportation systems (ITS). Prediction and patterns comprehensibility of traffic characteristic parameters such as average speed, flow, and travel time could be beneficiary both in advanced traveler information systems (ATIS) and in ITS traffic control systems. However, due to their complex nonlinear patterns, these systems are burdensome. In this paper, we have applied some supervised data mining techniques (i.e. Classification Tree, Random Forest, Naïve Bayesian and CN2) to predict the next state of Traffic by a categorical traffic variable (level of service (LOS)) in different shorttime intervals and also produce simple and easy handling ifthen rules to reveal road facility characteristic. The analytical results show prediction accuracy of 80% on average by using methods
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181
193


S.Mehdi
Hashemi
Department of Mathematical and Computer Science, Amirkabir University of Technology, Tehran, Iran
Department of Mathematical and Computer Science,
Iran
hashemi@aut.ac.ir


Mehrdad
Almasi
Department of Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Department of Computer Engineering, Isfahan
Iran
m.almasi@ec.iut.ac.ir


Roozbeh
Ebrazi
Department of Mathematical and Computer Science, Amirkabir University of Technology, Tehran, Iran
Department of Mathematical and Computer Science,
Iran
r.ebrazi@aut.ac.ir


Mohsen
Jahanshahi
Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Young Researchers and Elite club, Central
Iran
mjahanshahi@iauctb.ac.ir
traffic prediction
Level of Service Prediction
Data Mining
Naïve Bayesian
Random forest
Classification tree
CN2
[[1] Abdulhai, B. P. ,“Shortterm traffic flow prediction using neurogenetic algorithms”. ITS Journal, Vol.7, pp.341, 2002. ##[2] P. Allaby, B. Hellinga, and Bullock, M. ,“Variable Speed Limits: Safety and Operational Impacts of a Candidate Control Strategy for an Urban Freeway”, IEEE Intelligent Transportation Systems Conference. Toronto, Canada, 2006. ##[3] Y. Amit, & D. Geman ,“Shape Quantization and Recognition with Randomized Trees”. NEURAL COMPUTATION, Vol.9, Issue.7, pp.15451588, 1997. ##[4] F. Attneave ,“Applications of information theory to psychology: a summary of basic concepts, methods, and results”. Holt, 1959. ##[5] M. BenBassat, “Use of Distance Measures, Information Measures and Error Bounds in Feature Evaluation”, Handbook of Statistics, Classification, Pattern Recognition and Reduction of Dimensionality, Vol.2, pp.773791, 1982. ##[6] L. Breiman, “Bagging predictors”, Machine Learning, Vol.24, Issue.2, pp.123140, 1996. ##[7] L. Breiman, J. H. Friedman, R. A. Olshen, & C. J. Stone, “Classification and Regression Trees”, Chapman & Hall, New York, 1984. ##[8] L. Brieman, “Random Forests”. Machine Learning, Vol.45, Issue.1, pp.532, 2001. ##[9] M. Carey, M. Bowers, “A Review of Properties of Flow–Density Functions”, Transport Reviews, Vol.32, Issue.1, pp.4973, 2012. ##[10] M. CastroNeto, Y.S. Jeong, M.K. Jeong, & L. Han,” OnlineSVR for shortterm traffic flow prediction under typical and atypical traffic conditions”. Expert Systems with Applications, Vol.36, Issue.3, pp.61646173, 2009. ##[11] B. Cestnik, “Estimating probabilities: A crucial task in machine learning”, Ninth European Conference on Artificial Intelligenc, Stokholm, pp.147149, 1990. ##[12] C. Chen, Y. Wang, L. Li, J. Hu, & Z. Zhang. “The retrieval of intraday trend and its influence on traffic prediction”. Transportation Research Part C, Vol.22, Issue(June, 2012), pp.103118, 2012. ##[13] R. Chrobok, O. Kaumann, J. Wahle, M. Schreckenberg, “Different methods of traffic forecast based on real data”. European Journal of Operational Research , Vol.155 Issue.3, pp.558568, 2004. ##[14] P. Clark, R. Boswell, “Rule induction with CN2: Some recent improvements”. In Y. Kodratoff (Ed.) Proceedings of the 5th European conference, pp.151163, 1991. ##[15] P. Clark, & T. Niblett, “The CN2 Induction Algorithm. Machine Learning”, Vol.3, Issue.4, pp.261283, 1989. ##[16] E. Cook, L. Goldman,” Empiric comparison of multivariate analytic techniques: Advantages and disadvantages of recursive partitioning analysis”, Journal of Chronic Diseases, Vol.37, pp.721731, 1984. ##[17] T. G. Dietterich., “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting and Randomization”, Machine Learning, Vol.40, Issue.2, pp.139157, 2000. ##[18] S. Dzeroski, B. Cestnik , I. Petrovski., “Using the mestimate in rule induction”, Journal of Computing and Information Technology, Vol.1, Issue.1, pp.3746, 1993. ##[19] A. H. Ghods, L. Fu, A. RahimiKian, “An Efficient Optimization Approach to RealTime Coordinated and Integrated Freeway Traffic Control”, IEEE Transactions on Intelligent Transportation Systems, Vol.11, Issue.4, pp.872884, 2010. ##[20] J. Guo, B. Williams, B. Smith, “Data collection time intervals for stochastic shortterm traffic flow forecasting”, Transportation Research Record: Journal of the Transportation Research Board, Issue.2024, pp.1826, 2007. [21] J. Han, M. Kamber, J. Pei. “Data Mining Concepts and Techniques”, Morgan Kaufmann; 3rd edition, July 6, 2011. ##[22] A. Hegyi, B. Schutter. “Optimal Coordination of Variable Speed Limits to Suppress Shock Waves”, Transportation Research Record, No.1852, pp.167174, 2003. ##[23] T. K. Ho, “The Random Subspace Method for Constructing Decision Forests”, IEEE Transactions on Pattern Analysis and Machine Intelligence Pami, Vol.20, Issue.8, pp.832844, 1998. ##[24] W.C. Hong., “Traffic Flow Forecasting by Seasonal SVR with Chaotic Simulated Annealing Algorithm”, Neurocomputing, Vol.74, Issue.1213, pp.20962107, 2011. ##[25] G. V. Kass.,” An Exploratory Technique for Investigating Large Quantities of Categorical Data”, Applied Statistics, Vol.29, Issue.2, pp.119127, 1980. ##[26] N. Lavrac, B. Kavsek, P. Flach, L. Todorovski, “Subgroup Discovery with CN2SD”, Journal of Machine Learning Research, Vol.5, pp.153188, 2004. ##[27] J. Li, Q. Chen, D. Ni, H. Wang., “Analysis of LWR Model with Fundamental Diagram Subject to Uncertainty”, Greenshields 75 Symposium. Woods Hole MA: Transportation Research Board, pp.7483, 2011. ##[28] D. Lili, S. Peeta, Y. Hoon Kim. “An adaptive information fusion model to predict the shortterm link travel time distribution in dynamic traffic networks”. Transportation Research Part B, Vol.46, pp.235252, 2012. ##[29] W.Y. Loh, YS shih., “Split selection methods for classification trees”, Statistics Sinica, Vol.7, pp.815840, 1997. ##[30] R. Michalski., “On the quasiminimal solution of the general covering problem”, 5th Int. Symposium on Information Processing, pp.125128, Bled, Yugoslavia 1969. ##[31] M. Mozina, J. Demsar, M. Kattan, B. Zupan., “Nomograms for Visualization of Naive Bayesian Classifier”, Lecture Notes in Computer Science, Vol.3202, pp.337348, 2004. ##[32] T. Oda., “An algorithm for prediction of travel time using vehicle sensor data”, Third International Conference on Road Traffic Control, pp.4044. London, England, 1990. ##[33] M. Papageorgiou, I. Papamichail, A. Messmer, Y. Wang., “Traffic Simulation with METANET”, Fundamentals of Traffic Simulation, International Series in Operations Research & Management Science, pp.399430. New York Dordrecht Heidelberg London, Springer, 2010. ##[34] D. Park, L. R. Rilett, “Forecasting multipleperiod freeway link travel times using modular neural networks”. Transportation Research Record, Vol.1617, pp.6370, 1998. ##[35] J. Quinlan, “Induction of decision trees”, Machine Learning, pp.81106, 1986. ##[36] J. Quinlan, “Simplifying decision trees”. International Journal of Machine Studies, Vol.27, pp.221234, 1987. ##[37] J. R. Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann, 1993. ##[38] L. Rokach and O. Maimon. “Decision trees”. In Lior Rokach and Oded Maimon (eds) Data Mining and Knowledge Discovery Handbook, pp.165192, Springer, NY, 2010. ##[39] L. Rokach, O. Maimon, “TopDown Induction of Decision Trees Classifiers — A Survey”, IEEE Transaction on Systems, Man and Cybernetics—part C: applications and reviews, Vol.35, Issue.4, pp.476487, 2005. ##[40] B. Smith, M. Demetsky,“Traffic flow forecasting: comparison of modeling approaches”, Journal of Transportation Engineering, Vol.123, Issue.4, pp.261266, 1997. ##International Journal of Smart Electrical Engineering, Vol.1, No.3, Fall 2012 ISSN: 22519246 ##[41] B. Smith, B. Williams, R. Oswald. “Comparison of parametric and nonparametric models for traffic flow forecasting”, Transportation Research Part C. Emerging Technologies, Vol.10, Issue.4, pp.30332, 2002. ##[42] “Transportation Research Board”. Highway Capacity Manual. Washington DC: the National Research Council, 2000. ##[43] J. van Lint, “Online Learning Solutions for Freeway Travel Time Prediction”, IEEE Transactions on Intelligent Transportation Systems, pp.3847, 2008. ##[44] C. Wu, C. Wei, D. Su, M. Chang, J. Ho.,“Travel time prediction with support vector regression”, Intelligent Transportation Systems, pp.14381442, Shanghai, China, 2003. ##[45] K. Wunderlich, D. Kaufman, R. Smith,“Travel time prediction for decentralized route guidance architectures”, IEEE Transactions on Intelligent Transportation Systems, Vol.1, Issue.1, pp.414, 2000. ##[46] F. Yang, Z. Yin, H. Liu, B. Ran.,“On line recursive algorithm for shortterm traffic prediction”, Transportation Research Record: Journal of the Transportation Research Board, Vol.1879, pp.18, 2004. ##[47] J. Yang.,“A Study of Travel Time Modeling Via Time Series Analysis”, IEEE Conference on Control Applications, pp.855860, Toronto, Canada, 2005. ##[48] X. Zhang, J.Rice,“Shortterm Travel Time Prediction”. Transportation Research Part C, Vol.11, Issue.34, pp.187210, 2003. ##[49] Y. Zhang, Y. Liu,“Comparison of Parametric and Nonparametric Techniques for Nonpeak Traffic Forecasting”, World Academic of Science and Engineering Technology, Vol.51, 2009. [50] M. Zhong, S. Sharma, P. Lingras,“Analyzing the performance of genetically designed shortterm traffic prediction models based on road types and functional classes”, Lecture Notes in Computer Science, Vol.3029, pp.11331145, 2004.##]
EnergySaving in Wireless Sensor Networks Based on Optimization Sink Movement Control
2
2
A sensor network is made up of a large number of sensors with limited energy. Sensors collect environmental data then send them to the sink. Energy efficiency and thereby increasing the lifetime of sensor networks is important. Direct transfer of the data from each node to the central station will increase energy consumption. Previous research has shown that the organization of nodes in clusters and selection the appropriate cluster head increases the network lifetime. In this study, clustering, determine to cluster heads and the sink movement on the predefined paths has been done with fuzzy method. There are two inputs for the fuzzy model; residual energy of the node and distance from the sink. The output is priority of cluster heads. Sink moves base on the highest priorities on the predefined paths. Then by using genetic algorithm, the number of clusters, shape type and area is optimized. Fitness function is based on network lifetime.
1

195
198


Mozhgan
Toulabi
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
t_mozh@yahoo.com


Shahram
Javadi
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
sh.javadi@iauctb.ac.ir
Wireless Sensor Network
lifetime
fuzzy method
Genetic algorithm
mobile sink
[[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: A Survey”, The International Journal of Computer and Telecommunications Networking, Vol.38, No.4, pp.393–422, 2002. ##[2] D. Puccinelli and M. Haenggi, “Wireless Sensor Networks: Applications and Challenges of Ubiquitous Sensing”, IEEE Circuits and Systems Magazine, Vol.5, No.3, pp.19–31, 2005. ##[3] M. Haenggi, “Mobile SensorActuator Networks: Opportunities and Challenges”, In Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and Their Applications (CNNA ’02), pp.283–290, Frankfurt, Germany, July 2002. ##[4] Yanzhong Bi, Limin Sun, Jian Ma,3 Na Li, Imran Ali Khan, and Canfeng Chen, “An Autonomous Moving Strategy for Mobile Sinks in DataGathering Sensor Networks”, Hindawi Publishing Corporation, EURASIP Journal on Wireless Communications and Networking, Vol. 2007. ##[5] K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie, “Protocols for SelfOrganization of a Wireless Sensor Network”, IEEE Personal Communications, Vol.7, No.5, pp.16–27, 2000. ##[6] A. Boukerche and S. Nikoletseas, “Protocols for Data Propagation in Wireless Sensor Networks: A Survey”, Wireless Communications Systems and Networks, pp.23–51, Kluwer Academic Publishers, Boston, Mass, USA, 2004. ##[7] J. N. AlKaraki and A. E. Kamal, “Routing techniques in wireless sensor networks: a survey”, IEEEWireless Communications, Vol.11, No.6, pp.6–28, 2004. ##[8] D. Niculescu, “Communication Paradigms for Sensor networks”, IEEE Communications, Vol.43, No.3, pp.116122, 2005. [9] Liliana M.C. Arboleda, “Comparsion of Clustering Algorithm and Protocols for Wireless Sensor network“, Canadian Conference on Electrical and Computer Engineering, pp.17871792, May 2006. ##[10] Heinzelman W. R., A. P. Chandrakasan and H. Balakrishnan, “EnergyEfficient Communication Protocol for Wireless Microsensor Networks”, Proc. of the 33rd IEEE Int. Conf. on System Sciences Honolulu, USA, pp.1–10, Jan. 2000. ##[11] Heinzelman W. R., A. P. Chandrakasan and H. Balakrishnan, “An ApplicationSpecific Protocol Architecture for Wireless Microsensor Networks”, IEEE Trans. on Wireless Communications, Vol.1, No.4, pp. 660670, Oct.2002. ##[12] D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, “Optimizing Clustering Algorithm in Mobile Adhoc Networks Using Genetic Algorithmic Approach”, In Proceedings of the Global Telecommunications Conference (GLOBECOM), November 2002. ##[13] S. Gandham, M. Dawande, R. Prakash, and S. Venkatesan, “Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile Base Stations”, In Proceedings of IEEE Globecom, Vol.1, pp.377–381, Dec. 2003. [14] Singh, A.K., Alkesh, A., Purohit, N., ”Minimization of Energy Consumption of Wireless Sensor Networks Using Fuzzy”, International Conference on Computational Intelligence and Communication Systems, pp.519521, 2011.##]
Effective Feature Selection for PreCancerous Cervix Lesions Using Artificial Neural Networks
2
2
Since most common form of cervical cancer starts with precancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. There are 2 ways to stop this disease from developing. One way is to find and treat precancers before they become true cancers, and the other is to prevent the precancers in the first place. The presented approach uses precancerous images which are taken from a digital colposcope, and a set of texture and color features is extracted which includes low and high grade SIL (Squamous Interepithelial Lesion ) .After extracting, features are fed to a classifier, which could be KNN,RBF,MLP and NeuroFuzzy network and after training effective features are selected using UTA algorithm for each classifier individually. Finally, results come in a comparison table, show that the landa fourteenth, thetax and together with Neurofuzzy classifier have the best overall performance. This approach has an acceptable and simple early diagnosis of cervix cancer and may have found clinical application
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199
204


Farnaz
Rouhbakhsh
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
farnazruhbakhsh84@gmail.com


Fardad
Farokhi
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
f_farokhi@iauctb.ac.ir


Kaveh
Kangarloo
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
k_kangarloo@iauctb.ac.ir
Image classification
Artificial Neural Network
Feature Selection
Colposcopic images
[K. Tumer, N Ramanujam, J. Ghosh, R. Kortum, “Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Precancer”, IEEE Transactions on Biomedical Engineering, Vol.45, No.8, 1998. ##I. Claude, R. Winzenrieth, P. Pouletaut, J. Charles Boulanger, ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1, Page 10771. ##B. Tulpule, Sh. Yang, Y. Srinivasan, S. Mitra, B. Nutter, “Segmentation and Classification of Cervix Lesions by Pattern and Texture Analysis”, The 14th IEEE International Conference Fuzzy Systems FUZZ '05, 2005. ##Y. Artan, X. Huang , ”Combining Multiple 2νSVM Classifiers for Tissue Segmentation”, 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.488491, 2008. ##Y. Srinivasan, E. Corona, B. Nutter, S. Mitra, S.Bhattacharya, “A Unified ModelBased Image Analysis Framework for Automated Detection of Precancerous Lesions in Digitized Uterine Cervix Images”, IEEE Journal of Selected Topics in Signal Processing, Vol.3, No.1, 2009. ##A. Das, A. Kar, D. Bhattacharyya, ”Elimination of specular reflection and identification of ROI: The first step in automated detection of Cervical Cancer using Digital Colposcopy”, IEEE International Conference on Imaging Systems and Techniques (IST), pp.237–241, 2011. ##P. Hannequin and J. Mas, “Statistical and heuristic image noise extraction (SHINE): a new method for processing Poisson Noise in Scintigraphic Images,” Phys. In Med. & Biol., Vol.47, pp.4329–4344, 2002. ##J.van de Weijer, T. Gevers, J. M Geusebroek, “Edge and Corner Detection by Photometric Quasiinvariants”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.4, pp.625630, 2005. ##D. Nauck, R. Kruse, “A Fuzzy Neural Network Learning Fuzzy Control Rules and Membership Functions by Fuzzy Error Back propagation”, IEEE International Conference on Neural Networks, vol.2, pp.10221027, 1993. ##Li Xin Wang, “A Course in Fuzzy Systems and Control”. PrenticeHall Edition, chapter 13, pp.168172., 1997. ##M.F.Redondo, C.H.Espinosa, “A Comparison Among Feature Selection Method Based on Trained Network”, Neural Networks for Signal Processing IX Proceedings of the IEEE Signal Processing Society Workshop, pp.205214, 1999. ##S. Haykin, “Neural Networks, A Comprehensive Foundation”, Second edition, PrenticeHall Edition, chapter 5, 1999. ##S. Haykin, “Neural Networks, A Comprehensive Foundation”, Second edition, PrenticeHall Edition, chapter 3, 1999##]
An Approach for Accurate Edging using Dynamic Membership Functions
2
2
In this paper, by means of fuzzy approaches, an accurate method is introduced for edging of color photographs. The difference between our method with other similar methods is the use of a morphological operation to think or thick the obtained edges. In this proposed method, a 3×3 window is dragged on the photo. For each block, 12 point sets will be defined, each including two nonoverlapping point sets. Then, a fuzzy membership function will be designed for each point sets according to data of contrast. At last, the range of membership contrast degree for the points of second point sets will be assessed. A comparison of membership degree of second point sets with a predefined threshold indicates whether central point of window is bordered or nonbordered. The method was performed on some reference images and the results were compared with common edging methods. The results show that proposed method has a high capability to edge photographs
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205
209


Fatemeh
Khosravi Pourian
Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Young Researchers and Elite Club, Central
Iran
aith.khp7@gmail.com


Reza
SabbaghiNadooshan
Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
r_sabbaghi@iauctb.ac.ir
Fuzzy Edge Detection
Fuzzy logic
Variant Membership Function
[Duarte, A., Sanchez, A., Fernandez, F., Montemayor, A. S., “Improving Image Segmentation Quality Through Effective Region Merging Using a Hierarchical Social Metaheuristic”, Pattern Recognition Letters, Vol.27, pp.12391251, 2006. ##Hampton, C., Persons, T., Wyatt, C., “Survey of Image Segmentation”, 2000. ##McCane, B., “Edge Detection”, Course Note, Department of Computer Science, University of Outage, Dunedin, Newzeland, Feb. 2001. ##Gonzalez, R. C., Woods, R. E., “Digital image processing”, AddisonWesley, 2000. ##Eghbal, E., Mansoori, G., Eghbali, H. J., “Heuristic Edge Detection Using Fuzzy Rulebased Classifier”, Journal of Intelligent and Fuzzy System, Vol.17, pp.457 469, 2006. ##Gao, H., Siu, W. C., Hou, C. H., “Improved Techniques for Automatic Image Segmentation”, IEEE Trans. Circuits and Systems for Video Technology, Vol.11, pp.1273–1280, 2001. ##Montoya, M. D. G., Gil, C., Garcia, I., “The Load Unbalancing Problem for Region Growing Image Segmentation Algorithms”, J. Parallel Distrib. Comput., Vol.63, pp.387395, 2003. ##Suliman, C., Boldoisor, C., Bazavan, R., Moldoveanu, F., “A Fuzzy Logic Based Method for Edge Detection”, Bulletin of Transilvania University of Brasov, Engineering Sciences,. Vol.4, pp.159164, 2011. ##Borkhoda, W., Akhlaqian tab, F., Shahryan, O., “Fuzzy Edge Detection Based on Pixel`s Gradient and Standard Deviation Values”, Proceedings of the International Multiconference on Computer Science and Information Technology, pp.7–10, 2009. ##Afsari, F., Koohimoghadam, M., Nekoohimahane, M., Nezamabade, H., “A New Fuzzy Edge Detection Using Dynamic Membership Functions”, 8th conference of Intelligent Systems, Ferdowsi University of Mashhad, 2008.##]