2015
3
2
6
59
Design and PLC Implementation for Speed Control of DC Motor using Fuzzy Logic
2
2
In this article, a speed control of DC motor is designed and illustrated using fuzzy logicbased programmable logic controller (PLC). The DC motor is an attractive part of electrical equipment in many industrial applications requiring variable speed and load specifications due to its ease of controllability. The designed system is consisted of three main parts including programmable logic controller, pulse width modulation (PWM) bipolar drive and DC motor. In the control section, PLC is used as real time controller and fuzzy logic algorithm is designed based on nonlinear model of DC motor, and its parameters are optimized in MATLAB software. Then, it is implemented using rslogix5000 PLC and programming language ladder for speed control. Finally, with favorable results, the efficiency of the controller is successfully proved under different load conditions. The obtained results demonstrate the efficiency of the PLC intelligent controller in enhancing the accuracy and speed control of DC motor.
1

71
75


Jalal
Rostami Monfared
Department of Electronics, Darehshahr Branch, Islamic Azad University, Darehshahr, Iran
Department of Electronics, Darehshahr Branch,
Iran


Mehdi
Fazeli
Department of Electronics, Darehshahr Branch, Islamic Azad University, Darehshahr, Iran
Department of Electronics, Darehshahr Branch,
Iran
mehdi.fazeli.mut@gmail.com


Yaser
Lotfi
Department of Humanities, Darehshahr Branch, Islamic Azad University, Darehshahr, Iran
Department of Humanities, Darehshahr Branch,
Iran
yaser.lotfi@gmail.com
DC motor
Fuzzy logic
Programmable logic Controller
Speed control
[[1] G. Huang and S. Lee, "PCbased PID speed control in DC motor," in proc. 2008 IEEE International Conference on Audio, Language and Image Processing, pp. 400407. ##[2] P. Deshpande and A. Deshpande, "Inferential control of DC motor using Kalman filter," in: proc 2012 IEEE International Conference on Power, Control and Embedded Systems, pp. 15. ##[3] P.M. Meshram and R.G. Kanojiya, "Tuning of PID controller using ZieglerNichols method for speed control of DC motor," in proc. 2012 IEEE International Conference on Advances in Engineering, Science and Management, pp. 117122. [4] R.K. Munje, M.R. Roda, and B.E. Kushare, "Speed control of DC motor using PI and SMC," in proc. 2010 IEEE 2010 IPEC, pp. 945 – 950. ##[5] K. M. Passino and S. Yurkovich, Fuzzy Control, CA: Addison Wesley Longman, 1997. ##[6] L.A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338353, 1965. ##[7] M.T. Hagan, H.B. Demuth, and M.H. Beale, Neural Network Design, MA: PWS Publishing, 1996. ##[8] A.A. Abdulrahman and R.B. Mamat, "Modelling and simulation for industrial DC motor using intelligent control," International Symposium on Robotics and Intelligent Sensors, 71, pp. 420 425, 2012. ##[9] W. Yan, D. Wang, P. Jia, and W. Li, "The PWM speed regulation of DC motor based on intelligent Control," Systems Engineering Procedia the 2nd International Conference on Complexity Science & Information Engineering, vol. 3, pp. 259267, 2012. ##[10] R. Rahmani, M.S. Mahmodian, S. Mekhilef, and A.A. Shojaei, "Fuzzy logic controller optimized by particle swarm optimization for DC motor speed control," in proc. 2012 IEEE Student Conference on Research and Development, pp. 109113. ##[11] E.J. Hepzibah and R. Korah, "FPGA implementation for speed monitoring and speed control of a DC motor using fuzzy logic," in proc. 2012 IEEE International Conference on Emerging Trends in Electrical Engineering and Energy Management, pp. 222228. ##[12] A.S.Z. El Din, "PLCbased speed control of DC motor," in proc. 2006 CES/IEEE 5th International Power Electronics and Motion Control Conference, pp. 16. ##[13] Q. Jie, L. Shaobo, and H. Haisomg, "An application of fuzzy control based on PLC in the pressure control system of Chinese medicine water extraction," in proc. 2011 IEEE Fourth International Symposium on Computational Intelligence and Design, pp. 119122. ##[14] A. SyaichuRohman and R. Sirius, "Model predictive control implementation on a programmable logic controller for DC motor speed control," in proc. 2011 IEEE International Conference on Electrical Engineering and Informatics, pp. 14. [15] V.A. Maraba and A.E. Kuzucuoglu, "PID neural network based speed control of asynchronous motor using programmable logic controller," Advances in Electrical and Computer Engineering, vol. 11, pp. 2328, 2011. ##[16] H. Ferdinando, "The implementation of low cost fuzzy logic controller for PLC TSX 3721," in proc. 2007 IEEE International Conference on Intelligent and Advanced Systems, pp. 1081 1086. ##[17] M. Arrofiq and N. Saad, "A PLCbased selftuning PIfuzzy controller for linear and nonlinear drives control," in proc. 2008 IEEE 2nd International Power and Energy Conference, pp. 701706. ##[18] R. Bayindir and Y. Cetinceviz, "A water pumping control system with a programmable logic controller (PLC) and industrial wireless modules for industrial plants—An experimental setup," ISA Transactions, vol. 50, pp. 321328, 2011. [19] E.A. Ramadan, M. Elbardini, and M.A. Fkirin, "Design and FPGAimplementation of an improved adaptive fuzzy logic controller for DC motor speed control," Ain Shams Engineering Journal, vol. 5, pp. 803816, 2014. ##[20] T.J. Ross, Fuzzy Logic with Engineering Applications, Third ed., John Wiley & Sons, 2010. ##[21] N. Saad and M. Arrofiq, "A PLCbased modifiedfuzzy controller for PWMdriven induction motor drive with constant V/Hz ratio control," Robotics and ComputerIntegrated Manufacturing, vol. 28, pp. 95112, 2012.##]
Transmission Congestion Management Considering Uncertainty of Demand Response Resources’ Participation
2
2
Under the smart grid environment, demand response resources (DRRs) are introduced as a virtual power plant to enhance power system adequacy. DRRs often fail to reduce their load due to some external factors. In this paper, a reliability model of a DRR is constructed as multistate conventional generation units, where the probability, frequency of occurrence, and departure rate of each state can be acquired. DRRs as consequence of demand response program implementation can be applied to transmission congestion management. Therefore, this paper presents an optimal model of congestion management (CM) by means of multistate DRRs, namely CM_DRR. In the proposed approach, in addition to DRRs, independent system operator relieves the existing transmission line congestions using the combination of generating unit rescheduling and involuntary load shedding. The hourly historical data associated with the Connecticut region in New England is employed to achieve the DRRs’ participation regime. Moreover, the impact of different capacities of DRRs on the congestion management cost and load shedding cost is evaluated. Results of applying the aforementioned model to the 24bus Reliability Test System (RTS) indicate the efficiency of CM_DRR framework.
1

77
88


Abbas
Tabandeh
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid
Iran
abbas_tabandeh@yahoo.com


Amir
Abdollahi
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid
Iran
a.abdollahi@uk.ac.ir


Masoud
Rashidinejad
Department of Electrical Engineering, Shahid Bahonar University, Kerman, Iran
Department of Electrical Engineering, Shahid
Iran
Congestion management
Demand response resource
Customer’s uncertainty
Multistate model
Smart grid
[[1] F. D. Galiana and M. Ilic, "A mathematical framework for the analysis and management of power transactions under open access," Power Systems, IEEE Transactions on, vol. 13, pp. 681 687, 1998. ##[2] A. Kumar, S. C. Srivastava, and S. N. Singh, "Congestion management in competitive power market: A bibliographical survey," Electric Power Systems Research, vol. 76, pp. 153164, sept. 2005. ##[3] Y. R. Sood and R. Singh, "Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources," Renewable Energy, vol. 35, pp. 18281836, Aug. 2010. ##[4] M. Esmaili, N. Amjady, and H. A. Shayanfar, "Multiobjective congestion management by modified augmented εconstraint method," Applied Energy, vol. 88, pp. 755766, March. 2011. ##[5] M. Khanabadi, H. Ghasemi, and M. Doostizadeh, "Optimal transmission switching considering voltage security and N1 contingency analysis," Power Systems, IEEE Transactions on, vol. 28, pp. 542550, 2013. ##[6] L. S. Vargas, G. BustosTuru, and F. Larrain, "Wind power curtailment and energy storage in transmission congestion management considering power plants ramp rates," Power Systems, IEEE Transactions on, vol. 30, pp. 24982506, 2015. [7] J. Hu, A. Saleem, S. You, L. Nordström, M. Lind, and J. Østergaard, "A multiagent system for distribution grid congestion management with electric vehicles," Engineering Applications of Artificial Intelligence, vol. 38, pp. 4558, Feb. 2015. ##[8] M. A. López, S. Martín, J. A. Aguado, and S. de la Torre, "V2G strategies for congestion management in microgrids with high penetration of electric vehicles," Electric Power Systems Research, vol. 104, pp. 2834, Nov. 2013. ##[9] K. S. Verma, S. N. Singh, and H. O. Gupta, "Location of unified power flow controller for congestion management," Electric Power Systems Research, vol. 58, pp. 8996, June. 2001. ##[10] M. Esmaili, H. A. Shayanfar, and N. Amjady, "Congestion management considering voltage security of power systems," Energy Conversion and Management, vol. 50, pp. 25622569, Oct. 2009. ##[11] N. Amjady and M. Hakimi, "Dynamic voltage stability constrained congestion management framework for deregulated electricity markets," Energy Conversion and Management, vol. 58, pp. 6675, June. 2012. ##[12] F. C. Schweppe, R. D. Tabors, R. E. Bohn, and M.C. Caramanis, "Spot pricing of electricity", 1988 edition. Boston: Springer, 1988. ##[13] N. MahmoudiKohan, M. P. Moghaddam, and M. K. SheikhElEslami, "An annual framework for clusteringbased pricing for an electricity retailer," Electric Power Systems Research, vol. 80, pp. 10421048, Sept. 2010. [14] S. Yousefi, M. P. Moghaddam, and V. J. Majd, "Optimal real time pricing in an agentbased retail market using a comprehensive demand response model," Energy, vol. 36, pp. 57165727, Sept. 2011. ##[15] Z. Jun Hua, D. Zhao Yang, P. Lindsay, and W. Kit Po, "Flexible transmission expansion planning with uncertainties in an electricity market," Power Systems, IEEE Transactions on, vol. 24, pp. 479488, 2009. ##[16] N. Venkatesan, J. Solanki, and S. K. Solanki, "Residential demand response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, vol. 96, pp. 8491, Aug. 2012. ##[17] A. Abdollahi, M. P. Moghaddam, M. Rashidinejad, and M. K. SheikhelEslami, "Investigation of economic and environmentaldriven demand response measures incorporating UC," Smart Grid, IEEE Transactions on, vol. 3, pp. 1225, 2012. ##[18] M. Mollahassanipour, A. Abdollahi, and M. Rashidinejad, "Investigation of marketbased demand response impacts on securityconstrained preventive maintenance scheduling," Systems Journal, IEEE, vol. PP, pp. 111, 2015. ##[19] M. P. Moghaddam, A. Abdollahi, and M. Rashidinejad, "Flexible demand response programs modeling in competitiveelectricity markets," Applied Energy, vol. 88, pp. 32573269, Sept. 2011. ##[20] GAMS (General Algebraic Modeling System) software package. www.gams.com. ##[21] “The historical data of DRR in DRPs.” [Online]. Available: www.isone.com. ##[22] R. Billinton, Power system reliability evaluation: Taylor & Francis, 1970. ##[23] M. Čepin, Assessment of Power System Reliability: Methods and Applications: Springer Science & Business Media, 2011. ##[24] C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications: CRC Press, 2013. ##[25] F. Castro Sayas and R. N. Allan, "Generation availability assessment of wind farms," Generation, Transmission and Distribution, IEE Proceedings, vol. 143, pp. 507518, 1996. ##[26] W. Lei, M. Shahidehpour, and L. Tao, "Cost of reliability analysis based on stochastic unit commitment," Power Systems, IEEE Transactions on, vol. 23, pp. 13641374, 2008. ##[27] P. Wong, P. Albrecht, R. Allan, R. Billinton, Q. Chen, C. Fong, et al., "The IEEE reliability test system1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee," Power Systems, IEEE Transactions on, vol. 14, pp. 10101020, 1999. ##[28] M. Esmaili, N. Amjady, and H. A. Shayanfar, "Stochastic congestion management in power markets using efficient scenario approaches," Energy Conversion and Management, vol. 51, pp. 22852293, Nov. 2010.##]
Optimal Design of Axial Flux Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using GAand FEM
2
2
Axial Flux Permanent Magnet (AFPM) machines are attractive candidates for Electric Vehicles (EVs) applications due to their axial compact structure, high efficiency, high power and torque density. This paper presents general design characteristics of AFPM machines. Moreover, torque density of the machine which is selected as main objective function, is enhanced by using Genetic Algorithm (GA) and variation of PM characteristics, based on sizing equation and Finite Element Analysis (FEA). Then, torque ripple of the motor is reduced according to the effect of PM characteristics on Torque Ripple Factor (TRF). The designed machine produces sinusoidal backEMF waveform. The torque density is improved and the torque ripple is reduced. The results are validated by using 3DFEA (FEA) . Furthermore, to assess the obtained results by FEA method, an advanced vehicle simulator (ADVISOR) software is used to demonstrate the performance improvement over the Europe test drive cycles.
1

89
97


Mostafa
Ahmadi Darmani
Islamic Azad University, Science and Research Branch
Islamic Azad University, Science and Research
Iran
mostafa.ahmadi.d@gmail.com


Hooman
Hooshyar
Islamic Azad University, Science and Research Branch
Islamic Azad University, Science and Research
Iran
hoomanhooshyar@gmail.com
Axial Flux Permanent Magnet
Motor
Finite Element Analysis (FEA)
Genetic Algorithm (GA)
Torque Ripple Analysis (TRF)
Electric Vehicles (EVs)
[[1] M. J. Yang, H. L. Jhou, B. Y. Ma, K. Kai Shyu,”A CostEffective Method of Electric Brake With Energy Regeneration for Electric Vehicle” IEEE Trans. Ind. Electron.,vol. 56, no. 6, pp. 22032212, June. 2009. ##[2] G. Nanda, N. C. Kar, ”A Survey And Comparison Of Characteristics Of Motor Drives Used In Electric Vehicles” IEEE Electrical and Computer Engineering Conf, pp. 811814. ##[3] S. Onoda, A. Emadi,”PSIMBased Modeling of Automotive Power Systems Conventional, Electric, and Hybrid Electric Vehicle” IEEE Trans.Vehicular Technology, vol. 53, no. 2, pp. 390400, Mar. 2004. ##[4] Aydin, M., S. Huang, T.A. Lipo, “Axial Flux Permanent Magnet Disc Machines: A Review” Research Report,20042010. [5] S. AsgharGholamian, M.T. AbbasiAblouie, A. Mohseni and S. EsmaeiliJafarabadi, “Effect of Air Gap on Torque Density for DoubleSided Axial Flux Slotted Permanent Magnet Motors using Analytic and FEM Evaluation”, Journal of Applied Sciences Research, 5(9): 12301238, 2009. ##[6] J. Gieras, RongJiewang and J.Kamper, “Axial Flux Permanent Magnet Brushless Machines”, Publisher:Springer,2005. [7] D.C. Hanselman, “Brushless Permanentmagnet Motor Design”, Number 2, McGrawHill,Inc, 1994. ##[8] Funda. Sahin,”Design and development of high speed axialflux permanent magnet machines” Thesis, Doctor of Philosophy,CipData ClibraryTechnischeUniversiteit Eindhoven,2001. ##[9] Qu Ronghai, T.A. Lipo, “Analysis and modeling of airgap and zigzag leakage fluxes in a surfacemounted permanentmagnet,” Machine IEEE Transactions on Industry Applications, Volume 40, Issue 1, pp 121127, 2004 ##[10] Cirani M. &Lindstr¨om J. ”Electric hub wheel motors: An initial studyfor long haul applications” Technical report, Volvo 3P, 2009. ##[11] Chiristian DuBar, “Design of an axial flux machine for an inwheel motor application” Master of science thesis, Department of Energy and Environment, Chalmers University Of Technology, 2011.##]
Objects Identification in ObjectOriented Software Development  A Taxonomy and Survey on Techniques
2
2
Analysis and design of object oriented is onemodern paradigms for developing a system. In this paradigm, there are several objects and each object plays some specific roles. Identifying objects (and classes) is one of the most important steps in the objectoriented paradigm. This paper makes a literature review over techniques to identify objects and then presents six taxonomies for them. The first taxonomy is based on the documents exist for a domain. The second taxonomy is based on reusable previous knowledge and the third one relies on commonalities in a domain. The fourth taxonomy is concerned with decomposing a domain. The fifth taxonomy is based on experience view and sixth one is related to use the abstraction in a domain. In this paper, the constraints, strengths and weaknesses of the techniques in each taxonomy are described. Then, the techniques are evaluated in four systems inside an educational center in a university. A couple of approach is recommended for finding objects, based on some practical experiences obtained from the evaluation.
1

99
114


Hassan
Rashidi
Department of Statistics, Mathematics, and Computer Science, Allameh Tabataba’i University
Department of Statistics, Mathematics, and
Iran
hrashi@gmail.com
Taxonomy
Class
Object
ObjectOriented
Software Engineering
[[1] G. Bavota, A D. Lucia, A. Marcus, and R. Oliveto, “Automating extract class refactoring: an improved method and its evaluation,” Empirical Software Engineering, Vol. 19, pp. 1616 1664, 2014. ##[2] G. Nanda, N. C. Kar, “A Survey And Comparison Of Characteristics Of Motor Drives Used In Electric Vehicles,” IEEE Electrical and Computer Engineering Conf, pp. 811814. ##[3] G. Booch., “ObjectOriented Development,” IEEE Transaction on Software Engineering, 12 (2), pp. 211221, 1986 ##[4] G. Booch., “Software Engineering with Ada,” Benjamin/Cummings Publishing Co., Menlo Park, California, 1983 ##[5] G. Booch, J. Rumbaugh, and I. Jacobson, “The Unified Modeling Language User Guide,” Addison Wesley, 1998 ##[6] G. Booch, J. Rumbaugh, and I. Jacobson “The Unified Software Development Process,”AddisonWesley, 1998 ##[7] F. P. Brooks, “The Silver Bullet, Essence and Accidents of Software Engineering,” Information Processing '86. Ed., Kugler H. J., Elsevier Science Publishers B.B. (NorthHolland), 1986 ##[8] F. P. Brooks, “The Mythical Manmonth: Essay on Software Engineering,” AddisonWesley, 1982 ##[9] B. Bruegge, and A. H. Dutoit, “ObjectOriented Software Engineering: Using UML, Patterns, and Java,” Pearson Prentice Hall,2010 ##[10] G. Canforaa, A. Cimitilea, A. D Luciaa, and G. A. D Lucca, “Decomposing Legacy Systems into Objects: An Eclectic Approach,” Information and Software Technology, Vol. 43, pp. 401412, 2001 ##[11] Ch. Peter, “The entityrelationship modelToward a unified view of data,” ACM Trans. on Database Systems, Vol. 1(1.), pp. 936, 1976 ##[12] P. Coad, and E. Yourdon, “ObjectOriented Analysis,” Yourdon Press, 1991 ##[13] A. Cockburn., “Writing Effective Use Cases (Draft 3),” Addison Wesley Longman, 2000. ##[14] E. Codd, “Extending the database relational model to capture more meaning,” ACM Trans. on Database Systems, Vol. 4(4), pp. 397434, 1979 ##[15] A. V. Deursen, T. Kuipers, “Identifying Objects Using Cluster and Concept Analysis,” Proc. of 21st International Conference on Software Engineering, Los Angeles, CA, ACM Press, New York, pp.246255, 1999 ##[16] Faculty of Electrical, Computer and IT engineering, Islamic Azad University, Qazvin Branch, ##[17] M. Fokaefs, N. Tsantalis, E. Strouliaa, and A. Chatzigeorgioub, “Identification And Application Of Extract Class Refactoring In ObjectOriented Systems,” Journal of Systems and Software, Vol. 85 , pp. 2241–2260, 2012. ##[18] M. Fowler, , and K. Scott, “UML Distilled A Brief Guide to The Standard Object Modeling Guide,” 2ndEdition,Addison Wesley Longman, Inc, 1999 ##[19] N. Goldsein, and J. Alger “Developing ObjectOriented Software for the Macintosh Anaiysis, Design, and Programming,” AddisonWesley, 1992 ##[20] J.V.Gurp , and J. Bosch, “Design, Implementation and Evolution of ObjectOriented Frameworks: Concepts and Guidelines,” Software—Practice and Experience, Vol. 31, pp. 277300, 2001 ##[21] I. Jacobson. and G. Booch, “The Unified Software Development Process,” AddisonWesley, Reading, MA, 1999 ##[22] I. Jacobson, M.P. Christerson, and F. Overgaard, “ObjectOriented Software Engineering A Use Case Approach,” AddisonWesley, Wokingham, England, 1992 ##[23] Josuttis, M. Nicolai, “The C++ Standard Library: A Tutorial and Reference,” AddisonWesley, 1999 ##[24] R. King, “My Cat Is ObjectOriented”, ObjectOriented Concepts, Databases and Applications”, Addison Wesley, 1989 [25] M. Langer, “Analysis and Design of Information Systems”, 3rdEdition, SpringerVerlag London Limited, 2008 ##[26] R.C. Lee and W.M. Tepfenhart, “UML and C++: A Practical Guide to ObjectOriented Development,” 2ndEdition, Pearson Prentice Hall, 2005 ##[27] J. Martin, and J. Odell, ObjectOriented Analysis and Design, PrenticeHall, 1992 ##[28] S. M. McMennin, and J. F. Palmer. Essential System Analysis, Yourdon Press, 1984 ##[29] MerriamWebster Online (2011), Dictionary and Thesaurus, fromhttp:// www.merriamwebster.com ##[30] B. Meyer, “ObjectOriented Software Construction,” PrenticeHall International (UK) Ltd., Cambridge, UK, 1988 ##[31] Musser, R. David, and A. Saini., “STL Tutorial and Reference Guide C++ Programming with the Standard Template Library,” Addison Wesley, 1996 ##[32] S.h. Pfleeger, and J.M. Atlee, “Software Engineering: Theory and Practice,” 4th Edition, Pearson, 2010 ##[33] R. S. Pressman, “Software Engineering: A Practitioner's Approach,” 8th Edition, McGrawHill, 2015 ##[34] M. R. Quillian, “Semantic Memory In Marvin Minsky,” Semantic Information Processing. Cambridge, MIT Press, 1968 [35] H. Rashidi, “Software EngineeringA programming approach,” 2ndEdition, AllamehTabataba’i University Press (in Persian), Iran, 2014 ##[36] D. Ross, “Applications and Extensions of SADT,” IEEE Computer, 1985, Vol. 18 (4), pp. 2534. ##[37] J. Rumbaugh, “Getting Started: Using Use Cases To Capture Requirements,” ObjectOriented Programming, Vol. 7(5), pp. 8 12, 1994 ##[38] J. Rumbaugh, M. Blaha, W. Premerlani, F. Eddy, and W. Lorensen, “ObjectOriented Modeling and Design,” PrenticeHall, 1992 ##[39] S. Schlaer, and S. Melior, “ObjectOriented Systems Analysis: Modeling the World in Data,” Yourdon Press, 1988 ##[40] S. Schlaer, , and S. Melior. Object Lifecycles: Modeling the World in States,Yourdon Press, 1992 ##[41] Y. Sommerville, “Software Engineering,” 9th Edition, Pearson Education, 2010. ##[42] L. A. Stein, , H. Lieberman, and D.Ungar, “A shared view of sharing: The Treaty of Orlando,” ObjectOriented Concepts, Databases, and Applications”,Eds. by W. Kim , and F. H. Lechosky, ACM Press, New York, 1989 ## [43] B. Stroustroup, “The C++ Programming Language,” AddisonWesley, 1991 ##[44] K.S. Subhash et al., “NLP based ObjectOriented Analysis and Design from Requirement Specification,” International Journal of Computer Applications, , Vol. 47 (21), 2012 ##[45] M. E. Winston, R. Chaffer, and D. Herrmann, “A Taxonomy of PartWhole Relations,” Cognitive Science, Vol. 11, pp. 417444, 1987. ##[46] R. WirfsBrock, “Designing ObjectOriented Software,” PrenticeHall, 1990 ##[47] E. N. Yourdon, and L. L. Constatine,“Structured Design: Fundamentals of a Discipline of Computer Program and Systems Design,”PrenticeHall, Englewood Cliffs, New Jersey, 1979. ##[48] D. Rosenberg, and M. Stephens, “Use Case Driven Object Modeling with UML: Theory and Practice,” Apress, 2007. [49] C. Larman, “:Applying UML and Patterns – An Introduction to ObjectOriented Analysis and Design and Iterative Development”, 3rd edition, Prentice Hall, 2005. ##[50] H. Rashidi, “A Systematic Approach to Financial Planning in Firms and Its Implementation in an Enterprise,” Quarterly Journal of Fiscal and Economic Policies, Vol. 2 (8), PP. 7392, 2014. ##]
Modified Physical Optics Approximation for RCS Calculation of Electrically Large Objects with Coated Dielectric
2
2
The Radar Cross Section of a target plays an important role in the detection of targets by radars. This paper presents a new method to predict the bistatic and monostatic RCS of coated electrically large objects. The bodies can be covered by lossy electric and/or magnetic Radar Absorbing Materials (RAMs). These materials can be approximated by the Fresnel reflection coefficients. The proposed method uses modified Physical Optics (PO) approximation to obtain the object scattered field. One of the advantages is the use of Stationary Phase Method (SPM) to solve the PO integral. This is becausethe SPM reduces significantly the computation time required to solve this integral as compared to rigorously numerical integration techniques. Simulationresults are presented to verify the accuracy and efficiency of the proposed method. The results are compared with commercial FEKO and CST software in order to show its superiority as far as the computation time is concerned.
1

115
122


Hossein
Mohammadzadeh
Department of Electrical and Computer Engineering, Isfahan University of Technology (IUT), Isfahan, Iran
Department of Electrical and Computer
Iran


Abolghasem
ZeidaabadiNezhad1
Department of Electrical and Computer Engineering, Isfahan University of Technology (IUT), Isfahan, Iran
Department of Electrical and Computer
Iran


Zaker
Hossein Firouzeh
Department of Electrical and Computer Engineering, Isfahan University of Technology (IUT), Isfahan, Iran
Department of Electrical and Computer
Iran
Asymptotic techniques
Physical optics (PO)
Approximation
Radar cross section (RCS)
Coated objects
Modified PO
[[1] M. F. Catedra, C. Delgado, and I. G. Diego, "New physical optics approach for an efficient treatment of multiple bounces in curved bodies defined by an impedance boundary condition," IEEE Transactions on Antennas and Propagation, vol. 56, no. 3, pp. 728736, 2008. ##[2] F. S. de Adana, I. G. Diego, O. G. Blanco, P. Lozano, and M. F. Catedra, "Method based on physical optics for the computation of the radar cross section including diffraction and double effects of metallic and absorbing bodies modeled with parametric surfaces," IEEE Transactions on Antennas and Propagation, vol. 52, no. 5, pp. 32953303, 2004. ##[3] J. T. Hwang, S. Y. Hong, J. H. Song, and H. W. Kwon, "'Radar cross section analysis using physical optics and its applications to marine targets," Journal of Applied Mathematics and Physics, vol. 3, pp. 166171, 2015. ##[4] F. Weinmann, "The Influence of Surface Curvature on HighFrequency RCS Simulations" The Second European Conference on Antennas and Propagation EuCAP, Edinburgh, pp. 15, Nov. 2007. ##[5] C. Corbel, C. Bourlier, N. Pinel, and J. Chauveau, "Rough Surface RCS Measurements and Simulations Using the Physical Optics Approximation" IEEE Trans. Antennas Propag., vol. 61, no. 10, pp. 51555165, 2013. ##[6] F. Weinmann, "Ray tracing with po/ptd for rcs modeling of large complex objects," IEEE Trans. Antennas and Propaation., vol. 54, no. 6, pp. 17971806, 2006. ##[7] Y. An, D. Wang, R. Chen, "Improved multilevel physical optics algorithm for fast computation of monostatic radar cross section," IET Microwaves, Antennas & Propagation, vol. 8, no. 2, pp. 9398, 2014. ##[8] H. Mohammadzadeh, A. ZeidaabadiNezhad, and Z. H. Firouzeh, "Modified physical optics approximation and physical theory of diffraction for rcs calculation of dielectric coated pec," Antennas and Propagation Society International Symposium (APSURSI), OrlandoFL, pp. 1896 – 1897, 2013. ##[9] A. Noga, "Physical optics approximation for PEC objects coated with lossy material," 21st International Conference in Radioelektronika 2011, pp. 13, 2011. ##[10] W. C. Gibson, The Method of Moments in Electromagnetics, Chapman & Hall/CRC and Taylor & Francis Group, 2008. [11] X. J. Chen , X. W. Shi "Comments on a formulation in radar cross section," Journal of Electromagnetic Waves and Applications, vol. 21, no. 15, p. 23892394, 2007. ##[12] J. J. Stamnes, Waves in Focal Region, IOP Publisher, 1986. ##[13] L. P. Felsen, N. Marcuvitz, Radiation and Scattering of Waves, IEEE Press, 1994. ##[14] J. Perez, M. F. Catedra, "Application of physical optics to the RCS computation of bodies modeled with NURBS surfaces," IEEE Transactions on Antennas and Propagation, vol. 42, no. 10, pp. 14041411, 1994. ##[15] P. C. Lash, "Comparison of computational electromagnetic codes for prediction of lowfrequency radar cross section," Master of Science, Department of Electrical and Computer Engineering, Air Force Institute of Technology, 2006. ##[16] L. M. Brekhovskikh, Waves in Layered Media, 2nd ed., vol. 6, Academic Press, 1960. ##[17] R. S. Elliott, Antenna Theory and Design, 2nd ed., IEEE Press, 2003. ##[18] C. A. Balanis, Advanced Engineering Electromagnetics, 2nd ed., John Wiley & Sons, 2012. ##[19] D. Klement, J. Preissner, and V. Stein, "Special problems in applying the physical optics method for backscatter computations of complicated objects," IEEE Transactions on Antennas and Propagation, vol. 36, no. 2, pp. 228237, 1988.##]
A CSA Method for Assigning Client to Servers in Online Social Networks
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This paper deals with the problem of userserver assignment in online social network systems. Online social network applications such as Facebook, Twitter, or Instagram are built on an infrastructure of servers that enables them to communicate with each other. A key factor that determines the facility of communication between the users and the servers is the Expected Transmission Time (ETT). A smart userserver assignment can avoid the low quality links and improve the communication between nodes and also save the valuable communication resources. Unfortunately, finding the optimal assignment turns out to be a NPhard problem. This paper proposes the use of a heuristic algorithm named Centralized Simulated Annealing (CSA) to get a good near optimum solution for this problem. Simulation results of this investigation show that using a relatively small number of iterations, this approach achieves a very good performance improvement. On the other hand, the average number of iterations needed to achieve the nearoptimal solution, will be slightly increased when the number of users in the network increase.
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Shahriar
Minaee Jalil
Imam Khomeini International University, Qazvin, Iran
Imam Khomeini International University, Qazvin,
Iran
minaei@gmail.com


Ali
khaleghi
Imam Khomeini International University, Qazvin, Iran
Imam Khomeini International University, Qazvin,
Iran
Online social networks
Clientserver assignment
Centralized simulated
Annealing algorithm
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