Analyzing the Propagation Behavior of a Gaussian Laser Beam through Seawater and Comparing with Atmosphere 2 2 Study of the beam propagation behavior through oceanic media is a challenging subject. In this paper, based on generalized Collins integral, the mean irradiance profile of Gaussian laser beam propagation through ocean is investigated. Power In Special Bucket (PIB) is calculated. Using analytical expressions and calculating seawater transmission, the effects of absorption and scattering on beam propagation are studied. Based on these formulae, propagation in ocean and atmosphere are compared. The effects of some optical and environmental specifications, such as divergence angle and chlorophyll concentration in seawater on beam propagation by using mean irradiance, PIB and analytical formula of oceanic transmission are studied. The calculated results are shown graphically. 197 203 2013/03/14 1391/12/24 2013/12/22 1392/10/1 F Dabbagh Kashani F Dabbagh Kashani IUST Iran f_dk@iust.ac.ir M R Hedayati Rad M R Hedayati Rad Imam Hosien University Iran mrheda@gmail.com E Kazemian E Kazemian iust iran e_kazemian@iust.ac.ir Propagation of Gaussian Beams Absorption and Scattering Extinction Coefficient Seawater Mean Irradiance [1] F. Schill, U. R. Zimmer and J. Trumpf, “Visible Spectrum Communication and Distance Sensing for Underwater Application”, Australasian Conference Robotics and Automation, pp. 1-5, 2004.##[2] F. Hanson and S. Radic, “High bandwidth underwater optical communication”, Applied Optics, Vol. 47, No. 2, pp. 277-283, 2008.##[3] J. W. Giles and I. N. Bankman, “Underwater optical communications systems. Part 2: basic design considerations”, IEEE Military Communications Conference (MILCOM 2005), pp. 1700-1705, 2005.##[4] C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters, Academic press, 1994.##[5] W. Cox, “A 1 Mbps underwater communication system using a 405 nm laser diode and photomultiplier tube”, M. Sc. Thesis, North Carolina State University, Department of Electrical Engineering, 2007.##[6] H. Brundage, “Designing a wireless underwater optical communication system”, M. Sc. Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2010.##[7] J. Simpson, “A 1 Mbps underwater communications system using LEDs and photodiodes with signal processing capability”, M. Sc. Thesis, North Carolina State University, Department of Electrical Engineering, 2007.##[8] S. Jaruwatanadilok, “Underwater wireless optical communication channel modeling and performance evaluation using vector radiative transfer theory”, IEEE Journal on Selected Areas in Communications, Vol. 26, No. 9, pp. 1620-1627, 2008.##[9] LB. E. A. Saleh and M. C. Teich, Fundamentals of Photonics, Second edition Wiley series in pure and applied, New Jersey, 2007.##[10] L. C. Andrews and R. L. Phillips, Laser Beam Propagation Through Random Media, Second edition SPIE optical Engineering press, Bellingham, Washington USA, 2005.##[11] A. E. Siegman, Lasers, University Science Books Mill Valley, California, 1986.##[12] R. Kvicala, V. Kvicera, M. Grabner and O. Fiser, “BER and availability measured on FSO link”, Radio Engineering, Vol. 16, No. 3, pp. 7-12, 2007.##[13] M. A. Chancey, “Short range underwater optical communication links”, M. Sc. Thesis, North Carolina State University, 2005.## ##

Integrated fuzzy guidance law for high maneuvering targets based on proportional navigation guidance 2 2 An integrated fuzzy guidance (IFG) law for a surface to air homing missile is introduced. The introduced approach is a modification of the well-known proportional navigation guidance (PNG) law. The IFG law enables the missile to approach a high maneuvering target while trying to minimize control effort as well as miss-distance in a two-stage flight. In the first stage, while the missile is far from the intended target, the IFG tends to have low sensitivity to the target maneuvering seeking to minimize the overall control effort. When the missile gets closer to the target, a second stage is started and IFG law changes tactic by increasing that sensitivity attempting to minimize the miss-distance. A fuzzy-switching point (FSP) controller manages the transition between the two stages. The FSP is optimized based on variety of scenarios some of which are discussed in the paper. The introduced scheme depends on line-of-sight angle rate, closing velocity, and target-missile relative range. The performance of the new IFG law is compared with PNG law and the results show a relative superiority in wide variety of flight conditions. 204 214 2013/03/142013/02/11 1391/11/23 2013/12/222013/12/22 1392/10/1 L Hassan L Hassan Maleke Ashtar University iran lab.has77@yahoo.com h sadati h sadati Maleke Ashtar University Iran hsadati@aut.ac.ir j karimi j karimi Maleke Ashtar University Iran karimi_j@alum.sharif.edu Fuzzy Logic Homing Missile Proportional Navigation Guidance [1] B. T. Burchett, “Feedback linearization guidance for approach and landing of reusable launch vehicles”, Proceedings of the American Control Conference, pp. 2093-2097, June 2005.##[2] C. C. Kung, F. L. Chiang and K.Y. Chen, “Design A Three-dimensional pursuit guidance law with Feedback Linearization Method”, World Academy of Science, Engineering and Technology, Vol. 79, No. 1, pp. 136-141, 2011.##[3] F. K. Yeh, H. H. Chien and L. C. Fu, “Design of optimal midcourse guidance sliding-mode control for missiles with TVC”, IEEE Transaction on Aerospace and Electronic Systems, Vol. 39, No. 3, pp. 824-837, 2003.##[4] R. M. Shoucri, “Closed Form Solution of Line-of-Sight Trajectory for Maneuvering Target”, AIAA Journal of Guidance, Control, and Dynamics, Vol. 24, No. 2, pp. 408-409, 2001.##[5] C. M. Lin and Y. F. Peng, “Missile guidance law design using adaptive cerebellar model articulation controller”, IEEE Transaction on Neural Network, Vol. 16, No. 3, pp. 636-644, 2005.##[6] V. Stepanyan and N. Hovakimyan, “Adaptive disturbance rejection controller for visual tracking of a maneuvering target”, Journal of Guidance, Control and Dynamics, Vol. 30, No. 4, pp. 1090-1106, 2007.##[7] J. I. Lee, I. S. Jeon and M. J. Tahk, “Guidance law to control impact time and angle”, IEEE Aerospace and Electronic Systems, Vol. 43, No. 1, pp. 301-310, 2007.##[8] V. Savkin, P. Pathirana and F. A. Faruqi, “The Problem of Precision Missile Guidance: LQR and H∞ Frameworks”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 3, pp. 901-910, 2003.##[9] D. Deshkar, M. Kuber and P. Parakh, “Fuzzy logic guidance law with optimized membership function”, International Journal of Computer Science and Informatics, Vol. 1, No. 2, pp. 52-56, 2011.##[10] V. Rajasekhar and A. G. Sreenatha, “Fuzzy Logic Implementation of Proportional Navigation Guidance,” Acta Astronautica, Vol. 46, No. 1, pp.17-24, 2000.##[11] A. Moharampour, J. Poshtan and A. Khaki-Sedigh, “A Modified Proportional Navigation Guidance for Range Estimation”, Iranian Journal of Electrical & Electronic Engineering, Vol. 4, No. 3, pp.115-126, July 2008.##[12] A. Moharampour, J. Poshtan and A. Khaki-Sedigh, “A Modified Proportional Navigation Guidance for Accurate Target Hitting”, Iranian Journal of Electrical & Electronic Engineering, Vol. 6, No. 1, pp. 20-28, 2010.##[13] G. Siouris, Missile Guidance and Control Systems, Springer Verlag, New York, 2004.##[14] P. Zarchan, Tactical and Strategic Missile Guidance, Third Edition, AIAA, Chapter 6, 2002.##[15] C. L. Lin and Y. Y. Chen, “Design of Fuzzy Logic Guidance Law against High Speed Target”, Journal of Guidance, Control and Dynamics, Vol. 23, No. 1, pp. 17-25, 2000.##[16] J. S. R. Jang, “Adaptive-Network-Based Fuzzy Inference system”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, 1993.##[17] W. V. Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification”, Fuzzy Sets and Systems, Vol. 108, No. 2, pp. 159-178. 1999.##[18] J. Lindblad and N. Sladoje “Feature Based Defuzzification at Increased Spatial Resolution”, Proc., of 11th Intern. Workshop on Combinatorial Image Analysis, Berlin, Germany, Lecture Notes in Computer Science, LNCS. 4040, pp. 131-143, 2006.##[19] J. S. R. Jang, “Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm”, Proc. of the Ninth National Conf. on Artificial Intelligence (AAAI-91), pp. 762-767, July 1991.##[20] M. L. Padma, V. C. Veera and N. R. Sivarami, “Application of Fuzzy and ABC Algorithm for DG Placement for Minimum Loss in Radial Distribution System”, Iranian Journal of Electrical & Electronic Engineering, Vol. 6, No. 4, pp. 248-256, 2010.##[21] C. L. Lin, H. Z. Hung, Y. Y. Chen and B. S. Chen, “Development of an Integrated Fuzzy logic based missile guidance law against high speed target”, IEEE, Transaction on Fuzzy System, Vol. 12, No. 2, pp. 157-169, 2004.##[22] S. Chakraborty and C. Yeh, “A Simulation Based Comparative Study of Normalization Procedures in Multi-attribute Decision Making”, The 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Corfu Island, Greece, pp. 102-109, 2007.##[23] L. L. Chun, C. K. Tzu and T. W. Meng, “Design of a Fuzzified Terminal Guidance Law”, International Journal of Fuzzy Systems, Vol. 9, No. 2, pp. 110-115, 2007.##[24] S. Li and L. Yuan, “Design of Fuzzy Logic Missile Guidance Law with Minimal Rule Base”, Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 176-180, 2009.## ##

Real Time Pseudo-Range Correction Predicting by a Hybrid GASVM model in order to Improve RTDGPS Accuracy 2 2 Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. To overcome the degradation caused by the loss of Differential Global Positioning System (DGPS) Pseudo-Range Correction (PRC), predictions of PRC is possible. In this paper, the Support Vector Machine (SVM) and Genetic Algorithms (GAs) will be incorporated for predicting DGPS PRC information. The Genetic Algorithm is employed to feature subset selection. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy in Real Time DGPS. Given a set of data received from low cost GPS module, the GASVM can predict the PRC precisely when the PRC signal is lost for a short period of time. This method which is introduced for the first time for prediction of PRC is compared to other recently published methods. The experiments show that the total RMS prediction error of GASVM is less than 0.06m for on step and 0.16m for 10 second ahead cases 215 223 2013/03/142013/02/112013/04/09 1392/1/20 2013/12/222013/12/222013/12/22 1392/10/1 M H Refan M H Refan Shahid Rajaee University Iran refan@srttu.edu A Dameshghi A Dameshghi Shahid Rajaee University Iran adel_dameshghi@yahoo.com M Kamarzarrin M Kamarzarrin Shahid Rajaee University Iran Mehrnoosh_kamarzarrin@yahoo.com RTDGPS Pseudo-Range Correction Support Vector Machine Genetic Algorithm [1] M. H. Refan and H. Valizadeh, “Computer Network Time Synchronization using a Low Cost GPS Engine”, Iranian Journal of Electrical & Electronic Engineering, Vol. 8, No. 3, pp. 206-216, 2012.##[2] M. R. Mosavi, “An Adaptive Correction Technique for DGPS using Recurrent Wavelet Neural Network”, Int. Conference on Systems, Man and Cybernetics, pp. 3029-3033, 2007.##[3] D. Jwo, T. Sh. Lee and Y. W. Tseng, “ARMA Neural Networks for Predicting DGPS Pseudo-range Correction”, the journal of navigation, Vol. 57, No. 1, pp. 275-286, 2004.##[4] Y. Geng, “Online DGPS Correction Prediction using Recurrent Neural Networks with Unscented Kalman filter”, International Global Navigation Satellite Systems Society IGNSS Symposium, The University of New South Wales, Sydney, Australia, 4-6 Dec. 2007.##[5] M. R. Mosavi, “Comparing DGPS Corrections Prediction using Neural Network, Fuzzy Neural Network and Kalman Filter”, Journal of GPS Solutions, Vol. 10, No. 2, pp. 97-107. 2006.##[6] K. Kobayashi, K. C. Cheok, K. Watanabe and F. Munekata, “Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique”, IEEE Transaction on Industrial Electronics, Vol. 45, No. 3, pp. 510-518, 1998.##[7] F. E. H. Tay and L. Cao, “Application of support vector machines in financial time series forecasting, Omega”, The International Journal of Management Science, Vol. 29, No. 3, pp. 309-317, 2001.##[8] C. W. J. Granger, “Combining forecasts- Twenty years later”, Journal of Forecasting, Vol. 8, No. 4, pp. 167-173, 1989.##[9] J. H. Min and Y. C. Lee, “Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters”, Expert Systems with Applications, Vol. 28, No. 4, pp. 603-614, 2005.##[10] J. Sang, K. Kubik and L. Zhang, “Prediction of DGPS corrections with neural networks”, 1st Conference on Knowledge-based Intelligent Electronics Systems, Adelaide, Australia, 21-23 May 1997.##[11] M. Mohasseb, A. Rabbany, O. Alim and R. Rashad, “DGPS correction prediction using artificial neural networks”, The Journal of Navigation, Vol. 60, No. 2, pp. 291-301, 2007.##[12] Radio Technical Commission for Maritime Services Special Committee 104, “RTCM SC-104, Recommended Standards for Differential Navstar GPS Service”, Version 2.3, 2001.##[13] X. U. Yingle, I. L. Qunzhan, X. Shaofeng and Z. Liyan, “Study on Algorithm and Communication Protocol of Differential GPS Positioning based on Pseudo-range”, Int. Forum on Information Technology and Applications, pp. 606-609, 2009.##[14] B. Park, J. Kim and C. Kee, “RRC Unnecessary for DGPS Messages”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, No. 3, pp. 1149-1160, 2006.##[15] C. L. Huang and C. J. G. Wan, “A GA-based attribute selection and parameter optimization for support vector machine”, Expert Systems with Applications, Vol. 31, No .2, pp. 231-240, 2006.##[16] H. Drucker, C. Burges, L. Kaufman, A. Smola and V. N. Vapnik, “Support Vector Regression Machines”, Neural Information Processing Systems, MIT Press, Cambridge, MA, Vol. 9, pp. 155-161, 1997.##[17] V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Verlag, 1995.##[18] A. Farag and M. M. Refaat, “Regression Using Support Vector Machines: Basic Foundations”, Technical Report, Dec. 2004.##[19] C. J. C. Burgers, “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, Vol. 2, pp. 121-167, 1998.##[20] L. J. Cao and F. E. H. Tay, “Support vector machine with adaptive parameters in financial time series forecasting”, IEEE Transactions on Neural Network, Vol. 14, No. 6, pp. 1506-1518, 2003.##[21] H. Drucker, C. Burges, L. Kaufman, A. Smola and V. N. Vapnik, “Support Vector Regression Machines”, MIT Press, Cambridge, Vol. 9, pp. 155-161, 1997.##[22] P. Minqiang, Z. Dehuai and X. U. Gang, “Temperature Prediction of Hydrogen Producing reactor using SVM regression with PSO”, Journal of Computers, Vol. 5, No. 3, pp. 388-393, 2010.##[23] R. Yuan and B. Guangchen, “Determination of Optimal SVM Parameters by Using GA/PSO”, Journal of computers, Vol. 5, No. 8, pp.1160-1168, 2010.##[24] S. H. Zahiri, H. Rajabi Mashhadi and S. A. Seyedin, “Intelligent and Robust Genetic Algorithm Based Classifier”, Iranian Journal of Electrical & Electronic Engineering, Vol. 1, No. 3, pp. 1-9, 2005.##[25] M. Soleimanpour-Moghadam and S. Talebi, “A novel technique for steganography method based on improved genetic algorithm optimization in spatial domain”, Iranian Journal of Electrical & Electronic Engineering, Vol. 9, No. 2, pp. 67-75, 2013.##[26] P. P. Feng, H. W. Chiang and L. Y. Shen, “Determining Parameters of Support Vector Machines by Genetic Algorithms-Applications to Reliability Prediction”, International Journal of Operations Research, Vol. 2, No. 1, pp. 1-7, 2005.##[27] P. F. Pai, Ch. Sh. Lin, W. Ch. Hong and T. Chen, “Feature Selection Methods Involving SVMs for Prediction of Insolvency in Non-life Insurance Companies”, Information and Management Sciences, Vol. 17, No. 2, pp. 19-32, 2006.##[28] M12+ GPS Receiver User’s Guide, Motorola GPS Products - M12+ User\'s Guide Revision 6.X 09FEB05, 2004.## ##

A Sub-µW Tuneable Switched-Capacitor Amplifier-Filter for Neural Recording Using a Class-C Inverter 2 2 A two stage sub-µW Inverter-based switched-capacitor amplifier-filter is presented which is capable of amplifying both spikes and local field potentials (LFP) signals. Here we employ a switched capacitor technique for frequency tuning and reducing of 1/f noise of two stages. The reduction of power consumption is very necessary for neural recording devices however, in switched capacitor (SC) circuits OTA is a major building block that consumes most of the power. Therefore an OTA-less technique utilizing a class-C inverter is employed that significantly reduces the power consumption. A detailed analysis of noise performance for the inverter-based SC circuits is presented. A mathematical model useful for analysis of such SC integrators is derived and a good comparison is obtained between simulation and analytical technique. With a supply voltage of 0.7V and using 0.18 µm CMOS technology, this design can achieves a power consumption of about 538 nW. The designed amplifier-filter has the gains 18.6 dB and 28.2 dB for low pass only and cascaded filter, respectively. By applying different sampling frequencies, the filter attains a reconfigurable bandwidth. 224 231 2013/03/142013/02/112013/04/092013/04/13 1392/1/24 2013/12/222013/12/222013/12/222013/12/22 1392/10/1 A Ghorbani-Nejad A Ghorbani-Nejad Tarbiat Modares University Iran a.ghorbani@modares.ac.ir A Jannesari A Jannesari Tarbiat Modares University Iran Jannesari@modares.ac.ir Amplifier-Filter Inverter-Based Switched-Capacitor Circuit Neural Recording [1] K. J. Otto, M. Johnson and D. Kipke, "Voltage pulses change neural interface properties and improve unit recordings with chronically implanted microelectrodes", IEEE Trans. Biomed. Eng., Vol. 53, No. 2, pp. 333-340, 2006.##[2] R. A. Andersen, J. W. Burdick, S. Musallam, H. Scherberger, B. Pesaran, et al., "Recording advances for neural prosthetics", Proc. IEEE EMBS Conf., Vol. 2, pp. 5352-5355, 2004.##[3] L. Jongwoo, M. D. Johnson and D. R. Kipke, "A Tunable Biquad Switched-Capacitor Amplifier-Filter for Neural Recording", IEEE Trans. Biomed. Eng., Vol. 4, No. 5, pp. 295-300, 2010.##[4] C. Youngcheol and H. Gunhee, "Low Voltage, Low Power, Inverter-Based Switched-Capacitor Delta-Sigma Modulator", IEEE J. Solid-State Circuits, Vol. 44, No. 2, pp. 458-472, 2009.##[5] S. Mirhosseini and A. Ayatollahi, “A Low-Voltage, Low-Power, Two-Stage Amplifier for Switched-Capacitor Applications in 90 nm CMOS Process”, Iranian Journal of Electrical and Electronic Engineering, Vol. 6, No. 4, pp.199-204, 2010.##[6] F. Krummenacher, "Micropower switched capacitor biquadratic cell", IEEE J. Solid-State Circuits, Vol. 17, No. 3, pp. 507-512, 1982.##[7] B. Nauta, "A CMOS transconductance-C filter technique for very high frequencies", IEEE J. Solid-State Circuits, Vol. 27, No. 2, pp. 142-153, 1992.##[8] M. Kwon, Y. Chae and G. Han, "Sub-μW Switched-Capacitor Circuits Using a Class-C Inverter", IEICE Trans. Fundamentals, Vol. 88, No. 5, pp. 1313-1319, 2005.##[9] Y. Chae and G. Han, "A low power sigma-delta modulator using class-C inverter", Symp.VLSI Circuits Dig., pp. 240-241, Jun. 2007.##[10] R. H. Van Veldhoven, R. Rutten and L. J. Breems, "An inverter-based hybrid ΔΣ modulator", Int. IEEE Solid-State Circuits Conf. Dig. Tech. Papers, pp. 492-630, Feb. 2008.##[11] S. Tedja, J. Van der Spiegel and H. H. Williams, "Analytical and experimental studies of thermal noise in MOSFET\'s", IEEE Trans. Electron Devices, Vol. 41, No. 11, pp. 2069-2075, 1994.##[12] F. Shayegh, A. Mohammadi, A. Abdipour, V. Sedghi and R. Mirzavand, “The Signal and noise Analysis of Direct Conversion EHM”, Iranian Journal of Electrical and Electronic Engineering, Vol. 2, No. 1, pp. 16-25, 2006.##[13] G. Khodabandehloo, S. Mirzakuchaki and Gh. Karimi, “Modeling and Simulation of Substrate noise in Mixed-Signal”, Iranian Journal of Electrical and Electronic Engineering, Vol. 2, No. 1, pp. 8-15, 2006.##[14] C. C. Enz and G. C. Temes, "Circuit techniques for reducing the effects of op-amp imperfections: autozeroing, correlated double sampling, and chopper stabilization", Proceedings of the IEEE, Vol. 84, No. 11, pp. 1584-1614, 1996.##[15] S. Rabii and B. A. Wooley, "A 1.8-V digital-audio sigma-delta modulator in 0.8-μm CMOS", IEEE J. Solid-State Circuits, Vol. 32, No. 6, pp. 783-796, 1997.##[16] T. Denison, K. Consoer, W. Santa, A. T. Avestruz, J. Cooley and A. Kelly, "A 2 μW 100 nV/rtHz chopper-stabilized instrumentation amplifier for chronic measurement of neural field potentials", IEEE J. Solid-State Circuits, Vol. 42, No. 12, pp. 2934-2945, 2007.##[17] A. M. Sodagar, G. E. Perlin, Y. Yao, K. Najafi and K. D. Wise, "An implantable 64-channel wireless microsystem for single-unit neural recording", IEEE J. Solid-State Circuits, Vol. 44, No. 9, pp. 2591-2604, 2009.##[18] J. N. Y. Aziz, K. Abdelhalim, R. Shulyzki, R. Genov, B. L. Bardakjian, M. Derchansky, et al., "256-channel neural recording and delta compression microsystem with 3D electrodes", IEEE J. Solid-State Circuits, Vol. 44, No. 3, pp. 995-1005, 2009.##[19] W. S. Liew, X. Zou, L. Yao and Y. Lian, "A 1-V 60-μW 16-channel interface chip for implantable neural recording," Proc. IEEE CICC Conf., pp. 507-510, 2009.## ##

Spatial detection of ferromagnetic wires using GMR sensor and based on shape induced anisotropy 2 2 The purpose of this paper is to introduce a new technique for row spacing measurement in a wire array using giant magnetoresistive (GMR) sensor. The self-rectifying property of the GMR-based probes leads to accurately detection of the magnetic field fluctuations caused by surface-breaking cracks in conductive materials, shape-induced magnetic anisotropy, etc. The ability to manufacture probes having small dimensions and high sensitivity (11 mV/mT) to low magnetic fields over a broad frequency range (from dc up to 1 MHz) enhances the spatial resolution of such a probe that is applicable to eddy current testing (ECT) techniques. Here, an AC uniform magnetic field is formed using a Helmholtz coil in which by scanning the probe over an array of acupuncture needles, the distances between them are detected. The results verified the possibility and the performance of the proposed row spacing measurement using GMR sensor. 232 236 2013/03/142013/02/112013/04/092013/04/132013/01/12 1391/10/23 2013/12/222013/12/222013/12/222013/12/222013/12/22 1392/10/1 B Rezaeealam B Rezaeealam Lorestan University Iran rezaeealam@gmail.com Spacing Measurement GMR Sensor [1] H. J. Moller, “Wafering of silicon crystals”, Phys. Stat. Sol. (a), Vol. 203, No. 1, pp. 659-669, 2006.##[2] M. Bhagavat, V. Prasad and I. Kao, “Elasto-Hydrodynamic Interaction in the Free Abrasive Wafer Slicing Using a Wiresaw: Modeling and Finite Element Analysis”, Trans. of the ASME, Vol. 122, No. 1, pp. 394-404, 2000.##[3] S. M. Sakhaei, A. Mahloojifar and H. Ghassemian, “Parallel Beamformation Method to Enhance Ultrasound Images”, Iranian Journal of Electrical and Electronic Engineering, Vol. 2, No. 2, pp. 41-46, 2006.##[4] M. Hariri, S. B. Shokouhi and N. Mozayani, “An Improved Fuzzy Neural Network for Solving Uncertainty in Pattern Classification and Identification”, Iranian Journal of Electrical and Electronic Engineering, Vol. 4, No. 3, pp. 79-93, 2008.##[5] S. Mahmoudi-Barmas and S. Kasaei, “Contourlet-Based Edge Extraction for Image Registration”, Iranian Journal of Electrical and Electronic Engineering, Vol. 4, No. 1, pp. 17-34, 2008.##[6] K. Chomsuwan, S. Yamada and M. Iwahara, “Bare PCB inspection system with SV-GMR sensor eddy-current testing probe”, IEEE Sensors J., Vol. 7, No. 5, pp. 890-896, 2007.##[7] T. Dogaru and S. T. Smith, “Edge crack detection using a giant magnetoresistance based eddy current sensor”, Non-Destruct. Test. Eval., Vol. 16, No. 1, pp. 31-53, 2000.##[8] B. Lebrun, Y. Jayet and J. C. Baboux, “Pulsed eddy current application to the detection of deep cracks”, Mater. Eval., Vol. 53, No. 11, pp. 1296-1300, 1995.##[9] C. H. Smith and R. W. Schneider, “Magnetic field sensing utilizing GMR materials”, Sensor Review, Vol. 18, No. 4, pp. 230-236, 1998.##[10] A. J. Lopez-Martin and A. Carlosena, “Performance tradeoffs of three novel GMR contactless angle detectors”, IEEE Sensors J., Vol. 9, No. 3, pp. 191-198, 2009.##[11] M. M. Maqableh, T. Liwen, H. Xiaobo, R. Cobian, G. Norby, R. H. Victora and B. J. H. Stadler, “CPP GMR through nanowires”, IEEE Trans. Magnetics, Vol. 48, No. 5, pp. 1744-1750, 2012.##[12] Y. He, M. Pan, F. Luo and G. Tian, “Reduction of Lift-Off Effects in Pulsed Eddy Current for Defect Classification”, IEEE Trans. Magnetics, Vol. 47, No. 12, pp. 4753-4760, 2011.##[13] R. M. Bozorth, Ferromagnetism, Wiley-IEEE Press, New York, 1993.## ##

Effect of Remote Back-Up Protection System Failure on the Optimum Routine Test Time Interval of Power System Protection 2 2 Appropriate operation of protection system is one of the effective factors to have a desirable reliability in power systems, which vitally needs routine test of protection system. Precise determination of optimum routine test time interval (ORTTI) plays a vital role in predicting the maintenance costs of protection system. In the most previous studies, ORTTI has been determined while remote back-up protection system was considered fully reliable. This assumption is not exactly correct since remote back-up protection system may operate incorrectly or fail to operate, the same as the primary protection system. Therefore, in order to determine the ORTTI, an extended Markov model is proposed in this paper considering failure probability for remote back-up protection system. In the proposed Markov model of the protection systems, monitoring facility is taken into account. Moreover, it is assumed that the primary and back-up protection systems are maintained simultaneously. Results show that the effect of remote back-up protection system failures on the reliability indices and optimum routine test intervals of protection system is considerable. 237 245 2013/03/142013/02/112013/04/092013/04/132013/01/122013/04/16 1392/1/27 2013/12/222013/12/222013/12/222013/12/222013/12/222013/12/22 1392/10/1 Y Damchi Y Damchi Ferdowsi University of Mashhad Iran damchi_pe@yahoo.com J Sadeh J Sadeh Ferdowsi University of Mashhad Iran sadeh@um.ac.ir Markov Model Monitoring Primary and Remote Back-Up Protection Systems Reliability Routine Test Time Interval [1] L. Wang, G. Wang and Z. Sun, “Determination of the optimum routine maintenance intervals for protective systems”, IEEE Power Engineering Society General Meeting, pp. 1-5, Jul. 2009.##[2] K. Mazlum and H. A. Abyaneh, “Relay coordination and protection failure effects on reliability indices in an interconnected sub-transmission system”, Electric Power Systems Research, Vol. 79, No. 7, pp. 1011-1017, Jul. 2009.##[3] X. Yu and C. Singh, “A Practical approach for integrated power system vulnerability analysis with protection failures”, IEEE Transactions on Power Systems, Vol. 19, No. 4, pp. 1811-1820, Nov. 2004.##[4] X. Yu and C. Singh, “Power system reliability analysis considering protection failures”, IEEE Power Engineering Society Summer Meeting, pp. 963-968, Jul. 2002.##[5] J. J. Kumm, M. S. Weber, D. Hou and E. O. Schweitzer, “Predicting the optimum routine test interval for protection relays”, IEEE Transactions on Power Delivery, Vol. 10, No. 2, pp. 659-665, Apr. 1995.##[6] R. Billinton, M. Fotuhi-Firuzabad and T. S. Sidhu, “Determination of the optimum routine test and self-checking intervals in protective relaying using a reliability model”, IEEE Transactions on Power System, Vol. 17, No. 3, pp. 663-669, Aug. 2002.##[7] H. Seyedi, M. Fotuhi-Firuzabad and M. Sanaye-Pasand, “An extended Markov model to determine the reliability of protective system”, IEEE Power India Conference, pp. 1-5, Apr. 2006.##[8] P. M. Anderson and S. K. Agarwal, “An improved model for protective system reliability”, IEEE Transactions on Reliability, Vol. 41, No. 3, pp. 422-426, Sep. 1992.##[9] P. M. Anderson, G. M. Chintaluri, S. M. Magbuhat and R. F. Ghajar, “An improved reliability model for redundant protective systems Markov models”, IEEE Transactions on Power Systems, Vol. 12, No. 2, pp. 573-578, May 1997.##[10] S. T. J. A. Vermeulen, H. Rijanto and F. A. D. Schouten, “Modeling the influence of preventive maintenance on protection system reliability performance”, IEEE Transactions on Power Delivery, Vol. 13, No. 4, pp. 1027-1032, Oct. 1998.##[11] K. Kangvansaichol, P. Pittayapat and B. Eua-arporn, “Routine test interval decision for protective systems based on probabilistic approach”, IEEE Power System Technology Conference, pp. 977-988, Aug. 2000.##[12] K. Kangvansaichol, P. Pittayapat and B. Eua-arporn, “Optimal routine test intervals for pilot protection schemes using probabilistic methods”, IEE Power System Protection Conference, pp. 254-257, Apr. 2001.##[13] Y. Damchi and J. Sadeh, “Considering failure probability for back-up relay in determination of the optimum routine test interval in protective system using Markov model”, IEEE Power Engineering Society General Meeting, pp. 1-5, Jul. 2009.##[14] H. Etemadi and M. Fotuhi-Firuzabad, “Design and routine test optimization of modern protection systems with reliability and economic constraints”, IEEE Transactions on Power Delivery, Vol. 27, No. 1, pp. 271-278, Jan. 2012.##[15] M. Sefidgaran, M. Mirzaie and A. Ebrahimzadeh, “Reliability model of power transformer with ONAN cooling”, Iranian Journal of Electrical & Electronic Engineering, Vol. 6, No. 2, pp. 103-109, Jun. 2008.##[16] R. Billinton and R. N. Allan, Reliability Evaluation of Engineering Systems, New-York: Plenum Press, 1984.##[17] J. S. Arora, Introduction to Optimum Design, Elsevier Academic Press, Boston, 2004.## ##

An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems 2 2 The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods. 246 252 2013/03/142013/02/112013/04/092013/04/132013/01/122013/04/162013/03/10 1391/12/20 2013/12/222013/12/222013/12/222013/12/222013/12/222013/12/222013/12/22 1392/10/1 R Subramanian R Subramanian Akshaya College India ssst.m1m2m3@gmail.com K Thanushkodi K Thanushkodi Akshaya College India thanush12@gmail.com A Prakash A Prakash Akshaya College India prksh830@gmail.com Artificial Bee Colony optimization Bio geography-Based Optimization Economic Load Dispatch Firefly Algorithm Genetic Algorithm Particle Swarm Optimization [1] A. Badri, S. Jadid and M. Parsa-Moghaddam, “Impact of participants’ market power and transmission constraints on GenCos’ Nash equilibrium point”, Iranian Journal of Electrical and Electronic Engineering, Vol. 3, Nos. 1 & 2, pp.1-9, Jan. 2007.##[2] M. R. Aghamohammadi, “Static security constrained generation scheduling using sensitivity characteristics of neural network”, Iranian Journal of Electrical and ElectronicEngineering, Vol. 4, No. 3, pp. 104-114, Jul. 2008.##[3] L. Derong and C. Ying, “Taguchi Method for Solving the Economic Dispatch Problem With Non smooth Cost Functions”, IEEE transactions on power systems, Vol. 20, No. 4, pp. 2006-2014, Nov. 2005.##[4] S. Duman, U. Güvenç and N. Yörükeren, “Gravitational Search Algorithm for Economic Dispatch with Valve-Point Effects”, International Review of Electrical Engineering, Vol. 5, No. 6, pp. 2890-2895, Dec. 2010.##[5] D. C. Walters and G. B. Sheble, “Genetic Algorithm Solution of Economic Dispatch with Valve Point Loading”, IEEE Transactions on Power Systems,Vol. 8, No. 3, pp. 1325-1332, Aug. 1993.##[6] A. Bakirtzis, V. Petridis and S. Kazarlis, “Genetic Algorithm Solution to the Economic Dispatch Problem”, Proceedings. Inst. Elect. Eng. –Generation, Transmission Distribution, Vol. 141, No. 4, pp. 377-382, July 1994.##[7] G. B. Sheble and K. Brittig, “Refined genetic algorithm- economic dispatch example”, IEEE Trans. on Power Systems, Vol. 10, pp. 117-124, Feb. 1995.##[8] N. Sinha, R. Chakrabarti and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load dispatch”, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 1, pp. 83-94, Feb. 1996.##[9] C. T. Su and C. T. Lin, “New approach with a Hopfield modeling framework to economic dispatch”, IEEE Transactions on Power Systems, Vol. 15, No. 2, pp. 541-545, 2000.##[10] J. G. Vlachogiannis and K. Y. Lee, “Economic Load Dispatch - A Comparative Study on Heuristic Optimization Techniques with an Improved Coordinated Aggregation-Based PSO”, IEEE Transactions on Power Systems, Vol. 24, No. 2, pp. 991-1001, May 2009.##[11] J. B. Park, K. S. Lee, J. R. Shin and K. Y. Lee, “A Particle Swarm Optimization for Economic Dispatch with Nonsmooth Cost Functions”, IEEE Transactions on Power Systems, Vol. 20, No. 1, pp. 34-42, Feb. 2005.##[12] J. B. Park, Y. W. Jeong, J. R. Shin and K. Y. Lee, “An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems”, IEEE Transactions on Power Systems, Vol. 25, No. 1, pp. 156-166, Feb. 2010.##[13] I. A. Selvakumar and K. Thanushkodi, “A new particle swarm optimization solution to non convex economic load dispatch problems”, IEEE Transactions on Power Systems, Vol. 22, No. 1, pp. 42-51, Feb. 2007.##[14] B. K. Panigrahi and V. R. Pandi, “Bacterial foraging optimization: Nelder-Mead hybrid algorithm for economic load dispatch.” IET Gener. Transm, Distrib. Vol. 2, No. 4. pp. 556-565, 2008.##[15] D. Karaboga and B. Basturk, “Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems”, Springer-Verlag, IFSA, LNAI, Vol. 4529, pp. 789–798, 2007.##[16] R. Storn and K. Price, Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, International Computer Science Institute,, Berkeley, CA, 1995, Tech. Rep. TR-95-012.##[17] A. Bhattacharya and P. K. Chattopadhyay, “Biogeography-Based Optimization for Different Economic Load Dispatch Problems”, IEEE Transactions on Power Systems, Vol. 25, No. 2, pp. 1064-1077, May 2010.##[18] X. S. Yang, Firefly algorithm, Levy flights and global optimization, Research and Development in Intelligent Systems XXVI, Springer, London UK, pp. 209-218, 2010.##[19] X.-S. Yang, S. S. Sadat Hosseini, and A. H. Gandomi, “Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect”, Applied Soft Computing, Vol. 12, No. 3, pp. 1180-1186, 2012.##[20] X. S. Yang, “Firefly algorithms for multimodal optimization”, Proceedings of the Stochastic Algorithms: Foundations and Applications (SAGA ’09), Vol. 5792 of Lecture Notes in Computing Sciences, pp. 178-178, Springer, Sapporo, Japan, Oct. 2009.## ##