2017
49
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0
0
A Review of Application of Signal Processing Techniques for Fault Diagnosis of Induction Motors – Part I
2
2
Abstract  Use of efficient signal processing tools (SPTs) to extract proper indices for fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The Part1 of the two parts paper focuses on Fourierbased techniques including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault fetection in IMs are analyzed in details. Then, their competency and their drawbacks for extracting indices in transient and steady state modes are criticized from different aspects. The considerable experimental results are used to certificate demonstrated discussion. Different kinds of faults, including eccentricity, broken bar and bearing faults as major internal faults, in IMs are investigated. The use of efficient signal processing tools (SPTs) to extract proper indices for faultdetection in induction motors (IMs) is the essential part of any fault recognition procedure. In thefirst part of the present paper, we focus on Fourierbased techniques, including fast Fourier transformand short time Fourier transform. In this paper, all utilized SPTs which have been employed forfault detection in IMs are analyzed in detail. Then, their competency and their drawbacks to extractindices in transient and steady state modes are criticized from different aspects. Different kinds offaults, namely, eccentricity, broken bar, and bearing faults as the major internal faults in IMs, areinvestigated.
1

109
122


J.
Faiz
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
School of Electrical and Computer Engineering,
Iran
jfaiz@ut.ac.ir


A. M.
Takbash
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
Department of Electrical and Computer Engineering,
Iran


E.
MazaheriTehrani
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
School of Electrical and Computer Engineering,
Iran
Fault diagnosis
induction motors
Signal Processing
Fourier transform
eccentricity fault
broken bars fault
bearing fault
[[1] J. Faiz, B.M. Ebrahimi, M.B.B. Sharifian, Different Faults and Their Diagnosis Techniques in ThreePhase Squirrel Cage Induction Motors—A Review, Electromagnetics, 26 (2006) 543569. ##[2] S. Nandi, H.A. Toliyat, X. Li, Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review, IEEE Transactions on Energy Conversion, 20(4) (2005) 719729. ##[3] X. Ying, Characteristic Performance Analysis of Squirrel Cage Induction Motor With Broken Bars, IEEE Transactions on Magnetics, 45 (2009) 759766. ##[4] J. Faiz, B.M. Ebrahimi, A New Pattern for Detecting Broken Rotor Bars in Induction Motors During StartUp, IEEE Transactions on Magnetics, 44 (2008) 46734683. ##[5] J. Faiz, B.M. Ebrahimi, Determination of Number of Broken Rotor Bars and Static Eccentricity Degree in Induction Motor under Mixed Fault, Electromagnetics, 28 (2008) 433449. ##[6] J. Faiz, B.M. Ebrahimi, B. Akin, H.A. Toliyat, Comprehensive Eccentricity Fault Diagnosis in Induction Motors Using Finite Element Method, IEEE Transactions on Magnetics, 45 (2009) 17641767. ##[7] M. Drif, A.J.M. Cardoso, AirgapEccentricity Fault Diagnosis, in ThreePhase Induction Motors, by the Complex Apparent Power Signature Analysis, IEEE Transactions on Industrial Electronics, 55 (2008) 1404 1410. ##[8] X. Huang, T.G. Habetler, R.G. Harley, E.J. Wiedenbrug, Using a Surge Tester to Detect Rotor Eccentricity Faults in Induction Motors, IEEE Transactions on Industry Applications, 43 (2007) 11831190. ##[9] D.G. Dorrell, W.T. Thomson, S. Roach, Analysis of airgap flux, current, and vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3phase induction motors, IEEE Transactions on Industry Applications, 33 (1997) 2434. ##[10] J. Faiz, M. Ojaghi, InstantaneousPower Harmonics as Indexes for Mixed Eccentricity Fault in MainsFed and Open/ClosedLoop DriveConnected SquirrelCage Induction Motors, IEEE Transactions on Industrial Electronics, 56 (2009) 47184726. ##[11] S. Nandi, R.M. Bharadwaj, H.A. Toliyat, Performance analysis of a threephase induction motor under mixed eccentricity condition, IEEE Transactions on Energy Conversion, 17 (2002) 392399. ##[12] J. Faiz, B.M. Ebrahimi, B. Akin, H.A. Toliyat, Finite Element Transient Analysis of Induction Motors Under Mixed Eccentricity Fault, IEEE Transactions on Magnetics, 44 (2008) 6674. ##[13] M. Blodt, P. Granjon, B. Raison, G. Rostaing, Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring, IEEE Transactions on Industrial Electronics, 55 (2008) 18131822. ##[14] L. Frosini, E. Bassi, Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors, IEEE Transactions on Industrial Electronics, 57 (2010) 244251. ##[15] I.Y. Önel, M.E.H. Benbouzid, Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study, IEEE/ASME Transactions on Mechatronics, 13 (2008) 257262. ##[16] M.J. Devaney, L. Eren, Detecting motor bearing faults, IEEE Instrumentation and Measurement Magazine, 7 (2004) 3050. ##[17] L. Eren, M.J. Devaney, Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current, IEEE Transactions on Instrumentation and Measurement, 53 (2004) 431436. ##[18] B. Mirafzal, N.A.O. Demerdash, On innovative methods of induction motor interturn and brokenbar fault diagnostics, IEEE Transactions on Industry Applications, 42 (2006) 405414. ##[19] J. Faiz, I. Tabatabaei, Extension of winding function theory for nonuniform air gap in electric machinery, IEEE Transactions on Magnetics, 38 (2002) 36543657. ##[20] Z. Liu, X. Yin, Z. Zhang, D. Chen, W. Chen, Online Rotor Mixed Fault Diagnosis Way Based on Spectrum Analysis of Instantaneous Power in Squirrel Cage Induction Motors, IEEE Transactions on Energy Conversion, 19 (2004) 485490. ##[21] M.S. Ballal, Z.J. Khan, H.M. Suryawanshi, R.L. Sonolikar, Adaptive Neural Fuzzy Inference System for the Detection of InterTurn Insulation and Bearing Wear Faults in Induction Motor, IEEE Transactions on Industrial Electronics, 54 (2007) 250258. ##[22] S. Shin, J. Kim, S.B. Lee, C. Lim, E.J. Wiedenbrug, Evaluation of the Influence of Rotor Magnetic Anisotropy on Condition Monitoring of TwoPole Induction Motors, IEEE Transactions on Industry Applications, 51 (2015) 28962904. ##[23] A. Sadeghian, Z. Ye, B. Wu, Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks, IEEE Transactions on Instrumentation and Measurement, 58 (2009) 22532263. ##[24] J.F. Bangura, R.J. Povinelli, N.A.O. Demerdash, R.H. Brown, Diagnostics of eccentricities and bar/endring connector breakages in polyphase induction motors through a combination of timeseries data mining and timestepping coupled fe~statespace techniques, IEEE Transactions on Industry Applications, 39 (2003) 10051013. ##[25] S.H. Kia, H. Henao, G.A. Capolino, Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform Without Slip Estimation, IEEE Transactions on Industry Applications, 45 (2009) 13951404. ##[26] M.E.H. Benbouzid, G.B. Kliman, What stator current processingbased technique to use for induction motor rotor faults diagnosis?, IEEE Transactions on Energy Conversion, 18 (2003) 238244. ##[27] P.J. Rodriguez, A. Belahcen, A. Arkkio, Signatures of electrical faults in the force distribution and vibration pattern of induction motors, IEE Proceedings  Electric Power Applications, 153 (2006) 523. ##[28] C.C. Yeh, G.Y. Sizov, A. SayedAhmed, N.A.O. Demerdash, R.J. Povinelli, E.E. Yaz, D.M. Ionel, A Reconfigurable Motor for Experimental Emulation of Stator Winding Interturn and Broken Bar Faults in Polyphase Induction Machines, IEEE Transactions on Energy Conversion, 23 (2008) 10051014. ##[29] B. Mirafzal, N.A.O. Demerdash, Effects of Load Magnitude on Diagnosing Broken Bar Faults in Induction Motors Using the Pendulous Oscillation of the Rotor Magnetic Field Orientation, IEEE Transactions on Industry Applications, 41 (2005) 771783. ##[30] G. Didier, E. Ternisien, O. Caspary, H. Razik, Fault detection of broken rotor bars in induction motor using a global fault index, IEEE Transactions on Industry Applications, 42 (2006) 7988. ##[31] J. Milimonfared, H.M. Kelk, S. Nandi, A.D. Minassians, H.A. Toliyat, A novel approach for brokenrotorbar detection in cage induction motors, IEEE Transactions on Industry Applications, 35 (1999) 10001006. ##[32] A.M. da Silva, R.J. Povinelli, N.A.O. Demerdash, Induction Machine Broken Bar and Stator ShortCircuit Fault Diagnostics Based on ThreePhase Stator Current Envelopes, IEEE Transactions on Industrial Electronics, 55 (2008) 13101318. ##[33] C.E. Kim, Y.B. Jung, S.B. Yoon, D.H. Im, The fault diagnosis of rotor bars in squirrel cage induction motors by timestepping finite element method, IEEE Transactions on Magnetics, 33 (1997) 21312134. ##[34] M. Haji, H.A. Toliyat, Pattern recognitiona technique for induction machines rotor broken bar detection, IEEE Transactions on Energy Conversion, 16 (2001) 312317. ##[35] R. PuchePanadero, M. PinedaSanchez, M. Riera Guasp, J. RogerFolch, E. HurtadoPerez, J. Perez Cruz, Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip, IEEE Transactions on Energy Conversion, 24 (2009) 5259. ##[36] J.F. Bangura, N.A. Demerdash, Diagnosis and characterization of effects of broken bars and connectors in squirrelcage induction motors by a timestepping coupled finite elementstate space modeling approach, IEEE Transactions on Energy Conversion, 14 (1999) 11671176. ##[37] A. Bellini, F. Filippetti, G. Franceschini, C. Tassoni, G.B. Kliman, Quantitative evaluation of induction motor broken bars by means of electrical signature analysis, IEEE Transactions on Industry Applications, 37 (2001) 12481255. ##[38] R.F. Walliser, C.F. Landy, Determination of interbar current effects in the detection of broken rotor bars in squirrel cage induction motors, IEEE Transactions on Energy Conversion, 9 (1994) 152158. ##[39] J.F. Watson, D.G. Dorrell, The use of finite element methods to improve techniques for the early detection of faults in 3phase induction motors, IEEE Transactions on Energy Conversion, 14 (1999) 655660. ##[40] P.V. Goode, M.y. Chow, Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors. Part IMethodology, IEEE Transactions on Industrial Electronics, 42 (1995) 131138. ##[41] K. Gyftakis, J. AntoninoDaviu, R. GarciaHernandez, M. McCulloch, D. Howey, A. Cardoso, Comparative Experimental Investigation of the Broken Bar Fault Detectability in Induction Motors, IEEE Transactions on Industry Applications, 10 (2015) 11. ##[42] M. RieraGuasp, J. PonsLlinares, F. VedreñoSantos, J.A. AntoninoDaviu, M. Fernández Cabanas, Evaluation of the amplitudes of highorder fault related components in double bar faults, SDEMPED 2011  8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, (2011) 307315. ##[43] M. RieraGuasp, M. PinedaSanchez, J. PerezCruz, R. PuchePanadero, J. RogerFolch, J.A. AntoninoDaviu, Diagnosis of induction motor faults via gabor analysis of the current in transient regime, IEEE Transactions on Instrumentation and Measurement, 61 (2012) 15831596. ##[44] J. Faiz, B.M. Ebrahimi, M.B.B. Sharifian, TIME STEPPING FINITE ELEMENT ANALYSIS OF BROKEN BARS FAULT IN A THREEPHASE SQUIRRELCAGE INDUCTION MOTOR, Progress In Electromagnetics Research, 68 (2007) 5370. ##[45] J. Faiz, B.M. Ebrahimi, Locating rotor broken bars in induction motors using finite element method, Energy Conversion and Management, 50 (2009) 125131. ##[46] B. Akin, U. Orguner, H.A. Toliyat, M. Rayner, Low Order PWM Inverter Harmonics Contributions to the InverterFed Induction Machine Fault Diagnosis, IEEE Transactions on Industrial Electronics, 55 (2008) 610619. ##[47] M.h. Drif, A.J.M. Cardoso, The Use of Instantaneous PhaseAngle Signature Analysis for Airgap Eccentricity Diagnosis in ThreePhase Induction Motors, in: 2007 International Conference on Power Engineering, Energy and Electrical Drives, IEEE, 2007, pp. 100105. ##[48] J. Faiz, I.T. Ardekanei, H.A. Toliyat, An evaluation of inductances of a squirrelcage induction motor under mixed eccentric conditions, IEEE Transactions on Energy Conversion, 18 (2003) 252258. ##[49] W.T. Thomson, A. Barbour, Online current monitoring and application of a finite element method to predict the level of static airgap eccentricity in threephase induction motors, IEEE Transactions on Energy Conversion, 13 (1998) 347357. ##[50] J.F. Bangura, N.A. Demerdash, Effects of broken bars/ endring connectors and airgap eccentricities on ohmic and core losses of induction motors in ASDs using a coupled finite elementstate space method, IEEE Transactions on Energy Conversion, 15 (2000) 4047. ##[51] S. Nandi, S. Ahmed, H.A. Toliyat, Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages, IEEE Transactions on Energy Conversion, 16 (2001) 253260. ##[52] X. Li, Q. Wu, S. Nandi, Performance Analysis of a ThreePhase Induction Machine With Inclined Static Eccentricity, IEEE Transactions on Industry Applications, 43 (2007) 531541. ##[53] A.M. Knight, S.P. Bertani, Mechanical Fault Detection in a MediumSized Induction Motor Using Stator Current Monitoring, IEEE Transactions on Energy Conversion, 20 (2005) 753760. ##[54] S. Nandi, T.C. Ilamparithi, S.B. Lee, D. Hyun, Detection of eccentricity faults in induction machines based on nameplate parameters, IEEE Transactions on Industrial Electronics, 58 (2011) 16731683. ##[55] M. Ojaghi, Eccentricity fault diagnosis in threephase induction motors under mains voltage and DTC drive supply modes, Ph. D. thesis, School of Electrical and Computer Engineering, Univ. Tehran, Tehran, Iran, (2009). ##[56] W. Zhou, B. Lu, T.G. Habetler, R.G. Harley, Incipient Bearing Fault Detection via Motor Stator Current Noise Cancellation Using Wiener Filter, IEEE Transactions on Industry Applications, 45 (2009) 13091317. ##[57] Y. Liu, L. Guo, Q. Wang, G. An, M. Guo, H. Lian, Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT, Mechanical Systems and Signal Processing, 24 (2010) 29612971. ##[58] M. RieraGuasp, J.A. AntoninoDaviu, M. Pineda Sanchez, R. PuchePanadero, J. PerezCruz, A General Approach for the Transient Detection of SlipDependent Fault Components Based on the Discrete Wavelet Transform, IEEE Transactions on Industrial Electronics, 55 (2008) 41674180. ##[59] M. Timusk, M. Lipsett, C.K. Mechefske, Fault detection using transient machine signals, Mechanical Systems and Signal Processing, 22 (2008) 17241749. ##[60] A. Prudhom, J. AntoninoDaviu, H. Razik, V. Climente Alarcon, Timefrequency vibration analysis for the detection of motor damages caused by bearing currents, Mechanical Systems and Signal Processing, (2015) 116.##]
Data Hiding Method Based on Graph Coloring and Pixel Block‘s Correlation in Color Image
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2
An optimized method for data hiding into a digital color image in spatial domainis provided. The graph coloring theory with different color numbers is applied. To enhance thesecurity of this method, block correlations method in an image is used. Experimental results showthat with the same PSNR, the capacity is improved by %8, and also security has increased in themethod compared with other methods. In the correlation blockbased image method, data hidingcapacity of the host image varies according to image type and defined threshold level. In theproposed algorithm, during graph explanation, independent pixels placed side by side were colored.Then, based on “pixel block correlation data hiding” process is done. This method grows thesecurity and capacity of hiding process. Besides, this increases the effects of image format andcorrelation threshold on security and capacity.
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123
130


G.
Ghadimi
Dept. of Electrical Engineering, Emam Ali University, Tehran, Iran
Dept. of Electrical Engineering, Emam Ali
Iran
g_ghadimi@yahoo.com


M.
Nejati Jahromi
Dept. of Electrical Engineering, Shahid Sattary Aeronautical University of Science and Technology, Tehran, Iran
Dept. of Electrical Engineering, Shahid Sattary
Iran
nejati@aut.ac.ir


E. Ghaemi
Ghaemi
Dept. of Electrical Engineering, Ahar University, Ahar, Iran
Dept. of Electrical Engineering, Ahar University,
Iran
ghaemi_e78@yahoo.com
Data hiding
Graph coloring
Correlation
Threshold
Security
Color number
[[1] R.Z. Wang, C.F. Lin, J.C. Lin, Image hiding by optimal LSB substitution and genetic algorithm, Pattern recognition, 34(3) (2001) 671683. ##[2] M.A. AbdelWahab, H. Selim, U. Sayed, A new method for digital image watermarking based on Vector Quantization (VQ). ##[3] C.C. Chang, W.L. Tai, C.C. Lin, A reversible data hiding scheme based on side match vector quantization, IEEE Transactions on Circuits and Systems for Video Technology, 16(10) (2006) 13011308. ##[4 N.J. Hopper, Toward a theory of Steganography, CARNEGIEMELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE, 2004. ##[5] M.S. Shahreza, An improved method for steganography on mobile phone, WSEAS Transactions on Systems, 4(7) (2005) 955957. ##[6] J. Fridrich, P. Lisonek, Grid colorings in steganography, IEEE Transactions on Information Theory, 53(4) (2007) 15471549. ##[7] W. Zhang, X. Zhang, S. Wang, Twice Gric Colorings in Steganography, in: Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP’08 International Conference on, IEEE, 2008, pp. 13011304. ##[8] S. Yue, Z.H. Wang, C.Y. Chang, C.C. Chang, M.C. Li, Image data hiding schemes based on graph coloring, Ubiquitous Intelligence and Computing, (2011) 476489. ##[9] M. Nafari, G.H. Sheisi, M.N. Jahromi, New data hiding method based on neighboring correlation of blocked image, in: International Conference on Digital Information and Communication Technology and Its Applications, Springer, 2011, pp. 787801. ##[10] S.M. Douiri, M.O. Medeni, S. Elbernoussi, New steganography scheme using graphs product, in: Interactive Collaborative Learning (ICL), 2014 International Conference on, IEEE, 2014, pp. 525528. ##[11] W. Astuti, U.N. Wisety, Data Hiding Scheme on Medical Image using Graph Coloring, in: Journal of Physics: Conference Series, IOP Publishing, 2015, pp. 012028. ##[12] S.H. Chiang, J.H. Yan, On L (d, 1)labeling of Cartesian product of a cycle and a path, Discrete Applied Mathematics, 156(15) (2008) 28672881. ##[13] D.B. West, Introduction to graph theory, Prentice hall Upper Saddle River, 2001.##]
Performance Analysis Of Monobit Digital Instantaneous Frequency Measurement (Difm) Device
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Instantaneous Frequency Measurement (IFM) devices are the essential parts of anyESM, ELINT, and RWR receiver. Analog IFMs have been used for several decades. However, thesedevices are bulky, complex and expensive. Nowadays, there is a great interest in developing a wideband, high dynamic range, and accurate Digital IFMs. One Digital IFM that has suitably reached allthese requirements is monobit zerocrossing IFM, made by some different producers at present. Inthis paper, the performance of monobit digital Instantaneous Frequency Measurement (IFM) deviceis analyzed. This analysis includes quantization error, thermal noise, clock jitter, comparator bias andalso “PulseonPulse” occurrence. The error limits due to all these factors are computed and analyzed,and a unified approach to the system design is presentedIn this paper, the performance of monobit digital Instantaneous frequency measurement (IFM) device is analyzed. This analysis includes quantization error, additive (thermal) noise, clock jitter, comparator bias and also “PulseonPulse” occurrence. The error limits due to all these factors are computed and analyzed, and a unified approach to the system design is presented
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131
140


Y.
Norouzi
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Elrctrical Engineering, Amirkabir
Iran
y.norouzi@aut.ac.ir


H.
Shahbazi
Dept. of Science and Research, Azad University, Tehran, Iran
Dept. of Science and Research, Azad University,
Iran


S.
Mirzaei
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran,Iran
Dept. of Elrctrical Engineering, Amirkabir
Iran
Digital Instantaneous Frequency
Measurement (DIFM)
Monobit Receiver
Zerocrossing
[[1] H. Gruchala, M. CzyzEwski, The instantaneous frequency measurement receiver in the complex electromagnetic environment, in: Microwaves, Radar and Wireless Communications, 2004. MIKON2004. 15th International Conference on, IEEE, 2004, pp. 155158. ##[2] R.G. Wiley, ELINT: The interception and analysis of radar signals, Artech House, 2006. ##[3] A.B. Carlson, Communication system, Tata McGraw Hill Education, 2010. ##[4] A. Weiss, B. Friedlander, Simultaneous signals in IFM receivers, IEE ProceedingsRadar, Sonar and Navigation, 144(4) (1997) 181185. ##[5] P. East, Design techniques and performance of digital IFM, in: IEE Proceedings F (Communications, Radar and Signal Processing), IET, 1982, pp. 154163. ##[6] J. Hedge, W. McCormick, J.B. Tsui, IFM receiver with two correlators, in: Aerospace and Electronics Conference, 1989. NAECON 1989., Proceedings of the IEEE 1989 National, IEEE, 1989, pp. 898901. ##[7] H. Gruchala, M. CzyzEwski, The instantaneous frequency measurement receiver in the complex electromagnetic environment, in: Microwaves, Radar and Wireless Communications, 2004. MIKON2004. 15th International Conference on, IEEE, 2004, pp. 155158. ##[8] H. Gruchalla, M. Czyzewski, A. Slowik, The Instantaneous Frequency Measurement Receiver with Simultaneous Signal Capability, in: Microwaves, Radar & Wireless Communications, 2006. MIKon 2006. International Conference on, IEEE, 2006, pp. 554557. ##[9] M. Thornton, Ultrabroadband frequency discriminator designs for IFM receivers, in: MultiOctave Active and Passive Components and Antennas, IEE Colloquium on, IET, 1989, pp. 13/1113/14. ##[10] S.C. Sekhar, T.V. Sreenivas, Adaptive window zerocrossingbased instantaneous frequency estimation, EURASIP Journal on Advances in Signal Processing, 2004(12) (2004) 249858. ##[11] X. Zou, H. Chi, J. Yao, Microwave frequency measurement based on optical power monitoring using a complementary optical filter pair, IEEE Transactions on Microwave Theory and Techniques, 57(2) (2009) 505511. ##[12] X. Zou, J. Yao, An optical approach to microwave frequency measurement with adjustable measurement range and resolution, IEEE photonics technology letters, 20(23) (2008) 19891991. ##[13] J. Helton, C.I.H. Chen, D.M. Lin, J.B. Tsui, FPGAbased 1.2 GHz bandwidth digital instantaneous frequency measurement receiver, in: Quality Electronic Design, 2008. ISQED 2008. 9th International Symposium on, IEEE, 2008, pp. 568571. ##[14] J.B. Tsui, Digital techniques for wideband receivers, SciTech Publishing, 2004. ##[15] P. Gibson, A 9 Bit DIFM Receiver for KaBand, in: Microwave Conference, 1985. 15th European, IEEE, 1985, pp. 599604. ##[16] http://www.mwelisra.com/pdf/DIFM%20218.pdf ##[17] http://www.inphi corp.com/docs/Inphi_Military_and_ Aerospace_Solutions_Guide.pdf ##[18] A.A. Ivanov, O.G. Morozov, V.A. Andreev, A.A. Kuznetsov, L.M. Faskhutdinov, Radiophotonic method for instantaneous frequency measurement based on principles of “frequencyamplitude” conversion in Fiber Bragg grating and additional frequency separation, in: Antenna Theory and Techniques (ICATT), 2017 XI International Conference on, IEEE, 2017, pp. 425428. ##[19] M. Burla, X. Wang, M. Li, L. Chrostowski, J. Azaña, Onchip instantaneous microwave frequency measurement system based on a waveguide Bragg grating on silicon, in: CLEO: Science and Innovations, Optical Society of America, 2015, pp. STh4F. 7. ##[20] B. De Oliveira, M. de Melo, I. LlamasGarro, M. Espinosa Espinosa, M.T. de Oliveira, E. de Oliveira, Integrated instantaneous frequency measurement subsystem based on multibandstop filters, in: Microwave Conference (APMC), 2014 AsiaPacific, IEEE, 2014, pp. 910912. ##[21] F. Marvasti, Nonuniform sampling: theory and practice, Springer Science & Business Media, 2012. ##[22] P. Stoica, R.L. Moses, Introduction to spectral analysis, Prentice hall Upper Saddle River, NJ, 1997. ##[23] W.A. Gardner, Statistical Spectral Analysis: A Nonprobabilistic Approach, in, Prentice Hall, Englewood Cliffs, NJ, 1988.##]
Cascaded Multilevel Inverters with Reduced Structures Based on a Recently Proposed Basic Units: Implementing a 147level Inverter
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A multilevel inverter is capable of generating highquality stepwise pseudosinusoidalvoltage with low THD , applicable to highpower and highvoltage systems. These types of topologiesmay require a large number of switches and power supplies. This leads to much cost, large size, andcomplicated control algorithms. Thus, newer topologies are being proposed to decrease the numberof power electronic devices for a large number of levels in output voltage. Recently, a new multilevelinverter has been reported in the literature to reduce component count. Its structure requires a lowernumber of active switches as compared to the existing ones. The available literature presents ageneralization of the topology with an especial asymmetrical sources ratio, but no investigations aremade for other symmetrical or asymmetrical sources ratio with cascaded configurations. This studypresents a comprehensive analysis of cascaded topologies with the proposed basic units. The topologyis analysed for both symmetric and asymmetric DC source configurations. Also, two algorithms forasymmetric source configuration suitable for cascaded structures are proposed. Moreover, the designand simulation of a 147level inverter are presented under an optimal number of DC sources and powerswitches. Furthermore, experimental validation is performed by implementing a laboratory prototype.
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141
150


M. J.
Mojibian
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K.
Iran
mojibian@ee.kntu.ac.ir


M.
Tavakoli Bina
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K.
Iran
tavakoli@ieee.org


B.
Eskandari
Department of Electrical Engineering, Malayer University, Malayer, Iran
Department of Electrical Engineering, Malayer
Iran
b.eskandary@gmail.com
Asymmetrical DC Sources
Multilevel Inverter
Packed U cell
Reduced Structures
[[1] J. Rodríguez, S. Bernet, B. Wu, J.O. Pontt, S. Kouro, Multilevel voltagesourceconverter topologies for industrial mediumvoltage drives, IEEE Transactions on industrial electronics, 54(6) (2007) 29302945. ##[2] L.G. Franquelo, J. Rodriguez, J.I. Leon, S. Kouro, R. Portillo, M.A. Prats, The age of multilevel converters arrives, IEEE industrial electronics magazine, 2(2) (2008). ##[3] S. Kouro, M. Malinowski, K. Gopakumar, J. Pou, L.G. Franquelo, B. Wu, J. Rodriguez, M.A. Pérez, J.I. Leon, Recent advances and industrial applications of multilevel converters, IEEE Transactions on industrial electronics, 57(8) (2010) 25532580. ##[4] X. Zha, L. Xiong, J. Gong, F. Liu, Cascaded multilevel converter for mediumvoltage motor drive capable of regenerating with part of cells, IET Power Electronics, 7(5) (2014) 13131320. ##[5] R. Teichmann, M. Malinowski, S. Bernet, Evaluation of threelevel rectifiers for lowvoltage utility applications, IEEE Transactions on Industrial Electronics, 52(2) (2005) 471481. ##[6] S. Daher, J. Schmid, F.L. Antunes, Multilevel inverter topologies for standalone PV systems, IEEE Transactions on Industrial Electronics, 55(7) (2008) 27032712. ##[7] M. Malinowski, K. Gopakumar, J. Rodriguez, M.A. Perez, A survey on cascaded multilevel inverters, IEEE Transactions on industrial electronics, 57(7) (2010) 21972206. ##[8] M.J. Mojibian, M.T. Bina, Classification of multilevel converters with a modular reduced structure: implementing a prominent 31level 5 kVA class B converter, IET Power Electronics, 8(1) (2014) 2032. ##[9] J. Dixon, L. Moran, Highlevel multistep inverter optimization using a minimum number of power transistors, IEEE Transactions on Power Electronics, 21(2) (2006) 330337. ##[10] Y. Ounejjar, K. AlHaddad, L.A. Grégoire, Packed U cells multilevel converter topology: theoretical study and experimental validation, IEEE Transactions on Industrial Electronics, 58(4) (2011) 12941306. ##[11] G. Konstantinou, M. Ciobotaru, V. Agelidis, Selective harmonic elimination pulsewidth modulation of modular multilevel converters, IET Power Electronics, 6(1) (2013) 96107. ##[12] I. Colak, E. Kabalci, R. Bayindir, Review of multilevel voltage source inverter topologies and control schemes, Energy Conversion and Management, 52(2) (2011) 1114 1128. ##[13] A.M. AS, A. Gopinath, M. Baiju, A simple space vector PWM generation scheme for any general $ n $level inverter, IEEE Transactions on Industrial Electronics, 56(5) (2009) 16491656. ##[14] Z. Du, L.M. Tolbert, B. Ozpineci, J.N. Chiasson, Fundamental frequency switching strategies of a sevenlevel hybrid cascaded Hbridge multilevel inverter, IEEE Transactions on Power Electronics, 24(1) (2009) 2533.##]
Investigating Direct Torque Control of SixPhase Induction Machines Under Open Phase Fault Conditions
2
2
This paper presents analysis and evaluation of classical direct torque control(DTC), for controlling a symmetrical six phase induction motor (SPIM) under open phasefault conditions. The machine has two threephase windings spatially shifted by 60 electricaldegrees. The strategy of the proposed method consists of choosing the switching modesaccording to the configuration of living phases in such a way that it generates vectors thathave higher amplitude in αβ plane while their projections on z axis give zero or near zeroamplitude vectors. The goal is reducing parasitic currents and torque ripples of SPIM underfaulty mode. Based on the theoretical analysis, it will be shown that in the open phase faultconditions, the only nonpulsating operation is obtained by opening the fault threephasewinding. Experimental test results are provided o support theoretical analysis in open phasefault conditions for SPIM.
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151
160


R.
Kianinezhad
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shahid
Iran
reza.kiani@scu.ac.ir


A.
Hajary
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shahid
Iran
alihajary@gmail.com
six phase induction machine
direct torque control
open phase fault
[[1] J.R. Fu, T.A. Lipo, Disturbancefree operation of a multiphase currentregulated motor drive with an opened phase, IEEE Transactions on Industry Applications, 30(5) (1994) 12671274. ##[2] Y. Zhao, T.A. Lipo, Space vector PWM control of dual threephase induction machine using vector space decomposition, IEEE Transactions on industry applications, 31(5) (1995) 11001109. ##[3] R. Kianinezhad, B. NahidMobarakeh, L. Baghli, F. Betin, G.A. Capolino, Modeling and control of sixphase symmetrical induction machine under fault condition due to open phases, IEEE Transactions on Industrial Electronics, 55(5) (2008) 19661977. ##[4] A. Tani, M. Mengoni, L. Zarri, G. Serra, D. Casadei, Control of multiphase induction motors with an odd number of phases under opencircuit phase faults, IEEE Transactions on Power Electronics, 27(2) (2012) 565577. ##[5] H.S. Che, M.J. Duran, E. Levi, M. Jones, W.P. Hew, N.A. Rahim, Postfault operation of an asymmetrical sixphase induction machine with single and two isolated neutral points, IEEE Transactions on Power Electronics, 29(10) (2014) 54065416. ##[6] H.M. Ryu, J.W. Kim, S.K. Sul, Synchronous frame current control of multiphase synchronous motorpart II asymmetric fault condition due to open phases, in: Industry Applications Conference, 2004. 39th IAS Annual Meeting. Conference Record of the 2004 IEEE, IEEE, 2004. ##[7] Y. Zhao, T.A. Lipo, Modeling and control of a multiphase induction machine with structural unbalance, IEEE Transactions on energy conversion, 11(3) (1996) 570577. ##[8] Y. Zhao,T. A. Lipo, Modeling and control of a multiphase induction machine with structural unbalance Part II. Fieldoriented control andexperimental verification, , IEEE Transactions on energy conversion, 11(3) (1996) 578584. ##[9] J.P. Martin, F. MeibodyTabar, B. Davat, Multiplephase permanent magnet synchronous machine supplied by VSIs, working under fault conditions, in: Industry Applications Conference, 2000. Conference Record of the 2000 IEEE, IEEE, 2000, pp. 17101717. ##[10] D. Hadiouche, H. Razik, A. Rezzoug, Modelling of a double star induction motor for space vector pwm control, in: International conference on electrical machines, 2000, pp. 392396. ##[11] R. Kianinezhad, B. NahidMobarakeh, L. Baghli, F. Betin, G.A. Capolino, Torque ripples suppression for sixphase induction motors under open phase faults, in: IEEE Industrial Electronics, IECON 200632nd Annual Conference on, IEEE, 2006, pp. 13631368. ##[12] V. Talaeizadeh, R. Kianinezhad, S. Seyfossadat, H. Shayanfar, Direct torque control of sixphase induction motors using threephase matrix converter, Energy Conversion and Management, 51(12) (2010) 24822491. ##[13] R. Alcharea, R. Kianinezhad, B. NahidMobarakeh, F. Betin, G.A. Capolino;PWM Direct Torque Control of Symmetrical SixPhase Induction Machines, Conference of the IEEE Industrial Electronics Society, IECON, 2008.##]
Optimization of MixedInteger NonLinear Electricity Generation Expansion Planning Problem Based on Newly Improved Gravitational Search Algorithm
2
2
Electricity demand is forecasted to double in 2035, and it is vital to address the economicsof electrical energy generation for planning purposes. This study aims to examine the applicability ofGravitational Search Algorithm (GSA) and the newly improved GSA (IGSA) for optimization of themixedinteger nonlinear electricity generation expansion planning (GEP) problem. The performanceindex of GEP problem is defined as the total cost (TC) based on the sum of costs for investment andmaintenance, unserved load, and salvage. In IGSA, the search space is subdivided for escaping fromlocal minima and decreasing the computation time. Four different GEP case studies are considered toevaluate the performances of GSA and IGSA, and the results are compared with those from implementingparticle swarm optimization algorithm. It is found that IGSA results in lower TC by 7.01%, 4.08%,11.00%, and 6.40%, in comparison with GSA, for the four case studies. Moreover, as compared withGSA, the simulation results show that IGSA requires less computation time, in all cases.
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161
172


F .J.
Ardakani
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Energy Research Center, Department of Electrical
Iran
ardehali@aut.ac.ir


M. M.
Ardehali
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Energy Research Center, Department of Electrical
Iran
Generation expansion planning
Improved gravitational search
algorithm
Optimization
Power system planning
[[1] F. Ardakani, M. Ardehali, Novel effects of demand side management data on accuracy of electrical energy consumption modeling and longterm forecasting, Energy Conversion and Management, 78 (2014) 745752. ##[2] EIA, World Energy Outlook (2013). ##[3] EIA, World Energy Outlook (2009). ##[4] I. Statistics, Energy balances of nonOECD countries in 2011, Paris: International Energy Agency, (2011). ##[5] B. Alizadeh, S. Jadid, Reliability constrained coordination of generation and transmission expansion planning in power systems using mixed integer programming, IET generation, transmission & distribution, 5(9) (2011) 948960. ##[6] J.L.C. Meza, M.B. Yildirim, A.S. Masud, A multiobjective evolutionary programming algorithm and its applications to power generation expansion planning, IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans, 39(5) (2009) 10861096. ##[7] G. Liu, H. Sasaki, N. Yorino, Application of network topology to long range composite expansion planning of generation and transmission lines, Electric Power Systems Research, 57(3) (2001) 157162. ##[8] L. Wenyuan, R. Billinton, A minimum cost assessment method for composite generation and transmission system expansion planning, IEEE Transactions on Power Systems, 8(2) (1993) 628635. ##[9] C.H. Antunes, A.G. Martins, I.S. Brito, A multiple objective mixed integer linear programming model for power generation expansion planning, Energy, 29(4) (2004) 613627. ##[10] S. Majumdar, D. Chattopadhyay, A model for integrated analysis of generation capacity expansion and financial planning, IEEE transactions on power systems, 14(2) (1999) 466471. ##[11] H. Khodr, J. Gomez, L. Barnique, J. Vivas, P. Paiva, J. Yusta, A. Urdaneta, A linear programming methodology for the optimization of electric powergeneration schemes, IEEE Transactions on Power systems, 17(3) (2002) 864 869. ##[12] H. Tekiner, D.W. Coit, F.A. Felder, Multiperiod multiobjective electricity generation expansion planning problem with MonteCarlo simulation, Electric Power Systems Research, 80(12) (2010) 13941405. ##[13] C. UnsihuayVila, J.W. MarangonLima, A.Z. De Souza, I. PerezArriaga, Multistage expansion planning of generation and interconnections with sustainable energy development criteria: A multiobjective model, International Journal of Electrical Power & Energy Systems, 33(2) (2011) 258270. ##[14] A. Ramos, I.J. PerezArriaga, J. Bogas, A nonlinear programming approach to optimal static generation expansion planning, IEEE Transactions on Power Systems, 4(3) (1989) 11401146. ##[15] J.B. Park, Y.M. Park, J.R. Won, K.Y. Lee, An improved genetic algorithm for generation expansion planning, IEEE Transactions on Power Systems, 15(3) (2000) 916922. ##[16] J. Sirikum, A. Techanitisawad, Power generation expansion planning with emission control: a nonlinear model and a GA.based heuristic approach, International Journal of Energy Research, 30(2) (2006) 8199. ##[17] B. Alizadeh, S. Jadid, A dynamic model for coordination of generation and transmission expansion planning in power systems, International Journal of Electrical Power & Energy Systems, 65 (2015) 408418. ##[18] Z. Hejrati, E. Hejrati, A. Taheri, Optimization generation expansion planning by HBMO, Optimization, 37(7) (2012) 99108. ##[19] S.L. Chen, T.S. Zhan, M.T. Tsay, Generation expansion planning of the utility with refined immune algorithm, Electric Power Systems Research, 76(4) (2006) 251258. ##[20] B. HEDAYATFAR, A. BARJANEH, LeastCost Generation Expansion Planning Using an Imperialist Competitive Algorithm, Life Science Journal, 10(8s) (2013). ##[21] R.P. Kothari, D.P. Kroese, Optimal generation expansion planning via the crossentropy method, in: Winter Simulation Conference, Winter Simulation Conference, 2009, pp. 14821491. ##[22] S. Kannan, S.M.R. Slochanal, P. Subbaraj, N.P. Padhy, Application of particle swarm optimization technique and its variants to generation expansion planning problem, Electric Power Systems Research, 70(3) (2004) 203210. ##[23] M. Jadidoleslam, E. Bijami, N. Amiri, A. Ebrahimi, J. Askari, Application of shuffled frog leaping algorithm to long term generation expansion planning, International Journal of Computer and Electrical Engineering, 4(2) (2012) 115. ##[24] M. Jadidoleslam, A. Ebrahimi, Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm, International Journal of Electrical Power & Energy Systems, 64 (2015) 743751. ##[25] E. Rashedi, H. NezamabadiPour, S. Saryazdi, GSA: a gravitational search algorithm, Information sciences, 179(13) (2009) 22322248. ##[26] P.K. Roy, Solution of unit commitment problem using gravitational search algorithm, International Journal of Electrical Power & Energy Systems, 53 (2013) 8594. ##[27] P. Roy, B. Mandal, K. Bhattacharya, Gravitational search algorithm based optimal reactive power dispatch for voltage stability enhancement, Electric Power Components and Systems, 40(9) (2012) 956976. ##[28] A. Bhattacharya, P. Roy, Solution of multiobjective optimal power flow using gravitational search algorithm, IET generation, transmission & distribution, 6(8) (2012) 751763. ##[29] IAEA (International Atomic Energy Agency), Wien automatic system planning (WASP) package a computer code for power generating system expansion planning in, Vienna, 2001. ##[30] G.M. Cole, Surveyor reference manual, fifth ed., Professional publications Inc. (PPI), 2009. ##[31] M. Clerc, J. Kennedy, The particle swarmexplosion, stability, and convergence in a multidimensional complex space, IEEE transactions on Evolutionary Computation, 6(1) (2002) 5873. ##[32] E. Rashedi, H. NezamabadiPour, S. Saryazdi, BGSA: binary gravitational search algorithm, Natural Computing, 9(3) (2010) 727745. ##[33] C. Li, J. Zhou, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 52(1) (2011) 374381. ##[34] T.H. Huynh, A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers, in: Industrial Technology, 2008. ICIT 2008. IEEE International Conference on, IEEE, 2008, pp. 16. ##[35] A. David, Z. Rongda, An expert system with fuzzy sets for optimal planning (of power system expansion), IEEE Transactions on Power Systems, 6(1) (1991) 5965. ##]
Increasing Voltage Gain by New Structure of Inductive Switching DCDC Converter
2
2
In a photovoltaic system, sun light energy is converted to electricity. The generatedelectricity has a low DC voltage. In order to increase voltage generated by photovoltaic cells (PV),an additive DCDC converter is required to raise the low voltage to a good level which provides theconditions for connection to DCDC converters. Low wastes, low costs, and high efficiency are someother specifications of such converters. This paper presents a new structure for an additive DCDCconverter with inductive and capacitor switching for increasing high voltage gain to be used in PVsystem. It is based on the inductive and noninsulated switching which increases voltage in a duty cycleup to 10 times of input voltage. In addition, using a switch, low elements, and also low voltage stresson the switch is the advantage of this new setup. The easy increasing of levels to reach the highervoltages is another benefit of this structure. The paper continues with the analysis of circuit functionand PWM (Pulse Width Modulation) adjustments. PSCAD/EMTDC software is used for confirming theauthenticity of the performance of the suggested model. The results are presented.
1

173
178


S.
Nabati
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Electrical Eng., Science and
Iran
salman.nabati@yahoo.com


A.
Siadatan
Dept. of Electrical Eng., Faculty of Technical & Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
Dept. of Electrical Eng., Faculty of Technical
Iran


S. B.
Mozafari
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Electrical Eng., Science and
Iran
PV
DCDC Converter
High Voltage Gain
PWM
PSCAD Software
[[1] W.Y. Choi, J.S. Yoo, J.Y. Choi, High efficiency dcdc converter with high stepup gain for low PV voltage sources, in: Power Electronics and ECCE Asia (ICPE & ECCE), 2011 IEEE 8th International Conference on, IEEE, 2011, pp. 11611163. ##[2] Q. Zhao, F.C. Lee, Highefficiency, high stepup DCDC converters, IEEE Transactions on Power Electronics, 18(1) (2003) 6573. ##[3] B. Wu, S. Li, S. Keyue, A new hybrid boosting converter, in: Energy Conversion Congress and Exposition (ECCE), 2014 IEEE, IEEE, 2014, pp. 33493354. ##[4] M. Prudente, L.L. Pfitscher, G. Emmendoerfer, E.F. Romaneli, R. Gules, Voltage multiplier cells applied to nonisolated DC–DC converters, IEEE Transactions on Power Electronics, 23(2) (2008) 871887. ##[5] S. Lee, P. Kim, S. Choi, High stepup softswitched converters using voltage multiplier cells, IEEE Transactions on Power Electronics, 28(7) (2013) 33793387. ##[6] J.C. RosasCaro, J.M. Ramirez, F.Z. Peng, A. Valderrabano, A DC–DC multilevel boost converter, IET Power Electronics, 3(1) (2010) 129137. ##[7] F.L. Luo, H. Ye, Positive output multiplelift pushpull switchedcapacitor Luoconverters, IEEE transactions on industrial electronics, 51(3) (2004) 594602. ##[8] J.A. Starzyk, Y.W. Jan, F. Qiu, A DCDC charge pump design based on voltage doublers, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48(3) (2001) 350359. ##[9] F.L. Luo, H. Ye, Positive output superlift converters, IEEE Transactions on Power Electronics, 18(1) (2003) 105113. ##[10] N. Vazquez, L. Estrada, C. Hernandez, E. Rodriguez, The tappedinductor boost converter, in: Industrial Electronics, 2007. ISIE 2007. IEEE International Symposium on, IEEE, 2007, pp. 538543. ##[11] T.F. Wu, Y.S. Lai, J.C. Hung, Y.M. Chen, Boost converter with coupled inductors and buck–boost type of active clamp, IEEE Transactions on Industrial Electronics, 55(1) (2008) 154162. ##[12] L.S. Yang, T.J. Liang, J.F. Chen, Transformerless DC–DC converters with high stepup voltage gain, IEEE Transactions on Industrial Electronics, 56(8) (2009) 31443152.##]
Measurement and Computational Modeling of RadioFrequency Electromagnetic Power Density Around GSM Base Transceiver Station Antennas
2
2
Evaluating the power densities emitted by GSM1800 and GSM900 BTS antennas isconducted via two methods. Measurements are carried out in half a square meter grids around twoantennas. CST Microwave STUDIO software is employed to estimate the power densities in order fordetailed antenna and tower modeling and simulation of power density. Finally, measurements obtainedfrom computational and experimental methods were compared through the contour lines using thestatistical Surfer software. After measuring and simulating all values, it turns out that power density isgenerally lower than the permissible exposure limits although exceeds the limits in some sample points. According to the measurements, simulation error in stations GSM900 and GSM1800 are 10% and 8%,respectively. Findings from contourlinemaps illustrates that direct measurement method follows thesame emission pattern as the computational method does. It validates the computational approach andthe models attained for BTS power density estimation.
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179
186


P.
Nassiri
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering,
Iran
nassiri@sina.tums.ac.ir


M.
Saviz
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Biomedical Engineering, Amirkabir
Iran
msaviz@aut.ac.ir


M.
HelmikohnehShahri
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering,
Iran
m.hk680925@yahoo.com


M.
Pourhosein
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering,
Iran
mehr5632@yahoo.com


R.
Divani
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering,
Iran
m.hk680925@gmail.com
BTS antenna
simulation
power density
permissible exposure limits
[[1] P. Ushie, V.U. Nwankwo, A. Bolaji, O. Osahun, Measurement and Analysis of Radiofrequency Radiation Exposure Level from Different Mobile Base Transceiver Stations in Ajaokuta and Environs, Nigeria, arXiv preprint arXiv:1306.1475, (2013). ##[2] P. Baltrėnas, R. Buckus, Indoor measurements of the power density close to mobile station antenna, (2011). ##[3] Q.Q. He, W.C. Yang, Y.X. Hu, Accurate method to estimate EM radiation from a GSM base station, Progress In Electromagnetics Research M, 34 (2014) 1927. ##[4] M.A. Keow, S. Radiman, Assessment of radiofrequency/ microwave radiation emitted by the antennas of rooftopmounted mobile phone base stations, Radiation protection dosimetry, 121(2) (2005) 122127. ##[5] S. Miclaus, P. Bechet, Estimated and measured values of the radiofrequency radiation power density around cellular base stations, Romanian Journal of Physics, 52(3/4) (2007) 429. ##[6] W. Suwansin, P. Phasukkit, C. Pintavirooj, A. Sanpanich, Analysis of heat transfer and specific absorption rate of electromagnetic field in human body at 915 MHz and 2.45 GHz with 3D finite element method, in: Biomedical Engineering International Conference (BMEiCON), 2012, IEEE, 2012, pp. 14. ##[7] S. Banik, S. Bandyopadhyay, S. Ganguly, Bioeffects of microwave––a brief review, Bioresource technology, 87(2) (2003) 155159. ##[8] A. Khavanin, Nonthermal Effects of Radar Exposure on Human: A Review Article, Iranian Journal of Health, Safety and Environment, 1(1) (2014) 4352. ##[9] A. Vander Vorst, A. Rosen, Y. Kotsuka, RF/microwave interaction with biological tissues, John Wiley & Sons, 2006. ##[10] T. Alanko, M. Hietanen, P. Von Nandelstadh, Occupational exposure to RF fields from base station antennas on rooftops, annals of telecommunicationsannales des télécommunications, 63(12) (2008) 125132. ##[11] R. Kitchen, RF and microwave radiation safety handbook, Newnes, 2001. ##[12] I.C.o.N.I.R. Protection, Guidelines for limiting exposure to timevarying electric and magnetic fields (1 Hz to 100 kHz), Health physics, 99(6) (2010) 818836. ##[13] R.G. Sargent, Verification and validation of simulation models, in: Proceedings of the 37th conference on Winter simulation, winter simulation conference, 2005, pp. 130143. ##[14] Y. Alfadhl, Numerical evaluations on the interaction of electromagnetic fields with animals and with biological tissues, University of London, 2006. ##[15] Roof Top guide 122A, n.kouhestani cartographer2015. ##[16] Roof Top guide 195A n.kouhestani, cartographer2015 ##[17] P. Gajšek, D. šimunic, Occupational exposure to base stations—compliance with EU Directive 2004/40/ EC, International Journal of Occupational Safety and Ergonomics, 12(2) (2006) 187194.##]
Combination of Transformedmeans Clustering and Neural Networks for ShortTerm Solar Radiation Forecasting
2
2
In order to provide an efficient conversion and utilization of solar power, solar radiation datashould be measured continuously and accurately over the longterm period. However, the measurement ofsolar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence,several studies were proposed in the literature to find mathematical and physical models to estimate andforecast the amount of solar radiation such as stochastic prediction models based on time series methods. Thispaper proposes a hybridization framework, considering clustering, preprocessing, and training steps for shorttermsolar radiation forecasting. The proposed method is a combination of a novel data clustering method,timeseries analysis, and multilayer perceptron neural network (MLPNN). The proposed TransformedMeans clustering method is based on inverse data transformation and Kmeans algorithm that presents moreaccurate clustering results when compared to the KMeans algorithm; its improved version and also otherpopular clustering algorithms. The performance of the proposed TransformedMeans is evaluated usingseveral types of datasets and compared with different variants of Kmeans algorithm. The proposed methodclusters the input solar radiation timeseries data into an appropriate number of subdatasets which are thenpreprocessed by the timeseries analysis. The preprocessed timeseries data provide the input for the trainingstage where MLPNN is used to forecast the solar radiation. Solar timeseries data with different solar radiationcharacteristics are also used to determine the accuracy and the processing speed of the developed forecastingmethod with the proposed TransformedMeans and other clustering techniques.
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187
194


M.
Ghayekhloo
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin
Iran


M. B.
Menhaj
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Electrical Engineering, Amirkabir
Iran
mbmenhaj@yahoo.com
Data Mining
Time Series Analysis
Forecasting
Solar
KMeans
[[1] A. Mellit, M. Benghanem, A.H. Arab, A. Guessoum, Modelling of sizing the photovoltaic system parameters using artificial neural network, in: Proc. of IEEE, CCA, 2003, pp. 353357. ##[2] M. Nehrir, C. Wang, K. Strunz, H. Aki, R. Ramakumar, J. Bing, Z. Miao, Z. Salameh, A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control, and applications, IEEE Transactions on Sustainable Energy, 2(4) (2011) 392403. ##[3] E. Lorenz, D. Heinemann, Prediction of solar irradiance and photovoltaic power, (2012). ##[4] A. Mellit, Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review, International Journal of Artificial intelligence and soft computing, 1(1) (2008) 5276. ##[5] K. Tanaka, K. Uchida, K. Ogimi, T. Goya, A. Yona, T. Senjyu, T. Funabashi, C.H. Kim, Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error, IEEE transactions on smart grid, 2(3) (2011) 438444. ##[6] P. Zhang, Generation Scheduling for Supply and Demand Balancing in Power Systems with Renewable Power Generation, Kyushu University, 2013. ##[7] A. Yona, T. Senjyu, T. Funabshi, H. Sekine, Application of neural network to 24hoursahead generating power forecasting for PV system, IEEJ Transactions on Power and Energy, 128 (2008) 3339. ##[8] S. Cao, W. Weng, J. Chen, W. Liu, G. Yu, J. Cao, Forecast of solar irradiance using chaos optimization neural networks, in: Power and Energy Engineering Conference, 2009. APPEEC 2009. AsiaPacific, IEEE, 2009, pp. 14. ##[9] G. Capizzi, C. Napoli, F. Bonanno, Innovative secondgeneration wavelets construction with recurrent neural networks for solar radiation forecasting, IEEE Transactions on neural networks and learning systems, 23(11) (2012) 18051815. ##[10] J. Shi, W.J. Lee, Y. Liu, Y. Yang, P. Wang, Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Transactions on Industry Applications, 48(3) (2012) 10641069. ##[11] T.C. Yu, H.T. Chang, The forecast of the electrical energy generated by photovoltaic systems using neural network method, in: Electric Information and Control Engineering (ICEICE), 2011 International Conference on, IEEE, 2011, pp. 27582761. ##[12] S. Wang, N. Zhang, Y. Zhao, J. Zhan, Photovoltaic system power forecasting based on combined grey model and BP neural network, in: Electrical and Control Engineering (ICECE), 2011 International Conference on, IEEE, 2011, pp. 46234626. ##[13] T. Kohonen, The selforganizing map, Proceedings of the IEEE, 78(9) (1990) 14641480. ##[14] C. Cortes, V. Vapnik, Supportvector networks, Machine learning, 20(3) (1995) 273297. ##[15] A.K. Yadav, S. Chandel, Solar radiation prediction ##using Artificial Neural Network techniques: A review, Renewable and Sustainable Energy Reviews, 33 (2014) 772781. ##[16] S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renewable and sustainable energy reviews, 5(4) (2001) 373401. ##[17] H. Esen, M. Inalli, A. Sengur, M. Esen, Artificial neural networks and adaptive neurofuzzy assessments for groundcoupled heat pump system, Energy and Buildings, 40(6) (2008) 10741083. ##[18] C. Paoli, C. Voyant, M. Muselli, M.L. Nivet, Forecasting of preprocessed daily solar radiation time series using neural networks, Solar Energy, 84(12) (2010) 21462160. ##[19] M.S. Bobi, Use, operation and maintenance of renewable energy systems: Experiences and future approaches, Springer, 2014. ##[20] N. Sengupta, S. Aloka, B. Narayanaswamy, H. Ismail, S. Mathew, Time series data mining for demand side decision support, in: Innovative Smart Grid TechnologiesAsia (ISGT Asia), 2013 IEEE, IEEE, 2013, pp. 16. ##[21] Y. Yang, L. Dong, Shortterm PV generation system direct power prediction model on wavelet neural network and weather type clustering, in: Intelligent Human Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, IEEE, 2013, pp. 207211. ##[22] R. Li, H. Wang, Y. Cui, X. Huang, Solar flare forecasting using learning vector quantity and unsupervised clustering techniques, SCIENCE CHINA Physics, Mechanics & Astronomy, 54(8) (2011) 15461552. ##[23] K. Benmouiza, A. Cheknane, Forecasting hourly global solar radiation using hybrid kmeans and nonlinear autoregressive neural network models, Energy Conversion and Management, 75 (2013) 561569. ##[24] M.I. Malinen, R. MariescuIstodor, P. Fränti, Kmeans⁎: Clustering by gradual data transformation, Pattern Recognition, 47(10) (2014) 33763386. ##[25] http://cs.uef.fi/sipu/clustering/animator/. ##[26] D.J. Ketchen Jr, C.L. Shook, The application of cluster analysis in strategic management research: an analysis and critique, Strategic management journal, (1996) 441458. ##[27] http://cs.uef.fi/sipu/datasets. ##[28] https://archive.ics.uci.edu/ml/datasets. ##[29] http://mesonet.agron.iastate.edu ##[30] D. Arthur, S. Vassilvitskii, kmeans++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACMSIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 10271035. ##[31] J. Herbert, J. Yao, A gametheoretic approach to competitive learning in selforganizing maps, Advances in Natural Computation, (2005) 418418.##]
Implementation of a Low Cost Multi IMU by Using Information Form of a Steady State Kalman Filter
2
2
In this paper, a homogenous multisensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemented to test the method with three types of experiments: static test, constant rate, and oscillatingtest. Results of static test for zaxis show that ARW coefficient reduces to 0.0022°/√s and VRW error isdecreased by %50. Also, dynamic test results show the reduction of the standard deviation of combinedrate signal up to six times compared with a single sensor. A comparison between the proposed filter andthe simple averaging method is made in which the results indicate that the Kalman filter is more accuratecompared to the averaging method.
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195
204


A. M.
Shahri
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
Department of Electrical, Biomedical and
Iran
shahri@iust.ac.ir


R.
Rasoulzadeh
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
Department of Electrical, Biomedical and
Iran
r.rasoulzadeh@qiau.ac.ir
Multisensor fusion
IMU
information form of steadystate
Kalman filter
[[1] D.S. Bayard, S.R. Ploen, High accuracy inertial sensors from inexpensive components, in, Google Patents, 2005. ##[2] H. Chang, L. Xue, W. Qin, G. Yuan, W. Yuan, An integrated MEMS gyroscope array with higher accuracy output, Sensors, 8(4) (2008) 28862899. ##[3] H. Chang, L. Xue, C. Jiang, M. Kraft, W. Yuan, Combining numerous uncorrelated MEMS gyroscopes for accuracy improvement based on an optimal Kalman filter, IEEE Transactions on Instrumentation and Measurement, 61(11) (2012) 30843093. ##[4] C. Jiang, L. Xue, H. Chang, G. Yuan, W. Yuan, Signal processing of MEMS gyroscope arrays to improve accuracy using a 1st order markov for rate signal modeling, Sensors, 12(2) (2012) 17201737. ##[5] L. Xue, L. Wang, T. Xiong, C. Jiang, W. Yuan, Analysis of dynamic performance of a Kalman filter for combining multiple MEMS gyroscopes, micromachines, 5(4) (2014) 10341050. ##[6] H. Martin, P. Groves, M. Newman, R. Faragher, A new approach to better lowcost MEMS IMU performance using sensor arrays, in, The Institute of Navigation, 2013. ##[7] M. Tanenhaus, D. Carhoun, T. Geis, E. Wan, A. Holland, Miniature IMU/INS with optimally fused low drift MEMS gyro and accelerometers for applications in GPSdenied environments, in: Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, IEEE, 2012, pp. 259264. ##[8] I. Skog, J.O. Nilsson, P. Handel, An opensource multi inertial measurement unit (MIMU) platform, in: Inertial Sensors and Systems (ISISS), 2014 International Symposium on, IEEE, 2014, pp. 14. ##[9] R. Rasoulzadeh, A.M. Shahri, Implementation of A lowcost multiIMU hardware by using a homogenous multisensor fusion, in: Control, Instrumentation, and Automation (ICCIA), 2016 4th International Conference on, IEEE, 2016, pp. 451456. ##[10] G. Yuan, W. Yuan, L. Xue, J. Xie, H. Chang, Dynamic performance comparison of two Kalman filters for rate signal direct modeling and differencing modeling for combining a MEMS gyroscope array to improve accuracy, Sensors, 15(11) (2015) 2759027610. ##[11] I. Skog, J.O. Nilsson, P. Händel, A. Nehorai, Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound, IEEE Transactions on Signal Processing, 64(16) (2016) 42184227. ##[12] A. Unknown, IEEE Standard Specification Format Guide and Test Procedure for Coriolis Vibratory Gyros, IEEE Standards, 1431 179. ##[13] N. ElSheimy, H. Hou, X. Niu, Analysis and modeling of inertial sensors using Allan variance, IEEE Transactions on instrumentation and measurement, 57(1) (2008) 140149. ##[14] R.J. Vaccaro, A.S. Zaki, Statistical modeling of rate gyros, IEEE Transactions on Instrumentation and Measurement, 61(3) (2012) 673684. ##[15] R.E. Kalman, R.S. Bucy, New results in linear filtering and prediction theory, Journal of basic engineering, 83(1) (1961) 95108. ##[16] D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley & Sons, 2006. ##[17] R.S. Bucy, P.D. Joseph, Filtering for stochastic processes with applications to guidance, American Mathematical Soc., 1987. ##[18] M. Grewal, A. Andrews, Kalman theory, theory and practice using MATLAB, in, John Wiley & Sons, Inc, 2008. ##[19] C. Chen, Linear System Theory and Design. New York: Holt, Rinehart and Winston, Decoupling with stability for linear periodic systems, 765 (1984). ##[20] Q. Gan, C.J. Harris, Comparison of two measurement fusion methods for Kalmanfilterbased multisensor data fusion, IEEE Transactions on Aerospace and Electronic systems, 37(1) (2001) 273279. ##[21] N. Assimakis, M. Adam, A. Douladiris, Information filter and kalman filter comparison: Selection of the faster filter, International Journal of Information Engineering, 2(1) (2012) 15. ##[22] D.W. Allan, Statistics of atomic frequency standards, Proceedings of the IEEE, 54(2) (1966) 221230. ##[23] InvenSens MPU9150 Motion Sensor Document number: PSMPU9150A, Rev4.0 ##[24] NXP (Phillips),LPC17xx 32bit ARM CortexM3 microcontroller, Rev. 5.3.##]
Internal Fault Detection, Location, and Classification in Stator Winding of the Synchronous Generators Based on the Terminal Voltage Waveform
2
2
In this paper, a novel method is presented for detection and classification of the faultyphase/region in the stator winding of synchronous generators on the basis of the resulting harmoniccomponents that appear in the terminal voltage waveforms. Analytical results obtained through DecisionTree (DT) show that the internal faults are not only detectable but also they can be classified andthe related region can be estimated. Therefore, this scheme can be used to protect the synchronousgenerators against the various internal faults. Fuji technical documents and data sheets for an actualsalient pole synchronous generator (one unit of an Iran’s hydroelectric power plants) are used for themodeling. Simulations in Maxwell software environment are presented. All the related parameters, suchas BH curve, unsymmetrical air gap and pole saliency, slotteeth effect, and other actual parameters, areconsidered to obtain a comprehensive model to generate acceptable terminal voltage waveforms withoutany simplification.
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205
214


M.
Fayazi
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Shahid Beheshti
Iran
m.fayazi69@yahoo.com


F.
Haghjoo
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Shahid Beheshti
Iran
f_haghjoo@sbu.ac.ir
Synchronous Generator
Internal Faults
TurnTurn Faults
Phase To Ground Faults
Detection
Classification
Location
Harmonic Components
Decision Tree
[[1] B. Ravindranath, M. Chander, Power system protection and switchgear, New Age International, 1977. ##[2] M. Fayazi, F. Haghjoo, Turn to turn fault detection and classification in stator winding of synchronous generators based on terminal voltage waveform components, in: Power Systems Protection and Control Conference (PSPC), 2015 9th, IEEE, 2015, pp. 3641. ##[3] ABB Lecture, “Generator and Transformer Protection”, 3rd Edition, 1998. ##[4] K. Louie, A new accurate phasedomain synchronous generator model for transient simulation, in: Electrical and Computer Engineering, 2006. CCECE’06. Canadian Conference on, IEEE, 2006, pp. 22452248. ##[5] D.D.S. Muthumuni, E. Dirks, Internal fault simulation in synchronous machines, in: Electrical and Computer Engineering, 2000 Canadian Conference on, IEEE, 2000, pp. 12021206. ##[6] A. Megahed, O. Malik, Simulation of internal faults in synchronous generators, IEEE Transactions on Energy Conversion, 14(4) (1999) 13061311. ##[7] A. Megahed, O. Malik, Synchronous generator internal fault computation and experimental verification, IEE ProceedingsGeneration, Transmission and Distribution, 145(5) (1998) 604610. ##[8] P. Subramaniam, O. Malik, Digital simulation of a synchronous generator in directphase quantities, in: Proceedings of the Institution of Electrical Engineers, IET, 1971, pp. 153160. ##[9] D. Muthumuni, P. McLaren, E. Dirks, V. Pathirana, A synchronous machine model to analyze internal faults, in: Industry Applications Conference, 2001. ThirtySixth IAS Annual Meeting. Conference Record of the 2001 IEEE, IEEE, 2001, pp. 15951600. ##[10] X. Tu, L.A. Dessaint, M. El Kahel, A.O. Barry, A new model of synchronous machine internal faults based on winding distribution, IEEE Transactions on Industrial electronics, 53(6) (2006) 18181828. ##[11] S. Hemmati, S. Shokri, S. Saied, Modeling and simulation of internal short circuit faults in large hydro generators with wave windings, in: Power Engineering, Energy and Electrical Drives (POWERENG), 2011 International Conference on, IEEE, 2011, pp. 16. ##[12] A. Dehkordi, A. Gole, T. Maguire, P. Neti, A realtime model for testing statorground fault protection schemes of synchronous machines, in: International Conference on Power Systems Transients (IPST2009), 2009. ##[13] A. Sinha, D. Vishwakarma, R. Srivastava, Modeling and simulation of internal faults in salientpole synchronous generators with wave windings, Electric Power Components and Systems, 38(1) (2009) 100114. ##[14] X. Tu, L.A. Dessaint, M. El Kahel, A. Barry, Modeling and experimental validation of internal faults in salient pole synchronous machines including space harmonics, Mathematics and Computers in Simulation, 71(4) (2006) 425439. ##[15] A. Sinha, D. Vishwakarma, R. Srivastava, Modeling and Realtime Simulation of Internal Faults in Turbogenerators, Electric Power Components and Systems, 37(9) (2009) 957969. ##[16] X. Tu, L.A. Dessaint, N. Fallati, B. De Kelper, Modeling and realtime simulation of internal faults in synchronous generators with parallelconnected windings, IEEE Transactions on Industrial Electronics, 54(3) (2007) 14001409. ##[17] H. Jiang, R. Aggarwal, G. Weller, S. Ball, L. Denning, A new approach to synchronous generator internal fault simulation using combined winding function theory and direct phase quantities, (1999). ##[18] X. Wang, Y. Sun, B. Ouyang, W. Wang, Z. Zhu, D. Howe, Transient behaviour of salientpole synchronous machines with internal stator winding faults, IEE Proceedings Electric Power Applications, 149(2) (2002) 143151. ##[19] P. LeHuy, C. Larose, F. Giguère, Flexible Phase Domain SynchronousMachine Model with Internal Fault for Protection Relay Testing and related RealTime Implementation Issues, in: International Conference on Power Systems Transients (IPST2011), 2011. ##[20] M. Rahnama, J. Nazarzadeh, Synchronous machine modeling and analysis for internal faults detection, in: IEEE International Conference on Electric Machines & Drives (IEMDC’07), 2007. ##[21] N. Yadaiah, N. Ravi, Statistical method for fault detection in synchronous generators, in: Computer Communication and Informatics (ICCCI), 2012 International Conference on, IEEE, 2012, pp. 14. ##[22] A. Dehkordi, D. Ouellette, P. Forsyth, Protection testing of a 100% stator ground fault using a phase domain synchronous machine model in real time, (2010).##]
KComplex Detection Based on Synchrosqueezing Transform
2
2
Kcomplex is an underlying pattern in the sleep EEG. Due to the role of sleep studies inneurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this patternare of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysisof this pattern. SST is an EMDlike tool, which benefits from wavelet transform and reallocation approaches.This method is able to decompose signals into their timevarying oscillatory ingredients. In addition, itprovides a timefrequency representation with less blurring compared to wavelet transform. In this paper,firstly, the ability of SST is investigated by applying the ANOVA test, which is approved by proper pvalues.This paper proposes SST for Kcomplex detection. The proposed method is based on a socalled “detectionof Kcomplexes and sleep spindles” (DETOKS) framework. DETOKS is based on spares optimizationand decomposes signals into four components, namely transient, low frequency, oscillatory, and a residual.Applying the TeagerKaiser energy operator and setting a threshold on the lowfrequency component resultin Kcomplex detection. We modify DETOKS using SST. The proposed method is applied to DREAMSdataset. The dataset provides two visual scorings accompanied by an automatic one. As the visual labels wereextremely different, the automatic detection is considered as the third expert’s scoring and data is relabeledby a voting approach among three experts. For DETOKS, DETOKS modified by CWT, and the proposedmethod, MCC measure is 0.62, 0.71, and 0.76, respectively. It shows superiority of the proposed method.
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214
222


Z.
Ghanbari
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Faculty of Biomedical Engineering, Amirkabir
Iran
zahraghanbari@yahoo.com


M. H.
Moradi
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Faculty of Biomedical Engineering, Amirkabir
Iran
mhmoradi@aut.ac.ir
Kcomplex
Sleep EEG
Synchrosqueezing Transform (SST)
Sparse Optimization
TeagerKaiser Energy Operator
[[1] E. HernándezPereira, V. BolónCanedo, N. Sánchez Maroño, D. ÁlvarezEstévez, V. MoretBonillo, A. AlonsoBetanzos, A comparison of performance of Kcomplex classification methods using feature selection, Information Sciences, 328 (2016) 114. ##[2] T. Lajnef, S. Chaibi, J.B. Eichenlaub, P.M. Ruby, P.E. Aguera, M. Samet, A. Kachouri, K. Jerbi, Sleep spindle and Kcomplex detection using tunable Qfactor wavelet transform and morphological component analysis, Frontiers in human neuroscience, 9 (2015). ##[3] A.L. Pinto, I.S. Fernández, J.M. Peters, S. Manganaro, J.M. Singer, M. Vendrame, S.P. Prabhu, T. Loddenkemper, S.V. Kothare, Localization of sleep spindles, kcomplexes, and vertex waves with subdural electrodes in children, Journal of Clinical Neurophysiology, 31(4) (2014) 367374. ##[4] V. Kokkinos, G.K. Kostopoulos, Human non.rapid eye movement stage II sleep spindles are blocked upon spontaneous K.complex coincidence and resume as higher frequency spindles afterwards, Journal of sleep research, 20(1pt1) (2011) 5772. ##[5] http://www.tcts.fpms.ac.be/~devuyst/Databases/ DatabaseKcomplexes/ ##[6] V. Kokkinos, A.M. Koupparis, G.K. Kostopoulos, An intraKcomplex oscillation with independent and labile frequency and topography in NREM sleep, Frontiers in human neuroscience, 7 (2013). ##[7] W.O. Tatum IV, Handbook of EEG interpretation, Demos Medical Publishing, 2014. ##[8] T.A. Camilleri, K.P. Camilleri, S.G. Fabri, Automatic detection of spindles and Kcomplexes in sleep EEG using switching multiple models, Biomedical Signal Processing and Control, 10 (2014) 117127. ##[9] T. Babaie, S. Chawla, R. Abeysuriya, Sleep analytics and online selective anomaly detection, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2014, pp. 362371. ##[10] Z.R. Zamir, N. Sukhorukova, H. Amiel, A. Ugon, C. Philippe, Convex optimisationbased methods for kcomplex detection, Applied Mathematics and Computation, 268 (2015) 947956. ##[11] A. Parekh, I.W. Selesnick, D.M. Rapoport, I. Ayappa, Detection of Kcomplexes and sleep spindles (DETOKS) using sparse optimization, Journal of neuroscience methods, 251 (2015) 3746. ##[12] I. Daubechies, J. Lu, H.T. Wu, Synchrosqueezed wavelet transforms: An empirical mode decompositionlike tool, Applied and computational harmonic analysis, 30(2) (2011) 243261. ##[13] C. Yücelbaş, Ş. Yücelbaş, S. Özşen, G. Tezel, S. Küççüktürk, Ş. Yosunkaya, Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods, Neural Computing and Applications, (2016) 117. ##[14] G. Thakur, E. Brevdo, N.S. Fučkar, H.T. Wu, The synchrosqueezing algorithm for timevarying spectral analysis: Robustness properties and new paleoclimate applications, Signal Processing, 93(5) (2013) 10791094. ##[15] Z. Ghanbari, M.H. Moradi, Synchrosqueezing transform: Application in the analysis of the Kcomplex pattern, in: Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016 23rd Iranian Conference on, IEEE, 2016, pp. 221225. ##[16] M.M. Kabir, R. Tafreshi, D.B. Boivin, N. Haddad, Enhanced automated sleep spindle detection algorithm based on synchrosqueezing, Medical & biological engineering & computing, 53(7) (2015) 635644. ##[17] H.T. Wu, Y.H. Chan, Y.T. Lin, Y.H. Yeh, Using synchrosqueezing transform to discover breathing dynamics from ECG signals, Applied and Computational Harmonic Analysis, 36(2) (2014) 354359. ##[18] H.T. Wu, S.S. Hseu, M.Y. Bien, Y.R. Kou, I. Daubechies, Evaluating physiological dynamics via synchrosqueezing: Prediction of ventilator weaning, IEEE Transactions on Biomedical Engineering, 61(3) (2014) 736744. ##[19] S. Devuyst, T. Dutoit, P. Stenuit, M. Kerkhofs, Automatic Kcomplexes detection in sleep EEG recordings using likelihood thresholds, in: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, IEEE, 2010, pp. 46584661. ##[20] J.M. O’Toole, A. Temko, N. Stevenson, Assessing instantaneous energy in the EEG: a nonnegative, frequencyweighted energy operator, in: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE, 2014, pp. 32883291. ##[21] L.K. Krohne, R.B. Hansen, J.A. Christensen, H.B. Sorensen, P. Jennum, Detection of Kcomplexes based on the wavelet transform, in: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE, 2014, pp. 54505453.##]
Combination of Feature Selection and Learning Methods for IoT Data Fusion
2
2
In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (ReP), Relief and Genetic Algorithm Particle Swarm Optimization (ReGAPSO), Genetic Algorithm and Artificial Neural Network (GAANN), Rough and Perceptron (RoP)and Rough and GAPSO (RoGAPSO). All the schemes consist of four stages, including preprocessingthe data set based on curve fitting, reducing the data dimension and identifying the most effective featuresets according to data correlation, training classification algorithms, and finally predicting new databased on classification algorithms. The results derived from five compound schemes are investigated andcompared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and StorageCapacity (SC). While the ReP scheme is only capable of separating classes that are linearly separable,ReGAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. Onthe other hand, GAANN is a Wrapper method and despite Relief can adapt itself to the machine learningalgorithm. Meanwhile, RoP scheme is useful for analyzing vague and imprecise information and, unlikeGAANN, has less calculative costs. Among these five schemes, RoGAPSO is a more precise one, whichhas less calculative cost and does not become stuck in local minima. Experimental results show that RePoutperforms other proposed and existing methods in terms of computational time complexity.
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223
232


V.
SattariNaeini
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Dept. of Computer Engineering, Shahid Bahonar
Iran
vsnaeini@uk.ac.ir


Zahra
PariziNejad
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Dept. of Computer Engineering, Shahid Bahonar
Iran
za_parizi87@yahoo.com
Internet of Things
Data Fusion
Rough Set Theory
Perceptron
GAPSO
[[1] X. Qin, Y. Gu, Data fusion in the Internet of Things, Procedia Engineering, 15 (2011) 30233026. ##[2] H.Y. Shwe, X.H. Jiang, S. Horiguchi, Energy saving in wireless sensor networks, Journal of Communication and Computer, 6(5) (2009) 2027. ##[3] G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad hoc networks, 7(3) (2009) 537568. ##[4] M. Lewitt, R. Polikar, An ensemble approach for data fusion with Learn++, Multiple Classifier Systems, (2003) 161161. ##[5] W.T. Sung, M.H. Tsai, Data fusion of multisensor for IOT precise measurement based on improved PSO algorithms, Computers & Mathematics with Applications, 64(5) (2012) 14501461. ##[6] J. Zhou, L. Hu, F. Wang, H. Lu, K. Zhao, An efficient multidimensional fusion algorithm for IoT data based on partitioning, tsinghua science and technology, 18(4) (2013) 369378. ##[7] A.R. Pinto, C. Montez, G. Araújo, F. Vasques, P. Portugal, An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms, Information fusion, 15 (2014) 90101. ##[8] R. Gravina, P. Alinia, H. Ghasemzadeh, G. Fortino, Multisensor fusion in body sensor networks: Stateoftheart and research challenges, Information Fusion, 35 (2017) 6880. ##[9] M.M. Fouad, N.E. Oweis, T. Gaber, M. Ahmed, V. Snasel, Data mining and fusion techniques for WSNs as a source of the big data, Procedia Computer Science, 65 (2015) 778786. ##[10] M. Marjani, F. Nasaruddin, A. Gani, A. Karim, I.A.T. Hashem, A. Siddiqa, I. Yaqoob, Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges, IEEE Access, 5 (2017) 52475261. ##[11] D.C. Mocanu, E. Mocanu, P.H. Nguyen, M. Gibescu, A. Liotta, Big IoT data mining for realtime energy disaggregation in buildings, in: Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on, IEEE, 2016, pp. 003765003769. ##[12] L. Wald, Some terms of reference in data fusion, IEEE Transactions on geoscience and remote sensing, 37(3) (1999) 11901193. ##[13] E.F. Nakamura, A.A. Loureiro, A.C. Frery, Information fusion for wireless sensor networks: Methods, models, and classifications, ACM Computing Surveys (CSUR), 39(3) (2007) 9. ##[14] H. Almuallim, T.G. Dietterich, Learning With Many Irrelevant Features, in: AAAI, 1991, pp. 547552. ##[15] Y. Sun, D. Wu, A relief based feature extraction algorithm, in: Proceedings of the 2008 SIAM International Conference on Data Mining, SIAM, 2008, pp. 188195. ##[16] M.S. Mohamad, Feature selection method using genetic algorithm for the classification of small and high dimension data, in: Proc. Int. Symp. Info. Com. Tech., 2004, 2004, pp. 1316. ##[17] A. Golmohammadi, N. Shams Ghareneh, A. Keramati, B. Jahandideh, Importance analysis of travel attributes using a rough setbased neural network: The case of Iranian tourism industry, Journal of Hospitality and Tourism Technology, 2(2) (2011) 155171. ##[18] B. Ahn, S. Cho, C. Kim, The integrated methodology of rough set theory and artificial neural network for business failure prediction, Expert systems with applications, 18(2) (2000) 6574. ##[19] G.H. John, R. Kohavi, K. Pfleger, Irrelevant features and the subset selection problem, in: Machine learning: proceedings of the eleventh international conference, 1994, pp. 121129. ##[20] S. Yang, J. Gu, Feature selection based on mutual information and redundancysynergy coefficient, Journal of Zhejiang UniversityScience A, 5(11) (2004) 13821391. ##[21] D. Wei, Clustering algorithms for sensor networks and mobile ad hoc networks to improve energy efficiency, University of Cape Town, 2007. ##[22] Y. LiCF, W. ChenGH, An EnergyEfficient Unequal Clustering Mechanism for Wireless Sensor Networks, Proceedings of the Second IEEE International Conference on Mobile AdHoc and Sensor Systems (MASS2005), Washing ton, DC, (2005). ##[23] L. Fausett, L. Fausett, Fundamentals of neural networks: architectures, algorithms, and applications, Prentice Hall, 1994. ##[24] J. Langeveld, A.P. Engelbrecht, A generic setbased particle swarm optimization algorithm, in: International conference on swarm intelligence, ICSI, 2011, pp. 110. ##[25] http://web.mit.edu/cron/group/house_n/data/PlaceLab/ PlaceLab.htm [seen Aug., 2017] ##[26] D. Roobaert, G. Karakoulas, N. Chawla, Information gain, correlation and support vector machines, Feature extraction, (2006) 463470. ##[27] L. Yu, H. Liu, Feature selection for highdimensional data: A fast correlationbased filter solution, in: Proceedings of the 20th international conference on machine learning (ICML03), 2003, pp. 856863. ##]