2016
4
1
7
0
Fuzzy Neighbor Voting for Automatic Image Annotation
2
2
With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper, we propose a novel image annotation algorithm based on neighbor voting which uses fuzzy system. The performance of the model depends on selecting the right neighbors and a fuzzy system with the right combination of features it offers.Experimental results on Corel5k and IAPR TC12 benchmark annotated datasets, demonstrate that using the proposed method leads to good performance.
1

1
8


Vafa
Maihami
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran
Department of Electrical and Computer Engineering,
Iran
maihamy@gmail.com


Farzin
Yaghmaee
Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran
Department of Electrical and Computer Engineering,
Iran
f_yaghmaee@semnan.ac.ir
Automatic image
Annotation
fuzzy system
Image retrieval
Feature extraction
[[1] D. Zhang , Md. Monirul Islam, and Guo jun Lu, “A review on automatic image annotation techniques”, Pattern Recognition , vol. 45, pp. 346362, 2012. ##[2] F. Wang, “A survey on automatic image annotation and trends of the new age”, Procedia Engineering, vol. 23, pp. 434438, 2011. ##[3] R. Datta, D. Joshi, J. Li, and J. Wang, “Image retrieval: Ideas, influences, and trends of the new age”, ACM Comput. Surveys (CSUR), vol. 40, no. 2, pp. 5, 2008. ##[4] Y. Liu, D. Zhang, G. Lu, and W. Ma, “survey of contentbased image retrieval with highlevel semantics”, Pattern Recognition, vol. 40, no. 1, pp. 262282, 2007. ##[5] Yuan. Ying, F. Wu, J. Shao, and Y. Zhuang, “Image annotation by semisupervised crossdomain learning with group sparsity”, Journal of Visual Communication and Image Representation, vol. 24, no. 2, pp. 95102, 2013. ##[6] J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma, “Image annotation via graph learning, Pattern Recognition”, vol. 42, no. 2, pp. 218 228, Feb. 2009. ##[7] Ch. Huang, F. Meng, W. Luo, and Sh. Zhu, “Bird breed classification and annotation using saliency based graphical model”, Journal of Visual Communication and Image Representation, vol. 25, no. 6, pp. 12991307, 2014. ##[8] D. Zhang, , M. Islam, and G. Lu, “Structural image retrieval using automatic image annotation and region based inverted file”, Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 10871098, 2013. ##[9] JaHwung Su, et al. “Effective semantic annotation by imagetoconcept distribution model”, Multimedia, IEEE Transactions on, vol. 13.3, pp. 530538, 2011. ##[10] Y. Yang, Z. Huang, Y. Yang, J. Liu, H. Tao Shen, and J. Luo, “Local image tagging via graph regularized joint group sparsity”, Pattern Recognition, vol. 46, no. 5, pp. 13581368, 2012. ##[11] X. Li, C.G.M. Snoek, and M. Worring, “Learning Social Tag Relevance by Neighbor Voting”, IEEE Trans. Multimedia, vol. 11, no. 7, pp. 13101322, Nov. 2009. ##[12] L. Wu, R. Jin, and A. K. Jain, “Tag Completion for Image Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 716727, March 2013. ##[13] S. Lee, W. De Neve, Y. Man Ro, “Visually weighted neighbor voting for image tag relevance learning”, Multimed Tools Appl, April, 2013. DOI 10.1007/s1104201314393. ##[14] L. H. Zadeh, Fuzzy sets, Information and control, 1965. ##[15] Ross, J Timothy, “Fuzzy logic with engineering applications”, John Wiley & Sons, 2009. ##[16] J. A. Sanz, , M. Galar, A. Jurio, A. Brugos, M. Pagola, and H. Bustince, “Medical diagnosis of cardiovascular diseases using an intervalvalued fuzzy rulebased classification system”, Applied Soft Computing, 2013. ##[17] S. Dasiopoulou, C. Doulaverakis, V. Mezaris, I. Kompatsiaris, M.G. Strintzis, “An OntologyBased Framework for Semantic Image Analysis and Retrieval”, SemanticBased Visual Information Retrieval, YuJin ZHANG (Eds), Idea Group Inc., 2007. ##[18] Zh. Hua, X. Wang, Q. Liu, H. Lu, “Semantic knowledge extraction and annotation for web images”, Proceedings of the 13th annual ACM international conference on Multimedia, Hilton, Singapore, November 0611, 2005. ##[19] M. Han, X. Zhu, W. Yao, “Remote sensing image classification based on neural network ensemble algorithm”, Neurocomputing, vol. 78 (1), pp. 133138, 2012. ##[20] Y. Han, F. Wu, Q. Tian, Y. Zhuang “Image annotation by inputoutput structural grouping sparsity”, IEEE Transactions on Image Processing (99), 2012. ##[21] Z. Chen, Zh. Chi, H. Fu, D. Feng, “Multiinstance multilabel image classification: A neural approach”, Neurocomputing, vol. 99 , pp. 298306, 2013. ##[22] Sh. Zhang, J. Huang, H. Li, and D. N. Metaxas, “Automatic image annotation and retrieval using group sparsity”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 3, pp. 838849, 2012. ##[23] T. Chaira, and A. K. Ray, “Fuzzy measures for colour image retrieval”, Fuzzy Sets and Systems , pp. 545560 , 2005. [24] F. Long, H. Zhang, and D.D. Feng, “Fundamentals of contentbased image retrieval”, in: Multimedia Information Retrieval and Management: Technological Fundamentals and Applications, Springer, 2003. ##[25] S. Jeong, C.S. Won, R.M. Gray, Image retrieval using colour histograms generated by Gauss mixture vector quantization, Comput. Vision Image Underst. vol. 94 (1–3), pp. 4466, 2004. ##[26] Y. Yang, Z. Huang, H. T. Shen, Zhou, X., “Mining multitag association for image tagging”, World Wide Web vol. 14(2), 133156., 2011. ##[27] P. Villar, A. Fernandez, R. A. Carrasco, and F. Herrera, “Feature selection and granularity learning in genetic fuzzy rulebased classification systems for highly imbalanced datasets.”, International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems, vol. 20 (03),369397, 2012. ##[28] P. Duygulu, K. Barnard, J. De Freitas, and D. Forsyth, “Object recognition as machine translation:learning a lexicon for a fixed image vocabulary”, Proceedings of European Conferenceon Computer Vision(ECCV), vol. 2353, pp. 97112., 2002. ##[29] J. Huang, S. Kuamr, M. Mitra, W.J. Zhu, R. Zabih, “Image indexing using colour correlogram”, in: Proceedings of the CVPR97, pp. 762765., 1997. ##[30] H. Yu, M. Li, H. Zhang, and J. Feng, “Color texture moment for content based image retrieval”, in Proc. ICIP, pp. 929–932, 2002. ##[31] B. S. Manjunath, and W. Y. Ma, “Texture features for browsing and retrieval of image data, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol 18, no. 8, pp. 837842., 1996. ##[32] J. Jeon, V. Lavrenko, R. Manmatha, “Automatic image annotation and retrieval using crossmedia relevance models”, In: 26th annual international ACM SIGIR conference on research and development in information retrieval. ACM, Toronto, 28 July1 August 2003, pp 119126. ##[33] V. Lavrenko, R. Manmatha, J. Jeon, “A model for learning the semantics of pictures”, In: 16th conference on advances in neural information processing systems (NIPS 16), Vancouver. MIT Press, Canada,813 December 2003. ##[34] A. Yavlinsky, E. Schofield, and S. Ruger, “Automated image annotation using global features and robust nonparametric density estimation”, in Proc. ACM Int. Conf. Image Video Retrieval,pp. 507517, 2005. ##[35] S. Zhu, X. Tan, “A novel automatic image annotation method based on multiinstance learning”, Procedia Eng, vol. 15:3439 3444, 2011. ##[36] N. ElBendary , T. h. Kim , A. Hassanien , M. Sami, “Automatic image annotation approach based on optimization of classes scores”, Computing, 96(5), pp. 381402, 2014. ##[37] Li, Zhixin, L. Li, K. Yan, and C. Zhang, “Automatic image annotation based on fuzzy association rule and decision tree." InProceedings of the 7th International Conference on Internet Multimedia Computing and Service, p. 12. ACM, 2015. ##[38] S.L. Feng, R. Manmatha, and V. Lavrenko, “Multiple bernoulli relevance models for image and video annotation”, In Computer Vision and Pattern Recognition. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (vol. 2, pp. II1002). IEEE. 2004. ##[39] A. Makadia, V. Pavlovic, and S. Kumar, “A new baseline for image annotation”, In Computer VisionECCV 2008 (pp. 316 329). Springer Berlin Heidelberg. 2008. ##[40] D. AriasAranda, J. L. Castro, M. Navarro, J. M. Sánchez, and J. M. Zurita, “A fuzzy expert system for business management”, Expert Systems with Applications 37, no. 12 (2010): 75707580. ##[41] V. Maihami, F. Yaghmaee, “Color Features and Color Spaces Applications to the Automatic Image Annotation”, Book chapter in Emerging Technologies in Intelligent Applications for Image and Video Processing. 2016 Jan 7:378. ##[42] J. Johnson, L. Ballan, and L. FeiFei, “Love thy neighbors: Image annotation by exploiting image metadata”. In Proceedings of the IEEE International Conference on Computer Vision (pp. 46244632), 2015. ##[43] X. Li, T. Uricchio, L. Ballan, M. Bertini, CG. Snoek, A. Del Bimbo, “Socializing the semantic gap: A comparative survey on image tag assignment, refinement and retrieval”, ACM Computing Surveys. arXiv preprint arXiv:1503.08248. 2016.##]
MOCA ARM: Analog Reliability Measurement based on Monte Carlo Analysis
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2
Due to the expected increase of defects in circuits based on deep submicron technologies, reliability has become an important design criterion. Although different approaches have been developed to estimate reliability in digital circuits and some measuring concepts have been separately presented to reveal the quality of analog circuit reliability in the literature, there is a gap to estimate reliability when circuit includes analog and digital structures. In this paper, we propose a new classification method using Monte Carlo analysis to calculate the reliability of analog circuits and show its efficacy when it is used for a combination of analog and digital circuits. Our method is based on signal reliability concepts and measures the probability of passing correct or faulty values. Furthermore, we compare our reliability measurements with the reliability definitions come from other failure mechanisms in submicron technologies. Simulation results show the reliability measurement presented here which provides key information for reliability improvement and monitoring.
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9
14


Shiva
Taghipour
Department of Electrical Engineering, University of Guilan, Rasht, Iran
Department of Electrical Engineering, University
Iran
taghipoor_shiva@yahoo.com


Rahebeh
Niaraki Asli
Department of Electrical Engineering, University of Guilan, Rasht, Iran
Department of Electrical Engineering, University
Iran
raheasli@gmail.com
Analog reliability measurement
Deep submicron technologies
Failure mechanisms
Monte Carlo analysis
Mean time to failure
[[1] D. T. Franco, M. C. Vasconcelos, L. Navine, and J. F. Naviner, “Signal probability for reliability evaluation of logic circuits,” Microelectronics Reliability, vol. 48, pp. 15861591. 2008, DOI: 10.1016/j.microrel. 2008.07.002 ##[2] Y. Chen, C. Ye, X. Zhang, and R. Kang, D. Xue, “Reliability modeling method of electronic products considering failure mechanism dependence,” IEEE 4th Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 419423, 2014. DOI: 10.1109/CYBER.2014.6917500 ##[3] L. A. de B. Naviner, J. F. Naviner, T. Ban, and G.S. junior, “Reliability analysis based on significance,” Institut TELECOMParisTech, LTCICNRS, pp. 17. 2011. ##[4] Panasonic: Fauilure Mechanism of Semiconductor Devices, Japan (2009). ##[5] A. Birolini, “Reliability engineering,” (3rd eds.), Springer, Heidelberg. pp. 47. 1999. DOI: 10.1007/9783662037928. ##[6] A. T. de Almeida, C. A. V. Cavalcanteí, M. H. Alencar, R. J. P. Ferreira, and T. V. Garcez, “Multicriteria and Multiobjective Models for Risk Reliability and Maintenance Decision Analysis,” Springer. Switzerland, pp. 115121, 2015. DOI: 10.1007/9783319179698 ##[7] A. Balasinski, “Semiconductors integrated circuit design for manufacturability,” Taylor & Francis Group, London, 2012. [8] G. Groeseneken, R. Degraeve, T. Nigam, G. Van den bosch, and H. E. Maes, “Hot carrier degradation and timedependent dielectric breakdown in oxides,” Microelectronic Engineering, 49, pp. 2740. 1999. DOI: 10.1016/S01679317(99)00427X. ##[9] J. R. Black, “Electro migrationA Brief Survey and Some Recent Results,” IEEE Transactions on Electron Devices, 16 (4), pp. 338347. 1969. DOI: 10.1109/TED. 1969.16754. ##[10] J. Kumar, M. B. Tahoori, “A low power soft error suppression technique for dynamic logic,” 20th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, pp. 454462, 2005. DOI:10.1109/ DFTVS.2005.9. ##[11] J. M. Rabaey, A. Chandrakasan, B. Nikolic, “Digital integrated circuits. A design perspective,” (2nd eds.) Prentice Hall. pp. 331338, 2003. ##[12] Y. Sasaki, K. Namba, H. Ito, “Circuit and latch capable of masking soft errors with Schmitt trigger,” Springer Electronic Testing journal, 24 (13), pp. 11–19. 2008. DOI: 10.1007/s1083600750342 ##[13] D. T. Franco, M. C. Vasconcelos, L. Naviner, J. F. Naviner, “Reliability of logic circuits under multiple simultaneous faults,” in 51st Midwest Symposium on Circuits and Systems, pp. 265–268, 2008. DOI: 10.1109/ MWSCAS.2008.4616787 [14] ISCAS85 Benchmark Circuits Information [Online]. http://www.cbl.ncsu.edu/benchmarks/ISCAS85/ 2011. ##[15] H. Cha, J. H. Patel, “A logic level model for partical hits in cmos circuits," pp. 538542. 1993. DOI: 10.1109/ICCD.1993.393319 ##[16] V. A. Carreno, G. Chio, K.R. lyer, “Analogdigital simulation of transientinduced logic errors and upset susceptibility of an advanced control system,” In NASA Tech Memorandum 4241. pp. 1 – 20. 1990. Biographies##]
A Comprehensive Survey on GenCos’ Optimal Bidding Strategy Problem in Competitive Power Markets
2
2
This paper represents a complete survey on Generation Companies’ (GenCos’) optimal bidding strategy problem in restructured power markets. In this regard after an introduction to competitive electricity markets, concept of optimal bidding strategy is presented. Considering large amount of works accomplished in this area a novel classification is implemented in order to categorize the existing diverse studies. Accordingly, studies are classified in different categories based on market mechanism, trading mechanism, type of competition, transmission security, type of power plant, type of commodity and type of objective function. For each category, the corresponding studies are presented to show the effectiveness of each item. At the end, the impact of uncertainty and risk on GenCos’ optimal bidding strategy problem is represented and a number of applicable methods to simulate stochastic nature of the problem are investigated. The presented paper may be applicable for that group of researches that are interested in GenCos’ optimal bidding strategy to give a comprehensive perspective in this issue.
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15
24


Ali
Badri
Department of Faculty of Electrical Engineering Shahid Rajaee Teacher Training University
Department of Faculty of Electrical Engineering
Iran
a_badri73@yahoo.com
Generation company
Optimal bidding strategy
Uncertainty
Price
Moving average filter
Review
[[1] Y. Abbasi, N. Bigdeli, and K. Afshar, “RiskConstrained Optimal Bidding Strategy in PayasBid Electricity Markets,” IEEE int conference on management and service science, pp. 14, 2011. ##[2] F. Careri, C. Genesi, P. Marannino, M. Montagna, S. Rossi, and I. Siviero, “Bidding Strategies in DayAhead Energy Markets: System Marginal Price vs. Pay as Bid,” IEEE, Energy market, EEM, pp. 17, 2010. ##[3] M. Kazemi, B. MohammadiIvatloo, and M. Ehsan, “IGDT Based Riskconstrained Strategic Bidding of GenCos Considering Bilateral Contracts,” IEEE, ICEE, pp. 16, 2013. ##[4] H. Song, C. Liu, and J. Lawarree, "Nash equilibrium bidding strategy in a bilateral electricity market," IEEE Trans, on POWER SYSTEMS, vol. 17, no. 1, pp. 7379, 2002. ##[5] A. Badri, S. Jadid, M. Rashidinejad, M. P. moghaddam, “Optimal bidding strategies in oligopoly markets considering bilateral contracts and transmission constraints,” Electric Power Systems Research, vol. 17, pp. 10891098, 2007. ##[6] H. Niu, R. Baldick, and G. Zhu, “Supply Function Equilibrium Bidding StrategiesWith Fixed Forward Contracts,” IEEE Trans, on POWER SYSTEMS, vol. 20, no. 4, pp. 18591867, 2005. ##[7] F. C. Munhoz, P. B. Correia, “Bidding design for pricetaker sellers in bilateral electricity contract auctions,” Electrical Power and Energy Systems, vol. 30, pp. 491495, 2008. ##[8] N. Lucas, and P. Taylor, “Characterizing generator behavior: bidding strategies in the pool,” ButterworthHeinemann Ltd, 1993. ##[9] Z. Yuan, D. Liu, and C. Jiung, "Analysis of equilibrium about bidding strategy of suppliers with future contracts," Energy Conversion and Management, vol. 48, pp. 10161020, 2007. ##[10] A. Conejo, and F. Prieto, “Mathematical programming and electricity markets,” TOP, 9(1), pp. 147, 2001. ##[11] V. P. Gountis, A.G. Bakirtzis, “Bidding strategies for electricity producers in a competitive electricity market place,” IEEE Trans. Power Syst. 19, pp. 356–365, 2004. ##[12] A. Badri, and et al, “Impact of generators' behaviors on Nash equilibrium considering transmission constraints,” European transactions on electrical power, vol. 19, pp. 765777, 2008. ##[13] D. Zhang, Y.Wang, P.B. Luh, “Optimization based bidding strategies in the deregulated market,” IEEE Trans. Power Syst. 15, pp. 981–986, 2000. ##[14] T. Li, M. Shahidehpour, “Strategic bidding of transmissionconstrained GenCos with incomplete information,” IEEE Trans. Power Syst. 20, pp. 437–447, 2005. ##[15] F. S. Wen, A.K. David, “Oligopoly electricity market production under incomplete information,” IEEE Power Eng. Rev. 21, pp. 58–61, 2001. ##[16] R. Ferrero, J. Rivera, M. Shahidehpour, “Applications of games with incomplete information for pricing electricity in deregulated power pools,” IEEE Trans. Power Syst. 13, pp. 184–189, 1998. ##[17] R. Ferrero, M. Shahidehpour, V. Ramesh, “Transaction analysis in deregulated power systems,” IEEE Trans. Power Syst. 12, pp. 1340–1347, 1997. ##[18] X. Bai, M. Shahidehpour,V. Ramesh, E.Yu, “Transmission analysis by Nash game method,” IEEE Trans. Power Syst. 12, pp. 1046–1052, 1997. ##[19] L. Ma, F. Wen, Y. Ni, F.F. Wu, “Optimal bidding strategies for generation companies in electricity markets with transmission capacity constraints taken into account,” in: Proceedings of the IEEE/PES Summer Meeting 2, pp. 2604–2610, 2003. ##[20] Y. He, and Y.H. Song, “Integrated bidding strategies by optimal response to probabilistic locational marginal prices,” IEEE Proc. Gener. Transmission Distribution, vol. 149, no. 6, 2002. ##[21] P. Wang, and L. Goel, “Reliabilitybased reserve management in a bilateral power market,” Electric Power Systems Research, vol. 67, pp. 185189, 2003. ##[22] L. Wu, M. Shahidehpour, and Z. Li, “GenCo’s RiskConstrained Hydrothermal Scheduling,” IEEE Trans, on Power Systems, vol. 23, no. 4, pp. 18471858, 2008. ##[23] H. M. I. Pousinho, J. Contreras, A. G. Bakirtzis, and J. P. S. Catalão, “RiskConstrained Scheduling and Offering Strategiesof a PriceMaker Hydro Producer Under Uncertainty,” IEEE Trans, on Power Systems, pp. , 2013. ##[24] C. G. Baslis, and A. G. Bakirtzis, “MidTerm Stochastic Scheduling of a PriceMakerHydro Producer With Pumped Storage,” IEEE Trans, on Power Systems, vol. 26, no. 4, pp. 18561865, 2011. ##[25] F. S. Wen, and A. K. David, “Optimally coordinated bidding strategies in energy and ancillary service markets,” IEEE Proc.  Gener. Transm. Distrib. 149 (2002) 331–338. ##[26] T. Li, M. Shahidehpour, and Z. Li, “RiskConstrained Bidding Strategy With Stochastic Unit Commitment,” IEEE Trans, on Power Systems, vol. 22, no. 1, pp. 449458, 2007. ##[27] J. Khorasani, and H. RajabiMashhadi, “Bidding analysis in joint energy and spinning reserve markets based on payasbid pricing,” IET Gener. Transmission Distribution, vol. 6, pp. 79–87, 2012. ##[28] S. Soleymania, A.M. Ranjbara, and A.R. Shiranib, “New approach for strategic bidding of GenCos in energyand spinning reserve markets,” Energy Conversion and Management, vol. 48, pp. 2044– 2052, 2007. ##[29] F. Wen, and A. K. David, “Coordination of bidding strategies in dayahead energy and spinning reserve markets,” Electrical Power and Energy Systems, vol. 24, pp. 251261, 2002. ##[30] P. Chunhua, and S. Huijuan, “Multiobjective Optimal Strategy of Generating and Bidding on Power Selling Side Considering Environmental Protection and Bidding Risk,” IEEE Int conference on electric utility deregulation andpower technologies, pp. 263267, 2008. ##[31] X. R. Li, C. WaiYu, ZhaoXu, F. JiLuo, Z. YangDong, and K. P. Wong, “A Multimarket DecisionMaking Framework forGENCO Considering Emission Trading Scheme,” IEEE Trans, on Power Systems, vol. 28, no. 4, pp. 40994108, 2013. [32] X. Ma, “RandomFuzzy Programming and its Hybrid Intelligent Algorithm to Building Optimal Bidding Strategies for Generation Companies in Electricity Market,” International Conference on Computational Intelligence and Security, 2007. [33] L. A. Barroso, A. Street, S. Granville, and B. Bezerra, “Bidding Strategies in Auctions for LongTerm Electricity Supply Contracts ##for New Capacity”, IEEE, Power and energy society meeting, pp. 20 24, 2008. ##[34] X. Yin, J. Zhao, T. Kumar Saha, and Zh. Yang Dong, “Developing GenCo’s Strategic Bidding in an Electricity Market with Incomplete Information,” IEEE, Power and energy society meeting, pp. 17, 2007. ##[35] J. Liu, Q. Zhu, X. Ma, and Q. Ding, “RandomFuzzy Programming Based Bidding Strategies for Generation Companies in Electricity Market Environment,” International Conference on Sustainable Power Generation and Supply, 2009. ##[36] Y. Song, Y. Ni, F. Wen, Zhijian Hou, and F. F. Wu, “Conjectural variation based bidding strategy in spot markets: fundamentals and comparison with classical game theoretical bidding strategies,” Electric Power Systems Research, vol. 67, pp. 4551, 2003. ##[37] S. Nojavan, K. Zare, and M. R. Feyzi, “Optimal bidding strategy of generation station in power market usinginformation gap decision theory (IGDT),” Electric Power Systems Research, vol. 96, pp. 56– 63, 2013. ##[38] Ch. Boonchuay, and W. Ongsakul, “Optimal risky bidding strategy for a generating company by selforganising hierarchical particle swarm optimization,” Energy Conversion and Management, vol 52, pp. 1047–1053, 2011. ##[39] M. B. NaghibiSistani, M. R. AkbarzadehTootoonchi, M.H. JavidiDashteBayaz, and H. RajabiMashhadi, “Application of Qlearning with temperature variation for bidding strategies in market based power systems,” Energy Conversion and Management, vol. 47, pp. 1529–1538, 2006. ##[40] A. Badri, and M. Rashidinejad, “Security constrained optimal bidding strategy of GenCos in day ahead oligopolistic power markets: A Cournot based model,” Electrical Engineering, Springer, vol. 95, pp. 6372, 2013.##]
Estimating the Number of Wideband Radio Sources
2
2
In this paper, a new approach for estimating the number of wideband sources is proposed which is based on RSS or ISM algorithms. Numerical results show that the MDLbased and EITbased proposed algorithm havea much better detection performance than that in EGM and AIC cases for small differences between the incident angles of sources. In addition, for similar conditions, RSS algorithm offers higher detection probabilitycompared to ISM one, meanwhile it needs a heavy computational complexity than ISM. Furthermore, the effect of bandwidth on the performance of the proposed algorithm is studied. Simulation results show different detection probabilities for the proposed RSS and ISM algorithms meanwhile decreasing the bandwidth is the reason for increasing the performance of both RSSEIT and ISMEIT algorithms.
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25
30


Somaye
Jalaei
Digital Communications Signal Processing (DCSP) Research Lab., Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 1678815811, Tehran, Iran
Digital Communications Signal Processing
Iran


Shahriar
Shirvani Moghaddam
Digital Communications Signal Processing (DCSP) Research Lab., Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 1678815811, Tehran, Iran
Digital Communications Signal Processing
Iran
sh_shirvani@srttu.edu
Rotational signal subspace
Incoherent signal subspace
Minimum description length
Akaike's information criteria
Eigenvalue gradient method
Eigen increment threshold
[[1] Z. Hengli, Z. YongJun, “A novel method for fast estimating the number of wideband sources,” in Proc. 2008 Congress on Image and Signal Processing, vol. 5, pp. 2428. ##[2] S. ShirvaniMoghaddam, S. AlamsiMonfared, “Interference elimination and directionofarrival estimation of wideband cyclostationarycommunication signals,” Journal of Soft Computing and Information Technology, vol. 3, no. 2, pp. 920, Summer 2014 (In Farsi). ##[3] S. ShirvaniMoghaddam, Z. Ebadi, and V. TabatabaVakili, “Modified FOCbased rootMUSIC algorithm for DOA estimation of coherent signal groups,” Life Science Journal, vol. 10, no. 1, pp. 843 846, 2013. ##[4] J. Foutz, A. Spanias, and M. Banavar, Narrowband Direction of Arrival Estimation for Antenna Arrays, Morgan & Clypool Publisher, 2008. ##[5] R.O. Schmidt, “Multiple emitter location and signal parameter estimation,"IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276280, 1986. ##[6] M. Wax, T. Kailath, “Detection of signals by information theoretic criteria,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 33, no. 2, pp. 387–392, Apr. 1985. ##[7] D.B. Williams, Detection: Determining the Number of Sources, Digital Signal Processing Handbook. Chapter 67, CRC Press LCC, 1999. ##[8] S. ShirvaniMoghaddam, S. Jalaei, “Determining the number of coherent sources using FBSSbased methods,” Frontiers in Science, vol. 2, no. 6, pp. 203208, Dec. 2012. ##[9] S. ShirvaniMoghaddam, S. Jalaei, “A new method for detecting the number of coherent sources in the presence of colored noise,” Journal of Information Systems and Telecommunication, vol. 1, no. 3, pp. 5560, July/Sept. 2013. ##[10] Y. Hou, D. Han, and Y. Jin, “New realspace direction finding method for wideband sources,” in 2009 IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 39653968. ##[11] Y.S. Yoon, L.M. Kaplan, and J.H. McClellan, “TOPS: New DOA estimator for wideband signals,” IEEE Transactions on Signal Processing, vol. 54, no. 6, pp. 19771989, 2006. ##[12] P. Pal, P. P. Vaidyanathan, “A novel autofocusing approach for estimating directionsofarrival of wideband signals,” in proc. of 2009 Conference on Signals, Systems and Computer,pp. 16631667. ##[13] H. Wang, M. Kaveh, “Coherent signalsubspace processing for the detection and estimation of angles of arrival of multiple wideband sources,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 33, no. 4, pp. 823831, 1985. ##[14] S. Valaee, P. Kabal, “Wideband array processing using a twosided correlation transformation,” IEEE Transactions on Signal Processing, vol. 43, no. 1, pp. 160172, 1995. ##[15] H. Hung, M. Kaveh, “Focusing matrices for coherent signalsubspace processing,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 36, no. 8, pp.12721281, 1988. ##[16] Q. Zhang, Y. Yin, and J. Huang, “Detecting the number of sources using modified EGM,” in IEEE Region 10 Conference (TENCON2006), pp. 14. ##[17] F. Chu, J. Huang, M.Jiang, Q. Zhang, and T. Ma, “Detecting the number of sources using modified EIT,” in proc. of the 2009 IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 563566.##]
A Clustering Approach by SSPCO Optimization Algorithm Based on Chaotic Initial Population
2
2
Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. SSPCO optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called seesee partridge. One of the things that smart algorithms are applied to solve is the problem of clustering. Clustering is employed as apowerful tool in many data mining applications, data analysis, and data compression in order to group data on the number of clusters (groups). In the present article, a chaotic SSPCO algorithm is utilized for clusteringdata on different benchmarks and datasets; moreover, clustering with artificial bee colony algorithm and particle mass 9 clustering technique is compared. Clustering tests have been done on 13 datasets from UCImachine learning repository. The results show that clustering SSPCO algorithm is a clustering technique which is very efficient in clustering multivariate data.
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31
38


Rohollah
Omidvar
Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran
Young Researchers and Elite Club, Yasooj
Iran
r.omidvar.uni@gmail.com


Hamid
Parvin
Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
Young Researchers and Elite Club, Nourabad
Iran


Amin
Eskandari
Sama Technical and Vocational Training College, Azad University of Shiraz, Shiraz, Iran
Sama Technical and Vocational Training College,
Iran
eskandary.a@gmail.com
SSPCO algorithm
Chaotic
Clustering
Initial Population
Data set
[[1] R. Rajabioun, “Cuckoo Optimization Algorithm,” Applied Soft Computing, 2011. ##[2] J. Kennedy, and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, 1995. ##[3] M. Dorigo, M. Birattari, and T. Stutzle, “Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique,” IEEE Computational Intelligence Magazine, 2006. ##[4] S. Arora, and S. Singh, “The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection,” International Journal of Computer Applications, Vol. 69. 3, 2013. ##[5] L. Fister, L. Fister Jr, X. She yang, and J. Brest, “A comprehensive review of firefly algorithm,” Swarm and Evolutionary Computation, vol. 13, pp. 3446, Des. 2013. ##[6] D. Karaboga, and B. Basturk, “On the performance of artificial bee colony algorithm,” Applied Soft Computing, vol. 8, 2008. ##[7] D. Pham, A. Ghanbarzadeh, A. Koc, S. Otri, S. Rahim, and M. Zaidi, “The bees algorithm, Technical note, Cardiff university,” UK: Manufactoring Engineering center, 2005. ##[8] J. Han, and M. Kamber, “Data mining: Concept and Techniques,”Morgan Kaufmann publisher, 2001. ##[9] D. J. Hand, H. Mannila, and P. Smyte, “Principles of Data Mining,” The MIT Press, 2001. ##[10] M.P. Veyssieres, and R.E. Plant , “Identification of vegetation state and transition domains in California’s hardwood rangelands,” University of California, 1998. ##[11] R. Xu, and D. Wunsch, “Survey of Clustering Algorithms,” IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 16. 3, 2005. ##[12] A. Barladi, E. Alpaydin, “Constructive feedforward ART clustering networks,” Part I and II. IEEE Trans. Neural Netw, vol. 13. 3, pp. 662 – 677, May. 2002. ##[13] V. Cherkassky, and F. Mulier, “Learning From Data: Concepts, Theory, and Methods,” New York : Wiley, 1998. ##[14] A.K. Jain, M.N. Murty, and P.J. Flynn, “Data clustering: A review,” ACM Comput. Surv, vol. 31. 3, 1999. ##[15] L. Rokach, “A survey of Clustering Algorithms,” Data Mining and Knowledge Discovery Handbook, 2nd ed. Springer Science. 10.1007/9780387098234_14, 2010 . ##[16] Y. Marinakis, M. Marinaki, M. Doumpos, N. Matsatsinis, and C. Zopounidis, “A hybrid stochastic genetic—GRASP algorithm for clustering analysis,” Oper. Res. Int. J.(ORIJ) , vol. 8. 1, 2008. ##[17] D. Karaboga, and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Applied Soft Computing, Elsevier, 10.1016/j.asoc.12.025, 2009. ##[18] C.L. Blake, and C.J. Merz. The University of California at Irvine Repository of Machine, http://www.ics.uci.edu/ mlearn/MLRepository., 1998. ##[19] I. De Falco, A. Della Cioppa, and E. Tarantino, “Facing classification problems with Particle Swarm Optimization,” Appl. Soft Comput, vol. 7. 3, pp. 652658, 2007. ##[20] F. Jensen, “An Introduction to Bayesian Networks,” UCL Press/Springer–Verlag, 1996. ##[21] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning representation by backpropagation errors”, Nature, 323(9), pp. 533536, 1986. ##[22] M H. Hassoun, “Fundamentals of Artificial Neural Networks,” The MIT Press, Cambridge, MA, 1995. ##[23] J.C. Cleary, and L.E. Trigg, “An instancebased learner using an entropic distance measure,” Proceedings of the 12th International Conference on Machine Learning. pp. 108–114, 1995. ##[24] L. Breiman, “Bagging predictors,” Mach. Learn, vol. 24. 2, pp.123140, 1996. ##[25] G.I. Webb, “Multiboosting: a technique for combining boosting and wagging,” Mach. Learn, vol. 40. 2, pp. 159196, 2000. ##[26] R. Kohavi, “Scaling up the accuracy of naiveBayes classifiers: a decision tree hybrid, in: E. Simoudis, J.W. Han, U. Fayyad (Eds.),” Proceedings of the Second International ConferenceonKnowledge Discovery and Data Mining, AAAI Press. pp. 202–207, 1996. ##[27] P. Compton, and R. Jansen, “Knowledge in context: a strategy for expert system maintenance, in: C.J., Barter, M.J., Brooks (Eds.),” Proceedings of Artificial Intelligence LNAI, Berlin, Springer–Verlag, Adelaide, Australia, vol. 406. pp. 292–306, 1988. ##[28] G. Demiroz, and A. Guvenir, “Classification by voting feature intervals,” Proceedings of the Seventh European Conference on Machine Learning, pp. 85–92, 1997. ##[29] D. Rumelhart, E. Hinton, and J. Williams, Learning internal representation by error propagation, “Parallel Distribute Processing,” vol. 1, pp. 318362, 1986. ##[30] M. B. Menhaj, Principles of Neural Networks, Amirkabir University of Technology, second edition, pp.715, 2002. ##[31] R. Omidvar, H. Parvin, and F. Rad, “SSPCO Optimization Algorithm (SeeSee Partridge Chicks Optimization),” 14 thMexican international conferences on artificial intelligence, IEEE, 2015. ##[32] Statistical Consultant for Doctoral Students and Researchers, http://www.statisticallysignificantconsulting.com/Ttest.htm. ##[33] J. K. Kruschke, “Bayesian estimation supersedes the t test,” Journal of Experimental Psychology: General Version of May 31, 2012. ##[34] J. C. F. De. Winter, “Using the Student’s ttest with extremely small sample sizes,” Practical Assessment, Research & Evaluation, vol 18, no 10, 2013. ##[35] Y. He, J. Zhou, X. Xiang, H. Chen, and H. Qin, “Comparison of different chaotic maps in particle swarm optimization algorithm for longterm cascaded hydroelectric system scheduling,” Chaos Solitons Fractals 2009;42:316976. ##[36] L. Coelho, and V. Mariani, “Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization,” Expert Syst Appl 2008;34:190513. ##[37] H. Gao, Y. Zhang, S. Liang, and D. Li, “A new chaotic algorithm for image encryption,” Chaos Solitons Fractals 2006;29:3939. ##[38] D. Kuo, Chaos and its computing paradigm. IEEE Potentials Mag 2005;24:135. ##[39] J. Nayak, B. Naik, and H.S. Behera, “Fuzzy CMeans (FCM) lustering algorithm: a decade review from 2000 to 2014,” Comput. Intell. Data Min, vol. 2, pp. 133–149 (2014). ##[40] J. Nayak, M. Nanda, K. Nayak, B. Naik, and H.S. Behera, “An improved firefly fuzzy cmeans (FAFCM) algorithm for clustering real world data sets,” Smart Innov. Syst. Technol. Vol 27, pp. 339– 348, 2014. ##[41] X. Wu,B. Wu, J. Sun, S. Qiu, and X. Li, “A hybrid fuzzy Kharmonic means clustering algorithm,” Appl. Math. Model. vol 39(12), pp. 3398–3409, 2015. ##[42] S. Shamshirband, A. Amini, N B. Anuar, L M. Kiah, “DFICCA: a densitybased fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks,” Measurement, 55, pp. 212–226, 2014.##]
PassivityBased Stability Analysis and Robust Practical Stabilization of Nonlinear Affine Systems with Nonvanishing Perturbations
2
2
This paper presents some analyses about the robust practical stability of a class of nonlinear affine systems in the presence of nonvanishing perturbations based on the passivity concept. The given analyses confirm the robust passivity property of the perturbed nonlinear systems in a certain region. Moreover, robust control laws are designed to guarantee the practical stability of the perturbed systems. For this purpose, the control laws are designed in two cases. In the first case, it is assumed that the designer has freedom in choosing the outputs. In the second case, it is assumed that the outputs are predefined. In this case, first it is considered that the nominal system is passive between its inputs and outputs and then the control law is designed as static output feedback law for the perturbed system. Moreover, in the case that the nominal system is not passive, first, a law is designed such that the new nominal system is passive between the virtual inputs and the outputs. Then, the virtual input is designed as a static output feedback law such that the proposed controllers guarantee the practical stability of the perturbed system. Finally, the computer simulations are performed to show the efficacy and applicability of the designed controllers.
1

39
47


Hamed
Chenarani
Department of Electrical and Electronic Engineering, Shiraz university of Technology, Shiraz, Iran.
Department of Electrical and Electronic Engineerin
Iran
h.chenarani@sutech.ac.ir


Tahereh
Binazadeh
Department of Electrical and Electronic Engineering, Shiraz university of Technology, Shiraz, Iran.
Department of Electrical and Electronic Engineerin
Iran
binazadeh@sutech.ac.ir
Robust passivitybased Control
Practical stability
Nonvanishing perturbations
[[1] J. C. Willems, “Dissipative dynamical systems part I: General theory,” Archive for rational mechanics and analysis, vol. 45, no. 5, pp. 321–351, 1972. ##[2] X. Li, S. Yin, H. Gao, and O. Kaynak, “Robust Static OutputFeedback Control for Uncertain Linear DiscreteTime Systems via the Generalized KYP Lemma,” in World Congress, 2014, vol. 19, pp. 7430–7435. ##[3] D. Hill, P. Moylan, “The stability of nonlinear dissipative systems,” Automatic Control, IEEE Transactions on, vol. 21, no. 5, pp. 708–711, 1976. ##[4] C. King, R. Shorten, “An extension of the KYPlemma for the design of statedependent switching systems with uncertainty,” Systems & Control Lett, vol. 62, no. 8, pp. 626– 631, Aug. 2013. ##[5] S. You, J. C. Doyle, “A Lagrangian dual approach to the generalized KYP lemma.,” in CDC, 2013, pp. 2447–2452. ##[6] W. Paszke, and E. Rogers, and K. Galkowski, “Experimentally verified generalized KYP lemma based iterative learning control design,” Control Engineering Practice, Apr. 2016. ##[7] C.C. Tsai, H.L. Wu, “Passivity, global stabilization and disturbance attenuation of weakly minimumphase nonlinear uncertain systems with applications to mechatronic systems,” in ICCAS International Conference on Control, Automation and Systems, 2008, pp. 777–782. ##[8] C. I. Byrnes, A. Isidori, and J. C. Willems, “Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems,” IEEE Transactions on Automatic Control, vol. 36, no. 11, pp. 1228–1240, 1991. ##[9] T. Binazadeh, M. J. Yazdanpanah, “Application of passivity based control for partial stabilization,” Nonlinear Dynamics and Systems Theory, vol. 11, no. 4, pp. 373–382, 2011. ##[10] H. Chenarani, T. Binazadeh, “Flexible structure control of unmatched uncertain nonlinear systems via passivitybased sliding mode technique,” accepted for publication in Iranian Journal of Science & Technology, Transactions of Electrical Engineering. ##[11] T. Binazadeh, M. H., Shafiei, “Passivitybased optimal control of discretetime nonlinear systems,” Control and Cybernetics, vol. 42, no. 3, pp. 627637, 2013. ##[12] S. Kuntanapreeda, “Adaptive control of fractionalorder unified chaotic systems using a passivitybased control approach,” Nonlinear Dynamics, vol. 84, no. 4, pp. 1–11, 2016. ##[13] A. C. Leite, F. Lizarralde, “Passivitybased adaptive 3D visual servoing without depth and image velocity measurements for uncertain robot manipulators,” International Journal of Adaptive Control and Signal Processing, vol. 30, no. 810, pp. 12691297, 2016. ##[14] C.C. Tsai, H.L. Wu, “Robust passivitybased control of weakly minimum phase nonlinear uncertain systems: An application to manipulator,” in Asian Control Conference, 2009, pp. 919– 924. ##[15] W. Lin, T. Shen, “Robust passivity and feedback design for minimumphase nonlinear systems with structural uncertainty,” Automatica, vol. 35, no. 1, pp. 35–47, 1999. ##[16] S. Aranovskiy, R. Ortega, and R. Cisneros, “Robust PI passivitybased control of nonlinear systems: application to portHamiltonian systems and temperature regulation,” in American Control Conference (ACC), 2015, pp. 434–439. ##[17] N. Bu, M. Deng, “Passivitybased robust control for uncertain nonlinear feedback systems,” in International Conference on Advanced Mechatronic Systems (ICAMechS), , 2015, pp. 70–74. ##[18] A. Donaire, J. Guadalupe Romero, and T. Perez, “Passivitybased trajectorytracking for marine craft with disturbance rejection,” IFACPapersOnLine, vol. 48, no. 16, pp. 19–24, 2015. ##[19] M. S, T. N, “Application of passivity concept for split range control of heat exchanger networks,” Journal of Chemical Engineering & Process Technology, vol. 07, no. 01, 2015. ##[20] H. K. Khalil, J. Grizzle, Nonlinear Systems, Prentice Hall, Upper Saddle River, 2002. ##[21] N. Kalouptsidis, J. Tsinias, “Stability improvement of nonlinear systems by feedback,” IEEE Transactions on Automatic Control, vol. 29, no. 4, pp. 364–367, 1984. ##[22] D. T. Stansbery, J. R. Cloutier, “Position and attitude control of a spacecraft using the statedependent Riccati equation technique, ”Proceedings of the American Controls Conference, Chicago, lllinois, 2000, pp. 18671871.##]
Software Cost Estimation by a New Hybrid Model of Particle Swarm Optimization and KNearest Neighbor Algorithms
2
2
A successful software should be finalized with determined and predetermined cost and time. Software is a production which its approximate cost is expert workforce and professionals. The most important and approximate software cost estimation (SCE) is related to the trained workforce. Creative nature of software projects and its abstract nature make extremely cost and time of projects difficult to estimate. Various methods have been presented in the software project cost estimation for performing a software project in the area of software engineering. COCOMO II model is one of the most documented models among templatebased methods that has been proposed by Bohm. Common methods for estimating the time and cost are essentially abstract, accordingly, providing new methods for SCE is required and necessary. In this paper, a new method is presented to solve the problem of SCE by using hybrid particle swarm optimization (PSO) algorithm and Knearest neighbor (KNN) algorithm. The method was evaluated on 6 multiple datasets with 8 different evaluation criteria. Obtained results show the more accurate performance of the proposed method.
1

49
55


Mohsen
Hasanluo
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
Department of Computer Engineering, Urmia
Iran


Farhad
Soleimanian Gharehchopogh
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
Department of Computer Engineering, Urmia
Iran
bonab.farhad@gmail.com
Software Cost Estimation
PSO
KNN
Hybrid Method
Optimization
[[1] L. Zhangf, “Software Cost Estimation in Handbook of Software Engineering and Knowledge Engineering,” World Scientific Pub. Co, River Edge, NJ, 2001. ##[2] B.W. Boehm, S. Chulani, and D. Reifer, The Rosetta Stone: Making COCOMO 81 Files Work with COCOMO II,1998. ##[3] F. S.Gharehchopogh, L. Ebrahimi, I. Maleki, and S. Jodati, “A new novel PSObased approach with hybrid of fuzzy cmeans and learning automata in software cost estimation,” Indian Journal of Science and Technology, vol. 7, pp. 795803, 2014. ##[4] Ziauddin, Sh. Kamal, Sh. Khan, and J.A. Nasir, “A fuzzy logic based software cost estimation model,” International Journal of Software Engineering and Its Applications, vol. 7, no. 2, pp. 7 18, 2013. ##[5] P.V.G.D.P. Reddy, CH.V.M.K. Hari, and R.T. Srinivasa, “Multi objective particle swarm optimization for software cost estimation,” International Journal of Computer Applications, vol. 32, no.3, pp. 1317, 2011. ##[6] T.R. Benala, S. Dehuri, S.C. Satapathy, and S. Madhurakshara, “Genetic algorithm for optimizing functional link artificial neural network based software cost estimation,” in Proc. 2012 International Conference on Information Systems Design, pp. 75–82. ##[7] F. S.Gharehchopogh, Z.A. Dizaji, “A new approach in software cost estimation with hybrid of bee colony and chaos optimizations algorithms,” MAGNT Research Report, vol. 2, no. 6, pp. 1263127, 2014. ##[8] Z.A.Khalifelu, F.S.Gharehchopogh,“Comparison and evaluation data mining techniques with algorithmic models in software cost estimation,” ProcediaTechnology Journal, vol. 1, pp. 65 71, 2012. ##[9] Z.A. Dizaji, F. S.Gharehchopogh, “A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation,” Indian Journal of Science and Technology, vol. 8, no.2, pp.128–133,2015. ##[10] E.E. Miandoab, F.S. GHAREHCHOPOGH, “A novel hybrid algorithm for software cost estimation based on cuckoo optimization and knearest neighbors algorithms,” Engineering, Technology & Applied Science Research, vol. 6, no. 3, pp. 10181022, 2016. ##[11] L.F. Capretz, V. Marza, “Improving effort estimation by voting software estimation models,” Advances in Software Engineering, pp. 18, 2009. ##[12] S. Kumari, S. Pushkar, “Performance analysis of the software cost estimation methods: a review,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 229238, 2013. ##[13] T.M. Cover, P.E. Hart, “nearest neighbor pattern classification,” IEEE Trans. Inform. Theory, vol. 13, pp 221, 1967. [14] J. Kennedy, R.C. Eberhart, “Particle swarm optimization,” 1995 IEEE Conference on Neural Networks, Perth, Australia; pp. 1942–1948. ##[15] F.D. Mokri, M. Molanli, “software cost estimation using adaptive neuro fuzzy inference system,” International Journal of Academic Research in Computer Engineering, vol. 1, no. 1, pp. 3439, 2016. ##[16] F. S.Gharehchopogh, S. Jodati, and I. Maleki, “object oriented software engineering models in software industry,” International Journal of Computer Applications, vol. 95,no.3, pp. 1316, 2014.##]
Declarative Semantics in ObjectOriented Software Development  A Taxonomy and Survey
2
2
One of the modern paradigms to develop an application is object oriented analysis and design. In this paradigm, there are several objects and each object plays some specific roles in applications. In an application, we must distinguish between procedural semantics and declarative semantics for their implementation in a specific programming language. For the procedural semantics, we can write a set of instructions that must be executed sequentially. The declarative semantics declare a set of facts and rules. They do not specify the sequence of steps for doing the processing. In this paper, we present four taxonomies for the rules in objectoriented paradigm and discuss how the paradigm can be extended to support declarative semantic of applications. Then, the rules in the taxonomies are evaluated in four case studies. After that, an approach is recommended for finding and implementation of declarative semantics, based on some practical experience obtained from the evaluation.
1

57
68


Hassan
Rashidi
Department of Statistics, Mathematics, and Computer Science, Allameh Tabataba’i University, Tehran, Iran
Department of Statistics, Mathematics, and
Iran
rashidi@gmail.com
Taxonomy
Object
Semantics
ObjectOriented
Software Engineering
[[1] M. Langer, "Analysis and Design of Information Systems," 3rd ed., SpringerVerlag London Limited, 2008. ##[2] P. Coad, E. Yourdon, ObjectOriented Analysis, Yourdon Press, 1991. ##[3] S. H. Pfleeger, J. M. Atlee, "Software Engineering: Theory and Practice," 4th ed., Pearson, 2010. ##[4] R. S. Pressman, “Software Engineering: A Practitioner's Approach,” 8th ed., McGrawHill, 2015. ##[5] Y. Sommerville, “Software Engineering,” 10th ed., Pearson Education, 2016. ##[6] L. A. Stein, H. Lieberman, and D. Ungar, "A shared view of sharing: The Treaty of Orlando, ObjectOriented Concepts, Databases, and Applications”, W. Kim and F. H. Lechosky, Eds. New York: ACM Press, 1989. ##[7] M. Asadi, H. Rashidi, “A Model for ObjectOriented Software Maintainability Measurement,” International Journal of Intelligent Systems and Applications, pp. 6066, 2016. ##[8] G. Bavota, A. De. Lucia, A. Marcus, and R. Oliveto, “Automating extract class refactoring: an improved method and its evaluation,” Empirical Software Engineering, vol. 19, pp. 1616 1664, 2014. ##[9] K. Beck, W. Cunningham, "A laboratory for teaching object oriented thinking," OOPSLA '89 Conference proceedings on Objectoriented programming systems, languages and applications, ACM SIGPLAN Notices, 1989. ##[10] A. Cockburn, Writing Effective Use Cases (Draft 3), Addison Wesley Longman, 2000. ##[11] M. Fokaefs, N. Tsantalis, E. Strouliaa, and A. Chatzigeorgioub, “Identification and Application Of Extract Class Refactoring In ObjectOriented Systems,” Journal of Systems and Software, vol. 85, pp. 2241–2260, 2012. ##[12] H. Rashidi, “Objects Identification in ObjectOriented Software Development  A Taxonomy and Survey on Techniques”, Journal of Electrical and Computer Engineering Innovations, vol. 3(2), pp. 2743, 2015. ##[13] B. Bruegge, A. H. Dutoit, ObjectOriented Software Engineering: Using UML, Patterns, and Java, Pearson Prentice Hall, 2010. ##[14] I. Jacobson, M. P. Christerson, and F. Overgaard, ObjectOriented Software Engineering A Use Case Approach, AddisonWesley, Wokingham, England, 1992. ##[15] J. Rumbaugh, M. Blaha, W. Premerlani, E. Eddy, and W. Lorensen, ObjectOriented Modeling and Design, PrenticeHall, 1992. ##[16] R. King, My Cat Is ObjectOriented, ObjectOriented Concepts, Databases and Applications, Addison Wesley, 1989. [17] R. WirfsBrock, Designing ObjectOriented Software, PrenticeHall, 1990. ##[18] C. Larman, "Applying UML and Patterns – An Introduction to ObjectOriented Analysis and Design and Iterative Development," 3rd ed., Prentice Hall, 2005. ##[19] D. Rosenberg, M. Stephens, Use Case Driven Object Modeling with UML: Theory and Practice, Apress, 2007. ##[20] G. Canforaa, A. Cimitilea, A. D. Luciaa, and G. A. D. Lucca, “Decomposing Legacy Systems into Objects: An Eclectic Approach,” Information and Software Technology, vol. 43, pp. 401412, 2001. ##[21] M. Fowler, K. Scott, “UML Distilled A Brief Guide to The Standard Object Modeling Guide,” 2nd ed., Addison Wesley Longman, Inc, 1999. ##[22] N. Goldsein, J. Alger, Developing ObjectOriented Software for the Macintosh Analysis, Design, and Programming, AddisonWesley, 1992. ##[23] J. V. Gurp, J. Bosch, “Design, Implementation and Evolution of ObjectOriented Frameworks: Concepts and Guidelines,” Software—Practice and Experience, vol. 31, pp. 277300, 2001. ##[24] I. Jacobson, G. Booch, The Unified Software Development Process, AddisonWesley, Reading, MA, 1999. ##[25] J. Rumbaugh, “Getting Started: Using Use Cases To Capture Requirements,” ObjectOriented Programming, vol. 7(5), pp. 8 12, 1994. ##[26] S. Schlaer, S. Melior, Object Lifecycles: Modeling the World in States, Yourdon Press, 1992. ##[27] G. Booch, J. Rumbaugh, and I. Jacobson, The Unified Modeling Language User Guide, Addison Wesley, 1998. ##[28] J. Martin, J. Odell, ObjectOriented Analysis and Design, PrenticeHall, 1992. ##[29] R. C. Lee, W. M. Tepfenhart, "UML and C++: A Practical Guide to ObjectOriented Development," 2nd ed., Pearson Prentice Hall, 2005. ##[30] Z. Rashidi, “Properties of Relationships among objects in ObjectOriented Software Design,” International Journal of Programming Languages and Applications, vol. 5(4), pp. 113, 2015. ##[31] MerriamWebster Online (2011), Dictionary and Thesaurus, from http:// www.merriamwebster.com ##[32] K. S. Subhash, M. Navi, and B. Bhojane, “NLP based ObjectOriented Analysis and Design from Requirement Specification,” International Journal of Computer Applications, vol. 47(21), 2012. ##[33] H. Rashidi, Firm Planning, Using Computing Models, Eghtesad Farda Press (in Persian), 2014. ##[34] H. Rashidi, “Software EngineeringA programming approach,” 2nd ed., Allameh Tabataba’i University Press (in Persian), Iran, 2014.##]
Evaluation and Ranking of Discrete Simulation Tools
2
2
In studying through simulation, choosing an appropriate tool/language would be a difficult task because many of them are available. On the other hand, few research works focus on evaluation of simulation tools/languages and their comparison. This paper makes a couple of evaluations and ranks more than fifty simulation tools that are currently available. The first evaluation and ranking is in the approach of Analytic Hierarchy Process and the second one is in the Feature Analysis and Weighted Average Sum. The evaluations and rankings are based on thirteen indicators included in simulation tools, which are the general features, visual aspects, coding aspects, efficiency, modeling assistance, testability, software compatibility, input/output, experimental features, statistical facilities, user support, financial and technical features as well as pedigree. These evaluations and rankings provide significant information for any decisionmaker to choose favorite simulation tools.
1

69
84


Zeynab
Rashidi
Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
Department of Mathematics and Computer
Iran
zeynabrashidi@gmail.com


Zahra
Rashidi
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Department of Computer Engineering,
Iran
zrashi@ce.sharif.edu
[[1] M.C. Albrecht, and P.E. AZ, “Introduction to discrete event simulation”, available http://www.albrechts.com/mike/DES/index.html, 2010. ##[2] T.M.K. Roeder, “An information taxonomy for discrete event simulations,” Ph.D. Dissertation, University of California, Berkeley, 2004. ##[3] H. Rashidi, “Discrete simulation tools: A survey on taxonomies,” Journal of Simulation, vol. 4, pp. 111, 2016. ##[4] C.M. Overstreet, “Model specification and analysis for discrete event simulation,” Ph.D. Dissertation, Department of Computer Science, Virginia Polytechnic Institute and State University, 1982. ##[5] R.T. Ahmed, Hall and P. Wernick, “A proposed framework for evaluating software process simulation models,” Proceedings Prosim’03 May 3 4, Portland state University, 2003. ##[6] R.E. Nance, “A history of discrete event simulation programming languages,” ACM SIGPLAN Notices, vol. 28, no. 3, 1993, pp. 149175. ##[7] J. Banks, and R. Gibson, “Selecting simulation software,” IIE Solutions, pp. 3032, 1999. ##[8] C.M. Overstreet, and R. E. Nance, “Characterizations and relationships of world views,” in Proceedings of the 2004 Winter Simulation Conference, Edited by R.G. Ingalls,M. D. Rossetti, J. S. Smith, and B. A. Peters, 2004, pp. 279287. ##[9] A. Sulistio, C. Shinyeo, and R. Buyya, “Taxonomy of computerbased simulations and its mapping to parallel and distributed systems simulation tools,” Software Practice and Experience, vol. 34, pp. 653–673, 2004. ##[10] W. Wang, Y. Zhu, and Q. Li, “Serviceoriented simulation framework: an overview and unifying methodology,” Ph.D. dissertation, Changsha, China: National University of Defence Technology, 2010. ##[11] S. Suliza, R. Ibrahim, N. H. Zakaria, and A. H. Ab Hami, “comparing three simulation model using taxonomy: system dynamic simulation, discrete event simulation and agent based simulation,” International Journal of Management Excellence, vol. 1, no. 3, 2013, pp. 5359. ##[12] G.T. Mackulak, J.K. Cochran, and P.A. Savory, “Ascertaining important features for industrial simulation environments,” Simulation, vol. 63, no. 4, pp. 211–221, 1994. ##[13] E.H. Page, “Simulation modeling methodology principles and etiology of decision support,” Ph.D. Dissertation, Virginia Polytechnic Institute and State University, 1994. ##[14] V. Hlupic, Z. Irani, and R. J. Paul, “Evaluation framework for simulation software,” International Journal of Advanced Manufacturing Technology, vol. 15, no. 5, pp. 366382, 1999. ##[15] V. Hlupic and R.J. Paul, “Guidelines for selection of manufacturing simulation software,” IIE Transactions, vol. 31, no. 1, pp. 2129, 1999. ##[16] T.W. Tewoldeberhan, A. Verbraeck, E. Valentin, and G. Bardonnet, “An evaluation and selection methodology for discreteevent simulation software,” Proceedings of the Winter Simulation Conference, 2002, vol. 1, pp 67 75. ##[17] A.F. Seila, V. Ceric, and P. Tadikamalla, Applied simulation modeling, Thomson Learning, Australia: Thomson Learning, 2003. ##[18] B. Boehm, Software Engineering Economics, Prentice Hall Inc, 1981. ##[19] O.V. Lindland, G. Sindre, and A. Solvberg, “Understanding quality in conceptual modeling,” IEEE Software, pp. 42 49,1994. ##[20] B. Kitchenham, L. Picakrd, S. Linkman, and P. Jones, “A framework for evaluating software bidding model,” Proceedings of Conference on Empirical Assessment in Software Engineering, April 2002. ##[21] L.W. Schruben, “Mathematical programming models of discrete event system dynamics,” Proceedings of the 2000 Winter Simulation Conference, Edited by J. A. Jones, R. R. Barton, K. Kang, and P. A. Fishwick, Piscataway, NJ, USA: IEEE, 2000, pp. 381385. ##[22] A.S. Jadhava and R.M. 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Research of Low Voltage Shore Power Supply Used on Shipping Based on Sliding Control
2
2
In order to solve the problem of fuel pollution, the diesel fuel for power generation of marine was abandoned and shore power supply have been proposed at the historic moment. The Shore Power Supply which needs transferring 380V/50Hz to 450V/60Hz is used on shipping who need this kind of power, so effectively curbs the emissions of pollution gas into the air, and achieves the purpose of improving the environment. At the same time, in order to achieve better control, in this paper, sliding mode control is applied to the closed loop system. The structure of the main circuit, drive circuit and control system are described and the design of critical parameters are provided.
1

85
93


Guoliang
Yang
Key Lab of Power Electronicsfor Energy Comservation and Motor Driveof Hebei Province, YanShan University, Qinhuangdao,066004, China.
Key Lab of Power Electronicsfor Energy Comservatio
Iran
y99ygl@ysu.edu.cn


Yanxiao
Jia
Key Lab of Power Electronicsfor Energy Comservation and Motor Driveof Hebei Province, YanShan University, Qinhuangdao,066004, China.
Key Lab of Power Electronicsfor Energy Comservatio
Iran
892667864@qq.com


ManCuiHang
Qin
Key Lab of Power Electronicsfor Energy Comservation and Motor Driveof Hebei Province, YanShan University, Qinhuangdao,066004, China.
Key Lab of Power Electronicsfor Energy Comservatio
Iran


Yiming
Fang
Key Lab of Power Electronicsfor Energy Comservation and Motor Driveof Hebei Province, YanShan University, Qinhuangdao,066004, China.
Key Lab of Power Electronicsfor Energy Comservatio
Iran
fyming@ysu.edu.cn
SPS
Diodeclamped threelevel inverter
Sliding Control
SVPWM
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