2012
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The IUFP Algorithm for Generating Simulation Heart
The IUFP Algorithm for Generating Simulation Heart
2
2
In all systems simulation, random variates are considered as a main factor and based of simulation heart. Actually, randomization is inducted by random variates in the simulation. Due to the importance of such a problem, a new method for generation of random variates from continuous distributions is presented in this paper. The proposed algorithm, called uniform fractional part (UFP) is simpler and more efficient compared with other methods of random variates generation. Despite useful consequences, this algorithm has several shortcomings such as 1) being approximate, 2) not accessibility of the inverse of cumulative density function (CDF) for all distributions in order to determine the cutoff points and 3) truncating the tails of infinite distributions, which all of the aforementioned shortcomings reduce the precision and speed of the algorithm. The main goal of this research is proposing the improved version of this algorithm (IUFP) through recognizing its deficiencies.
1
In all systems simulation, random variates are considered as a main factor and based of simulation heart. Actually, randomization is inducted by random variates in the simulation. Due to the importance of such a problem, a new method for generation of random variates from continuous distributions is presented in this paper. The proposed algorithm, called uniform fractional part (UFP) is simpler and more efficient compared with other methods of random variates generation. Despite useful consequences, this algorithm has several shortcomings such as 1) being approximate, 2) not accessibility of the inverse of cumulative density function (CDF) for all distributions in order to determine the cutoff points and 3) truncating the tails of infinite distributions, which all of the aforementioned shortcomings reduce the precision and speed of the algorithm. The main goal of this research is proposing the improved version of this algorithm (IUFP) through recognizing its deficiencies.
1
10
Elham
Shadkam
Elham
Shadkam
Corresponding Author, E. Shadkam, PhD student, Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
Corresponding Author, E. Shadkam, PhD student,
Iran
ie.el.shadkam@gmail.com
Abdollah
Aghaie
Abdollah
Aghaie
A. Aghaie, Professor, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran (aaghaie@kntu.ac.ir
A. Aghaie, Professor, Department of Industrial
Iran
aaghaie@kntu.ac.ir
Random Variates generation
Simulation
Uniform Fractional Part (UFP)
[[1] Banks, J.; Handbook of simulation principle, methodology, advances, applications and practice, 3nd Edition, New York: John Wiley & Sons, 1998. ##[2] Hung, Y. C.; Balakrishnan, N.; Cheng C. W.; “Evaluation of algorithms for generating Dirichlet random vectors”, Journal of Statistical Computation and Simulation, 2010. ##[3] Banks, J.; Carson, J.S.; Nelson, B.L.; Nicol, D.M.; Discreteevent system simulation, 1nd Edition, Upper Saddle River: Pearson Prentice Hall, 2005. ##[4] Ormann, W.; Erflinger, G.; “The transformed rejection method for generating random variables, an alternative to the ratio of uniforms method” Communications in Statistics  Simulation and Computation, vol. 23, 3, p.p. 847 – 860, 1994. ##[5] Mahlooji, H.; Jahromi, A.E.; Mehrizi, H.A.; Izady,N.; “Uniform Fractional Part: A simple fast method for generating continuous random variates”, Scientia Iranica, vol. 15,5, p.p. 613622, 2008. ##[6] Jones, M. C A.; Lunn, D.; “Transformations and random variate generation: generalised ratioofuniforms methods”, Journal of Statistical Computation and Simulation, vol. 55, 1, p.p. 4955, 1996. ##[7] Cheng, R. C. H.; Feast, G. M.; “Some simple gamma variate generators”, appl statist, vol. 28,3, p.p. 290295, 1979. ##[8] Morgan, B.J.T.; Elements of simulation, 1ed Edition, London: Chapman and Hall, 1984. ##[9] Mahlooji, H.; Izady, N.; “Developing a Wide EasytoGenerate Class of Bivariate Copulas”, Communications in Statistics Theory and Methods, vol. 37, p.p. 1919–1929, 2008. ##[10] Mahlooji, H.; Mehrizi, H.A.; Farzan, A.; “A fast method for generating continuous order statics based on uniform fractional part”, Proc. 35th International Conference on Computers and Industrial Engineering, p.p. 13551360, 2004. ##Mahlooji, H.; Mehrizi, H.; Sedghi, N.; “An efficient, fast and portable random number generator”, Proc.35th International ##]
AN Improved UTD Based Model For The Multiple Building Diffraction Of Plane Waves In Urban Environments By Using Higher Order Diffraction Coeficients
AN Improved UTD Based Model For The Multiple Building Diffraction Of Plane Waves In Urban Environments By Using Higher Order Diffraction Coeficients
2
2
This paper describes an improved model for multiple building diffraction modeling based on the uniform theory of diffraction (UTD). A wellknown problem in conventional uniform theory of diffraction (CUTD) is multipleedge transition zone diffraction. Here, higher order diffracted fields are used in order to improve the result; hence, we use higher order diffraction coefficients to improve a hybrid physical optics (PO)CUTD model, the results show that the new model corrects errors of the POCUTD model. Therefore, the proposed model can find application in the development of theoretical models to predict more realistic path loss in urban environments when multiplebuilding diffraction is considered.
1
This paper describes an improved model for multiple building diffraction modeling based on the uniform theory of diffraction (UTD). A wellknown problem in conventional uniform theory of diffraction (CUTD) is multipleedge transition zone diffraction. Here, higher order diffracted fields are used in order to improve the result; hence, we use higher order diffraction coefficients to improve a hybrid physical optics (PO)CUTD model, the results show that the new model corrects errors of the POCUTD model. Therefore, the proposed model can find application in the development of theoretical models to predict more realistic path loss in urban environments when multiplebuilding diffraction is considered.
11
17
A.
Tajvidyi
A.
Tajvidyi
Corresponding Author, A. Tajvidy is with the Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Corresponding Author, A. Tajvidy is with
Iran
A.
Ghorbanii
A.
Ghorbanii
A. Ghorbani is with the Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran (email: ghorbani@aut.ac.ir).
A. Ghorbani is with the Department of Electrical
Iran
M.
Nasermoghaddasi
M.
Nasermoghaddasi
M. Nasermoghaddasi is with the Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran (email: mn.moghaddasi@srbiau.ac.ir).
M. Nasermoghaddasi is with the Department
Iran
Higher order diffraction coefficient
Multipleedge Diffraction
UTD
[[1] H. L. Bertoni,” Radio Propagation for Modern Wireless Systems” PrenticeHall, Englewood Cliffs, NJ, 2000. ##[2] COST 231, European Commission, “Digital mobile radio toward future generation systems” Brussels, Belgium, 1999. ##[3] W. Zhang, “Fast twodimensional diffraction modeling for sitespecific propagation prediction in urban microcellular environments,” IEEE Transaction o Antennas and Propagation, Vol. 49, No. 2, 428–436, Mar. 2000. ##[4] J. Wolfish, and H. L. Bertoni, “A theoretical model of UHF propagation in urban environments,” IEEE Transaction on Antennas and Propagation, Vol. 36, No.12, 1788–1796, Dec. 1988. ##[5] S. R. Saunders, and F. R. Bonar, “Explicit multiple building diffraction attenuation function for mobile radio wave propagation,” Electron. Letter, Vol. 27, No. 14, 1276–1277, Jul. 1991. ##[6] S. R. Saunders, and F. R. Bonar, “Prediction of mobile radio wave propagation over buildings of irregular heights and spacings,” IEEE Transaction on Antennas and Propagation, Vol. 42, No. 2, 137–144, Feb. 1994. ##[7] M. J. Neve, and G. B. Rowe, “Contributions toward the development of a UTDbased model for cellular radio propagation prediction,” Proc. IEE Microwave. Antennas Propagation, Vol. 141, No. 5, 407–414, Oct. 1994. ##[8] W. Zhang, “A more rigorous UTDbased expression for multiple diffractions by buildings,” Proc. IEE—Microwave. Antennas Propagation, vol. 142, no. 6, pp. 481–484, Dec. 1995. ##[9] W. Zhang, “A wideband propagation model based on UTD for cellular mobile radio communications“, IEEE Transactions on Antennas and Propagation, vol. 45, no. 11, pp. 1669–1678, Nov. 1997. ##[10] L. JuanLlácer and N. Cardona, “UTD solution for the multiple building diffraction attenuation unction for mobile radio wave propagation," Electron. Letters, vol. 33, no. 1, pp. 92–93, Jan. 1997. ##[11] A. Kara and E. Yazgan, “UTDbased propagation model for the path loss characteristics of cellular mobile communications system,” in Proc. IEEE Int.Symp. Antennas and Propagation Society, vol. 1, Orlando, FL, pp. 392–395, 1999. ##[12] C. Tzaras and S. R. Saunders, “An improved heuristic UTD solution for multipleedge transition zone diffraction,” IEEE Transactions on Antennas and Propagation., vol. 49, pp. 1678–1682, Dec. 2001. ##[13] L. JuanLlácer and J. L. Rodríguez, “A UTDPO solution for diffraction of plane waves by an array of perfectly conducting wedges,” IEEE Transactions on Antennas and Propagation, vol. 50, no. 9, pp. 1–5, Sep.2002. ##[14] R. Arablouei and A. Ghorbani, “A new UTDbased model for multiple diffractions by buildings,” in Proc. 3rd Int. Conf. Microwave and Milimeter Wave Technology, St. Petersburg, Russia, pp.484–488, Jun. 2002. ##[15] D. Erricolo, G. D’Elia, and P. L. E. Uslenghi, “Measurements on scaled models of urban environments and comparisons with raytracing propagation simulation,” IEEE Transactions on Antennas and Propagation, vol. 50, no. 5, pp.727–729, May 2002. ##[16] D. Erricolo, “Experimental validation of secondorder diffraction coefficients for computation of pathloss past buildings,” IEEE Transaction Electromagnet. Compact, vol. 44, no. 1, pp. 272–273, Feb. 2002. ##[17] J.V. Rodríguez, J.M. MolinaGarcíaPardo and L. Juan.Llácer, “An improved solution expressed in terms of UTD coefficients for the multiplebuilding diffraction of plane waves,” IEEE Antennas and Wireless Propagation Letters, vol. 4, 2005. ##[18] E. Torabi, , A. Ghorbani and H.R. Amindavar, “Modification of the UTD Model for Cellular Mobile Communication in an Urban Environment,” Electromagnetics, Vol. 27, 263285,Jun. 2007. ##[19] A. Tajvidy, and A. Ghorbani, “A New Uniform TheoryofDiffractionBased Model for the Multiple Building Diffraction of Spherical Waves in Microcell Environments,” Electromagnetics, Vol. 28, 375388, Jun. 2008. ##[20] E. Torabi, A. Ghorbani and A. Tajvidy, "A Modified Diffraction Coefficient for Imperfect Conducting Wedges and Buildings with Finite Dimensions" IEEE Transactions on Antennas And Propagation. Vol. 57, No. 4, 11971207, Apr. 2009. ##[21] R. G. Kouyoumjian and P. H. Pathak, "A uniform geometrical theory of diffraction for an edge in a perfectly conducting surface". Proc. IEEE. Vol. 62, 1448–1461, 1974. ##[22] M. F. Catedra and Jesus PerezArriaga, Cell Planning For Wireless Communication, Artech House, Inc., 1999, ch. 9. ##[23] P. D. Holm, "Calculation of Higher Order Diffracted Fields for MultipleEdge Transition Zone Diffraction", IEEE Transactions on Antennas and Propagation, Vol. 52, No. 5, pp. 13501355, MAY. 2004. ##[24] P. D. Holm, “A new heuristic UTD diffraction coefficient for no perfectly conducting wedge,” IEEE Transactions Antennas and Propagation, Vol. 48, No.8, 12111219, Aug. 2000. ##]
Genetic and Memetic Algorithms for Sequencing a New JIT MixedModel Assembly Line
Genetic and Memetic Algorithms for Sequencing a New JIT MixedModel Assembly Line
2
2
This paper presents a new mathematical programming model for the bicriteria mixedmodel assembly line balancing problem in a justintime (JIT) production system. There is a set of criteria to judge sequences of the product mix in terms of the effective utilization of the system. The primary goal of this model is to minimize the setup cost and the stoppage assembly line cost, simultaneously. Because of its complexity to be optimally solved in a reasonable time, we propose and develop two evolutionary metaheuristics based on a genetic algorithm (GA) and a memetic algorithm (MA). The proposed heuristics are evaluated by the use of random iterations, and the related results obtained confirm their efficiency and effectiveness in order to provide good solutions for medium and largescale problems.
1
This paper presents a new mathematical programming model for the bicriteria mixedmodel assembly line balancing problem in a justintime (JIT) production system. There is a set of criteria to judge sequences of the product mix in terms of the effective utilization of the system. The primary goal of this model is to minimize the setup cost and the stoppage assembly line cost, simultaneously. Because of its complexity to be optimally solved in a reasonable time, we propose and develop two evolutionary metaheuristics based on a genetic algorithm (GA) and a memetic algorithm (MA). The proposed heuristics are evaluated by the use of random iterations, and the related results obtained confirm their efficiency and effectiveness in order to provide good solutions for medium and largescale problems.
17
28
R.
TavakkoliMoghaddam
R.
TavakkoliMoghaddam
Corresponding Author, R. TavakkoliMoghaddam is a professor in Department of Industrial Engineering, College of Engineering, University
of Tehran, Tehran, Iran (email: tavakoli@ut.ac.ir)
Corresponding Author, R. TavakkoliMoghaddam
Iran
Y.
GholipourKanani
Y.
GholipourKanani
Y. GholipourKanani is a faculty member in Department of Management, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
(email: gholipourkanani@yahoo.com)
Y. GholipourKanani is a faculty member in
Iran
R.
Cheraghalizadeh
R.
Cheraghalizadeh
R. Cheraghalizadeh has received her M.Sc. degree from Mazandaran University of Science & Technology, Babol, Iran
(email: r_cheraghalizadeh@yahoo.com)
R. Cheraghalizadeh has received her M.Sc.
Iran
JIT mixedmodel assembly line balancing
Setup cost
Stoppage cost
Genetic Algorithm
Memetic algorithm
[[1] P. M. Vilarinho and A.S. Simaria, “A twostage heuristic method for balancing mixedmodel assembly lines with parallel workstations”, International Journal of Production Research, Vol. 40, pp. 14051420, 2002. ##[2] Y. Zhang and P.B. Luh, K.Yoneda, T. Kano and Y. Kyoya, “MixedModel Assembly Line Scheduling Using the Lagrangian Relaxation Technique”, Institute of Industrial Engineering, Vol. 32, 125134, 2000. ##[3] N. V. Hop, “A heuristic solution for fuzzy mixedmodel line balancing problem”, European Journal of Operational Research , Vol. 168 (3), pp. 798–810, 2006. ##[4] Y. Monden, Toyota Production System, second Ed. The Institute of Industrial Engineers, Norcross, GA, 1993. ##[5] J. Miltenburg, “Level schedules for mixedmodel assembly lines in justintime production systems”, Management Science, Vol. 35, pp. 192–207, 1989. ##[6] J. Miltenburg, G. Steiner and S. Yeomans, “A dynamic programming algorithm for scheduling mixedmodel justintime production systems”, Mathematical Computation Modeling, Vol. 13, pp. 57–66, 1990. ##[7] P.R. Inman and R. L. Bulfin, “Note on sequencing JIT mixedmodel assembly lines”, Management Science, Vol. 37, pp. 904–910, 1991. ##[8] C.A. Yano, and R. Rachamadugu, “Sequencing to minimize work overload in assembly lines with product options”, Management Science, Vol. 37, pp. 572–586, 1991. ##[9] J.F. Bard, E.M. DarEl, and A. Shtub, “An analytic framework for sequencing mixed model”, International Journal of Production Research, Vol. 30, pp. 35–48, 1992. ##[10] K. Okamura, and H. Yamshina, “A heuristic algorithm for the assembly line modelmix sequencing problem to minimize the risk of stopping the conveyor”, International Journal of Production Research, Vol. 17, 233–247, 1979. ##[11] I. Baybars, “A survey of exact algorithms for the simple assembly line balancing problem”, Management Science, Vol. 2, pp. 909–932, 1986. ##[12] S. Ghosh, and R. J. Gagnon, “A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems”. International Journal of Production Research, Vol. 27, pp. 637670, 1989. ##[13] A. Scholl, Balancing and sequencing of assembly lines, Physica, Heidelberg, second Ed., 1999. ##[14] Fokkert, J.I.V.Z.D. and de Kok, T.G., “The mixed and multimodel line balancing problem: A comparison”, European Journal of Operational Research, Vol. 100, pp. 399–412, 1997. ##[15] W.B. Helgeson, and D.P. Birnie, “Assembly line balancing using the ranked positional weight technique”, Journal of Industrial Engineering, Vol. 12, pp. 394–398, 1961. ##[16] A.L. Gutjahr and G.L. Nemhauser, “An algorithm for the line balancing problem”, Management Science, Vol. 11, pp. 308–315, 1964. ##[17] E. M. Mansoor, “Assembly Line Balancing – An Improvement on the Ranked Positional Weight Technique”, Journal of Industrial Engineering, Vol. 15, pp. 7378, 1964. ##[18] A. Kabir, and M. Tabucanon, “BatchModel Assembly Line Balancing: A Multi Attribute Decision Making Approach”, Int. J. of Production Economics, Vol. 41, pp. 193201, 1995. ##[19] H. Gokcen and E. Erel, “A Goal Programming Approach to MixedModel Assembly Line Balancing Problem”, Int. J. of Production Economics, Vol. 48, pp. 177185, 1997. ##[20] H. Gokcen and E. Erel, “Binary integer formulation for mixedmodel assembly line balancing problem”, Computers and Industrial Engineering, Vol. 34, pp. 451–461, 1998. ##[21] E. Erel and H. Gokcen, “Shortestroute formulation of mixedmodel assembly line balancing problem”, European Journal of Operational Research, Vol. 116, pp. 194–204, 1999. ##[22] R.F. Deckro and S. Rangachari, “A goal approach to assembly line balancing”, Computers and Operations Research, Vol. 17, pp. 509–521, 1990. ##[23] A.L. Gutjahr and G.L. Nemhauser, “An algorithm for the line balancing problem”, Management Science, Vol. 11, 1964, pp. 308– 315, 1964. ##[24] S.D. Roberts and C.D. Villa, “On a multiproduct assembly linebalancing problem”, AIIE Transactions, Vol. 2, pp. 361–364, 1970. ##[25] R. TavakkoliMoghaddam, G. Moslehi, M. Vasei and A. Azaron, “Optimal scheduling for a single machine to minimize the sum of maximum earliness and tardiness considering idle insert”, Applied Mathematics and Computation, Vol. 167, pp. 1430–1450, 2005. ##[26] R. TavakkoliMoghaddam, G. Moslehi, M. Vasei and A. Azaron, “A branchandbound algorithm for a single machine sequencing to minimize the sum of maximum earliness and tardiness with idle insert”, Applied Mathematics and Computation, Vol. 17, pp. 388–408, 2006. ##[27] J.F. Bard, A. Shtub and S.B. Joshi, “Sequencing mixedmodel assembly lines to level parts usage and minimize the length”, International Journal of Production Research, Vol. 32, pp. 2431–2454, 1994. ##[28] C.J. Hyun, Y. Kim and Y.K. Kim, “A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines”, Computers and Operations Research, Vol. 25, pp. 675–690, 1998. ##[29] T. Korkmazel and S. Meral, “Bicriteria sequencing methods for the mixedmodel assembly line in justintime production systems”, European Journal of Operational Research, Vol. 131, pp. 188–207, 2001. ##[30] Y. Monden, Toyota Production System, Institute of Industrial Engineers Press, Atlanta, 1983. ##[31] P.R. McMullen and G.V. Frazier, “A simulated annealing approach to mixedmodel sequencing with multiple objectives on a JIT line”, IIE Transactions, Vol. 3, pp. 679–686, 2000. ##[32] P.R. McMullen, “JIT sequencing for mixedmodel assembly lines with setups using tabu search”, Production Planning and Control, Vol. 9, pp. 504–510, 1998. ##[33] P.R. McMullen, “An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics”, Computers and Industrial Engineering, Vol. 41, pp.335–353, 2001. ##[34] P.R. McMullen, “A Kohonen selforganizing map approach to addressing a multiple objective, mixedmodel JIT sequencing problem”, International Journal of Production Economics, Vol. 72, pp. 59–71, 2001. ##[35] P.R. McMullen, “An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives”, Artificial Intelligence in Engineering, Vol. 15, pp. 309–317, 2001. ##[36] S.A. Mansouri, “A multiobjective genetic algorithm for mixedmodel sequencing on JIT assembly lines”, European Journal of Operational Research, Vol. 167, pp. 696–716, 2005. ##[37] R. TavakkoliMoghaddam, N. Safaei and M. Babakhani, “Solving a dynamic cell formation problem with machine cost and alternative process plan by memetic algorithms”, in: O.B. Lupanov, O.M. KasimZade, A.V. Chaskin, K. Steinhofel (Eds.), Stochastic Algorithms: Foundation and Applications, Lecture Notes in Computer Science, SpringerVerlag, Berlin, vol. 3777, ## pp. 213–227, 2005. ##[38] S. Emde and N. Boysen, “Optimally routing and scheduling tow trains for JITsupply of mixedmodel assembly lines”, European Journal of Operational Research, Vol. 217, 287–299, 2012. ##[39] S. Emde and N. Boysen, “Optimally locating inhouse logistics areas to facilitate JITsupply of mixedmodel assembly lines”, International Journal of Production Economics, Vo. 135, 393–402, 2012. ##[40] Q.Y. Dong, J. Lu, and Y. Gui, “Integrated Optimization of Production Planning and Scheduling in Mixed Model Assembly Line”, Procedia Engineering, Vol. 29, 3340–3347, 2012. ##[41] A. Hamzadayi and G. Yildiz, “A genetic algorithm based approach for simultaneously balancing and sequencing of mixedmodel Ulines with parallel workstations and zoning constraints”, Computers & Industrial Engineering Volume 62, 206– 215, 2012. ##[42] X. Zenga, W. K. Wonga and S. Y. Leung, “An operator allocation optimization model for balancing control of the hybrid assembly lines using Pareto utility discrete differential evolution algorithm”, Computers & Operations Research, Vol. 39, 1145–1159, 2012. ##[43] J. Bautista, A. Cano and R. Alfaro, “Modeling and solving a variant of the mixedmodel sequencing problem with work overload minimization and regularity constraints. An application in Nissan’s Barcelona Plant”, Expert Systems with Applications, Available online 14 March 2012. ##[44] N. Boysena and S. Bock, “Scheduling justintime part supply for mixedmodel assembly lines”, European Journal of Operational Research, Vol. 211, 1525, 2011. ##[45] Q. Zhenga,Y. Lia and M. Li, “Assembly Line Balancing Model Based on Ant Colony Optimization Algorithm”, Energy Procedia, Vol. 13, 5366–5372, 2011. ##[46] S. Akpınar and G. M. Bayhan, “A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints”, Engineering Applications of Artificial Intelligence, Vol. 24, 449–457, 2011. ##[47] S.J. Hua, J. Kob, L. Weyandc, H.A. ElMaraghyd, T.K. Liene, Y. Korena, H. Bleyc, G. Chryssolourisf, N. Nasrg and M. Shpitalnih, “Assembly system design and operations for product variety”, CIRP Annals  Manufacturing Technology, Vol. 60, 715–733, 2011. ##[48] U, Özcan, “Balancing stochastic twosided assembly lines: A chanceconstrained, piecewiselinear, mixed integer program and a simulated annealing algorithm”, European Journal of Operational Research, Vol. 205, 81–97, 2010. ##[49] V. Giard and J. Jeunet, “Optimal sequencing of mixed models with sequencedependent setups and utility workers on an assembly line”, International Journal of Production Economics, Vol. 123, 290–300, 2010. ##[50] L. Yang and X. Zhang, “Design and Application of Kanban Control System in a MultiStage, MixedModel Assembly Line”, Systems Engineering  Theory & Practice, Vol. 29, 6472, 2009. ##[51] N. Boysena, M. Fliednerb and A. Scholl, “The product rate variation problem and its relevance in real world mixedmodel assembly lines”, European Journal of Operational Research, Vol. 197, 818–824, 2009. ##[52] J.F. Bard, E.M. DarEl and A. Shtub, , “An analytic framework for sequencing mixed model”, International Journal of Production Research, Vol. 30, pp. 35–48, 1992. ##[53] C.J. Hyun, Y. Kim and Y.K. Kim, “A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines”, Computers and Operations Research, Vol. 25, pp. 675–690, 1998. ##[54] T. Hoffmann, “Eureka: A hybrid system for assembly line balancing”, Int. J. of Management Science, Vol. 38, pp. 3947, 1992. ##[55] B.B. Malakooti, “Assembly line balancing with buffers by multiple criteria optimization”, Int. J. of Production Research, Vol. 32, ## pp. 21592178, 1994. ##[56] P.P Sonekar, S.M. Sindhi, J.V.L. Venkatesh, B.M. Dabade and S.P. Kallurkar, “A multiple criterion heuristic software for the practical assembly line balancing problem”, Stochastic Models Optimization Techniques and Computer Applications, pp. 303313, 1994. ##[57] F. Boctor, “A multiplerule heuristic for assembly line balancing”, Int. J. of Operational Research Society, Vol. 46, pp. 6269, 1995. ##[58] A. Enmer, J. Favrel and J. Gauthie, “Balancing an assembly line for industrial truck engines”, Proceedings for IFAC Intelligent Manufacturing System, Bucharest, Romania, pp. 163165, 1995. ##[59] R. Roy and M.J. Allchurch, “Development of a knowledgebased system for balancing complex mixed model assembly lines”, International Journal of Computer Integrated Manufacturing, Vol. 9, pp. 205216, 1996. ##[60] K. Oh, “Expert line balancing system (ELBS)”, Computer & Industry Engineering, Vol. 33, pp. 303306, 1997. ##[61] A. Kumar and B. Malakooti, “A knowledgebased system for solving multiobjective assembly line balancing problems”, International Journal of Production Research, Vol. 34, pp. 25332552, 1996. ##[62] B. Azinze and F. Partovi, “A knowledge based method for designing precedence networks and performing job allocation in line balancing”, Compute Industry Engineering, Vol. 18, pp. 351364, 1990. ##[63] K. Sudhir and K. Rajagopalan, “An artificial approach to precedence network generation for assembly line balancing”, Computers in Industry, Vol. 18, pp. 177191, 1992. ##[64] R. TavakkoliMoghaddam, Y. GholipourKanani, and R. Cheraghalizadeh, “A genetic algorithm and memetic algorithm to sequencing and scheduling of cellular manufacturing systems”, International Journal of Management Science and Engineering Management, Vol. 3, pp. 119130, 2008. ##[65] P. Moscato, On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms, Technical Report C3P 826. Caltech Concurrent Computation Program, California Institute of Technology, Pasadena: CA, 1989. ##[66] P. Moscato, “Memetic algorithms: A short introduction, In: D. Corne, M. Dorigo, F. Glover (Eds.), “New ideas in optimization”, McGrawHill, London, pp. 219213, 1999. ##[67] A. S. Mendes, F. M. Muller, a.P.M. Franc and P. Moscato, Comparing metaheuristic approaches for parallel machine scheduling problems with sequencedependent setup times, Proceedings of the 15th Int. Conf. on CAD/CAM Robotics and Factories of the Future, A ` guas de Lindo` ia, SP, Brazil, 1999. ##[68] D.E. Goldberg, Genetic algorithms in search, optimization and machines learning, AddisonWesley, Reading, MA, 1989. ##[69] J. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, 1975. ##[70] J. Knowles and D. Corne, “The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimization”, The Proc. of CEC’99, 98–105, 1999. ##]
A Mushy State Simulated Annealing
A Mushy State Simulated Annealing
2
2
It is a long time that the Simulated Annealing (SA) procedure is introduced as a modelfree optimization for solving NPhard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules starts or the time schedule itself. There are several parameters in the process of annealing, the adjustment of which affects the overall performance. This paper focuses on the importance of initial temperature and then proposes a lower temperature with low energy to speed up the process, using an auxiliary memory to buffer the best solution. Such an annealing indeed starts from a “mushy state” rather than a quite liquid molten material. The mushy state characteristics depends on the problems that SA is being applied to solve for. In this paper, the Mushy State Simulated Annealing (MSSA) is fully developed and then applied to the popular Traveling Salesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of a Wise Traveling Salesman, who starts from a randomly chosen city and seeks for the nearest one as the next, is also applied to initiate SA by a lowenergy, lowtemperature state. This fast method results in quite accurate solutions compared to the methods recently cited in the literature.
1
It is a long time that the Simulated Annealing (SA) procedure is introduced as a modelfree optimization for solving NPhard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules starts or the time schedule itself. There are several parameters in the process of annealing, the adjustment of which affects the overall performance. This paper focuses on the importance of initial temperature and then proposes a lower temperature with low energy to speed up the process, using an auxiliary memory to buffer the best solution. Such an annealing indeed starts from a “mushy state” rather than a quite liquid molten material. The mushy state characteristics depends on the problems that SA is being applied to solve for. In this paper, the Mushy State Simulated Annealing (MSSA) is fully developed and then applied to the popular Traveling Salesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of a Wise Traveling Salesman, who starts from a randomly chosen city and seeks for the nearest one as the next, is also applied to initiate SA by a lowenergy, lowtemperature state. This fast method results in quite accurate solutions compared to the methods recently cited in the literature.
1
8
حامد
شکوری گنجوی
Hamed
Shakouri G.
Corresponding Author,S.T. Rizvi is a PhD Student in School of Astronautics, Beijing University of Aeronautics and Astronautics, 37XueYuan
Road, 100191 Beijing, China. (rizvi.aeng@gmail.com)
Corresponding Author,S.T. Rizvi is a PhD
Iran
h.shakouri@gmail.com


Kambiz
Shojaee
Iran
k.shojaee@ece.ut.ac.ir


Mohammad B.
Menhaj
Iran
mbmenhaj@yahoo.com
Combinatorial Optimization
Traveling Salesman Problem
Simulated Annealing
Initial Condition
Optimal Trajectory Study of a Small Size Waverider and WingBody Reentry Vehicle at Suborbital Entry Speed of Approximately 4 km/s with Dynamic Pressure and Heat Rate Constraint
Optimal Trajectory Study of a Small Size Waverider and WingBody Reentry Vehicle at Suborbital Entry Speed of Approximately 4 km/s with Dynamic Pressure and Heat Rate Constraint
2
2
A numerical trajectory optimization study of two types of liftingentry reentry vehicle has been presented at low suborbital speed of 4.113 km/s and 15 degree entry angle. These orbital speeds are typical of medium range ballistic missile with ballistic range of approximately 2000 km at optimum burnout angle of approximately 41 degree for maximum ballistic range. A lifting reentry greatly enhances the reentry range which leads to a higher overall range of approximately 3000 km for the same ΔV. The optimum reentry angle of lifting reentry vehicle for medium range missiles under constrained gload lies between 15 to 20 degree for limited gload trajectories. These entry angles result in high decent rates and the vehicle quickly approaches the heat rate boundary. The heat rate problem is more severe for small size vehicle because of small noseradius. Limiting the heat rate restricts the trajectory and lowers the downrange/crossrange performance of the reentry vehicle. A wingbody reentry vehicle has a larger nose radius as compared to a waverider which results in comparatively low heat rates during flight. This type of a vehicle has lower lifttodrag ratio and therefore lesser range in comparison to a waverider type design. The performance of the two vehicle types is studied at various heat rate limits with the objective to calculate the optimum control deflections that would maximize the cross range. The results provide performance of the two designs visàvis maximum heat rate constraint at the stagnation point along with the required control history. General pseudospectral optimal control software, GPOPS has been used for the optimal trajectory studies.
1
A numerical trajectory optimization study of two types of liftingentry reentry vehicle has been presented at low suborbital speed of 4.113 km/s and 15 degree entry angle. These orbital speeds are typical of medium range ballistic missile with ballistic range of approximately 2000 km at optimum burnout angle of approximately 41 degree for maximum ballistic range. A lifting reentry greatly enhances the reentry range which leads to a higher overall range of approximately 3000 km for the same ΔV. The optimum reentry angle of lifting reentry vehicle for medium range missiles under constrained gload lies between 15 to 20 degree for limited gload trajectories. These entry angles result in high decent rates and the vehicle quickly approaches the heat rate boundary. The heat rate problem is more severe for small size vehicle because of small noseradius. Limiting the heat rate restricts the trajectory and lowers the downrange/crossrange performance of the reentry vehicle. A wingbody reentry vehicle has a larger nose radius as compared to a waverider which results in comparatively low heat rates during flight. This type of a vehicle has lower lifttodrag ratio and therefore lesser range in comparison to a waverider type design. The performance of the two vehicle types is studied at various heat rate limits with the objective to calculate the optimum control deflections that would maximize the cross range. The results provide performance of the two designs visàvis maximum heat rate constraint at the stagnation point along with the required control history. General pseudospectral optimal control software, GPOPS has been used for the optimal trajectory studies.
29
36


S.
Tauqeer ul Islam Rizvi
Iran


He
Linshu
Iran


Tawfiqur
Rahman
Iran
tawfiqurrahman@hotmail.com
trajectory optimization
Optimal control
Reentry Guidance
Lifting Reentry
Conceptual design
Ballistic Missiles
Radau Pseudospectral Method
[[1] C. E. Crockrell, L. D. Huebner, and D. B. Finley, "Aerodynamic Performance and Flow Field Characteristics of two waverider Derived Hypersonic Cruise Vehicles " in AIAA 33rd Aerospace Science Meeting and Exhibit Reno, NV: AIAA Paper 950736, 1995. ##[2] T. H. Phillips, "A Common Aero Vehicle (CAV) Model, Description, and Employment Guide," Schafer Corporation for AFRL & AFSPC 2003. ##[3] S. A. Whitmore and B. J. Dunbar, "Orbital Space Plane: Past, Present, and Future," in IAA/ ICAS International Air and Space Symposium and Exposition, Dayton, Ohio, 2003. ##[4] "X41 Common Aero Vehicle," GlobalSecurity.org, 2010. ##[5] M. S. ParishII, "Optmality of Aeroassisted Orbital Plane Changes," in Naval Postgraduate School. vol. Maters of Science Thesis Monterey, CA, 1995, p. 110. ##[6] W. E. Bornemann and T. E. Surber, "Aerodynamic Design of the Space Shuttle Orbiter." ##[7] T. E. Surber and D. C. Oslen, "Shuttle Orbiter Aerodynamic Development," Journal of Spacecraft, vol. 15, pp. 4047, 1978. ##[8] S. A. Whitmore, Daniel W. Banks, B. M. Andersen, and P. R. Jolley, "DriectEntry, Aerobraking, and Lifting Aerocapture for Human Rated Lunar Return Vehicles," in 44th AIAA Aerospace Science Meeting and Exhibit, Reno, Nevada, 2006, p. 29. ##[9] F. Zimmermann and A. J. Calise, "Numerical Optimization Study of Aeroassisted Orbital Transfer," Journal of Guidance Control and Dynamics, vol. 21, pp. 127133, JanuaryFeburary 1998. ##[10] D. G. Hull and J. L. Speyer, "Optimal reentry and Plane Change Trajectories," The Journal of Astronautical Sciences, vol. 30, pp. 117130, 1982. ##[11] S. T. Rizvi and L. He, "Optimal Performance Study of WingBody Reentry Vehicle for Medium to Intermediate Range Ballistic Missile Applications," Beijing University of Aeronautics and Astronautics, Beijing, China., 2012. ##[12] J. John D. Anderson, "Hypersonic and High Temperature Gas Dynamics," in Hypersonic and High Temperature Gas Dynamics: McGrawHill Book Company, 1989, pp. 291292. ##[13] J. A. Love and L. W. Neustadt, "A simple reentry guidance system," Guidance and Control, p. 49, 1963. ##[14] C. D. Scot, R. C. Ried, R. J. Maraia, C. P. Li, and S. M. Derri, "An AOTV Aeroheating and Thermal Protection Study," in Thermal Design of Aeroassisted Orbital Transfer Vehicles. vol. 96, F. Nelson, Ed. New York: AIAA, 1985. ##[15] E. V. Zoby, K. P. Lee, R. N. Gupta, and R. A. Thompson, "Nonequilibrium Viscous Shock Layers Solutions for Hypersonic Flow Over Slender Bodies," in Eighth National Aero Space Plane Technology Symposium, Monterey, CA, Mar, 1990. ##[16] J. J. Bertin, "Hypersonic Aerothermodynamics," in Hypersonic Aerothermodynamics Washington, DC: AIAA Education Series, 1994, pp. 257262. ##[17] "Launch Vehicle Design," H. Linshu, Ed.: BUAA Press, 2004, pp. 8087. ##[18] C. L. Darby and A. V. Rao, "MinimumFuel LowEarthOrbit Aeroassisted Orbital Transfer of Small Spacecraft," Journal of Spacecraft and Rockets, vol. 48, Jul.Aug., 2011. ##[19] A. V. Rao, "User's Manual for GPOPS Version 4.0," August, 2011.##]
Modeling of Jitter Characteristics for the Second Order BangBang CDR
Modeling of Jitter Characteristics for the Second Order BangBang CDR
2
2
BangBang clock and data recovery (BBCDR) circuits are hard nonlinear systems due to the nonlinearity introduced by the binary phase detector (BPD). The specification of the CDR frequency response is determined by jitter tolerance and jitter transfer. In this paper, jitter transfer and jitter tolerance of the secondorder BBCDR are characterized by formulating the time domain waveforms. As a result, a new equation is presented to obtain corner frequency. Also, the jitter tolerance is expressed in closed form as a function of loop parameters. The proposed method is general enough to be used for designing BBCDR. The analysis is verified using behavioral simulations in MATLAB. Simulation results demonstrate the validity of the result obtained by analytical equations.
1
BangBang clock and data recovery (BBCDR) circuits are hard nonlinear systems due to the nonlinearity introduced by the binary phase detector (BPD). The specification of the CDR frequency response is determined by jitter tolerance and jitter transfer. In this paper, jitter transfer and jitter tolerance of the secondorder BBCDR are characterized by formulating the time domain waveforms. As a result, a new equation is presented to obtain corner frequency. Also, the jitter tolerance is expressed in closed form as a function of loop parameters. The proposed method is general enough to be used for designing BBCDR. The analysis is verified using behavioral simulations in MATLAB. Simulation results demonstrate the validity of the result obtained by analytical equations.
37
45


Habib
Adrangi
Iran


Hossein
Miar Naimi
Iran
Clock and Data Recovery (CDR)
BangBang Phase Detector (BPD)
Jitter Transfer and Jitter Tolerance
[[1] J. K. Kim, J. Kim, G. Kim and D. K. Jeong, “A fully integrated 0.13μm CMOS 40Gb/s serial link transceiver,” IEEE J. SolidState Circuits., vol. 44, no. 5, pp. 15101521, May. 2009. ##[2] N. Da Dalt, E. Thaller, P. Gregorius and L. Gazsi., “A compact tripleband lowjitter digital LC PLL with programmable coil in 130nm CMOS,” IEEE J. SolidState Circuits., vol. 40, no. 7, pp. 14821490, Jul. 2005. ##[3] Y. S. Seo, J. W. Lee, H. J. Kim, Ch. Yoo, J. J. Lee and Ch. S. Jeong, “A 5gbit/s clock and data recovery circuit with 1/8rate linear phase detector in 0.18μm CMOS technology,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 56, no. 1, pp. 610, Jan. 2009. ##[4] B. Razavi, “Challenges in the design of highspeed clock and data recovery circuits,” IEEE Commun. Mag.,vol 40, no. 8, pp. 94101, Aug. 2002. ##[5] N. Da Dalt, “Markov chainsbased derivation of the phase detector gain in bangbang PLLs,” IEEE Trans. Circuits and Syst. II, Exp. Briefs, vol. 53, no. 11, pp. 1195–1199, Nov. 2006. ##[6] B. Chun and M. P. Kennedy, “Statistical properties of firstorder bangbang PLL with nonzero loop delay,” IEEE Trans. Circuits and Syst. II, Exp. Briefs, vol. 55, no. 10, pp. 1016–1020, Oct. 2008. ##[7] S. Tertinek, J. P. Gleeson, and O. Feely, “Statistical analysis of firstorder bangbang phaselocked loops using signdependent random walk theory,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 57, no. 9, pp. 2367–2380, Sep. 2010. ##[8] S. Cheng, H. Tong, J. SilvaMartinez and A. I. Karsilayan, “Steadystate analysis of phaselocked loops using binary phase detector,” IEEE Trans. Circuits and Syst. II., vol.54, no.6, pp. 474478, Jun. 2007. ##[9] R. C. Walker, “Designing bangbang PLLs for clock and data recovery in serial data transmission systems” in Phaselocking in High Performance Systems, B. Razavi, Ed. Piscataway, NJ: IEEE Press, pp. 34–45, 2003.##]
Analysis of Vector Estimating Modulation Method to Eliminate Common Mode Voltage
Analysis of Vector Estimating Modulation Method to Eliminate Common Mode Voltage
2
2
Abstract The problem of common mode voltage in inverters can be considered as a major issue which leads to motor bearing failures. To eliminate these voltages, proposing some methods seems to be necessary. This paper has a comparative study on estimating modulation methods of eliminating common mode voltage. The main idea of these methods is based on generation of reference vector with nearest vector/ vectors with zero common mode voltage. Depending on the number of delivering nearest vectors, there are two estimating methods. For the reference method, reference vector is synthesized only by the nearest vector. But for the proposed method, the reference vector is synthesized by more than one vector. Dwell time calculations of these vectors are based on the distance between the aforementioned vectors and the reference vector. In this paper, some characteristics such as linear relationships among output voltage and modulation index, and also total harmonic distortion of output voltage and stator current are considered. Finally, it is concluded that the new method has more advantages such as more linear relationships and lower THD of current with respect to the reference method.
1
Abstract The problem of common mode voltage in inverters can be considered as a major issue which leads to motor bearing failures. To eliminate these voltages, proposing some methods seems to be necessary. This paper has a comparative study on estimating modulation methods of eliminating common mode voltage. The main idea of these methods is based on generation of reference vector with nearest vector/ vectors with zero common mode voltage. Depending on the number of delivering nearest vectors, there are two estimating methods. For the reference method, reference vector is synthesized only by the nearest vector. But for the proposed method, the reference vector is synthesized by more than one vector. Dwell time calculations of these vectors are based on the distance between the aforementioned vectors and the reference vector. In this paper, some characteristics such as linear relationships among output voltage and modulation index, and also total harmonic distortion of output voltage and stator current are considered. Finally, it is concluded that the new method has more advantages such as more linear relationships and lower THD of current with respect to the reference method.
47
53


N.
Rashidiradi
Iran


A.
Rahmati
Iran


A.
Abrishamifar
Iran
Common Mode Voltage
Modulation
Harmonic Distortion
Modulation Index
[Periodicals: ##[1] Jose Rodriguez, Jorge Pontt, Pablo Correa, Patricio Cortes, Cesar Silva, “A New Modulation Method to Reduce CommonMode Voltages in Multilevel Inverters”, IEEE TRANSACTIONS ON INDUSTRY ELECTRONICS, VOL.51, NO.4, AUGUST 2004. ##[2] Shaotang Chen, Thomas A.lipo, Dennis Fitzgerald, “Modeling of Motor Bearing Currents in PWM Inverter Drives”,IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 32, NO. 6, NOVEMBER/ DECEMBER 1996. ##[3] A. Muetze and A. Binder, “Don’t lose your bearings—Mitigation techniques for bearing currents in invertersupplied drive systems,” IEEE Ind. Appl. Mag., VOL. 12, NO. 4, JULY/AUGUST 2006. ##[4] Haoran Zhang, Annette von Jouanne, Shaoan Dai, Alan K.Wallace, Fei Wang, “Multilevel Inverter Modulation Schemes to Eliminate Common‐Mode Voltages”, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 36, NO. 6, NOVEMBER /DECEMBER 2000. ##[5] Wenix Yao, Haibing Hu, Zhengyu Lu, “ Comparsions of Spacevector Modulation and CarrierBased Modulation of Multilevel Inverter”, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 23, NO. 1, JANURAY 2008. ##[6] Nasim Rashidirad, Abdolreza Rahmati, Adib Abrishamifar, “Comparison of Reliability in Modular Multilevel Inverters”, Electrical Review journal (PRZEGLAD ELEKTROTECHNICZNY), ISSN: 00332097, 2012 ##[7] Nasim Rashidirad, Abdolreza Rahmati, Adib Abrishamifar, “A Novel Scheme to Eliminate Common Mode Voltage in Multilevel Inverters”, International Journal of Scientific and Engineering Research, Jun 2011. ##Papers from Conference Proceedings (Published): ##[8] N. Rashidi‐rad, A. Rahmati and A. Abrishamifar, “A New Modulation Method to Eliminate Common Mode Voltages in Modular Multilevel Inverters”, in Proc. 2011 IEEE International Conference on Information and Industrial Electronics, pp. 113116. ##Dissertations: ##[9] Maryam Saeedifard, “Space Vector Modulation of MultiLevel and MultiModule Converters for High Power Applications”, Ph.D. dissertation, Dept. Elec. Eng., Univ. Toronto, 2008.##]
A Hybrid Framework for Building an Efficient Incremental Intrusion Detection System
A Hybrid Framework for Building an Efficient Incremental Intrusion Detection System
2
2
In this paper, a boostingbased incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As the framework has low computational complexity, it is suitable for realtime or online learning. We use incremental centroidbased “online kMean” clustering algorithm to implement anomaly detection system. Experimental evaluations on KDD Cup dataset have shown that the proposed framework has high clustering quality, relatively low computational complexity and fast convergence.
1
In this paper, a boostingbased incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As the framework has low computational complexity, it is suitable for realtime or online learning. We use incremental centroidbased “online kMean” clustering algorithm to implement anomaly detection system. Experimental evaluations on KDD Cup dataset have shown that the proposed framework has high clustering quality, relatively low computational complexity and fast convergence.
55
67


Amin
Rasoulifard
Iran


Abbas
Ghaemi Bafghi
Iran
Intrusion Detection System
Neural Network
Incremental Learning
Boosting Ensemble Learning
clustering
Weak Classifiers
Weak Learner
[[1] D. Anderson, T. Frivold, and A. Valdes, "NextGeneration Intrusion Detection Expert System", (NIDES)A Summary, Technical Report SRICLS9507, SRI, May 1995. ##[2] D. Barbarra, J. Couto, S. Jajodia, L. Popyack, and N. Wu, “ADAM: Detecting Intrusion by Data Mining”, Proceedings of the 2001 IEEE, Workshop on Information Assurance and Security T1A3 1100 United States Military Academy, West Point, NY, June 2001. ##[3] C. Amza, C.Leordeanu, V. Cristea, "Hybrid network Intrusion Detection ", IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 2011, Page(s): 503 – 510.. ##[4] DARPA Intrusion detection evaluation: http://www.ll.mit.edu/SSt/ideval/result/result_index.html. ##[5] O. Depren, M. Topallar, E. Anarim, and M. K. Ciliz, "An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks", Expert Systems with Applications Volume 29, Issue 4, Pages 713722, , November 2005. ##[6] E. Eleazar, "Anomaly Detection over Noisy Data using Learned Probability Distributions'', ICML00, Palo Alto, CA: July, 2000. ##[7] Y. Freund and R. Schapire, “A decision theoretic generalization of online learning and an application to boosting”, Comput. Syst. Sci., vol. 57, no. 1, pp. 119–139, 1997. ##[8] G. Giacinto, F. Roli, and L. Didaci, "Fusion of multiple classifiers for intrusion detection in computer networks", Pattern Recognition Letters, 24(12), pp. 17951803, 2003. ##[9] R. Heady, G. Luger, A. Maccabe, and M. Servilla. "The architecture of a network level intrusion detection system", Technical Report CS9020, Department of Computer Science, University of New Mexico, August 1990. ##[10] W. Hu and W. Hu, "Networkbased Intrusion Detection Using Adaboost Algorithm", Proceedings of the 2005 IEEE/WIC/ACM International conference on Web Intelligence(WI'05), 076952415X/05, 2005. ##[11] K. Hwang, M. Cai, Y. Chen, and M. Qin, "Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes", IEEE Transaction on Dependable and Secure Computing , Vol. 4, No. 1, pp. 4155, JanuaryMarch 2007. ##[12] KDD Cup 1999 Intrusion detection dataset, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. ##[13] K. Leung and C. Leckie, “Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters”, Australasian Computer Science Conference, Newcastle, NSW, Australia, 2005. ##[14] P. Lichodzijewski, A.N. ZincirHeywood, and M. I. Heywood, “Hostbased intrusion detection using selforganizing maps,” Proceedings of the 2002 IEEE World Congress on Computational Intelligence, 2002. ##[15] U. Lindqvist and P.A. Porras, "Detecting computer and network misuse through the productionbased expert system toolset (PBEST)", Proceedings of the 1999 IEEE symposium on security and privacy, pp. 146161, IEEE Computer Socitey, Los Alamitos, CA., 1999. ##[16] N. Littlestone and M.Warmuth, “Weighted majority algorithm”, Inform. Comput. vol. 108, pp. 212–261, 1994. ##[17] M. Locasto, K. Wang, A. Keromytis, and S. Stolfo. Flips: Hybrid adaptive intrusion prevention. In Proceedings of the 8th International Symposium on Recent Advances in Intrusion Detection (RAID), September 2005. ##[18] Mounji, B.L. Charlier, D. Zampuniéris, and N. Habra, "Distributed audit trail analysis:, Proceedings of the ISOC’95 symposium on network and distributed system security, pp. 102112, IEEE Computer Society, Los Alamitos, CA., 1995. ##[19] S. Peddabachigaria, A. Abrahamb, I. Grosanc, and J. Thomas, "Modeling intrusion detection system using hybrid intelligent systems", Published by Elsevier Ltd, 2005. ##[20] S. Peddabachigaria, A. H. Sung, and A. Abraham, "Intrusion detection using an ensemble of intelligent paradigms", Published by Elsevier Ltd, 2004. ##[21] R. Polikar, L. Udpa, and V. Honavar, “Learn++: An incremental learning algorithm for supervised neural networks”, IEEE Transactions on System, Man and Cybernetics (C), Special Issue on Knowledge Management, vol. 31, no. 4, pp. 497508, 2001. ##[22] P. Porras and G. P. Neumann, "EMERALD: Event Monitoring Enabling Responses to Anomalous Live Disturbances", In Proceedings of 20th National Information Systems Security Conference, 1997. ##[23] S.T. Powers and J. He, "A hybrid artificial immune system and Self Organizing Map for network intrusion detection", Information Sciences 178, pp. 3024–3042, 2008. ##[24] R. Rangadurai Karthick, V.P.Hattiwale, B. Ravindran, "Adaptive network intrusion detection system using a hybrid approach", Fourth International Conference on Communication Systems and Networks (COMSNETS), 2012, Page(s): 1 – 7. ##[25] Rasoulifard and A. Ghaemi Bafghi, "Incremental Intrusion Detection Using Learn++ algorithm", 3rd conference on Information and Knowledge Technology, Ferdowsi University of Mashhad, Faculty of Engineering, IKT2007, Nov. 2729 2007. ##[26] Rasoulifard, A. Ghaemi Bafghi, and M. kahani, "Incremental Hybrid Intrusion Detection Using Ensemble of Weak Classifiers", 13th Int'l CSI Computer Conference (CSICC'08), March 911, 2008. ##[27] M. Sabhnani and G. Serpen, "Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context", EECS Dept, University of Toledo, Toledo, Ohio 43606 USA. ##[28] K. Shah, N. Dave, S. Chavan, S. Mukherjee, A. Abraham, and S. Sanyal, "Adaptive NeuroFuzzy Intrusion Detection System", IEEE International Conference on ITCC'04, Vol. 1, pp. 7074, 2004. ##[29] K, Selvamani; S, Anbuchelian; S, Kanimozhi; R, Elakkiya; S, Bose; A, Kannan, "A hybrid framework of intrusion detection system for resource consumption based attacks in wireless adhoc networks", International Conference on Systems and Informatics (ICSAI), 2012, Page(s): 8 – 12.. ##[30] T. Shon and J. Moon, "A hybrid machine learning approach to network anomaly detection", Information Sciences 177, pp.3799–3821, 2007. ##[31] E. Tombini, H. Debar, L. Mé, and M. Ducassé, "A Serial Combination of Anomaly and Misuse IDSes Applied to HTTP Traffic", In proceedings of the Annual Computer Security Applications Conference (ACSAC). December 2004. ##[32] K. Wang and S. J. Stolfo. "Anomalous Payloadbased Network Intrusion Detection", In Proceedings of the 7th International Symposium on Recent Advances in Intrusion Detection (RAID), pages 203222, September 2004. ##[33] Xiang and S.M. Lim, "Design of MultipleLevel Hybrid Classifier for Intrusion Detection System", Proceeding of Machine Learning for Signal Processing, 2005 IEEE Workshop on Volume , Issue , 2828 ,PP 117 – 122, Sept. 2005. ##[34] L. Xu, A. Krzyzak, and Y.Ching, "Methods of Combining Multiple Classifier and Their Application to Handwriting Recognition", IEEE TRANSACTION ON SYSTEMS, MAN AND CYBERNETICS, VOL. 22, NO. 3, MAY/JUNE 1992. ##[35] W. Yang, X.C. Yun, and L.J. Zhang, "Using Incremental Learning Method For Adaptive Network Intrusion Detection", Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 1821 August 2005. ##[36] Z. Yu and J.P. Tsai, "A MultiClass SLIPPER System for Intrusion Detection," compsac, pp. 212217, 28th Annual International Computer Software and Applications Conference (COMPSAC'04), 2004. ##[37] Z. Yu and J.P. Tsai, "An efficient intrusion detection system using a boostingbased learning algorithm," International Journal of Computer Applications in Technology, Vol. 27, No.4 pp. 223 – 231, 2006. ##[38] J. Zhang and M. Zulkernine, “Anomaly based network intrusion detection with unsupervised outlier detection”, The 2006 IEEE International Conference on Communications, Istanbul, Turkey, June 2006. ##[39] J. Zhang and M. Zulkernine, “A Hybrid Network Intrusion Detection Technique Using Random Forests”, Proc. of the International Conference on Availability, Reliability and Security (AReS), IEEE CS Press, pp. 262269, Vienna, Austria, April 2006. ##[40] S. Zhong, T. Khoshgoftaar, and N. Seliya, "ClusteringBased Network Intrusion Detection", International Journal of Reliability, Quality and Safety Engineering. ##]
Algorithms for Computing Limit distributions of Oscillating Systems with Finite Capacity
Algorithms for Computing Limit distributions of Oscillating Systems with Finite Capacity
2
2
We address the batch arrival systems with finite capacity under partial batch acceptance strategy where service times or rates oscillate between two forms according to the evolution of the number of customers in the system. Applying the theory of Markov regenerative processes and resorting to Markov chain embedding, we present a new algorithm for computing limit distributions of the number customers in the system. The numerical results are given in the paper for a clearer expression of the proposed computational methodologies.
1
We address the batch arrival systems with finite capacity under partial batch acceptance strategy where service times or rates oscillate between two forms according to the evolution of the number of customers in the system. Applying the theory of Markov regenerative processes and resorting to Markov chain embedding, we present a new algorithm for computing limit distributions of the number customers in the system. The numerical results are given in the paper for a clearer expression of the proposed computational methodologies.
69
78


Mohammad
Taremi
Iran
Systems
Finite Capacity
Oscillating Systems
Acceptance Strategy
Batch Arrivals
[[1] Altman, E. and A. JeanMarie (1998). Loss probabilities for messages with redundant packets feeding a finite buffer. IEEE Journal of Selected Areas in Communications 16 (5), 779787. ##[2] Bahary, E. and P. Kolesar (1972). Multilevel bulk service queues. Operations Research 20, 406420. ##[3] Bekker, R., S. C. Borst, O. J. Boxma, and O. Kella (2004). Queues with workloaddependent arrival and service rates. Queuing Systems 46 (34), 537556. ##[4] Bratiychuk, M. and A. Chydzinski (2003). On the ergodic distribution of oscillating queuing systems. Journal of Applied Mathematics and Stochastic Analysis 16 (4), 311326. ##[5] Choi, B. D. and D.I. Choi (1996). Queuing system with queue length dependent service times and its application to cell discarding scheme in ATM networks. IEE Proceedings Communications 143 (1), 511. ##[6] Choi, B. D., Y. C. Kim, Y. W. Shin, and C. E. M. Pearce (2001). The M/G/1 queue with queue length dependent service times. Journal of Applied Mathematics and Stochastic Analysis 14 (4), 399419. ##[7] Choi, D. I., C. Knessl, and C. Tier (1999). A queuing system with queue length dependent service times, with applications to cell discarding in ATM networks. Journal of Applied Mathematics and Stochastic Analysis 12 (1), 3562 ##[8] Chydzinski, A. (2002). The M/GG/1 oscillating queuing system. Queuing Systems 42 (3), 255268. ##[9] Chydzinski, A. (2003). The MM/G/1type oscillating systems. Cybernetics and Systems Analysis 39 (2), 316324. ##[10] Chydzinski, A. (2004). The oscillating queue with finite buffer. Performance Evaluation 57 (3), 341355. ##[11] Fakinos, D. and A. Economou (2001). A new approach for the study of the M/G/1 queue using renewal arguments. Stochastic Analysis and Applications 19, 151156. ##[12] Federgruen, A. and H. C. Tijms (1980). Computation of the stationary distribution of the queue size in an M/G/1 queuing system with variable service rate. 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