2013
45
2
2
0
Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems
Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems
2
2
Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems.
1
Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems.
1
10
Samira
Babalou
Samira
Babalou
MSC student, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran
MSC student, Department of Computer Engineering,
Iran
Mohammad Javad
Kargar
Mohammad Javad
Kargar
Assistant Professor, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran
Assistant Professor, Department of Computer
Iran
showcaran@gmail.com
Seyyed Hashem
Davarpanah
Seyyed Hashem
Davarpanah
Assistant Professor, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran
Assistant Professor, Department of Computer
Iran
davarpanah@usc.ac.ir
Ontology matching
Clustering method
Large-scale matching
Semantic graph
[[1] Hendler, J. “Agents and the semantic web,” Intelligent Systems, IEEE, vol. 16, no.2, pp. 30-37, 2001.##[2] Euzenat, J., C. Meilicke, H. Stuckenschmidt, P. Shvaiko, and C. Trojahn, “Ontology alignment evaluation initiative: six years of experience,” in Journal on data semantics XV, Springer. pp. 158-192, 2011.##[3] Euzenat, J., A. Ferrara, W. van Hage, L. Hollink, C. Meilicke, A. Nikolov, et al. “Results of the ##Ontology Alignment Evaluation Initiative 2011.” in 6th OM workshop, 2011.##[4] Hu, W, Y. Qu, and G. Cheng, “Matching large ontologies: A divide-and-conquer approach,” Data & Knowledge Engineering, vol. 67, no. 1, pp. 140-160, 2008.##[5] Algergawy, A., S. Massmann, and E. Rahm. “A clustering-based approach for large-scale ontology matching,” in Advances in Databases and Information Systems, Springer, 2011.##[6] Wang, Z., Y. Wang, S. Zhang, G. Shen, and T. Du, “Matching large scale ontology effectively,” in The Semantic Web–ASWC 2006, Springer, pp. 99-105, 2006.##[7] Khan, M., N. Javaid, M. Khan, A. Javaid, Z. Khan, and U. Qasim, “Hybrid DEEC: Towards Efficient Energy Utilization in Wireless Sensor Networks,” arXiv preprint arXiv:1303.4679, 2013.##[8] Bsoul, M., A. Al-Khasawneh, A.E. Abdallah, E.E. Abdallah, and I. Obeidat, “An energy-efficient threshold-based clustering protocol for wireless sensor networks,” Wireless personal communications, vol. 70, no. 1, pp. 99-112, 2013.##[9] Saruladha, K., G. Aghila, and B. Sathiya. “A partitioning algorithm for large scale ontologies,” in Recent Trends In Information Technology (ICRTIT), 2012 International Conference on. IEEE, 2012.##[10] Zhou, Y, H. Cheng, and J. X. Yu, “Graph clustering based on structural/attribute similarities,” Proceedings of the VLDB Endowment, vol. 2, no. 1, pp. 718-729, 2009.##[11] Hu, W. and Y. Qu, “Falcon-AO: A practical ontology matching system. Web Semantics,” Science, Services and Agents on the World Wide Web, vol. 6, no.3, pp. 237-239, 2008.##[12] Do, H.-H. and E. Rahm, “Matching large schemas Approaches and evaluation,” Information Systems, vol. 32, no.6, pp. 857-885, 2007.##[13] Hu, W., Y. Zhao, and Y. Qu, “Partition-based block matching of large class hierarchies,” in The Semantic Web–ASWC 2006, Springer, pp. 72-83, 2006.##[14] Jiménez-Ruiz, E. and B.C. Grau, “Logmap: Logic-based and scalable ontology matching,” in The Semantic Web–ISWC 2011, Springer, pp. 273-288, 2011.##[15] Kirsten, T., A. Gross, M. Hartung, and E. Rahm, “GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution,” J. Biomedical Semantics, vol. 2, pp. 6, 2011.##[16] Ngo, D. and Z. Bellahsene, “YAM++: a multi-strategy based approach for ontology matching task,” in Knowledge Engineering and Knowledge Management, Springer, pp. 421-425, 2012.##[17] Grau, B.C., I. Horrocks, Y. Kazakov, and U. Sattler. “Just the right amount: extracting modules from ontologies,” in Proceedings of the 16th international conference on World Wide Web, ACM, 2007.##[18] Wang, Z., Y. Wang, S. Zhang, G. Shen, and T. Du, “Ontology Pasing Graph-based Mapping: A Parsing Graph-based Algorithm for Ontology Mapping,” Journal of Donghua University, vol. 23, no.6, pp. 5, 2006.##[19] Yuruk, N., M. Mete, X. Xu, and T.A. Schweiger. “AHSCAN: Agglomerative hierarchical structural clustering algorithm for networks. in Social Network Analysis and Mining,” ASONAM'09. International Conference on Advances in. 2009 IEEE, 2009.##[20] Guha, S., R. Rastogi, and K. Shim, “ROCK: A robust clustering algorithm for categorical attributes,” Information systems, vol. 25, no.5, pp. 345-366, 2000.##[21] Hamdi, F., B. Safar, C. Reynaud, and H. Zargayouna, “Alignment-based partitioning of large-scale ontologies,” in Advances in knowledge discovery and management, Springer, pp. 251-269, 2010.##[22] Zhang, X., H. Li, and Y. Qu," Finding important vocabulary within ontology ", in The Semantic Web–ASWC 20062006, Springer. p. 106-112..##[23] Graves, A., S. Adali, and J. Hendler. “A Method to Rank Nodes in an RDF Graph,” International Semantic Web Conference (Posters & Demos). 2008.##[24] Kermarrec, A.-M., E. Le Merrer, B. Sericola, and G. Trédan, “Second order centrality: Distributed assessment of nodes criticity in complex networks,” Computer Communications, vol. 34, no. 5, pp. 619-628, 2011.##[25] Freeman, L.C," A set of measures of centrality based on betweenness ". Sociometry, 1977: p. 35-41.##[26] Hage, P. and F. Harary, “Eccentricity and centrality in networks,” Social networks, vol. 17, no.1, pp. 57-63, 1995.##[27] Koschützki, D., K.A. Lehmann, L. Peeters, S. Richter, D. Tenfelde-Podehl, and O. Zlotowski, “Centrality indices,” Network analysis, Springer, pp. 16-61, 2005.##[28] Zhang, X., G. Cheng, and Y. Qu, “Ontology summarization based on rdf sentence graph,”Proceedings of the 16th international conference on World Wide Web, ACM, 2007.##[29] Stuckenschmidt, H, “Network analysis as a basis for partitioning class hierarchies,” W8: Semantic Network Analysis, pp. 43, 2005.##[30] Algergawy, A., R. Nayak, and G. Saake, “Element similarity measures in XML schema matching,” Information Sciences, vol. 180, no. 24, pp. 4975-4998, 2010.##[31] Levenshtein, V.I., “Binary codes capable of correcting deletions, insertions and reversals,” in Soviet physics doklady, 1966.##[32] Lin, F. and K. Sandkuhl, “A survey of exploiting wordnet in ontology matching,” in Artificial Intelligence in Theory and Practice II2008, Springer, pp. 341-350, 2008.##]
Suppressing Vibration In A Plate Using Particle Swarm Optimization
Suppressing Vibration In A Plate Using Particle Swarm Optimization
2
2
In this paper a mesh-free model of the functionally graded material (FGM) plate is presented. The piezoelectric material as a sensor and actuator has been distributed on the top and bottom of the plate, respectively. The formulation of the problem is based on the classical laminated plate theory (CLPT) and the principle of virtual displacements. Moreover, the Particle Swarm optimization (PSO) algorithm is used for the vibration control of the (FGM) plate. In this study a function of the sliding surface is considered as an objective function and then the control effort is produced by the particle swarm method and sliding mode control strategy. To verify the accuracy and stability of the proposed control system, a traditional sliding mode control system is designed to suppressing the vibration of the FGM plate. Besides, a genetic algorithm sliding mode (GASM) control system is also implemented to suppress the vibration of the FGM plate. The performance of the proposed PSO sliding mode than the GASM and traditional sliding mode control system are demonstrated by some simulations.
1
In this paper a mesh-free model of the functionally graded material (FGM) plate is presented. The piezoelectric material as a sensor and actuator has been distributed on the top and bottom of the plate, respectively. The formulation of the problem is based on the classical laminated plate theory (CLPT) and the principle of virtual displacements. Moreover, the Particle Swarm optimization (PSO) algorithm is used for the vibration control of the (FGM) plate. In this study a function of the sliding surface is considered as an objective function and then the control effort is produced by the particle swarm method and sliding mode control strategy. To verify the accuracy and stability of the proposed control system, a traditional sliding mode control system is designed to suppressing the vibration of the FGM plate. Besides, a genetic algorithm sliding mode (GASM) control system is also implemented to suppress the vibration of the FGM plate. The performance of the proposed PSO sliding mode than the GASM and traditional sliding mode control system are demonstrated by some simulations.
11
22
J.
Javadi Moghaddam
J.
Javadi Moghaddam
Phd Student Department of Mechanical Engineering, University of Guilan
Phd Student Department of Mechanical Engineering,
Iran
A.
Bagheri
A.
Bagheri
Professor Department of Mechanical Engineering, University of Guilan
Professor Department of Mechanical Engineering,
Iran
Plate
Particle Swarm Optimization
Sliding mode
FGM
GASM
[[1] Zhao X., Lee Y. Y. and Liew K. M., ‘‘Free vibrationan alysis of functionally graded plates using the element-free kp-Ritz method,’’Journal of Sound and Vibration, vol. 319, pp. 918–939, 2009.##[2] Liu G. R., Zhao X., Dai K. Y., Zhong Z. H., Li G.Y. and Han X. ‘‘Static and free vibration analysis##of laminated composite plates using the conforming radial point interpolation method,’’ Composites Science and Technology , vol. 68, pp. 354– 366, 2008.##[3] Belytschko T, Lu Y and Gu L. ‘‘Element-free Galerkin methods,’’ Int J Numer Methods Eng, vol. 37, pp. 229– 56, 1994.##[4] Liu G.R, Liu M.B. ‘‘Smoothed particle hydrodynamics: a meshfree particle method,’’ New Jersey: World Scientific, 2003.##[5] Liu W.K, Jun S and Zhang Y.F. ‘‘Reproducing kernel particle methods,’’ Int J Numer Methods Fluid , vol. 20, pp. 1081– 106, 1995.##[6] Batra R.C, Zhang G.M. ‘‘Analysis of adiabatic shear bands in elastothermo-viscoplastic materials by modified smoothed-particle hydrodynamics (MSPH) method,’’ J Comput Phys , vol. 201, pp. 172– 90, 2004.##[7] Atluri S. N, Zhu T. ‘‘A new meshless local Petrov-Galerkin (MLPG) approach in computational mechanics,’’ Comput Mech , vol. 22, pp. 117–27, 1998.##[8] Chen J. K, Beraun J. E and Jih C. J. ‘‘Completeness of corrective smoothed particle method for linear elastodynamics,’’ Comput Mech, vol. 24, pp. 273– 85, 1999.##[9] Wang J. G, Liu G. R. ‘‘A point interpolation meshless method based on radial basis functions,’’ Int J Numer Method Eng , vol. 54, pp. 1623– 48, 2002.##[10] Bui T. Q., Nguyen M. N., ‘‘A moving Kriging interpolation-based meshfree method for free vibration analysis of Kirchhoff plates,’’ Computers and Structures , vol. 89, pp. 380– 394, 2011.##[11] K.Y. Dai, G.R. Liu, X. Han and K.M. Lim. ‘‘Thermo mechanical analysis of functionally graded material (FGM) plates using element-free Galerkin method,” Computers and Structures , vol. 83, pp. 1487– 1502, 2005.##[12] Kennedy J., Eberhart R. ‘‘Particle swarm optimization,’’ In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4. Perth, Australia, pp. 1942– 1948, 1995.##[13] Jiang J., Kwong C. K., Chen Z. and Ysim Y. C. ‘‘Chaos particle swarm optimization and T–S fuzzy modeling approaches to constrained predictive control,” Expert Systems with Applications, 2011.##[14] Marinaki M., Marinakis Y and Stavroulakis G. E., ‘‘Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization,” Expert Systems with Applications, vol. 38, pp. 6872–6883, 2011.##[15] Bachlaus M., Shukla N., Tiwari M. K. and Shankar R. ‘‘Optimization of system reliability using chaos-embedded self-organizing hierarchical particle swarm optimization,” Proceedings of the institution of mechanical engineers , vol. 220, pp. 77– 91, 2006.##[16] Chen J. S., Pan C., Wu C. T. and Liu W. K. ‘‘Reproducing kernel particle methods for large deformation analysis of nonlinear structures,’’ Computer Methods in Applied Mechanics and Engineering, vol. 139, pp. 195– 227, 1996.##[17] Liew K. M., He X. Q. and Kitipornchai S., ‘‘Finite element method for the feedback control of FGM shells in the frequency domain via piezoelectric sensors and actuators,’’ Comput. Methods Appl. Mech. Energy., vol. 193, pp. 257–273, 2004.##[18] Touloukian, Y. S. Thermophysical Properties of High Temperature Solid Materials, Macmillian, New York, 1967.##[19] Reddy, J. N., Mechanics of Laminated composite plates and shells: Theory and Analysis, 2NdEd CRC Press, Boca Raton, London New York Washington, D.C 2004.##[20] Slotine J. J. E., Li, W., Applied Nonlinear Control, Prentice-Hall, Englewood Clifs, NJ, 1991.##[21] K. J. Astrom, B. Wittenmark, Adaptive Control, Addison-Wesley, New York, 1995.##]
Eigenvalue Assignment Of Discrete-Time Linear Systems With State And Input Time-Delays
Eigenvalue Assignment Of Discrete-Time Linear Systems With State And Input Time-Delays
2
2
Time-delays are important components of many dynamical systems that describe coupling or interconnection between dynamics, propagation or transport phenomena, and heredity and competition in population dynamics. The stabilization with time delay in observation or control represents difficult mathematical challenges in the control of distributed parameter systems. It is well-known that the stability of closed-loop system achieved by some stabilizing output feedback laws may be destroyed by whatever small time delay there exists in observation. In this paper a new method for eigenvalue assignment of discrete-time linear systems with state and input time-delays by static output feedback matrix is presented. The main result is an iterative method that only requires linear equations to be solved at each iteration. In this scheme, first a linear delayed system by defining an augmented vector is changed to standard form, then output feedback matrix K is calculated by inverse eigenvalue problem. We investigate all types of delays in the states, inputs or both for discrete – time linear systems. A simple algorithm and an illustrative example are presented to show the advantages of this new technique.
1
Time-delays are important components of many dynamical systems that describe coupling or interconnection between dynamics, propagation or transport phenomena, and heredity and competition in population dynamics. The stabilization with time delay in observation or control represents difficult mathematical challenges in the control of distributed parameter systems. It is well-known that the stability of closed-loop system achieved by some stabilizing output feedback laws may be destroyed by whatever small time delay there exists in observation. In this paper a new method for eigenvalue assignment of discrete-time linear systems with state and input time-delays by static output feedback matrix is presented. The main result is an iterative method that only requires linear equations to be solved at each iteration. In this scheme, first a linear delayed system by defining an augmented vector is changed to standard form, then output feedback matrix K is calculated by inverse eigenvalue problem. We investigate all types of delays in the states, inputs or both for discrete – time linear systems. A simple algorithm and an illustrative example are presented to show the advantages of this new technique.
23
30
H. A.
Tehrani
H. A.
Tehrani
Assistant Professor, Department of Mathematics, University of Shahrood, Shahrood, Iran
Assistant Professor, Department of Mathematics,
Iran
N.
Ramroodi
N.
Ramroodi
MSc Student, Department of Mathematics, University of Shahrood, Shahrood, Iran
MSc Student, Department of Mathematics, University
Iran
Eigenvalue assignment
Inverse eigenvalue problem
Output feedback
Time-delay system
[[1] H. Ahsani Tehran, “Localization of Eigenvalues in Small Specified Regions of Complex Plane by State##Feedback Matrix,” J. Sci. Islam. Repub. Iran, vol 25(2), pp. 157 – 164.##[2] M.T. Chu, “Inverse eigenvalue problems,” SIAM, vol 40, pp. 1-39, 1998.##[3] Y. Dong and J. Wei, “Output feedback stabilization of nonlinear discrete-time systems with time-delay,”##Advances in Difference Equations, vol 73, pp. 1-11,2012.##[4] R. Dorf and R.H. Bishop, Modern control system,11th Edition, Prentice Hall 2007.##[5] G.H. Golub and C.F. Van Loan, Matrix Computations, 4th Edition, The Johns Hopkins##University Press, Baltimore 2013.##[6] S.M. Karbassi and D.J. Bell “Parametric timeoptimal control of linear discrete-time systems by##state feedback-Part 1: Regular Kronecker invariants,” International Journal of Control, vol.##57, pp. 817-830, 1993.##[7] S.M. Karbassi and F. Saadatjou, “A parametric approach for eigenvalue assignment by static output##feedback,” Journal of the Franklin Institute, vol. 346,pp. 289-300, 2009.##[8] S.M. Karbassi and H.A. Tehrani, “Parameterizations of the state feedback controllers for linear##multivariable systems,” Computers and Mathematics with Applications, vol. 44, pp. 1057-1065, 2002.##[9] R.W. Koepcke, “On the control of linear systems with pure time-delay,” Trans. ASME Journal of##Basic Engineering, vol. 87, pp. 74-80, 1965.##[10] F. Kurzweil, “The control of multivariable processes in the presence of the pure transport delays,” IEEE##Trans. Automatic Control, vol. 8, pp. 27- 35, 1963.##[11] N. Li, “An iterative method for pole assignment,”Linear Algebra and Its applications, vol. 23, pp. 77-##102, 2001.##[12] N. Li, “An inverse eigenvalue problem and feedback control,” proceedings of the 4th Biennial##engineering mathematics and applications conference, vol. 124, pp. 183-186, 2000.##[13] X. Li and H. Gao, “A new model transformation of discrete-time systems with time-varying delay and##its application to stability analysis,” IEEE Transactions on Automatic Control, vol. 56, no. 9,##pp. 2172–2178, 2011.##[14] S.M. Modarres and S.M. Karbassi, “Time-optimal control of discrete time linear systems with state and##input time-delays,” International Journal of Innovative Computing, Information and Control,vol. 5, no. 9, pp. 2619-2625, 2009.##[15] Y. Xia and G. Liu and P. Shi and D. Rees and E. Thomas, “New stability and stabilization conditions##for systems with time-delay,” International Journal of Systems Science, vol. 38, no. 1, pp. 17–24, 2007. ##]
Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm
Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm
2
2
One of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. In this paper, the decoupled sliding mode control technique (DSMC) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. Furthermore, a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is implemented for optimizing the DSMC parameters in order to decrease the normalized angle error of the pole and normalized distance error of the cart, simultaneously. The results of simulation are presented which consist of results with and without disturbances. The proposed Pareto front for the DSMC problem demonstrates that the Ingenious-MOPSO operates much better than other multi-objective evolutionary algorithms.
1
One of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. In this paper, the decoupled sliding mode control technique (DSMC) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. Furthermore, a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is implemented for optimizing the DSMC parameters in order to decrease the normalized angle error of the pole and normalized distance error of the cart, simultaneously. The results of simulation are presented which consist of results with and without disturbances. The proposed Pareto front for the DSMC problem demonstrates that the Ingenious-MOPSO operates much better than other multi-objective evolutionary algorithms.
31
40
M.
Bisheban
M.
Bisheban
School of Mechanical and Aerospace Engineering, the George Washington University, Washington DC, USA.
School of Mechanical and Aerospace Engineering,
Iran
M.J.
Mahmoodabadi
M.J.
Mahmoodabadi
Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.
Department of Mechanical Engineering, Sirjan
Iran
Decoupled Sliding Mode Control
Multi-objective Algorithm
Particle Swarm Optimization
Inverted Pendulum System
[[1] J. Kennedy, R.C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE##International Conference on Neural Networks, vol.IV, Perth, Australia, pp. 1942-1948, 1995.##[2] P.J. Angeline, “Using selection to improve particle swarm optimization,” in Proceedings of the IEEE##Congress on Evolutionary Computation,Anchorage, AK, pp. 84-89, May 1998.##[3] R. C. Eberhart, Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation,Anchorage, AK, pp. 611-616, May 1998.##[4] H. Yoshida, K. Kawata, Y. Fukuyama, “A particle swarm optimization for reactive power and voltage##control considering voltage security assessment,” IEEE Transactions on Power Systems, vol. 15, pp.##1232-1239, 2000.##[5] X. Hu, R. C. Eberhart, “Multi-objective optimization using dynamic neighborhood particle swarm optimization,” In Proceedings of the IEEE World Congress on Computational Intelligence (CEC’02), pp. 1677-1681, 2002.##[6] J. E. Fieldsend, S. Singh, “A multi-objective algorithm based upon particle swarm optimization##and efficient data structure and turbulence,” In Workshop on Computational Intelligence, pp. 34-##[7] S. Mostaghim, J. Teich, “Strategies for finding good local guides in multi- objective particle##swarm optimization (MOPSO),” In Proceedings of the IEEE Swarm Intelligence Symposium, pp. 26-##[8] K. E. Parsopoulos, D. K. Tasoulis, M. N. Vrahatis, “Multi-objective optimization using parallel vector##evaluated particle swarm optimization,” Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, vol. 2, pp. 823-828, 2004.##[9] P. K. Tripathi, S. Bandyopadhyay, S. K. Pal,“Multi-objective particle swarm optimization with##time variant inertia and acceleration coefficients,”Information Sciences, vol. 177, pp. 5033-5049,##[10] S. J. Tsai, T. Y. Sun, C. C. Liu, S. T. Hsieh, W. C. Wu, S. Y Chiu, “An improved multi-objective##particle swarm optimizer for multi-objective problems,” Expert Systems with Applications, vol.##37, pp. 5872-5886, 2010.##[11] Y. Wang, Y. Yang, “Particle swarm optimization with preference order ranking for multi-objective##optimization,” Information Sciences, vol. 179, pp.1944-1959, 2009.##[12] S. Mostaghim, J. Teich, “The role of ε-dominance in multi objective particle swarm optimization##methods,” Congress on Evolutionary Computation,vol. 3, pp. 1764-1771, 2003.##[13] A. G. Hernandez-Diaz, L. V. Santana-Quintero, C.A. Coello Coello, J. Molina, R. Caballero,##“Improving the efficiency of ε-dominance based grids, Information Sciences,” vol. 181, no. 15, pp.##3101-3129, 2011. ##[14] K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali, A. Jamali, “Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms,” Energy Conversion and Management, vol. 48, pp. 1029-1041, 2007.##[15] M. J. Mahmoodabadi, A. Bagheri, S. Arabani Mostaghim, M. Bisheban, “Simulation of stability using Java application for Pareto design of controllers based on a new multi-objective particle swarm optimization,” Mathematical and Computer Modelling, vol. 54, no. 5-6, pp. 1584-1607, 2011.##[16] M. J. Mahmoodabadi, M. Taherkhorsandi, A. Bagheri, “Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO,” Neurocomputing, vol. 124, pp. 194–209.##[17] P. Fleming, R. Purshouse, “Evolutionary algorithms in control systems engineering: a survey,” Control Engineering Practice, vol. 10, no. 11, pp. 1223-1241, 2002.##[18] C. Fonseca, P. Fleming, “Multi-objective optimal controller design with genetic algorithms,” International Conference on Control, vol. 1, March, pp. 745-749, 1994.##[19] G. Sanchez, M. Villasana, M. Strefezza, “Multi-objective pole placement with evolutionary algorithms,” Lecture Notes in Computer Science, vol. 4403, pp. 417, 2007.##[20] W. Qiao, G. Venayagamoorthy, R. Harley, “Design of optimal PI controllers for doubly fed induction generators driven by wind turbines using particle swarm optimization,” International Joint Conference on Neural Networks, pp. 1982-1987, 2006.##[21] Z. L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE Transaction on Energy Conversion, vol. 19, no. 2, pp. 384-391, 2004.##[22] K. C. Ng, Y. Li, D. Munay-Smith, K.C. Shaman, “Genetic algorithm applied to fuzzy sliding mode controller design,” Genetic Algorithms in Engineering Systems, Innovations and Applications 12-14, Conference Publication No. 414, IEE, pp. 220-225, 1995.##[23] C. C Wong, S.Y. Chang, “Parameter selection in the sliding mode control design using genetic algorithms,” Tamkang Journal of Science and Engineering vol. 1 no. 2, pp. 115-122, 1998.##[24] C. C. Kung, T. H. Chen, L. H. Kung, “Modified adaptive fuzzy sliding mode controller for uncertain nonlinear systems,” IEICE Transaction on Fundamentals of Electronics, Communications##and Computer Sciences, vol. 88, no. 5, pp. 1328-1334, 2005.##[25] N. Yagiz, Y. Hacioglu, “Robust control of a spatial robot using fuzzy sliding modes,” Mathematical and Computer Modeling vol. 49, no. 1-2, pp. 114-127, 2009.##[26] C. Zhi-mei, M. Wen-jun, Z. Jing-gang, Z. Jian-chao, “Scheme of sliding mode control based on modified particle swarm optimization,” Systems Engineering-Theory & Practice, vol. 29, no. 5, pp. 137-141, 2009.##[27] E. Alfaro-Cid, E. W. McGookina, D. J. Murray-Smith, T. I. Fossen, “Genetic algorithms optimization of decoupled Sliding Mode controllers: simulated and real results,” Control Engineering Practice, vol. 13, pp. 739-748, 2005.##[28] M. J. Mahmoodabadi, A. Bagheri, N. Nariman-zadeh, A. Jamali, R. Abedzadeh Maafi, “Pareto design of decoupled sliding-mode controllers for nonlinear systems based on a multiobjective genetic algorithm,” Journal of Applied Mathematics pp. 22, 2012.##[29] M. J. Mahmoodabadi, S. Arabani Mostaghim, A. Bagheri, N. Nariman-zadeh, “Pareto optimal design of the decoupled sliding mode controller for an inverted pendulum system and its stability simulation via Java programming,” Mathematical and Computer Modelling, vol. 57, pp. 1070–1082, 2013.##[30] A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, 2005.##[31] R. C. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory,” Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39-43, 1995.##[32] A. Ratnaweera, S. K. Halgamuge, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240-255, 2004.##[33] C. A. Coello Coello, D. A. Van Veldhuizen, G. B. Lamont, In: Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic, Dordrecht, 2002.##[34] A. Jamali, A. Hajiloo, N. Nariman-zadeh, “Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform- diversity genetic algorithm,” (MUGA), Expert Systems with Applications, vol. 37, no. 1, pp. 401–413, 2010.##[35] W. S. Lin, C. S. Chen, “Robust adaptive sliding mode control using fuzzy modeling for a class of uncertain MIMO nonlinear systems,” ControlTheory and Applications, IEE, vol. 149, no. 3, pp. 193-201, 2002.##[36] J. Jing, Q.H. Wuan, “Intelligent sliding mode control algorithm for position tracking servo system,” International Journal of Information Technology, vol. 12, no. 7, pp. 57-62, 2006.##[37] Utkin, Sliding Modes and Their Application in Variable Structure Systems, Central Books Ltd, 1978##[38] M. Dotoli, P. Lino, B. Turchiano, “A decoupled fuzzy sliding mode approach to swing-Up and stabilize an inverted pendulum,” The CSD03, The 2nd IFAC Conference on Control Systems Design, Bratislava, Slovak Republic, pp. 113-120, September 2003.##[39] R. Toscana, “A simple robust PI/PID controller design via numerical optimization approach,” Journal of Process Control, vol. 15, pp. 81–88, 2005.##[40] W.A. Wolovich, Automatic Control Systems, Harcourt Brace College Publication Orlando, Saunders College Publishing, USA, 1994.##]
Second Order Sliding Mode Control With Finite Time Convergence
Second Order Sliding Mode Control With Finite Time Convergence
2
2
In this paper, a new smooth second order sliding mode control is proposed. This algorithm is a modified form of Super Twisting algorithm. The Super Twisting guarantees the asymptotic stability, but the finite time stability of proposed method is proved with introducing a new particular Lyapunov function. The Proposed algorithm which is able to control nonlinear systems with matched structured uncertainty, is able to guarantee the finite time stability. The main advantage of this second order sliding mode control is reaching to sliding surface with high precision without chattering in control signal. In simulation section, the proposed algorithm is compared with the boundary layer sliding mode control and then is applied to designing a finite time nonlinear guidance law that is robust with respect to target maneuvers. Simulation results show that the control input in this algorithm is smooth and has no chattering and by applying this method, sliding variables will converge to zero in a given desired finite time.
1
In this paper, a new smooth second order sliding mode control is proposed. This algorithm is a modified form of Super Twisting algorithm. The Super Twisting guarantees the asymptotic stability, but the finite time stability of proposed method is proved with introducing a new particular Lyapunov function. The Proposed algorithm which is able to control nonlinear systems with matched structured uncertainty, is able to guarantee the finite time stability. The main advantage of this second order sliding mode control is reaching to sliding surface with high precision without chattering in control signal. In simulation section, the proposed algorithm is compared with the boundary layer sliding mode control and then is applied to designing a finite time nonlinear guidance law that is robust with respect to target maneuvers. Simulation results show that the control input in this algorithm is smooth and has no chattering and by applying this method, sliding variables will converge to zero in a given desired finite time.
41
52
V.
Behnamgol
V.
Behnamgol
PhD Student, Department of Control Engineering, Malek Ashtar University of Technology
PhD Student, Department of Control Engineering,
Iran
A. R.
Vali
A. R.
Vali
Associate professor, Control Engineering Department, Malek Ashtar University of Technology
Associate professor, Control Engineering
Iran
I.
Mohammadzaman
I.
Mohammadzaman
Assistant professor, Control Engineering Department, Malek Ashtar University of Technology
Assistant professor, Control Engineering
Iran
Second Order Sliding Mode
Finite Time Stability
Chattering
Uncertainty
[[1] Slotine, J. J. E., and Li, W., Applied Nonlinear Control, Prentice-Hall, Upper Saddle River, NJ,##pp. 276-309, 1991.##[2] Khalil, H. K, Nonlinear Systems, Prentice-Hall, Upper Saddle River, NJ, pp. 601-617, 1996.##[3] Evangelista, C., Puleston, P., and Valenciaga, F.,“Wind turbine efficiency optimization.##Comparative study of controllers based on second order sliding modes,” International Journal of##Hydrogen Energy ,vol. 35, pp. 5934–5939, 2010.##[4] Fridman, L., Moreno, J. and Iriarte, R., Sliding Modes after the First Decade of the 21st Century,##Springer, 2011.##[5] Kaveh, P. and Shtessel, Y. B., “Blood glucose regulation using higher-order sliding mode##control,” International Journal of Robust and Nonlinear Control, John Wiley & Sons, Ltd, 2007.##[6] Shtessel, Y. B., Shkolnikov, I. A., and Brown, M. D. J., “An Asymptotic Second-Order Smooth##Sliding Mode Control,” Asian Journal of Control,Vol. 5, No. 4, pp. 498-504, 2003.##[7] Levant, A., “Principles of 2-slidingmode design,”Automatica, vol. 43, pp. 576 – 586, 2007.##[8] Mondal, S., and Mahanta, Ch., “Nonlinear sliding surface based second order sliding mode controller##for uncertain linear systems,” Commun Nonlinear Sci Numer Simulat, vol.16, pp. 3760–3769, 2011.##[9] Mihoub, M., Nouri, A. S. and Abdennour, R. B., “Real-time application of discrete second order##sliding mode control to a chemical reactor,”Elsevier Control Engineering Practice, vol. 17, pp.##1089–1095, 2009.##[10] Bhat, S. P. and Bernstain, D. S., “Continues finite time stabilization of the translational and rotational##double integrators,” IEEE Transaction on Automatic Control, vol. 43, pp. 678-682, 1998.##[11] Hong, Y, “Finite-time stabilization and stabilizability of a class of controllable systems,”##Systems & Control letters, vol. 46, pp. 231-236, 2002.##[12] Hong, Y., Huang, J. and Xu, Y., “On an output feedback finite-time stabilization problem,” IEEE##Transaction on Automatic Control, vol. 46, pp.305-309, 2001.##[13] Rew, D.Y., Tahk, M. J. and Cho, H., “Short Time Stability of Proportional Navigation Guidance##Loop,” IEEE Transaction on Aerospace and Electronic Systems, vol. 32, pp. 1107-1115, 1996.##[14] Zhou, D. and Sun, S, “Guidance Laws with Finite Time Convergence,” Journal of Guidance, Control,##and Dynamics, vol. 32, pp. 1838-1846, 2009.##[15] Shtessel, Y. B., Shkolnikov, I. A., and Levant, A., “Smooth second-order sliding modes: Missile##guidance application,” Automatica, vol. 43, pp.1470 – 1476, 2007.##[16] Siouris, G. M., “Missile Guidance and Control Systems,” Springer, pp. 194–228, 2005.##[17] Moon, J., Kim, K., and Kim, Y., “Design of Missile Guidance Law via Variable Structure##Control,” Journal of Guidance, Control, and Dynamics, vol. 24, no. 4, pp. 659 – 664, 2001.##[18] Zarchan, P., “Tactical and Strategic Missile Guidance,” AIAA Series, Vol. 199, pp. 143–152,##]
Type-2 Fuzzy Hybrid Expert System For Diagnosis Of Degenerative Disc Diseases
Type-2 Fuzzy Hybrid Expert System For Diagnosis Of Degenerative Disc Diseases
2
2
One-third of the people with an age over twenty have some signs of degenerated discs. However, in most of the patients the mere presence of degenerative discs is not a problem leading to pain, neurological compression, or other symptoms. This paper presents an interval type-2 fuzzy hybrid rule-based system to diagnose the abnormal degenerated discs where pain variables are represented by interval type-2 membership functions. For this purpose, Mamdani interval type-2 fuzzy sets are utilized in the inference engine. The main contribution of this paper is to present the interval type-2 fuzzy hybrid rule-based system, which is the combination of forward and backward chaining approach in its inference engine. Combining forward and backward chaining leads to detect the exact location of degenerated disc that shows some spinal instability. The phase of forward chaining diagnoses the severity of the degeneration based on taking history of the patient. The second phase uses backward chaining approach to find the exact location of the degenerated disc by investigating related clinical examinations. Using parametric operations for the fuzzy calculations increases the robustness of the system. The system is tested for 11 patients and the results are compared with the neurosurgeon’s diagnosis. Results indicate that the hybrid of forward and backward chaining approaches provide fast and accurate diagnosis of degenerative disc disease, and determine the necessity of taking MRI. Concluding, the proposed system could be a valuable tool in hand of the physicians in clinics and imaging centers to support diagnosis of the degenerated discs.
1
One-third of the people with an age over twenty have some signs of degenerated discs. However, in most of the patients the mere presence of degenerative discs is not a problem leading to pain, neurological compression, or other symptoms. This paper presents an interval type-2 fuzzy hybrid rule-based system to diagnose the abnormal degenerated discs where pain variables are represented by interval type-2 membership functions. For this purpose, Mamdani interval type-2 fuzzy sets are utilized in the inference engine. The main contribution of this paper is to present the interval type-2 fuzzy hybrid rule-based system, which is the combination of forward and backward chaining approach in its inference engine. Combining forward and backward chaining leads to detect the exact location of degenerated disc that shows some spinal instability. The phase of forward chaining diagnoses the severity of the degeneration based on taking history of the patient. The second phase uses backward chaining approach to find the exact location of the degenerated disc by investigating related clinical examinations. Using parametric operations for the fuzzy calculations increases the robustness of the system. The system is tested for 11 patients and the results are compared with the neurosurgeon’s diagnosis. Results indicate that the hybrid of forward and backward chaining approaches provide fast and accurate diagnosis of degenerative disc disease, and determine the necessity of taking MRI. Concluding, the proposed system could be a valuable tool in hand of the physicians in clinics and imaging centers to support diagnosis of the degenerated discs.
53
62
S.
Rahimi Damirchi-Darasi
S.
Rahimi Damirchi-Darasi
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Industrial Engineering, Amirkabir
Iran
M.H.
Fazel Zarandi
M.H.
Fazel Zarandi
Knowledge Intelligent System Laboratory, University of Toronto, Toronto, Canada
Knowledge Intelligent System Laboratory,
Iran
M.
Izadi
M.
Izadi
Sub-special Neurosurgery, Fayyazbakhsh and Erfan Hospital, Tehran, Iran
Sub-special Neurosurgery, Fayyazbakhsh and
Iran
Type-2 Fuzzy Expert System
Forward-Backward Chaining
Degenerative Disc Diseases
Diagnosis System
[[1] “Conditions Treated Degenerative Disc Disease,” Available:http://neurosurgery.ucla.edu/body.cfm?id=1123&ref=111&action=detail, Feb. 24.##[2] J. P.Revord. “Typical Symptoms of a Herniated Disc,” Available: http://www.spine-health.com/conditions/herniated-disc/typical-symptoms-a-herniated-disc, Feb. 17.##[3] R. Staehler. “Cervical Herniated Disc Symptoms and Treatment Options,” Available: http://www.spine-health.com/conditions/herniated-disc/cervical-herniated-disc-symptoms-and-treatment-options. Feb. 04.##[4] J. Koh, V. Chaudhary, E. K. Jeon, and G. Dhillon, "Automatic spinal canal detection in lumbar MR images in the sagittal view using dynamic programming," Computerized Medical Imaging and Graphics, vol. 38, pp. 569-579, 10//.##[5] R. Alomari, J. Corso, V. Chaudhary, and G. Dhillon, "Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model," in Computational Methods and Clinical Applications for Spine Imaging. vol. 17, J. Yao, T. Klinder, and S. Li, Eds., ed: Springer International Publishing, pp. 87-98.##[6] J. Koh, V. Chaudhary, and G. Dhillon, "Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier," Int J Comput Assist Radiol Surg, vol. 7, pp. 861-9, Nov 2012.##[7] S. Ghosh, R. S. Alomari, V. Chaudhary, and G. Dhillon, "Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI," Conf Proc IEEE Eng Med Biol Soc, vol. 2011, pp. 5068-71, 2011.##[8] S. Ghosh, R. S. Alomari, V. Chaudhary, and G. Dhillon, "Computer-aided diagnosis for lumbar mri using heterogeneous classifiers," in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, pp. 1179-1182, 2011.##[9] R. S. Alomari, J. J. Corso, V. Chaudhary, and G. Dhillon, "Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI," Int J Comput Assist Radiol Surg, vol. 5, pp. 287-93, May 2010.##[10] S. K. Michopoulou, L. Costaridou, E. Panagiotopoulos, R. Speller, G. Panayiotakis, and A. Todd-Pokropek, "Atlas-based segmentation of##degenerated lumbar intervertebral discs from MR images of the spine," IEEE Trans Biomed Eng, vol. 56, pp. 2225-31, Sep 2009.##[11] M. Toth-Tascau, D. I. Stoia, and D. Andrei, "Integrated methodology for a future expert system used in low back pain management," in Applied Computational Intelligence and Informatics (SACI), 2012 7th IEEE International Symposium on, pp. 315-320, 2012.##[12] O. A. R. M. a. O. A. D. Kandil, "The use of Knowledge Based Expert System Approach in Examining Causes of Low Back Pain in Computer users," Eur. J. Sci. Res, vol. 50, pp. 352-362, March 2011.##[13] M. A. Kadhim, M. A. Alam, and H. Kaur, "Design and Implementation of Fuzzy Expert System for Back pain Diagnosis," International Journal of Innovative Technology & Creative Engineering, IJITCE, vol. U, pp. 16-22, 2011.##[14] M. Sari, E. Gulbandilar, and A. Cimbiz, "Prediction of low back pain with two expert systems," J Med Syst, vol. 36, pp. 1523-7, Jun 2012.##[15] MatheMEDics. "EasyDiagnosis Modules," Internet: http://easydiagnosis.comlmodules.html, Jan. 20, 2012 [Feb. 08, 2012].##[16] J.M. Mendel, R.I. John, “Type-2 fuzzy sets made simple,” Fuzzy Systems, IEEE Transactions on, 2002.##[17] J.M. Mendel, R.I. John, F. Liu, Interval type-2 fuzzy logic systems made simple, IEEE Transactions on Fuzzy Systems vol. 14, no.6, pp. 808–821, 2006.##[18] J.M. Mendel, Type-2 fuzzy sets and systems: an overview, IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 20–29, 2007.##[19] I. B. Turksen, "Type 2 representation and reasoning for CWW," Fuzzy Sets and Systems, vol. 127, pp. 17-36, 4/1/ 2002.##[20] D. Wu, W.W. Tan, “A type-2 fuzzy logic controller for the liquid-level process,” in: Proc. IEEE International Conference on Fuzzy Systems, Hungary, pp. 953–958, 2004.##[21] M.H. Fazel Zarandi, S.R. Damirchi-Darasi, M. Izadi, I.B. Turksen, M.A. Ghahazi, "Fuzzy rule based expert system to diagnose spinal cord disorders," in Norbert Wiener in the 21st Century (21CW), IEEE Conference on , vol., no., pp.1-5, 24-26 June.##]