ORIGINAL_ARTICLE
NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different strategies of Game Theory are proposed to provide a competitive game for nonwinningneurons to participate in the learning phase and obtain more input patterns. The performanceof the proposed clustering analysis is evaluated and compared with that of the K-means, SOM andNG methods using different types of data. The clustering results of the proposed method and existingstate-of-the-art clustering methods are also compared which demonstrates a better accuracy of theproposed clustering method.
https://miscj.aut.ac.ir/article_850_f0ab9b6e8ac07d0e8ea61b493ea2e467.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
133
142
10.22060/miscj.2016.850
clustering
game theory
self-organizing map
vector quantization
M.
Ghayekhloo
m.ghayekhlou@gmail.com
true
1
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
M. B.
Menhaj
menhaj@aut.ac.ir
true
2
Dept. of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
R.
Azimi
azimi.rasool@gmail.com
true
3
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
E.
Shekari
e.shekari@gmail.com
true
4
Dept. of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Dept. of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Dept. of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
AUTHOR
[1] R. Duwairi, M. Abu-Rahmeh, A novel approach for initializing the spherical K-means clustering algorithm, Simulation Modelling Practice and Theory, 54 (2015) 49-63.
1
[2] H. Mashayekhi, J. Habibi, S. Voulgaris, M. van Steen, GoSCAN: Decentralized scalable data clustering, Computing, 95(9) (2013) 759-784.
2
[3] S.M.R. Zadegan, M. Mirzaie, F. Sadoughi, Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets, Knowledge-Based Systems, 39 (2013) 133-143.
3
[4] J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA., 1967, pp. 281-297.
4
[5] H.-S. Park, C.-H. Jun, A simple and fast algorithm for K-medoids clustering, Expert systems with applications, 36(2) (2009) 3336-3341.
5
[6] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, (1973).
6
[7] S. Miyamoto, K. Umayahara, Methods in hard and fuzzy clustering, in: Soft computing and human-centered machines, Springer, 2000, pp. 85-129.
7
[8] T. Johnson, S.K. Singh, Genetic algorithms based enhanced K Strange points clustering algorithm, in: Computing and Network Communications (CoCoNet), 2015 International Conference on, IEEE, 2015, pp. 737-741.
8
[9] A. Likas, N. Vlassis, J.J. Verbeek, The global k-means clustering algorithm, Pattern recognition, 36(2) (2003) 451-461.
9
[10] D. Arthur, S. Vassilvitskii, k-means++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.
10
[11] C. Zhang, D. Ouyang, J. Ning, An artificial bee colony approach for clustering, Expert Systems with Applications, 37(7) (2010) 4761-4767.
11
[12] W. Kwedlo, A clustering method combining differential evolution with the K-means algorithm, Pattern Recognition Letters, 32(12) (2011) 1613-1621.
12
[13] T. Kohonen, The self-organizing map, Proceedings of the IEEE, 78(9) (1990) 1464-1480.
13
[14] S. Wu, T.W. Chow, Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density, Pattern Recognition, 37(2) (2004) 175-188.
14
[15] Y. Dogan, D. Birant, A. Kut, SOM++: integration of self-organizing map and k-means++ algorithms, in: International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, 2013, pp. 246-259.
15
[16] A. Neme, S. Hernández, O. Neme, L. Hernández, Self-Organizing Maps with Non-cooperative Strategies (SOM-NC), in: WSOM, Springer, 2009, pp. 200-208.
16
[17] A.P. Engelbrecht, Computational intelligence: an introduction, John Wiley & Sons, 2007.
17
[18] L. Pavel, Game theory for control of optical networks, Springer Science & Business Media, 2012.
18
[19] J. Shen, S.I. Chang, E.S. Lee, Y. Deng, S.J. Brown, Determination of cluster number in clustering microarray data, Applied Mathematics and Computation, 169(2) (2005) 1172-1185.
19
[20] S. Subramani, S. Balasubramaniam, Post mining of diversified multiple decision trees for actionable knowledge discovery, in: International Conference on Advanced Computing, Networking and Security, Springer, 2011, pp. 179-187.
20
[21] T. Martinetz, K. Schulten, A" neural-gas" network learns topologies, (1991).
21
[22] B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii, Scalable k-means++, Proceedings of the VLDB Endowment, 5(7) (2012) 622-633.
22
[23] http://cs.uef.fi/sipu/datasets.
23
[24] https://archive.ics.uci.edu/ml/datasets.
24
ORIGINAL_ARTICLE
Thermo-Electro Mechanical Impedance based Structural Health Monitoring: Euler- Bernoulli Beam Modeling
In recent years, impedance measurement method by piezoelectric (PZT) wafer activesensor (PWAS) has been widely adopted for non-destructive evaluation (NDE). In this method, theelectrical impedance of a bonded PWAS is used to detect a structural defect. The electro-mechanicalcoupling of PZT materials constructs the original principle of this method. Accordingly, the electricalimpedance of PWAS can sense any change in the mechanical impedance of the structure. A thermalstress on a structure, which was generated by environmental temperature, could change the electricalimpedance of PWAS. The thermal stress which affects the output impedance of PWAS is alsodeveloped. A temperature-dependent model, the temperature dependency of PWAS, and structurematerial properties are investigated for a PWAS bonded to an Euler Bernoulli clamped-clamped beam.The Rayleigh-Ritz and spectral element methods are studied and, then, verified by 3D finite elementmethod (FEM).
https://miscj.aut.ac.ir/article_841_2effa710256b56de16a48c760007bd92.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
143
152
10.22060/miscj.2016.841
Thermal Stress
Euler Bernoulli Beam
Spectral Element Method
Impedance-based Structural Health
Monitoring
3D FEM
N.
Sepehry
naser.sepehry@gmail.com
true
1
Faculty of Mechanical and Mechatronics Engineering, Shahrood University of Technology, Shahrood, Iran
Faculty of Mechanical and Mechatronics Engineering, Shahrood University of Technology, Shahrood, Iran
Faculty of Mechanical and Mechatronics Engineering, Shahrood University of Technology, Shahrood, Iran
AUTHOR
F.
Bakhtiari-Nejad
baktiari@aut.ac.ir
true
2
Dept. of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
M.
Shamshirsaz
shamshir@aut.ac.ir
true
3
New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran
New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran
New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] V. Giurgiutiu, C. Rogers, Electro-mechanical (E/M) impedance method for structural health monitoring and nondestructive evaluation, Structural Health Monitoring—Current Status and Perspective, (1997) 18-20.
1
[2] G. Park, H.H. Cudney, D.J. Inman, Feasibility of using impedance‐based damage assessment for pipeline structures, Earthquake engineering & structural dynamics, 30(10) (2001) 1463-1474.
2
[3] S. Bhalla, A.S.K. Naidu, C.K. Soh, Influence of structure-actuator interactions and temperature on piezoelectric mechatronic signatures for NDE, in: Smart Materials, Structures, and Systems, International Society for Optics and Photonics, 2003, pp. 263-270.
3
[4] K.-Y. Koo, S. Park, J.-J. Lee, C.-B. Yun, Automated impedance-based structural health monitoring incorporating effective frequency shift for compensating temperature effects, Journal of Intelligent Material Systems and Structures, 20(4) (2009) 367-377.
4
[5] G. Park, K. Kabeya, H.H. Cudney, D.J. Inman, Impedance-based structural health monitoring for temperature varying applications, JSME International Journal Series A Solid Mechanics and Material Engineering, 42(2) (1999) 249-258.
5
[6] A. Bastani, H. Amindavar, M. Shamshirsaz, N. Sepehry, Identification of temperature variation and vibration disturbance in impedance-based structural health monitoring using piezoelectric sensor array method, Structural Health Monitoring, 11(3) (2012) 305-314.
6
[7] N. Sepehry, M. Shamshirsaz, F. Abdollahi, Temperature variation effect compensation in impedance-based structural health monitoring using neural networks, Journal of Intelligent Material Systems and Structures, 22(17) (2011) 1975-1982.
7
[8] N. Sepehry, M. Shamshirsaz, A. Bastani, Experimental and theoretical analysis in impedance-based structural health monitoring with varying temperature, Structural Health Monitoring, 10(6) (2011) 573-585.
8
[9] V. Giurgiutiu, Structural health monitoring: with piezoelectric wafer active sensors, Academic Press, 2007.
9
[10] A.N. Zagrai, V. Giurgiutiu, Electro-mechanical impedance method for crack detection in thin wall structures, in: 3rd Int. Workshop of Structural Health Monitoring, 2001, pp. 12-14.
10
[11] S. Bhalla, C.K. Soh, Electromechanical impedance modeling for adhesively bonded piezo-transducers, Journal of Intelligent Material Systems and Structures, 15(12) (2004) 955-972.
11
[12] D.M. Peairs, D.J. Inman, G. Park, Circuit analysis of impedance-based health monitoring of beams using spectral elements, Structural Health Monitoring, 6(1) (2007) 81-94.
12
[13] S. Bhalla, C.K. Soh, Structural health monitoring by piezo-impedance transducers. I: Modeling, Journal of Aerospace Engineering, 17(4) (2004) 154-165.
13
[14] W. Yan, W. Chen, C. Lim, J. Cai, Application of EMI technique for crack detection in continuous beams adhesively bonded with multiple piezoelectric patches, Mechanics of Advanced Materials and Structures, 15(1) (2008) 1-11.
14
[15] U. Lee, Spectral element method in structural dynamics, John Wiley & Sons, 2009.
15
[16] Y. Kiani, S. Taheri, M. Eslami, Thermal buckling of piezoelectric functionally graded material beams, Journal of Thermal Stresses, 34(8) (2011) 835-850.
16
ORIGINAL_ARTICLE
Generalized Aggregate Uncertainty Measure 2 for Uncertainty Evaluation of a Dezert-Smarandache Theory based Localization Problem
In this paper, Generalized Aggregated Uncertainty measure 2 (GAU2), as a newuncertainty measure, is considered to evaluate uncertainty in a localization problem in which cameras’images are used. The theory that is applied to a hierarchical structure for a decision making to combinecameras’ images is Dezert-Smarandache theory. To evaluate decisions, an analysis of uncertainty isexecuted at every level of the decision-making system. The second generalization of AggregatedUncertainty measure (GAU2) which is applicable for DSmT results is used as a supervisor. TheGAU2 measure in spite of the GAU1 measure can be applied to the problems with vague borders orcontinuous events. This measure may help to make decisions based on better preference combinationsof sensors or methods of fusion. GAU2 is used to evaluate uncertainty after applying classic DSmTand hybrid DSmT with extra knowledge. Therefore by using the decision making system, results withless uncertainty are generated in spite of high conflict sensory data.
https://miscj.aut.ac.ir/article_826_62c1ad4ffed85ea7ed0c44a461c36d2a.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
153
162
10.22060/miscj.2016.826
Data Fusion
Camera Image
Uncertainty Measurement
Dezert-Smarandache Theory
A.
Mohammad-Shahri
shahri@iust.ac.ir
true
1
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
LEAD_AUTHOR
M.
Khodabandeh
khodabandeh@hut.ac.ir
true
2
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
[1] P. Pong, S. Challa, Empirical analysis of generalized uncertainty measures with Dempster-Shafer fusion, in: 10th Int. Conf. on Information Fusion, Quebec, Que, 9-12 July, 2007.
1
[2] J. Esteban, Starr A., Willetts R., Hannah P., Bryanston-Cross P., A review of data fusion models and architectures: towards engineering guidelines, Neural Computing & Application, 14 (2005) 273-281.
2
[3] G. Shafer, A mathematical theory of evidence, Princeton Univ. Press, Princeton, NJ, 1976.
3
[4] R.R. Yager, Hedging in the combination of evidence, Journal of Information Optimization Science, 4 (1983) 73-81.
4
[5] R.R. Yager, On the Dempster–Shafer framework and new combination rules, Information Science, 41 (1987) 93-138.
5
[6] P. Smets, R. Kennes, The transferable belief model, Artificial Intelligence, 66(2) 191-234.
6
[7] D. Dubois, H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, 4 (1988) 244-264.
7
[8] J.P. Yang, H.Z. Huang, Y. Liu, Y.F. Li, Quantification classification algorithm of multiple sources of evidence, International Journal of Information Technology & Decision Making, 14(5) (2015) 1017-1034.
8
[9] J. Dezert, Foundation for a new theory of plausible and paradoxical reasoning, Information Security Journal, 9 (2002) 13-57.
9
[10] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 1, American Research Press, 2004.
10
[11] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 2, American Research Press, 2006.
11
[12] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 3, American Research Press, 2009.
12
[13] R.V.L. Hartley, Transmission of information, Bell Systems & Technology Journal, 7(535-563) (1928).
13
[14] C.E. Shannon, A mathematical theory of communication, Bell Systems & Technology Journal, 27 (1948) 379-423 and 623-656.
14
[15] G.J. Klir, M.J. Wierman, Uncertainty-based information 2nd ed. series, Studies in Fuzziness and Soft Computing 15, Physica–Verlag, Heidelberg, Germany, 1999.
15
[16] M. Beynon, D. Cosker, D. Marshall, An expert system for multicriteria decision making using Dempster Shafer theory, Expert Systems with Applications, 20 (2001) 357-367.
16
[17] Z. Hua, B. Gong, X. Xu, A DS–AHP approach for multiattribute decision making problem with incomplete information, Expert Systems with Applications, 34 (2008) 2221-2227.
17
[18] J. Dezert, J.M. Tacnet, M. Batton-Hubert, F. Smarandache, Multi- criteria decision making based on DSmT-AHP, in: Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2015.
18
[19] N. Abbas, Y. Chibani, A. Martin, F. Smarandache, The effective use of the DSmT for multi-class classification in: Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2015, pp. 359.
19
[20] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 4, American Research Press, 2015.
20
[21] R.R. Yager, Entropy and specificity in a mathematical theory of evidence, International Journal of General Systems, 9(4) (1983) 249-260.
21
[22] G.J. Klir, A. Ramer, Uncertainty in Dempster–Shafer theory: a critical re-examination, International Journal of General Systems, 18(2) (1990) 155-166.
22
[23] T. George, N.R. Pal, Quantification of conflict in Dempster–Shafer framework: a new approach, International Journal of General Systems, 24(4) (1996) 407-423.
23
[24] N.R. Pal, J.C. Bezdek, R. Hemasinha, Uncertainty measures for evidential reasoning I: a review, International Journal of Approximate Reasoning, 7(3/4) (1992) 165-183.
24
[25] N.R. Pal, J.C. Bezdek, R. Hemasinha, Uncertainty measures for evidential reasoning II: a new measure of total uncertainty, International Journal of Approximate Reasoning, 8(1) (1993) 1-16.
25
[26] A. Ramer, J. Hiller, Total uncertainty revisited, International Journal of General Systems, 26(3) (1997) 223-237.
26
[27] J.A. Herencia, M.T. Lamata, A generalization of entropy using Dempster–Shafer theory, International Journal of General Systems, 29(5) (2000) 719-735.
27
[28] Y. Maeda, H.T. Nguyen, H. Ichihashi, Maximum entropy algorithms for uncertainty measures, International Journal of Uncertainty, Fuzziness & Knowledge Based Systems, 1(1) (1993) 69-93.
28
[29] D. Harmanec, G.J. Klir, Measuring total uncertainty in Dempster–Shafer theory, International Journal of General Systems, 22(4) (1994) 405-419.
29
[30] D. Harmanec, Uncertainty in Dempster–Shafer theory, State University of New York, New York, NY, 1996.
30
[31] A.L. Jousselme, C. Liu, D. Grenier, E. Bosse, IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 36(5) (2006) 890-903.
31
[32] G.J. Klir, H.W. Lewis, Remarks on measuring ambiguity in the evidence theory, IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 38(4) (2008) 995-999.
32
[33] M. Vatsa, R. Singh, A. Noore, M.M. Houck, Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory, International Journal of Approximate Reasoning, 50 (2008) 51-61.
33
[34] M. Vatsa, R. Singh, A. Noore, Unification of evidence-theoretic fusion algorithms; a case study in level-2 and level-3 fingerprint features, IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 39(1) (2009) 47-56.
34
[35] G.J. Klir, Uncertainty and information, foundation of generalized information theory, John Wiley & Sons Inc., Hoboken, NJ, 2006.
35
[36] M. Khodabandeh, A. Mohammad-Shahri, Two generalizations of aggregated uncertainty measure for evaluation of Dezert-Smarandache theory, International Journal Information Technology & Decision Making (IJITDM), 11(1) (2012) 119-142.
36
[37] M. Khodabandeh, A. Mohammad-Shahri, Uncertainty evaluation for a Dezert-Smarandache theory based localization problem, International Journal of General Systems, 43(6) (2014) 610-632.
37
[38] M. Khodabandeh, A. Mohammad-Shahri, Uncertainty evaluation for an ultrasonic data fusion based target differentiation problem using Generalized Aggregated Uncertainty measure 2, Measurement, 59 (2015) 139-144.
38
[39] E. Garcia, Altamirano L., Multiple cameras fusion based on DSmT for tracking objects on ground plane, in: F. Smarandache, Dezert J. (Eds.) Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2009, pp. 691-704.
39
ORIGINAL_ARTICLE
Near-Optimal Controls of a Fuel Cell Coupled with Reformer using Singular Perturbation methods
A singularly perturbed model is proposed for a system comprised of a PEM Fuel Cell(PEM-FC) with Natural Gas Hydrogen Reformer (NG-HR). This eighteenth order system is decomposedinto slow and fast lower order subsystems using singular perturbation techniques that provides tools forseparation and order reduction. Then, three different types of controllers, namely an optimal full-order,a near-optimal composite controller based on the slow and the fast subsystems, and a near-optimalreduced-order controller based on the reduced-order model, are designed. The comparison of closedloopresponses of these three controllers shows that there are minimal degradations in the performanceof the composite and the reduced order controllers.
https://miscj.aut.ac.ir/article_872_be5eb01dfaf7d24ab72538250530a60b.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
163
172
10.22060/miscj.2016.872
singular perturbation technique
two-time scale systems
Schur decomposition method
near-optimal controller
slow/fast subsystems
S.
Nazem-Zadeh
nazemzadeh_sh@yahoo.com
true
1
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
M.T.
Hamidi-Beheshti
mbehesht@modares.ac.ir
true
2
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
[1] J.T. Pukrushpan, Modeling and control of fuel cell systems and fuel processors, University of Michigan Ann Ar bor, Michigan, USA, 2003.
1
[2] J.T. Pukrushpan, A.G. Stefanopoulou, H. Peng, Control of fuel cell breathing, IEEE Control Systems, 24(2) (2004) 30-46.
2
[3] Pukrushpan, J., Stefanopoulou, A., and Peng, H., Control of fuel cell breathing: Initial Results on the oxygen starvation problem, Ann Arbor, National Science Foundation & Automotive Research Center of University of Michigan.
3
[4] J.T. Pukrushpan, A.G. Stefanopoulou, H. Peng, Control of fuel cell power systems: principles, modeling, analysis and feedback design, Springer Science & Business Media, 2004.
4
[5] V. Tsourapas, A.G. Stefanopoulou, J. Sun, Model-based control of an integrated fuel cell and fuel processor with exhaust heat recirculation, IEEE Transactions on control systems technology, 15(2) (2007) 233-245.
5
[6] K. Kodra, Z. Gajic, Order reduction via balancing and suboptimal control of a fuel cell–reformer system, International Journal of Hydrogen Energy, 39(5) (2014) 2215-2223.
6
[7] J. OReilly, P. Kokotovic, H. Khalil, Singular Perturbation Methods in Control: Analysis and Design, in, Academic Press New York, 1986.
7
[8] M. Skataric, Z. Gajic, Slow and fast dynamics of a natural gas hydrogen reformer, International Journal of Hydrogen Energy, 38(35) (2013) 15173-15179.
8
[9] D.S. Naidu, Singular perturbation methodology in control systems, IET, 1988.
9
[10] A. Rao, S. Lamba, S. Rao, Comments on" A note on selecting a low-order system by Davison's model simplification technique, IEEE Transactions on Automatic Control, 24(1) (1979) 141-142.
10
[11] K.B. Datta, Matrix and linear algebra, Prentice-Hall of India New Delhi, India, 1991.
11
[12] B. Noble, J.W. Daniel, Applied linear algebra, Prentice-Hall New Jersey, 1988.
12
[13] A.J. Fossard, M. Berthelot, J. Magni, On coherency-based decomposition algorithms, Automatica, 19(3) (1983) 247-253.
13
ORIGINAL_ARTICLE
A Hybrid Modeling for Continuous Casting Scheduling Problem
This paper deals with a multi-agent-based interval type-2 fuzzy (IT2F) expert systemfor scheduling steel continuous casting. Continuous caster scheduling is a complex and extensiveprocess that needs expert staff. In this study, a distributed multi-agent-based structure is proposed as asolution. The agents used herein can cooperate with each other via various communication protocols.To facilitate such communication, an appropriate negotiation protocol (i.e., contract net protocol)is proposed. The due dates specified by expert staff are represented by IT2F membership functions(MFs). As a part of the objective functions, a simple procedure is proposed to calculate the totalearliness and tardiness penalty when the due date’s MFs are IT2F. The proposed hybrid multi-agentbasedsystem combines the multi-agent systems with type-2 fuzzy concepts which conforms to thereal-world continuous casting problem.
https://miscj.aut.ac.ir/article_891_7933ef1a4ae92bc899f1c0c44ef86358.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
173
180
10.22060/miscj.2017.11583.4946
Steel production
Continuous caster scheduling
Agent-based system
Negotiation
Fuzzy system
M. H.
Fazel Zarandi
zarandi@aut.ac.ir
true
1
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
F.
Kashani Azad
f_kashani90@aut.ac.ir
true
2
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
A. H.
Karimi Kashani
karimikashani54@yahoo.com
true
3
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] D. Ouelhadj, A multi-agent system for the integrated dynamic scheduling of steel production, University of Nottingham, 2003.
1
[2] P.I. Cowling, D. Ouelhadj, S. Petrovic, Dynamic scheduling of steel casting and milling using multi-agents, Production Planning & Control, 15(2) (2004) 178-188.
2
[3] Y. Li, J.-Q. Zheng, S.-L. Yang, Multi-agent-based fuzzy scheduling for shop floor, The International Journal of Advanced Manufacturing Technology, 49(5) (2010) 689-695.
3
[4] M.F. Zarandi, P. Ahmadpour, Fuzzy agent-based expert system for steel making process, Expert systems with applications, 36(5) (2009) 9539-9547.
4
[5] O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz, Type-2 fuzzy logic: theory and applications, in: Granular Computing, 2007. GRC 2007. IEEE International Conference on, IEEE, 2007, pp. 145-145.
5
[6] N.N. Karnik, J.M. Mendel, Operations on type-2 fuzzy sets, Fuzzy sets and systems, 122(2) (2001) 327-348.
6
[7] J.M. Mendel, R.B. John, Type-2 fuzzy sets made simple, IEEE Transactions on fuzzy systems, 10(2) (2002) 117-127.
7
[8] M.F. Zarandi, R. Gamasaee, Type-2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process, Soft Computing, 16(8) (2012) 1287-1302.
8
[9] Q. Liang, J.M. Mendel, Interval type-2 fuzzy logic systems: theory and design, IEEE Transactions on Fuzzy systems, 8(5) (2000) 535-550.
9
[10] J. Dorn, W. Slany, A flow shop with compatibility constraints in a steelmaking plant, na, 1994.
10
[11] J. Dorn, Iterative improvement methods for knowledge-based scheduling, AI communications, 8(1) (1995) 20-34.
11
[12] F. Liu, J.M. Mendel, An interval approach to fuzzistics for interval type-2 fuzzy sets, in: Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International, IEEE, 2007, pp. 1-6.
12
[13] B. Lally, L. Biegler, H. Henein, A model for sequencing a continuous casting operation to minimize costs, Mathematical Modelling of Materials Processing Operations, (1987) 1157-1172.
13
[14] L. Tang, J. Liu, A. Rong, Z. Yang, A mathematical programming model for scheduling steelmaking-continuous casting production, European Journal of Operational Research, 120(2) (2000) 423-435.
14
[15] S.Y. Chang, M.-R. Chang, Y. Hong, A lot grouping algorithm for a continuous slab caster in an integrated steel mill, Production planning & control, 11(4) (2000) 363-368.
15
ORIGINAL_ARTICLE
Developing a Model for Measuring Severity of Effects Caused by Interconnected Units in Electronic Supply Chains
For many electronic supply chain networks in the world that can comprise hundreds ofcompanies with several tiers of suppliers and intermediate customers, there are numerous presentingrisks to consider. In the electronic supply chain, the situation are even worse, for the characteristics ofthis supply chain: excessive lean management, global sourcing and the rather more uncertain marketdemand. Electronic companies are forced to manage their supply chains effectively to increase efficiencyand reactivity. This paper proposes a mathematical model for estimating the severity of interactionsbetween supply chain’s units and how their affect on the entire supply chain. Based on the model,scholars can model supply chains easily with considering interconnected units. Basic characteristics ofsupply chains are considered in the model. The units, which are used to simulate the members of supplychains, produce appropriate products by intelligent choices. The relationships on units are connected bytheir activities ; then, the proposed model is applied to an experimental example. The model yields itsnumerical parameters and responses by means of Lingo software.
https://miscj.aut.ac.ir/article_928_1afc3206ba6ce995a47c88b84e962235.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
181
186
10.22060/miscj.2017.11487.4943
Supply chain
Interconnected units
Propagated effect
A.
Kazemi
abkaazemi@gmail.com
true
1
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
L.
Ahmadpour
leila8061@gmail.com
true
2
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
[1] G. Applequist, J. Pekny, G. Reklaitis, Risk and uncertainty in managing chemical manufacturing supply chains, Computers & Chemical Engineering, 24(9-10) (2000) 2211-2222.
1
[2] T. Assavapokee, W. Wongthatsanekorn, Reverse production system infrastructure design for electronic products in the state of Texas, Computers & Industrial Engineering, 62(1) (2012) 129-140.
2
[3] W.H. Baker, L. Wallace, Is information security under control?: Investigating quality in information security management, IEEE Security & Privacy, 5(1) (2007).
3
[4] S. Bose, J. Pekny, A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain, Computers & Chemical Engineering, 24(2-7) (2000) 329-335.
4
[5] M.A. Cohen, H.L. Lee, Resource deployment analysis of global manufacturing and distribution networks, Journal of manufacturing and operations management, 2(2) (1989) 81-104.
5
[6] C.W. Craighead, J. Blackhurst, M.J. Rungtusanatham, R.B. Handfield, The severity of supply chain disruptions: design characteristics and mitigation capabilities, Decision Sciences, 38(1) (2007) 131-156.
6
[7] F.D. Mele, G. Guillén, A. Espuna, L. Puigjaner, An agent-based approach for supply chain retrofitting under uncertainty, Computers & chemical engineering, 31(5-6) (2007) 722-735.
7
[8] P. Georgiadis, M. Besiou, Sustainability in electrical and electronic equipment closed-loop supply chains: a system dynamics approach, Journal of Cleaner Production, 16(15) (2008) 1665-1678.
8
[9] A. Gupta, C.D. Maranas, A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty, Industrial & Engineering Chemistry Research, 39(10) (2000) 3799-3813.
9
[10] A. Gupta, C.D. Maranas, Managing demand uncertainty in supply chain planning, Computers & chemical engineering, 27(8-9) (2003) 1219-1227.
10
[11] J.K. Deane, C.T. Ragsdale, T.R. Rakes, L.P. Rees, Managing supply chain risk and disruption from IT security incidents, Operations Management Research, 2(1-4) (2009) 4.
11
[12] J. Li, F.T. Chan, An agent-based model of supply chains with dynamic structures, Applied Mathematical Modelling, 37(7) (2013) 5403-5413.
12
[13] Y. Li, Z. Lin, L. Xu, A. Swain, “Do the electronic books reinforce the dynamics of book supply chain market?”–A theoretical analysis, European Journal of Operational Research, 245(2) (2015) 591-601.
13
[14] P.-K. Marhavilas, D. Koulouriotis, V. Gemeni, Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000–2009, Journal of Loss Prevention in the Process Industries, 24(5) (2011) 477-523.
14
[15] A. Mele, Asymmetric stock market volatility and the cyclical behavior of expected returns, Journal of financial economics, 86(2) (2007) 446-478.
15
[16] S. Mohebbi, X. Li, Designing intelligent agents to support long-term partnership in two echelon e-Supply Networks, Expert Systems with Applications, 39(18) (2012) 13501-13508.
16
[17] A. Nagurney, J. Cruz, J. Dong, D. Zhang, Supply chain networks, electronic commerce, and supply side and demand side risk, European journal of operational research, 164(1) (2005) 120-142.
17
[18] E. Perea-Lopez, B.E. Ydstie, I.E. Grossmann, A model predictive control strategy for supply chain optimization, Computers & Chemical Engineering, 27(8-9) (2003) 1201-1218.
18
[19] M. Sakawa, I. Nishizaki, Y. Uemura, Fuzzy programming and profit and cost allocation for a production and transportation problem, European Journal of Operational Research, 131(1) (2001) 1-15.
19
[20] D. Simchi-Levi, E. Simchi-Levi, P. Kaminsky, Designing and managing the supply chain: Concepts, strategies, and cases, McGraw-Hill New York, 1999.
20
[21] G. Stoneburner, A.Y. Goguen, A. Feringa, Sp 800-30. risk management guide for information technology systems, (2002).
21
[22] C.H. Timpe, J. Kallrath, Optimal planning in large multi-site production networks, European Journal of Operational Research, 126(2) (2000) 422-435.
22
[23] W. Wang, Y. Zhang, Y. Li, X. Zhao, M. Cheng, Closed-loop supply chains under reward-penalty mechanism: Retailer collection and asymmetric information, Journal of cleaner production, 142 (2017) 3938-3955.
23
ORIGINAL_ARTICLE
Partial Observation in Distributed Supervisory Control of Discrete-Event Systems
Distributed supervisory control is a method to synthesize local controllers in discrete-eventsystems with a systematic observation of the plant. Some works were reported on extending this methodby which local controllers are constructed so that observation properties are preserved from monolithic todistributed supervisory control, in an up-down approach. In this paper, we find circumstances in whichobservation properties are preserved from monolithic to distributed supervisory control. Local observationproperties, i.e. local normality and local relative observability are employed for investigating observationproperties of each local controller, which are constructed by any localization algorithm that preserves controlequivalency to the monolithic supervisor with respect to the plant. These properties enable us to investigatethe observation properties from monolithic to distributed supervisory control. Moreover, observationequivalence property is defined according to the control equivalence in a distributed supervisory controlwith partial observation. It is proved that with preserving observation equivalence of the local controllers tothe monolithic supervisor, the control equivalence is satisfied, if and only if the intersection of local eventsets is a subset of or equal to the global observable event set.
https://miscj.aut.ac.ir/article_929_16b8a43c2081f9e64e34c7aedf3b6d5e.pdf
2017-12-01T11:23:20
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187
198
10.22060/miscj.2017.12041.4997
Distributed Supervisory Control
Local Normality
Local Relative Observability
Observation Equivalent
V.
Saeidi
vahidsaidi@gmail.com
true
1
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
AUTHOR
A.
Afzalian
afzalian@sbu.ac.ir
true
2
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
AUTHOR
D.
Gharavian
d_gharavian@sbu.ac.ir
true
3
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
AUTHOR
[1] P.J. Ramadge, W.M. Wonham, Supervisory control of a class of discrete event processes, SIAM journal on control and optimization, 25(1) (1987) 206-230.
1
[2] F. Lin, W.M. Wonham, Decentralized control and coordination of discrete-event systems with partial observation, IEEE Transactions on automatic control, 35(12) (1990) 1330-1337.
2
[3] J. Komenda, J.H. van Schuppen, Modular control of discrete-event systems with coalgebra, IEEE Transactions on Automatic Control, 53(2) (2008) 447-460.
3
[4] H. Zhong, W.M. Wonham, On the consistency of hierarchical supervision in discrete-event systems, IEEE Transactions on automatic Control, 35(10) (1990) 1125-1134.
4
[5] K.C. Wong, W.M. Wonham, Hierarchical control of discrete-event systems, Discrete Event Dynamic Systems, 6(3) (1996) 241-273.
5
[6] K.C. Wong, W.M. Wonham, Modular control and coordination of discrete-event systems, Discrete Event Dynamic Systems, 8(3) (1998) 247-297.
6
[7] K. Schmidt, T. Moor, S. Perk, Nonblocking hierarchical control of decentralized discrete event systems, IEEE Transactions on Automatic Control, 53(10) (2008) 2252-2265.
7
[8] K. Schmidt, C. Breindl, Maximally permissive hierarchical control of decentralized discrete event systems, IEEE Transactions on Automatic Control, 56(4) (2011) 723-737.
8
[9] L. Feng, W.M. Wonham, Supervisory control architecture for discrete-event systems, IEEE Transactions on Automatic Control, 53(6) (2008) 1449-1461.
9
[10] T.-S. Yoo, S. Lafortune, A general architecture for decentralized supervisory control of discrete-event systems, Discrete Event Dynamic Systems, 12(3) (2002) 335-377.
10
[11] K. Rudie, W.M. Wonham, Think globally, act locally: Decentralized supervisory control, IEEE transactions on automatic control, 37(11) (1992) 1692-1708.
11
[12] K. Cai, R. Zhang, W.M. Wonham, On relative observability of discrete-event systems, in: Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on, IEEE, 2013, pp. 7285-7290.
12
[13] J. Komenda, T. Masopust, J.H. van Schuppen, Coordination control of discrete-event systems revisited, Discrete Event Dynamic Systems, 25(1-2) (2015) 65-94.
13
[14] J. Komenda, T. Masopust, J.H. van Schuppen, On conditional decomposability, Systems & Control Letters, 61(12) (2012) 1260-1268.
14
[15] K. Cai, W.M. Wonham, Supervisor localization: a top-down approach to distributed control of discrete-event systems, IEEE Transactions on Automatic Control, 55(3) (2010) 605-618.
15
[16] F. Lin, W.M. Wonham, On observability of discrete-event systems, Information sciences, 44(3) (1988) 173-198.
16
[17] K. Cai, R. Zhang, W.M. Wonham, Relative observability of discrete-event systems and its supremal sublanguages, IEEE Transactions on Automatic Control, 60(3) (2015) 659-670.
17
[18] E. José, N. Patrícia, L. Stéphane, Verification of Nonconflict of Supervisors Using Abstractions, (2009).
18
[19] R. Zhang, K. Cai, W.M. Wonham, Supervisor localization of discrete-event systems under partial observation, Automatica, 81 (2017) 142-147.
19
[20] V. Saeidi, A.A. Afzalian, D. Gharavian, Distributed supervisory control with partial observation, in: Control, Instrumentation, and Automation (ICCIA), 2016 4th International Conference on, IEEE, 2016, pp. 136-141.
20
[21] S. Mohajerani, R. Malik, M. Fabian, An algorithm for weak synthesis observation equivalence for compositional supervisor synthesis, IFAC Proceedings Volumes, 45(29) (2012) 239-244.
21
[22] M. Noorbakshsh, A. Afzalian, Design and PLC based implementation of supervisory control for under-load tap-changing transformers, in: Control, Automation and Systems, 2007. ICCAS'07. International Conference on, IEEE, 2007, pp. 901-906.
22
[23] A. Afzalian, A. Saadatpoor, W. Wonham, Systematic supervisory control solutions for under-load tap-changing transformers, Control Engineering Practice, 16(9) (2008) 1035-1054.
23
[24] R. Zhang, K. Cai, Y. Gan, W.M. Wonham, Distributed supervisory control of discrete-event systems with communication delay, Discrete Event Dynamic Systems, 26(2) (2016) 263-293.
24
[25] W. Wonham, Control design software: TCT, Developed by Systems Control Group, Univ. Toronto. Toronto, Canada, (2014).
25
ORIGINAL_ARTICLE
Saturated Neural Adaptive Robust Output Feedback Control of Robot Manipulators:An Experimental Comparative Study
In this study, an observer-based tracking controller is proposed and evaluatedexperimentally to solve the trajectory tracking problem of robotic manipulators with the torque saturationin the presence of model uncertainties and external disturbances. In comparison with the state-of-the-artobserver-based controllers in the literature, this paper introduces a saturated observer-based controllerbased on a radial basis function neural network. This technique helps the controller produce feasiblecontrol signals for the robot actuators. As a result, it efficiently diminishes the actuators saturation riskand consequently, a better transient performance is obtained. The stability analyses of the dynamicsof the tracking errors and state estimation errors are given with the help of a Lyapunov-based stabilityanalysis method. The theoretical analyses will systematically prove that the errors are semi-globallyuniformly ultimately bounded and they converge to a small set around the origin whose size is adjustableby a suitable tuning of parameters. At last, some real experiments are performed on a laboratory roboticarm to illustrate the efficiency of the proposed control system for real industrial applications.
https://miscj.aut.ac.ir/article_927_8fb0769fff0992f1289a320fb75d553d.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
199
208
10.22060/miscj.2017.12177.5010
Actuator saturation
Adaptive robust control
Observer-based control
RBF neural networks
Robot manipulators
M.
Pourrahim
pde.mohammad@gmail.com
true
1
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
AUTHOR
K.
Shojaei
khoshnam.shojaee@gmail.com
true
2
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
LEAD_AUTHOR
A.
Chatraei
abbas.chatraei@gmail.com
true
3
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
AUTHOR
O.
Shahnazari
dep_omid@yahoo.com
true
4
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
AUTHOR
[1] H. Berghuis, H. Nijmeijer, A passivity approach to controller-observer design for robots, IEEE Transactions on robotics and automation, 9(6) (1993) 740-754.
1
[2] M.A. Arteaga, R. Kelly, Robot control without velocity measurements: New theory and experimental results, IEEE Transactions on Robotics and Automation, 20(2) (2004) 297-308.
2
[3] D.J. López-Araujo, A. Zavala-Río, V. Santibáñez, F. Reyes, Output-feedback adaptive control for the global regulation of robot manipulators with bounded inputs, International Journal of Control, Automation and Systems, 11(1) (2013) 105-115.
3
[4] M. Mendoza, A. Zavala-Río, V. Santibáñez, F. Reyes, Output-feedback proportional–integral–derivative-type control with simple tuning for the global regulation of robot manipulators with input constraints, IET Control Theory & Applications, 9(14) (2015) 2097-2106.
4
[5] W.E. Dixon, Adaptive regulation of amplitude limited robot manipulators with uncertain kinematics and dynamics, IEEE Transactions on Automatic Control, 52(3) (2007) 488-493.
5
[6] C. Huang, X. Peng, C. Jia, J. Huang, Guaranteed robustness/performance adaptive control with limited torque for robot manipulators, Mechatronics, 18(10) (2008) 641-652.
6
[7] W.E. Dixon, M.S. de Queiroz, F. Zhang, D.M. Dawson, Tracking control of robot manipulators with bounded torque inputs, Robotica, 17(2) (1999) 121-129.
7
[8] E. Aguiñaga-Ruiz, A. Zavala-Río, V. Santibanez, F. Reyes, Global trajectory tracking through static feedback for robot manipulators with bounded inputs, IEEE Transactions on Control Systems Technology, 17(4) (2009) 934-944.
8
[9] A. Laib, Adaptive output regulation of robot manipulators under actuator constraints, IEEE Transactions on Robotics and Automation, 16(1) (2000) 29-35.
9
[10] Y. Su, P.C. Muller, C. Zheng, Global asymptotic saturated PID control for robot manipulators, IEEE Transactions on Control Systems Technology, 18(6) (2010) 1280-1288.
10
[11] V. Santibañez, K. Camarillo, J. Moreno-Valenzuela, R. Campa, A practical PID regulator with bounded torques for robot manipulators, International Journal of Control, Automation and Systems, 8(3) (2010) 544-555.
11
[12] A. Loria, R. Kelly, R. Ortega, V. Santibanez, On global output feedback regulation of Euler-Lagrange systems with bounded inputs, IEEE Transactions on Automatic Control, 42(8) (1997) 1138-1143.
12
[13] J. Moreno-Valenzuela, V. Santibáñez, R. Campa, On output feedback tracking control of robot manipulators with bounded torque input, International Journal of Control, Automation, and Systems, 6(1) (2008) 76-85.
13
[14] F.L. Lewis, D.M. Dawson, C.T. Abdallah, Robot manipulator control: theory and practice, CRC Press, 2003.
14
[15] M.W. Spong, S. Hutchinson, M. Vidyasagar, Robot modeling and control, Wiley New York, 2006.
15
[16] P.A. Ioannou, J. Sun, Robust adaptive control, PTR Prentice-Hall Upper Saddle River, NJ, 1996.
16
[17] B. Yao, Adaptive robust control of nonlinear systems with application to control of mechanical systems, University of California, Berkeley, 1996.
17
[18] L. Xu, B. Yao, Output feedback adaptive robust precision motion control of linear motors, Automatica, 37(7) (2001) 1029-1039.
18
[19] K. Shojaei, A. Chatraei, A Saturating Extension of an Output Feedback Controller for Internally Damped Euler‐Lagrange Systems, Asian Journal of Control, 17(6) (2015) 2175-2187.
19
[20] M. Pourrahim, K. Shojaei, A. Chatraei, O.S. Nazari, Experimental evaluation of a saturated output feedback controller using RBF neural networks for SCARA robot IBM 7547, in: Electrical Engineering (ICEE), 2016 24th Iranian Conference on, IEEE, 2016, pp. 1347-1352.
20
ORIGINAL_ARTICLE
Adaptive Control Strategy for a Bilateral Tele- Surgery System Interacting with Active Soft Tissues
In this paper, the problem of control and stabilization of a bilateral tele-surgery roboticsystem in interaction with an active soft tissue is considered. To the best of the authors’ knowledge, theprevious works did not consider a realistic model for a moving soft tissue like heart tissue in beating heartsurgery. Here, a new model is proposed to indicate significant characteristics of a moving soft tissue,rolling as the teleoperation system environment. The model is formed by a parallel combination of aviscoelastic passive part and an active part. Furthermore, the delays in communication and parameteruncertainties of the master and slave robot dynamics are considered. Using an adaptive control strategy,the ultimate boundedness of the system trajectories while interacting with the active environment iscertified, and this ultimate bound is calculated. Moreover, to evaluate the theoretical results, simulationresults are presented.
https://miscj.aut.ac.ir/article_931_282271bd5de6a49ee9ee04e5d61d6a2b.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
209
216
10.22060/miscj.2017.12074.5000
active soft tissue
viscoelastic model
bilateral tele robotic surgery
communication time delay
Adaptive control
M.
Sharifi
sharifi.m@ut.ac.ir
true
1
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
H. A.
Talebi
alit@aut.ac.ir
true
2
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
[1] N. Chopra, M.W. Spong, R. Lozano, Synchronization of bilateral teleoperators with time delay, Automatica, 44(8) (2008) 2142-2148.
1
[2] N. Chopra, M.W. Spong, Passivity-based control of multi-agent systems, in: Advances in robot control, Springer, 2006, pp. 107-134.
2
[3] N. Chopra, M.W. Spong, R. Ortega, N.E. Barabanov, On tracking performance in bilateral teleoperation, IEEE Transactions on Robotics, 22(4) (2006) 861-866.
3
[4] X. Liu, M. Tavakoli, Inverse dynamics-based adaptive control of nonlinear bilateral teleoperation systems, in: Robotics and Automation (ICRA), 2011 IEEE International Conference on, IEEE, 2011, pp. 1323-1328.
4
[5] X. Liu, R. Tao, M. Tavakoli, Adaptive control of uncertain nonlinear teleoperation systems, Mechatronics, 24(1) (2014) 66-78.
5
[6] N. Chopra, M.W. Spong, R. Lozano, Synchronization of bilateral teleoperators with time delay, Automatica, 44(8) (2008) 2142-2148.
6
[7] E. Nuño, R. Ortega, L. Basañez, An adaptive controller for nonlinear teleoperators, Automatica, 46(1) (2010) 155-159.
7
[8] I.G. Polushin, P.X. Liu, C.-H. Lung, G.D. On, Position-error based schemes for bilateral teleoperation with time delay: theory and experiments, Journal of dynamic systems, measurement, and control, 132(3) (2010) 031008.
8
[9] C.-C. Hua, X.P. Liu, Delay-dependent stability criteria of teleoperation systems with asymmetric time-varying delays, IEEE Transactions on Robotics, 26(5) (2010) 925-932.
9
[10] F. Hashemzadeh, M. Tavakoli, Position and force tracking in nonlinear teleoperation systems under varying delays, Robotica, 33(4) (2015) 1003-1016.
10
[11] P. Moreira, N. Zemiti, C. Liu, P. Poignet, Viscoelastic model based force control for soft tissue interaction and its application in physiological motion compensation, Computer methods and programs in biomedicine, 116(2) (2014) 52-67.
11
[12] L. Loeffler, K. Sagawa, A one-dimensional viscoelastic model of cat heart muscle studied by small length perturbations during isometric contraction, Circulation research, 36(4) (1975) 498-512.
12
[13] Y.-c. Fung, Biomechanics: mechanical properties of living tissues, Springer Science & Business Media, 2013.
13
[14] L. Barbé, B. Bayle, M. de Mathelin, A. Gangi, In vivo model estimation and haptic characterization of needle insertions, The International Journal of Robotics Research, 26(11-12) (2007) 1283-1301.
14
[15] W. Bachta, P. Renaud, E. Laroche, A. Forgione, J. Gangloff, Cardiolock: An active cardiac stabilizer. First in vivo experiments using a new robotized device, Computer Aided Surgery, 13(5) (2008) 243-254.
15
[16] F.L. Lewis, C.T. Abdallah, D.M. Dawson, Control of robot manipulators, Macmillan New York, 1993.
16
[17] P. Moreira, C. Liu, N. Zemiti, P. Poignet, Soft tissue force control using active observers and viscoelastic interaction model, in: Robotics and Automation (ICRA), 2012 IEEE International Conference on, IEEE, 2012, pp. 4660-4666.
17
[18] H.K. Khalil, Nonlinear systems. 2002, ISBN, 130673897 (2002) 9780130673893.
18
[19] P. Jordan, S. Socrate, T. Zickler, R. Howe, Constitutive modeling of porcine liver in indentation using 3D ultrasound imaging, Journal of the mechanical behavior of biomedical materials, 2(2) (2009) 192-201.
19
[20] Y. Kobayashi, A. Onishi, H. Watanabe, T. Hoshi, K. Kawamura, M.G. Fujie, In vitro validation of viscoelastic and nonlinear physical model of liver for needle insertion simulation, in: Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, IEEE, 2008, pp. 469-476.
20
[21] S. Bhasin, K. Dupree, P.M. Patre, W.E. Dixon, Neural network control of a robot interacting with an uncertain viscoelastic environment, IEEE Transactions on Control Systems Technology, 19(4) (2011) 947-955.
21
[22] M. Sharifi, H.A. Talebi, Adaptive control of a telerobotic surgery system interacting with non-passive soft tissues, in: Control, Instrumentation, and Automation (ICCIA), 2016 4th International Conference on, IEEE, 2016, pp. 214-219.
22
ORIGINAL_ARTICLE
3-RPS Parallel Manipulator Dynamical Modelling and Control Based on SMC and FL Methods
In this paper, a dynamical model-based SMC (Sliding Mode Control) is proposed fortrajectory tracking of a 3-RPS (Revolute, Prismatic, Spherical) parallel manipulator. With ignoring smallinertial effects of all legs and joints compared with those of the end-effector of 3-RPS, the dynamical model ofthe manipulator is developed based on Lagrange method. By removing the unknown Lagrange multipliers, thedistribution matrix of control input vector disappears from the dynamical equations. Therefore, the calculationof the aforementioned matrix is not required for modeling the manipulator. It in trun results in decreasedmathematical manipulation and low computational burden. As a robust nonlinear control technique, a SMCsystem is designed for the tracking of the 3-RPS manipulator. According to Lyapunov’s direct method, theasymptotic stability and the convergence of 3-RPS manipulator to the desired reference trajectories areproved. Based on computer simulations, the robust performance of the proposed SMC system is evaluatedwith respect to FL (feedback linearization) method. The proposed model and control algorithms can beextended to different kinds of holonomic and non-holonomic constrained parallel manipulators.
https://miscj.aut.ac.ir/article_992_2482dcbd28346f86423d607657ec9d64.pdf
2017-12-01T11:23:20
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217
226
10.22060/miscj.2017.11945.4985
Parallel manipulator
Dynamic modeling
Trajectory tracking
Feedback linearization
sliding mode control
M.
Shahidi
m.shahidi@tabrizu.ac.ir
true
1
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
LEAD_AUTHOR
J.
Keighobadi
keighobadi@tabrizu.ac.ir
true
2
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
A. R.
Khoogar
khoogar@yahoo.com
true
3
Department of Mechanical Engineering, Maleke-Ashtar University of Technology, Tehran, Iran
Department of Mechanical Engineering, Maleke-Ashtar University of Technology, Tehran, Iran
Department of Mechanical Engineering, Maleke-Ashtar University of Technology, Tehran, Iran
AUTHOR
[1] P. Nanua, K.J. Waldron, V. Murthy, Direct kinematic solution of a Stewart platform, IEEE Transactions on Robotics and Automation, 6(4) (1990) 438-444.
1
[2] P. Ji, H. Wu, A closed-form forward kinematics solution for the 6-6/sup p/Stewart platform, IEEE Transactions on robotics and automation, 17(4) (2001) 522-526.
2
[3] J. Schadlbauer, D. Walter, M. Husty, The 3-RPS parallel manipulator from an algebraic viewpoint, Mechanism and Machine Theory, 75 (2014) 161-176.
3
[4] J.-P. Merlet, Parallel robots, Springer Science & Business Media, 2006.
4
[5] J.-P. Merlet, Direct kinematics of parallel manipulators, IEEE transactions on robotics and automation, 9(6) (1993) 842-846.
5
[6] C.-f. Yang, S.-t. Zheng, J. Jin, S.-b. Zhu, J.-w. Han, Forward kinematics analysis of parallel manipulator using modified global Newton-Raphson method, Journal of Central South University of Technology, 17(6) (2010) 1264-1270.
6
[7] W.-H. Ding, H. Deng, Q.-M. Li, Y.-M. Xia, Control-orientated dynamic modeling of forging manipulators with multi-closed kinematic chains, Robotics and Computer-Integrated Manufacturing, 30(5) (2014) 421-431.
7
[8] S.-H. Lee, J.-B. Song, W.-C. Choi, D. Hong, Position control of a Stewart platform using inverse dynamics control with approximate dynamics, Mechatronics, 13(6) (2003) 605-619.
8
[9] M.-J. Liu, C.-X. Li, C.-N. Li, Dynamics analysis of the Gough-Stewart platform manipulator, IEEE Transactions on Robotics and Automation, 16(1) (2000) 94-98.
9
[10] W. Khalil, S. Guegan, Inverse and direct dynamic modeling of Gough-Stewart robots, IEEE Transactions on Robotics, 20(4) (2004) 754-761.
10
[11] H. Pendar, M. Vakil, H. Zohoor, Efficient dynamic equations of 3-RPS parallel mechanism through Lagrange method, in: Robotics, Automation and Mechatronics, 2004 IEEE Conference on, IEEE, 2004, pp. 1152-1157.
11
[12] E. Özgür, N. Andreff, P. Martinet, Linear dynamic modeling of parallel kinematic manipulators from observable kinematic elements, Mechanism and Machine Theory, 69 (2013) 73-89.
12
[13] M. Diaz-Rodriguez, A. Valera, V. Mata, M. Valles, Model-based control of a 3-DOF parallel robot based on identified relevant parameters, IEEE/ASME Transactions on Mechatronics, 18(6) (2013) 1737-1744.
13
[14] M. Zeinali, L. Notash, Adaptive sliding mode control with uncertainty estimator for robot manipulators, Mechanism and Machine Theory, 45(1) (2010) 80-90.
14
[15] J. Cazalilla, M. Vallés, V. Mata, M. Díaz-Rodríguez, A. Valera, Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models, Robotics and Computer-Integrated Manufacturing, 30(5) (2014) 468-477.
15
[16] M.R. Sirouspour, S.E. Salcudean, Nonlinear control of hydraulic robots, IEEE Transactions on Robotics and Automation, 17(2) (2001) 173-182.
16
[17] I. Davliakos, E. Papadopoulos, Model-based control of a 6-dof electrohydraulic Stewart–Gough platform, Mechanism and machine theory, 43(11) (2008) 1385-1400.
17
[18] M.A. Khosravi, H.D. Taghirad, Robust PID control of fully-constrained cable driven parallel robots, Mechatronics, 24(2) (2014) 87-97.
18
[19] J.-M. Yang, J.-H. Kim, Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots, IEEE Transactions on robotics and automation, 15(3) (1999) 578-587.
19
[20] M.A. Hussain, P.Y. Ho, Adaptive sliding mode control with neural network based hybrid models, Journal of Process Control, 14(2) (2004) 157-176.
20
[21] P. Doostdar, J. Keighobadi, Design and implementation of SMO for a nonlinear MIMO AHRS, Mechanical Systems and Signal Processing, 32 (2012) 94-115.
21
[22] K.-M. Lee, D.K. Shah, Kinematic analysis of a three-degrees-of-freedom in-parallel actuated manipulator, IEEE Journal on Robotics and Automation, 4(3) (1988) 354-360.
22
[23] J.J. Craig, Introduction to robotics: mechanics and control, Pearson Prentice Hall Upper Saddle River, 2005.
23
[24] X. Yang, H. Wu, Y. Li, B. Chen, A dual quaternion solution to the forward kinematics of a class of six-DOF parallel robots with full or reductant actuation, Mechanism and Machine Theory, 107 (2017) 27-36.
24
[25] K.H. Harib, Dynamic modeling, identification and control of Stewart platform-based machine tools, The Ohio State University, 1997.
25
[26] L.-W. Tsai, Robot analysis: the mechanics of serial and parallel manipulators, John Wiley & Sons, 1999.
26
[27] F.L. Lewis, C.T. Abdallah, D.M. Dawson, Control of robot manipulators, Macmillan New York, 1993.
27
ORIGINAL_ARTICLE
A Comparison Between Fourier Transform Adomian Decomposition Method and Homotopy Perturbation ethod for Linear and Non-Linear Newell-Whitehead-Segel Equations
In this paper, a comparison among the hybrid of Fourier Transform and AdomianDecomposition Method (FTADM) and Homotopy Perturbation Method (HPM) is investigated.The linear and non-linear Newell-Whitehead-Segel (NWS) equations are solved and the results arecompared with the exact solution. The comparison reveals that for the same number of componentsof recursive sequences, the error of FTADM is much smaller than that of HPM. For the non-linearNWS equation, the accuracy of FTADM is more pronounced than HPM. Moreover, it is shown thatas time increases, the results of FTADM, for the linear NWS equation, converges to zero. And for thenon-linear NWS equation, the results of FTADM converges to 1 with only six recursive components.This is in agreement with the basic physical concept of NWS diffusion equation which is in turn inagreement with the exact solution.
https://miscj.aut.ac.ir/article_990_904b8331d108cdb9ea1cc3cdaaec53af.pdf
2017-12-01T11:23:20
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227
238
10.22060/miscj.2017.12051.4998
Fourier Transform and Adomian
Decomposition Method
Homotopy Perturbation Method
Newell–Whitehead-Segel Equation
Nonlinear Partial Differential
Equation
S. S.
Nourazar
icp@aut.ac.ir
true
1
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
H.
Parsa
hasan_parsa@aut.ac.ir
true
2
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
A.
Sanjari
a_70_s@yahoo.com
true
3
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] J.-H. He, Application of homotopy perturbation method to nonlinear wave equations, Chaos, Solitons & Fractals, 26(3) (2005) 695-700.
1
[2] J.-H. He, Homotopy perturbation method for bifurcation of nonlinear problems, International Journal of Nonlinear Sciences and Numerical Simulation, 6(2) (2005) 207-208.
2
[3] J.-H. He, Homotopy perturbation method for solving boundary value problems, Physics letters A, 350(1) (2006) 87-88.
3
[4] A.-M. Wazwaz, Partial differential equations and solitary waves theory, Springer Science & Business Media, 2010.
4
[5] A. Yildirim, Homotopy perturbation method for the mixed Volterra–Fredholm integral equations, Chaos, Solitons & Fractals, 42(5) (2009) 2760-2764.
5
[6] S.S. Nourazar, M. Soori, A. Nazari-Golshan, On the exact solution of Newell-Whitehead-Segel equation using the homotopy perturbation method, arXiv preprint arXiv:1502.08016, (2015).
6
[7] M.M. Rashidi, H. Shahmohamadi, Analytical solution of three-dimensional Navier–Stokes equations for the flow near an infinite rotating disk, Communications in Nonlinear Science and Numerical Simulation, 14(7) (2009) 2999-3006.
7
[8] O.A. Bég, M. Rashidi, T.A. Bég, M. Asadi, Homotopy analysis of transient magneto-bio-fluid dynamics of micropolar squeeze film in a porous medium: a model for magneto-bio-rheological lubrication, Journal of Mechanics in Medicine and Biology, 12(03) (2012) 1250051.
8
[9] M.H. Abolbashari, N. Freidoonimehr, F. Nazari, M.M. Rashidi, Entropy analysis for an unsteady MHD flow past a stretching permeable surface in nano-fluid, Powder Technology, 267 (2014) 256-267.
9
[10] S. Nourazar, A. Nazari-Golshan, A. Yıldırım, M. Nourazar, On the hybrid of Fourier transform and Adomian decomposition method for the solution of nonlinear Cauchy problems of the reaction-diffusion equation, Zeitschrift für Naturforschung A, 67(6-7) (2012) 355-362.
10
[11] A. Nazari-Golshan, S. Nourazar, H. Ghafoori-Fard, A. Yildirim, A. Campo, A modified homotopy perturbation method coupled with the Fourier transform for nonlinear and singular Lane–Emden equations, Applied Mathematics Letters, 26(10) (2013) 1018-1025.
11
[12] A. Saravanan, N. Magesh, A comparison between the reduced differential transform method and the Adomian decomposition method for the Newell–Whitehead–Segel equation, Journal of the Egyptian Mathematical Society, 21(3) (2013) 259-265.
12
[13] G. Adomian, Solving Frontier Problems of Physics: The Decomposition MethodKluwer, Boston, MA, (1994).
13
[14] A.-M. Wazwaz, M.S. Mehanna, The combined Laplace-Adomian method for handling singular integral equation of heat transfer, International Journal of Nonlinear Science, 10(2) (2010) 248-252.
14
[15] R.G. Pratt, C. Shin, G. Hick, Gauss–Newton and full Newton methods in frequency–space seismic waveform inversion, Geophysical Journal International, 133(2) (1998) 341-362.
15
[16] R.G. Pratt, M. Worthington, Inverse theory applied to multi-source cross-hole tomography. Part 1: Acoustic wave-equation method, Geophysical prospecting, 38(3) (1990) 287-310.
16
[17] T. Wu, Z. Chen, A dispersion minimizing subgridding finite difference scheme for the Helmholtz equation with PML, Journal of Computational and Applied Mathematics, 267 (2014) 82-95.
17
ORIGINAL_ARTICLE
A Survey of Dynamic Replication Strategies for Improving Response Time in Data Grid Environment
Large-scale data management is a critical problem in a distributed system such as cloud,P2P system, World Wide Web (WWW), and Data Grid. One of the effective solutions is data replicationtechnique, which efficiently reduces the cost of communication and improves the data reliability andresponse time. Various replication methods can be proposed depending on when, where, and howreplicas are generated and removed. In this paper, different replication algorithms are investigated todetermine which attributes are assumed in a given algorithm and which are declined. We provide a tabularrepresentation of important factors to facilitate the future comparison of data replication algorithms. Thispaper also presents some interesting discussions about future works in data replication by proposingsome open research challenges.
https://miscj.aut.ac.ir/article_874_28197c0a307fd6468830364b3b3d1ed2.pdf
2017-12-01T11:23:20
2018-11-19T11:23:20
239
264
10.22060/miscj.2016.874
Data Grid
Dynamic replication
Data Availability
Simulation
N.
Mansouri
najme.mansouri@gmail.com
true
1
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
M. M.
Javidi
javidi@mail.uk.ac.ir
true
2
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
[1] M. Chetty, R. Buyya, Weaving computational grids: How analogous are they with electrical grids?, Computing in Science and Engineering, 4(4) (2002) 61.
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[4] A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, S. Tuecke, The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets, Journal of Network and Computer Applications, 23(3) (2000) 187-200.
4
[5] G. Zhou, F. Lian, G. Li, Influence of alloy elements on magnetic properties of Fe-based amorphous alloys, JOURNAL OF MATERIALS SCIENCE AND TECHNOLOGY-SHENYANG-, 16(2) (2000) 157-158.
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[6] N. Chau, N.H. Luong, N.X. Chien, P.Q. Thanh, L.V. Vu, Influence of P substitution for B on the structure and properties of nanocrystalline Fe73.5Si15.5Nb3Cu1B7−xPx alloys, Physica B: Condensed Matter, 327(2–4) (2003) 241-243.
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[8] W. Liu, J. Tang, Y. Du, Nanocrystalline soft magnetic ribbon with α″-Fe16N2 nanocrystallites embedded in amorphous matrix, Journal of Magnetism and Magnetic Materials, 320(21) (2008) 2752-2754.
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[9] I. Maťko, E. Illeková, P.Š. Sr, P. Švec, D. Janičkovič, V. Vodárek, Microstructural study of the crystallization of amorphous Fe–Sn–B ribbons, Journal of Alloys and Compounds, 615, Supplement 1(0) (2014) S462-S466.
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[10] R.M. Rahman, R. Alhajj, K. Barker, Replica selection strategies in data grid, Journal of Parallel and Distributed Computing, 68(12) (2008) 1561-1574.
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[11] D.T. Nukarapu, B. Tang, L. Wang, S. Lu, Data replication in data intensive scientific applications with performance guarantee, IEEE Transactions on Parallel and Distributed Systems, 22(8) (2011) 1299-1306.
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[12] C.Z. X. Meng, An ant colony model based replica consistency maintenance strategy in unstructured P2P networks, Computer Networks, 62 (2014) 11.
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[13] R.M. Rahman, K. Barker, R. Alhajj, Replica placement strategies in data grid, Journal of Grid Computing, 6(1) (2008) 103-123.
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[14] A. Benoit, V. Rehn-Sonigo, Y. Robert, Replica placement and access policies in tree networks, IEEE Transactions on Parallel and Distributed Systems, 19(12) (2008) 1614-1627.
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[15] H.H.E. Al-Mistarihi, C.H. Yong, On fairness, optimizing replica selection in data grids, IEEE Transactions on Parallel and Distributed Systems, 20(8) (2009) 1102-1111.
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[16] K. Skakowski, R. Sota, D. Król, J. Kitowski, QoS-based storage resources provisioning for grid applications, Future Generation Computer Systems, 29(3) (2013) 713-727.
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[17] Y.S. G. Belalem, A Consistency Protocol Multi-Layer for Replicas Management in Large Scale Systems, World Academy of Science
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Engineering and Technology, 16 (2008) 6.
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