ORIGINAL_ARTICLE
SFLA Based Gene Selection Approach for Improving Cancer Classification Accuracy
In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification. Therefore, selection of the appropriate genes is important in bioinformatics and machine learning. The proposed method combines the advantage of wrapper and filter methods for gene subset selection. SFLA-FS consists of two phases. In the first phase a filter method is used for gene ranking from high dimensional microarray data and in the second phase, SFLA is applied to gene selection. The performance of SFLA-FS evaluated for cancer classification using seven standard microarray cancer datasets. Experimental results are compared with those of obtained from several existing well-known gene selection algorithm. The experimental results show that SFLA-FS has a remarkable ability to generate reduced size of genes while yielding significant classification accuracy in cancer classification.
http://miscj.aut.ac.ir/article_542_4e2a9aa10e1406551bb9b2b4154a1053.pdf
2015-05-22T11:23:20
2018-01-23T11:23:20
1
8
10.22060/miscj.2015.542
Bioinformatics
Cancer Classification
gene Selection
SFLA
Microarray Data
Jamshid
Pirgazi
j.pirgazi@znu.ac.ir
true
1
University of Zanjan Department of Computer Engineering Zanjan, Iran
University of Zanjan Department of Computer Engineering Zanjan, Iran
University of Zanjan Department of Computer Engineering Zanjan, Iran
AUTHOR
Ali Reza
Khanteymoori
khanteymoori@gmail.com
true
2
University of Zanjan Department of Computer Engineering Zanjan, Iran
University of Zanjan Department of Computer Engineering Zanjan, Iran
University of Zanjan Department of Computer Engineering Zanjan, Iran
LEAD_AUTHOR
[1] Chang.C and Lin.C.J,” LIBSVM:a library for support vector machines,” ACM Trans Intell, SystTechnol, vol. 2, no. 27, pp. 1–27, 2011.
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[2] Hajiloo.M, Rabiee.H.R and Anooshahpour.M,”Fuzzy support vector machine: an efficient rule-based classification technique for microarrays,” BMC bioinformatics /1471-2105/14/s13/s4. 2013.
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[3] Ammu.K and Preeja.V,”Feature Selection for high Dimensional DNA Microarray data using hybrid approaches,” Bioinformatics, vol. 9, no. 16, pp. 824-828, 2013.
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[4] Abedini.M,Kirly.M and Chiong.R, ”Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data,” Australasian Medical Journal AMJ, vol. 6, no. 5, pp. 272-279, 2013.
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[5] Ben-Bassat.M,”Pattern recognition and reduction of dimensionality,” In Krishnaiah.P and Kanal,L, (eds.) Handbook of Statistics II, Vol. 1.North-Holland, Amsterdam. pp. 773–791, 1982.
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[6] Yu.L and Liu.H, “Efficient feature selection via analysis of relevance and redundancy,” J. Mach. Learn. Res, vol. 5, pp. 1205–1224, 2004.
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[7] ElAlami.M.E,”A filter model for feature subset selection based on genetic algorithm,” Knowledge-Based Systems, vol. 22, pp. 356–362, 2009.
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[9] Foithong. S, Pinngern. O, and Attachoo.B, ”Feature subset selection wrapper based on mutual information and rough sets,” Expert Systems with Applications, vol. 39, pp. 574–584, 2012.
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[10] Yang.W,Li.D and Zhu.L, ”An improved genetic algorithm for optimal feature subset selection from multi-character feature set,” Expert Systems with Applications, vol. 38, pp.2733–2740, 2011.
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[12] Yassi .M and Moattar .M.H ,” Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification ,” Biochemical and Biophysical Research Communications, vol. 446, pp. 850–856, 2014.
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[15] MonirulKabir .M.D, Shahjahan .M.D and Murase .K, “A new hybrid ant colony optimization algorithm for feature selection,” Expert Systems with Applications, vol. 39, pp. 3747–3763, 2012.
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[17] Sivagaminathan .R and Ramakrishnan .M,”A hybrid approach for feature subset selection using neural networks and ant colony optimization,” Expert Systems with Applications, vol. 33, pp. 49–60, 2007.
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[18] Shengqiao.Li, James .H.E and Adjeroh. D.A, “Random KNN feature selection - a fast and stable Alternative to Random Forests,” BMC Bioinformatics, vol. 12, pp. 450, 2011.
18
[19] Huang .J and et al,”Decision forest for classification of gene expression data,” Computers in biology and medicine, vol. 40, pp.698-704, 2010.
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[20] Li .X and Zhao .H, “Weighted random subspace method for high dimensional data classification,” Stat Interface, vol. 2, pp. 153–159, 2009.
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[22] Duan, Q.Y, et al, “Shuffled complex evolution approach for effective and efficient global minimization,” journal of optimization theory and application, vol. 76, no. 3, pp. 501-521, 2009.
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[24] Alon .U, Barkai .N and Notterman .D.A, Gishdagger .k, Ybarradagger .S, Mackdagger .D and Levine .A.J. “Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays,” Proc. Nat’l Academy of Sciences USA, vol. 96, no. 08, pp. 6745-6750. June 1999.
24
[25] Singh .D and et al. “Gene Expression Correlates of Clinical Prostate Cancer Behavior,” Cancer Cell, vol. 0, no. 8, pp. 803-809, 2000.
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[26] Shipp .M.A and et al, “Diffuse Large B-Cell Lymphoma Outcome Prediction by Gene-Expression Profiling and Supervised Machine Learning,” Nature Medicine, vol. 2, no.0, pp. 62-74.Jan, 2008.
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[27] Pomeroy .S.L and et al, “Prediction of Central Nervous System Embryonic Tumor Outcome Based on Gene Expression,” Nature, vol. 405, pp. 865-870, 2008.
27
[28] Gordon GJ, Jensen RV, Hsiao LL, Gullans SR, Blumenstock JE, Ramaswami S ,Richards WG, Sugarbaker DJ, Bueno R: “Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma,” Cancer Res, vol. 62, pp. 4963–4967, 2002.
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[29] Stuart RO, Wachsman W, Berry CC, Wang-Rodriguez J, Wasserman L, Klacansky I, Masys D, Arden K, Goodison S, McClelland M, Wang Y, Sawyers A, Kalcheva I, Tarin D, and Mercola D, “In silico dissection of cell-type-associated patterns of gene expression in prostate cancer,” Proc Natl Acad Sci USA, 101:615–62, 2004.
29
ORIGINAL_ARTICLE
A New Near Optimal High Gain Controller For The Non-Minimum Phase Affine Nonlinear Systems
In this paper, a new analytical method to find a near-optimal high gain controller for the non-minimum phase affine nonlinear systems is introduced. This controller is derived based on the closed form solution of the Hamilton-Jacobi-Bellman (HJB) equation associated with the cheap control problem. This methodology employs an algebraic equation with parametric coefficients for the systems with scalar internal dynamics and a differential equation for those systems with the internal dynamics of order higher than one. It is shown that 1) if the system starts from different initial conditions located in the close proximity of the origin the regulation error of the closed-loop system with the proposed controller is less than that of the closed-loop system with the high gain LQR, which is surely designed for the linearized system around the origin, 2). for the initial conditions located in a region far from the origin, the proposed controller significantly outperforms the LQR controller.
http://miscj.aut.ac.ir/article_543_9fcceb3e1d8a7a3118f720fdd22e1b0f.pdf
2015-05-22T11:23:20
2018-01-23T11:23:20
9
20
10.22060/miscj.2015.543
Non-Minimum Phase
Nonlinear Systems
Cheap Control
Optimal Controller
Hamilton-Jacobi-Bellman Equation (HJB)
Hoda N.
Foghahaayee
true
1
Assistant Professor, Department of Electrical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Assistant Professor, Department of Electrical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Assistant Professor, Department of Electrical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
AUTHOR
Mohammad B.
Menhaj
true
2
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Heidar A.
Talebi
true
3
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] J. B. Lasserre, C. Prieur, and D. Henrion.“Nonlinear optimal control: numerical approximation via moments and LMI relaxations.in joint,” IEEE Conference on Decision and Control and European Control Conference, 2005.
1
[2] J. Arthur, E. Bryson, and Y.-C. Ho, Applied Optimal Control: Optimization, Estimation and
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Control: Hemisphere Publ. Corp., Washington D.C, 1975.
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[3] W. Grimm and A. Markl, “Adjoint estimation from a direct multiple shooting method,” Journal of
4
Optimization Theory and Applications, vol. 92,No. 2, pp- 263–283, 1997.
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[4] J. Stoer and R. Bulirsch, Introduction to Numerical Analysis, ed. T. Edition, New York: Springer-
6
Verlag, 2002.
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[5] O. v. Stryk and R. Bulirsch, “Direct and indirect methods for trajectory optimization,” annals of
8
operations research, vol. 37, No. 1, pp-357–373,1992.
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[6] R. Fletcher, Practical methods of optimization:Unconstrained optimization, John Wiley & Sons,
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Ltd., Chichester, 1980.
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[7] P. E. Gill, W. Murray, and M. H. Wright, Practical optimization. : Academic Press, Inc., London-New
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York, 1981.
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[8] D. E. Kirk, Optimal Control Theory: An Introduction, Prentice-Hall, Englewood Cliffs, NJ:
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Dover Publications, USA, 1998.
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[9] D. S. Naidu, Optimal Control Systems: CRC Press, Idaho State University, USA,, 2003.
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[10] S. Subchan and R. Zbikowski, computational Optimal Control: Tools and Practice, United
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Kingdom: John Wiley & Sons, 2009.
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[11] K. Zhou, J. C. Doyle, and K. Glover, Robust and optimal control: Prentice Hall, Englewood Cliffs,
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[12] A. Ben-Tal and A. Nemirovski, “Robust Convex Optimization,” Mathematics of operations
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research, vol. 23, No. 4, pp-769-805, 1998.
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[13] A. Ben-Tal and A. Nemirovski, “Lectures on Modern Convex Optimization: Analysis,
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Algorithms, and Engineering Applications,” MPSSIAM Series on Optimization. MPS-SIAM,
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Philadelphia, 2001.
24
[14] Z. K. Nagy and R. Braatz, “Open-loop and closedloop robust optimal control of batch processes
25
using distributional and worst-case analysis,”Journal of Process Control, vol. 14, No. 4, pp-411–422, 2004.
26
[15] M. Diehl, H. G. Bock, and E. Kostina, “An approximation technique for robust nonlinear
27
optimization,” Mathematical Programming, vol.
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107, No. 1-2, pp-213–230, 2006.
29
[16] S. Seshagiria and H. K. Khalilb, “Robust output feedback regulation of minimum-phase nonlinear
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systems usingconditional integrators,” Automatica,vol. 41, No. 1, pp-43 – 54, 2005.
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[17] H. Nogami and H. Maeda, “Robust Stabilization of Multivariable High Gain Feedback Systems,”
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Transactions of the Society of Instrument and Control Engineers, vol. E, No. 1, pp- 83-91, 2001.
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[18] W. Maas and A. v. d. Schaft. “Singular nonlinear Hinf optimal control by state feedback,” in 33th
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IEEE conference on decision & control. Lake Buena Vista, USA, 1994.
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[19] A. Astolfi. “singular Hinf control,” in 33th IEEE conference on decision and control. Lake Buena
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Vista, USA, 1994.
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[20] R. Marino, et al., “Nonlinear Hinf almost disturbance decoupling,” systems & control letters,vol. 23, No. 3, pp- 159-168, 1994.
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[24] M. Seron, et al., Feedback limitations in nonlinear systems: From Bode integrals to cheap control, University of California: Santa Barbara, 1997.
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[25] M. M. Seron, et al., “Feedback limitations in nonlinear systems: From bode integrals to cheap control,” IEEE Transactions on Automatic Control, vol. 44, No. 4, pp-829–833, 1999.
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[26] M. Krstic', I. Kanellakopoulos, and P. Kokotovic', Nonlinear and adaptive control design: John Wiley & Sons, 1995.
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[27] J. W. Helton, et al., “Singularly perturbed control systems using non-commutative computer algebra,” International Journal of Robust and Nonlinear Control, Special Issue: GEORGE ZAMES COMMEMORATIVE ISSUE, vol. 10, No. 11-12, pp- 983–1003, 2000.
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[28] H. Kwakernaak and R. Sivan, Linear Optimal Control Systems, New York, Chichester, Brisbane, Toronto: Wiley-Interscience, a division of John Wiley & Sons, Inc, 1972a.
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[30] U. Shaked, Singular and cheap optimal control: the minimum and nonminimum phase cases, National Research Institute for Mathematical Sciences: Pretoria, Republic of South Africa, 1980.
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[31] A. Jameson and R. E. O'Malley, “Cheap control of the time-invariant regulator,” applied mathematics & optimization, vol. 1, No. 4, pp-337-354, 1975.
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[32] R. Sepulchre, et al., Constructive nonlinear control, Springer, Editor: London, 1997.
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[33] P. V. Kokotovic, H. K. Khalil, and J. O’Reilly, Singular Perturbations Methods in Control: Analysis and Design, New York: Academic Press, 1986.
51
ORIGINAL_ARTICLE
Developing Robust Project Scheduling Methods for Uncertain Parameters
A common problem arising in project management is the fact that the baseline schedule is often disrupted during the project execution because of uncertain parameters. As a result, project managers are often unable to meet the deadline time of the milestones. Robust project scheduling is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during project execution. In order to apply robust scheduling principles to real projects, one should make assumptions close to the actual conditions of the project as much as possible. In this paper, in terms of uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust scheduling methods under different conditions. Finally, we formulate recommendations regarding the best method of robust scheduling based on the results of these experiments.
http://miscj.aut.ac.ir/article_544_334238d446a312022c05d16a9787cc0c.pdf
2015-05-22T11:23:20
2018-01-23T11:23:20
21
32
10.22060/miscj.2015.544
Project Scheduling
Uncertainty Modeling
Robustness
simulation
I.
Bossaghzadeh
true
1
MSc of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
MSc of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
MSc of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
LEAD_AUTHOR
S. R.
Hejazi
true
2
Professor of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
Professor of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
Professor of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Z.
Pirmoradi
true
3
PhD of Product Design and Optimization Lab, Simon Fraser University, Surrey, Canada
PhD of Product Design and Optimization Lab, Simon Fraser University, Surrey, Canada
PhD of Product Design and Optimization Lab, Simon Fraser University, Surrey, Canada
AUTHOR
[1] W. Herroelen, R. Leus, “Project scheduling under uncertainty: Survey and research potentials: On the merits and pitfalls of critical chain scheduling,” European Journal of Operational Research, vol. 165, pp. 289-306, 2005.
1
[2] R. Leus, “The generation of stable project plans,” Ph.D. dissertation, Department of applied
2
economics, Katholieke Universiteit Leuven, Belgium, 2003.
3
[3] S. Van de Vonder, E. Demeulemeester, W. Herroelen, R. Leus, “The use of buffers in project management: The trade-off between stability and Makespan,” International Journal of Production Economics, vol. 97, pp. 227-240, 2005.
4
[4] E. M. Goldratt, “Critical chain,” The North River Press Publishing Corporation, Great Barrington, 1997.
5
[5] S. Van de Vonder, F. Ballestin, E. Demeulemeester, W. Herroelen, “Heuristic procedures for reactive project scheduling,” Computers & Industrial Engineering, vol. 52, pp. 11–28, 2007.
6
[6] W. Herroelen, R. Leus, “On the merits and pitfalls of critical chain scheduling,” Journal of Operations Management, vol. 19, pp. 559-577, 2001.
7
[7] M. A. Al-Fawzan, M. Haouari, “A bi-objective model for robust resource constrained project scheduling,” International Journal of Production Economics, vol. 96, pp. 175-187, 2005.
8
[8] S. Danka, “Robust resource constrained project scheduling with uncertain-but-bounded activity duration and cash flows,” International Journal of Optimization in Civil Engineering, vol. 3, no. 4, pp. 527-542, 2013.
9
[9] W. Herroelen, R. Leus, “The construction of stable baseline schedules,” European Journal of Operational Research, vol. 156, pp. 550– 565, 2004.
10
[10] S. Van de Vonder, E. Demeulemeester, W. Herroelen, R. Leus, “The trade-off between stability and makespan in resource constrained project scheduling,” International Journal of Production Research, vol. 44, no. 2, pp. 215-236, 2006.
11
[11] S. Van de Vonder, E. Demeulemeester, W. Herroelen, “Proactive heuristic procedures for robust project scheduling: An experimental analysis,” European Journal of Operational Research, vol. 189, no. 3, pp. 723-733, 2008.
12
[12] O. Lambrechts, E. Demeulemeester, W. Herroelen, “Proactive and reactive strategies for resource constrained project scheduling with uncertain resource availabilities,” Journal of scheduling, vol. 11, no. 2, pp. 121-136, 2008.
13
[13] O. Lambrechts, E. Demeulemeester, W. Herroelen, “Time slack-based techniques for robust project scheduling subject to resource uncertainty,” Annals of Operations Research, vol. 186, no. 1, pp. 443-464, 2010.
14
[14] W. Herroelen, B. De Reyck, E. Demeulemeester, “A note on the paper „Resource-constrained project scheduling: notation, classification, models and methods‟ by Brucker et al,” European Journal of Operational Research, vol. 128, pp. 679–688, 2001.
15
[15] A. Sprecher, R. Kolisch, A. Drexl, “Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 80, pp. 94–102, 1995.
16
[16] V. Valls, F. Ballestin, S. Quintanilla, “A hybrid genetic algorithm for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 185, pp. 495–508, 2008.
17
[17] J. L. Ringuest, Multi objective optimization: Behavioral and Computational Considerations, Kluwer Academic publishers, 1992.
18
[18] M. Vanhoucke, J. Coelho, D. Debels, B. Maenhout, L. V. Tavares, “An evaluation of the adequacy of project network generators with systematically sampled networks,” European Journal of Operational Research, vol. 187, pp. 511-524, 2008.
19
ORIGINAL_ARTICLE
Peaking Attenuation in High-Gain Observers Using Adaptive Saturation: Application to a Ball and Wheel System
Despite providing robustness, high-gain observers impose a peaking phenomenon, which may cause instability, on the system states. In this paper, an adaptive saturation is proposed to attenuate the undesirable mentioned phenomenon in high-gain observers. A real-valued and differentiable sigmoid function is considered as the saturating element whose parameters (height and slope) are adaptively tuned. The corresponding feedback and adaptation laws are derived based on the Lyapunov and LaSalle theorems to guarantee the asymptotic stability property for the closed-loop system’s equilibrium point. Compared to the conventional high-gain observers which suffer from states’ peaking, it is possible to increase the observer’s gain, up to a higher level, under which not only all system states and the adaptive saturation elements remain stable, but also robustness is reinforced in the presence of uncertainties and/or non-similarities in the system and observer’s dynamics, respectively. Both theoretical analysis and simulation results confirm the efficiency of the proposed scheme.
http://miscj.aut.ac.ir/article_545_cd44e0048aae526d2f448d9de254f0ff.pdf
2015-05-22T11:23:20
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33
40
10.22060/miscj.2015.545
High-gain Observer
Adaptive Saturation
Lyapunov Stability
S. D.
Yazdi Mirmokhalesouni
true
1
Advanced Control Systems Lab, Control and Intelligent Processing Center of Excellence (CIPCE) at School of Electrical and
Advanced Control Systems Lab, Control and Intelligent Processing Center of Excellence (CIPCE) at School of Electrical and
Advanced Control Systems Lab, Control and Intelligent Processing Center of Excellence (CIPCE) at School of Electrical and
AUTHOR
M. J. Yazdanpanah
Yazdanpanah
true
2
Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
[1] M. Farza, A. Sboui, E. Cherrier, and M. M’Saad,“High-gain observer for a class of time-delay
1
nonlinear systems,” International Journal of Control, vol. 83, no. 2, pp. 273–280, February 2010.
2
[2] M.-T. Ho, Y.-W. Tu, and H.-S. Lin, “Controlling of ball and wheel system using full-state feedback
3
linearization,” IEEE Control System Magazine,October 2009.
4
[3] J. C. Doyle and G. Stein, “Robustness with observers,” IEEE Transactions on Automatic
5
Control, vol. 24, pp. 607 – 611, August 1979.
6
[4] A. Saberi and P. Sannuti, “Observer design for loop transfer recovery for uncertain dynamical
7
systems,” IEEE Transactions on Automatic
8
Control, vol. 35, no. 8, pp. 878–897, August 1990.
9
[5] P. Dorl´eans, j. F. Massieu, and T. Ahmed-Ali, “High-gain observer design with sampled measurements: application to inverted pendulum,”International Journal of Control, vol. 84, no. 4, pp. 801–807, April 2011.
10
[6] Y. Li, X. Xia, and Y. Shen, “A high-gain-based global finite-time nonlinear observer,” International Journal of Control, vol. 86, no. 5, pp. 759–767, 2013.
11
[7] H. J. Sussmann and P. V. Kokotovic, “Peaking and stabilization,” IEEE Conference on Decision and Control, vol. 2, pp. 1379 – 1384, December 1989.
12
[8] T. Mita, “On zeros and responses of linear regulators and linear observers,” IEEE Transactions on Automatic Control, vol. 22, no. 3, pp. 423–428, June 1977.
13
[9] F. Esfandiari and H. K. Khalil, “Output feedback stabilization of fully linearized systems,” International Journal of Control, vol. 56, no. 3, pp. 1007–1037, December 1992.
14
[10] A. A. Ball and H. K. Khalil, “Analysis of a nonlinear high-gain observer in the presence of measurement noise,” American Control Conference, pp. 2584 – 2589, July 2011.
15
[11] H. K. Khalil, Nonlinear Systems. Upper Saddle River, New Jersey: Prentice Hall, 2002.
16
[12] J. H. Ahrens and H. K. Khalil, “Output feedback control using high-gain observers in the presence of measurement noise,” American Control Conference, vol. 5, pp. 4114 – 4119, July 2004..
17
[13] H. K. Khalil, “High-gain observer in nonlinear feedback control,” International Conference on Control, Automation and Systems, pp. xlvii – lvii, October 2008.
18
[14] A. Teel and L. Praly. Global stabilizability and observability imply semi-global stabilizability by output feedback. Syst. Contr. Lett., 22:313–325, 1994.
19
ORIGINAL_ARTICLE
A New Job Scheduling in Data Grid Environment Based on Data and Computational Resource Availability
Data Grid is an infrastructure that controls huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. The heterogeneity and geographic dispersion of grid resources and applications place some complex problems such as job scheduling. Most existing scheduling algorithms in Grids only focus on one kind of Grid jobs which can be data-intensive or computation-intensive. However, only considering one kind of jobs in scheduling does not result in suitable scheduling in the viewpoint of all systems, and sometimes causes wasting of resources on the other side. To address the challenge of simultaneously considering both kinds of jobs, a new Integrated Job Scheduling Strategy (IJSS) is proposed in this paper. At one hand, the IJSS algorithm considers both data and computational resource availability of the network, and on the other hand, considering the corresponding requirements of each job, it determines a value called W to the job. Using the W value, the importance of two aspects (being data or computation intensive) for each job is determined, and then the job is assigned to the available resources. The simulation results with OptorSim show that IJSS outperforms comparing to the existing algorithms mentioned in literature as number of jobs increases.
http://miscj.aut.ac.ir/article_546_64c3d087030b308d78beceedd2210185.pdf
2015-05-22T11:23:20
2018-01-23T11:23:20
41
53
10.22060/miscj.2015.546
Data Grid
Scheduling
Access Pattern
simulation
Najme
Mansouri
true
1
Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
[1] D. Fernandez-Baca, “Allocating modules to processors in a distributed system,” IEEE Transactions on Software Engineering, 15: pp. 427-1436, 1989.
1
[2] S. Kardani-Moghadam, F. Khodadadi, and R. Entezari-Maleki, A. Movaghar, “A hybrid genetic algorithm and variable neighborhood search for task scheduling problem in grid environment,” Procedia Engineering, 29: pp. 3808-3814, 2012.
2
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ORIGINAL_ARTICLE
Magnetic Calibration of Three-Axis Strapdown Magnetometers for Applications in Mems Attitude-Heading Reference Systems
In a strapdown magnetic compass, heading angle is estimated using the Earth's magnetic field measured by Three-Axis Magnetometers (TAM). However, due to several inevitable errors in the magnetic system, such as sensitivity errors, non-orthogonal and misalignment errors, hard iron and soft iron errors, measurement noises and local magnetic fields, there are large error between the magnetometers' outputs and actual geomagnetic field vector. This is the necessity of magnetic calibration of TAM, especially in navigation application to achieve the true heading angle. In this paper, two methodologies, including clustering swinging method and clustering velocity vector method are presented for magnetic compass calibration. Several factors for clustering process have been introduced and analyzed. The algorithms can be applied in both low-cost MEMS magnetometer and high-accuracy magnetic sensors. The proposed calibration algorithms have been evaluated using in-ground and in-flight tests. It can be concluded from the experimental results that, applying the clustering calibration algorithms bring about a considerable enhancement in the accuracy of magnetic heading angle
http://miscj.aut.ac.ir/article_547_f9dbfe47f53936796d67b88c4d2a02ad.pdf
2015-05-22T11:23:20
2018-01-23T11:23:20
55
65
10.22060/miscj.2015.547
Magnetic Calibration
Magnetic heading angle
clustering calibration method
Swinging method
Velocity vector method
Hamed
Milanchian
hamed.milanchian@gmail.com
true
1
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
Jafar
Keighobadi
jkeighobadi@tabrizu.ac.ir
true
2
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
Hossein
Nourmohammadi
hnourmohammadi@tabrizu.ac.ir
true
3
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran
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