2012
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Effective Calculation of Multiple Solutions of Mixed Convection in a Porous Medium
Effective Calculation of Multiple Solutions of Mixed Convection in a Porous Medium
2
2
This paper considers an important model of boundary value problem with a condition at infinity namely combined free and forced convection over a plane of arbitrary shape embedded in a fluidsaturated porous medium; this model admits dual solutions, and uses a technique, which is to some extent modification of homotopy analysis method (HAM), in order to obtain dual solutions analytically with high accuracy.
1
This paper considers an important model of boundary value problem with a condition at infinity namely combined free and forced convection over a plane of arbitrary shape embedded in a fluidsaturated porous medium; this model admits dual solutions, and uses a technique, which is to some extent modification of homotopy analysis method (HAM), in order to obtain dual solutions analytically with high accuracy.
1
6

عباس بندی
S.
Abbasbandy
Corresponding Author, S. Abbasbandy is with the Department of Mathematics, Imam Khomeini International University, Qazvin, Iran (email:
abbasbandy@yahoo.com).
Corresponding Author, S. Abbasbandy is with
Iran


E.
Shivanian
E. Shivanian is with the Department of Mathematics, Imam Khomeini International University, Qazvin, Iran (email: shivanian@ikiu.ac.ir).
E. Shivanian is with the Department of Mathematics
Iran
Homotopy analysis method
rule of multiplicity of solutions
prescribed parameter
convergencecontroller parameter
[[1] S. J. Liao, Beyond Perturbation: Introduction to the Homotopy Analysis Method, Chapman Hall CRC/Press, Boca Raton, 2003. ##[2] T. Hayat, N. Ahmed, M. Sajid, S. Asghar, “On the MHD flow of a second grade fluid in a porous channel,” Comput Math Appl,vol. 3, pp. 549557, Apr. 1988. 2007;54:1440. ##[3] S. Abbasbandy, “Soliton solutions for the 5thorder KdV equation with the homotopy analysis method,” Nonlinear Dyn,vol. 51, pp. 8387, Apr. 2008. ##[4] S. Abbasbandy, “The application of the homotopy analysis method to solve a generalized HirotaSatsuma coupled KdV equation,” Phys Lett A, vol. 361, pp. 478483, Apr. 2007. ##[5] S. Abbasbandy , “The application of the homotopy analysis method to nonlinear equations arising in heat transfer,” Phys Lett A, vol. 360, pp. 109113, Apr. 2006. ##[6] S. P. Zhu, “An exact and explicit solution for the valuation of American put options,” Quant Fin, vol. 6, pp. 229242, Apr.2006. ##[7] M. Yamashita, K. Yabushita, K. Tsuboi, “An analytic solution of projectile motion with the quadratic resistance law using the homotopy analysis method,” J Phys A, vol. 40, pp. 84038416,Apr. 2007. ##[8] Y. Bouremel, “Explicit series solution for the Glauertjet problem by means of the homotopy analysis method,” Commun Nonlinear Sci Numer Simulat, vol. 12(5), pp. 714724, Apr.2007. ##[9] L. Tao, H. Song, S. Chakrabarti, “Nonlinear progressive waves in water of finite depthan analytic approximation,” vol. 54, pp.549825834, Apr. 2007. ##[10] H. Song H, L. Tao, “Homotopy analysis of 1D unsteady,nonlinear groundwater flow through porous media,” J Coastal Res, vol. 50, pp. 292305, Apr. 2007. ##[11] A. S. Bataineh, M. S. M. Noorani, I. Hashim, “Solutions of timedependent Emden–Fowler type equations by homotopy analysis method,” Phys Lett A, vol. 371, pp. 7282, Apr. 2007. ##[12] Z. Wang, L. Zou, H. Zhang, “Applying homotopy analysis method for solving differentialdifference equation,” Phys Lett A, vol. 369, pp. 7784, Apr. 2007. ##[13] Inc. Mustafa, “On exact solution of Laplace equation with Dirichlet and Neumann boundary conditions by the homotopy analysis method,” Phys Lett A, vol. 365, pp. 412415, Apr. 2007. ##[14] S. Abbasbandy, E. Magyari, E. Shivanian, “The homotopy analysis method for multiple solutions of nonlinear boundary value problems,” Commun Nonlinear Sci Numer Simulat, vol.14, pp. 35303536, Apr. 2009. ##[15] S. Abbasbandy, E. Shivanian, “Prediction of multiplicity of solutions of nonlinear boundary value problems: novel application of homotopy analysis method,” Commun Nonlinear Sci Numer Simulat, vol. 15, pp. 38303846, Apr. 2010. ##[16] A. Nakayama, H. Koyama, “A general similarity transformation for combined free and forcedconvection flows within a fluidsaturated ##porous medium,” ASME J. Heat Trans, vol. 109, pp. 10411045, Apr. 1987. ##[17] E. Magyari, I. Pop, B. Keller, “Exact dual solutions occurring in Darcy mixed convection flow,” Int Journal Heat Mass Transf, vol. 44, pp. 45634566, Apr. 2001.##]
A Fuzzy Based Approach for Rate Control in Wireless Multimedia Sensor Networks
A Fuzzy Based Approach for Rate Control in Wireless Multimedia Sensor Networks
2
2
Wireless Multimedia Sensor Networks (WMSNs) undergo congestion when a link (or a node) becomes overpopulated in terms of incoming packets. In WMSNs this happens especially in upstream nodes where all incoming packets meet and directed to the sink node. Congestion in networks, if not handled properly, might lead to congestion collapse which deteriorates the quality of service (QoS). Therefore, in order to avoid such situations corresponding actions should be taken into account so that to yield lower packet loss and consequently energy loss that is of utmost importance in WSNs. However, the term "packet loss" as implied by today's literature might not be effective in many applications especially in multimedia sensor networks. In this paper a new weighted packet loss metric is proposed which is best suited for multimedia sensor networks that convey packets of different priority classes. The proposed method then tries to minimize the aforementioned criterion by means of fuzzy queue management and a newly introduced adaptive rate control mechanism, in the presence of both abrupt and gradual changes in network dynamics. The employment of these two techniques provides us a synergy to handling short term and long term variations arising through the underlying simulated networks. The simulation results approve the superiority of the proposed approach over the selected competitive method when dealing with packets of different priorities.
1
Wireless Multimedia Sensor Networks (WMSNs) undergo congestion when a link (or a node) becomes overpopulated in terms of incoming packets. In WMSNs this happens especially in upstream nodes where all incoming packets meet and directed to the sink node. Congestion in networks, if not handled properly, might lead to congestion collapse which deteriorates the quality of service (QoS). Therefore, in order to avoid such situations corresponding actions should be taken into account so that to yield lower packet loss and consequently energy loss that is of utmost importance in WSNs. However, the term "packet loss" as implied by today's literature might not be effective in many applications especially in multimedia sensor networks. In this paper a new weighted packet loss metric is proposed which is best suited for multimedia sensor networks that convey packets of different priority classes. The proposed method then tries to minimize the aforementioned criterion by means of fuzzy queue management and a newly introduced adaptive rate control mechanism, in the presence of both abrupt and gradual changes in network dynamics. The employment of these two techniques provides us a synergy to handling short term and long term variations arising through the underlying simulated networks. The simulation results approve the superiority of the proposed approach over the selected competitive method when dealing with packets of different priorities.
7
20


Mohammad Hossein
Yaghmaee Moghaddam
Corresponding Author, M.H. Yaghmaee Moghaddam is with the Department of Computer Engineering and Center of Excellence on Soft
Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran (email: yaghmaee@ieee.org).
Corresponding Author, M.H. Yaghmaee Moghaddam
Iran
yaghmaee@ieee.org


Hamid Reza
Hassanzadeh
Hamid Reza Hassanzadeh is a graduate student in Department of Computer Engineering, Ferdowsi University of Mashhad (FUM), Mashhad,
Iran (email:ha.hassanzadeh@ieee.org)
Hamid Reza Hassanzadeh is a graduate student
Iran
Adaptive rate control
Multimedia sensor networks
Fuzzy queue management
[[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks 38 (4) (2002) 393– 422. ##[2] I.F. Akyildiz, T. Melodia, T.R. Chowdhury, A survey on wireless multimedia sensor networks, Computer Networks 51 (2007) 921–960. ##[3] V. Jacobson, Congestion Avoidance and Control, Symposium proceeding on communications architecture and protocols, (1988) 314329. ##[4] Yassine Hadjadj Aoul, Ahmaed Mehaoua, Charalabos Skianis, A Fuzzy LogicBased AQM for RealTime Traffic Over Internet, Computer Networks 51 (2007) 46174633. ##[5] S. Floyd, K. Fall, Random early detection gateways for congestion avoidance, IEEE/ACM Transactions on Networking (August) (1993). ##[6] Zadeh, L.: Fuzzy Sets, Information and Control, 8:338 – 353, (1965). ##[7] Zargar, S.T., Yaghmaee, M.H.; Fard, A.M , Fuzzy Proactive Queue Management Technique, Annual India Conference, (2006). ##[8] Tapio Frantti, Fuzzy Congestion Control In Packet Networks, Springer Berlin / Heidelberg, Volume 2/2005. ##[9] B. Safaiezadeh, A.M. Rahmani, E. Mahdipour, A New Fuzzy Congestion Control in Computer Networks, International Conference on Future Computer and Communication, (2009) 314318 . ##[10] C.N. Nyirenda, D.S. Dawoud, “SelfOrganization in a Particle Swarm Optimized Fuzzy Logic Con gestion Detection Mechanism for IP Networks”, Submitted to Scientia Iranica, International Journal of Science and Technology. ##[11] C. Wang, Member, K. Sohraby, M. Daneshmand, Y. Hu, Upstream congestion control in wireless sensor networks through crosslayer optimization, IEEE Journal on Selected Areas in Communications 25 (4) (2007) 786–795. ##[12] C.T. Ee, R. Bajcsy, Congestion control and fairness for manyto one routing in sensor networks, in: Proceedings of ACM Sensys, November 2004. ##[13] C.Y. Wan, S.B. Eisenman, A.T. Campbell, CODA: congestion detection and avoidance in sensor networks, in: Proceedings of ACM Sensys’03, Los Angeles, CA, November 5–7, 2003. ##[14] H. Zhang et al., Reliable bursty convergecast in wireless sensor networks, in: Proceedings of ACM Mobihoc’05, UrbanaChampain, IL, May 25–28, 2005. ##[15] Mohammad Hossein Yaghmaee a,*, Donald A. Adjeroh, Prioritybased rate control for service differentiation and congestion control in wireless multimedia sensor networks, Computer Networks 53 (2009) 1798–1811.##]
Large Deformation Characterization of Mouse Oocyte Cell Under Needle Injection Experiment
Large Deformation Characterization of Mouse Oocyte Cell Under Needle Injection Experiment
2
2
In order to better understand the mechanical properties of biological cells, characterization and investigation of their material behavior is necessary. In this paper hyperelastic NeoHookean material is used to characterize the mechanical properties of mouse oocyte cell. It has been assumed that the cell behaves as continuous, isotropic, nonlinear and homogenous material for modeling. Then, by matching the experimental data with finite element (FE) simulation result and using the Levenberg–Marquardt optimization algorithm, the nonlinear hyperelastic model parameters have been extracted. Experimental data of mouse oocyte captured from literatures. Advantage of the developed model is that it can be used to calculate accurate reaction force on surgical instrument or it can be used to compute deformation or force in virtual reality based medical simulations.
1
In order to better understand the mechanical properties of biological cells, characterization and investigation of their material behavior is necessary. In this paper hyperelastic NeoHookean material is used to characterize the mechanical properties of mouse oocyte cell. It has been assumed that the cell behaves as continuous, isotropic, nonlinear and homogenous material for modeling. Then, by matching the experimental data with finite element (FE) simulation result and using the Levenberg–Marquardt optimization algorithm, the nonlinear hyperelastic model parameters have been extracted. Experimental data of mouse oocyte captured from literatures. Advantage of the developed model is that it can be used to calculate accurate reaction force on surgical instrument or it can be used to compute deformation or force in virtual reality based medical simulations.
21
25


Ali A.
Abbasi
Corresponding Author, School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran, Email: Ali.eng.edu@gmail.com.
Corresponding Author, School of Mechanical
Iran


M.T.
Ahmadian
Center of Excellence in Design, Robotics and Automation (CEDRA), School of Mechanical Engineering, Sharif University of Technology,
Tehran, Iran.
Center of Excellence in Design, Robotics
Iran
Biological cells
Levenberg–Marquardt optimization algorithm
Inverse finite element
hyperelastic material
[[1] C.T. Lim, E.H. Zhou, and S.T. Quek, "Mechanical models for living cells—a review,” Journal of Biomechanics, vol. 39, pp.195–216. 2006. ##[2] Y. Tan, D. Sun, W. Huang, and S. Han Cheng, "Characterizing Mechanical Properties of Biological Cells by Microinjection,” IEEE Transactions on Nanobioscience, Vol. 9, No. 3, September 2010. ##[3] F.P.T. Baaijens, W.R. Trickey, T.A. Laursen, and F. Guilak, "Large deformation ﬁnite element analysis of micropipette aspiration to determine the mechanical properties of the chondrocyte". Annals of Biomedical Engineering, vol.33, No.4, pp. 494–501. 2005. ##[4] Y. Tan, D. Sun, and W. Huang, “Mechanical modeling of red blood cells during optical stretching,” J. Biomech. Eng.Trans. ASME, vol. 132, pp. 044504, 2010. ##[5] Y. Kim, J. H. Shin, and J. Kim, “Atomic Force Microscopy Probing for Biomechanical Characterization of Living Cells,” Proceedings of the 2nd Biennial IEEE/RASEMBS International Conference on Biomedical Robotics and Biomechatronics Scottsdale, AZ, USA, October 1922, 2008. ##[6] M.T. Ahmadian, G.R. Vossoughi, A.A. Abbasi, P. Raeissi,“Modeling Of Cell Deformation Under External Force Using Artificial Neural Network” , Proceedings of the ASME 2010 International Mechanical Engineering Congress & Exposition, November 1218, 2010, Vancouver, British Columbia, Canada. ##[7] M.T. Ahmadian, G.R. Vossoughi, A.A. Abbasi, P. Raeissi, “Cell Deformation Modeling Under External Force Using Artificial Neural Network” Journal of Solid Mechanics Vol. 2, No. 2, pp 190198. 2010. ##[8] A. A. Abbasi, G.R. Vossoughi, M.T. Ahmadian “Deformation Prediction Of Mouse Embryos In Cell Injection Experiment By A Feed forward Artificial Neural Network” Proceedings of the ASME International Design Engineering Technical Conferences &Computers and Information in Engineering Conference, August 2931, 2011, Washington, DC, USA. ##[9] A. A. Abbasi, H. Sayyaadi, G.R. Vossoughi, “Sensitivity Analysis Of Mouse Embryos In Needle Injection Experiment Using Artificial Neural Network”, 2nd International Conference on Future Information Technology (ICFIT 2011), 1618 September 2011, Singapore. ##[10] A. A. Abbasi, G.R. Vossoughi, M.T. Ahmadian “Deformation Prediction By A Feed forward Artificial Neural Network during mouse embryo micromanipulation” Animal cells and systems, Vol. 16, No. 2, pp.121126,2012. ##[11] A. A. Abbasi, G.R. Vossoughi, M.T. Ahmadian “Application Of Adaptive Neural Fuzzy Inference Technique For Biological Cell Modeling –Part A:Deformation Prediction” 2nd International Conference on Future Information Technology (ICFIT 2011), 1618 September 2011, Singapore. ##[12] A. A. Abbasi, G.R. Vossoughi, M.T. Ahmadian “Application of Adaptive Neural Fuzzy Inference Technique for Biological Cell Modeling –Part B: Prediction of External Applied Force”, 2nd International Conference on Future Information Technology (ICFIT 2011), 1618 September 2011, Singapore. ##[13] L.G. Alexopoulos, M.A. Haider, T.P. Vail, and F. Guilak, “Alterations in the mechanical properties of the human chondrocyte pericellular matrix with osteoarthritis”. Journal of Biomechanical Engineering 125 (3), 323–333, 2003. ##[14] E.H. Zhou, C.T. Lim, and S.T. Quek, “Finite element simulation of the micropipette aspiration of a living cell undergoing large viscoelastic deformation” Mechanics of Advanced Materials and Structures, vol. 12, No. 6, pp. 501–512, 2005. ##[15] T. Boudou, J. Ohayon, Y. Arntz, G. Finet, C. Picart, P. Tracqui,. An extended modeling of the micropipette aspiration experiment for the characterization of the Young’s modulus and Poisson’s ratio of adherent thin biological samples: numerical and experimental studies. Journal of Biomechanics, vol. 39 No. 9, pp.1677–1685, 2006. ##[16] M. Flückiger, “Cell Membrane Mechanical Modeling for Microrobotic Cell Manipulation”, Diploma Thesis, ETHZ Swiss Federal Institute of Technology, Zurich, WS03/04, 2004. ##[17] Y. Sun, K.T. Wan, K.P. Roberts, J.C. Bischof, and B.J. Nelson, “Mechanical Property Characterization of Mouse Zona Pellucida” IEEE Transactions on Nanobioscience, vol. 2, pp.279286, 2003. ##[18] A. Bummo and J. Kim, “an Efficient Soft Tissue Characterization Method for Haptic Rendering of Soft Tissue Deformation in Medical Simulation”, Frontiers in the Convergence of Bioscience and Information Technologies 2007.IEEE. ##[19] Abaqus/CAE user’s manual, Version 6.9, Inc., Providence, RI, USA, 2009. ##[20] W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery, Numerical recipes in C++. The art of scientiﬁc computing, second ed. Cambridge University Press, 1992. ##[21] A. A. Abbasi, “Modeling of biological cells with applications to the design of a nanomicro gripper used in cell manipulation”, M.S. thesis, Sharif University of technology, Tehran, Iran, 2011. ##[22] E. Samur, M. Sedef, C. Basdogan, L. Avtan and O. Duzgun, “A robotic indenter for minimally invasive measurement and characterization of soft tissue response”, Medical Image Analysis vol. 11, pp. 361–373, 2007.##]
Crosslayer Packetdependant OFDM Scheduling Based on Proportional Fairness
Crosslayer Packetdependant OFDM Scheduling Based on Proportional Fairness
2
2
This paper assumes each user has more than one queue, derives a new packetdependant proportional fairness power allocation pattern based on the sum of weight capacity and the packet’s priority in users’ queues, and proposes 4 new crosslayer packetdependant OFDM scheduling schemes based on proportional fairness for heterogeneous classes of traffic. Scenario 1, scenario 2 and scenario 3 lead respectively artificial fish swarm algorithm, selfadaptive particle swarm optimization algorithm and cloud adaptive particle swarm optimization algorithm into subcarrier allocation in packetdependant proportional fairness scheduling, and use respectively new power allocation pattern, selfadaptive particle swarm optimization algorithm and population migration algorithm to allocate power. Scenario 4 uses greedy algorithm concerning fairness to allocate subcarriers, and uses new power allocation pattern to allocate power. Simulation indicates scenario 1,scenario 2 and scenario 3 raise the system’s total rate on the basis of undertaking the fairness among users’ rates and average packet delay; scenario 4 not only meets users’ rates and average packet delay demands, but also improve the fairness among users’ rates.
1
This paper assumes each user has more than one queue, derives a new packetdependant proportional fairness power allocation pattern based on the sum of weight capacity and the packet’s priority in users’ queues, and proposes 4 new crosslayer packetdependant OFDM scheduling schemes based on proportional fairness for heterogeneous classes of traffic. Scenario 1, scenario 2 and scenario 3 lead respectively artificial fish swarm algorithm, selfadaptive particle swarm optimization algorithm and cloud adaptive particle swarm optimization algorithm into subcarrier allocation in packetdependant proportional fairness scheduling, and use respectively new power allocation pattern, selfadaptive particle swarm optimization algorithm and population migration algorithm to allocate power. Scenario 4 uses greedy algorithm concerning fairness to allocate subcarriers, and uses new power allocation pattern to allocate power. Simulation indicates scenario 1,scenario 2 and scenario 3 raise the system’s total rate on the basis of undertaking the fairness among users’ rates and average packet delay; scenario 4 not only meets users’ rates and average packet delay demands, but also improve the fairness among users’ rates.
27
39


Hua
Hou
Corresponding Author Hua Hou is with school of Information Science and Electrical Engineering, Hebei University of Engineering, Handan ,
P.R.China ( Email: hh110040@gmail.com).
Corresponding Author Hua Hou is with school
Iran


Gen
xuan
Genxuan Li is with School of Information Science and Electrical Engineering, Hebei University of Engineering, Handan , P.R.China ( Email:
hh110040@gmail.com ).
Genxuan Li is with School of Information
Iran
Multiuser OFDM
Scheduling
Proportional fairness
Swarm Intelligence Algorithm
Crosslayer
Resource allocation
Particle swarm algorithm
Population migration algorithm
Artificial fish swarm algorithm
Packetdependant
[[1] Y.L. Liu, M.Y. Jiang.Adaptive resource allocation in multiuser OFDM system based on hopfield neural networks. JOURNAL OF CIRCUITS AND SYSTEMS,2010,15(2):4751. ##[2] D.X. Yu, Y.M. Cai,D. Wu,W. Zhong.Subcarrier and Power Allocation Based on Game Theory in Uplink OFDMA Systems. Journal of Electronics and Information Technology,2010,32(4):775779. ##[3] L. Peng, M.Y. Jiang. Adaptive crosslayer resource allocation scheme resisting delay sensibility. Application Research of Computers, 2010,27(3):11221125. ##[4] N.Zhou, X.zhu, Y.Huang, H.Lin. Low Complexity CrossLayer Design with Packet Dependent Scheduling for Heterogeneous Traffic in Multiuser OFDM Systems. Wireless Com.,IEEE,Jun.2010,9(6):1912– 1923. ##[5] Z.K.Shen, J.G. Andrews, B.L. Evans. Adaptive Resource Allocation in Multiuser OFDM Systems With Proportional Rate Constraints. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, NOVEMBER 2005,4(6): 27262737. ##[6] X. Ma,Q. Liu. Artificial fish swarm algorithm for multiple knapsack problem. Journal of Computer Applications, 2010,30(2):469471. ##[7] Y.M. Cheng, M.Y. Jiang. Adaptive resource allocation in multiuser OFDM system based on improved artificial fish swarm algorithm. Application Research of Computers, 2009,26(6):20922094. ##[8] X.J. Bi, W.W. Cao. Adaptive subcarrier allocation for an orthogonal frequency division multiple access system based on a particle swarm optimization algorithm. Journal of Harbin Engineering University,2010,32(4):775779. ##[9] K. Niu, W.W. Sun, W.J. Xu, Z.Q. He.The Distributed Power Allocation used in OFDMA systems Based on Particle Swarm Optimization Algorithm. China,201010033918.8,2010. ##[10] J. Li, Ch. Wang. A modified selfadaptive particle swarm optimization.Journal of Huazhong University of Science and Technology(Natural Science Edition),2008,36(3):118121. ##[11] X.Q. Wei,Y.Q. Zhou,H.J. Huang,D.X. Luo. Adaptive particle swarm optimization algorithm based on cloud theory.Computer Engineering and Application, 2009,45(1):4850. ##[12] A.J.OuYang, W.W. Zhang, Y.Q. Zhou. Hybrid global optimization algorithm based on simplex and population migration.Computer Engineering and Applications, 2010,46(4):2931. ##]
Investigating Effective Parameters in Tactile Determination of Artery included in Soft Tissue by FEM
Investigating Effective Parameters in Tactile Determination of Artery included in Soft Tissue by FEM
2
2
One of the newest ways of surgery is known as Minimally Invasive Surgery (MIS), which in spite of its benefits, because of surgeon's tactile sensing omission, causes some problems with detection of arteries and their exact positions in tissue during a surgery. In this study, tactile detection of an artery in tissue has been modeled by finite element method. In this modeling, three 2D models of tissue have been created: tissue, tissue including a tumor, and tissue including an artery. After solving the three models with similar boundary conditions and loadings, the 2D tactile mappings and stress graphs for upper nodes of models, which have the role of transferring tactile data, have been explored. Comparing these results showed that stress graphs of upper nodes of tissue including an artery is timedependent. However, for two other models it is constant. Then, the effect of variation of different parameters of the model on artery detection such as tissue thickness, artery diameter, and elastic module of artery wall has been studied.
1
One of the newest ways of surgery is known as Minimally Invasive Surgery (MIS), which in spite of its benefits, because of surgeon's tactile sensing omission, causes some problems with detection of arteries and their exact positions in tissue during a surgery. In this study, tactile detection of an artery in tissue has been modeled by finite element method. In this modeling, three 2D models of tissue have been created: tissue, tissue including a tumor, and tissue including an artery. After solving the three models with similar boundary conditions and loadings, the 2D tactile mappings and stress graphs for upper nodes of models, which have the role of transferring tactile data, have been explored. Comparing these results showed that stress graphs of upper nodes of tissue including an artery is timedependent. However, for two other models it is constant. Then, the effect of variation of different parameters of the model on artery detection such as tissue thickness, artery diameter, and elastic module of artery wall has been studied.
41
46


Ali
Abouei Mehrizi
A. Abouei Mehrizi is with the Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (email:
abouei.ali@gmail.com).
A. Abouei Mehrizi is with the Faculty of
Iran


Siamak
Najarian
Corresponding Author, S. Najarian is with the Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (email:
najarian@aut.ac.ir).
Corresponding Author, S. Najarian is with
Iran
najarian@aut.ac.ir


Majid
Moini
M. Moini is with Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran (email:
moinim@hotmail.com).
M. Moini is with Sina Trauma and Surgery
Iran


Pedram
Pahlavan
P. Pahlavan is with the Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran (email: pe_pedram@yahoo.com).
P. Pahlavan is with the Faculty of Biomedical
Iran


Javad
Dargahi
Professor J. Dargahi, Mechanical &Industrial Engineering Dept., Concordia University, Montreal, Canada.
Professor J. Dargahi, Mechanical &Industrial
Iran
Soft tissue
artery
tactile detection
Physical Properties
Finite Element Method (FEM)
[[1] A. Abouei Mehrizi, S. Najarian and M. Moini, "Modeling of tactile detection of an artery in a soft tissue by finite element analysis," Amirkabir Journal of Science and technology, to be published. ##[2] W. J. Peine, "Remote palpation instruments for minimally invasive surgery," Ph.D. dissertation, Div. of Eng. and Appl. Sci., HarvardUniversity, 1998. ##[3] J. Dargahi and S. Najarian, "Human tactile perception as a standard for artificial tactile sensing a review”, Inter. J. of Med. Rob. Comp. Ass. Surg., vol. 1, pp. 2335, 2004. ##[4] M. E. H. Eltaib and J. R. Hewit, "Tactile sensing technology for minimal access surgerya review", Mechatronics, vol. 13, p.p. 11631177, 2003. ##[5] J. Dargahi and S. Najarian, "A Supported membrane type sensor for medical tactile mapping", Sensor Review, vol. 24, pp. 284297, 2004. ##[6] S. M. Hosseini, S. Najarian, S. Motaghinasab, and J. Dargahi, "Detection of tumors using computational tactile sensing approach", Inter. J. of Med. Rob. Comp. Ass. Surg., vol. 2, no. 4, p.p. 333340, 2006. ##[7] S. M. Hosseini, S. Najarian, S. Motaghinasab, "Analysis of effects of tumors in tissue using of artificial tactile modeling", Amirkabir Journal, to be published. ##[8] S. M. Hosseini, S. Najarian, S. Motaghinasab, and S. Torabi, "Experimental and numerical verification of artificial tactile sensing approach for predicting tumor existence in virtual soft tissue", Proc. of the 15th AnnualInternational Conf. of Mechanical Engineering, 2007. ##[9] P. Dario, M. Bergamasco, "An advanced robot system for automated diagnostic tasks through palpation" IEEE Trans. on Biomed. Eng., vol. 35, no. 2, p.p. 11826, 1988. ##[10] W. J. Peine, S. Son, and R. D. Howe, "A palpation system for artery localization in laparoscopic surgery", First Inter. Symp. on Medical Robotics and ComputerAssisted Surgery, Pittsburgh, 1994. ##[11] R. A. Beasley and R. D. Howe, "Tactile tracking of arteries in robotic surgery", Proc. of IEEE, International Conf. on Robotics&Automation, Washington, DC, 2002. ##[12] M. H. Lee and H. R. Nicholls, "Review article tactile sensing for mechatronicsa state of the art survey", Mechatronics, vol. 9, pp. 131, 1999. ##[13] D. J. Mozersky, D. Sumnfer, D.E. Hokanson, and D.E. Strandness, "Transcutaneous measurement of the elastic properties of the human femoral artery", Circulation AHA Journal, v. XLVI, 1972. ##[14] A. E. Kerdoke, S. M. Cotin, M. P. Ottensmeyer, A. M. Galea, R. D. Howe, and S. L. Dawson, "Truth cube: establishing physical standard for soft tissue simulation", Med. Imag. Anal., vol. 7, pp. 283291, 2003.##]
A MultiPeriod 1Center Location Problem in the Presence of a Probabilistic Line Barrier
A MultiPeriod 1Center Location Problem in the Presence of a Probabilistic Line Barrier
2
2
This paper investigates a multiperiod rectilinear distance 1center location problem considering a lineshaped barrier, in which the starting point of the barrier follows the uniform distribution function. In addition, the existing points are sensitive to demands and locations. The purpose of the presented model is to minimize the maximum barrier distance from the new facility to the existing facilities during the finite planning horizon. Additionally, a lower bound problem is generated. The presented model is mixedinteger nonlinear programming (MINLP); however, an optimum solution is reached.
1
This paper investigates a multiperiod rectilinear distance 1center location problem considering a lineshaped barrier, in which the starting point of the barrier follows the uniform distribution function. In addition, the existing points are sensitive to demands and locations. The purpose of the presented model is to minimize the maximum barrier distance from the new facility to the existing facilities during the finite planning horizon. Additionally, a lower bound problem is generated. The presented model is mixedinteger nonlinear programming (MINLP); however, an optimum solution is reached.
47
55


M.
AmiriAref
Iran


N.
Javadian
Iran


R.
TavakkoliMoghaddam
Iran


M. B.
Aryanezhad
Iran
Multiperiod Center location problem
Probabilistic line barrier
Rectilinear distance
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Numerical Computation Of MultiComponent TwoPhase Flow in Cathode Of PEM Fuel Cells
Numerical Computation Of MultiComponent TwoPhase Flow in Cathode Of PEM Fuel Cells
2
2
A twodimensional, unsteady, isothermal and twophase flow of reactantproduct mixture in the airside electrode of proton exchange membrane fuel cells (PEMFC) is studied numerically in the present study. The mixture is composed of oxygen, nitrogen, liquid water and water vapor. The governing equations are two species conservation, a single momentum equation for mobile mixture, liquid mass conservation, and the whole mixture mass conservation. In this study, liquid mass conservation is used to calculate the saturation, so, the effect of liquid phase velocity and also saturation at previous time step are accounted in calculating the next time step saturation. The capillary pressure was used to express the slip velocity between the phases. The strongly coupled equations are solved using the finite volume SIMPLER scheme of Patankar (1984). The computational domain consists of an open area (gas delivery channel), and a porous Gas Diffusion Layer (GDL). A single set of governing equations are solved for both sub domains with respect to each sub domain property. The comparison between the numerical current density and that of experimental (Ticianelli et al.(1988)) shows a good agreement.
1
A twodimensional, unsteady, isothermal and twophase flow of reactantproduct mixture in the airside electrode of proton exchange membrane fuel cells (PEMFC) is studied numerically in the present study. The mixture is composed of oxygen, nitrogen, liquid water and water vapor. The governing equations are two species conservation, a single momentum equation for mobile mixture, liquid mass conservation, and the whole mixture mass conservation. In this study, liquid mass conservation is used to calculate the saturation, so, the effect of liquid phase velocity and also saturation at previous time step are accounted in calculating the next time step saturation. The capillary pressure was used to express the slip velocity between the phases. The strongly coupled equations are solved using the finite volume SIMPLER scheme of Patankar (1984). The computational domain consists of an open area (gas delivery channel), and a porous Gas Diffusion Layer (GDL). A single set of governing equations are solved for both sub domains with respect to each sub domain property. The comparison between the numerical current density and that of experimental (Ticianelli et al.(1988)) shows a good agreement.
57
65


M.
Khakbaz Baboli
Graduate Student, member of Energy Conversion Research Laboratory, Department of Mechanical Engineering Amirkabir University of
Technology (Tehran Polytechnic) Tehran, Iran, 15875—4413, mobinkhakbaz@gmail.com.
Graduate Student, member of Energy Conversion
Iran


M. J.
Kermani
Iran
CFD
PEM Fuel Cells
TwoPhase
Two Component
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