Topology optimization considering the requirements of deep-drawn sheet metals

Size: px
Start display at page:

Download "Topology optimization considering the requirements of deep-drawn sheet metals"

Transcription

1 th World Congress on Structural and Multdscplnary Optmsaton 7 th - th, June 5, Sydney Australa Topology optmzaton consderng the requrements of deep-drawn sheet metals Robert Denemann, Axel Schumacher, Serk Febg, Unversty of Wuppertal, Faculty D Mechancal Engneerng, Char for Optmzaton of Mechancal Structures, Wuppertal, Germany denemann@un-wuppertal.de schumacher@un-wuppertal.de Volkswagen AG, Braunschweg, Germany, serk.febg@volkswagen.de. Abstract Topology-optmzed desgns for mnmum complance or mnmum stress at mnmum mass are often framework structures due to ther homogeneous stress dstrbuton over the cross secton and therefore the best possble materal utlzaton. From the manufacturng s pont of vew complex framework structures, whch often develops durng topology optmzaton, are dffcult to manufacture because of possble undercuts. Manufacturng of these desgns s often only possble by jonng of numerous components or by D prntng. For mass producton sheet metal parts manufactured by deep drawng are often more effcent concernng the costs n relaton to ther performance. Therefore we mplemented a manufacturng constrant to the D topology optmzaton based on the densty method ensurng that thn walled structure results. Thereby more flexblty for the md surface desgn and also for cut-outs s reached compared to the optmzaton based on CAD-parameters. Also a varable thckness dstrbuton for talored blanks can be acheved. Results for deep drawng structures wth optmzed topologes wll be compared wth optmzed structures wthout manufacturng restrcton due to ther performance.. Keywords: topology optmzaton, sheet metals, deep drawng, manufacturng constrant, thn walled structures. Introducton The optmzaton of shell structures s mportant n the feld of mechancal engneerng, but also n cvl engneerng and archtecture (roof structures). In these felds a strengthened research has taken place n recent years. Ansola et. al [] propose a combnaton of CAD-parameters for the md surface descrpton and the SIMP-algorthm for the dentfcaton of optmal cut-outs. Thereby the optmzaton algorthm runs serally through the shape optmzaton of the md surface and afterwards the topology optmzaton. Ths approach was taken up by Hassan et al. [] and a smultaneous shape- and topology-optmzaton was ntroduced. The shape optmzaton takes place n the Fnte Element Model, whch shape can be modfed by control ponts of splnes. Both methods hghly depend on the parametrzaton of the md surface. Zenkewcz and Campbell [] use the de coordnates as desgn varables nstead of the CAD-parameters. Thereby a larger freedom of desgn s acheved. However by usng senstvtes of coordnates of boundary des the fnte element mesh becomes rregular. Yonekura et al. [] keep the mesh regularty for small shape modfcatons. In lterature there are few attempts for the optmzaton of shells based on sold elements. Lochner-Aldnger and Schumacher [5] use the densty method and extract sosurfaces of the element denstes as md surfaces.. The new approach for topology optmzaton for deep-drawn sheet metals Our new approach uses the homogensaton method [6] on a lnear voxel mesh. The derved method Sold Isotropc Materal wth Penalsaton (SIMP) ntroduces materal wth the artfcal densty < ρ and Young s modulus E n element (see equaton ). E s the Young s modulus of the basc materal. By ncreasng the penalty exponent s over. ntermedate denstes are penalzed and thereby the optmzed desgn rather converges to a black&whte desgn. E s ρ E = () Because of the use of senstvtes our approach s sutable for lnear statc load cases. All knds of objectve functons or constrants can be used, f ther senstvtes are kwn.

2 .. Calculaton of the md surface To allow the manufacturng by deep drawng n a sngle formng step, the optmzed structure must have - undercuts. - a constant wall thckness. Thereby the thnnng durng the formng process s neglected. By t consderng the formng process also the materal hardenng and resdual stresses are gred. These two manufacturng constrants can be acheved by modfyng the senstvtes of the objectve functon. An ncrease of the element denstes s only allowed near the current md surface. Thus the md surface can move accordng to the senstvtes. The md surface can be found by calculatng the average of the element coordnates n the punch drecton weghted wth the element denstes. Fgure shows the procedure of dervng the md surface from the volume mesh. Only a sngle cross secton s dsplayed. Intally the user has to defne the global punch drecton. The mesh s dvded nto columns wth the same wdth w, whch s the element edge length. The mdpont of each column s calculated by equaton. element densty ξ element mdpont punch drecton column boundary ground lne element poston ξ pont of md surface Fgure : Calculaton of md surface ξ = ρ ξ m () ρ ξ are the dstances between the element mdponts from a ground lne. For one exemplary column these dstances are marked as grey arrows. The mdpont of each element decdes to whch column the element belongs. The connecton of all mdponts wth dstance ξ m present the md surface.. Penalzaton of senstvtes In order to get a shell structure senstvtes far away from the md surface are penalzed. The penalzaton factor P for the senstvtes of each element s calculated by equaton. = a P atan b d π b () d s the mnmum dstances between the mdpont of element and the md surface. b s the user defned desred wall thckness, a / b descrbes the dscreteness of the penalty functon (see Fgure ). A larger quotent a / b ensures that the shell thckness does t exceed b, but slows down the convergence rate. The penalsaton factor s P,. rmalzed ] [

3 P b / d Fgure : Graph of penalsaton functon for element senstvtes as a functon of the dstance from the md surface.. Convergence The movement of the md surface can stagnate, f the penalsaton of the senstvtes s stronger than the mprovement of the objectve functon. Ths problem s solved by alternatng the desred wall thckness b. By ncreasng the desred wall thckness, elements are accumulated at the sde of the shell, where the senstvtes are larger, by decreasng the desred wall thckness the shell s mdface has moved to an mproved desgn. Also the penalsaton of ntermedate denstes has an nfluence on the convergence. Fgure shows the movement of the md surface. Only a sngle cross secton s dsplayed. Even f a lower located md surface would be better for the possble objectve functon complance, the stffness of the structure would temporarly decrease due to the lower stffness of elements wth penalzed ntermedate densty (mage at the rght). That s the reason why the optmzaton starts wthout penalsaton of ntermedate denstes (penalty exponent s = ) untl a convergence crteron s reached. Thereby at least the tensle/compressve stffness remans the same between the mages on the left and the rght. After the ncrease of the penalty exponent ths convergence problem s also solved by alternatng the desred wall thckness. element densty md surface Fgure : Movement of md surface through change of elements denstes. Optmzaton procedure Fgure shows the optmzaton algorthm. Convergence crteron can be the change of the objectve functon from one teraton to the next one or the maxmum change of element denstes. Convergence crteron s the mprovement of the objectve functon after the alternaton of the desred wall thckness. Durng the alternaton of the desred wall thckness and at the start of the optmzaton, the current desred wall thckness bcurr s larger than the desred wall thckness b. 5. Examples In the followng example topology optmzatons of a cantlever beam (see Fgure 5a) wth and wthout manufacturng constrant are performed. The complance s mnmzed consderng a volume fracton constrant of 6.5 %. The desgn space s dscretsed by 8 8 elements. One end of the structure s fxed, at one edge a lne load of N/mm s appled. The elements at the lne load are defned as n-desgn space. A senstvty flter wth the radus of r = (.7 element edge lengths) and a penalty exponent s = are used. The materal s steel wth Young s Modulus E = MPa and Posson s Rato ν =..

4 Start desgn, penalty exponent, desred wall thckness, current desred wall thckness Calculaton of senstvtes Flterng & penalzaton of senstvtes Update of element denstes Fnte element analyss Decrease current desred wall thckness Convergence crteron reached? Desred wall thckness b = current desred wall? thckness Increase current desred wall thckness Convergence crteron reached? Penalty exponent Penalty exponent? Optmzed deep drawable sheet metal found Fgure : Optmzaton procedure mm z Mses Stress [MPa] 8 6 y x a) b) Complance [Nm] 5. Cantlever Beam wthout manufacturng constrant Wthout the manufacturng constrant a complance of 8.7 Nmm s acheved (see Fgure 5b/c). 6 teratons were necessary followed by a fnal converson to a black&whte desgn. The convergence crteron s the mprovement of the objectve functon per teraton of less than. % per teraton. 8 Iteraton c) Fgure 5: Cantlever Beam: a) FE-Model wth loads and boundary condton, b) Stresses of fnal desgn (converted to black&whte desgn) wthout manufacturng constrant, c) Complance hstory

5 5. Cantlever Beam wth manufacturng constrant The same optmzaton task as n chapter 5. s performed by usng the optmzaton procedure for thn walled structures descrbed n chapter.. The desred wall thckness s b = ( element edge lengths). Ths s the thnnest possble structure that ensures that a bendng stress state can be represented wth lnear volume elements. The punch drecton was chosen as z. The convergence crtera and were the mprovement of the objectve functon per teraton of less than. %. The penalsaton parameter for the manufacturng restrcton s chosen as a = 5. Fgure 6a shows the complance hstory of the topology optmzaton process. In Fgure 6b) the success of the alternaton of the desred wall thckness between ntermedate result and can be seen. In Fgure 7 the desgn changes durng the optmzaton process are shown. 6 Iteraton Current desred wall thckness [multple of element edge length] Complance [Nm] SIMP penalty exponent s Complance [Nm] 8 5 Iteraton a) 5 b) Fgure 6: Complance hstory: a) whole Optmzaton (logarthmc scale) wth change of penalty exponent s, b) detal of convergence hstory wth change of desred wall thckness Element densty Iteraton Iteraton 9 Iteraton 68 5 Iteraton 76 Iteraton 77 6 Iteraton 5 Fgure 7: Element denstes of ntermedate results durng the optmzaton (elements wth densty x >. ) 5

6 In Fgure 8a the fnal black&whte desgn of the shell structure s shown. Ths structure reaches a complance of. Nmm at a bucklng safety of In comparson to the optmzaton wthout manufacturng constrant the complance s.7 % worse, whereby the manufacturng s much easer. Mses Stress [MPa] 8 6 a) b) Fgure 8: Stress a) of fnal desgn wth manufacturng constrant (converted to black&whte desgn), b) converted to surface model wth shell elements In order to check the qualty of the fnte element model wth sold elements, a surface model of the optmzed desgn wth the same volume has been created. Thereby the complance ncreases by.6 %. As to be seen n Fgure 8, also the stresses are very smlar, although the sold model s calculated wth only three lnear voxels across the sheet metal thckness. 6. Fnal remarks Besdes the shown applcaton examples, the manufacturng constrant for the topology optmzaton of deep drawable sheet metals has been tested for several structures wth multple load cases. The results are promsng, but we have to te that the gradent based optmzaton method wll fnd most probably only local optma. Compared to gradent based topology optmzatons wthout manufacturng constrant the presented method needs more teratons and the objectve functon of the optmzed desgns s usually worse, but t can be guaranteed that the structures can be manufactured easly. Further research actvtes wll focus on the mprovement of the computatonal effcency, multshell structures, stress- and bucklng constrants, the mplementaton of the deep drawng smulaton n the optmzaton and automatzaton of the converson to a surface model n order to perform a followng shape optmzaton. 7. References [] R. Ansola, J. Canales, J.A. Tarrago and J. Rasmussen, On smultaneous shape and materal layout optmzaton of shell structures, Structural and Multdscplnary Optmzaton,, 75-8,. [] B. Hassan, S.M. Tavakkol and H. Ghasemnejad, Smultaneous shape and topology optmzaton of shell structures, Structural and Multdscplnary Optmzaton, 8, -,. [] O.C. Zenkewcz and J.S. Campbell, Shape Optmzaton and sequental lnear programmng, In: R.H. Gallagher and O.C. Zenkewcz, Optmum Structural Desgn, John Wley & Sons, Chchester, 97. [] M. Yonekura, M. Shmoda and Y. Lu, Optmal Free-form Desgn of Shell Structure for Stress Mnmzaton, th World Congress on Structural and Multdscplnary Optmzaton, Orlando, USA,. [5] I. Lochner-Aldnger and A. Schumacher, Homogenzaton method, In: S. Adraenssens, P. Block, D. Veenendaal, C. Wllams, Shell Structures for Archtecture Form Fndng and Optmzaton, Routledge, New York, [6] M.P. Bendsøe, Optmal shape desgn as a materal dstrbuton problem, Structural Optmzaton,, 9-, 989 6

Design of Structure Optimization with APDL

Design of Structure Optimization with APDL Desgn of Structure Optmzaton wth APDL Yanyun School of Cvl Engneerng and Archtecture, East Chna Jaotong Unversty Nanchang 330013 Chna Abstract In ths paper, the desgn process of structure optmzaton wth

More information

Cable optimization of a long span cable stayed bridge in La Coruña (Spain)

Cable optimization of a long span cable stayed bridge in La Coruña (Spain) Computer Aded Optmum Desgn n Engneerng XI 107 Cable optmzaton of a long span cable stayed brdge n La Coruña (Span) A. Baldomr & S. Hernández School of Cvl Engneerng, Unversty of Coruña, La Coruña, Span

More information

LS-TaSC Version 2.1. Willem Roux Livermore Software Technology Corporation, Livermore, CA, USA. Abstract

LS-TaSC Version 2.1. Willem Roux Livermore Software Technology Corporation, Livermore, CA, USA. Abstract 12 th Internatonal LS-DYNA Users Conference Optmzaton(1) LS-TaSC Verson 2.1 Wllem Roux Lvermore Software Technology Corporaton, Lvermore, CA, USA Abstract Ths paper gves an overvew of LS-TaSC verson 2.1,

More information

Topology Design using LS-TaSC Version 2 and LS-DYNA

Topology Design using LS-TaSC Version 2 and LS-DYNA Topology Desgn usng LS-TaSC Verson 2 and LS-DYNA Wllem Roux Lvermore Software Technology Corporaton, Lvermore, CA, USA Abstract Ths paper gves an overvew of LS-TaSC verson 2, a topology optmzaton tool

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

Structural Optimization Using OPTIMIZER Program

Structural Optimization Using OPTIMIZER Program SprngerLnk - Book Chapter http://www.sprngerlnk.com/content/m28478j4372qh274/?prnt=true ق.ظ 1 of 2 2009/03/12 11:30 Book Chapter large verson Structural Optmzaton Usng OPTIMIZER Program Book III European

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

A Weight Balanced Multi-Objective Topology Optimization for Automotive Development

A Weight Balanced Multi-Objective Topology Optimization for Automotive Development A Weght Balanced Mult-Objectve Topology Optmzaton for Automotve Development Nkola Aulg 1, Emly Nutwell 2, Stefan Menzel 1, Duane Detwler 3 1 Honda Research Insttute Europe GmbH, Offenbach/Man, Germany

More information

Unsupervised Learning

Unsupervised Learning Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS Non-Parametrc Approach Mxture of Denstes Maxmum-Lkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and

More information

Research on Topology optimization Method for Tanker Structures in

Research on Topology optimization Method for Tanker Structures in Research on Topology optmzaton Method for Tanker Structures n Cargo Tank Regon QIU WeqangMarc_quwq@163.com,GAO Chu,SUN L, LUO Renje Marne Desgn & Research Insttute of Chna (MARIC), Shangha, Chna Abstract

More information

Multicriteria Decision Making

Multicriteria Decision Making Multcrtera Decson Makng Andrés Ramos (Andres.Ramos@comllas.edu) Pedro Sánchez (Pedro.Sanchez@comllas.edu) Sonja Wogrn (Sonja.Wogrn@comllas.edu) Contents 1. Basc concepts 2. Contnuous methods 3. Dscrete

More information

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

陳申岳 S-Y. Chen, 2007, Gradient-Based Structural and CFD Global Shape Optimization with SmartDO and the Response Smoothing Technology, Proceeding of

陳申岳 S-Y. Chen, 2007, Gradient-Based Structural and CFD Global Shape Optimization with SmartDO and the Response Smoothing Technology, Proceeding of 陳申岳 S-Y. Chen, 2007, Gradent-Based Structural and CFD Global Shape Optmzaton wth SmartDO and the Response Smoothng Technology, Proceedng of the 7 th World Congress of Structural and Multdscplnary Optmzaton

More information

MULTISTAGE OPTIMIZATION OF AUTOMOTIVE CONTROL ARM THROUGH TOPOLOGY AND SHAPE OPTIMIZATION. 1 Duane Detwiler, 2 Emily Nutwell*, 2 Deepak Lokesha

MULTISTAGE OPTIMIZATION OF AUTOMOTIVE CONTROL ARM THROUGH TOPOLOGY AND SHAPE OPTIMIZATION. 1 Duane Detwiler, 2 Emily Nutwell*, 2 Deepak Lokesha 6 th BETA CAE Internatonal Conference MULTISTAGE OPTIMIZATION OF AUTOMOTIVE CONTROL ARM THROUGH TOPOLOGY AND SHAPE OPTIMIZATION. 1 Duane Detwler, 2 Emly Nutwell*, 2 Deepak Lokesha 1 Honda R&D Amercas,

More information

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

Complexity Control in the Topology Optimization of Continuum Structures

Complexity Control in the Topology Optimization of Continuum Structures E. L. Cardoso Grupo de Mecânca Aplcada Departamento de Engenhara Mecânca UFRGS Rua Sarmento Lete, 425 95-7 Porto Alegre, RS. Brazl eduardo_lenz@yahoo.com.br J. S. O. Fonseca Grupo de Mecânca Aplcada Departamento

More information

Finite Element Analysis of Rubber Sealing Ring Resilience Behavior Qu Jia 1,a, Chen Geng 1,b and Yang Yuwei 2,c

Finite Element Analysis of Rubber Sealing Ring Resilience Behavior Qu Jia 1,a, Chen Geng 1,b and Yang Yuwei 2,c Advanced Materals Research Onlne: 03-06-3 ISSN: 66-8985, Vol. 705, pp 40-44 do:0.408/www.scentfc.net/amr.705.40 03 Trans Tech Publcatons, Swtzerland Fnte Element Analyss of Rubber Sealng Rng Reslence Behavor

More information

Sequential Projection Maximin Distance Sampling Method

Sequential Projection Maximin Distance Sampling Method APCOM & ISCM 11-14 th December, 2013, Sngapore Sequental Projecton Maxmn Dstance Samplng Method J. Jang 1, W. Lm 1, S. Cho 1, M. Lee 2, J. Na 3 and * T.H. Lee 1 1 Department of automotve engneerng, Hanyang

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

Detecting Maximum Inscribed Rectangle Based On Election Campaign Algorithm Qing-Hua XIE1,a,*, Xiang-Wei ZHANG1,b, Wen-Ge LV1,c and Si-Yuan CHENG1,d

Detecting Maximum Inscribed Rectangle Based On Election Campaign Algorithm Qing-Hua XIE1,a,*, Xiang-Wei ZHANG1,b, Wen-Ge LV1,c and Si-Yuan CHENG1,d 6th Internatonal onference on Advanced Desgn and Manufacturng Engneerng (IADME 2016) Detectng Maxmum Inscrbed Rectangle Based On Electon ampagn Algorthm Qng-Hua XIE1,a,*, Xang-We ZHAG1,b, Wen-Ge LV1,c

More information

SHAPE OPTIMIZATION OF STRUCTURES BY MODIFIED HARMONY SEARCH

SHAPE OPTIMIZATION OF STRUCTURES BY MODIFIED HARMONY SEARCH INTERNATIONAL JOURNAL OF OPTIMIZATION IN CIVIL ENGINEERING Int. J. Optm. Cvl Eng., 2011; 3:485-494 SHAPE OPTIMIZATION OF STRUCTURES BY MODIFIED HARMONY SEARCH S. Gholzadeh *,, A. Barzegar and Ch. Gheyratmand

More information

Shape Optimization of Shear-type Hysteretic Steel Damper for Building Frames using FEM-Analysis and Heuristic Approach

Shape Optimization of Shear-type Hysteretic Steel Damper for Building Frames using FEM-Analysis and Heuristic Approach The Seventh Chna-Japan-Korea Jont Symposum on Optmzaton of Structural and Mechancal Systems Huangshan, June, 18-21, 2012, Chna Shape Optmzaton of Shear-type Hysteretc Steel Damper for Buldng Frames usng

More information

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION 24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and

More information

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD Analyss on the Workspace of Sx-degrees-of-freedom Industral Robot Based on AutoCAD Jn-quan L 1, Ru Zhang 1,a, Fang Cu 1, Q Guan 1 and Yang Zhang 1 1 School of Automaton, Bejng Unversty of Posts and Telecommuncatons,

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Active Contours/Snakes

Active Contours/Snakes Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng

More information

Modeling of Fillets in Thin-walled Beams Using Shell/Plate and Beam Finite. Elements

Modeling of Fillets in Thin-walled Beams Using Shell/Plate and Beam Finite. Elements Modelng of Fllets n Thn-walled Beams Usng Shell/Plate and Beam Fnte Elements K. He Graduate Research Assstant W.D. Zhu Professor and Correspondng Author Emal: wzhu@umbc.edu; Tel: 410-455-3394 Department

More information

Plate/shell topological optimization subjected to linear buckling constraints by adopting composite exponential filtering function

Plate/shell topological optimization subjected to linear buckling constraints by adopting composite exponential filtering function Acta Mech. Sn. 2016) 324):649 658 DOI 10.1007/s10409-015-0531-5 RESEARCH PAPER Plate/shell topologcal optmzaton subjected to lnear bucklng constrants by adoptng composte exponental flterng functon Hong-Lng

More information

Computational layout design optimization of frame structures

Computational layout design optimization of frame structures Interfaces: archtecture.engneerng.scence 5-8th September, 017, Hamburg, Germany Annette Bögle, Manfred Grohmann (eds.) Computatonal layout desgn optmzaton of frame structures Jun Ye*, Paul Shepherd a,

More information

Fitting: Deformable contours April 26 th, 2018

Fitting: Deformable contours April 26 th, 2018 4/6/08 Fttng: Deformable contours Aprl 6 th, 08 Yong Jae Lee UC Davs Recap so far: Groupng and Fttng Goal: move from array of pxel values (or flter outputs) to a collecton of regons, objects, and shapes.

More information

Springback Reduction in Stamping of Front Side Member with a Response Surface Method

Springback Reduction in Stamping of Front Side Member with a Response Surface Method Sprngback Reducton n Stampng of Front Sde Member wth a Response Surface Method Jung-Han Song *, Hoon Huh *, Se-Ho Km **, Sung-Ho Park *** * Department of Mechancal Engneerng, Korea Advanced Insttute of

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Classification / Regression Support Vector Machines

Classification / Regression Support Vector Machines Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM

More information

Meta-heuristics for Multidimensional Knapsack Problems

Meta-heuristics for Multidimensional Knapsack Problems 2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,

More information

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water.

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water. Purpose Theory REFRACTION a. To study the refracton of lght from plane surfaces. b. To determne the ndex of refracton for Acrylc and Water. When a ray of lght passes from one medum nto another one of dfferent

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned

More information

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like: Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A

More information

Structural optimization using artificial bee colony algorithm

Structural optimization using artificial bee colony algorithm 2 nd Internatonal Conference on Engneerng Optmzaton September 6-9, 2010, Lsbon, ortugal Structural optmzaton usng artfcal bee colony algorthm Al Hadd 1, Sna Kazemzadeh Azad 2, Saed Kazemzadeh Azad Department

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

NGPM -- A NSGA-II Program in Matlab

NGPM -- A NSGA-II Program in Matlab Verson 1.4 LIN Song Aerospace Structural Dynamcs Research Laboratory College of Astronautcs, Northwestern Polytechncal Unversty, Chna Emal: lsssswc@163.com 2011-07-26 Contents Contents... 1. Introducton...

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT 3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ

More information

Investigations of Topology and Shape of Multi-material Optimum Design of Structures

Investigations of Topology and Shape of Multi-material Optimum Design of Structures Advanced Scence and Tecnology Letters Vol.141 (GST 2016), pp.241-245 ttp://dx.do.org/10.14257/astl.2016.141.52 Investgatons of Topology and Sape of Mult-materal Optmum Desgn of Structures Quoc Hoan Doan

More information

A high precision collaborative vision measurement of gear chamfering profile

A high precision collaborative vision measurement of gear chamfering profile Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) A hgh precson collaboratve vson measurement of gear chamferng profle Conglng Zhou, a, Zengpu Xu, b, Chunmng

More information

Design for Reliability: Case Studies in Manufacturing Process Synthesis

Design for Reliability: Case Studies in Manufacturing Process Synthesis Desgn for Relablty: Case Studes n Manufacturng Process Synthess Y. Lawrence Yao*, and Chao Lu Department of Mechancal Engneerng, Columba Unversty, Mudd Bldg., MC 473, New York, NY 7, USA * Correspondng

More information

Performance improvement for optimization of non-linear geometric fitting problem in manufacturing metrology*

Performance improvement for optimization of non-linear geometric fitting problem in manufacturing metrology* Measurement Scence and Technology 1 Performance mprovement for optmzaton of non-lnear geometrc fttng problem n manufacturng metrology* Govann Moron,Wahyudn P. Syam, and Stefano Petrò Mechancal Engneerng

More information

Multiobjective fuzzy optimization method

Multiobjective fuzzy optimization method Buletnul Ştnţfc al nverstăţ "Poltehnca" dn Tmşoara Sera ELECTRONICĂ ş TELECOMNICAŢII TRANSACTIONS on ELECTRONICS and COMMNICATIONS Tom 49(63, Fasccola, 24 Multobjectve fuzzy optmzaton method Gabrel Oltean

More information

Determining the Optimal Bandwidth Based on Multi-criterion Fusion

Determining the Optimal Bandwidth Based on Multi-criterion Fusion Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn

More information

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell Module 6: FEM for Plates and Shells Lecture 6: Fnte Element Analyss of Shell 3 6.6. Introducton A shell s a curved surface, whch by vrtue of ther shape can wthstand both membrane and bendng forces. A shell

More information

THE PULL-PUSH ALGORITHM REVISITED

THE PULL-PUSH ALGORITHM REVISITED THE PULL-PUSH ALGORITHM REVISITED Improvements, Computaton of Pont Denstes, and GPU Implementaton Martn Kraus Computer Graphcs & Vsualzaton Group, Technsche Unverstät München, Boltzmannstraße 3, 85748

More information

A Saturation Binary Neural Network for Crossbar Switching Problem

A Saturation Binary Neural Network for Crossbar Switching Problem A Saturaton Bnary Neural Network for Crossbar Swtchng Problem Cu Zhang 1, L-Qng Zhao 2, and Rong-Long Wang 2 1 Department of Autocontrol, Laonng Insttute of Scence and Technology, Benx, Chna bxlkyzhangcu@163.com

More information

Intra-Parametric Analysis of a Fuzzy MOLP

Intra-Parametric Analysis of a Fuzzy MOLP Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

Automatic selection of reference velocities for recursive depth migration

Automatic selection of reference velocities for recursive depth migration Automatc selecton of mgraton veloctes Automatc selecton of reference veloctes for recursve depth mgraton Hugh D. Geger and Gary F. Margrave ABSTRACT Wave equaton depth mgraton methods such as phase-shft

More information

VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES

VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES UbCC 2011, Volume 6, 5002981-x manuscrpts OPEN ACCES UbCC Journal ISSN 1992-8424 www.ubcc.org VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES

More information

Load-Balanced Anycast Routing

Load-Balanced Anycast Routing Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance

More information

CONCURRENT OPTIMIZATION OF MULTI RESPONCE QUALITY CHARACTERISTICS BASED ON TAGUCHI METHOD. Ümit Terzi*, Kasım Baynal

CONCURRENT OPTIMIZATION OF MULTI RESPONCE QUALITY CHARACTERISTICS BASED ON TAGUCHI METHOD. Ümit Terzi*, Kasım Baynal CONCURRENT OPTIMIZATION OF MUTI RESPONCE QUAITY CHARACTERISTICS BASED ON TAGUCHI METHOD Ümt Terz*, Kasım Baynal *Department of Industral Engneerng, Unversty of Kocael, Vnsan Campus, Kocael, Turkey +90

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

Multi-stable Perception. Necker Cube

Multi-stable Perception. Necker Cube Mult-stable Percepton Necker Cube Spnnng dancer lluson, Nobuuk Kaahara Fttng and Algnment Computer Vson Szelsk 6.1 James Has Acknowledgment: Man sldes from Derek Hoem, Lana Lazebnk, and Grauman&Lebe 2008

More information

Vibration Characteristic Analysis of Axial Fan Shell Based on ANSYS Workbench

Vibration Characteristic Analysis of Axial Fan Shell Based on ANSYS Workbench Internatonal Conference on Logstcs Engneerng, Management and Computer Scence (LEMCS 2015) Vbraton Characterstc Analyss of Axal Fan Shell Based on ANSYS Workbench Lchun Gu College of Mechancal and Electrcal

More information

FAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks

FAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks 2017 2nd Internatonal Semnar on Appled Physcs, Optoelectroncs and Photoncs (APOP 2017) ISBN: 978-1-60595-522-3 FAHP and Modfed GRA Based Network Selecton n Heterogeneous Wreless Networks Xaohan DU, Zhqng

More information

Study of key algorithms in topology optimization

Study of key algorithms in topology optimization Int J Adv Manuf Technol (2007) 32: 787 796 DOI 101007/s00170-005-0387-0 ORIGINAL ARTICLE Kong-Tan Zuo L-Png Chen Yun-Qng Zhang Jngzhou Yang Study of key algorthms n topology optmzaton Receved: 30 Aprl

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

More information

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process

More information

International Journal of Industrial Engineering Computations

International Journal of Industrial Engineering Computations Internatonal Journal of Industral Engneerng Computatons 4 (2013) 51 60 Contents lsts avalable at GrowngScence Internatonal Journal of Industral Engneerng Computatons homepage: www.growngscence.com/jec

More information

Motivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to:

Motivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to: 4.1 4.2 Motvaton EE 457 Unt 4 Computer System Performance An ndvdual user wants to: Mnmze sngle program executon tme A datacenter owner wants to: Maxmze number of Mnmze ( ) http://e-tellgentnternetmarketng.com/webste/frustrated-computer-user-2/

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress Analyss of 3D Cracks n an Arbtrary Geometry wth Weld Resdual Stress Greg Thorwald, Ph.D. Ted L. Anderson, Ph.D. Structural Relablty Technology, Boulder, CO Abstract Materals contanng flaws lke nclusons

More information

Robust data analysis in innovation project portfolio management

Robust data analysis in innovation project portfolio management MATEC Web of Conferences 70, 007 (08) SPbWOSCE-07 https://do.org/0.05/matecconf/0870007 Robust data analyss n nnovaton project portfolo management Bors Ttarenko,*, Amr Hasnaou, Roman Ttarenko 3 and Llya

More information

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method NICOGRAPH Internatonal 2012, pp. 114-119 3D Vrtual Eyeglass Frames Modelng from Multple Camera Image Data Based on the GFFD Deformaton Method Norak Tamura, Somsangouane Sngthemphone and Katsuhro Ktama

More information

Active Contour Models

Active Contour Models Actve Contour Models By Taen Lee A PROJECT submtted to Oregon State Unversty n partal fulfllment of The requrements for the Degree of Master of Scence n Computer Scence Presented September 9 005 Commencement

More information

Review of approximation techniques

Review of approximation techniques CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated

More information

Adaptive Weighted Sum Method for Bi-objective Optimization

Adaptive Weighted Sum Method for Bi-objective Optimization 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamcs & Materals Conference 19-22 Aprl 2004, Palm Sprngs, Calforna AIAA 2004-1680 Adaptve Weghted Sum Method for B-objectve Optmzaton Olver de Weck

More information

CS246: Mining Massive Datasets Jure Leskovec, Stanford University

CS246: Mining Massive Datasets Jure Leskovec, Stanford University CS46: Mnng Massve Datasets Jure Leskovec, Stanford Unversty http://cs46.stanford.edu /19/013 Jure Leskovec, Stanford CS46: Mnng Massve Datasets, http://cs46.stanford.edu Perceptron: y = sgn( x Ho to fnd

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

Machine Learning 9. week

Machine Learning 9. week Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below

More information

Dynamic wetting property investigation of AFM tips in micro/nanoscale

Dynamic wetting property investigation of AFM tips in micro/nanoscale Dynamc wettng property nvestgaton of AFM tps n mcro/nanoscale The wettng propertes of AFM probe tps are of concern n AFM tp related force measurement, fabrcaton, and manpulaton technques, such as dp-pen

More information

Parallel Artificial Bee Colony Algorithm for the Traveling Salesman Problem

Parallel Artificial Bee Colony Algorithm for the Traveling Salesman Problem Parallel Artfcal Bee Colony Algorthm for the Travelng Salesman Problem Kun Xu, Mngyan Jang, Dongfeng Yuan The School of Informaton Scence and Engneerng Shandong Unversty, Jnan, 250100, Chna E-mal: xukun_sdu@163.com,

More information

A Performance Measure Approach to composites reliability: a transmission loss application

A Performance Measure Approach to composites reliability: a transmission loss application 10 th World Congress on Structural and Multdscplnary Optmzaton May 19-24, 2013, Orlando, Florda, USA A Performance Measure Approach to compostes relablty: a transmsson loss applcaton Roberto d Ippolto

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

The Evaluation and Correction of the Reconstructed NURBS Surface Smoothing and Accuracy

The Evaluation and Correction of the Reconstructed NURBS Surface Smoothing and Accuracy Sensors & Transducers 2013 by IFSA http://www.sensorsportal.com The Evaluaton and Correcton of the Reconstructed NURBS Surface Smoothng and Accuracy Shgang WANG, Janzhou ZHANG, Yong YAN School of Mechatroncs

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

OPTIMIZATION OF SKELETAL STRUCTURES USING IMPROVED GENETIC ALGORITHM BASED ON PROPOSED SAMPLING SEARCH SPACE IDEA

OPTIMIZATION OF SKELETAL STRUCTURES USING IMPROVED GENETIC ALGORITHM BASED ON PROPOSED SAMPLING SEARCH SPACE IDEA INTERNATIONAL JOURNAL OF OPTIMIZATION IN CIVIL ENGINEERING Int. J. Optm. Cvl Eng., 2018; 8(3): 415-432 OPTIMIZATION OF SKELETAL STRUCTURES USING IMPROVED GENETIC ALGORITHM BASED ON PROPOSED SAMPLING SEARCH

More information

THE design of mechanical systems exploits numerical

THE design of mechanical systems exploits numerical Symbolc Stffness Optmzaton of Planar Tensegrty Structures BRAM DE JAGER, * AND ROBERT E. SKELTON Department of Mechancal Engneerng, Technsche Unverstet Endhoven, P.O. Box 5, 56 MB, Endhoven, The Netherlands

More information

Minimization of the Expected Total Net Loss in a Stationary Multistate Flow Network System

Minimization of the Expected Total Net Loss in a Stationary Multistate Flow Network System Appled Mathematcs, 6, 7, 793-87 Publshed Onlne May 6 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/.436/am.6.787 Mnmzaton of the Expected Total Net Loss n a Statonary Multstate Flow Networ System

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information