CONTROLLER DESIGN FOR OFF-TRACKING ELIMINATION IN MULTI-ARTICULATED VEHICLES. S. A. Manesis, G. N. Davrazos, N.T. Koussoulas

Size: px
Start display at page:

Download "CONTROLLER DESIGN FOR OFF-TRACKING ELIMINATION IN MULTI-ARTICULATED VEHICLES. S. A. Manesis, G. N. Davrazos, N.T. Koussoulas"

Transcription

1 Copyrght IFAC 5th Treal World Cogress, Barceloa, pa CONTROER DEIGN FOR OFF-TRACKING EIMINATION IN MUTI-ARTICUATED VEHICE. A. Maess, G. N. Davrazos, N.T. Koussoulas Electrcal ad Computer Egeerg Dept. Dvso of ystems ad Cotrol Uversty of Patras <stam.maess, gdavrazo, Abstract: The moto of a mult-body autoomous robot as well as of a tra-lke multartculated trasportato vehcle s characterzed by the devato of the path of each termedate vehcle from that of the leadg oe (off-trackg. I ths paper, we make use of a ovatve jucto techque, whch allows the kgp to slde alog the axs of the leadg vehcle, somethg that proved to be very effectve reducg offtrackg. We propose two cotrollers for the elmato of the off-trackg pheomeo, both robotc ad trasportato mult-artculated vehcles; the oe s heurstcally derved whle the other oe s based o steady-state off-trackg whe a -traler vehcle moves o a crcular trajectory. mulato results for varous cases, wthout ad wth the sldg kgp system, showed that sgfcat off-trackg reducto or eve elmato ca be acheved. Keywords: Trasportato systems, mult-artculated vehcles, vehcle tras, offtrackg.. INTRODUCTION I the autoomous robotcs feld, the goal s to buld physcal systems that accomplsh useful tasks wthout huma terveto whle operatg ukow evromets. O the other had, Itellget Trasportato ystems the goal s smlarly to costruct trasportato vehcles, tellget eough to be drve wth as less as possble huma terveto. I groud freght trasportato, heavy-duty trucks may be combed to form a truck tra or road tra, cosstg of a umber of sem-tralers ad a sgle hgh-power tractor [Maess 998]. A smlar tra s formed mult-body autoomous robots. Off-trackg, that s the devato of the path of each traler from those of the leadg vehcle or robot, s amog the basc techcal problems that must be solved [Bushell et. al 994 ], [Altaf ad Gutma 998 ]. The combato of may tralers wth a sgle tractor formg a road tra or truck-tra s called hard platoog. Road tras may cosst of a umber of tralers (at least 3 ad possbly up to ad a lead tractor. For such a road tra of trucks to be safely drve a mult-lae hghway, a umber of ssues must be dealt wth. Besdes ecoomc ad poltcal cosderatos, techcal ssues clude the ecessty for a set of traffc ad drvg rules that must be defed ad obeyed, the soluto of the pathfollowg problem, the mechacal realzato, ad the space lmtatos outsde a hghway. The advatages of usg truck tras hghway freght trasportato are dscussed [Maess ].

2 The moto of the -traler system s subject to oholoomc costrats (rollg wthout slppg so t has bee studed as a class of oholoomc systems by may researchers ad has both theoretcal ad practcal terest. The work [Kolmaovsky ad McClamroch, 995] s a excellet survey of recet advaces cotrol of oholoomc systems. The ma problem that has attracted most of the atteto s path followg. We kow of a few works oly that cosder the off-trackg problem. A closed-form expresso for the off-trackg of the rear pvot pot of a smple tractor-sem-traler vehcle ca be foud [Alexader ad Maddocks, 998] whle offtrackg bouds for a car pullg tralers have bee derved [Bushell et. al., 994]. For example [Altaf, 998] the path-followg problem wth reduced off-trackg s addressed for the -traler system. Ths s acheved by keepg track of the error dstace of each of the mddle pots of the axles of the vehcle from the path usg dfferet movg frames. I [Nakamura et. al. ] dfferet passve steerg mechasms as well as cotrol laws are preseted for oholoomc traler systems. The ma focus of such mechasms s o reducg passve trackg error from tractor s trajectory ad lttle atteto was pad o actve moto cotrol. I ecto II we descrbe the mult-artculated vehcle model ad the off-trackg problem. ecto III cotas a bref descrpto of the sldg kgp system together wth the state equatos of the multartculated vehcle whe sldg s appled. I secto IV we descrbe two ew cotrollers oe of heurstc org ad the other based o the compesato for the steady-state off-trackg whe the leadg vehcle moves a crcular trajectory, whle secto V smulato results are preseted wth ad wthout sldg. ecto VI cotas coclusos ad dscusso about the results ad some future research problems.. THE MUTI-ARTICUATED VEHICE I ths secto we descrbe brefly the model of the mult-artculated vehcle that s commo for both robotc ad trasportato vehcles. It s a log ad complex vehcle system cosstg of a hgh power tractor pullg a umber of passve robot bodes or sem-tralers as show Fg.. The state equatos of the above system, called also -traler system, wth a drvg axle ad hece a steerg agle for the tractor are x! cosϑ y! sϑ ϕ! U = taϕ U = s( ϑ ϑ ( y ϑ x ϑ (X,Y ϑ ϑ Fg.. Illustrato of the mult-artculated vehcle coordates. U = cos( ϑ ϑs( ϑ ϑ " U = cos( ϑj ϑj s( ϑ ϑ =,..., j = where x, y are the Cartesa coordates of the leadg vehcle (tractor ad U,U are the two cotrol puts, the lear velocty ad the steerg agle rate respectvely [aumod 993]. The above equatos are derved from algebrac mapulato of the holoomc costrats ad the + oholoomc costrats uder the assumpto of the same legth for all tralers. The oly dfferece betwee the mult-body robotc systems ad trucktra s the magtude of the dfferet physcal quattes (legth, velocty, steerg agle lmts, weght, etc. Off-trackg s defed as the devato of the semtralers axles or the kgp htch from the path of the steerg axle of the leadg vehcle. I the case of truck-tras, t s more mperatve tha ay other case that the last sem-traler follow exactly the path of the lead tractor durg a tur for lae chage or a tur due to the curvature of the hghway. Otherwse t wll be possble for the last sem-traler to volate the outer boudary of the hghway or to crash wth a adjacet car durg a lae chage although both keep varat ther relatve velocty. It s kow that the drver of ay log truck-tra, because of the offtrackg of the rear tralers, turs the tractor far towards the desred path order to avod ths pheomeo. Whe we deal wth moble robots the major problems are to fd a obstacle-free path ad path followg cotrol. However, the case of mult-artculated robotc vehcles we must take to cosderato the off-trackg pheomeo whe fdg a obstacle-free path. The reaso s that the last traler may collde wth obstacles f the vehcle attempts to follow the desged path for the leadg vehcle wth off-trackg eglected. Oe effcet way to solve ths problem s to fd a obstacle-free path for the leadg vehcle, add a cotroller for path φ

3 followg ad use aother kgp cotroller for offtrackg elmato. 3. THE IDING KINGPIN YTEM The off-trackg ca be elmated by sldg each traler wth respect to the prevous oe, a techque frstly descrbed [Maess 998]. Accordg to ths techque the kgp htch each sem-traler sldes a drecto perpedcular to the logtudal axle (.e. alog the rear axle of the traler by a dstace. I ths secto we preset brefly the sldg kgp system ad the state-equatos of the multartculated vehcle whe sldg s used, together wth the assumptos that are made durg the dervato of the equatos. Cosder two termedate sem-tralers of a truck tra as show Fg.. The posto of each sem-traler P, s take to be the mddle pot of the th sem-traler s rear axle. Y y ys y+ P+ θ + em-traler + P x+ x xs θ θ Ps em-traler Fg.. The kgp sldes alog the axle whe the sem-traler turs. Posto P s defed by the par ( x, y the Cartesa coordates system whle ϑ s the oretato of the th sem-traler wth respect to the horzotal axs. To smplfy dervato of the truck tra model we wll ot cosder tally a steerg agle for the tractor, sce the exteso of the model to cover ths case s smple. It has bee poted out [Bushell et. al 994] that whe the lead car of a sgle traler system s travelg alog a crcle of radus R l, the the traler s travelg alog a crcle of radus R t wth the same ceter, where. I order to compesate for R < R t l X ths path devato of the traler, we suppose that the kgp htchg pot sldes from the pot P to the pot P s by a dstace. The followg assumptos are ecessary for dervg the mathematcal model: a All tralers have the same legth. b Each traler s modeled as havg oly oe axle. c Each traler s assumed to be hooked up to the mdpot of the rear axle of the precedg traler. d By sldg the locato of the kgp, the weght of the traler shfts toward a outer drecto, whch does t affect the kematc behavor of the tra. e The ubalaced pullg pot (whe the kgp sldg s ozero does ot cause skddg of the whole axle. f The sldg of the kgp ca be performed wth the traler fully loaded va a hydraulc mechasm. I the geeral case of a -traler truck tra, we have the classcal ( + oholoomc costrats mposed by the rollg ad o-slppg codto x! s θ y! cosθ = ( ad holoomc equatos troduced by the correspodg lks, whch, because of the sldg = P P s (see Fg., are of the form x y + + = x cos θ + = y s θ + + s θ cos θ (3 Takg the dervatves of the holoomc Eq. (3, combg them wth Eq. ( ad elmatg x!, y! leads to a system of + equatos. The soluto of ths system combed wth the equatos of moto of the tractor wth steerg agle ad uder the assumpto! =, yelds x! cosϑ y! sϑ ϕ! θ! = = U = taϕ U = s( ϑ ϑ [ + taϕ ] θ! 3 " [ + ( t taϕ ] s( θ θ [ cos( θ θ + ( t s( θ θ ] u + + = [ + ( t taϕ ] s( θ θ [ cos( θ θ + ( t s( θ θ ] (4

4 where x, y are the Cartesa coordates of the leadg vehcle, ϑ ts oretato, ϑ, =,,... the oretato agle of the th traler, U,U the two cotrol puts lear velocty ad steerg agle rate respectvely, the legth of each traler ad the sldg dstace, whch s determed from the cotroller. 4. CONTROER DEIGN Equatos ( ad (4 descrbe the kematc behavor of a -traler system wthout ad wth sldg, respectvely. I a mult-artculated vehcle two dfferet cotrollers are used the oe for path followg ad the other for off-trackg elmato regulatg the sldg dstace the sldg kgp system. For path followg ssues the lear velocty ad the steerg agle rate of the leadg vehcle are the cotrol puts. I the classcal case, the drver regulates the above cotrol puts such a way as to acheve kematc stablty ad the desrable trajectory trackg. For a autoomous mult-body robot movg sde a lmted laboratory or dustral evromet, the embedded cotroller regulates the cotrol puts based o a cotrol algorthm for path followg. The overall structure of the cotrol system for a -traler vehcle s depcted Fg. 3. The frst each kgp of the th traler. It s kow that the curve radus for a vehcle s gve by U U r = =. o ω geeral ad for the th traler wll be gve by U r = ϑ! (6. From the set of equatos ( we have that U ϑ! = cos( ϑ j ϑ j s( ϑ ϑ (7 j = By combg (6, (7 ad takg to cosderato the relato U cos( j ϑ j+ j= ϑ (8 ad after some algebrac mapulato t yelds that r = cot( ϑ ϑ (9 We coclude from the last relato that the curve rad for dfferet tralers are dfferet so t s logcal to troduce dfferet sldg for each traler. I [Bushell et. al. 994] was prove that f the leadg vehcle travels alog a crcular trajectory wth radus r (whereas r > the the traler coverges to a crcular trajectory wth radus R = r. I order for the leadg vehcle ad the sem-traler follows the same crcular trajectory we troduce the followg lemma. U, U traler Y=tate traler N-traler ystem ldg mechasms ΔU, ΔU cos( ϑ ϑ = s( ϑ ϑ Huma observato ad terveto or Path followg cotroller Fg. 3. The overall cotrol system for the -traler vehcle. cotroller for off-trackg elmato that we use s heurstcally foud based o basc cotrol egeerg prcples ad s gve by: = K ϑ! (5 where ϑ! s the oretato of the leadg vehcle. Followg the procedure below we derve the equatos for the secod closed-loop cotroller for emma If the kgp sldg s gve by = r + r the the traler the steady state follows the crcular trajectory wth radus r of the leadg vehcle, Proof Accordg to [Bushell et. al. 994] the traler steady-state wll travel a crcular trajectory of radus

5 R ss = rz (Fg. 4, whereas r z r + =. o we have that Rss = rz = ( r + =... After some algebrac mapulatos, we coclude that R = r. ss gve by (. Observg the fgures we otce that the smulato results are much better whe cotroller ( s used tha the cotroller (5. I all smulatos we assume a mult-artculated vehcle wth the same legth for all tralers ad tractor, equal to oe ut legth. The above lemma ca be exteded for the tralers case followg the same procedure, so the sldg for the th traler wll be gve by = r + r ( By combg (9 ad ( we fd that the dfferet sldg dstaces that we must apply to the dfferet tralers are gve by the relato cos( ϑ ϑ = ( s( ϑ ϑ y (ut legth : thrd traler -. secod traler -- frst traler - tractor Kgp pot trajectory wth sldg r z x (ut legth Fg.5. The tractor of a tra wth 3 tralers follows (bold le a +8 /-9 arc wthout sldg. : thrd traler -. secod traler -- frst traler - tractor r y (ut legth R Traler trajectory wthout sldg Tractor trajectory Fg.4. A smple tractor-traler system depctg the trajectores of the traler, tractor, ad kgp pot. 5. IMUATION REUT To test the cotrollers descrbed the last secto, the Matlab/mulk smulato evromet was used ad depedetly verfed through Mathematca. mulato results wthout the sldg kgp mechasm are show Fg. 5. The dvdual trajectores of a truck tra wth 3 tralers travelg o a ¾ crcular arc, emphasze the offtrackg devato. Fg. 6 shows the correspodg trajectores derved after the applcato of the sldg kgp mechasm, where the sldg dstace s determed from cotroller gve by (5. Fg. 7 shows the correspodg trajectores, whe cotroller x (ut legth Fg.6. The vehcle of Fg. 4 wth the tractor tracg the same trajectory but ow wth the kgp sldg all tralers ad heurstcally foud cotroller gve by (5. y (ut legth : thrd traler -. secod traler -- frst traler - tractor x (ut legth Fg.7. The vehcle of Fg. 4 wth the tractor tracg the same trajectory but ow wth the kgp sldg all tralers ad cotroller gve by (.

6 6. CONCUION Off trackg s oe of the most sgfcat problems occurrg artculated vehcles. The sldg kgp s a techque for correctg such devatos. I ths paper, a cotroller for adjustg the sldg dstace of a kgp sldg mechasm has bee proposed based o the theoretcal steady-state off-trackg whe the leadg vehcle moves a crcular trajectory. Its respose was compared to aother heurstcally foud cotroller that performs also well. Both desgs have bee valdated through smulato, whose results showed satsfactory performace of both desgs. However, the aalytcally desged cotroller fared better the steady state part of the crcular trajectory. The ma topc for future research s to derve equatos for the -traler system wth the sldg kgp mechasm wthout makg the assumpto that the dervatve of sldg dstace s zero ad fd out how the desged cotroller affects the behavor of the vehcle ths stuato. REFERENCE Alexader J.C. ad J.H. Maddocks, (988 O the Maeuverg of Vehcles, IAM Joural Appled Mathematcs, pp.38-5, vol.48, No.. Altaf C., (998 Path followg problem wth reduced off-trackg for the -traler system Proc. of the 37 th IEEE Cof. O Decso ad Cotrol Altaf C. ad Gutma P-O., (998 Path followg wth reduced off-trackg for the -traler system, Proceedgs of 37th IEEE Cof. O Decso ad Cotrol, Florda, December. Bushell., B. Mrtch, A. aha ad M. ecor, (994 Off-trackg Bouds for a Car Pullg Tralers wth Kgp Htchg, Proceedgs of the 33 rd Coferece o Decso ad Cotrol, pp , ake Buea Vsta, F. Kolmaovsky I., McClamroch Harrs N., (995 Develompets oholoomc cotrol problems IEEE Cotrol ystems Magaze Vol.5, No.6, Dec aumod J.-P., (993 Cotrollablty of a Multbody Moble Robot IEEE Tras. o Robotcs ad Automato, Vol. 9, No. 6. Maess., (998 Off-trackg elmato roadtras of heavy duty trucks wth multple semtralers, Proceedgs of 8th IFAC/IFIP/IFOR/ IMAC o arge cale ystems: Theory & Applcatos ( 98, Patras Greece. Maess., ( Hard platoog versus soft platoog large-scale hghway trasportato systems Proc. of 9 th IFAC/IFIP/IFOR/IMAC o arge cale ystems: Theory & Applcatos ( Bucharest, Romaa, Murray R. M., Z., hastry.., (997 A Mathematcal Itroducto to Robotc Mapulato, CRC Press. Nakamura Y., Ezak H., Ta Y., Chug W., ( Desg of steerg mechasm ad cotrol of oholoomc traler systems Proc. of IEEE It. Cof. O Robotcs ad Automato Ackowledgemets Ths research work s partally supported by Karatheodor Program of the Research Commsso of the Uversty of Patras.

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

8.1 Hashing Algorithms

8.1 Hashing Algorithms CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega

More information

On the Modeling and Simulation of Collision and Collision-Free Motion for Planar Robotic Arm Galia V. Tzvetkova

On the Modeling and Simulation of Collision and Collision-Free Motion for Planar Robotic Arm Galia V. Tzvetkova Iteratoal Joural of Egeerg Research & Scece (IJOER [Vol-, Issue-9, December- 25] O the Modelg ad Smulato of Collso ad Collso-Free Moto for Plaar Robotc Arm Gala V. Tzvetova Isttute of mechacs, Bulgara

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan M E M O R A N D U M DATE: 1 September, 1999 TO: Jm Russell FROM: Peter Tkack RE: Aalyss of wde ply tube wdg as compared to Kova Kore CC: Larry McMlla The goal of ths report s to aalyze the spral tube wdg

More information

Rademacher Complexity. Examples

Rademacher Complexity. Examples Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS Şl uv dr g Ja-Crsta GRIGORE, Uverstatea d Pteşt, strtîrgu dvale Nr Prof uv dr g Ncolae PANDREA, Uverstatea d Pteşt, strtîrgu dvale Nr Cof

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov Iteratoal Boo Seres "Iformato Scece ad Computg" 97 MULTIIMNSIONAL HTROGNOUS VARIABL PRICTION BAS ON PRTS STATMNTS Geady Lbov Maxm Gerasmov Abstract: I the wors [ ] we proposed a approach of formg a cosesus

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10,

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10, PHYS Look over Chapter 9 Sectos - Eamples:, 4, 5, 6, 7, 8, 9, 0, PHYS Look over Chapter 7 Sectos -8 8, 0 eamples, 3, 4, 6, 7, 8,9, 0 ad How To ake Phscs Pa We wll ow look at a wa of calculatg where the

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Bounds for the Connective Eccentric Index

Bounds for the Connective Eccentric Index It. J. Cotemp. Math. Sceces, Vol. 7, 0, o. 44, 6-66 Bouds for the Coectve Eccetrc Idex Nlaja De Departmet of Basc Scece, Humates ad Socal Scece (Mathematcs Calcutta Isttute of Egeerg ad Maagemet Kolkata,

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions. It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A

More information

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract Numercal Smulatos of the Complex Moded Korteweg-de Vres Equato Thab R. Taha Computer Scece Departmet The Uversty of Georga Athes, GA 002 USA Tel 0-542-2911 e-mal thab@cs.uga.edu Abstract I ths paper mplemetatos

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

Consensus Control for a Class of High Order System via Sliding Mode Control

Consensus Control for a Class of High Order System via Sliding Mode Control Cosesus Cotrol for a Class of Hgh Order System va Sldg Mode Cotrol Chagb L, Y He, ad Aguo Wu School of Electrcal ad Automato Egeerg, Taj Uversty, Taj, Cha, 300072 Abstract. I ths paper, cosesus problem

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

More information

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission

2/20/2013. Topics. Power Flow Part 1 Text: Power Transmission. Power Transmission. Power Transmission. Power Transmission /0/0 Topcs Power Flow Part Text: 0-0. Power Trassso Revsted Power Flow Equatos Power Flow Proble Stateet ECEGR 45 Power Systes Power Trassso Power Trassso Recall that for a short trassso le, the power

More information

Pinaki Mitra Dept. of CSE IIT Guwahati

Pinaki Mitra Dept. of CSE IIT Guwahati Pak Mtra Dept. of CSE IIT Guwahat Hero s Problem HIGHWAY FACILITY LOCATION Faclty Hgh Way Farm A Farm B Illustrato of the Proof of Hero s Theorem p q s r r l d(p,r) + d(q,r) = d(p,q) p d(p,r ) + d(q,r

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

Dynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load

Dynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load Dyamc Aalyss of Axally Beam o Vsco - Elastc Foudato wth Elastc Supports uder Movg oad Saeed Mohammadzadeh, Seyed Al Mosayeb * Abstract: For dyamc aalyses of ralway track structures, the algorthm of soluto

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK Ram Rzayev Cyberetc Isttute of the Natoal Scece Academy of Azerbaa Republc ramrza@yahoo.com Aygu Alasgarova Khazar

More information

A Remark on the Uniform Convergence of Some Sequences of Functions

A Remark on the Uniform Convergence of Some Sequences of Functions Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

More information

Logistic regression (continued)

Logistic regression (continued) STAT562 page 138 Logstc regresso (cotued) Suppose we ow cosder more complex models to descrbe the relatoshp betwee a categorcal respose varable (Y) that takes o two (2) possble outcomes ad a set of p explaatory

More information

Computational Geometry

Computational Geometry Problem efto omputatoal eometry hapter 6 Pot Locato Preprocess a plaar map S. ve a query pot p, report the face of S cotag p. oal: O()-sze data structure that eables O(log ) query tme. pplcato: Whch state

More information

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar

More information

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous

More information

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

CS286.2 Lecture 4: Dinur s Proof of the PCP Theorem

CS286.2 Lecture 4: Dinur s Proof of the PCP Theorem CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat

More information

5 Short Proofs of Simplified Stirling s Approximation

5 Short Proofs of Simplified Stirling s Approximation 5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:

More information

Descriptive Statistics

Descriptive Statistics Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Bezier curve and its application

Bezier curve and its application , 49-55 Receved: 2014-11-12 Accepted: 2015-02-06 Ole publshed: 2015-11-16 DOI: http://dx.do.org/10.15414/meraa.2015.01.02.49-55 Orgal paper Bezer curve ad ts applcato Duša Páleš, Jozef Rédl Slovak Uversty

More information

Chapter 5. Curve fitting

Chapter 5. Curve fitting Chapter 5 Curve ttg Assgmet please use ecell Gve the data elow use least squares regresso to t a a straght le a power equato c a saturato-growthrate equato ad d a paraola. Fd the r value ad justy whch

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

The Effect of Distance between Open-Loop Poles and Closed-Loop Poles on the Numerical Accuracy of Pole Assignment

The Effect of Distance between Open-Loop Poles and Closed-Loop Poles on the Numerical Accuracy of Pole Assignment Proceedgs of the 5th Medterraea Coferece o Cotrol & Automato, July 7-9, 007, Athes - Greece T9-00 The Effect of Dstace betwee Ope-Loop Poles ad Closed-Loop Poles o the Numercal Accuracy of Pole Assgmet

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

A NEW FINITE ELEMENT CONSIDERING SHEAR LAG

A NEW FINITE ELEMENT CONSIDERING SHEAR LAG Bullet of the raslvaa Uversty of Braşov CIBv 204 Vol. 7 (56) Specal Issue No. - 204 A NEW FINIE EEMEN CONSIDERING SHEAR AG A. PROIC M. VOJNIC PURCAR D. UIC Abstract: A ew model of descrbg the shear lag

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

More information

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s). CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The

More information

QR Factorization and Singular Value Decomposition COS 323

QR Factorization and Singular Value Decomposition COS 323 QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?

More information

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,

More information

n -dimensional vectors follow naturally from the one

n -dimensional vectors follow naturally from the one B. Vectors ad sets B. Vectors Ecoomsts study ecoomc pheomea by buldg hghly stylzed models. Uderstadg ad makg use of almost all such models requres a hgh comfort level wth some key mathematcal sklls. I

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES

02/15/04 INTERESTING FINITE AND INFINITE PRODUCTS FROM SIMPLE ALGEBRAIC IDENTITIES 0/5/04 ITERESTIG FIITE AD IFIITE PRODUCTS FROM SIMPLE ALGEBRAIC IDETITIES Thomas J Osler Mathematcs Departmet Rowa Uversty Glassboro J 0808 Osler@rowaedu Itroducto The dfferece of two squares, y = + y

More information

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each

Log1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each 01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may

More information

Applied Fitting Theory VII. Building Virtual Particles

Applied Fitting Theory VII. Building Virtual Particles Appled Fttg heory II Paul Avery CBX 98 38 Jue 8, 998 Apr. 7, 999 (rev.) Buldg rtual Partcles I Statemet of the problem I may physcs aalyses we ecouter the problem of mergg a set of partcles to a sgle partcle

More information

Multiple Linear Regression Analysis

Multiple Linear Regression Analysis LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple

More information

The Primitive Idempotents in

The Primitive Idempotents in Iteratoal Joural of Algebra, Vol, 00, o 5, 3 - The Prmtve Idempotets FC - I Kulvr gh Departmet of Mathematcs, H College r Jwa Nagar (rsa)-5075, Ida kulvrsheora@yahoocom K Arora Departmet of Mathematcs,

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

More information

Lecture 5: Interpolation. Polynomial interpolation Rational approximation

Lecture 5: Interpolation. Polynomial interpolation Rational approximation Lecture 5: Iterpolato olyomal terpolato Ratoal appromato Coeffcets of the polyomal Iterpolato: Sometme we kow the values of a fucto f for a fte set of pots. Yet we wat to evaluate f for other values perhaps

More information

Unsupervised Learning and Other Neural Networks

Unsupervised Learning and Other Neural Networks CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all

More information

RECURSIVE FORMULATION FOR MULTIBODY DYNAMICS

RECURSIVE FORMULATION FOR MULTIBODY DYNAMICS ultbody Dyamcs Lecture Uversty of okyo, Japa Dec. 8, 25 RECURSIVE FORULAION FOR ULIODY DYNAICS Lecturer: Sug-Soo m Professor, Dept. of echatrocs Egeerg, orea Vstg Professor, Ceter for Collaboratve Research

More information