IS THE MINIMUM-TRACE DATUM DEFINITION THEORETICALLY CORRECT AS APPLIED IN COMPUTING 2D AND 3D DISPLACEMENTS?

Size: px
Start display at page:

Download "IS THE MINIMUM-TRACE DATUM DEFINITION THEORETICALLY CORRECT AS APPLIED IN COMPUTING 2D AND 3D DISPLACEMENTS?"

Transcription

1 Procdgs, h FIG Symposum o Dformao asurms, Saor, Grc, 003. IS HE INIU-RACE DAU DEFINIION HEOREICALLY CORREC AS APPLIED IN COPUING D AND 3D DISPLACEENS? Wold Prószyńsk Isu of Appld Godsy, Warsaw Uvrsy of chology, Pl. Polchk, PL , Warszawa, Polad Asrac I s show ha h mmum-rac daum dfo as usd for compug D ad 3D posos ad dsplacm vcors as wll as hr accuracy characrscs dos o yld a physcally rpral daum wh rspc o orao. hs s xplad o h ass of h aalyss of muual rlaoshps w daum cosras h al o-lar Gauss-arkov modl (G) for a local work ad h corrspodg cosras h larzd sysm. A propry of mmum-rac daum for D works s provd whch, sp of h aov mod drawack, covcs o of h saf us of hs daum pracc. h propry s llusrad o a smpl praccal xampl, g a D lar-agular moorg work. o kp h cos of h papr wh h lms s y h Symposum orgasrs, h rlva proofs, xcp for o, ar gv a rf oul oly.. Iroduco A physcally rpral daum s a prcpal lm of vry procss of drmg h posos or dsplacm vcors. I addo o lmag h dfc of h moorg work, such a daum should gv a ral ss oh o h compud quas ad hr accuracy characrscs. A quso arss whhr h frquly usd mmum-rac daum dfo fulfls h scod rqurm. h daum orgad h sxs as a cor cocp of fr adjusm ad has sc xsvly vsgad y may rsarchrs such as ssl (96), rmyr (97), Plzr (97), Grafard, Schaffr (974), Caspary (988), Papo, Prlmur (989) o mo u a fw. hr rsarch cocrad o lar (larzd) modls, gomrcal rprao of h daum cosras ad h proprs of h las squars smaors. From h aalyss of hos fdgs, whch ar o coclusv as rgards h orao of D ad 3D works, sms ha h quso of physcal rpraly of h mmum-rac daum ds a spcal ram. Followg hs osrvao, h prs papr focuss o h quso how hs daum dfs h orao of h work, ad hc, h orao of h dsplacm vcors. h physcally-ord approach appld hs papr s rflcd h followg wll-kow prcpls: - a co-orda sysm coms dfal a physcal raly y aachg uquly o a group of physcal pos (.. work moums). h rqurm for uqu aachm follows from h fac ha h drmd posos of hs pos ar sochasc quas; - h covarac marx for a vcor of posos (or poso chags) mus hav a physcally dfal zro-varac as. I a local work h zro-varac as s dfd o a spcfd sus of h work pos;

2 h ass of h aalyss wll h rlaoshps w h cosras h al o-lar G ad hos s larzd form. I s h formr modl ha cosus a propr mahmacal modl of h posog or moorg ask.. Daum-dfg cosras a o-lar G ad s larzd form L us rcall oh a paramrc o-lar modl wh mmum cosras ad s larzd form F(X) + = y ; (, C ) ===> Ax + = y y ; ( 0, C ) () - 0 ap - G(X) = c ===> Sx = 0... whr: X, x = h u vcors of paramrs, F(X), G(X) = h ad d fucoal vcors, such ha d[ F( X)] d[ G( X)] A = dx X, S = ap dx X ; A ( u)marx; rak(a) = u d ap (d h work dfc); S (d u), rak(s) = d; rak[ A S ] = u; X ap c y y ap = h u vcor of approxma co-ordas = h d vcor of cosas; = h x vcor of ukow radom rrors; = h vcor of osrvaos; = h vcor of h approxma osrvao valus; C = h covarac marx of osrvaos (pos. df). or spcfcally, h cosras whch cosu h dfo of h daum for a local work should wr as G ( X ) = c ad S x = 0 o dca ha hy ar dfd o a chos sus P of h work pos. h al o-lar G, wh usd for posog, ca rprd as a dscrpo of h followg ask (s Fg.a for D cas): - cosruc fgur Fg (y) ad loca uquly h co-orda sysm,.. accordg o h spcfd valus of h locao paramrs G(X), hr Xo = Xo ( X), Yo = Yo ( X), αo = αo ( X). l 0 l 0 α 0 a) posos ) dsplacms α 0 G(X) Fg( y) Fg( X ) G(X) Fg( y) Fg( X ) X P 0 P 0 G(X) Fg( y) Fg( X) 0 0 X 0 0 (X, Y ) (X, Y ) Y Fg. Spcfcao of h posog ask (a) ad h moorg ask () Y

3 I h cas of moorg h dsplacms h dscrpo of h ask wll as follows (s Fg. ): - cosruc fgur Fg( y ) ad fgur Fg( y ), corrspodg o wo dffr m moms, ad loca ach uquly h co-orda sysm,.. accordg o h spcfd valus of locao paramrs G(X), hr Xo = X o ( X), Y o = Y o ( X ), α o = αo ( X). No ha w oa h suprposo of h fgurs Fg( y ), Fg( y ) h drc way,.. hrough locag ach h sam daum. Sc X o = 0; Yo = 0; αo = 0, h po Po ad h l l o dfd o h posos of h rfrc pos cosu also h zro-varac as for h accuracy characrscs of compud posos or dsplacm vcors. From h aalyss prsd so far follows ha h mmum cosras G(X) = c should sasfy h followg rqurms:. physcal rpraly ach cosra should corrspod o a spcfd dgr of frdom of Fg( y) a co-orda sysm;. dffraly h possly of oprag wh a lar modl Sx = 0. O hs ass w may formula h corrspodg rqurms for lar cosras Sx = 0 wh hy ar crad for a larzd modl whou pror spcfyg G(X) = c: Ax + = y y ; ( 0, C ) (s ()) ap -. graly h possly of oag h rlaoshps of h yp G(X) = c. physcal rpraly ach rlaoshp oad from grao should dfal as a cosra corrspodg o a spcfd dgr of frdom of Fg( y) a co-orda sysm. 3. Shorcomgs of h mmum-rac daum dfo I s a spcfc cas of daum for a local work, logg o h class of h mmumcosras daums. I uss S = (or mor spcfcally S = S ]) such ha S [, 0 AS = 0, whch alog wh v C v = m ylds xˆ = m (ad hc r( Cx ˆ, ) = m). Also ach of h marcs S = BS, whr B s o-sgular, ca usd quvally. h cosras ar formulad hr a a lvl of h larzd modl so h rlaoshps corrspodg o () ca wr as follows F(X) + = y ; ( 0, C ) ====> Ax + = y y ; ( 0, C ) - ap - () G(X) = c <=? == S x 0 = Accordg o Sco, h lar cosras S x = 0 should sasfy h rqurms of graly ad physcal rpraly. I s asy o s ha such s h cas wh posoal cosras, whr w oa afr grao X C = cos. ; Y C = cos. ; Z C = cos.,c g

4 h gravy cr of h group of rfrc pos. Drc grag of h orao ad scal cosras ylds h corrspodg cosras for h al G h form G ( X, Xap ) = c. hy do o fulfl h rqurm of g Xap - dpd,.. G(X) = c. Skg such a form w shall cosdr h cosras S x = 0 wh h approxma coordas g rplacd y h corrspodg varals. h, o grag h scal cosra w oa mmdaly ρ,c = cos., whch y aalogy o cocps usd mchacs (Lyko 997), may calld a polar mom of ra for h group of rfrc pos wh rspc o s gravy cr C. As rgards h orao cosras S x = 0, h proof gv Appdx B allows o o draw h followg coclusos: 3D works - for 3 h orao cosras ar o gral; D works - for 3 h orao cosra s o gral. Summg up h aov, w may df fr adjusm as solvg h al fucoal modl as () wh h cosras as follows: G p ( X ) = c p ; G α ( X, X ap ) = cα ; G s ( X) = cs, whr p, α, s doo posoal, orao ad scal cosras rspcvly. For a D work whou scal dfc h modl ca rprd as h dscrpo of h followg ask (s Fg.): Fg( y) - cosruc fgur ad loca uquly h co-orda sysm accordg o h spcfd valus of h locao paramrs G p ( X),.. X C, Y C, ad h zro valu of h rlav orao paramr G α ( X, X ap ),.. A( X, Xap ) - g h algrac sum of h hachd aras. Fg. h lack of XOY-rlad orao paramr for a D work Fg( X) For 3 h orao of h rsulg fgur cao dfd drcly rlav o h axs of h co-orda sysm,.. hrough G α (X). Covrsly, sc h l l o cao dfd w cao rproduc h orao of h X0Y sysm ad w do o hav h zrovarac as for work orao. h possly of mprovg h accuracy of X ap h succssv raos, v ll Xˆ X ap G α ( X). coms glgly small, dos o lad o fdg h rqurd orao paramr

5 4. Advaagous proprs of h mmum-rac daum dfo I ca provd (s Appdx C) ha for ach po of D work h followg propry holds (s Fg.3): - h agal compo of h soluo vcor oad wh mmum rac daum s a wghd ma of h agal compos (j =,,...,) of soluo vcors oad w wh h daums, ach g po C fxd, h l (for h formula ad h proof s Appdx C). (j) CP j fxd ; h wghs g p (j) = L j C P m (j) () max W C,max,max P L,, L Fg.3 Spcfc propry of h mmum-rac daum for D works hs mpls h followg proprs: * g a wghd ma, s always sd h rval < m, max > ad hus sd h rval oad wh physcally rpral daums; * for as a wghd ma, w hav, <, max. h aov proprs ar coss wh hos formulad for mmum-cosras daums wh h gravy cr fxd (s rlaoshps (A), Appdx A). No also h proprs of mmum-rac daum rlad o h rfrc pos (quals (A5) ad (A6), Appdx A). Praccal xampl. Fgur shows h agal compos ad j =,,...,6 of dsplacm vcors for som pos of h agular-lar moorg work. W ca s ha h arragm of h valus s smlar for all h hr work pos. (j) 5. Cocludg rmarks O h ass of h aalyss carrd ou hs papr o may formula h followg coclusos: - for 3 h mmum-rac daum dfo dos o spcfy a physcally rpral rfrc as for h orao of D ad 3D works, ad hc for h dsplacm vcors ad hr accuracy characrscs; - s a advaagous propry of h mmum-rac daum dfo for D works ha h rsulg soluo vcors for ach work po fall w hos oad wh physcally rpral daums. O may xpc ha h aalogous proprs ca foud for 3D works;

6 Fg.4 h agal compos of dsplacm vcors a moorg work - vw of praccal applcaos h advaagous propry as aov compsas for h lack of physcally rpral rfrc as for orao; - h accuracy of h approxma co-ordas, whch ca mprovd succssv rao sps, s hr h caus or adds o h shorcomgs of h mmum-rac daum dfo. Rfrcs Baarda W. (973). S-rasformaos ad crro marcs. Nh. Comm., Pul. O Godsy, Nw Srs 5, No., Dlf. Caspary W.F. (988). Cocps of work ad dformao aalyss. oograph, School of Survyg, h Uvrsy of Nw Souh Wals, Ksgo, N.S.W., Ausrala. Chr S.S., Ch W.H., Lam K.S. (999). Lcurs o dffral gomry. Srs o Uvrsy ahmacs,. World Scfc Pulshg Co., Ic., Rvr Edg, NJ. Domrz W. (00). Chck o graly of orao cosras mmum-rac daum dfo (ral rpor). Faculy of ahmacs ad Iformao Scc, Warsaw Uvrsy of chology. Grafard E., Schaffr B. (974). Uasd fr adjusm. Survy Rvw, XXII, 7. Lyko J. (997). Gral mchacs Dyamcs ( Polsh). PWN, Warszawa. ssl P. (96). D Ir Gaugk s Pukhaufs. Os. Zfv, 50 (5):59-65; 50(6): rmayr E. (97). Zur Ausglchug frr Nz. Zfv 97(): Papo H., Prlmur A. (989). Daum of a dyamcal work. auscrpa Godaca 4: Plzr H. (97). Zur Aalys Godaschr Dformaosmssug. DGK, uch, Rh C, Hf 64. Prószyƒsk W. (980). Gomrcal faurs of a s of pos ad accuracy of hr drmao o h ass of grg survys ( Polsh). Godzja Karografa, z.3/4: Rao C.R. (973). Lar sascal frc ad s applcaos, Wly, Nw York. Appdx A: Proprs of h mmum-cosras daums wh fxd gravy cr (D ad 3D works) L us cosdr h followg wo opos of mmum-cosras daum for a work whou scal dfc: S x = 0 S x = 0 mmum-rac daum N daum wh fxd gravy cr ad orao cosras ohr ha hos aov,

7 S p, 0 Sp, 0 x whr: S = [ S, 0] = ; S N = [ S N, 0] = ; S β, 0 x = S α, 0 xc h dcs ad c do hos work pos whch form h rfrc as () ad hos whch do o log o hs as (c); p sads for posos, α ad β - sad for orao. L Lˆ do h lgh of h l jog h gravy cr C ad a po P ad - h Lˆ, sadard dvao of Lˆ oad from h LS smao PROPERIES. For ay work po oh Lˆ cosras,.. ad ar vara o h choc of orao Lˆ Lˆ ( S N ) = Lˆ ( S ), ( S N ) = ( S ) =,,..., (A) Lˆ, Lˆ, PROOF. Applyg a wll-kow smlary rasformao (hr somry rasformao) s Baarda (973), w g whr G = I S x ˆ = Gx (A) N ˆ * S N S * ) ( S S p, Sp,c N ; S* = ; Sβ, Sβ, c xˆ xˆ = x ˆ,,c ; ˆx N xˆ = x ˆ N, N,c ad carryg ou a squc of smpl opraos (cludg h rducos of muually orhogoal facors), w oa whr Q xˆ N = U 0 xˆ,, (A3) Ic xˆ,c Q = I Sβ, ( Sα, Sβ, ) Sα, U = Sβ, c ( Sα, Sβ, ) Sα,. I a lar modl () w ca rprs Lˆ as Lˆ = Lap + gxˆ, whr g = [0,0,0,..., X, Y, Z,...,0,0,0] L I s asy o chck ha g,sβ, = 0 ad g,cs β, c = 0, ad hc N,, ap g x ˆ = g xˆ ad g x ˆ = g xˆ, (A4), whch ylds mmdaly,,c N, c,c Lˆ ( S N ) = Lˆ ( S ) for =,,...,., c From (A4) follows ha xˆ,n g C g = g C xˆ, g ad hus ( S N ) = ( S ) for =,,..., (Q.E.D.) Lˆ, Lˆ,

8 Fgur 5 llusras hs proprs for a D work. h soluo vcor x N,o ad h rror ar B N,o corrspod o a daum havg a orao cosra dα = 0, whr α s a arg of h l CP ad h sadard llpsods.. h aalogous ouds u h form of plas xs for 3D soluo vcors L P ap, x N x x N,o E N P E B N,o C CP Fg.5 h dsac (a) ad s sadard dvao orao cosra h proprs (A) ca xdd upo h works wh scal dfccy. C Lˆ L () as vara o h choc of,,, L r,, do rspcvly h radal ad h agal compos of h soluo vcor x for h h work po. Sc x < x ad r( C ) < r( C ), ad accordg o (A), N, xˆ, xˆ N, r ( S ) = r ( S ) ad S ) = ( S ), w oa mmdaly N r, ( N r, L ad ( () ' " ' " ) + ( ) < ( N ) + ( N ) () () () + (), (), < (), N + (), N (A5) ad for D works < N ad, <, N (A6) () () () () Appdx B: Igrag h cosras quaos mmum-rac daum dfo L (x,y,z) = 3 R whr ( x,..., x, y,..., y, z,..., z ) 3. L D do h followg Pfaffa sysm o dy = 0 ; y dz zdy = 0 dz = 0 ; z dx xdz = 0 dx = 0 ; x dy y dx = 0 do h sadard co-ordas sysm o 3 R :

9 PROPOSIION D s o gral. Proof. L us do I s asy o s ha ( ) = dy = y dz zdy 3 = dz 3 = z dx xdz α = dx ; β = x dy y dx α ; β α ; β ( ) α = d x ; α = d y ; α3 = d ( z ) ; hrfor d α = 0, =,,3 ad d = dx dy. W df γ s a 8-form o β γ = d β, 3 R. By drc calculao w oa γ( α α α3 β β β3,,,,,,, x y x y z x3 z z3 ) = = (x3y y3 xy3 x3y y3 + x y3 + xyy3 x yy3 x 3y + xyy3 x yy3 - xyy + x y + x yy3 + x3y x yy3 xy + x yy ) whch s a polyomal of h 3 rd dgr. As γ dos o vash o ay op sus ofr, y Frous horm (s Chr, Ch ad Lam 999) D s o gral (Q.E.D.). 3 Appdx C: A spcfc propry of h mmum-rac daum dfo for D works L x N( j) j =,,..., do h soluo vcors oad wh mmum-cosras daums ach wh h gravy cr fxd ad h orao cosra dα j = 0 ad x - h soluo vcor oad wh mmum-rac daum. L N( j) = x N( j) j =,,..., ad = x, whr s h ( ) roao marx, h rspcv agal compos of h soluo vcors.

10 PROPERY L j p( j) N( j) = j =,,...,, whr p ( j) = (C) j= L j j= PROOF Sc x N( j) = G N( j) x (s A) h rlaoshp (C) aks h form p( j) G N( j) x = Τx j= (C) I ca show ha (C) holds ru, f ( p ( j) G N( j) Τ) Go = 0 (C3) j= o ) whr G = I S ( S S S. akg us of (A3) w ca wr h codo (C3) h form Q 0 Q 0 I 0 p ( ) G = 0 U I p() U I 0 I o c c c ad furhr o p( j) Q p j ( j) I U j 0 G 0 o = 0 Vrfyg ha hs codo s sasfd compls h proof.

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao

More information

Control Systems (Lecture note #6)

Control Systems (Lecture note #6) 6.5 Corol Sysms (Lcur o #6 Las Tm: Lar algbra rw Lar algbrac quaos soluos Paramrzao of all soluos Smlary rasformao: compao form Egalus ad gcors dagoal form bg pcur: o brach of h cours Vcor spacs marcs

More information

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23 BIO53 Bosascs Lcur 04: Cral Lm Thorm ad Thr Dsrbuos Drvd from h Normal Dsrbuo Dr. Juchao a Cr of Bophyscs ad Compuaoal Bology Fall 06 906 3 Iroduco I hs lcur w wll alk abou ma cocps as lsd blow, pcd valu

More information

Lecture 12: Introduction to nonlinear optics II.

Lecture 12: Introduction to nonlinear optics II. Lcur : Iroduco o olar opcs II r Kužl ropagao of srog opc sgals propr olar ffcs Scod ordr ffcs! Thr-wav mxg has machg codo! Scod harmoc grao! Sum frqucy grao! aramrc grao Thrd ordr ffcs! Four-wav mxg! Opcal

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Chap 2: Reliability and Availability Models

Chap 2: Reliability and Availability Models Chap : lably ad valably Modls lably = prob{s s fully fucog [,]} Suppos from [,] m prod, w masur ou of N compos, of whch N : # of compos oprag corrcly a m N f : # of compos whch hav fald a m rlably of h

More information

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces Srs of Nw Iforao Dvrgcs, Proprs ad Corrspodg Srs of Mrc Spacs K.C.Ja, Praphull Chhabra Profssor, Dpar of Mahacs, Malavya Naoal Isu of Tchology, Japur (Rajasha), Ida Ph.d Scholar, Dpar of Mahacs, Malavya

More information

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system 8. Quug sysms Cos 8. Quug sysms Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs lc8. S-38.45 Iroduco o Tlraffc Thory Srg 5 8. Quug sysms 8.

More information

Almost unbiased exponential estimator for the finite population mean

Almost unbiased exponential estimator for the finite population mean Almos ubasd poal smaor for f populao ma Rajs Sg, Pakaj aua, ad rmala Saa, Scool of Sascs, DAVV, Idor (M.P., Ida (rsgsa@aoo.com Flor Smaradac ar of Dparm of Mamacs, Uvrs of Mco, Gallup, USA (smarad@um.du

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of

More information

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS 3. INTRODUCTION Th Ivrs Expoal dsrbuo was roducd by Kllr ad Kamah (98) ad has b sudd ad dscussd as a lfm modl. If a radom varabl

More information

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year Gau Thors Elmary Parcl Physcs Sro Iraco Fomoloy o Bo cadmc yar - Gau Ivarac Gau Ivarac Whr do Laraas or Hamloas com from? How do w kow ha a cra raco should dscrb a acual hyscal sysm? Why s h lcromac raco

More information

Almost Unbiased Exponential Estimator for the Finite Population Mean

Almost Unbiased Exponential Estimator for the Finite Population Mean Rajs Sg, Pakaj aua, rmala Saa Scool of Sascs, DAVV, Idor (M.P., Ida Flor Smaradac Uvrs of Mco, USA Almos Ubasd Epoal Esmaor for F Populao Ma Publsd : Rajs Sg, Pakaj aua, rmala Saa, Flor Smaradac (Edors

More information

Variable Satellite Usage in GPS Receivers

Variable Satellite Usage in GPS Receivers Procdgs of h orld Cogrss o grg ad Compur Scc 00 Vol I CCS 00, Ocobr 0-, 00, Sa Fracsco, USA Varabl Sall Usag GPS Rcvrs L Dg, Hyosop L, Ha Yu, Drrc Crwsy, X L, ad Crag C. Douglas, Mmbr, IANG Absrac Cosumr

More information

Two-Dimensional Quantum Harmonic Oscillator

Two-Dimensional Quantum Harmonic Oscillator D Qa Haroc Oscllaor Two-Dsoal Qa Haroc Oscllaor 6 Qa Mchacs Prof. Y. F. Ch D Qa Haroc Oscllaor D Qa Haroc Oscllaor ch5 Schrödgr cosrcd h cohr sa of h D H.O. o dscrb a classcal arcl wh a wav ack whos cr

More information

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Las squars ad moo uo Vascoclos ECE Dparm UCSD Pla for oda oda w wll dscuss moo smao hs s rsg wo was moo s vr usful as a cu for rcogo sgmao comprsso c. s a gra ampl of las squars problm w wll also wrap

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug

More information

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder Nolr Rgrsso Mjor: All Egrg Mjors Auhors: Aur Kw, Luk Sydr hp://urclhodsgusfdu Trsforg Nurcl Mhods Educo for STEM Udrgrdus 3/9/5 hp://urclhodsgusfdu Nolr Rgrsso hp://urclhodsgusfdu Nolr Rgrsso So populr

More information

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

Improvement of the Reliability of a Series-Parallel System Subject to Modified Weibull Distribution with Fuzzy Parameters

Improvement of the Reliability of a Series-Parallel System Subject to Modified Weibull Distribution with Fuzzy Parameters Joural of Mahmacs ad Sascs Rsarch Arcls Improvm of h Rlably of a Srs-Paralll Sysm Subjc o Modfd Wbull Dsrbuo wh Fuzzy Paramrs Nama Salah Youssf Tmraz Mahmacs Dparm, Faculy of Scc, Taa Uvrsy, Taa, Egyp

More information

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals MECE 330 MECE 330 Masurms & Isrumao Sac ad Damc Characrscs of Sgals Dr. Isaac Chouapall Dparm of Mchacal Egrg Uvrs of Txas Pa Amrca MECE 330 Sgal Cocps A sgal s h phscal formao abou a masurd varabl bg

More information

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19) TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal

More information

Overview. Introduction Building Classifiers (2) Introduction Building Classifiers. Introduction. Introduction to Pattern Recognition and Data Mining

Overview. Introduction Building Classifiers (2) Introduction Building Classifiers. Introduction. Introduction to Pattern Recognition and Data Mining Ovrv Iroduco o ar Rcogo ad Daa Mg Lcur 4: Lar Dcra Fuco Irucor: Dr. Gova Dpar of Copur Egrg aa Clara Uvry Iroduco Approach o uldg clafr Lar dcra fuco: dfo ad urfac Lar paral ca rcpro crra Ohr hod Lar Dcra

More information

Inference on Curved Poisson Distribution Using its Statistical Curvature

Inference on Curved Poisson Distribution Using its Statistical Curvature Rsarch Joural of Mahacal ad Sascal Sccs ISSN 3 647 ol. 5 6-6 Ju 3 Rs. J. Mahacal ad Sascal Sc. Ifrc o Curvd Posso Dsrbuo Usg s Sascal Curvaur Absrac Sal Babulal ad Sadhu Sachaya Dpar of sascs Th Uvrsy

More information

Algorithms to Solve Singularly Perturbed Volterra Integral Equations

Algorithms to Solve Singularly Perturbed Volterra Integral Equations Avalabl a hp://pvamudu/aam Appl Appl Mah ISSN: 9-9 Vol Issu Ju pp 9-8 Prvousl Vol Issu pp Applcaos ad Appld Mahmacs: A Iraoal Joural AAM Algorhms o Solv Sgularl Prurbd Volrra Igral Equaos Marwa Tasr Alqura

More information

Chain DOUBLE PITCH TYPE RS TYPE RS POLY-STEEL TYPE

Chain DOUBLE PITCH TYPE RS TYPE RS POLY-STEEL TYPE d Fr Flw OULE IC YE YE OLY-EEL YE Oubard wh d s (d ) s usd fr fr flw vya. Usually w srads ar usd h qupm. d s basd sadard rllr ha wh sd rllrs salld xdd ps. hr ar hr yps f bas ha: (1) ubl ph rllr ha wh sadard

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Chapter 4. Continuous Time Markov Chains. Babita Goyal

Chapter 4. Continuous Time Markov Chains. Babita Goyal Chapr 4 Couous Tm Markov Chas Baba Goyal Ky words: Couous m sochasc procsss, Posso procss, brh procss, dah procss, gralzd brh-dah procss, succssv occurrcs, r-arrval m. Suggsd radgs:. Mdh, J. (996, Sochasc

More information

IMPUTATION USING REGRESSION ESTIMATORS FOR ESTIMATING POPULATION MEAN IN TWO-PHASE SAMPLING

IMPUTATION USING REGRESSION ESTIMATORS FOR ESTIMATING POPULATION MEAN IN TWO-PHASE SAMPLING Joural of Rlal ad asal uds; I (Pr: 097-80, (Ol:9- ol., Issu (0: - IPUAIO UIG RGRIO IAOR FOR IAIG POPUAIO A I WO-PHA APIG ardra gh hakur, Kalpaa adav ad harad Pahak r for ahmaal s (, Baashal Uvrs, Rajasha,

More information

Akpan s Algorithm to Determine State Transition Matrix and Solution to Differential Equations with Mixed Initial and Boundary Conditions

Akpan s Algorithm to Determine State Transition Matrix and Solution to Differential Equations with Mixed Initial and Boundary Conditions IOSR Joural o Elcrcal ad Elcrocs Egrg IOSR-JEEE -ISSN: 78-676,p-ISSN: 3-333, Volu, Issu 5 Vr. III Sp - Oc 6, PP 9-96 www.osrourals.org kpa s lgorh o Dr Sa Traso Marx ad Soluo o Dral Euaos wh Mxd Ial ad

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Delay-Dependent State Estimation for Time Delay Systems

Delay-Dependent State Estimation for Time Delay Systems WSEAS TRANSACTIONS o SYSTEMS ad CONTROL Mohammad Al Pakzad, Bja Moav Dlay-Dpd Sa Esmao for Tm Dlay Sysms MOHAMMAD ALI PAKZAD Dparm of Elcrcal Egrg Scc ad Rsarch Brach, Islamc Azad Uvrsy Thra IRAN m.pakzad@srbau.ac.r

More information

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia Par B: rasform Mhods Profssor E. Ambikairaah UNSW, Ausralia Chapr : Fourir Rprsaio of Sigal. Fourir Sris. Fourir rasform.3 Ivrs Fourir rasform.4 Propris.4. Frqucy Shif.4. im Shif.4.3 Scalig.4.4 Diffriaio

More information

Continous system: differential equations

Continous system: differential equations /6/008 Coious sysm: diffrial quaios Drmiisic modls drivaivs isad of (+)-( r( compar ( + ) R( + r ( (0) ( R ( 0 ) ( Dcid wha hav a ffc o h sysm Drmi whhr h paramrs ar posiiv or gaiv, i.. giv growh or rducio

More information

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time Phys 31. No. 3, 17 Today s Topcs Cou Chap : lcomagc Thoy, Phoos, ad Lgh Radg fo Nx Tm 1 By Wdsday: Radg hs Wk Fsh Fowls Ch. (.3.11 Polazao Thoy, Jos Macs, Fsl uaos ad Bws s Agl Homwok hs Wk Chap Homwok

More information

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems Vo 3 No Mod Appd Scc Exsc of Nooscaoy Souos fo a Cass of N-od Nua Dffa Sysms Zhb Ch & Apg Zhag Dpam of Ifomao Egg Hua Uvsy of Tchoogy Hua 4 Cha E-ma: chzhbb@63com Th sach s facd by Hua Povc aua sccs fud

More information

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns Summary: Solvng a Homognous Sysm of Two Lnar Frs Ordr Equaons n Two Unknowns Gvn: A Frs fnd h wo gnvalus, r, and hr rspcv corrspondng gnvcors, k, of h coffcn mar A Dpndng on h gnvalus and gnvcors, h gnral

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

Asymptotic Behavior of Finite-Time Ruin Probability in a By-Claim Risk Model with Constant Interest Rate

Asymptotic Behavior of Finite-Time Ruin Probability in a By-Claim Risk Model with Constant Interest Rate Th Uvrsy of Souhr Msssspp Th Aqula Dgal Commuy Sud ublcaos 8-5-4 Asympoc Bhavor of F-Tm Ru robably a By-Clam Rs Modl wh Cosa Irs Ra L Wag Uvrsy of Souhr Msssspp Follow hs ad addoal wors a: hps://aqula.usm.du/sud_pubs

More information

EMPIRICAL STUDY IN FINITE CORRELATION COEFFICIENT IN TWO PHASE ESTIMATION

EMPIRICAL STUDY IN FINITE CORRELATION COEFFICIENT IN TWO PHASE ESTIMATION MPIRIAL TDY I FIIT ORRLATIO OFFIIT I TWO PHA TIMATIO M. Khohva Lcurr Grffh vry chool of Accoug ad Fac Aurala. F. Kaymarm Aa Profor Maachu Iu of Tchology Dparm of Mchacal grg A; currly a harf vry Thra Ira.

More information

ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM RISK MODEL WITH CONSTANT INTEREST RATE

ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM RISK MODEL WITH CONSTANT INTEREST RATE Joural of Mahmacs ad Sascs 3: 339-357 4 ISSN: 549-3644 4 Scc Publcaos do:.3844/mssp.4.339.357 Publshd Ol 3 4 hp://www.hscpub.com/mss.oc ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM

More information

Fourier Series: main points

Fourier Series: main points BIOEN 3 Lcur 6 Fourir rasforms Novmbr 9, Fourir Sris: mai pois Ifii sum of sis, cosis, or boh + a a cos( + b si( All frqucis ar igr mulipls of a fudamal frqucy, o F.S. ca rprs ay priodic fucio ha w ca

More information

Computing OWA weights as relevance factors

Computing OWA weights as relevance factors Compug OWA wghs as rlvac facors Agl Caţaro Dparm of Elcrocs ad Compurs, TRANSILVANIA Uvrsy of Braşov, Romaa -mal: caaro@vgaubvro Răzva Ado Compur Scc Dparm, Cral Washgo Uvrsy, Ellsburg, USA -mal: ado@cwudu

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

A NOVEL DIFFERENCE EQUATION REPRESENTATION FOR AUTOREGRESSIVE TIME SERIES

A NOVEL DIFFERENCE EQUATION REPRESENTATION FOR AUTOREGRESSIVE TIME SERIES Joural of Thorcal ad Appld Iformao Tchology h Spmbr 4. Vol. 67 No. 5-4 JATIT & LLS. All rghs rsrvd. ISSN: 99-8645 www.a.org E-ISSN: 87-395 A NOVEL DIFFERENCE EQUATION REPRESENTATION FOR AUTOREGRESSIVE

More information

(Reference: sections in Silberberg 5 th ed.)

(Reference: sections in Silberberg 5 th ed.) ALE. Atomic Structur Nam HEM K. Marr Tam No. Sctio What is a atom? What is th structur of a atom? Th Modl th structur of a atom (Rfrc: sctios.4 -. i Silbrbrg 5 th d.) Th subatomic articls that chmists

More information

Bayesian Reliability Modeling Using Monte Carlo Integration

Bayesian Reliability Modeling Using Monte Carlo Integration Joural o Modr Appld asal Mods Volu 4 Issu Arl 8 5--5 Baysa laly Modl Us Mo Carlo Irao V A.. Caara Uvrsy o ou Florda vaara@. Crs P. Tsokos Uvrsy o ou Florda prop@as.us.du Follow s ad addoal works a: p://daloos.way.du/as

More information

Survival Analysis for Randomized Clinical Trials II Cox Regression. Ziad Taib Biostatistics AstraZeneca February 26, 2008

Survival Analysis for Randomized Clinical Trials II Cox Regression. Ziad Taib Biostatistics AstraZeneca February 26, 2008 Survval alyss for Raomz Clcal rals II Cox Rgrsso a ab osascs sraca Fbruary 6, 8 la Irouco o proporoal azar mol H aral lkloo Comparg wo groups umrcal xampl Comparso w log-rak s mol xp z + + k k z Ursag

More information

Mellin Transform Method for the Valuation of the American Power Put Option with Non-Dividend and Dividend Yields

Mellin Transform Method for the Valuation of the American Power Put Option with Non-Dividend and Dividend Yields Joural of Mahmacal Fac, 5, 5, 49-7 Publshd Ol Augus 5 ScRs. h://www.scr.org/joural/jmf h://dx.do.org/.436/jmf.5.533 Mll Trasform Mhod for h Valuao of h Amrca Powr Pu Oo wh No-Dvdd ad Dvdd Ylds Suday Emmaul

More information

State Observer Design

State Observer Design Sa Obsrvr Dsgn A. Khak Sdgh Conrol Sysms Group Faculy of Elcrcal and Compur Engnrng K. N. Toos Unvrsy of Tchnology Fbruary 2009 1 Problm Formulaon A ky assumpon n gnvalu assgnmn and sablzng sysms usng

More information

Ruin Probability in a Generalized Risk Process under Rates of Interest with Homogenous Markov Chain Claims

Ruin Probability in a Generalized Risk Process under Rates of Interest with Homogenous Markov Chain Claims ahmaca Ara, Vl 4, 4, 6, 6-63 Ru Prbably a Gralzd Rs Prcss udr Ras f Irs wh Hmgus arv Cha Clams Phug Duy Quag Dparm f ahmcs Frg Trad Uvrsy, 9- Chua Lag, Ha, V Nam Nguy Va Vu Tra Quc Tua Uvrsy Nguy Hg Nha

More information

On the Existence and uniqueness for solution of system Fractional Differential Equations

On the Existence and uniqueness for solution of system Fractional Differential Equations OSR Jourl o Mhms OSR-JM SSN: 78-578. Volum 4 ssu 3 Nov. - D. PP -5 www.osrjourls.org O h Es d uquss or soluo o ssm rol Drl Equos Mh Ad Al-Wh Dprm o Appld S Uvrs o holog Bghdd- rq Asr: hs ppr w d horm o

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Special Curves of 4D Galilean Space

Special Curves of 4D Galilean Space Irol Jourl of Mhml Egrg d S ISSN : 77-698 Volum Issu Mrh hp://www.jms.om/ hps://ss.googl.om/s/jmsjourl/ Spl Curvs of D ll Sp Mhm Bkş Mhmu Ergü Alpr Osm Öğrmş Fır Uvrsy Fuly of S Dprm of Mhms 9 Elzığ Türky

More information

1- I. M. ALGHROUZ: A New Approach To Fractional Derivatives, J. AOU, V. 10, (2007), pp

1- I. M. ALGHROUZ: A New Approach To Fractional Derivatives, J. AOU, V. 10, (2007), pp Jourl o Al-Qus Op Uvrsy or Rsrch Sus - No.4 - Ocobr 8 Rrcs: - I. M. ALGHROUZ: A Nw Approch To Frcol Drvvs, J. AOU, V., 7, pp. 4-47 - K.S. Mllr: Drvvs o or orr: Mh M., V 68, 995 pp. 83-9. 3- I. PODLUBNY:

More information

Hence, Consider the linear, time-varying system with state model. y( t) u(t) H(t, )

Hence, Consider the linear, time-varying system with state model. y( t) u(t) H(t, ) Df. h mpul rpo mar of a lar, lumpd, m-varyg ym a mar map H, : RR R rm gv by H, = [h,,, h m, ], whr ach colum h, rpr h rpo of h ym o h mpulv pu u = -, R m, = [ ] occur a h h compo, h m of applcao of h pu

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

On nonnegative integer-valued Lévy processes and applications in probabilistic number theory and inventory policies

On nonnegative integer-valued Lévy processes and applications in probabilistic number theory and inventory policies Amrca Joural of Thorcal ad Appld Sascs 3; (5: - Publshd ol Augus 3 3 (hp://wwwsccpublshggroupcom/j/ajas do: 648/jajas35 O ogav gr-valud Lévy procsss ad applcaos probablsc umbr hory ad vory polcs Humg Zhag

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

Linear System Review. Linear System Review. Descriptions of Linear Systems: 2008 Spring ME854 - GGZ Page 1

Linear System Review. Linear System Review. Descriptions of Linear Systems: 2008 Spring ME854 - GGZ Page 1 8 Sprg ME854 - Z Pg r Sym Rvw r Sym Rvw r Sym Rvw crpo of r Sym: p m R y R R y FT : & U Y Trfr Fco : y or : & : d y d r Sym Rvw orollbly d Obrvbly: fo 3.: FT dymc ym or h pr d o b corollbl f y l > d fl

More information

Frequency Response. Response of an LTI System to Eigenfunction

Frequency Response. Response of an LTI System to Eigenfunction Frquncy Rsons Las m w Rvsd formal dfnons of lnary and m-nvaranc Found an gnfuncon for lnar m-nvaran sysms Found h frquncy rsons of a lnar sysm o gnfuncon nu Found h frquncy rsons for cascad, fdbac, dffrnc

More information

A Simple Representation of the Weighted Non-Central Chi-Square Distribution

A Simple Representation of the Weighted Non-Central Chi-Square Distribution SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch

More information

Chapter 5 Transient Analysis

Chapter 5 Transient Analysis hpr 5 rs Alyss Jsug Jg ompl rspos rs rspos y-s rspos m os rs orr co orr Dffrl Equo. rs Alyss h ffrc of lyss of crcus wh rgy sorg lms (ucors or cpcors) & m-ryg sgls wh rss crcus s h h quos rsulg from r

More information

CHAPTER 7. X and 2 = X

CHAPTER 7. X and 2 = X CHATR 7 Sco 7-7-. d r usd smors o. Th vrcs r d ; comr h S vrc hs cs / / S S Θ Θ Sc oh smors r usd mo o h vrcs would coclud h s h r smor wh h smllr vrc. 7-. [ ] Θ 7 7 7 7 7 7 [ ] Θ ] [ 7 6 Boh d r usd sms

More information

Poisson Arrival Process

Poisson Arrival Process Poisso Arrival Procss Arrivals occur i) i a mmylss mar ii) [ o arrival durig Δ ] = λδ + ( Δ ) P o [ o arrival durig Δ ] = λδ + ( Δ ) P o P j arrivals durig Δ = o Δ f j = 2,3, o Δ whr lim =. Δ Δ C C 2 C

More information

Poisson Arrival Process

Poisson Arrival Process 1 Poisso Arrival Procss Arrivals occur i) i a mmorylss mar ii) [ o arrival durig Δ ] = λδ + ( Δ ) P o [ o arrival durig Δ ] = 1 λδ + ( Δ ) P o P j arrivals durig Δ = o Δ for j = 2,3, ( ) o Δ whr lim =

More information

Physics 160 Lecture 3. R. Johnson April 6, 2015

Physics 160 Lecture 3. R. Johnson April 6, 2015 Physics 6 Lcur 3 R. Johnson April 6, 5 RC Circui (Low-Pass Filr This is h sam RC circui w lookd a arlir h im doma, bu hr w ar rsd h frquncy rspons. So w pu a s wav sad of a sp funcion. whr R C RC Complx

More information

Part I- Wave Reflection and Transmission at Normal Incident. Part II- Wave Reflection and Transmission at Oblique Incident

Part I- Wave Reflection and Transmission at Normal Incident. Part II- Wave Reflection and Transmission at Oblique Incident Apl 6, 3 Uboudd Mda Gudd Mda Chap 7 Chap 8 3 mls 3 o 3 M F bad Lgh wavs md by h su Pa I- Wav Rlo ad Tasmsso a Nomal Id Pa II- Wav Rlo ad Tasmsso a Oblqu Id Pa III- Gal Rlao Bw ad Wavguds ad Cavy Rsoao

More information

Linear Systems Analysis in the Time Domain

Linear Systems Analysis in the Time Domain Liar Sysms Aalysis i h Tim Domai Firs Ordr Sysms di vl = L, vr = Ri, d di L + Ri = () d R x= i, x& = x+ ( ) L L X() s I() s = = = U() s E() s Ls+ R R L s + R u () = () =, i() = L i () = R R Firs Ordr Sysms

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,

More information

EE 232 Lightwave Devices. Photodiodes

EE 232 Lightwave Devices. Photodiodes EE 3 Lgwav Dvcs Lcur 8: oocoucors a p-- ooos Rag: Cuag, Cap. 4 Isrucor: Mg C. Wu Uvrsy of Calfora, Brkly Elcrcal Egrg a Compur Sccs Dp. EE3 Lcur 8-8. Uvrsy of Calfora oocoucors ω + - x Ara w L Euval Crcu

More information

S.Y. B.Sc. (IT) : Sem. III. Applied Mathematics. Q.1 Attempt the following (any THREE) [15]

S.Y. B.Sc. (IT) : Sem. III. Applied Mathematics. Q.1 Attempt the following (any THREE) [15] S.Y. B.Sc. (IT) : Sm. III Applid Mahmaics Tim : ½ Hrs.] Prlim Qusion Papr Soluion [Marks : 75 Q. Amp h following (an THREE) 3 6 Q.(a) Rduc h mari o normal form and find is rank whr A 3 3 5 3 3 3 6 Ans.:

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Boyce/DiPrima 9 th ed, Ch 7.6: Complex Eigenvalues

Boyce/DiPrima 9 th ed, Ch 7.6: Complex Eigenvalues BocDPm 9 h d Ch 7.6: Compl Egvlus Elm Dffl Equos d Boud Vlu Poblms 9 h do b Wllm E. Boc d Rchd C. DPm 9 b Joh Wl & Sos Ic. W cosd g homogous ssm of fs od l quos wh cos l coffcs d hus h ssm c b w s ' A

More information

On the Class of New Better than Used. of Life Distributions

On the Class of New Better than Used. of Life Distributions Appld Mahacal Sccs, Vol. 6, 22, o. 37, 689-687 O h Class of Nw Br ha Usd of Lf Dsrbos Zohd M. Nofal Dpar of Sascs Mahacs ad Israc Facl of Corc Bha Uvrs, Egp dr_zofal@hoal.co Absrac So w rsls abo NBU3 class

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule Lcur : Numrical ngraion Th Trapzoidal and Simpson s Rul A problm Th probabiliy of a normally disribud (man µ and sandard dviaion σ ) vn occurring bwn h valus a and b is B A P( a x b) d () π whr a µ b -

More information

1973 AP Calculus BC: Section I

1973 AP Calculus BC: Section I 97 AP Calculus BC: Scio I 9 Mius No Calculaor No: I his amiaio, l dos h aural logarihm of (ha is, logarihm o h bas ).. If f ( ) =, h f ( ) = ( ). ( ) + d = 7 6. If f( ) = +, h h s of valus for which f

More information

AIAA Robert L. Sierakowski Chief Scientist Air Force Research Laboratory AFRL/MNG 101 W. Eglin BLVD, Ste.105, Eglin AFB, Florida

AIAA Robert L. Sierakowski Chief Scientist Air Force Research Laboratory AFRL/MNG 101 W. Eglin BLVD, Ste.105, Eglin AFB, Florida Opmal Rgulaor 4 Dsg of Opmal Rgulaors* Alxadr A. Bolo NRC Sor Rsarch Assoca Ar Forc Rsarch Laoraor 3 Avu R, #6-F, Brool, NY 9, USA TF 78-339-4563, abolo@uo.com, aolo@gmal.com, hp:bolo.arod.ru AIAA-3-5538

More information

EXERCISE - 01 CHECK YOUR GRASP

EXERCISE - 01 CHECK YOUR GRASP DEFNTE NTEGRATON EXERCSE - CHECK YOUR GRASP. ( ) d [ ] d [ ] d d ƒ( ) ƒ '( ) [ ] [ ] 8 5. ( cos )( c)d 8 ( cos )( c)d + 8 ( cos )( c) d 8 ( cos )( c) d sic + cos 8 is lwys posiiv f() d ( > ) ms f() is

More information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

Advanced Queueing Theory. M/G/1 Queueing Systems

Advanced Queueing Theory. M/G/1 Queueing Systems Advand Quung Thory Ths slds ar rad by Dr. Yh Huang of Gorg Mason Unvrsy. Sudns rgsrd n Dr. Huang's ourss a GMU an ma a sngl mahn-radabl opy and prn a sngl opy of ah sld for hr own rfrn, so long as ah sld

More information

ISSN No. (Print) :

ISSN No. (Print) : Iraoal Joural o Emrgg Tchologs (Scal Issu NCETST-07) 8(): 88-94(07) (Publshd by Rsarch Trd, Wbs: www.rsarchrd.) ISSN No. (Pr) : 0975-8364 ISSN No. (Ol) : 49-355 Comarso bw Baysa ad Mamum Lklhood Esmao

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

( A) ( B) ( C) ( D) ( E)

( A) ( B) ( C) ( D) ( E) d Smsr Fial Exam Worksh x 5x.( NC)If f ( ) d + 7, h 4 f ( ) d is 9x + x 5 6 ( B) ( C) 0 7 ( E) divrg +. (NC) Th ifii sris ak has h parial sum S ( ) for. k Wha is h sum of h sris a? ( B) 0 ( C) ( E) divrgs

More information

Modern Topics in Solid-State Theory: Topological insulators and superconductors

Modern Topics in Solid-State Theory: Topological insulators and superconductors Mor Topc ol-a Thor: Topolocal ulaor uprcoucor ra P. chr Ma-Plac-Iu für örprforchu, uar Uvrä uar Jauar 6 Topolocal ulaor uprcoucor. Topolocal b hor - Wha opolo? - H mol polacl) - hr ulaor IQH. Topolocal

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

RELIABILITY STOCHASTIC MODELING FOR REPAIRABLE PHYSICAL ASSETS. CASE STUDY APPLIED TO THE CHILEAN MINING

RELIABILITY STOCHASTIC MODELING FOR REPAIRABLE PHYSICAL ASSETS. CASE STUDY APPLIED TO THE CHILEAN MINING Rlably Sochasc Modlg for Rparabl Physcal Asss. Cas sudy appld o h Chla Mg P. Vvros, A. Crspo, R. Tapa, F. Krsjapollr, V. Gozálz-Prda RELIABILITY STOCHASTIC MODELING FOR REPAIRABLE PHYSICAL ASSETS. CASE

More information

Quantum Theory of Open Systems Based on Stochastic Differential Equations of Generalized Langevin (non-wiener) Type

Quantum Theory of Open Systems Based on Stochastic Differential Equations of Generalized Langevin (non-wiener) Type ISSN 063-776 Joural of Exprmal ad Thorcal Physcs 0 Vol. 5 No. 3 pp. 3739. Plads Publshg Ic. 0. Orgal Russa Tx A.M. Basharov 0 publshd Zhural Esprmal o Torchso Fz 0 Vol. 4 No. 3 pp. 4944. ATOMS MOLECULES

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

More information

NEW FLOODWAY (CLOMR) TE TE PIN: GREENS OF ROCK HILL, LLC DB: 12209, PG: ' S67 46'18"E APPROX. FLOODWAY NEW BASE FLOOD (CLOMR)

NEW FLOODWAY (CLOMR) TE TE PIN: GREENS OF ROCK HILL, LLC DB: 12209, PG: ' S67 46'18E APPROX. FLOODWAY NEW BASE FLOOD (CLOMR) W LOOWY (LOMR) RVRWLK PKWY ROK HLL, S PPROX. LOOWY W BS LOO (LOMR) lient nformation 4 SS- RM:4 V : PV Pipe V OU: PV Pipe JB SS- RM: V OU: PV Pipe RU R " PV Pipe @. LO SPS OL SSBL GRL ORMO: S OS: M BS LOO

More information

System-reliability-based design and topology optimization of structures under constraints on first-passage probability

System-reliability-based design and topology optimization of structures under constraints on first-passage probability Srucural Safy 76 (09) 8 94 Cos lss avalabl a SccDrc Srucural Safy o u r a l h o m p a g : w w w. l s v r. c o m / l o c a / s r u s a f Sysm-rlably-basd dsg ad opology opmzao of srucurs udr cosras o frs-passag

More information

The Variance-Covariance Matrix

The Variance-Covariance Matrix Th Varanc-Covaranc Marx Our bggs a so-ar has bn ng a lnar uncon o a s o daa by mnmzng h las squars drncs rom h o h daa wh mnsarch. Whn analyzng non-lnar daa you hav o us a program l Malab as many yps o

More information

Lecture Y4: Computational Optics I

Lecture Y4: Computational Optics I Phooc ad opolcoc chologs DPMS: Advacd Maals Udsadg lgh ma acos s cucal fo w applcaos Lcu Y4: Compuaoal Opcs I lfos Ldoks Room Π, 65 746 ldok@cc.uo.g hp://cmsl.maals.uo.g/ldoks Rflco ad faco Toal al flco

More information

Why Use Markov-switching Models in Exchange Rate Prediction?

Why Use Markov-switching Models in Exchange Rate Prediction? Why Us Markov-swchg Mols xchag Ra Prco? Hsu-Yu L a a Show-L Ch a Dparm of coomcs, Naoal Chug Chg Uvrsy, Tawa Dparm of coomcs, u-j Uvrsy, Tawa Asrac A larg amou of mprcal sus fs h suprory of h rgm-swchg

More information