An AGV-Routing Algorithm in the Mesh Topology with Random Partial Permutation

Size: px
Start display at page:

Download "An AGV-Routing Algorithm in the Mesh Topology with Random Partial Permutation"

Transcription

1 A AGV-Rou Alorhm he Mesh Topoloy wh Radom aral ermuao Ze Jaya, Hsu We-J ad Vee Voo Yee ere for Advaed Iformao Sysems, Shool of ompuer eer Naya Teholoal Uversy, Sapore {p8589, hsu, Absra I hs paper, we model a reals AGV sysem by a mul robos sysem wh mesh layou Based o era reasoable assumpos, we propose a mproved rou alorhm, ad prove ha has a ood me performae wh hh probably Iroduo Auomaed Guded Vehles (or AGVs for shor) have beome a mpora opo maeral hadl [-7, 9-, 6 I may applaos, suh as oaer ermals[, 9-, he serve area s ofe arraed o reaular bloks, whh leads o a mesh-lke pah opoloy Therefore, develop effe alorhms for AGV rou o hs opoloy has beome a mpora researh op There are may exs resuls abou AGV [5 However, relavely lle s kow abou rou o he mesh opoloy [- ave he aalyss of me ad spae omplexes for some bas AGV rou operaos o -mesh opoloy The per bouds of me ad spae omplexes for AGV rou are Θ( ) ad Θ( ) respevely, where deoes he umber of odes he pah opoloy However, he paper does o ve he deals of he rou alorhms ad ehques o avod oeso, ofls, deadloks, e [6-7 preseed dffere mehods o shedule ad roue smulaeously a mesh-lke pah opoloy I hese papers, he rou proess s formulaed as a sor problem Alhouh here are o ofls dur he permuao, requres seps of well-defed physal moves, whh requres AGVs o ravel exra dsae ad osume exra eery o fsh he asks Aually a AGV sysem s also a mul robo sysem There has bee researh doe o he rou sraey he mul robo sysem[-5, bu hese soluos assume a small umber of robos o he mesh layou o more ha O ( N ) or O ( ) for a N mesh layou However, se here are odes he mesh layou, should be able o aommodae more AGVs/ robos I [5, O( ) umber of robos s osdered, ad a lo ood rou alorhm s preseed o fsh all asks O ( ) seps wh hh probably I hs paper, we mprove he rou alorhm of [5 ad we show ha us our rou alorhm, he permuao asks a be fshed O ( ) seps wh hher probably ha ha [5 The remader of he paper s orazed as follows Seo desrbes he rou model Seo ves he rou alorhm I Seo, we aalyze he me performae of he rou alorhm Fally, Seo 5 dsusses possbles of relax era osras ad pos ou dreos of fuure sudy Rou model I our AGV sysem, here are oal bloks, amely bloks eah olum ad bloks eah row ah blok has he same sze ah blok has oe

2 / k -rop off sao (or / sao for shor), loaed a he per rh ad per op orer of he blok O he per-lef sde, here s a vehle park where all AGVs are saoed ally ad o whh hey wll reur o ompleo of all asks We oraze he AGV movemes o hree phases I he frs phase, le AGVs se ou from he park o her pk saos I he seod phase, le AGVs pk loads ad ravel o her desaos ad drop-off loads I he hrd phase, le AGVs reur o he park from her drop-off saos Beause s easy for us o dspah he AGV mov whou ay ofl he frs phase ad he hrd phase, we wll fous oly o he seod phase whe he loaded AGVs move o he mesh layou I he follow, a sep of a AGV meas ha moves from oe ode o oe of s ehbor odes I he mesh opoloy, we assume ha he umber of!!" AGVs, m, s bouded by O( ) Thus, he lo Fure Reals mesh layou Alhouh here are some mpora deals for AGV rou, suh as he sze of he juo, he radus of urs, he leh of he AGV, e[-7, s reasoable ad reals for us o smplfy he mesh model for oveee of aalyss ad dsusso I he smplfed mesh layou model, as show Fure, here are juos of pahways A juo ad he assoaed ehbor sao are ollevely rearded as a ode ah ode s assed wh he oordaes as s address or I, where x ad y represe respevely he row ad olum Is Ths mesh layou s modeled by a raph The veres of he raph represe juo odes, ad he b-dreoal edes represe wo pahs bewee wo adjae juo odes, ad he leh of eah ede s a osa #%$%& ')( * +!, - follow, we spose ha m m lo The moveme paer s a - paral permuao, whh s defed as follows { σ σ : Z Z Z Z, σs, σ m }, where m O( ) lo A he same me, we assume ha he ommuao mehasm amo all AGVs allows eah AGV o dee he AGVs whh are oe u dsae aroud As show Fure, he AGV eer a dee he AGVs he "do" pos Fure ommuao level As [5, he mesh layou for rou s paroed o maary squares ah square osss of lo lo odes of he rds, as show Fure Fure Smplfed mesh rou model (a) There are lo rows of squares ad lo

3 olums of squares ah square s marked by he oordaes (,j) as s address or I, where ad j represe respevely he row ad olum A he same me, we assume all AGVs eah square a oly ravel he pre-spefed yle dreo show Fure (b) The dreos of ay wo ehbor yles are dffere The yles are represeed by L, L, L,, L k, where L k represes he boudary, ad L represes he ex eral yle,, L k represes he ermos yle he square lo lo efo (Job): A job s defed by a ordered par J((,Y),(,Y)), where (,Y) represes he address of he pk sao, (,Y) represes he address of he drop-off sao, ad (,Y ) (,Y ) efo (Or square job se): A or square job se S (, j) deo a job se whh eah job s oraed from he square (,j), e S (, j) { J((, Y),(, Y))(, Y) square(, j)} L L L L Noe ha, by our assumpo, S lo (, j) efo (esao square job se): A desao square job se S (, j) deo a job se whh eah job s desed o he square (,j), e (a)the paro of mesh layou (b) Imaary yles eah square maary squares Fure re-spealzao of he mesh layou A he same me, we follow he formal defo of ood paral permuaos defed by [5 efo A: For a permuao σ : Z Z Z Z, σ, f a mos lo AGVs are oraed from (or desed o) every square, we all σ a ood paral permuao, where m max{,6 } Se mesh layou, a radom σ s a ood paral permuao wh hh probably, for lare, s reasoable for us o assume ha our rou sysem, he permuao s a ood paral permuao Our rou alorhm s based o hs assumpo I Seo 5, we wll show how o deal wh he rou problem f hs assumpo s relaxed Based o he pre-spefed squares he mesh layou ad he ood paral permuao, we formally defe he follow oaos S (, j) { J((, Y),(, Y))(, Y) square(, j)} Noe ha, by our assumpo, S lo (, j) efo (Square yle): A square yle L deo a yle a AGV s job se whh eah job s desed o he square (,j), e S (, j) { J((,Y),(,Y)) (,Y) square(, j)} Noe ha, by our assumpo, S lo (, j) efo (AGV s saus): A AGV s saus deo he poso of he AGV he mesh layou s defed by A(( S,S ),L,( x,y )), where ( S,S ) s he AGV s square I, ad ( x,y) s he AGV s poso I wh he square ( S,S ) L s he AGV s yle poso he square efo (rory)[5: The prory s ha AGVs whh oue rl o he same square boudary are preferred over AGVs ha ry o o o a ehbor square boudary For example, he rh sde of Fure 5, f he AGV o ode 7 was o o o he boudary of he square o s rh had sde, he AGV o ode 5 has hher prory

4 Rou alorhm [5 proposed Square Alorhm, as llusraed Fure 5, whh osss of hree phases: I he frs phase, every robo res o move from s or o he boudary of he square, us he maary eral Hamloa yle he square oa s or I he seod phase, us oly odes o he boudary of he square ha oas s desao I he hrd phase, every robo moves from he square s boudary o he desao sde he square, also walk hrouh he Hamloa yle 8 7 Fure 5 The square alorhm [5 Our AGV rou alorhm also osss of hree phases The dfferee s ha he frs ad las phases, we use square yles sead of Hamloa yles I he mddle phase, we have more ha oe pah o o, o oly oe spefed way as he square alorhm [5 Based o he same assumpo of ood paral permuao, our rou alorhm s ve as follows Spose ha a AGV s saus s A(( S,S ),L,( x, y )) 5 6, ' ' ad s job s J((, Y),(, Y)) Square ( S,S ) s he ehbor square of S,S ), he we kow ha S ' S or S S ' ± x x ± y y ( The alorhm, dvded o hree phases, s ve as follows hase Move he AGVs from her ors o he square s boudary Repea for lo ha he wors ase, afer lo seps ( Seo, we wll prove seps, all AGVs wll fsh her frs phase) If he AGV s o he boudary L The advae o he boudary lokwse dreo lse f he AGV s o he yle L ad here s o AGV wh hher prory o he yle L -, The moves o he L - lse advaes o he yle L hase Move he AGVs from her or square boudares o her desao square boudares Repea a eah sep If S,S ) (,Y ) ( The sars las phase ' lse If S S + s( ) ad here s o x x ' ' AGV wh hher prory ( S,S ) for AGV A (where s(-), f >; oherwse, s(-)) ' ' The moves o he square ( S,S ) lse advaes o he boudary of square S,S ) ( hase Move he AGVs from he boudary of he desao s square o her desao Repea for lo ha wors ase, afer lo x seps ( Seo, we wll prove y seps, all AGVs wll fsh her las phase) If he AGV reahes s desao (,Y ) The eers he buffer ad leaves he mesh lse f he AGV s o he yle L ad here s o AGV wh hher prory o he yle L +, The jumps o he L + lse advaes o he yle L

5 ( lo ) seps o reah he yle L, he would ake aoher ( lo ) seps, he wors ase We (a) a aalyze he smlar ases for he oher yles Therefore, we e he ru me of he frs phase he wors ase T ( lo ) + [ ( lo ) + ( lo ) + + [ ( lo ) + ( lo ) + lo lam : I he wors ase, he hrd phase our rou alorhm akes O(lo ) seps (b) Fure 6 Our rou alorhm Aalyss of me omplexy We aalyze he me performae of eah phase our rou alorhm lam : I he wors ase, he frs phase our rou alorhm akes O(lo ) seps for all AGVs o omplee her permuao operaos [roof: Se he dreos of wo ehbor yles are dffere, he AGVs o oe yle a oly dsurb eah AGV o aoher yle oe ( dsurb meas a AGV--A bloks aoher oe o ome o he same yle beause of A has hher prory) Beause lo > lo, for he seod yle L, whh s lose o he square boudary ad he sze of whh s ( lo ) ( lo ), he wors ase, a AGV o wll ake ( lo ) seps o reah he boudary Smlarly, for he AGV o he ex yle L, wll ake [roof: The proof s very smlar o ha of lam ad s herefore omed lam : I he seod phase of our rou alorhm, wh hh probably ( ) lo, all AGVs wll reah her desao square s boudary O ( ) seps [roof: The proof uses a arume smlar o ha of [5 The follow verso of heroff boud [8 s used our proof heroff Boud[8 Le p, p,, p R wh p, for,,, Le ad p p + p + + m p, ad le,,, be depede Beroull radom varables wh p r ob[ p, for,,,, S The for r 6m, r ob[ S r r We also eed eed he follow lemma 5

6 Lemma ( ) lo, Afer he frs phase, wh probably dur eah of he frs lo rouds, every AGV moves o he ex square s pah, ad dur hese rouds, a eah sep, every square has o more ha lo AGVs, where 5 + ad he A also ours [roof of lam : The proof s he same as ha of [5 lam : For every r ob[ B [roof of lam : See Appedx A, lo [roof of Lemma : Frsly, le s rodue he defos of era eves also defed [5 {a mos lo AGVs are ouboud every or square}, {a mos lo AGVs are boud o arrve a des every square}, ad, or des where max{6, } m For > A {a roud all ouboud AGVs move o he ex square her pah }, B {a ed of roud here are a mos lo every square }, AGVs ad { A B }, where 5 + ad Se we assume he ood paral permuao, r ob[ I order o prove he lemma, we rodue he follow lams lam : For every, f ours, lo Based o lam ad lam, we olude ha r ob[ r ob[ B r ob[ A B Therefore, ( lo we have r ob[ r ob[ r ob[ r ob[ r ob[ Subsu lo ) ( r ob[ ) o he equaly, we have lo lo r ob[ ( ) Thus, we e he proof of Lemma Aord o Lemma, a eah oe of he frs lo rouds, all AGVs move o he ex square dur 6

7 her pahs ah pah oa a mos lo squares, lam : I our rou alorhm, wh hh probably, all AGVs wll reah her desaos O ( ) seps ad eah roud eeds wh probably lo ( ) lo seps, so we kow ha lo lo O( ) seps Therefore, we e he proof of lam, he seod phase akes [roof: Based o lam, lam ad lam, ad se O(lo ) O( ), we a easly e he proof 5 sussos ad olusos I hs paper, we have aalyzed a reals AGV sysem wh a mesh layou, ad osdered he ase where he umber of AGVs s bouded by O( ) Based o lo (k,l) some pre-spefed pah of he mesh layou ad he ood paral permuao, we prese a mproved rou alorhm, ad prove ha wh hh probably, a be doe O( ) seps (k,l) (,j) (h,) (a) I he square alorhm (,j) (h,) dow lef Our alorhm s a mproveme over he resuls [5 I he seod phase of he rou alorhm [5, eah robo a oly ravel oe speal pah o reah s desao I our rou alorhm, every AGV has more pahs o hoose from ha he square alorhm, whe res o move owards s desao Iuvely, beause we allow AGVs o move o ay square ha dereases he square dsae o her desaos, should have more haes o avod poeal ofls, so s easer o reah s desao From he probably aalyss, has also bee ofrmed We assume ha he AGVs have ood paral permuao However, whe hs assumpo s o sasfed, we a use a b Hamloa yle he whole mesh layou, he he wors ase, he permuaos whh are o rh ood paral oes a be fshed O( ) seps (b) I our rou alorhm Fure 7 The squares of (, j ) The pah marked by dashed le s he oe for he job J((k,l),(h,)) For a (, ) square (, j ) j, k + l j Our rou alorhm reles o he mmal loal ommuao mehasm However, he ommuao level a be exeded The here should exs a more effe rou alorhm for fsh he permuao operao We have assumed ha he permuaos are -, ad eah AGV s oly assed o oe job These assumpos a 7

8 also be relaxed Whe a AGV jus fshes dropp off a box (or oaer, e) ad pks a ew oe, we a reard a ew AGV ora a ha me mome (spose ha he assumpo of ood paral permuao s sll sasfed Therefore, remov hs assumpo would o add muh dffuly o our aalyss I hs paper, we oly osder he me performae he rou alorhm Bu our mesh rou alorhm, all AGVs should make may urs before hey reah her desaos, hus, hey osume more eery ha some oher reedy rou alorhms [5 Therefore, s mpora for us o osder he eery effey he rou alorhm There are sll may ope ssues for fuure researh Frsly, how o exed he smplfed rou model whh eah blok s o a square, bu sead, a reale Seodly, we assumed ha he buffer of eah ode a oly aommodae oe AGV, ad here s o queue he rou model How o deerme he sze of he buffer ad he queue, f he assumpo s relaxed? Thrdly, our sudy, we dd o osder he ase whe some AGVs break dow, or whe he ommuao sysem s broke These falures ould lead o a serous problem of he whole sysem Therefore, s esseal o osder faul-olera alorhms Akowledme We akowlede he Marme ad or Auhory, A*STAR ad Naya Teholoal Uversy, all of Sapore, for her spor of he researh proje Referees [ vers, J J M ad S A J Koppers Auoma uded vehle raff orol a a oaer ermal Trasporao Researh ar A, ():-,996 [ HSU, W-J ad HUANG, S-Y, 99, Roue pla of auomaed uded vehles roeeds of Ielle Vehles, ars, pp79-85 [ Hua, S-Y ad W-J Hsu Rou auomaed uded vehles o mesh lke opoloes I roeeds of Ieraoal oferee o Auomao, Robos ad ompuer Vso, 99 [ Qu, L ad W-J Hsu, A b-dreoal pah layou for ofl-free rou of AGVs Ieraoal Joural of roduo Researh, 9(): 77-95, [5 Qu, L, W J Hsu, Shell-Y Hua, ad Ha Wa, "Shedul ad Rou Alorhms for AGVs: a Survey" Ieraoal Joural of roduo Researh, Vol, No, pp 75-76, [6 Qu, L, W J Hsu, "Rou AGVs o a Mesh-lke ah Topoloy" I roeeds of he I Ielle Vehles Symposum (IVS ), pp 9-97, earbor, Mha, USA, O -5, [7 Qu, L, W J Hsu, "Alorhms for Rou AGVs o a Mesh Topoloy" I roeeds of he uropea oferee o arallel ompu (uro-par ), pp , Tehal Uversy of Muh, Muh, Germay, Au 9-Sep, [8 T Haer ad Rub, A uded our of heroff bouds Iformao roess Leers, 5-8, [9 Ye, R, W-J Hsu, ad V-Y Vee srbued rou ad smulao of auomaed uded vehles I roeeds of TNON, volume II, paes 5-, Kuala Lumpur, Malaysa, Sepember -7, [ Ye, R, V-Y Vee, W-J Hsu ad SN Shah arallel smulao of AGVs oaer por operaos I roeeds of h Ieraoal oferee/xhbo o Hh erformae ompu Asa-paf Reo (H-ASIA ), volume I, paes 58-6, Bej, ha, May -7, [5 Yu, ad S-Y Hua, A eralzed Rou Alorhm for AGVS oaer ors I roeeds of he h Ieraoal oferee o ompuer Ieraed Maufaur, Sapore, paes 589-6, 997 [ Y Moses ad M Teeholz, O ompuaoal aspes of arfal soal sysems I roeeds of AI-9, 99 8

9 [ Y Shoham ad M Teeholz, O raff laws for moble robos I Frs oferee o AI la Sysems, 99 [ Y Shoham ad M Teeholz, O soal laws for arfal ae soeew: Off-le des Arfal Iellee, vol 7, 995 [5 remer, S omplexy aalyss of moveme mul robo sysem Maser s hess, eparme of Appled Mahemas, he Wezma Isue of See, Rehovo, Israel, 995 [6 Ze, J, Hsu W J ad Qu L A ery-ffe Alorhm For ofl-free AGV Rou O A Lear ah Layou I roeeds of he Ieraoal ompuer Symposum (IS ), Huala, Tawa, e 8-, Appedx A: roof of lam [roof: From lam, f, he A ours, amely, all ouboud AGVs whh are o he boudary of he square a he be of roud, wll leave he square dur he roud So we oly eed o osder he AGVs eer he square a he -h roud For hs purpose, we osder he follow eves (, j ) B {a mos lo AGVs are arrv o square (,j) a roud }, he we kow ha (, j ) B (, j ) B { he se of all squares ha are a a dsae of squares from (,j)}, The squares of (, j ) are show Fure 7 (b) We use he follow Beroull varables f he m-h AGV ora orae (k, l) ), (, j) where (k, l), ad m lo (by he assumpo of ood paral permuaos) I order o use he heroff boud, eah varable mus be depede However, our formula, depede of (, j ) ( k',l' ),m' s o (, j ), for ( k, l ), m ( k', l' ), m' ah of he four ses marked by dffere paers Fure 7 (b) s depede of he ohers, so we rodue he follow depede eves aord o Fure 7 (b) ( {, j ) (, ) ( k,l ), k,m lo } j < ( {, j ) dow (, ) ( k,l ), k,m lo } j > ( {, j ) rh (, ) ( k,l ),l j, k,m lo } j > ( {, j ) lef (, ) ( k,l ),l j, k,m lo } j < Now we wll alulae r ob[, r ob[ dow, r ob[ lef, ad r ob[ rh respevely, where deoes he ompleme of r ob[ r ob[ r ob[ r ob[ r ob[ r ob[ ( ) r ob[ r ob[ ( r ob[ ) Aord o Fure 7 (b), we kow ha here are a leas from (k, l) has (, j) o s pah ( lo ) (for ) odes ha a be he (, j ) desaos of AGVs ha orae ( k,l ) for he m-h oherwse (lud he ase whh less ha m AGVs AGV(all he odes mus he odes of he se) Wha eres us are he odes ha a be possble 9

10 desao odes Aord o Lemma A, he lares umber of squares mos lo here are a mos (, j ) s 5, ad here are a lo desao odes every square So 5 5 lo lo odes lo Beause here are a mos eah square, ad > r ob{ (, ( k,l ) r ob{ (, ( k,l ) j ),l > j, k,m j ),l > j, k,m lo AGVs ora, we have (, j ) (, j ) lo } lo } ha a be possble desao odes Therefore, we have 5 lo (, j ) [ 5lo lo Nex, order o use he heroff boud, we arue ha (, j ) (, ) ( k,l ), < k, m s sohasally domaed by he j sum Y, where j j wh suess probably here are oally se) Thus we have [ lo Y are depede Beroull rals j 5 lo (we sum o se lo odes he 5lo Y j (, j ) (, j ) ( k,l ), k,m [ j < By heroff boud we e for r ob{ (, ( k,l ) r ob{ Y Therefore, we have r ob[ j (, j ) lo j ),< k,m } lo lo } Se he dow se s symmeral o he se, we have r ob[ dow ( 5 lo prob[ B So r ob[ Smlarly we e r ob[ rh lef Now we oue o prove lam ( Se B, j ) ( ), we e r ob[ B (, j ) r ob[ Thus, we have r ob[ B (, j ) dow r ob[ B (, j ) (, j ) prob[ B lo dow rh rh r ob[ (, j ), j ) (, j ) lef lef dow r ob[ B (, j ) Therefore we omplee he proof of lam rh Lemma A: osder a mesh wh umber of squares For a ve squares (,j), here are a mos 5 possble squares ha are he desaos of he AGVs ha orae from square whh s have he square (,j) o her pahs lef ad (, j )

11 [roof: Whe (,j) s he eer of he mesh, we have he maxmum of he possble desao, where For oveee, we se (,j) o be he (,) po of he (, j ) oordaes For a ve square (k,l), ad ay square (h,) s a square ha has he AGVs ha orae from square (k,l) ad have square (,j) o her pahs, as ( k + l )! show Fure 7 (b), here are oally S k!l! square pahs from (k,l) o (,j), ad oally ( h + )! S square pahs from (,j) o (h,) A he same h!! ( k + l + h + )! me, here are oally S pahs from ( h + k )!( l + )! (,j) o (h,) The probably, of whh (h,) a be he square ora ad hav he square (,j) o s pah, s ve as follows ( h, ) S S r S ( k + h )( k + h )( k + ) ( l + )( l + )(l + ) ( k + l + h + )( k + l + h + )( h + + ) where k + l ad h, Whe h or reases, r dereases Spose ha here are a mos S possble squares ha sasfes he requreme, we have # S r h ( h, ) For h x( x > ), we have x ( x,x ) r < x Therefore, we have # S ( + r h ( h, ) ) + dx + x ( ( + ) x x 5 )

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

Chebyshev Polynomials for Solving a Class of Singular Integral Equations

Chebyshev Polynomials for Solving a Class of Singular Integral Equations Appled Mahemas, 4, 5, 75-764 Publshed Ole Marh 4 SRes. hp://www.srp.org/joural/am hp://d.do.org/.46/am.4.547 Chebyshev Polyomals for Solvg a Class of Sgular Iegral Equaos Samah M. Dardery, Mohamed M. Alla

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1)

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1) Pendulum Dynams Consder a smple pendulum wh a massless arm of lengh L and a pon mass, m, a he end of he arm. Assumng ha he fron n he sysem s proporonal o he negave of he angenal veloy, Newon s seond law

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

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

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.

For the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body. The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe

More information

Learning of Graphical Models Parameter Estimation and Structure Learning

Learning of Graphical Models Parameter Estimation and Structure Learning Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model

More information

A Theoretical Framework for Selecting the Cost Function for Source Routing

A Theoretical Framework for Selecting the Cost Function for Source Routing A Theoreal Framework for Seleg he Cos Fuo for Soure Roug Gag Cheg ad Nrwa Asar Seor ember IEEE Absra Fdg a feasble pah sube o mulple osras a ework s a NP-omplee problem ad has bee exesvely suded ay proposed

More information

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p Reearch Joural of Aled Scece Eeer ad Techoloy (6): 28-282 22 ISSN: 2-6 Maxwell Scefc Orazao 22 Submed: March 26 22 Acceed: Arl 22 Publhed: Auu 5 22 The MacWllam Idey of he Lear ode over he R F +uf +vf

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos

More information

KEY EQUATIONS. ES = max (EF times of all activities immediately preceding activity)

KEY EQUATIONS. ES = max (EF times of all activities immediately preceding activity) KEY EQUATIONS CHATER : Oeraos as a Comeve Weao. roduvy s he rao of ouu o u, or roduv y Ouu Iu SULEMENT A: Deso Makg. Break-eve volume: Q F. Evaluag roess, make-or-buy dfferee quay: Q F m b F b m CHATER

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

2007 Spring VLSI Design Mid-term Exam 2:20-4:20pm, 2007/05/11

2007 Spring VLSI Design Mid-term Exam 2:20-4:20pm, 2007/05/11 7 ri VLI esi Mid-erm xam :-4:m, 7/5/11 efieτ R, where R ad deoe he chael resisace ad he ae caaciace of a ui MO ( W / L μm 1μm ), resecively., he chael resisace of a ui PMO, is wo R P imes R. i.e., R R.

More information

Key words: Fractional difference equation, oscillatory solutions,

Key words: Fractional difference equation, oscillatory solutions, OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg

More information

Analyzing Control Structures

Analyzing Control Structures Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

Extremal graph theory II: K t and K t,t

Extremal graph theory II: K t and K t,t Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee

More information

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION MACROECONOMIC THEORY T. J. KEHOE ECON 85 FALL 7 ANSWERS TO MIDTERM EXAMINATION. (a) Wh an Arrow-Debreu markes sruure fuures markes for goods are open n perod. Consumers rade fuures onras among hemselves.

More information

A Mean- maximum Deviation Portfolio Optimization Model

A Mean- maximum Deviation Portfolio Optimization Model A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information

Application of GA Based Fuzzy Neural Networks for Measuring Fouling in Condenser

Application of GA Based Fuzzy Neural Networks for Measuring Fouling in Condenser pplao of G Based Fuzzy eural eors for Measur Foul Codeser Fa Shao-she Chasha Uversy of See ad Teholoy Chasha 40077 Cha fss508@63.om Wa Yao-a Hua Uversy Chasha 4008 Cha yaoa@63.om bsra - ovel approah for

More information

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

Fuzzy Possibility Clustering Algorithm Based on Complete Mahalanobis Distances

Fuzzy Possibility Clustering Algorithm Based on Complete Mahalanobis Distances Ieraoal Joural of Sef Egeerg ad See Volue Issue. 38-43 7. ISSN (Ole): 456-736 Fuzzy Possbly Cluserg Algorh Based o Colee ahalaobs Dsaes Sue-Fe Huag Deare of Dgal Gae ad Aao Desg Tae Uvey of are Tehology

More information

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,

VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China, Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs

More information

Design maintenanceand reliability of engineering systems: a probability based approach

Design maintenanceand reliability of engineering systems: a probability based approach Desg mateaead relablty of egeerg systems: a probablty based approah CHPTER 2. BSIC SET THEORY 2.1 Bas deftos Sets are the bass o whh moder probablty theory s defed. set s a well-defed olleto of objets.

More information

A note on Turán number Tk ( 1, kn, )

A note on Turán number Tk ( 1, kn, ) A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco

More information

FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang

FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS J. Samuel Mllá Faulad de Igeería Uversdad Auóoma del Carme Méxo. M Lus A. PérezP Isuo de Físa F UNAM MéxoM xo. Chum Wag Isuo de Ivesgaoes

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

Spring Ammar Abu-Hudrouss Islamic University Gaza

Spring Ammar Abu-Hudrouss Islamic University Gaza ١ ١ Chapter Chapter 4 Cyl Blo Cyl Blo Codes Codes Ammar Abu-Hudrouss Islam Uversty Gaza Spr 9 Slde ٢ Chael Cod Theory Cyl Blo Codes A yl ode s haraterzed as a lear blo ode B( d wth the addtoal property

More information

ES 330 Electronics II Homework 03 (Fall 2017 Due Wednesday, September 20, 2017)

ES 330 Electronics II Homework 03 (Fall 2017 Due Wednesday, September 20, 2017) Pae1 Nae Soluios ES 330 Elecroics II Hoework 03 (Fall 017 ue Wedesday, Sepeber 0, 017 Proble 1 You are ive a NMOS aplifier wih drai load resisor R = 0 k. The volae (R appeari across resisor R = 1.5 vols

More information

Queuing Theory: Memory Buffer Limits on Superscalar Processing

Queuing Theory: Memory Buffer Limits on Superscalar Processing Cle/ Model of I/O Queug Theory: Memory Buffer Lms o Superscalar Processg Cle reques respose Devce Fas CPU s cle for slower I/O servces Buffer sores cle requess ad s a slower server respose rae Laecy Tme

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

Pure Math 30: Explained!

Pure Math 30: Explained! ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial

More information

Quantitative Portfolio Theory & Performance Analysis

Quantitative Portfolio Theory & Performance Analysis 550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)

More information

Actuarial Society of India

Actuarial Society of India Acuarial Sociey of Idia EXAMINAIONS Jue 5 C4 (3) Models oal Marks - 5 Idicaive Soluio Q. (i) a) Le U deoe he process described by 3 ad V deoe he process described by 4. he 5 e 5 PU [ ] PV [ ] ( e ).538!

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

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017 Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy

More information

Frequency Transformation with Pascal Matrix Equations

Frequency Transformation with Pascal Matrix Equations Worl Aaemy of See, Eeer a Teholoy Ieraoal Joural of Eleo a Commuao Eeer Vol:, No:, 6 Frequey Traformao wh Paal Max Equao Phuo S Nuye Ieraoal See Iex, Eleo a Commuao Eeer Vol:, No:, 6 wae.or/publao/3378

More information

MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI

MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI MAHALAKSHMI EGIEERIG COLLEGE TIRUCHIRAALLI 6 QUESTIO BAK - ASWERS -SEMESTER: V MA 6 - ROBABILITY AD QUEUEIG THEORY UIT IV:QUEUEIG THEORY ART-A Quesio : AUC M / J Wha are he haraerisis of a queueig heory?

More information

N! AND THE GAMMA FUNCTION

N! AND THE GAMMA FUNCTION N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio

More information

Fully Fuzzy Linear Systems Solving Using MOLP

Fully Fuzzy Linear Systems Solving Using MOLP World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,

More information

Lecture Notes 4: Consumption 1

Lecture Notes 4: Consumption 1 Leure Noes 4: Consumpon Zhwe Xu (xuzhwe@sju.edu.n) hs noe dsusses households onsumpon hoe. In he nex leure, we wll dsuss rm s nvesmen deson. I s safe o say ha any propagaon mehansm of maroeonom model s

More information

General Complex Fuzzy Transformation Semigroups in Automata

General Complex Fuzzy Transformation Semigroups in Automata Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

ECE-314 Fall 2012 Review Questions

ECE-314 Fall 2012 Review Questions ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.

More information

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005 Seto 2:00 ~ 2:50 pm Thursday Marylad 202 Sep. 29, 2005. Homework assgmets set ad 2 revews: Set : P. A box otas 3 marbles, red, gree, ad blue. Cosder a expermet that ossts of takg marble from the box, the

More information

The Cell Transmission Model, Newell s Cumulative Curves and Min-Plus Algebra

The Cell Transmission Model, Newell s Cumulative Curves and Min-Plus Algebra The Cell Trasmsso Model eell s Cumulae Cures ad M-Plus lgebra Takash kamasu Deember 003. Prelmares Dagazo s Cell Trasmsso Model Suppose ha he relaoshp beee raff flo q ad desy k a homogeeous road seo s

More information

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May Exercise 3 Sochasic Models of Maufacurig Sysems 4T4, 6 May. Each week a very popular loery i Adorra pris 4 ickes. Each ickes has wo 4-digi umbers o i, oe visible ad he oher covered. The umbers are radomly

More information

Square law expression is non linear between I D and V GS. Need to operate in appropriate region for linear behaviour. W L

Square law expression is non linear between I D and V GS. Need to operate in appropriate region for linear behaviour. W L MOS Feld-Effec Trassrs (MOSFETs ecure # 4 MOSFET as a Amplfer k ( S Square law express s lear bewee ad. Need perae apprprae reg fr lear behaur. Cpyrgh 004 by Oxfrd Uersy Press, c. MOSFET as a Amplfer S

More information

Union-Find Partition Structures Goodrich, Tamassia Union-Find 1

Union-Find Partition Structures Goodrich, Tamassia Union-Find 1 Uio-Fid Pariio Srucures 004 Goodrich, Tamassia Uio-Fid Pariios wih Uio-Fid Operaios makesex: Creae a sileo se coaii he eleme x ad reur he posiio sori x i his se uioa,b : Reur he se A U B, desroyi he old

More information

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process

Ruin Probability-Based Initial Capital of the Discrete-Time Surplus Process Ru Probablty-Based Ital Captal of the Dsrete-Tme Surplus Proess by Parote Sattayatham, Kat Sagaroo, ad Wathar Klogdee AbSTRACT Ths paper studes a surae model uder the regulato that the surae ompay has

More information

Key Questions. ECE 340 Lecture 16 and 17: Diffusion of Carriers 2/28/14

Key Questions. ECE 340 Lecture 16 and 17: Diffusion of Carriers 2/28/14 /8/4 C 340 eure 6 ad 7: iffusio of Carriers Class Oulie: iffusio roesses iffusio ad rif of Carriers Thigs you should kow whe you leave Key Quesios Why do arriers use? Wha haes whe we add a eleri field

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

Moment Generating Function

Moment Generating Function 1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example

More information

Real-time Classification of Large Data Sets using Binary Knapsack

Real-time Classification of Large Data Sets using Binary Knapsack Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule

More information

Chapter 10. Laser Oscillation : Gain and Threshold

Chapter 10. Laser Oscillation : Gain and Threshold Chaper 0. aser Osillaio : Gai ad hreshold Deailed desripio of laser osillaio 0. Gai Cosider a quasi-moohromai plae wave of frequey propaai i he + direio ; u A he rae a whih

More information

Linear Quadratic Regulator (LQR) - State Feedback Design

Linear Quadratic Regulator (LQR) - State Feedback Design Linear Quadrai Regulaor (LQR) - Sae Feedbak Design A sysem is expressed in sae variable form as x = Ax + Bu n m wih x( ) R, u( ) R and he iniial ondiion x() = x A he sabilizaion problem using sae variable

More information

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25 Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:

More information

Density estimation III. Linear regression.

Density estimation III. Linear regression. Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg

More information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below. Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50

More information

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for

-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my

More information

Union-Find Partition Structures

Union-Find Partition Structures Uio-Fid //4 : Preseaio for use wih he exbook Daa Srucures ad Alorihms i Java, h ediio, by M. T. Goodrich, R. Tamassia, ad M. H. Goldwasser, Wiley, 04 Uio-Fid Pariio Srucures 04 Goodrich, Tamassia, Goldwasser

More information

Design and Optimization for Energy-Efficient Cooperative MIMO Transmission in Ad Hoc Networks

Design and Optimization for Energy-Efficient Cooperative MIMO Transmission in Ad Hoc Networks Ths arle has bee aeped for publao a fuure ssue of hs joural, bu has o bee fully eded. Coe may hage pror o fal publao. Cao formao: DOI 0.09/TVT.06.536803, IEEE Trasaos o Vehular Tehology Desg ad Opmzao

More information

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information

Computational results on new staff scheduling benchmark instances

Computational results on new staff scheduling benchmark instances TECHNICAL REPORT Compuaonal resuls on new saff shedulng enhmark nsanes Tm Curos Rong Qu ASAP Researh Group Shool of Compuer Sene Unersy of Nongham NG8 1BB Nongham UK Frs pulshed onlne: 19-Sep-2014 las

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as

More information

Supplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion

Supplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion Suppleme Maeral for Iverse Probably Weged Esmao of Local Average Treame Effecs: A Hger Order MSE Expaso Sepe G. Doald Deparme of Ecoomcs Uversy of Texas a Aus Yu-C Hsu Isue of Ecoomcs Academa Sca Rober

More information

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.

The textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period. ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce

More information

What is a Communications System?

What is a Communications System? Wha is a ommuiaios Sysem? Aual Real Life Messae Real Life Messae Replia Ipu Sial Oupu Sial Ipu rasduer Oupu rasduer Eleroi Sial rasmier rasmied Sial hael Reeived Sial Reeiver Eleroi Sial Noise ad Disorio

More information

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations Ope Joural of Appled Sees 5- do:436/oapps6 Publshed Ole Jue (hp://wwwsrporg/oural/oapps) Colloao Mehod for olear Volerra-Fredhol Iegral Equaos Jafar Ahad Shal Parvz Daraa Al Asgar Jodayree Akbarfa Depare

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

Mathematical Foundations -1- Choice over Time. Choice over time. A. The model 2. B. Analysis of period 1 and period 2 3

Mathematical Foundations -1- Choice over Time. Choice over time. A. The model 2. B. Analysis of period 1 and period 2 3 Mahemaial Foundaions -- Choie over Time Choie over ime A. The model B. Analysis of period and period 3 C. Analysis of period and period + 6 D. The wealh equaion 0 E. The soluion for large T 5 F. Fuure

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

B. Maddah INDE 504 Simulation 09/02/17

B. Maddah INDE 504 Simulation 09/02/17 B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service

More information

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead)

Week 8 Lecture 3: Problems 49, 50 Fourier analysis Courseware pp (don t look at French very confusing look in the Courseware instead) Week 8 Lecure 3: Problems 49, 5 Fourier lysis Coursewre pp 6-7 (do look Frech very cofusig look i he Coursewre ised) Fourier lysis ivolves ddig wves d heir hrmoics, so i would hve urlly followed fer he

More information