On the Transient and Steady-State Analysis of a Special Single Server Queuing System with HOL Priority Scheduling

Size: px
Start display at page:

Download "On the Transient and Steady-State Analysis of a Special Single Server Queuing System with HOL Priority Scheduling"

Transcription

1 96 On the Transent and Steady-State Analyss of a Specal On the Transent and Steady-State Analyss of a Specal Sngle Server Queung Syste wth HOL Prorty Schedulng Faou Kaoun Duba Uversty College, College of Inforaton Technology, PO Box 443 Duba, Uted Arab Erates faoun@ducacae Abstract In ths paper, we consder a specal dscrete-te queung syste wth two head-of-lne (HOL prorty queues and a x of correlated and uncorrelated arrvals The arrval process to the hgh prorty queue s correlated and conssts of a tran of a fxed nuber of fxed-length pacets, whle the low prorty traffc conssts of batch arrvals that are ndependent and dentcally dstrbuted fro slot-to-slot We derve an expresson for the functonal equaton descrbng the transent evoluton of ths prorty queung syste Ths functonal equaton s then apulated and transfored nto a atheatcal tractable for By applyng the fnal-value theore, the correspondng exact expressons for the steady-state argnal pgfs are derved We also show how the transent analyss provdes nsghts nto the dervaton of the syste s busy perod dstrbuton Fnally, we llustrate our soluton techque wth soe nuercal exaples, whereby we deonstrate the negatve effect of correlaton (n the hgh-prorty queue on the perforance of the low-prorty queue Introducton ATM and next generaton coucatons networs are beng bult around the otvaton of havng a sngle cost-effectve pacet-based networ that s capable of supportng dverse classes of servces, each wth ts own QoS requreent To acheve ths goal, varous pacet servce dscplnes that deterne the order by whch pacets are served have been proposed Aong the splest prorty schedulng dscplnes, the non-preeptve head-of-lne (HOL prorty schedulng has been proposed to provde dfferentated servces to the hgh-prorty traffc class Under ths schee, whenever the server s dle, t always schedules the delay-senstve traffc frst (f present In the lterature, there have been varous contrbutons towards the perforance analyss of HOL-based prorty systes (see for exaple [], [], [3], [4] Ths paper focuses on a specal sngle-server dscrete-te queung syste wth two head-of-lne (HOL prorty queues The arrval process to the hgh prorty queue s correlated and conssts of a tran of a fxed nuber of cells, whle the low prorty traffc conssts of batch arrvals that are ndependent and dentcally dstrbuted fro slot-to-slot Our wor departs fro the prevous contrbutons, cted above n the lterature, n at least four aspects: cgests-oct5

2 GESTS Int l Trans Coputer Scence and Engr, Vol8, o 97 Frst, unle prevous studes that erely focused on the steady-state behavor of prorty systes, our analyss as towards explorng the transent behavor of such systes as well As such, the present wor can serve as a bass towards the transent analyss of other prorty systes, under dfferent arrval odels and servce-te dstrbutons Second, whle the effect of correlaton on the hgh-prorty queue (wth correlated arrvals has been thoroughly nvestgated (see for exaple [5], ts pact on the behavor of the low-prorty queue (fed by uncorrelated pacet arrvals has not been addressed It s one of the obectves of ths wor to explore ths portant perforance ssue Thrd, we show how the transent analyss leads to the characteraton of the dstrbuton of the busy perod of the syste, whch s another dstnctve contrbuton of ths paper Fnally, our proposed approach whch s purely based on probablty generatng functons s entrely analytcal, does not nvolve any atrx concepts and provdes a ufed fraewor to extract transent as well as steady-state perforance easures The reang of ths paper s orgaed as follows: In sectons and 3, we descrbe the queung odel and present the functonal equaton for the ont pgf of the state vector In secton 4, the resultng functonal equaton s transfored nto a new for that s atheatcally tractable In sectons 5 and 6, we present the exact transent analyss The correspondng steady-state results are derved n sectons 7 and 8 In sectons 9, we charactere the dstrbuton of the busy perod of the prorty syste uercal results that provde nsghts nto the behavor of the queung syste are provded n secton Fnally, a suary of the an fndngs of the paper and recoendatons for future research are provded n secton Analytcal Model Descrpton In ths paper, we consder a dscrete-te queung syste wth nfte buffer capactes and a sngle (FCFS deterstc server The te axs s dvded nto equal length slots and pacet transsson s synchroed to occur at the slot boundares Here a slot s the te perod requred to transt exactly one pacet fro the buffers We consder two types of prorty traffc, naely a hgh-prorty and a low prorty traffc (thereafter referred to as type- and type, respectvely The schedulng s based on a HOL te-prorty schee, whereby type- pacets have absolute nonpreventve prorty over type- pacets Under ths prorty schee, the server wll always serve type- pacets (f any based on a FCFS bass If there are no type- pacets n the hgh-prorty queue, then type- pacets (f any wll be served; agan based on a FCFS rule We odel the arrval process to the hgh-prorty queue by the correlated real-te traffc coposed of a fxed-length pacet-tran arrval process Correlated tran arrvals are encountered n varous applcatons whereby custoers are essages (eg Fraes or ubo pacets that consst of ultple fxed-length pacets; see eg [6] More precsely, the hgh-prorty queue s fed wth nput lns, recevng fxedlength essages of pacets, each These essages enter the syste at a fxed rate of one pacet per slot In addton, traffc on dfferent nput lns s assued to be GESTS-Oct5

3 98 On the Transent and Steady-State Analyss of a Specal ndependent and wth the sae statstcal characterstcs On any nput ln, the probablty that the frst (leadng pacet of a essage enters the buffer n any gven slot s q f the frst pacet of the prevous essage on ths ln dd not enter the buffer durng the prevous ( - slots and t s ero otherwse Further, let {c ; } be a seres of ndependent and dentcal Bernoull rando varables wth pgf C ( q+ q ew essage arrvals to the low-prorty queue are assued to be d fro slot-toslot and charactered by the sae pgf B ( b E, ndependent of For splcty, t s assued that that each type- essage arrves n a batch of pacets; though our approach can be easly extended to handle varable-length essages Further, we defne the load offered by class- pacets as ρ (,, whle we denote by + the total load to the syste For stablty, we assue that ρ < T T 3 Syste Evoluton and Prelnares The prorty queung odel can be forulated as a dscrete-te Marov chan The state of the syste s defned by ( u,, u,, a,, a,, a, where u, are the syste contents of class (, queue at the end of the th slot, whle an, ( n s the nuber of nput lns havng sent the n th pacet of a essage to type- buffer n slot The total syste content ( u, + u wll be denoted by u, T, Clearly [5]: a a, n ( n, + n, I ( a c ; I a, + n, n The evoluton of the syste contents s descrbed by the followng syste equatons +, +, [, ] + n, + n u u u a u, + + u, [ u, ] + b+ f u, u, + b+ f u, > (3 where [ x ] + s a rando varable whch taes the value f x > and otherwse ext, defne the ont generatng functon of the prorty syste at the end of the th slot as follows: Q x Q x x x E x x u, u, a, a, (,, (,,,, Fro the above and usng (-3, we can easly derve the followng functonal equaton relatng the ont pgf of the syste between two consecutve slots: cgests-oct5

4 GESTS Int l Trans Coputer Scence and Engr, Vol8, o 99 Q(,, ϒ(, x, ϒ(, x Q + (,, x B( [ C( x ] ( Q (,,, + ( Q (,, + (4 x where ϒ (, x and x for notatonal purposes Cx ( Fro the above, t s clear that the presence of the ϒ(, x ters n the rght-hand sde of (4 coplcates the analyss For nstance, tang the lt as on both sdes of (4 does not help n extractng the steady-state ont pgf of the syste In the sequel, we show how to handle the above functonal equaton to extract not only the steadystate but also the transent ont pgf of the queung syste 4 Transforng the Functonal Equaton nto a ew For In ths secton, we show how to transfor the functonal equaton (4 of the syste nto a new for that wll lead tself to a soluton Wthout any loss of generalty, we wll assue ero tal condtons, whereby the syste s tally epty, wth all lns beng n an dle state, e Q (,, x Further, because of the Marovan property of the syste, the steady-state behavor s ndependent of the tal condtons Wth ero tal condtons and by expandng Q + (,, x n (4 for the frst few values of, we can prove by sple nducton the followng aor result whch enables us to express Q (,, x n a ore sutable for 4 Theore : Transent Jont PGF of the Prorty Syste Under ero tal condtons, the functonal equaton (4 descrbng the queung odel under consderaton can be wrtten as follows: Q (,, x where: B( ( [ J, x ( ] + [ J, x ( ] ( + [ J, x ( ] B( Q (,,, B( Q (,,, (5 GESTS-Oct5

5 On the Transent and Steady-State Analyss of a Specal, x ( C, x ( λ ( J (6 + q x λ ( C, x ( λ ( (7 λ ( ( ( q and λ s (,, are the dstnct roots of the characterstc equaton: ( λ ( ( q λ( q (8 The proof of ths theore s by nducton (n a slar way as n [7] and wll not be gven here Further, fro (8, t s obvous that one of the roots has the property that λ ( Ths partcular root s thereafter denoted by λ ( 5 Deternaton of the Transent Boundary Ters To derve the transent boundary ters Q (,, and Q (,, appearng n (5, we proceed as follows: Frst, let us defne the followng three transfors ( w < : Q (,, xw, Q(,, xw ; Pw ( Q(,, w ; R (, w Q(,, w (9 By substtutng Q (,, x fro (5 nto Q (,, xw, as defned above, and usng (6, we derve the followng expresson for the w-transfor of the transent ont pgf of the syste: Q(,, x, w ( R(, w + ( +! n! n! n! wb n + n + + n P( w B( w ( C, x ( ( λ ( n + n + + n! n! n! n! wb( ( C, x ( λ ( n λ ( The above expresson wll not only be useful to derve the two transent boundary ters Q (,, and Q (,,, appearng n (5, but the steady-state results related to our queung odel, as well ( cgests-oct5

6 GESTS Int l Trans Coputer Scence and Engr, Vol8, o 5 Deternaton of the Transent Boundary Constants The transent probabltes of an epty syste Q (,, can be readly obtaned fro ( as follows: Frst settng n ( yelds the followng expresson for the w-transfor of the transent ont pgf of the total syste contents ut, and the nuber of actve nput lnes Q(,, x, w n + n + + n + ( P( w B(! n! n! n! wb w ( C, x( ( n + n + + n λ ( (! n! n! n! wb( ( C, x( λ ( λ ( ext we deterne Pwfro ( the above expresson by nvong the analytc property of ( nsde the polyds ( ; w < For ths purpose, let H λ ( and * ( denote by Y (, w the uque root nsde the ut crcle of the equaton: w B( H ( ( Then, followng a slar approach as n [7], we can apply Rouchés theore to show that P( w (3 * Applyng Lagrange s theore to (-3, allows us to express the boundary functon P(w as follows: whch ples that: [ B( H ( ] w d P ( w + (4! d ( B ( H ( d Q (,, (! d ( or equvalently ( usng Leb s rule for the th dervatve of a product : (5 GESTS-Oct5

7 On the Transent and Steady-State Analyss of a Specal { ( ( } d B ( H Q (,, (6! d 5 Deternaton of the Transent Boundary Functons The boundary ters Q (,, appearng n (5 can be evaluated by explotng the analytcal property of ( nsde the polyds ( ; w < (,, whereby whenever the denonator of Q (,, xw, vashes, the nuerator ust also vash at the sae values We frst derve the w-transfor R(, w of ths boundary ter, then tae the nverse w-transfor to obtan Q (,,, Agan, Let H λ ( * and for a gven < denote by X (, the uque ( root nsde the ut crcle < of the equaton: Fro Rouchés theore, t can be shown that R(, w w B( H ( X (, w( P( w X (, w w (7 (8 The transent boundary functons Q (,, can now be obtaned by tang the nverse w-transfor of (8, gvng: Where: Q (,,, Γ ( + ( Γ( Q (,, (9 ( B ( ( d H Γ (! d and Q (,, are as defned n (6 Fro the above, t s clear that equatons (5-7, (6 and (9- fully charactere the transent ont pgf Q (,, x of the queung syste at the end of the th slot We also note that snce type- traffc s not nfluenced by the low-prorty traffc, the hgh-prorty queue could have been analyed n solaton fro the class- queue A detaled analyss of the resultng sngle-class queue can be found n [7] Our transent and steady-state results related to the hgher- prorty queue, whch are otted heren ( cgests-oct5

8 GESTS Int l Trans Coputer Scence and Engr, Vol8, o 3 due to lac of space, are also n agreeent wth the correspondng results derved n [7] Thus, n the sequel, our analyss wll anly focus on the lower-prorty queue 6 Transent Analyss of Class- (Low-Prorty Queue The argnal pgf P, ( of class- syste contents of at the end of the th slot s readly obtaned fro (5: P, ( ( [ B( ] + [ B( ] [ Q (,,, Q (,,] ( where the boundary ters Q (,, and Q (,,, are as prevously defned n (6 and (9-, respectvely Fro the above argnal pgf, te-dependent perforance easures can be derved For nstance, let, denote the ean queue length of class- queue at the end of the th slot Then fro ( we can show that: dp ( B'( Q (,,, Q (,, (,, 7 Steady-State Jont PGF of the Syste The steady-state ont pgf of the syste s readly obtaned by applyng the fnal-value theore to (: Q (,, x l ( wq (,, xw, (3 or equvalently: Q(,, x ( Q(,,, + ( w [ Q(,, ] ( C x B, ( ( (! n n n n n n + + +!!! B( λ ( λ (4 The only unnowns n the above expresson are the boundary ters Q(,,, and Q (,, These can be readly deterned fro ther transent counterparts, as shown below The frst boundary ter Q(,, s derved by applyng the fnal value-theore to (3: GESTS-Oct5

9 4 On the Transent and Steady-State Analyss of a Specal or equvalently: Q w (,, l( w P( w l w w Y (, w dw Q(,, B'( q * d w + ( q Ths also ples that the total load of the syste s q ( q ρ T B '( + + ext, the second boundary ter Q(,,, appearng n (4 s derved by applyng the fnal value-theore to (8: Q X ( ( (5 (6 (,,, l( w R(, w ( ρ (7 T w X ( where, for gven <, X s the uque root nsde the ut crcle < of the ( ( equaton: B H ( ext, by substtutng (7 nto (4, we obtan the followng equvalent expresson for the steady-state ont pgf of the syste: Q(,, x ( ρ ( B( T! n! n! n n + n + + n X ( X ( ( C, x ( λ (! B( λ ( (8 8 Steady-State Analyss of Class- (Low- Prorty Queue The argnal pgf P ( of class- syste contents s readly obtaned by settng x (, - n (8; gvng: P ( X ( B( ( ρ ( (9 ( X ( ( B( T Alternatvely, by settng x (, - n (4, we can express ( P n ters of the two boundary ters, as follows: cgests-oct5

10 GESTS Int l Trans Coputer Scence and Engr, Vol8, o 5 Q(,,, (,, ( ( ( Q B P (3 B( Applyng the noralaton condton, P ( to (3, yelds ρ q and ρ ρ ρ B'( T + ( q ext let denote the ean buffer occupancy of the low-prorty queue n steadystate By dfferentatng (3 twce wth respect to and settng n the resultng expresson, we get after few nteredate steps: ρ H''( H ''( B''( ( ρ ρ ρt ( ρt ( ρ ( ρt ( ρt (3 where: ( ρ ρ + ρ H''( ( 9 Busy Perod Analyss of the Prorty Queung Syste In order to charactere the probablty dstrbuton of the busy perod of the prorty syste, we wll use the techque descrbed n [] Specfcally, let: Ω ( pr b slots (,,, denote the probablty that the syste s busy for slots Assung that the last te the prorty-syste was epty at the end of the (- th slot, we can express the transent probabltes of an epty syste as: Q (,, Ω ( Q (,, ( > (3 ext, defne the followng w-transfor: Bw ( Ω ( w By substtutng for Q (,, as n (3 nto P(w as gven n (9 we get: Pw ( Bw ( (33 wp( w Snce n general we defne the busy perod of a queung syste as the te between two consecutve dle perods, then the busy perod ust consst of at least one slot (e tated by at least one arrval, whch occurs wth probablty B(( q Un- GESTS-Oct5

11 6 On the Transent and Steady-State Analyss of a Specal der ths conventon, let the rando varable b denote the length of an arbtrary busy perod n nuber of slots and let Bwbe ( the correspondng pgf Clearly: Ω ( ( Ω ( probablty[ b slots] B(( q ( It follows that: * wb(( q Bw ( w B(( q where * s as defned n ( Fro the above pgf, we can use Lagrange s theore to derve the expresson for the correspondng probablty ass functon Ω ( : (34 (35 d B( H( Ω ( ( > B(( q ( +! d + (36 uercal Exaples and Dscussons of the Results In ths secton, we llustrate our approach through soe nuercal exaples, and probe further nto the nterplay between the correlaton of type- traffc and the perforance of class- queue All the nuercal results presented were obtaned usng Maple [8] coputatonal software We have assued that the nuber of essage arrvals to the low-prorty (type- queue follows a Geoetrc Batch arrval process, ρ charactered by the pgf B ( ( + ( In fgure, we plot the transent ean queue length, (, of type- buffer, as well as the transent ean of the total syste content T, In partcular, we note that whle the exponental rse behavor n the transent eante curve of type- queue, s typcal n any other queung systes, the transent ean-te curve of type- queue exhbts a sharper lnear growth behavor Ths can be explaned by the earler observaton that all of the 4 pacets of a type- essage enter ther buffer durng the sae slot, whereas type- essages enter ther buffer at the fxed rate of one pacet/slot cgests-oct5

12 GESTS Int l Trans Coputer Scence and Engr, Vol8, o 7 Transent ean of queue length T,,, Te (nuber of slots Fg Transent eans of buffer occupances ( ρ 8; ρ ; 4; ; 8 ext to nvestgate the effect of type- correlaton on the behavor of type- queue, we plot n fgure, the steady-state ean buffer content of type- queue versus the total load ρt ρ+ ρfor dfferent values of type- correlaton ndcator ( Here, we assued 5, 5, and fxed ρ Steady-state ean buffer occupancy Total load Fg Steady-state ean buffer occupancy of the low-prorty queue As ay be seen fro fgure, the correlaton factor (assocated wth the hghprorty buffer has a sgfcant nfluence on the ean buffer occupancy of type- queue For the sae traffc loads and traffc paraeters, ncreases rapdly as ncreases The above leads us to conclude that the correlaton of type- traffc has a drect negatve pact not only on the hgh-prorty queue t s feedng, but also on the low-prorty queue Therefore pror studes whch gnored ths correlaton effect should be revsted as they ght lead to naccurate buffer densong, adsson and congeston control polces GESTS-Oct5

13 8 On the Transent and Steady-State Analyss of a Specal Conclusons and Suggestons for Further Research In ths paper, we have proposed an alternate soluton techque towards the transent and steady-state analyss of a specal non-preeptve HOL prorty queung syste Our wor revealed that n a HOL-based prorty syste, the correlaton of pacet arrvals to the hgh-prorty queues affects not only these queues, but the lowerprorty queues as well Ths wor can be further explored n any drectons For exaple, varants to our queung odel, such as dfferent arrval processes, fte buffers and ult-server cases can be nvestgated These are left for future research References [] J Walraevens, and H Bruneel, Perforance Analyss of a Sngle-Server ATM Queue wth Prorty Schedulng, Coputers and Operatons Research, Vol 3, o, 3, pp [] M Mehet Al and X, Song, A Perforance Analyss of a Dscrete-Te Prorty Queung Syste wth Correlated Arrvals, Perforance Evaluaton, o 57, 4, pp [3] K Laevens, and H Bruneel, Dscrete-Te Mult-server Queues wth Prortes, Perforance Evaluaton, o 33, 998, pp [4] R Jafar and K Sohraby, Perforance Analyss of a Prorty Based ATM Multplexer wth Correlated Arrvals, IEEE Infoco 99, 999, pp [5] Y Xong and H Bruneel, Perforance of Statstcal ultplexers wth Fte uber of Inputs and Tran Arrvals, n proceedngs of IFOCOM, 99, pp [6] H Bruneel, Pacet Delay and Queue Length of Statstcal Multplexers wth Low- Speed Access Lnes, Coputer etwors, Vol 5, 993, pp [7] F Kaoun, Perforance Analyss of a Dscrete-Te Queung Syste wth a Correlated Tran Arrval Process, Perforance Evaluaton, In Press [8] Maple s a regstered tradear of Maplesoft ( Bography Faou Kaoun PO Box 443 Duba UAE Holds a PhD degree n Electrcal and Coputer Engneerng fro Concorda Uversty, and an MBA degree n Manageent fro McGll Uversty, Canada He oned the College of IT at Duba Uversty College n Septeber, as an Assstant Professor Pror to that, he has been wth ortel etwors (Canada snce 995, where pror to hs departure n, he was a Seor Techcal Advsor n the H-CAP Optcal etwors Dvson Hs research nterests are n the odelng and perforance analyss of coucatons networs Tel: E-al: faoun@ducacae cgests-oct5

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

Analysis of Discrete Time Queues (Section 4.6)

Analysis of Discrete Time Queues (Section 4.6) Analyss of Dscrete Tme Queues (Secton 4.6) Copyrght 2002, Sanjay K. Bose Tme axs dvded nto slots slot slot boundares Arrvals can only occur at slot boundares Servce to a job can only start at a slot boundary

More information

XII.3 The EM (Expectation-Maximization) Algorithm

XII.3 The EM (Expectation-Maximization) Algorithm XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles

More information

Departure Process from a M/M/m/ Queue

Departure Process from a M/M/m/ Queue Dearture rocess fro a M/M// Queue Q - (-) Q Q3 Q4 (-) Knowledge of the nature of the dearture rocess fro a queue would be useful as we can then use t to analyze sle cases of queueng networs as shown. The

More information

Equilibrium Analysis of the M/G/1 Queue

Equilibrium Analysis of the M/G/1 Queue Eulbrum nalyss of the M/G/ Queue Copyrght, Sanay K. ose. Mean nalyss usng Resdual Lfe rguments Secton 3.. nalyss usng an Imbedded Marov Chan pproach Secton 3. 3. Method of Supplementary Varables done later!

More information

Excess Error, Approximation Error, and Estimation Error

Excess Error, Approximation Error, and Estimation Error E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple

More information

arxiv: v2 [math.co] 3 Sep 2017

arxiv: v2 [math.co] 3 Sep 2017 On the Approxate Asyptotc Statstcal Independence of the Peranents of 0- Matrces arxv:705.0868v2 ath.co 3 Sep 207 Paul Federbush Departent of Matheatcs Unversty of Mchgan Ann Arbor, MI, 4809-043 Septeber

More information

COS 511: Theoretical Machine Learning

COS 511: Theoretical Machine Learning COS 5: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #0 Scrbe: José Sões Ferrera March 06, 203 In the last lecture the concept of Radeacher coplexty was ntroduced, wth the goal of showng that

More information

On the number of regions in an m-dimensional space cut by n hyperplanes

On the number of regions in an m-dimensional space cut by n hyperplanes 6 On the nuber of regons n an -densonal space cut by n hyperplanes Chungwu Ho and Seth Zeran Abstract In ths note we provde a unfor approach for the nuber of bounded regons cut by n hyperplanes n general

More information

Journal of Global Research in Computer Science A MARKOV CHAIN MODEL FOR ROUND ROBIN SCHEDULING IN OPERATING SYSTEM

Journal of Global Research in Computer Science A MARKOV CHAIN MODEL FOR ROUND ROBIN SCHEDULING IN OPERATING SYSTEM Volue 2, No 6, June 20 Journal of Global Research n Coputer Scence RESEARCH AER Avalable Onlne at wwwjgrcsnfo A MARKOV CHAIN MODEL FOR ROUND ROBIN SCHEDULING IN OERATING SYSTEM Deepak Ssoda *, Dr Sohan

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2014. All Rghts Reserved. Created: July 15, 1999 Last Modfed: February 9, 2008 Contents 1 Lnear Fttng

More information

Applied Mathematics Letters

Applied Mathematics Letters Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć

More information

PROBABILITY AND STATISTICS Vol. III - Analysis of Variance and Analysis of Covariance - V. Nollau ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE

PROBABILITY AND STATISTICS Vol. III - Analysis of Variance and Analysis of Covariance - V. Nollau ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE V. Nollau Insttute of Matheatcal Stochastcs, Techncal Unversty of Dresden, Gerany Keywords: Analyss of varance, least squares ethod, odels wth fxed effects,

More information

Elastic Collisions. Definition: two point masses on which no external forces act collide without losing any energy.

Elastic Collisions. Definition: two point masses on which no external forces act collide without losing any energy. Elastc Collsons Defnton: to pont asses on hch no external forces act collde thout losng any energy v Prerequstes: θ θ collsons n one denson conservaton of oentu and energy occurs frequently n everyday

More information

Solutions for Homework #9

Solutions for Homework #9 Solutons for Hoewor #9 PROBEM. (P. 3 on page 379 n the note) Consder a sprng ounted rgd bar of total ass and length, to whch an addtonal ass s luped at the rghtost end. he syste has no dapng. Fnd the natural

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form SET OF METHODS FO SOUTION THE AUHY POBEM FO STIFF SYSTEMS OF ODINAY DIFFEENTIA EUATIONS AF atypov and YuV Nulchev Insttute of Theoretcal and Appled Mechancs SB AS 639 Novosbrs ussa Introducton A constructon

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

1 Review From Last Time

1 Review From Last Time COS 5: Foundatons of Machne Learnng Rob Schapre Lecture #8 Scrbe: Monrul I Sharf Aprl 0, 2003 Revew Fro Last Te Last te, we were talkng about how to odel dstrbutons, and we had ths setup: Gven - exaples

More information

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS Darusz Bskup 1. Introducton The paper presents a nonparaetrc procedure for estaton of an unknown functon f n the regresson odel y = f x + ε = N. (1) (

More information

,..., k N. , k 2. ,..., k i. The derivative with respect to temperature T is calculated by using the chain rule: & ( (5) dj j dt = "J j. k i.

,..., k N. , k 2. ,..., k i. The derivative with respect to temperature T is calculated by using the chain rule: & ( (5) dj j dt = J j. k i. Suppleentary Materal Dervaton of Eq. 1a. Assue j s a functon of the rate constants for the N coponent reactons: j j (k 1,,..., k,..., k N ( The dervatve wth respect to teperature T s calculated by usng

More information

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c Internatonal Conference on Appled Scence and Engneerng Innovaton (ASEI 205) Several generaton ethods of ultnoal dstrbuted rando nuber Tan Le, a,lnhe,b,zhgang Zhang,c School of Matheatcs and Physcs, USTB,

More information

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e. SSTEM MODELLIN In order to solve a control syste proble, the descrptons of the syste and ts coponents ust be put nto a for sutable for analyss and evaluaton. The followng ethods can be used to odel physcal

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Chapter 12 Lyes KADEM [Thermodynamics II] 2007

Chapter 12 Lyes KADEM [Thermodynamics II] 2007 Chapter 2 Lyes KDEM [Therodynacs II] 2007 Gas Mxtures In ths chapter we wll develop ethods for deternng therodynac propertes of a xture n order to apply the frst law to systes nvolvng xtures. Ths wll be

More information

Finite Vector Space Representations Ross Bannister Data Assimilation Research Centre, Reading, UK Last updated: 2nd August 2003

Finite Vector Space Representations Ross Bannister Data Assimilation Research Centre, Reading, UK Last updated: 2nd August 2003 Fnte Vector Space epresentatons oss Bannster Data Asslaton esearch Centre, eadng, UK ast updated: 2nd August 2003 Contents What s a lnear vector space?......... 1 About ths docuent............ 2 1. Orthogonal

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

ON A CLASS OF RENEWAL QUEUEING AND RISK PROCESSES

ON A CLASS OF RENEWAL QUEUEING AND RISK PROCESSES ON A CLASS OF RENEWAL QUEUEING AND RISK ROCESSES K.K.Thap a, M.J.Jacob b a Departent of Statstcs, SNMC, M.G.Unversty, Kerala INDIA b Departent of Matheatcs, NITC Calcut, Kerala - INDIA UROSE:- In ths paper,

More information

1 Definition of Rademacher Complexity

1 Definition of Rademacher Complexity COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #9 Scrbe: Josh Chen March 5, 2013 We ve spent the past few classes provng bounds on the generalzaton error of PAClearnng algorths for the

More information

The Parity of the Number of Irreducible Factors for Some Pentanomials

The Parity of the Number of Irreducible Factors for Some Pentanomials The Party of the Nuber of Irreducble Factors for Soe Pentanoals Wolfra Koepf 1, Ryul K 1 Departent of Matheatcs Unversty of Kassel, Kassel, F. R. Gerany Faculty of Matheatcs and Mechancs K Il Sung Unversty,

More information

Preference and Demand Examples

Preference and Demand Examples Dvson of the Huantes and Socal Scences Preference and Deand Exaples KC Border October, 2002 Revsed Noveber 206 These notes show how to use the Lagrange Karush Kuhn Tucker ultpler theores to solve the proble

More information

Quantum Particle Motion in Physical Space

Quantum Particle Motion in Physical Space Adv. Studes Theor. Phys., Vol. 8, 014, no. 1, 7-34 HIKARI Ltd, www.-hkar.co http://dx.do.org/10.1988/astp.014.311136 Quantu Partcle Moton n Physcal Space A. Yu. Saarn Dept. of Physcs, Saara State Techncal

More information

The optimal delay of the second test is therefore approximately 210 hours earlier than =2.

The optimal delay of the second test is therefore approximately 210 hours earlier than =2. THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

EXACT TRAVELLING WAVE SOLUTIONS FOR THREE NONLINEAR EVOLUTION EQUATIONS BY A BERNOULLI SUB-ODE METHOD

EXACT TRAVELLING WAVE SOLUTIONS FOR THREE NONLINEAR EVOLUTION EQUATIONS BY A BERNOULLI SUB-ODE METHOD www.arpapress.co/volues/vol16issue/ijrras_16 10.pdf EXACT TRAVELLING WAVE SOLUTIONS FOR THREE NONLINEAR EVOLUTION EQUATIONS BY A BERNOULLI SUB-ODE METHOD Chengbo Tan & Qnghua Feng * School of Scence, Shandong

More information

Meenu Gupta, Man Singh & Deepak Gupta

Meenu Gupta, Man Singh & Deepak Gupta IJS, Vol., o. 3-4, (July-December 0, pp. 489-497 Serals Publcatons ISS: 097-754X THE STEADY-STATE SOLUTIOS OF ULTIPLE PARALLEL CHAELS I SERIES AD O-SERIAL ULTIPLE PARALLEL CHAELS BOTH WITH BALKIG & REEGIG

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

Chapter One Mixture of Ideal Gases

Chapter One Mixture of Ideal Gases herodynacs II AA Chapter One Mxture of Ideal Gases. Coposton of a Gas Mxture: Mass and Mole Fractons o deterne the propertes of a xture, we need to now the coposton of the xture as well as the propertes

More information

AN ANALYSIS OF A FRACTAL KINETICS CURVE OF SAVAGEAU

AN ANALYSIS OF A FRACTAL KINETICS CURVE OF SAVAGEAU AN ANALYI OF A FRACTAL KINETIC CURE OF AAGEAU by John Maloney and Jack Hedel Departent of Matheatcs Unversty of Nebraska at Oaha Oaha, Nebraska 688 Eal addresses: aloney@unoaha.edu, jhedel@unoaha.edu Runnng

More information

Uncertainty and auto-correlation in. Measurement

Uncertainty and auto-correlation in. Measurement Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

TCOM 501: Networking Theory & Fundamentals. Lecture 7 February 25, 2003 Prof. Yannis A. Korilis

TCOM 501: Networking Theory & Fundamentals. Lecture 7 February 25, 2003 Prof. Yannis A. Korilis TCOM 501: Networkng Theory & Fundamentals Lecture 7 February 25, 2003 Prof. Yanns A. Korls 1 7-2 Topcs Open Jackson Networks Network Flows State-Dependent Servce Rates Networks of Transmsson Lnes Klenrock

More information

Final Exam Solutions, 1998

Final Exam Solutions, 1998 58.439 Fnal Exa Solutons, 1998 roble 1 art a: Equlbru eans that the therodynac potental of a consttuent s the sae everywhere n a syste. An exaple s the Nernst potental. If the potental across a ebrane

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016 ME140 - Lnear rcuts - Wnter 16 Fnal, March 16, 2016 Instructons () The exam s open book. You may use your class notes and textbook. You may use a hand calculator wth no communcaton capabltes. () You have

More information

1. Statement of the problem

1. Statement of the problem Volue 14, 010 15 ON THE ITERATIVE SOUTION OF A SYSTEM OF DISCRETE TIMOSHENKO EQUATIONS Peradze J. and Tsklaur Z. I. Javakhshvl Tbls State Uversty,, Uversty St., Tbls 0186, Georga Georgan Techcal Uversty,

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

Integral Transforms and Dual Integral Equations to Solve Heat Equation with Mixed Conditions

Integral Transforms and Dual Integral Equations to Solve Heat Equation with Mixed Conditions Int J Open Probles Copt Math, Vol 7, No 4, Deceber 214 ISSN 1998-6262; Copyrght ICSS Publcaton, 214 www-csrsorg Integral Transfors and Dual Integral Equatons to Solve Heat Equaton wth Mxed Condtons Naser

More information

On Pfaff s solution of the Pfaff problem

On Pfaff s solution of the Pfaff problem Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

THE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 6 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutons to assst canddates preparng for the eamnatons n future years and for

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Worst Case Interrupt Response Time Draft, Fall 2007

Worst Case Interrupt Response Time Draft, Fall 2007 Worst Case Interrupt esponse Te Draft, Fall 7 Phlp Koopan Carnege Mellon Unversty Copyrght 7, Phlp Koopan eproducton and dssenaton beyond students of CMU ECE 8-348 s prohbted.. Overvew: Interrupt Servce

More information

Finite Fields and Their Applications

Finite Fields and Their Applications Fnte Felds and Ther Applcatons 5 009 796 807 Contents lsts avalable at ScenceDrect Fnte Felds and Ther Applcatons www.elsever.co/locate/ffa Typcal prtve polynoals over nteger resdue rngs Tan Tan a, Wen-Feng

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

AGC Introduction

AGC Introduction . Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

6.3.7 Example with Runga Kutta 4 th order method

6.3.7 Example with Runga Kutta 4 th order method 6.3.7 Example wth Runga Kutta 4 th order method Agan, as an example, 3 machne, 9 bus system shown n Fg. 6.4 s agan consdered. Intally, the dampng of the generators are neglected (.e. d = 0 for = 1, 2,

More information

Application of Queuing Theory to Waiting Time of Out-Patients in Hospitals.

Application of Queuing Theory to Waiting Time of Out-Patients in Hospitals. Applcaton of Queung Theory to Watng Tme of Out-Patents n Hosptals. R.A. Adeleke *, O.D. Ogunwale, and O.Y. Hald. Department of Mathematcal Scences, Unversty of Ado-Ekt, Ado-Ekt, Ekt State, Ngera. E-mal:

More information

Slobodan Lakić. Communicated by R. Van Keer

Slobodan Lakić. Communicated by R. Van Keer Serdca Math. J. 21 (1995), 335-344 AN ITERATIVE METHOD FOR THE MATRIX PRINCIPAL n-th ROOT Slobodan Lakć Councated by R. Van Keer In ths paper we gve an teratve ethod to copute the prncpal n-th root and

More information

Fermi-Dirac statistics

Fermi-Dirac statistics UCC/Physcs/MK/EM/October 8, 205 Fer-Drac statstcs Fer-Drac dstrbuton Matter partcles that are eleentary ostly have a type of angular oentu called spn. hese partcles are known to have a agnetc oent whch

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

Determination of the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH Algorithm

Determination of the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH Algorithm Internatonal Conference on Inforaton Technology and Manageent Innovaton (ICITMI 05) Deternaton of the Confdence Level of PSD Estaton wth Gven D.O.F. Based on WELCH Algorth Xue-wang Zhu, *, S-jan Zhang

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

SOJOURN TIME IN A QUEUE WITH CLUSTERED PERIODIC ARRIVALS

SOJOURN TIME IN A QUEUE WITH CLUSTERED PERIODIC ARRIVALS Journal of the Operatons Research Socety of Japan 2003, Vol. 46, No. 2, 220-241 2003 he Operatons Research Socety of Japan SOJOURN IME IN A QUEUE WIH CLUSERED PERIODIC ARRIVALS Da Inoue he oko Marne and

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Analysis of Queuing Delay in Multimedia Gateway Call Routing

Analysis of Queuing Delay in Multimedia Gateway Call Routing Analyss of Queung Delay n Multmeda ateway Call Routng Qwe Huang UTtarcom Inc, 33 Wood Ave. outh Iseln, NJ 08830, U..A Errol Lloyd Computer Informaton cences Department, Unv. of Delaware, Newark, DE 976,

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

Limited Dependent Variables

Limited Dependent Variables Lmted Dependent Varables. What f the left-hand sde varable s not a contnuous thng spread from mnus nfnty to plus nfnty? That s, gven a model = f (, β, ε, where a. s bounded below at zero, such as wages

More information

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Chapter 13. Gas Mixtures. Study Guide in PowerPoint. Thermodynamics: An Engineering Approach, 5th edition by Yunus A. Çengel and Michael A.

Chapter 13. Gas Mixtures. Study Guide in PowerPoint. Thermodynamics: An Engineering Approach, 5th edition by Yunus A. Çengel and Michael A. Chapter 3 Gas Mxtures Study Gude n PowerPont to accopany Therodynacs: An Engneerng Approach, 5th edton by Yunus A. Çengel and Mchael A. Boles The dscussons n ths chapter are restrcted to nonreactve deal-gas

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information

Digital Signal Processing

Digital Signal Processing Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over

More information

Queueing Networks II Network Performance

Queueing Networks II Network Performance Queueng Networks II Network Performance Davd Tpper Assocate Professor Graduate Telecommuncatons and Networkng Program Unversty of Pttsburgh Sldes 6 Networks of Queues Many communcaton systems must be modeled

More information

COMPLETE BUFFER SHARING IN ATM NETWORKS UNDER BURSTY ARRIVALS

COMPLETE BUFFER SHARING IN ATM NETWORKS UNDER BURSTY ARRIVALS COMPLETE BUFFER SHARING WITH PUSHOUT THRESHOLDS IN ATM NETWORKS UNDER BURSTY ARRIVALS Ozgur Aras and Tugrul Dayar Abstract. Broadband Integrated Servces Dgtal Networks (B{ISDNs) are to support multple

More information

Xiangwen Li. March 8th and March 13th, 2001

Xiangwen Li. March 8th and March 13th, 2001 CS49I Approxaton Algorths The Vertex-Cover Proble Lecture Notes Xangwen L March 8th and March 3th, 00 Absolute Approxaton Gven an optzaton proble P, an algorth A s an approxaton algorth for P f, for an

More information

Study of the possibility of eliminating the Gibbs paradox within the framework of classical thermodynamics *

Study of the possibility of eliminating the Gibbs paradox within the framework of classical thermodynamics * tudy of the possblty of elnatng the Gbbs paradox wthn the fraework of classcal therodynacs * V. Ihnatovych Departent of Phlosophy, Natonal echncal Unversty of Ukrane Kyv Polytechnc Insttute, Kyv, Ukrane

More information

Two Conjectures About Recency Rank Encoding

Two Conjectures About Recency Rank Encoding Internatonal Journal of Matheatcs and Coputer Scence, 0(205, no. 2, 75 84 M CS Two Conjectures About Recency Rank Encodng Chrs Buhse, Peter Johnson, Wlla Lnz 2, Matthew Spson 3 Departent of Matheatcs and

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Introduction to Continuous-Time Markov Chains and Queueing Theory

Introduction to Continuous-Time Markov Chains and Queueing Theory Introducton to Contnuous-Tme Markov Chans and Queueng Theory From DTMC to CTMC p p 1 p 12 1 2 k-1 k p k-1,k p k-1,k k+1 p 1 p 21 p k,k-1 p k,k-1 DTMC 1. Transtons at dscrete tme steps n=,1,2, 2. Past doesn

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

The Expectation-Maximization Algorithm

The Expectation-Maximization Algorithm The Expectaton-Maxmaton Algorthm Charles Elan elan@cs.ucsd.edu November 16, 2007 Ths chapter explans the EM algorthm at multple levels of generalty. Secton 1 gves the standard hgh-level verson of the algorthm.

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

E Tail Inequalities. E.1 Markov s Inequality. Non-Lecture E: Tail Inequalities

E Tail Inequalities. E.1 Markov s Inequality. Non-Lecture E: Tail Inequalities Algorthms Non-Lecture E: Tal Inequaltes If you hold a cat by the tal you learn thngs you cannot learn any other way. Mar Twan E Tal Inequaltes The smple recursve structure of sp lsts made t relatvely easy

More information

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18 Multpont Analyss for Sblng ars Bostatstcs 666 Lecture 8 revously Lnkage analyss wth pars of ndvduals Non-paraetrc BS Methods Maxu Lkelhood BD Based Method ossble Trangle Constrant AS Methods Covered So

More information

ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES

ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Journal of Algebra, Nuber Theory: Advances and Applcatons Volue 3, Nuber, 05, Pages 3-8 ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Feldstrasse 45 CH-8004, Zürch Swtzerland e-al: whurlann@bluewn.ch

More information

Designing Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate

Designing Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate The Frst Internatonal Senar on Scence and Technology, Islac Unversty of Indonesa, 4-5 January 009. Desgnng Fuzzy Te Seres odel Usng Generalzed Wang s ethod and Its applcaton to Forecastng Interest Rate

More information

Queuing system theory

Queuing system theory Elements of queung system: Queung system theory Every queung system conssts of three elements: An arrval process: s characterzed by the dstrbuton of tme between the arrval of successve customers, the mean

More information