523 P a g e. is measured through p. should be slower for lesser values of p and faster for greater values of p. If we set p*

Size: px
Start display at page:

Download "523 P a g e. is measured through p. should be slower for lesser values of p and faster for greater values of p. If we set p*"

Transcription

1 R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: Vol., Issue 4, July-August 0, pp.5-58 Constructon Of Mxed Smplng Plns Indexed Through Mpd And Aql Wth Condtonl Double Smplng Pln As Attrbute Pln Usng Weghted Posson Dstrbuton R. Smpth Kumr, R. Kruthk nd R. Rdhkrshnn. Assstnt Professor, Deprtment of Sttstcs, Government Arts College, Combtore Assstnt Professor n Sttstcs, Klgnr Krunndh Insttute of Technology, Combtore Assocte Professor, Deprtment of Sttstcs, PSG College of Arts nd Scence, Combtore ABSTRACT Ths pper presents the procedure for the constructon nd selecton of mxed smplng pln wth MAPD s qulty stndrd nd Condtonl double smplng pln s ttrbute pln usng Weghted Posson dstrbuton s bse lne dstrbuton. The plns re constructed ndexed through MAPD nd AQL nd lso compred. Tbles re constructed for the esy selecton of the plns. Keywords nd Phrses: Mxmum Allowble Percent Defectve, Acceptble qulty level, Opertng Chrcterstc, Tngent ntercept. AMS (000) Subject Clssfcton Number: Prmry: 6P0 Secondry: 6D05. INTRODUCTION Mxed smplng plns consst of two stges of rther dfferent nture. Durng the frst stge the gven lot s consdered s smple from the respectve producton process nd crteron by vrbles s used to check process qulty. If process qulty s judged to be suffcently good, the lot s ccepted. Otherwse the second stge of the smplng pln s entered nd lot qulty s checked drectly by mens of n ttrbute smplng pln. There re two types of mxed smplng plns clled ndependent nd dependent plns. If the frst stge smple results re not utlzed n the second stge, then the pln s sd to be ndependent otherwse dependent. The prncpl dvntge of mxed smplng pln over pure ttrbute smplng plns s reducton n smple sze for smlr mount of protecton. It s the usul prctce tht whle selectng smplng nspecton pln, to fx the opertng chrcterstc (OC) curve n ccordnce wth the desred degree of dscrmnton. The smplng pln s n turn fxed through sutbly chosen prmeters. The entry prmeters used n the cceptnce smplng lterture re cceptble qulty level (AQL), ndfference qulty level (IQL), lmtng qulty level (LQL) nd mxmum llowble percent defectve (MAPD). Severl uthors hve provded procedures to desgn the smplng plns ndexed through these prmeters for vrous cceptnce smplng plns. The concept of MAPD ( p * ) ws ntroduced by Myer (967) nd further studed by Soundrrjn (975) s the qulty level correspondng to the nflecton pont on the OC curve. The degree of shrpness of nspecton bout ths qulty level p * s mesured through p t, the pont t whch the tngent to the OC curve t the nflecton pont cuts the proporton defectve xs. One of the desred propertes of n OC curve s tht the decrese of ( p) should be slower for lesser vlues of p nd fster for greter vlues of p. If we set p* s the qulty stndrd, the bove property of the OC curve s obtned snce p* corresponds to the nflecton pont of the OC curve nd hence d P ( p) / dp = 0 for p= p * P d P ( p) / dp < 0 for p< * d P ( p) / dp > 0 for p> p * The mxed smplng pln hs been desgned under two cses of sgnfcnt nterest. In the frst cse smple sze n s fxed nd pont on the OC curve s gven. In the second cse plns re desgned when two ponts on the OC curve re gven. The procedure for desgnng the mxed smplng plns to stsfy the bove mentoned condtons ws provded by Schllng (967). Usng Schllng s procedure, Devrul (00) hs constructed tbles for mxed p 5 P g e

2 R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: Vol., Issue 4, July-August 0, pp.5-58 smplng plns (ndependent cse) hvng vrous smplng plns s ttrbute plns. Rdhkrshnn nd Smpth Kumr (006, 006 b, 007, 009) nd Rdhkrshnn et.l (00) hve mde contrbutons to mxed smplng pln for ndependent cse. Smpth Kumr (007) hs constructed mxed smplng pln wth vrous smplng plns s ttrbute plns usng Posson dstrbuton s bse lne dstrbuton. Rdhkrshn Ro (977) suggested tht the Weghted Bnoml dstrbuton cn be used n desgnng smplng plns. Sudeswr (00) studed the constructon of smplng plns usng Weghted Posson dstrbuton s bse lne dstrbuton. Mohn Pry (008), Rdhkrshnn nd Mohn Pry (008, 008 b) hve constructed the smplng plns usng Weghted Posson dstrbuton s bse lne dstrbuton. The Weghted Posson dstrbuton plys n mportnt role n the cceptnce smplng, mnly n the constructon of smplng plns. Ech outcome (number of defectves) s specfc but cn be ssgned wth dfferent weghts bsed on ts mportnce or usge. In ths pper, mxed smplng pln (ndependent cse) wth Condtonl double smplng pln s ttrbute pln s constructed usng Weghted Posson dstrbuton s bse lne dstrbuton. The plns ndexed through MAPD nd AQL re constructed seprtely by fxng the vlues of c, c, c nd β j '. The mxed plns ndexed through MAPD (p * ) nd AQL (p ) re lso compred.. GLOSSARY OF SYMBOLS The symbols used n ths pper re s follows: p : submtted qulty of lot or process P (p) : probblty of cceptnce for gven qulty p : mxmum llowble percent defectve p * p t : the pont t whch the nflecton tngent of the OC curve cuts the p xs p : the submtted qulty level such tht P (p ) = 0.95 (lso clled AQL) h * : reltve slope t p * n n : smple sze for the vrble smplng pln : smple sze for the ttrbute smplng pln c : cceptnce number β j : probblty of cceptnce for the lot qulty p j β j ' : probblty of cceptnce ssgned to frst stge for percent defectve p j β j '' : probblty of cceptnce ssgned to second stge for percent defectve p j k X A = U - k : vrble fctor such tht lot s ccepted f. OPERATING PROCEDURE OF MIXED SAMPLING PLAN HAVING CONDITIONAL DOUBLE SAMPLING PLAN AS ATTRIBUTE PLAN Schllng (967) hs gven the followng procedure for the ndependent mxed smplng pln wth upper specfcton lmt (U) nd stndrd devton (σ).. Determne the prmeters of the mxed smplng pln n, n,, n,, k, c, c nd c.. Tke rndom smple of sze n from the lot.. If smple verge X A = U - k, ccept the lot 4. If the smple verge X > A = U - k, tke second smple of sze n, nd count the number of defectves d n the smple. 5. If the number of defectves d c, ccept the lot. 6. If c + d c, tke second smple of sze n, from the remnng lot nd fnd the number of defectves d 7. If d c or d +d c ccept the lot, otherwse reject the lot. 4. CONSTRUCTION OF MIXED SAMPLING PLAN HAVING CONDTIONAL DOUBLE SAMPLING PLAN AS ATTRIBUTE PLAN USING WEIGHTED POISSON DISTRIBUTION The detled procedure dopted n ths pper for the constructon of mxed smplng pln hvng Condtonl double smplng s ttrbute pln usng Weghted Posson dstrbuton ndexed through MAPD s gven below: Assume tht the mxed pln s ndependent Decde the smple sze n (for vrble smplng pln) to be used. Clculte the cceptnce lmt for the vrble smplng pln s A U [ z( p ' * ) { z( * ) / n }], where z s stndrd norml vrte correspondng to t such tht t = z( t) e u du Splt the probblty of cceptnce β * s β * ' nd β * ''such tht β * = β * ' + (- β * ') β * ''. Fx the vlue of β * ' 54 P g e

3 R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: Vol., Issue 4, July-August 0, pp.5-58 Determne β * '', the probblty of cceptnce P (p) = ssgned to the ttrbute pln ssocted wth the c c c c c second stge smple s β * ''= (β * - β * ')/ (- β * '). P Pc q Pc q... Determne the pproprte second stge smple of n from the relton β * '' = np e ( np) c c c c c c cwhere P P Where P c q P np e ( np) ( )! c, q q... P c e mnp ( mnp) ( )! Usng the bove procedure, tbles hve been constructed to fcltte esy selecton of mxed smplng pln usng Condtonl Double Smplng pln s ttrbute plns ndexed through MAPD. 4. Constructon of tbles The OC functon of Weghted Posson dstrbuton s gven by, P ( p) x x0 x p( x, ) p( x; ) ; x=0,, The probblty of cceptnce for Condtonl double smplng pln under Weghted Posson dstrbuton when α = s used n ths pper for determnng the second stge probbltes nd s gven by q q e P mnp, ( mnp) ( )! ( )!, m. c c P c In ths pper, the probblty mss functon of the condtonl double smplng pln s used for m=. For n, = n, = n (sy) the nflecton pont (p * ) s d p ( p) obtned by usng 0 dp d p ( p) nd 0. dp The reltve slope of the OC curve h * s gven by, h * = p P ( p) dp ( p) dp t p=p *. The nflecton tngent of the OC curve cuts the p xs t p t =p * + (p * /h * ). The vlues of n p *, h *, n p t nd R= p t / p * re clculted for the specfed β * ' = 0.5 usng C progrm nd presented n Tble. q Tble : Vrous chrcterstcs of the mxed smplng pln when (p *, β * ) nd (p, β ) re known for specfed β * ' = 0.5, β =0.95, β ' =0.5 c c c np β * β * '' np * h * np t R =p t /p * P g e

4 R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: Vol., Issue 4, July-August 0, pp Selecton of the Pln Tble s used to construct the plns when MAPD (p * ) nd tngent ntercept (p t ) re gven. For ny gven vlues of c,c, p t nd p * one cn fnd the rto R = p t / p *. Correspondng to the vlue of c nd c fnd the vlue n Tble under the column R whch s equl to or just greter thn the specfed rto, the correspondng vlue of c s noted from ths c,c nd c vlues one cn determne the vlue of n usng n = n p * / p *. Exmple : Gven c = 6, c = 0, p * = 0.09, p t =0. nd β * ' = 0.5. Fnd the rto R= p t /p * =.0. Usng Tble, correspondng to c = 6, c =0 select the vlue of R equl to or just greter thn ths rto s.67 whch s ssocted wth c = 6, c =0, c = nd n = n p * / p * = (7.66/0.09) = 85.The Mxed Smplng pln wth Condtonl double smplng pln s ttrbute pln s n, = 85,n, =85, c = 6, c =0 nd c =. Prctcl problem: Suppose the pln n =, k=.0 s to be ppled to the lot by lot cceptnce nspecton of lptop bttery, the chrcterstc to be nspected s the opertng temperture nd mbent temperture of the bttery for whch there s specfed upper lmt of F wth known S.D (σ) of.0 0 F. In ths exmple, U= F, σ=.0 0 F nd k=.0 A = U-kσ = 60 - (.0) (.0) = 60-4 =56 0 F. Now, by pplyng the vrble nspecton frst, tke rndom smple of sze n = from the lot. Record the smple results nd fnd X. If X A = U - k =56 0 F, then ccept the lot. If X >A, tke rndom smple sze n, nd pply the ttrbute nspecton. Under ttrbutes nspecton, by tkng Condtonl double smplng pln s ttrbute pln, f the mnufcturer fxes the vlues p * =0.09 (9 non conformtes out of 00), p t =0.( non conformtes out of 00) nd β * ' =0.5, tke the smple of sze n, =85 nd observe the number of defectves (d ). If 56 P g e

5 R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: Vol., Issue 4, July-August 0, pp.5-58 d 6 ccept the lot nd f d >, reject the lot. If 7 d, tke second smple of sze n, =85 from the remnng lot nd fnd the number of defectves (d ). If d 0 or d +d ccept the lot, otherwse reject the lot nd nform the Mngement for further cton. 5. SELECTION OF MIXED SAMPLING PLAN HAVING CONDITIONAL DOUBLE SAMPLING PLAN AS ATTRIBUTE PLAN INDEXED THROGH AQL The generl procedure gven n secton 4 s used for desgnng the mxed smplng pln hvng Condtonl double smplng pln s ttrbute pln ndexed through AQL (p ). For n, = n, =n(sy),under the ssumpton β =0.95 nd β ' =0.5, the np vlues re clculted for dfferent vlues of c,c nd c usng C progrm nd presented n Tble. 5. Selecton of the Pln Tble s used to construct the plns when AQL (p ), c, c, c vlues re gven. For ny specfed vlues of p,c, c nd c one cn determne n vlue usng n= np / p. Exmple : Gven p = 0.0, c = 4,c = 7,c = 0 nd β ' =0.5. Usng Tble, fnd n=np /p = * OC Curves re drwn Tble : Comprson of plns c c Gven Vlues p * p t Through MAPD n,c.6594/0.0=89. For fxed β ' =0.5, the Mxed Smplng pln wth Condtonl double smplng pln s ttrbute pln s n, = 89, n, =89, c = 4, c =7 nd c =0. 6. COMPARISON OF PLANS INDEXED THROUGH MAPD AND AQL In ths secton the mxed smplng pln wth Condtonl double smplng pln s ttrbute pln ndexed through MAPD s compred wth the mxed smplng pln wth Condtonl double smplng pln s ttrbute pln ndexed through AQL by fxng the prmeters c,c nd β '. For the specfed vlues of p * nd p t, one cn fnd the vlues of c,c, c nd n ndexed through MAPD for β * ' = 0.5 s gven n secton 4. By fxng the vlues of c,c, c nd n, fnd the vlue of p by equtng P (p) = β = By fxng β ' =0.5 nd c,c, c nd for the vlue of p, one cn fnd the vlue of n usng n = np /p from Tble,for dfferent combntons of c,c, p * nd p t, the vlues of n, c (ndexed through MAPD) nd n, c (ndexed through AQL) re clculted nd presented n Tble. Through AQL n,c , 6 9, , 5 69, , 7, , 7 84, , * 9, * 57 P g e

6 R. Smpth Kumr, R. Kruthk, R. Rdhkrshnn / Interntonl Journl of Engneerng Reserch nd Applctons (IJERA) ISSN: Vol., Issue 4, July-August 0, pp CONCLUSION In ths pper the procedure for constructng mxed smplng pln s ndexed through MAPD wth Weghted Posson dstrbuton s the bselne dstrbuton s presented. Sutble tbles re lso provded for the esy selecton of the plns for the engneers who re workng on the floor of the ssembly. It s concluded from the study tht the second smple sze requred for mxed smplng pln wth Condtonl double smplng pln s ttrbute pln ndexed through MAPD s less thn tht of the second stge smple sze of the Mxed smplng pln wth Condtonl double smplng pln s ttrbute pln ndexed through AQL, justfed by Smpth Kumr (008).These plns defntely help the producers, becuse of the lesser smple sze whch drectly result n lesser smplng cost nd ndrectly reduces the totl cost of the product. The dfferent smplng plns cn lso be constructed by chngng the frst stge probbltes (β * ' nd β ') nd cn be compred for ther effcency. REFERENCES [] Devrul, S., 00, Certn studes reltng to mxed smplng plns nd relblty bsed smplng plns, PhD Dssertton, Deprtment of Sttstcs, Bhrthr Unversty, Combtore, Tml Ndu, Ind. [] Myer, P.L., 967, A note on sum of Posson Probbltes nd n Applcton, Industrl Qulty Control, Vol 9, No.5, pp.-5. [] Mohn Pry, L., 008,Constructon nd Selecton of cceptnce smplng plns bsed on Weghted Posson dstrbuton, PhD Dssertton, Deprtment of Sttstcs, Bhrthr Unversty, Combtore, Tml Ndu, Ind. [4] Rdhkrshn Ro, C., 977, A nturl exmple of Weghted Bnoml dstrbuton, The Amercn Sttstcn, Vol,, No.. [5] Rdhkrshnn, R., nd Mohn Pry, L., 008, Selecton of sngle smplng pln usng Condtonl Weghted Posson dstrbuton, Interntonl Journl of Sttstcs nd Systems, Vol, No., pp [6] Rdhkrshnn, R., nd Mohn Pry, L., 008 b, Comprson of CRGS plns usng Posson nd Weghted Posson dstrbuton, Probstt Forum, Vol, No., pp [7] Rdhkrshnn, R., nd Smpth Kumr, R., 006, Constructon of mxed smplng pln ndexed through MAPD nd IQL wth sngle smplng pln s ttrbute pln, Ntonl Journl of Technology, Vol, No., pp [8] Rdhkrshnn, R., nd Smpth Kumr, R., 006 b, Constructon of mxed smplng plns ndexed through MAPD nd AQL wth chn smplng pln s ttrbute pln, STARS Int. Journl, Vol 7, No, pp. 4-. [9] Rdhkrshnn, R., nd Smpth Kumr, R., 007, Constructon of mxed smplng plns ndexed through MAPD nd IQL wth double smplng pln s ttrbute pln, The Journl of the Kerl Sttstcl Assocton, Vol 8, pp. -. [0] Rdhkrshnn, R., nd Smpth Kumr, R., 009,Constructon nd comprson of mxed smplng plns hvng ChSP-(0,) pln s ttrbute pln, The Interntonl Journl of Sttstcs nd Mngement System,Vol 4, No.-, pp [] Rdhkrshnn, R., Smpth Kumr, R., nd Mlth, M., 00, Selecton of mxed smplng pln wth TNT- (n, n ; 0) pln s ttrbute pln ndexed through MAPD nd MAAOQ, Interntonl Journl of Sttstcs nd System, Vol 5, No 4, pp [] Smpth Kumr, R., 007, Constructon nd selecton of mxed vrbles-ttrbutes smplng pln - PhD Dssertton, Deprtment of Sttstcs, Bhrthr Unversty, Combtore, Tml Ndu, Ind. [] Schllng, E.G., 967, A generl method for determnng the opertng chrcterstcs of mxed vrbles-ttrbutes smplng plns sngle sde specfctons, S.D.known, PhD Dssertton- Rutgers- The Stte Unversty, New Brunswck, New Jersy. [4] Soundrrjn, V., 975, Mxmum llowble percent defectve (MAPD) sngle smplng nspecton by ttrbute pln, Journl of Qulty Technology, Vol 7, No. 4, pp.7-8. [5] Sudeswr 00, Desgnng of smplng pln usng Weghted Posson dstrbuton, M.Phl. Dssertton, Deprtment of Sttstcs, PSG College of Arts nd Scence, Combtore. 58 P g e

A New Markov Chain Based Acceptance Sampling Policy via the Minimum Angle Method

A New Markov Chain Based Acceptance Sampling Policy via the Minimum Angle Method Irnn Journl of Opertons Reserch Vol. 3, No., 202, pp. 04- A New Mrkov Chn Bsed Acceptnce Smplng Polcy v the Mnmum Angle Method M. S. Fllh Nezhd * We develop n optmzton model bsed on Mrkovn pproch to determne

More information

Applied Statistics Qualifier Examination

Applied Statistics Qualifier Examination Appled Sttstcs Qulfer Exmnton Qul_june_8 Fll 8 Instructons: () The exmnton contns 4 Questons. You re to nswer 3 out of 4 of them. () You my use ny books nd clss notes tht you mght fnd helpful n solvng

More information

SELECTION OF MIXED SAMPLING PLANS WITH CONDITIONAL DOUBLE SAMPLING PLAN AS ATTRIBUTE PLAN INDEXED THROUGH MAPD AND LQL USING IRPD

SELECTION OF MIXED SAMPLING PLANS WITH CONDITIONAL DOUBLE SAMPLING PLAN AS ATTRIBUTE PLAN INDEXED THROUGH MAPD AND LQL USING IRPD R. Samath Kumar, R. Vaya Kumar, R. Radhakrshnan /Internatonal Journal Of Comutatonal Engneerng Research / ISSN: 50 005 SELECTION OF MIXED SAMPLING PLANS WITH CONDITIONAL DOUBLE SAMPLING PLAN AS ATTRIBUTE

More information

4. Eccentric axial loading, cross-section core

4. Eccentric axial loading, cross-section core . Eccentrc xl lodng, cross-secton core Introducton We re strtng to consder more generl cse when the xl force nd bxl bendng ct smultneousl n the cross-secton of the br. B vrtue of Snt-Vennt s prncple we

More information

A Family of Multivariate Abel Series Distributions. of Order k

A Family of Multivariate Abel Series Distributions. of Order k Appled Mthemtcl Scences, Vol. 2, 2008, no. 45, 2239-2246 A Fmly of Multvrte Abel Seres Dstrbutons of Order k Rupk Gupt & Kshore K. Ds 2 Fculty of Scence & Technology, The Icf Unversty, Agrtl, Trpur, Ind

More information

Acceptance Double Sampling Plan using Fuzzy Poisson Distribution

Acceptance Double Sampling Plan using Fuzzy Poisson Distribution World Appled Scences Journl 6 (): 578-588, 22 SS 88-4952 DOS Publctons, 22 Acceptnce Double Smplng Pln usng Fuzzy Posson Dstrbuton Ezztllh Blou Jmkhneh nd 2 Bhrm Sdeghpour Gldeh Deprtment of Sttstcs, Qemshhr

More information

Acceptance Sampling by Attributes

Acceptance Sampling by Attributes Introduction Acceptnce Smpling by Attributes Acceptnce smpling is concerned with inspection nd decision mking regrding products. Three spects of smpling re importnt: o Involves rndom smpling of n entire

More information

Construction and Selection of Single Sampling Quick Switching Variables System for given Control Limits Involving Minimum Sum of Risks

Construction and Selection of Single Sampling Quick Switching Variables System for given Control Limits Involving Minimum Sum of Risks Construction nd Selection of Single Smpling Quick Switching Vribles System for given Control Limits Involving Minimum Sum of Risks Dr. D. SENHILKUMAR *1 R. GANESAN B. ESHA RAFFIE 1 Associte Professor,

More information

UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II

UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II Mcroeconomc Theory I UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS MSc n Economcs MICROECONOMIC THEORY I Techng: A Lptns (Note: The number of ndctes exercse s dffculty level) ()True or flse? If V( y )

More information

Statistics and Probability Letters

Statistics and Probability Letters Sttstcs nd Probblty Letters 79 (2009) 105 111 Contents lsts vlble t ScenceDrect Sttstcs nd Probblty Letters journl homepge: www.elsever.com/locte/stpro Lmtng behvour of movng verge processes under ϕ-mxng

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter.4 Newton-Rphson Method of Solvng Nonlner Equton After redng ths chpter, you should be ble to:. derve the Newton-Rphson method formul,. develop the lgorthm of the Newton-Rphson method,. use the Newton-Rphson

More information

Lecture 4: Piecewise Cubic Interpolation

Lecture 4: Piecewise Cubic Interpolation Lecture notes on Vrtonl nd Approxmte Methods n Appled Mthemtcs - A Perce UBC Lecture 4: Pecewse Cubc Interpolton Compled 6 August 7 In ths lecture we consder pecewse cubc nterpolton n whch cubc polynoml

More information

IJMIE Volume 2, Issue 4 ISSN:

IJMIE Volume 2, Issue 4 ISSN: SELECTION OF MIXED SAMPLING PLAN WITH SINGLE SAMPLING PLAN AS ATTRIBUTE PLAN INDEXED THROUGH (MAPD, MAAOQ) AND (MAPD, AOQL) R. Sampath Kumar* R. Kiruthika* R. Radhakrishnan* ABSTRACT: This paper presents

More information

Two Coefficients of the Dyson Product

Two Coefficients of the Dyson Product Two Coeffcents of the Dyson Product rxv:07.460v mth.co 7 Nov 007 Lun Lv, Guoce Xn, nd Yue Zhou 3,,3 Center for Combntorcs, LPMC TJKLC Nnk Unversty, Tnjn 30007, P.R. Chn lvlun@cfc.nnk.edu.cn gn@nnk.edu.cn

More information

DCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x)

DCDM BUSINESS SCHOOL NUMERICAL METHODS (COS 233-8) Solutions to Assignment 3. x f(x) DCDM BUSINESS SCHOOL NUMEICAL METHODS (COS -8) Solutons to Assgnment Queston Consder the followng dt: 5 f() 8 7 5 () Set up dfference tble through fourth dfferences. (b) Wht s the mnmum degree tht n nterpoltng

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology Int. J. Pure Appl. Sc. Technol., () (), pp. 44-49 Interntonl Journl of Pure nd Appled Scences nd Technolog ISSN 9-67 Avlle onlne t www.jopst.n Reserch Pper Numercl Soluton for Non-Lner Fredholm Integrl

More information

Quiz: Experimental Physics Lab-I

Quiz: Experimental Physics Lab-I Mxmum Mrks: 18 Totl tme llowed: 35 mn Quz: Expermentl Physcs Lb-I Nme: Roll no: Attempt ll questons. 1. In n experment, bll of mss 100 g s dropped from heght of 65 cm nto the snd contner, the mpct s clled

More information

Time Truncated Two Stage Group Sampling Plan For Various Distributions

Time Truncated Two Stage Group Sampling Plan For Various Distributions Time Truncted Two Stge Group Smpling Pln For Vrious Distributions Dr. A. R. Sudmni Rmswmy, S.Jysri Associte Professor, Deprtment of Mthemtics, Avinshilingm University, Coimbtore Assistnt professor, Deprtment

More information

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC

Rank One Update And the Google Matrix by Al Bernstein Signal Science, LLC Introducton Rnk One Updte And the Google Mtrx y Al Bernsten Sgnl Scence, LLC www.sgnlscence.net here re two dfferent wys to perform mtrx multplctons. he frst uses dot product formulton nd the second uses

More information

CHI-SQUARE DIVERGENCE AND MINIMIZATION PROBLEM

CHI-SQUARE DIVERGENCE AND MINIMIZATION PROBLEM CHI-SQUARE DIVERGENCE AND MINIMIZATION PROBLEM PRANESH KUMAR AND INDER JEET TANEJA Abstrct The mnmum dcrmnton nformton prncple for the Kullbck-Lebler cross-entropy well known n the lterture In th pper

More information

Principle Component Analysis

Principle Component Analysis Prncple Component Anlyss Jng Go SUNY Bufflo Why Dmensonlty Reducton? We hve too mny dmensons o reson bout or obtn nsghts from o vsulze oo much nose n the dt Need to reduce them to smller set of fctors

More information

Dennis Bricker, 2001 Dept of Industrial Engineering The University of Iowa. MDP: Taxi page 1

Dennis Bricker, 2001 Dept of Industrial Engineering The University of Iowa. MDP: Taxi page 1 Denns Brcker, 2001 Dept of Industrl Engneerng The Unversty of Iow MDP: Tx pge 1 A tx serves three djcent towns: A, B, nd C. Ech tme the tx dschrges pssenger, the drver must choose from three possble ctons:

More information

Hybrid Group Acceptance Sampling Plan Based on Size Biased Lomax Model

Hybrid Group Acceptance Sampling Plan Based on Size Biased Lomax Model Mthemtics nd Sttistics 2(3): 137-141, 2014 DOI: 10.13189/ms.2014.020305 http://www.hrpub.org Hybrid Group Acceptnce Smpling Pln Bsed on Size Bised Lomx Model R. Subb Ro 1,*, A. Ng Durgmmb 2, R.R.L. Kntm

More information

Remember: Project Proposals are due April 11.

Remember: Project Proposals are due April 11. Bonformtcs ecture Notes Announcements Remember: Project Proposls re due Aprl. Clss 22 Aprl 4, 2002 A. Hdden Mrov Models. Defntons Emple - Consder the emple we tled bout n clss lst tme wth the cons. However,

More information

PLEASE SCROLL DOWN FOR ARTICLE

PLEASE SCROLL DOWN FOR ARTICLE Ths rtcle ws downloded by:ntonl Cheng Kung Unversty] On: 1 September 7 Access Detls: subscrpton number 7765748] Publsher: Tylor & Frncs Inform Ltd Regstered n Englnd nd Wles Regstered Number: 17954 Regstered

More information

The Number of Rows which Equal Certain Row

The Number of Rows which Equal Certain Row Interntonl Journl of Algebr, Vol 5, 011, no 30, 1481-1488 he Number of Rows whch Equl Certn Row Ahmd Hbl Deprtment of mthemtcs Fcult of Scences Dmscus unverst Dmscus, Sr hblhmd1@gmlcom Abstrct Let be X

More information

Variable time amplitude amplification and quantum algorithms for linear algebra. Andris Ambainis University of Latvia

Variable time amplitude amplification and quantum algorithms for linear algebra. Andris Ambainis University of Latvia Vrble tme mpltude mplfcton nd quntum lgorthms for lner lgebr Andrs Ambns Unversty of Ltv Tlk outlne. ew verson of mpltude mplfcton;. Quntum lgorthm for testng f A s sngulr; 3. Quntum lgorthm for solvng

More information

Designing of Combined Continuous Lot By Lot Acceptance Sampling Plan

Designing of Combined Continuous Lot By Lot Acceptance Sampling Plan Internatonal Journal o Scentc Research Engneerng & Technology (IJSRET), ISSN 78 02 709 Desgnng o Combned Contnuous Lot By Lot Acceptance Samplng Plan S. Subhalakshm 1 Dr. S. Muthulakshm 2 1 Research Scholar,

More information

Jean Fernand Nguema LAMETA UFR Sciences Economiques Montpellier. Abstract

Jean Fernand Nguema LAMETA UFR Sciences Economiques Montpellier. Abstract Stochstc domnnce on optml portfolo wth one rsk less nd two rsky ssets Jen Fernnd Nguem LAMETA UFR Scences Economques Montpeller Abstrct The pper provdes restrctons on the nvestor's utlty functon whch re

More information

1 Probability Density Functions

1 Probability Density Functions Lis Yn CS 9 Continuous Distributions Lecture Notes #9 July 6, 28 Bsed on chpter by Chris Piech So fr, ll rndom vribles we hve seen hve been discrete. In ll the cses we hve seen in CS 9, this ment tht our

More information

Definition of Tracking

Definition of Tracking Trckng Defnton of Trckng Trckng: Generte some conclusons bout the moton of the scene, objects, or the cmer, gven sequence of mges. Knowng ths moton, predct where thngs re gong to project n the net mge,

More information

KINETICS Pipe & Duct Seismic Application Manual

KINETICS Pipe & Duct Seismic Application Manual KINETIC pe & Duct esmc Applcton Mnul CODE BAED EIMIC DEIGN FORCE 5.1 Introducton: The code bsed horzontl sesmc force requrements for ppe nd duct re ether clculted by the sesmc restrnt mnufcturer s prt

More information

SUMMER KNOWHOW STUDY AND LEARNING CENTRE

SUMMER KNOWHOW STUDY AND LEARNING CENTRE SUMMER KNOWHOW STUDY AND LEARNING CENTRE Indices & Logrithms 2 Contents Indices.2 Frctionl Indices.4 Logrithms 6 Exponentil equtions. Simplifying Surds 13 Opertions on Surds..16 Scientific Nottion..18

More information

Many-Body Calculations of the Isotope Shift

Many-Body Calculations of the Isotope Shift Mny-Body Clcultons of the Isotope Shft W. R. Johnson Mrch 11, 1 1 Introducton Atomc energy levels re commonly evluted ssumng tht the nucler mss s nfnte. In ths report, we consder correctons to tomc levels

More information

LOCAL FRACTIONAL LAPLACE SERIES EXPANSION METHOD FOR DIFFUSION EQUATION ARISING IN FRACTAL HEAT TRANSFER

LOCAL FRACTIONAL LAPLACE SERIES EXPANSION METHOD FOR DIFFUSION EQUATION ARISING IN FRACTAL HEAT TRANSFER Yn, S.-P.: Locl Frctonl Lplce Seres Expnson Method for Dffuson THERMAL SCIENCE, Yer 25, Vol. 9, Suppl., pp. S3-S35 S3 LOCAL FRACTIONAL LAPLACE SERIES EXPANSION METHOD FOR DIFFUSION EQUATION ARISING IN

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

Lecture 5 Single factor design and analysis

Lecture 5 Single factor design and analysis Lectue 5 Sngle fcto desgn nd nlss Completel ndomzed desgn (CRD Completel ndomzed desgn In the desgn of expements, completel ndomzed desgns e fo studng the effects of one pm fcto wthout the need to tke

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

Tests for the Ratio of Two Poisson Rates

Tests for the Ratio of Two Poisson Rates Chpter 437 Tests for the Rtio of Two Poisson Rtes Introduction The Poisson probbility lw gives the probbility distribution of the number of events occurring in specified intervl of time or spce. The Poisson

More information

Introduction to Numerical Integration Part II

Introduction to Numerical Integration Part II Introducton to umercl Integrton Prt II CS 75/Mth 75 Brn T. Smth, UM, CS Dept. Sprng, 998 4/9/998 qud_ Intro to Gussn Qudrture s eore, the generl tretment chnges the ntegrton prolem to ndng the ntegrl w

More information

Lecture 36. Finite Element Methods

Lecture 36. Finite Element Methods CE 60: Numercl Methods Lecture 36 Fnte Element Methods Course Coordntor: Dr. Suresh A. Krth, Assocte Professor, Deprtment of Cvl Engneerng, IIT Guwht. In the lst clss, we dscussed on the ppromte methods

More information

We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b.

We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b. Mth 255 - Vector lculus II Notes 4.2 Pth nd Line Integrls We begin with discussion of pth integrls (the book clls them sclr line integrls). We will do this for function of two vribles, but these ides cn

More information

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede Fll Anlss of Epermentl Mesurements B. Esensten/rev. S. Errede Monte Crlo Methods/Technques: These re mong the most powerful tools for dt nlss nd smulton of eperments. The growth of ther mportnce s closel

More information

Chapter Newton-Raphson Method of Solving a Nonlinear Equation

Chapter Newton-Raphson Method of Solving a Nonlinear Equation Chpter 0.04 Newton-Rphson Method o Solvng Nonlner Equton Ater redng ths chpter, you should be ble to:. derve the Newton-Rphson method ormul,. develop the lgorthm o the Newton-Rphson method,. use the Newton-Rphson

More information

The Schur-Cohn Algorithm

The Schur-Cohn Algorithm Modelng, Estmton nd Otml Flterng n Sgnl Processng Mohmed Njm Coyrght 8, ISTE Ltd. Aendx F The Schur-Cohn Algorthm In ths endx, our m s to resent the Schur-Cohn lgorthm [] whch s often used s crteron for

More information

CALIBRATION OF SMALL AREA ESTIMATES IN BUSINESS SURVEYS

CALIBRATION OF SMALL AREA ESTIMATES IN BUSINESS SURVEYS CALIBRATION OF SMALL AREA ESTIMATES IN BUSINESS SURVES Rodolphe Prm, Ntle Shlomo Southmpton Sttstcl Scences Reserch Insttute Unverst of Southmpton Unted Kngdom SAE, August 20 The BLUE-ETS Project s fnnced

More information

AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio

AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio 7 CHANGE-POINT METHODS FOR OVERDISPERSED COUNT DATA THESIS Brn A. Wlken, Cptn, Unted Sttes Ar Force AFIT/GOR/ENS/7-26 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wrght-Ptterson

More information

LAPLACE TRANSFORM SOLUTION OF THE PROBLEM OF TIME-FRACTIONAL HEAT CONDUCTION IN A TWO-LAYERED SLAB

LAPLACE TRANSFORM SOLUTION OF THE PROBLEM OF TIME-FRACTIONAL HEAT CONDUCTION IN A TWO-LAYERED SLAB Journl of Appled Mthemtcs nd Computtonl Mechncs 5, 4(4), 5-3 www.mcm.pcz.pl p-issn 99-9965 DOI:.75/jmcm.5.4. e-issn 353-588 LAPLACE TRANSFORM SOLUTION OF THE PROBLEM OF TIME-FRACTIONAL HEAT CONDUCTION

More information

Attribute reduction theory and approach to concept lattice

Attribute reduction theory and approach to concept lattice Scence n Chn Ser F Informton Scences 2005 Vol48 No6 713 726 713 Attrbute reducton theory nd pproch to concept lttce ZHANG Wenxu 1, WEI Lng 1,2 & QI Jnun 3 1 Insttute for Informton nd System Scences, Fculty

More information

INTRODUCTION TO COMPLEX NUMBERS

INTRODUCTION TO COMPLEX NUMBERS INTRODUCTION TO COMPLEX NUMBERS The numers -4, -3, -, -1, 0, 1,, 3, 4 represent the negtve nd postve rel numers termed ntegers. As one frst lerns n mddle school they cn e thought of s unt dstnce spced

More information

MATHEMATICAL MODEL AND STATISTICAL ANALYSIS OF THE TENSILE STRENGTH (Rm) OF THE STEEL QUALITY J55 API 5CT BEFORE AND AFTER THE FORMING OF THE PIPES

MATHEMATICAL MODEL AND STATISTICAL ANALYSIS OF THE TENSILE STRENGTH (Rm) OF THE STEEL QUALITY J55 API 5CT BEFORE AND AFTER THE FORMING OF THE PIPES 6 th Reserch/Exert Conference wth Interntonl Prtcton QUALITY 009, Neum, B&H, June 04 07, 009 MATHEMATICAL MODEL AND STATISTICAL ANALYSIS OF THE TENSILE STRENGTH (Rm) OF THE STEEL QUALITY J55 API 5CT BEFORE

More information

Using the Econometric Models in Planning the Service of Several Machines at Random Time Intervals. Authors:

Using the Econometric Models in Planning the Service of Several Machines at Random Time Intervals. Authors: Usng the Econometrc Models n Plnnng the Servce of Severl Mchnes t Rndom Tme Intervls. Authors: ) Ion Constntn Dm, Unversty Vlh of Trgovste, Romn ) Mrce Udrescu, Unversty Artfex of Buchrest, Romn Interntonl

More information

THE COMBINED SHEPARD ABEL GONCHAROV UNIVARIATE OPERATOR

THE COMBINED SHEPARD ABEL GONCHAROV UNIVARIATE OPERATOR REVUE D ANALYSE NUMÉRIQUE ET DE THÉORIE DE L APPROXIMATION Tome 32, N o 1, 2003, pp 11 20 THE COMBINED SHEPARD ABEL GONCHAROV UNIVARIATE OPERATOR TEODORA CĂTINAŞ Abstrct We extend the Sheprd opertor by

More information

Lecture 3 Gaussian Probability Distribution

Lecture 3 Gaussian Probability Distribution Introduction Lecture 3 Gussin Probbility Distribution Gussin probbility distribution is perhps the most used distribution in ll of science. lso clled bell shped curve or norml distribution Unlike the binomil

More information

4 7x =250; 5 3x =500; Read section 3.3, 3.4 Announcements: Bell Ringer: Use your calculator to solve

4 7x =250; 5 3x =500; Read section 3.3, 3.4 Announcements: Bell Ringer: Use your calculator to solve Dte: 3/14/13 Objective: SWBAT pply properties of exponentil functions nd will pply properties of rithms. Bell Ringer: Use your clcultor to solve 4 7x =250; 5 3x =500; HW Requests: Properties of Log Equtions

More information

Research Article On the Upper Bounds of Eigenvalues for a Class of Systems of Ordinary Differential Equations with Higher Order

Research Article On the Upper Bounds of Eigenvalues for a Class of Systems of Ordinary Differential Equations with Higher Order Hndw Publshng Corporton Interntonl Journl of Dfferentl Equtons Volume 0, Artcle ID 7703, pges do:055/0/7703 Reserch Artcle On the Upper Bounds of Egenvlues for Clss of Systems of Ordnry Dfferentl Equtons

More information

International Journal of Engineering Research and Modern Education (IJERME) Impact Factor: 7.018, ISSN (Online): (

International Journal of Engineering Research and Modern Education (IJERME) Impact Factor: 7.018, ISSN (Online): ( CONSTRUCTION AND SELECTION OF CHAIN SAMPLING PLAN WITH ZERO INFLATED POISSON DISTRIBUTION A. Palansamy* & M. Latha** * Research Scholar, Department of Statstcs, Government Arts College, Udumalpet, Tamlnadu

More information

For the percentage of full time students at RCC the symbols would be:

For the percentage of full time students at RCC the symbols would be: Mth 17/171 Chpter 7- ypothesis Testing with One Smple This chpter is s simple s the previous one, except it is more interesting In this chpter we will test clims concerning the sme prmeters tht we worked

More information

Linear and Nonlinear Optimization

Linear and Nonlinear Optimization Lner nd Nonlner Optmzton Ynyu Ye Deprtment of Mngement Scence nd Engneerng Stnford Unversty Stnford, CA 9430, U.S.A. http://www.stnford.edu/~yyye http://www.stnford.edu/clss/msnde/ Ynyu Ye, Stnford, MS&E

More information

Study on the Normal and Skewed Distribution of Isometric Grouping

Study on the Normal and Skewed Distribution of Isometric Grouping Open Journ of Sttstcs 7-5 http://dx.do.org/.36/ojs..56 Pubshed Onne October (http://www.scp.org/journ/ojs) Study on the orm nd Skewed Dstrbuton of Isometrc Groupng Zhensheng J Wenk J Schoo of Economcs

More information

The solution of transport problems by the method of structural optimization

The solution of transport problems by the method of structural optimization Scentfc Journls Mrtme Unversty of Szczecn Zeszyty Nukowe Akdem Morsk w Szczecne 2013, 34(106) pp. 59 64 2013, 34(106) s. 59 64 ISSN 1733-8670 The soluton of trnsport problems by the method of structurl

More information

Read section 3.3, 3.4 Announcements:

Read section 3.3, 3.4 Announcements: Dte: 3/1/13 Objective: SWBAT pply properties of exponentil functions nd will pply properties of rithms. Bell Ringer: 1. f x = 3x 6, find the inverse, f 1 x., Using your grphing clcultor, Grph 1. f x,f

More information

Review of linear algebra. Nuno Vasconcelos UCSD

Review of linear algebra. Nuno Vasconcelos UCSD Revew of lner lgebr Nuno Vsconcelos UCSD Vector spces Defnton: vector spce s set H where ddton nd sclr multplcton re defned nd stsf: ) +( + ) (+ )+ 5) λ H 2) + + H 6) 3) H, + 7) λ(λ ) (λλ ) 4) H, - + 8)

More information

ψ ij has the eigenvalue

ψ ij has the eigenvalue Moller Plesset Perturbton Theory In Moller-Plesset (MP) perturbton theory one tes the unperturbed Hmltonn for n tom or molecule s the sum of the one prtcle Foc opertors H F() where the egenfunctons of

More information

Measurements of effective delayed neutron fraction in a fast neutron reactor. using the perturbation method *

Measurements of effective delayed neutron fraction in a fast neutron reactor. using the perturbation method * Mesurements of effectve delyed neutron frcton n fst neutron rector usng the perturbton method * ZHOU Ho-Jun( 周浩军 ) 1, ; 1) YIN Yn-Peng( 尹延朋 ) FAN Xo-Qng( 范晓强 ) LI Zheng-Hong( 李正宏 ) PU Y-Kng( 蒲以康 ) 1 1

More information

Stratified Extreme Ranked Set Sample With Application To Ratio Estimators

Stratified Extreme Ranked Set Sample With Application To Ratio Estimators Journl of Modern Appled Sttstcl Metods Volume 3 Issue Artcle 5--004 Strtfed Extreme Rned Set Smple Wt Applcton To Rto Estmtors Hn M. Smw Sultn Qboos Unversty, smw@squ.edu.om t J. Sed Sultn Qboos Unversty

More information

M/G/1/GD/ / System. ! Pollaczek-Khinchin (PK) Equation. ! Steady-state probabilities. ! Finding L, W q, W. ! π 0 = 1 ρ

M/G/1/GD/ / System. ! Pollaczek-Khinchin (PK) Equation. ! Steady-state probabilities. ! Finding L, W q, W. ! π 0 = 1 ρ M/G//GD/ / System! Pollcze-Khnchn (PK) Equton L q 2 2 λ σ s 2( + ρ ρ! Stedy-stte probbltes! π 0 ρ! Fndng L, q, ) 2 2 M/M/R/GD/K/K System! Drw the trnston dgrm! Derve the stedy-stte probbltes:! Fnd L,L

More information

Marginal Effects in Probit Models: Interpretation and Testing. 1. Interpreting Probit Coefficients

Marginal Effects in Probit Models: Interpretation and Testing. 1. Interpreting Probit Coefficients ECON 5 -- NOE 15 Margnal Effects n Probt Models: Interpretaton and estng hs note ntroduces you to the two types of margnal effects n probt models: margnal ndex effects, and margnal probablty effects. It

More information

The graphs of Rational Functions

The graphs of Rational Functions Lecture 4 5A: The its of Rtionl Functions s x nd s x + The grphs of Rtionl Functions The grphs of rtionl functions hve severl differences compred to power functions. One of the differences is the behvior

More information

The Study of Lawson Criterion in Fusion Systems for the

The Study of Lawson Criterion in Fusion Systems for the Interntonl Archve of Appled Scences nd Technology Int. Arch. App. Sc. Technol; Vol 6 [] Mrch : -6 Socety of ducton, Ind [ISO9: 8 ertfed Orgnzton] www.soeg.co/st.html OD: IAASA IAAST OLI ISS - 6 PRIT ISS

More information

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

Lecture 16 Statistical Analysis in Biomaterials Research (Part II) 3.051J/0.340J 1 Lecture 16 Statstcal Analyss n Bomaterals Research (Part II) C. F Dstrbuton Allows comparson of varablty of behavor between populatons usng test of hypothess: σ x = σ x amed for Brtsh statstcan

More information

A Tri-Valued Belief Network Model for Information Retrieval

A Tri-Valued Belief Network Model for Information Retrieval December 200 A Tr-Vlued Belef Networ Model for Informton Retrevl Fernndo Ds-Neves Computer Scence Dept. Vrgn Polytechnc Insttute nd Stte Unversty Blcsburg, VA 24060. IR models t Combnng Evdence Grphcl

More information

Investigation phase in case of Bragg coupling

Investigation phase in case of Bragg coupling Journl of Th-Qr Unversty No.3 Vol.4 December/008 Investgton phse n cse of Brgg couplng Hder K. Mouhmd Deprtment of Physcs, College of Scence, Th-Qr, Unv. Mouhmd H. Abdullh Deprtment of Physcs, College

More information

Math 426: Probability Final Exam Practice

Math 426: Probability Final Exam Practice Mth 46: Probbility Finl Exm Prctice. Computtionl problems 4. Let T k (n) denote the number of prtitions of the set {,..., n} into k nonempty subsets, where k n. Argue tht T k (n) kt k (n ) + T k (n ) by

More information

Solubilities and Thermodynamic Properties of SO 2 in Ionic

Solubilities and Thermodynamic Properties of SO 2 in Ionic Solubltes nd Therodync Propertes of SO n Ionc Lquds Men Jn, Yucu Hou, b Weze Wu, *, Shuhng Ren nd Shdong Tn, L Xo, nd Zhgng Le Stte Key Lbortory of Checl Resource Engneerng, Beng Unversty of Checl Technology,

More information

fractions Let s Learn to

fractions Let s Learn to 5 simple lgebric frctions corne lens pupil retin Norml vision light focused on the retin concve lens Shortsightedness (myopi) light focused in front of the retin Corrected myopi light focused on the retin

More information

GMM Estimation of the GB2 class of Income Distributions from Grouped Data

GMM Estimation of the GB2 class of Income Distributions from Grouped Data GMM Estmton of the GB clss of Income Dstrbutons from Grouped Dt Gholmre Hjrgsht Deprtment of Economcs Unversty of Melbourne, Austrl Wllm E. Grffths Deprtment of Economcs Unversty of Melbourne, Austrl Joseph

More information

Smart Motorways HADECS 3 and what it means for your drivers

Smart Motorways HADECS 3 and what it means for your drivers Vehcle Rentl Smrt Motorwys HADECS 3 nd wht t mens for your drvers Vehcle Rentl Smrt Motorwys HADECS 3 nd wht t mens for your drvers You my hve seen some news rtcles bout the ntroducton of Hghwys Englnd

More information

Sequences of Intuitionistic Fuzzy Soft G-Modules

Sequences of Intuitionistic Fuzzy Soft G-Modules Interntonl Mthemtcl Forum, Vol 13, 2018, no 12, 537-546 HIKARI Ltd, wwwm-hkrcom https://doorg/1012988/mf201881058 Sequences of Intutonstc Fuzzy Soft G-Modules Velyev Kemle nd Huseynov Afq Bku Stte Unversty,

More information

1 Module for Year 10 Secondary School Student Logarithm

1 Module for Year 10 Secondary School Student Logarithm 1 Erthquke Intensity Mesurement (The Richter Scle) Dr Chrles Richter showed tht the lrger the energy of n erthquke hs, the lrger mplitude of ground motion t given distnce. The simple model of Richter

More information

Lesson 2. Thermomechanical Measurements for Energy Systems (MENR) Measurements for Mechanical Systems and Production (MMER)

Lesson 2. Thermomechanical Measurements for Energy Systems (MENR) Measurements for Mechanical Systems and Production (MMER) Lesson 2 Thermomechncl Mesurements for Energy Systems (MEN) Mesurements for Mechncl Systems nd Producton (MME) 1 A.Y. 2015-16 Zccr (no ) Del Prete A U The property A s clled: «mesurnd» the reference property

More information

4. More general extremum principles and thermodynamic potentials

4. More general extremum principles and thermodynamic potentials 4. More generl etremum prncples nd thermodynmc potentls We hve seen tht mn{u(s, X )} nd m{s(u, X)} mply one nother. Under certn condtons, these prncples re very convenent. For emple, ds = 1 T du T dv +

More information

DESIGN OF MULTILOOP CONTROLLER FOR THREE TANK PROCESS USING CDM TECHNIQUES

DESIGN OF MULTILOOP CONTROLLER FOR THREE TANK PROCESS USING CDM TECHNIQUES DESIGN OF MULTILOOP CONTROLLER FOR THREE TANK PROCESS USING CDM TECHNIQUES N. Kngsb 1 nd N. Jy 2 1,2 Deprtment of Instrumentton Engneerng,Annml Unversty, Annmlngr, 608002, Ind ABSTRACT In ths study the

More information

Demand. Demand and Comparative Statics. Graphically. Marshallian Demand. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert

Demand. Demand and Comparative Statics. Graphically. Marshallian Demand. ECON 370: Microeconomic Theory Summer 2004 Rice University Stanley Gilbert Demnd Demnd nd Comrtve Sttcs ECON 370: Mcroeconomc Theory Summer 004 Rce Unversty Stnley Glbert Usng the tools we hve develoed u to ths ont, we cn now determne demnd for n ndvdul consumer We seek demnd

More information

CHOVER-TYPE LAWS OF THE ITERATED LOGARITHM FOR WEIGHTED SUMS OF ρ -MIXING SEQUENCES

CHOVER-TYPE LAWS OF THE ITERATED LOGARITHM FOR WEIGHTED SUMS OF ρ -MIXING SEQUENCES CHOVER-TYPE LAWS OF THE ITERATED LOGARITHM FOR WEIGHTED SUMS OF ρ -MIXING SEQUENCES GUANG-HUI CAI Receved 24 September 2004; Revsed 3 My 2005; Accepted 3 My 2005 To derve Bum-Ktz-type result, we estblsh

More information

Chapter 1: Fundamentals

Chapter 1: Fundamentals Chpter 1: Fundmentls 1.1 Rel Numbers Types of Rel Numbers: Nturl Numbers: {1, 2, 3,...}; These re the counting numbers. Integers: {... 3, 2, 1, 0, 1, 2, 3,...}; These re ll the nturl numbers, their negtives,

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

Case Study of Cascade Reliability with weibull Distribution

Case Study of Cascade Reliability with weibull Distribution ISSN: 77-3754 ISO 900:008 Certfed Internatonal Journal of Engneerng and Innovatve Technology (IJEIT) Volume, Issue 6, June 0 Case Study of Cascade Relablty wth webull Dstrbuton Dr.T.Shyam Sundar Abstract

More information

Dynamic Power Management in a Mobile Multimedia System with Guaranteed Quality-of-Service

Dynamic Power Management in a Mobile Multimedia System with Guaranteed Quality-of-Service Dynmc Power Mngement n Moble Multmed System wth Gurnteed Qulty-of-Servce Qnru Qu, Qng Wu, nd Mssoud Pedrm Dept. of Electrcl Engneerng-Systems Unversty of Southern Clforn Los Angeles CA 90089 Outlne! Introducton

More information

GAUSS ELIMINATION. Consider the following system of algebraic linear equations

GAUSS ELIMINATION. Consider the following system of algebraic linear equations Numercl Anlyss for Engneers Germn Jordnn Unversty GAUSS ELIMINATION Consder the followng system of lgebrc lner equtons To solve the bove system usng clsscl methods, equton () s subtrcted from equton ()

More information

Chapter 5 Supplemental Text Material R S T. ij i j ij ijk

Chapter 5 Supplemental Text Material R S T. ij i j ij ijk Chpter 5 Supplementl Text Mterl 5-. Expected Men Squres n the Two-fctor Fctorl Consder the two-fctor fxed effects model y = µ + τ + β + ( τβ) + ε k R S T =,,, =,,, k =,,, n gven s Equton (5-) n the textook.

More information

CHAPTER - 7. Firefly Algorithm based Strategic Bidding to Maximize Profit of IPPs in Competitive Electricity Market

CHAPTER - 7. Firefly Algorithm based Strategic Bidding to Maximize Profit of IPPs in Competitive Electricity Market CHAPTER - 7 Frefly Algorthm sed Strtegc Bddng to Mxmze Proft of IPPs n Compettve Electrcty Mrket 7. Introducton The renovton of electrc power systems plys mjor role on economc nd relle operton of power

More information

6 Roots of Equations: Open Methods

6 Roots of Equations: Open Methods HK Km Slghtly modfed 3//9, /8/6 Frstly wrtten t Mrch 5 6 Roots of Equtons: Open Methods Smple Fed-Pont Iterton Newton-Rphson Secnt Methods MATLAB Functon: fzero Polynomls Cse Study: Ppe Frcton Brcketng

More information

In this Chapter. Chap. 3 Markov chains and hidden Markov models. Probabilistic Models. Example: CpG Islands

In this Chapter. Chap. 3 Markov chains and hidden Markov models. Probabilistic Models. Example: CpG Islands In ths Chpter Chp. 3 Mrov chns nd hdden Mrov models Bontellgence bortory School of Computer Sc. & Eng. Seoul Ntonl Unversty Seoul 5-74, Kore The probblstc model for sequence nlyss HMM (hdden Mrov model)

More information

AP Calculus Multiple Choice: BC Edition Solutions

AP Calculus Multiple Choice: BC Edition Solutions AP Clculus Multiple Choice: BC Edition Solutions J. Slon Mrch 8, 04 ) 0 dx ( x) is A) B) C) D) E) Divergent This function inside the integrl hs verticl symptotes t x =, nd the integrl bounds contin this

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

7.2 Volume. A cross section is the shape we get when cutting straight through an object.

7.2 Volume. A cross section is the shape we get when cutting straight through an object. 7. Volume Let s revew the volume of smple sold, cylnder frst. Cylnder s volume=se re heght. As llustrted n Fgure (). Fgure ( nd (c) re specl cylnders. Fgure () s rght crculr cylnder. Fgure (c) s ox. A

More information

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives Block #6: Properties of Integrls, Indefinite Integrls Gols: Definition of the Definite Integrl Integrl Clcultions using Antiderivtives Properties of Integrls The Indefinite Integrl 1 Riemnn Sums - 1 Riemnn

More information

CS 109 Lecture 11 April 20th, 2016

CS 109 Lecture 11 April 20th, 2016 CS 09 Lecture April 0th, 06 Four Prototypicl Trjectories Review The Norml Distribution is Norml Rndom Vrible: ~ Nµ, σ Probbility Density Function PDF: f x e σ π E[ ] µ Vr σ x µ / σ Also clled Gussin Note:

More information

Physics 121 Sample Common Exam 2 Rev2 NOTE: ANSWERS ARE ON PAGE 7. Instructions:

Physics 121 Sample Common Exam 2 Rev2 NOTE: ANSWERS ARE ON PAGE 7. Instructions: Physcs 121 Smple Common Exm 2 Rev2 NOTE: ANSWERS ARE ON PAGE 7 Nme (Prnt): 4 Dgt ID: Secton: Instructons: Answer ll 27 multple choce questons. You my need to do some clculton. Answer ech queston on the

More information