Optimal Sensor Placement for Cooperative Distributed Vision

Size: px
Start display at page:

Download "Optimal Sensor Placement for Cooperative Distributed Vision"

Transcription

1 Opmal Senso Placemen fo Coopeave Dsbued Vson Lus E. Navao-Semen, John M. Dolan, and Padeep K. Khosla, Depamen of Eleccal and Compue Engneeng he Robocs Insue Canege Mellon Unves Psbugh, PA, USA {lenscmu, jmd, Absac hs pape descbes a mehod fo obsevng maneuveng ages usng a goup of moble obos equpped wh vdeo cameas. hese obos ae pa of a eam of small-sze (7x7x7 cm) obos confgued fom modula componens ha collaboae o accomplsh a gven ask. he cameas seek o obseve he age whle facng as much as possble fom he especve vewpons. hs wok consdes he poblem of schedulng and maneuveng he cameas based on he evaluaon of he cuen posons n ems of how well can he manan a fonal vew of he age. We descbe ou appoach, whch dsbues he ask among seveal obos and avods exensve eneg consumpon on a sngle obo. We exploe he concep n smulaon and pesen esuls. Kewods-senso placemen; coopeave sensos; dsbued vson; auomac suvellance. I. INRODUCION Suvellance, econnassance, and secu ae good examples of dffcul o edous undeakngs ha can benef fom he use of eams of oboc sensos capable of modfng he vanage pons. A eam of obos has dsnc advanages ove sngle obos wh espec o sensng. eam membes exchange senso nfomaon, collaboae o ack and denf ages, o even asss each ohe o ovecome obsacles. B coodnang s membes, a eam can explo nfomaon deved fom mulple dspaae vewpons. A sngle obo, hough poenall equpped wh a lage aa of dffeen sensng modales, s lmed a an one me o a sngle vewpon. Moeove, a eam of obos can smulaneousl collec nfomaon fom mulple locaons. One mpoan aspec of suvellance and vglance asks ha s fequenl addessed s ha of obsevng and ackng he movemens of people o vehcles navgang whn he confnes of a pacula aea. In he case of obsevng people, s ofen mpoan no onl o deec and ack a peson, bu also o denf hm/he, o a leas o poduce mages ha could be used n he fuue o deemne whehe a peson was pesen n a pacula aea a an gven me/dae. he coec placemen of a vdeo camea wh espec o he subjec o age s cucal o poduce a useful mage. A good phoogaphe alwas es o capue a scene fom he bes possble angle. hee ae seveal facos o be consdeed when evaluang how good a pacula camea poson s n ems of he chaacescs of he mage ha wll be poduced. Fo example, consde a convenonal mug sho made fo polce fles. If he suspec s locaed ehe oo close o o oo fa awa fom he camea, dsncve chaacescs of he suspec s face can be los n he pcue. In he same manne, s mpoan ha he suspec faces he camea a cean angles fo boh he fonal and pofle shos. Reseach n hs aea pcall consdes sac placemen of sensos, and deals wh ssues elaed o polgon vsbl [7]. Some ohe eseaches have consdeed he use of sensos ha shf he posons ove me o ensue ha ages eman unde suvellance a all mes [][]. Howeve, mos of hese appoaches do no ake no accoun ha some applcaons eque he senso o be posoned wh a pacula oenaon, and offe soluons n whch jus he deecon of a age s consdeed sasfaco. In hs pape we descbe some pelmna wok on an auomac age obsevaon ssem o be used pmal b ou eam of cenmee-scale obos, called Mllbos []. he Mllbos ae capable of cang dffeen pes of sensos whle mananng poson esmaes, and cuenl ae able o collaboae o exploe and map aeas naccessble o lage obos. We ae cuenl developng a ssem n whch vdeo cameas seek o obseve he age whle fonall facng as much as possble fom he especve vewpons fo suvellance asks. he same ssem can be used fo ohe applcaons ha eque he obsevaon of a movng age fo dffeen facng angles. II. PROBLEM DESCRIPION he Opmal Senso Placemen poblem suded n hs pape s defned as follows: gven S: A fne wo-dmensonal spaal egon, wh enances and exs. R: A subse of M obos equpped wh vdeo cameas, deploed as pa of a lage se of N obos R. { } R =,,, M : A se of K ages navgang whn S. { } S =,,, K

2 x x wh ange [,], whee x ( ) and x ( ) ae sae vecos ha epesen he Defne he evaluaon funcon E ( (), () ) poson and oenaon of obo (o camea) and age especvel. hs evaluaon funcon akes no accoun he camea s opcal confguaon (.e., focal lengh, feld of vew, ec.), he desed mage sze of he age, he dsance fom he camea o he age, and he angle beween he age s facng decon and he vew decon of he camea. A obo R s opmall placed wh espec o he age a me when he evaluaon funcon E( (), () ) = he evaluaon funcon E ( (), ()) x x. he goal s o maxmze he mean value of x x ove me hough he adequae selecon and maneuveng of he se of obos R, =,, M ove me seps. III. RELAED WORK he wok mos closel elaed o ous nvolves he concep of acve vson, whch mples compue vson mplemened wh a movable camea. Seveal eseaches have exploed he use of mulple cameas fo coopeave mulsenso suvellance; Collns e al. [8] descbe a mulcamea suvellance ssem ha allows a sngle human opeao o mono acves n a clueed envonmen usng a dsbued newok of acve vdeo sensos. Masuama e al. [7] nvesgae he use of evaluaon funcons fo plannng laou of mulple cameas n D sac scenes. he wok assumes ha he cameas eman n fxed posons; he senso placemen poblem s solved n he off-lne camea wok plannng. hs wok dffes fom ous n ha ou obos mus dnamcall shf he posons ove me o ensue a fonal vew of he age. Pake e al [] descbe he use of mulple obos fo coopeave obsevaon of ages. Howeve, hs wok assumes obos wh 6 feld of vew sensos, and does no enfoce an pacula senso-age aangemen. IV. APPROACH he aonale behnd ou saeg s smple: he obos wll eman opeaonal fo as long as he eneg souces can suppl he powe equed. I s well known ha one of he mos eneg-consumng asks ha an moble obo can pefom s o move fom one place o anohe. Consequenl, n ode o conseve eneg, we wan he obos o shf he posons as lle as possble. Fo hs eason, s cucal o know exacl whee he cameas should be placed, n ode o use he avalable eneg effcenl. I s easonable o assume ha he closes obo o he age should be he one assgned o obseve. he shoe he dsance a obo has o move, he bee n ems of eneg. Howeve, once a obo has moved o he opmal obsevaon poson, wll ve lkel become he closes obo o he opmal poson mos of he me, hus ceang a suaon n whch one obo s pemanenl assgned o obseve he age. Alhough hee ae a numbe of easons wh hs suaon ma be dsadvanageous, we ae mosl concened abou he exensve use of a sngle obo s eneg eseve. hs s paculal mpoan n he case of he Mllbos, snce he baees end o de fase when he shf he posons connuousl. Consequenl, we seek o dsbue he obsevaon ask among mulple obos. hee ae wo basc poblems nvolved n ou appoach: ) fndng he opmal senso placemen, and ) selecon of he combnaon of sensos o be used a an gven me. hese opcs ae descbed n he followng secons. A. Senso Evaluaon Funcon We have modfed he evaluaon funcons used n [7] and exended he concep fom sacall o dnamcall posoned cameas. All pons wh an unobsuced vew of a age ae evaluaed wh espec o wo vaables: dsance o age, and he angle beween he age s facng decon and he vew decon of he camea. he dsance o age affecs he appaen sze of he age n he mage. he opmal dsance o age dop s deemned befoehand, and akes no accoun he opcal confguaon of he camea, and he sze of he age. he desed angle beween he age s facng decon and he vew decon of he camea s ou pma concen, and s deemned b he naue of he applcaon. In hs pape we consde he full fonal facng angle o be opmal (.e., he age s facng sagh a he camea), alhough n some cases he use mgh wsh o obseve he age a a dffeen angle. Le us consde a age a me. he cuen age sae s descbed b x = x ϕ, whch ncludes s cuen poson and facng angle. Smlal, assume ha he sae veco descbng a vdeo camea caed b a obo s gven b x = x θ. he evaluaon of (, ) age s gven b ( x (), x ()) E ds exp x wh espec o s dsance o he = ( x x ) + ( ) d σ op Equaon () esembles a Gaussan dsbuon, whch peaks when he dsance beween ( x, ) and ( x, ) s equal o d op. he paamee σ adjuss how seveel he devaon fom he opmal dsance, d op, s penalzed. In ode o evaluae he angle beween he age s facng decon and he vew decon of he camea, he vew am decon ha cenes he age nsde he mage fame, θ, s compued fs. hs s smpl he angle of he un veco ha, x,, as adaes fom he pon ( x ) owad he pon ( ) ()

3 age ( x, ) Fgue. Geome of he Camea-age aangemen. am shown n Fg.. he compuaon of θ smplfes he calculaon of he opmal vew decon, snce educes he seach space fo he opmal soluon. We make he assumpon ha he cameas ae dven b a holonomc dve mechansm, so he can be oened equall well along an beang, nowhsandng he oenaon of he obo. he opmal vew decon of he camea occus when he age s facng sagh a he camea. As shown n Fg., when am am θ = θ he angula dsance θ ϕ povdes an ndcaon of how well he camea manans a fonal vew of he age. he evaluaon of ( x, ) wh espec o he facng angle s gven b am Efacng ( x (), x ()) = ( cos( ) θ ϕ ) () am Equaon () has a mnmum when θ = ϕ,.e., when boh he camea and he age ae ponng n he same decon. On he ohe hand, () peaks when he angula dsance equals π (o π), when he camea and age pon a one anohe. he oal evaluaon s hen compued b mulplng he effecs of boh evaluaon funcons () and (): ( (), () ) E x x = E E () oal ds facng he opmal camea poson fo a age can hen be deemned b fndng he poson n S ha maxmzes he evaluaon funcon: x = ag max E () op Camea ( x, ) ϕ oal am θ = θ ( x, ) am θ Fg. llusaes he use of () fo evaluang all pons suoundng a age. As expeced, he opmal camea poson s ndcaed b a global maxmum locaed n fon of he age, a a dsance d op. Y poson E oal : age (face angle = π/) - X poson Fgue. hs plo llusaes he pefomance of he evaluaon funcon. In hs plo, d op = and σ =.. he evaluaon funcon peaks a he pon (,), whch ndcaes he opmal camea poson. B mulplng he oal evaluaon funcon () compued ndvduall fo each age, s possble o deemne he opmal placemen of a camea wh espec o mulple ages: E = E, () combned oal K K Ls whee Ls denoes a ls of he objecs o be obseved b he camea. Fo example, consde he case n whch one camea s equed o obseve wo ages, as shown n Fg.. he opmal camea poson s compued fo each age sepaael, and hen he poduc of he ndvdual evaluaon funcons s calculaed. As expeced, hee s a pon n he (x, ) plane ha maxmzes he evaluaon funcon fo boh ages. Howeve, s neesng o noe ha hs pon, hough poduces he bes possble vew of wo ages a he same me, mgh no poduce a useful pcue of boh ages (fo nsance, n Fg. he opmal camea poson was evaluaed a onl 87%). Y poson - - Maxmum value = X poson ages Fgue.One camea obsevng wo ages. he bes mage poduced s evaluaed as 87% of he maxmum value.

4 Noneheless, he value of hs combned evaluaon funcon s sll a useful ndcao ha could be used o decde whehe one o wo cameas should be scheduled o obseve boh ages. B. Saeg fo Senso Selecon In hs secon, an algohm fo senso selecon and schedulng s pesened. As menoned befoe, he goal s o maxmze he mean value of he evaluaon funcon E ( x (), ()) x ove me hough he adequae selecon and maneuveng of obos R, =,, M ove me seps, whle educng he collecve use of bae powe. he evaluaon funcon () s used o fnd he opmal camea poson, and plas a cenal ole n ou algohm. he algohm s he followng: Gven R, S, and, and he opeaon paamees d op, σ,, execue he followng seps eve seconds:. Oban an esmae of x fo all navgang n S a me. (hs s usuall done usng ohe obos n R ).. Compue opmal camea posons fo all, usng (). If an opmal camea poson canno be found (.e. no sngle global maxmum s pesen), o he maxmum evaluaon numbe s below a cean heshold (.e., he vew s no good enough), ncease he numbe of cameas o obseve he ages b one unl a se of opmal posons s found.. Fnd he senso R ha s closes (Eucldean dsance) o each opmal poson found n sep, and assgn. If hee s a conflc (.e. one obo s closes o wo o moe ages ha canno be obseved smulaneousl), he nex closes obo s assgned.. Maneuve all assgned senso no he coespondng ages. he nex secon pesens smulaons ha llusae he pefomance of hs algohm. V. SIMULAIONS We have conduced a sees of smulaon expemens o valdae ou deas. he objecve s o llusae he compuaon of opmal camea poson usng evaluaon funcons. Ou expemens wee pefomed usng a Mllbo smulao. hs smulao has been exensvel used b ou eam, and esembles he mos elevan chaacescs of he Mllbos wh a good degee of fdel. I s assumed ha all sensos know he posons a all mes, as he Mllbos do. he Appendx descbes he Mllbos and he localzaon ssem n deal. he ackng conolle used s descbed n [6]. In he fs smulaon he goal s o manan a fonal vew of boh objecs as much as possble, usng as few cameas as possble. hs expemen nvolves wo objecs movng a consan bu dffeen speeds, as shown n Fg.. wo sees of bg ccles denoe he poson and oenaon of he wo objecs a eve me sep. he elave dsance beween consecuve ccles on each objec s pah s dffeen, snce he objecs move a dffeen veloces. wo cameas ae deploed n he aea. Boh cameas can dnamcall shf he posons. he squae maks ndcae he nal poson of he cameas. he sees of small ccles movng fom lef o uppe gh ndcaes he ajeco of he opmal camea poson, f a sngle camea wee used o obseve he wo objecs smulaneousl fo he duaon of he expemen. hs opmal poson s compued usng (). As shown n he op plo of Fg., dung he fs seconds he opmal obsevaon poson s no able o poduce an adequae mage of boh objecs. he evaluaon numbe emans below., whch was se as he heshold value. Consequenl, camea s nall assgned o obseve Objec, whle Objec s obseved b camea. he evaluaon of he opmal camea placemen evenuall eaches he heshold afe seconds, ndcang ha a camea s able b self o manan an adequae fonal vew of boh objecs smulaneousl. A ha me, camea s he closes o he opmal camea poson and consequenl s assgned o obseve boh objecs, whle camea s eleased and sopped. Boh camea evaluaons ae shown n Fg., boom plo. he second smulaon s smla. he goal s also o manan a fonal vew of boh objecs as much as possble, usng as few cameas as possble. hs expemen nvolves wo objecs movng fom lef o gh a consan bu dffeen speeds, as shown n Fg. 6. wo sees of bg ccles denoe he poson and oenaon of he wo objecs a eve me sep. he elave dsance beween consecuve ccles on each objec s pah s dffeen, snce he objecs move a dffeen veloces. Egh cameas ae deploed along an magna hallwa. hs me, he cameas can dnamcall shf he posons. he squae maks ndcae he nal poson and vew decon of he cameas. he sees of small ccles movng fom lef o gh ndcae he ajeco of he opmal camea poson, f a sngle camea wee used o obseve he wo objecs smulaneousl fo he duaon of he expemen. Y poson Objec Objec - 6 X poson Fgue. Obsevng mulple objecs. Inall he opmal obsevaon poson s no able o poduce an adequae mage of boh objecs. Evenuall, camea s assgned o obseve boh objecs. Opmal pah (fo a sngle camea obsevng wo objecs)

5 hs opmal poson s compued usng (). Inall, camea s able b self o manan an adequae fonal vew of boh objecs smulaneousl. hs s obseved n he ajeco of camea, whch nall follows he ajeco of he opmal camea poson. Due o he dffeence n speeds, he opmal obsevaon poson s no longe able o poduce a sasfaco mage of boh objecs and he evaluaon numbe dops below., whch was se as he heshold value. Consequenl, camea 6 s called no acon afe seconds. he esuls ae shown n Fg. 7. I s neesng o obseve n Fg.7 ha he evaluaon funcon fo camea eaches a sead sae below he maxmum of. hs s due o he fac ha he poson conolle used fo ackng fals o keep up he pace and lags wh espec o he desed poson. hs esembles he behavo obseved n he eal obos. I s also neesng o Y poson Camea evaluaon (one camea obsevng wo objecs) me, sec me, sec Camea evaluaon (one camea pe objec) Objec Objec Camea Camea Fgue.Camea evaluaon fo mulple objecs. he op plo shows he evaluaon of one camea obsevng boh ages. When he opmal poson s able o poduce a sasfaco mage, camea obseves boh objecs, and camea s eleased Opmal pah (fo a sngle camea obsevng wo objecs) X poson Fgue 6. Obsevng mulple objecs. Camea s nall able o obseve boh objecs smulaneousl. When he evaluaon of camea dopped below he heshold, a second camea was assgned o obseve Objec, whle Camea was assgned o obseve Objec. 7 Camea evaluaon (one camea obsevng wo objecs) me, sec.. Camea evaluaon (one camea pe objec) Camea Camea 6 me, sec. Fgue 7.Camea evaluaon fo mulple objecs. he op plo shows he evaluaon of camea obsevng boh ages. When he opmal poson was no able o manan a mnmum evaluaon, a second camea s called n, esulng n good obsevaon of boh objecs. noe ha he handoff ansen oveshoos fs, and hen falls befoe eachng he sead sae. hs s agan due o he behavo of he ackng conolle, whle he obo maneuves no he obsevng poson. VI. CONCLUSIONS AND FURHER WORK We have descbed a mehod fo obsevng maneuveng ages usng a goup of moble obos equpped wh vdeo cameas. We poposed an algohm fo schedulng and maneuveng he cameas based on he evaluaon of he cuen posons n ems of how well can he ake a fonal pcue of he age. We pesened smulaon esuls ha seem o ndcae he poenal of he suggesed appoach. he expemens descbed n he pecedng secon ae pelmna. he smulaons pesened do no accoun fo ohe obsacles n he envonmen, whch mos lkel wll affec he evaluaon funcon of pons n he plane. In addon, alhough he algohm s scalable, we need o fuhe es he pefomance fo seveal camea-age combnaons, and fo dffeen age maneuveng ndces. hee ae a numbe of decons n whch we would lke o expand hs eseach. In ode o be effecve, he algohm should ncopoae mechansms fo navgaon (.e. collson avodance, ec). Fuhemoe, he algohm could benef fom leanng abou he mos ansed pahs n he egon, so ha he sensos would gavae owads hgh-age-dens aeas. APPENDIX A. he Mllbos and he localzaon ssem he Mllbos ae small obos (7x7x7 cm) confgued fom modula componens. hs modula desgn allows he eam o be easl confgued o accomplsh he ask a hand. he cuen senso se ncludes RF communcaon, compuaon,

6 mobl, vdeo camea, sona (sho and long ange), IR angefnde, and PIR senso modules. Because of he small sze, he compuaonal and sensng capables of Mllbos ae lmed []. Noneheless, b explong he popees of specalzaon and collaboaon he dsadvanages mposed b small obo sze ae ovecome. B havng specalzed senso modules he eam as a whole can opmze on sze and esouces - specfcall powe. A senso s acvaed onl when he msson eques. Collaboaon s necessa o coodnae and collec nfomaon so ha he ndvdual membes of he eam can ac as a sngle logcal en. A cusom-bul localzaon ssem ha ulzes dead eckonng and ulasonc dsance measuemens beween obos enables he Mllbos o accomplsh asks such as mappng and exploaon []. he dsance beween wo obos s measued usng snchonzed ulasound and RF pulses. A concal efleco mouned above a low-cos ansduce allows he Mllbos o deec and ansm ulasonc pulses n an decon. Peodcall, each obo ha seves as a beacon ems smulaneousl a ado fequenc (RF) pulse and an ulasonc pulse. Usng he RF pulse fo snchonzaon, he dsance o he beacon s measued as he me-of-flgh of he ulasonc pulse mulpled b he speed of sound (m/s a C). he eam leade coodnaes he pngng sequence o ensue ha beacon sgnals fom mulple obos do no nefee wh one anohe. o mpove he accuac, hs pocedue s epeaed seveal mes and he sample mean s ulzed o esmae he dsance o he beacon. All he Mllbos ansm he dsance measuemens o he eam leade who calculaes he new obo posons. A maxmum lkelhood algohm deemnes he mos lkel poson of he obo gven he measued dsances o he cuen beacons. Assumng ha he dead eckonng and dsance measuemens ae nomall dsbued andom vaables, he lkelhood of beng locaed a a poson (x,) s gven b (, d, d, b(), b( ),, b( )) m P x x m ( d, σx ) ( d, σ ) ( () b (), σb ) N x x N N whee N(p,σ ) s a nomal dsbuon wh zeo mean and vaance of σ evaluaed a p, (x d, d ) s he poson measued = = (6) hough dead eckonng, () s he dsance fom he beacon o he Mllbo, m s he numbe of beacons, and b () s he sample mean of he dsance measuemens fom beacon o he Mllbo. he poblem of faul olean localzaon fo he Mllbos s addessed n []. hs wok focuses on deecng and solang measuemen fauls ha commonl occu n hs localzaon ssem. Such falues nclude dead eckonng eos when he obos collde wh undeeced obsacles, and dsance measuemen eos due o desucve nefeence beween dec and mul-pah ulasound wavefons. REFERENCES [] A. Howad, M. J. Maac, and G. S. Sukhame, Moble senso newok deplomen usng poenal felds: A dsbued, scalable soluon o he aea coveage poblem, In Poceedngs of he 6h Inenaonal Smposum on Dsbued Auonomous Robocs Ssems (DARS) Fukuoka, Japan, June -7,. [] L. E. Pake and B. Emmons, Coopeave mul-obo obsevaon of mulple movng ages, In Poceedngs of he 997 IEEE Inenaonal Confeence on Robocs and Auomaon, pp. 8-89, Albuqueque, New Mexco, Apl 997. [] L.E. Navao-Semen, C.J.J. Paeds, and P.K. Khosla, "A beacon ssem fo he localzaon of dsbued oboc eams," Inenaonal Confeence on Feld and Sevce Robocs, Psbugh, PA pp. -7, 999. [] R. Gabowsk, L. E. Navao-Semen, C.J.J. Paeds, and P. K. Khosla, "Heeogeneous eams of modula obos fo mappng and exploaon," Auonomous Robos, vol. 8, pp. 9-8,. [] R. nos, L.E. Navao-Semen, and C.J.J. Paeds, Faul olean localzaon fo eams of dsbued obos, Poceedngs of he IEEE/RSJ Inenaonal Confeence on Inellgen Robos and Ssems. Page(s): 6-66 vol.,. [6] S.O. Lee, Y.J. Cho, M.Hwang-Bo, B.J. You, and S.R. Oh, A sable age-ackng conol fo unccle moble obos, Poceedngs of he IEEE/RSJ Inenaonal Confeence on Inellgen Robos and Ssems, pp. 8-87,. [7]. Masuama,. Wada, and S. oka, Acve mage capung and dnamc scene vsualzaon b coopeave dsbued vson, Poc. s Inenaonal Confeence on Advanced Mulmeda Conen Pocessng AMCP98, pp.6-9, 998. [8] R. Collns, A. Lpon, H. Fujosh, and. Kanade, Algohms fo coopeave mulsenso suvellance, Poceedngs of he IEEE, Vol. 89, No., Ocobe,, pp

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( ) 5-1. We apply Newon s second law (specfcally, Eq. 5-). (a) We fnd he componen of he foce s ( ) ( ) F = ma = ma cos 0.0 = 1.00kg.00m/s cos 0.0 = 1.88N. (b) The y componen of he foce s ( ) ( ) F = ma = ma

More information

Chapter 3: Vectors and Two-Dimensional Motion

Chapter 3: Vectors and Two-Dimensional Motion Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon

More information

Fast Calibration for Robot Welding System with Laser Vision

Fast Calibration for Robot Welding System with Laser Vision Fas Calbaon fo Robo Weldng Ssem h Lase Vson Lu Su Mechancal & Eleccal Engneeng Nanchang Unves Nanchang, Chna Wang Guoong Mechancal Engneeng Souh Chna Unves of echnolog Guanghou, Chna Absac Camea calbaon

More information

N 1. Time points are determined by the

N 1. Time points are determined by the upplemena Mehods Geneaon of scan sgnals In hs secon we descbe n deal how scan sgnals fo 3D scannng wee geneaed. can geneaon was done n hee seps: Fs, he dve sgnal fo he peo-focusng elemen was geneaed o

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION HAPER : LINEAR DISRIMINAION Dscmnan-based lassfcaon 3 In classfcaon h K classes ( k ) We defned dsmnan funcon g () = K hen gven an es eample e chose (pedced) s class label as f g () as he mamum among g

More information

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr. Moden Enegy Funconal fo Nucle and Nuclea Mae By: lbeo noosa Teas &M Unvesy REU Cycloon 008 Meno: D. Shalom Shlomo Oulne. Inoducon.. The many-body poblem and he aee-fock mehod. 3. Skyme neacon. 4. aee-fock

More information

1 Constant Real Rate C 1

1 Constant Real Rate C 1 Consan Real Rae. Real Rae of Inees Suppose you ae equally happy wh uns of he consumpon good oday o 5 uns of he consumpon good n peod s me. C 5 Tha means you ll be pepaed o gve up uns oday n eun fo 5 uns

More information

Handling Fuzzy Constraints in Flow Shop Problem

Handling Fuzzy Constraints in Flow Shop Problem Handlng Fuzzy Consans n Flow Shop Poblem Xueyan Song and Sanja Peovc School of Compue Scence & IT, Unvesy of Nongham, UK E-mal: {s sp}@cs.no.ac.uk Absac In hs pape, we pesen an appoach o deal wh fuzzy

More information

L4:4. motion from the accelerometer. to recover the simple flutter. Later, we will work out how. readings L4:3

L4:4. motion from the accelerometer. to recover the simple flutter. Later, we will work out how. readings L4:3 elave moon L4:1 To appl Newon's laws we need measuemens made fom a 'fed,' neal efeence fame (unacceleaed, non-oang) n man applcaons, measuemens ae made moe smpl fom movng efeence fames We hen need a wa

More information

Name of the Student:

Name of the Student: Engneeng Mahemacs 05 SUBJEC NAME : Pobably & Random Pocess SUBJEC CODE : MA645 MAERIAL NAME : Fomula Maeal MAERIAL CODE : JM08AM007 REGULAION : R03 UPDAED ON : Febuay 05 (Scan he above QR code fo he dec

More information

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1 ps 57 Machne Leann School of EES Washnon Sae Unves ps 57 - Machne Leann Assume nsances of classes ae lneal sepaable Esmae paamees of lnea dscmnan If ( - -) > hen + Else - ps 57 - Machne Leann lassfcaon

More information

Lecture 5. Plane Wave Reflection and Transmission

Lecture 5. Plane Wave Reflection and Transmission Lecue 5 Plane Wave Reflecon and Tansmsson Incden wave: 1z E ( z) xˆ E (0) e 1 H ( z) yˆ E (0) e 1 Nomal Incdence (Revew) z 1 (,, ) E H S y (,, ) 1 1 1 Refleced wave: 1z E ( z) xˆ E E (0) e S H 1 1z H (

More information

Field due to a collection of N discrete point charges: r is in the direction from

Field due to a collection of N discrete point charges: r is in the direction from Physcs 46 Fomula Shee Exam Coulomb s Law qq Felec = k ˆ (Fo example, f F s he elecc foce ha q exes on q, hen ˆ s a un veco n he decon fom q o q.) Elecc Feld elaed o he elecc foce by: Felec = qe (elecc

More information

s = rθ Chapter 10: Rotation 10.1: What is physics?

s = rθ Chapter 10: Rotation 10.1: What is physics? Chape : oaon Angula poson, velocy, acceleaon Consan angula acceleaon Angula and lnea quanes oaonal knec enegy oaonal nea Toque Newon s nd law o oaon Wok and oaonal knec enegy.: Wha s physcs? In pevous

More information

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic * Avalable onlne a wwwscencedeccom ScenceDec oceda Engneeng 69 4 85 86 4h DAAAM Inenaonal Smposum on Inellgen Manufacung and Auomaon Behavo of Inegal Cuves of he uaslnea Second Ode Dffeenal Equaons Alma

More information

Performance-Driven Resource Management In Layered Sensing

Performance-Driven Resource Management In Layered Sensing h Inenaonal Confeence on Infomaon Fuson Seale, WA, USA, July 6-9, 009 Pefomance-Dven Resouce Managemen In Layeed Sensng Chun Yang Sgem echnology, Inc. San Maeo, CA 9440 chunynag@sgem.com Ivan Kada Inelnk

More information

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED)

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED) FIRMS IN THE TWO-ERIO FRAMEWORK (CONTINUE) OCTOBER 26, 2 Model Sucue BASICS Tmelne of evens Sa of economc plannng hozon End of economc plannng hozon Noaon : capal used fo poducon n peod (decded upon n

More information

When to Treat Prostate Cancer Patients Based on their PSA Dynamics

When to Treat Prostate Cancer Patients Based on their PSA Dynamics When o Tea Posae Cance Paens Based on he PSA Dynamcs CLARA day on opeaons eseach n cance eamen & opeaons managemen Novembe 7 00 Mael S. Lave PhD Man L. Pueman PhD Sco Tyldesley M.D. Wllam J. Mos M.D CIHR

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

CHAPTER 3 DETECTION TECHNIQUES FOR MIMO SYSTEMS

CHAPTER 3 DETECTION TECHNIQUES FOR MIMO SYSTEMS 4 CAPTER 3 DETECTION TECNIQUES FOR MIMO SYSTEMS 3. INTRODUCTION The man challenge n he paccal ealzaon of MIMO weless sysems les n he effcen mplemenaon of he deeco whch needs o sepaae he spaally mulplexed

More information

Modeling Background from Compressed Video

Modeling Background from Compressed Video Modelng acgound fom Compessed Vdeo Weqang Wang Daong Chen Wen Gao Je Yang School of Compue Scence Canege Mellon Unvesy Psbugh 53 USA Insue of Compung echnology &Gaduae School Chnese Academy of Scences

More information

Real-coded Quantum Evolutionary Algorithm for Global Numerical Optimization with Continuous Variables

Real-coded Quantum Evolutionary Algorithm for Global Numerical Optimization with Continuous Variables Chnese Jounal of Eleconcs Vol.20, No.3, July 2011 Real-coded Quanum Evoluonay Algohm fo Global Numecal Opmzaon wh Connuous Vaables GAO Hu 1 and ZHANG Ru 2 (1.School of Taffc and Tanspoaon, Souhwes Jaoong

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

Solution of Non-homogeneous bulk arrival Two-node Tandem Queuing Model using Intervention Poisson distribution

Solution of Non-homogeneous bulk arrival Two-node Tandem Queuing Model using Intervention Poisson distribution Volume-03 Issue-09 Sepembe-08 ISSN: 455-3085 (Onlne) RESEARCH REVIEW Inenaonal Jounal of Muldscplnay www.jounals.com [UGC Lsed Jounal] Soluon of Non-homogeneous bulk aval Two-node Tandem Queung Model usng

More information

Simulation of Non-normal Autocorrelated Variables

Simulation of Non-normal Autocorrelated Variables Jounal of Moden Appled Sascal Mehods Volume 5 Issue Acle 5 --005 Smulaon of Non-nomal Auocoelaed Vaables HT Holgesson Jönöpng Inenaonal Busness School Sweden homasholgesson@bshse Follow hs and addonal

More information

Integer Programming Models for Decision Making of. Order Entry Stage in Make to Order Companies 1. INTRODUCTION

Integer Programming Models for Decision Making of. Order Entry Stage in Make to Order Companies 1. INTRODUCTION Inege Pogammng Models fo Decson Makng of Ode Eny Sage n Make o Ode Companes Mahendawah ER, Rully Soelaman and Rzal Safan Depamen of Infomaon Sysems Depamen of Infomacs Engneeng Insu eknolog Sepuluh Nopembe,

More information

Tecnologia e Inovação, Lisboa, Portugal. ABB Corporate Research Center, Wallstadter Str. 59, Ladenburg, Germany,

Tecnologia e Inovação, Lisboa, Portugal. ABB Corporate Research Center, Wallstadter Str. 59, Ladenburg, Germany, A New Connuous-Tme Schedulng Fomulaon fo Connuous Plans unde Vaable Eleccy Cos Pedo M. Caso * Io Hajunkosk and Ignaco E. Gossmann Depaameno de Modelação e Smulação de Pocessos Insuo Naconal de Engenhaa

More information

SCIENCE CHINA Technological Sciences

SCIENCE CHINA Technological Sciences SIENE HINA Technologcal Scences Acle Apl 4 Vol.57 No.4: 84 8 do:.7/s43-3-5448- The andom walkng mehod fo he seady lnea convecondffuson equaon wh axsymmec dsc bounday HEN Ka, SONG MengXuan & ZHANG Xng *

More information

Physics 201 Lecture 15

Physics 201 Lecture 15 Phscs 0 Lecue 5 l Goals Lecue 5 v Elo consevaon of oenu n D & D v Inouce oenu an Iulse Coens on oenu Consevaon l oe geneal han consevaon of echancal eneg l oenu Consevaon occus n sses wh no ne eenal foces

More information

A hybrid method to find cumulative distribution function of completion time of GERT networks

A hybrid method to find cumulative distribution function of completion time of GERT networks Jounal of Indusal Engneeng Inenaonal Sepembe 2005, Vol., No., - 9 Islamc Azad Uvesy, Tehan Souh Banch A hybd mehod o fnd cumulave dsbuon funcon of compleon me of GERT newos S. S. Hashemn * Depamen of Indusal

More information

High-level Hierarchical Semantic Processing Framework for Smart Sensor Networks

High-level Hierarchical Semantic Processing Framework for Smart Sensor Networks HSI 2008 Kakow, Poland, May 25-27, 2008 Hgh-level Heachcal Semanc Pocessng Famewok fo Sma Senso Newoks Dema Buckne, Membe, IEEE, Jamal Kasb Rosemae Velk, Membe, IEEE, and Wolfgang Hezne, Membe, IEEE Venna

More information

ESS 265 Spring Quarter 2005 Kinetic Simulations

ESS 265 Spring Quarter 2005 Kinetic Simulations SS 65 Spng Quae 5 Knec Sulaon Lecue une 9 5 An aple of an lecoagnec Pacle Code A an eaple of a knec ulaon we wll ue a one denonal elecoagnec ulaon code called KMPO deeloped b Yohhau Oua and Hoh Mauoo.

More information

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have:

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have: Poblem Se #3 Soluons Couse 4.454 Maco IV TA: Todd Gomley, gomley@m.edu sbued: Novembe 23, 2004 Ths poblem se does no need o be uned n Queson #: Sock Pces, vdends and Bubbles Assume you ae n an economy

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

Reflection and Refraction

Reflection and Refraction Chape 1 Reflecon and Refacon We ae now neesed n eplong wha happens when a plane wave avelng n one medum encounes an neface (bounday) wh anohe medum. Undesandng hs phenomenon allows us o undesand hngs lke:

More information

) from i = 0, instead of i = 1, we have =

) from i = 0, instead of i = 1, we have = Chape 3: Adjusmen Coss n he abou Make I Movaonal Quesons and Execses: Execse 3 (p 6): Illusae he devaon of equaon (35) of he exbook Soluon: The neempoal magnal poduc of labou s epesened by (3) = = E λ

More information

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions: esng he Random Walk Hypohess If changes n a sees P ae uncoelaed, hen he followng escons hold: va + va ( cov, 0 k 0 whee P P. k hese escons n un mply a coespondng se of sample momen condons: g µ + µ (,,

More information

A Methodology for Detecting the Change of Customer Behavior based on Association Rule Mining

A Methodology for Detecting the Change of Customer Behavior based on Association Rule Mining A Mehodology fo Deecng he Change of Cusome Behavo based on Assocaon Rule Mnng Hee Seo Song, Soung He Km KAIST Gaduae School of Managemen Jae Kyeong Km KyungHee Unvesy Absac Undesandng and adapng o changes

More information

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova The XIII Inenaonal Confeence Appled Sochasc Models and Daa Analyss (ASMDA-009) Jne 30-Jly 3, 009, Vlns, LITHUANIA ISBN 978-9955-8-463-5 L Sakalaskas, C Skadas and E K Zavadskas (Eds): ASMDA-009 Seleced

More information

Computer Propagation Analysis Tools

Computer Propagation Analysis Tools Compue Popagaion Analysis Tools. Compue Popagaion Analysis Tools Inoducion By now you ae pobably geing he idea ha pedicing eceived signal sengh is a eally impoan as in he design of a wieless communicaion

More information

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter Finite Difference Method for Ordinary Differential Equations Chape 8.7 Fne Dffeence Mehod fo Odnay Dffeenal Eqaons Afe eadng hs chape, yo shold be able o. Undesand wha he fne dffeence mehod s and how o se o solve poblems. Wha s he fne dffeence mehod? The fne dffeence

More information

Basic molecular dynamics

Basic molecular dynamics 1.1, 3.1, 1.333,. Inoducon o Modelng and Smulaon Spng 11 Pa I Connuum and pacle mehods Basc molecula dynamcs Lecue Makus J. Buehle Laboaoy fo Aomsc and Molecula Mechancs Depamen of Cvl and Envonmenal Engneeng

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY Unvesy of Calfona, Davs Dae: June xx, 009 Depamen of Economcs Tme: 5 hous Mcoeconomcs Readng Tme: 0 mnues PRELIMIARY EXAMIATIO FOR THE Ph.D. DEGREE Pa I ASWER KEY Ia) Thee ae goods. Good s lesue, measued

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment

Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment Unsupevsed Coss-Doman ansfe n Polcy Gaden Renfocemen Leanng va Manfold Algnmen Haham Bou Amma Unv. of Pennsylvana hahamb@seas.upenn.edu Ec Eaon Unv. of Pennsylvana eeaon@cs.upenn.edu Paul Ruvolo Oln College

More information

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs)

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs) USOD 005 uecal Sudy of Lage-aea An-Reonan Reflecng Opcal Wavegude (ARROW Vecal-Cavy Seconduco Opcal Aplfe (VCSOA anhu Chen Su Fung Yu School of Eleccal and Eleconc Engneeng Conen Inoducon Vecal Cavy Seconduco

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering Qualy Technology & Quanave Managemen Vol. 9, No., pp. 97-, 202 QTQM ICAQM 202 Effcen Bayesan Newok Leanng fo Sysem Opmzaon n Relably Engneeng A. Gube and I. Ben-Gal Depamen of Indusal Engneeng, Faculy

More information

ajanuary't I11 F or,'.

ajanuary't I11 F or,'. ',f,". ; q - c. ^. L.+T,..LJ.\ ; - ~,.,.,.,,,E k }."...,'s Y l.+ : '. " = /.. :4.,Y., _.,,. "-.. - '// ' 7< s k," ;< - " fn 07 265.-.-,... - ma/ \/ e 3 p~~f v-acecu ean d a e.eng nee ng sn ~yoo y namcs

More information

MIMO Capacity for UWB Channel in Rectangular Metal Cavity

MIMO Capacity for UWB Channel in Rectangular Metal Cavity MMO Capacy o UB Channel n Recangula Meal Cavy Zhen u, Dalwnde ngh Depamen o Eleccal and Compue Engneeng Cene o Manuacung Reseach Tennessee Tech Unvesy zhu@nech.edu, dsngh@nech.edu Robe Qu (Conac Auho)

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

Optimized Braking Force Distribution during a Braking-in- Turn Maneuver for Articulated Vehicles

Optimized Braking Force Distribution during a Braking-in- Turn Maneuver for Articulated Vehicles 56 Opmzed Bakng Foce Dsbuon dung a Bakng-n- Tun Maneuve o Aculaed Vehcles E. Esmalzadeh, A. Goodaz and M. Behmad 3 Downloaded om www.us.ac. a 3:04 IRST on Fday Novembe 3d 08,* Faculy o Engneeng and Appled

More information

Mechanics Physics 151

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

More information

calculating electromagnetic

calculating electromagnetic Theoeal mehods fo alulang eleomagne felds fom lghnng dshage ajeev Thoapplll oyal Insue of Tehnology KTH Sweden ajeev.thoapplll@ee.kh.se Oulne Despon of he poblem Thee dffeen mehods fo feld alulaons - Dpole

More information

Hierarchical Production Planning in Make to Order System Based on Work Load Control Method

Hierarchical Production Planning in Make to Order System Based on Work Load Control Method Unvesal Jounal of Indusal and Busness Managemen 3(): -20, 205 DOI: 0.389/ujbm.205.0300 hp://www.hpub.og Heachcal Poducon Plannng n Make o Ode Sysem Based on Wok Load Conol Mehod Ehsan Faah,*, Maha Khodadad

More information

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS In. J. Nume. Meh. Fluds 005; 49:99 116 Publshed onlne 3 June 005 n Wley IneScence www.nescence.wley.com). DOI: 10.100/d.997 An axsymmec ncompessble

More information

Reinforcement learning

Reinforcement learning Lecue 3 Reinfocemen leaning Milos Hauskech milos@cs.pi.edu 539 Senno Squae Reinfocemen leaning We wan o lean he conol policy: : X A We see examples of x (bu oupus a ae no given) Insead of a we ge a feedback

More information

A Novel Fast Otsu Digital Image Segmentation Method

A Novel Fast Otsu Digital Image Segmentation Method The Inenaonal Aab Jounal of Infomaon Technology, Vol. 3, No. 4, July 06 47 A Novel Fas Osu Dgal Image Segmenaon Mehod Duaa AlSaeed,, Ahmed oudane,, and Al El-Zaa 3 Depamen of Compue Scence and Dgal Technologes,

More information

Mechanics Physics 151

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

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

A VISCOPLASTIC MODEL OF ASYMMETRICAL COLD ROLLING

A VISCOPLASTIC MODEL OF ASYMMETRICAL COLD ROLLING SISOM 4, BUCHAEST, - May A VISCOPLASTIC MODEL OF ASYMMETICAL COLD OLLING odca IOAN Spu Hae Unvesy Buchaes, odcaoan7@homal.com Absac: In hs pape s gven a soluon of asymmecal sp ollng poblem usng a Bngham

More information

Introduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms

Introduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms Course organzaon Inroducon Wee -2) Course nroducon A bref nroducon o molecular bology A bref nroducon o sequence comparson Par I: Algorhms for Sequence Analyss Wee 3-8) Chaper -3, Models and heores» Probably

More information

Clustering (Bishop ch 9)

Clustering (Bishop ch 9) Cluserng (Bshop ch 9) Reference: Daa Mnng by Margare Dunham (a slde source) 1 Cluserng Cluserng s unsupervsed learnng, here are no class labels Wan o fnd groups of smlar nsances Ofen use a dsance measure

More information

Go over vector and vector algebra Displacement and position in 2-D Average and instantaneous velocity in 2-D Average and instantaneous acceleration

Go over vector and vector algebra Displacement and position in 2-D Average and instantaneous velocity in 2-D Average and instantaneous acceleration Mh Csquee Go oe eco nd eco lgeb Dsplcemen nd poson n -D Aege nd nsnneous eloc n -D Aege nd nsnneous cceleon n -D Poecle moon Unfom ccle moon Rele eloc* The componens e he legs of he gh ngle whose hpoenuse

More information

MCTDH Approach to Strong Field Dynamics

MCTDH Approach to Strong Field Dynamics MCTDH ppoach o Song Feld Dynamcs Suen Sukasyan Thomas Babec and Msha Ivanov Unvesy o Oawa Canada Impeal College ondon UK KITP Sana Babaa. May 8 009 Movaon Song eld dynamcs Role o elecon coelaon Tunnel

More information

Journal of Engineering Science and Technology Review 7 (1) (2014) Research Article

Journal of Engineering Science and Technology Review 7 (1) (2014) Research Article Jes Jounal o Engneeng Scence and echnology Revew 7 5 5 Reseach Acle JOURNAL OF Engneeng Scence and echnology Revew www.jes.og Sudy on Pedcve Conol o ajecoy ackng o Roboc Manpulao Yang Zhao Dep. o Eleconc

More information

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing Peenaon fo Theoecal Condened Mae Phyc n TU Beln Geen-Funcon and GW appoxmaon Xnzheng L Theoy Depamen FHI May.8h 2005 Elecon n old Oulne Toal enegy---well olved Sngle pacle excaon---unde developng The Geen

More information

Time-interval analysis of β decay. V. Horvat and J. C. Hardy

Time-interval analysis of β decay. V. Horvat and J. C. Hardy Tme-nerval analyss of β decay V. Horva and J. C. Hardy Work on he even analyss of β decay [1] connued and resuled n he developmen of a novel mehod of bea-decay me-nerval analyss ha produces hghly accurae

More information

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations Today - Lecue 13 Today s lecue coninue wih oaions, oque, Noe ha chapes 11, 1, 13 all inole oaions slide 1 eiew Roaions Chapes 11 & 1 Viewed fom aboe (+z) Roaional, o angula elociy, gies angenial elociy

More information

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t

, on the power of the transmitter P t fed to it, and on the distance R between the antenna and the observation point as. r r t Lecue 6: Fiis Tansmission Equaion and Rada Range Equaion (Fiis equaion. Maximum ange of a wieless link. Rada coss secion. Rada equaion. Maximum ange of a ada. 1. Fiis ansmission equaion Fiis ansmission

More information

2 shear strain / L for small angle

2 shear strain / L for small angle Sac quaons F F M al Sess omal sess foce coss-seconal aea eage Shea Sess shea sess shea foce coss-seconal aea llowable Sess Faco of Safe F. S San falue Shea San falue san change n lengh ognal lengh Hooke

More information

The Application of Fuzzy Comprehensive Evaluations in The College Education Informationization Level

The Application of Fuzzy Comprehensive Evaluations in The College Education Informationization Level IOSR Jounal of Reseach & Mehod n Educaon IOSR-JRME) e- ISSN: 3 7388,p-ISSN: 3 737X Volume 8, Issue 3 Ve IV May June 8), PP -7 wwwosjounalsog The Applcaon of Fuzzy Compehensve Evaluaons n The College Educaon

More information

Molecular dynamics modeling of thermal and mechanical properties

Molecular dynamics modeling of thermal and mechanical properties Molecula dynamcs modelng of hemal and mechancal popees Alejando Sachan School of Maeals Engneeng Pudue Unvesy sachan@pudue.edu Maeals a molecula scales Molecula maeals Ceamcs Meals Maeals popees chas Maeals

More information

P 365. r r r )...(1 365

P 365. r r r )...(1 365 SCIENCE WORLD JOURNAL VOL (NO4) 008 www.scecncewoldounal.og ISSN 597-64 SHORT COMMUNICATION ANALYSING THE APPROXIMATION MODEL TO BIRTHDAY PROBLEM *CHOJI, D.N. & DEME, A.C. Depatment of Mathematcs Unvesty

More information

to Assess Climate Change Mitigation International Energy Workshop, Paris, June 2013

to Assess Climate Change Mitigation International Energy Workshop, Paris, June 2013 Decomposng he Global TIAM-Maco Maco Model o Assess Clmae Change Mgaon Inenaonal Enegy Wokshop Pas June 2013 Socaes Kypeos (PSI) & An Lehla (VTT) 2 Pesenaon Oulne The global ETSAP-TIAM PE model and he Maco

More information

Bayes rule for a classification problem INF Discriminant functions for the normal density. Euclidean distance. Mahalanobis distance

Bayes rule for a classification problem INF Discriminant functions for the normal density. Euclidean distance. Mahalanobis distance INF 43 3.. Repeon Anne Solberg (anne@f.uo.no Bayes rule for a classfcaon problem Suppose we have J, =,...J classes. s he class label for a pxel, and x s he observed feaure vecor. We can use Bayes rule

More information

Accelerated Sequen.al Probability Ra.o Test (SPRT) for Ongoing Reliability Tes.ng (ORT)

Accelerated Sequen.al Probability Ra.o Test (SPRT) for Ongoing Reliability Tes.ng (ORT) cceleaed Sequen.al Pobably Ra.o Tes (SPRT) fo Ongong Relably Tes.ng (ORT) Mlena Kasch Rayheon, IDS Copygh 25 Rayheon Company. ll ghs eseved. Cusome Success Is Ou Msson s a egseed adema of Rayheon Company

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

WORK POWER AND ENERGY Consevaive foce a) A foce is said o be consevaive if he wok done by i is independen of pah followed by he body b) Wok done by a consevaive foce fo a closed pah is zeo c) Wok done

More information

PHYS 1443 Section 001 Lecture #4

PHYS 1443 Section 001 Lecture #4 PHYS 1443 Secon 001 Lecure #4 Monda, June 5, 006 Moon n Two Dmensons Moon under consan acceleraon Projecle Moon Mamum ranges and heghs Reerence Frames and relae moon Newon s Laws o Moon Force Newon s Law

More information

A Compact Representation of Spatial Correlation in MIMO Radio Channels

A Compact Representation of Spatial Correlation in MIMO Radio Channels A Compac epesenaon of Spaal Coelaon n MIMO ado Channels A. van Zels Endhoven Unves of echnolog P.O. Box 53 5600 MB Endhoven he Nehelands e-mal: A.v.Zels@ue.nl and Agee Ssems P.O. Box 755 3430 A Neuwegen

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Mau Lkelhood aon Beln Chen Depaen of Copue Scence & Infoaon ngneeng aonal Tawan oal Unvey Refeence:. he Alpaydn, Inoducon o Machne Leanng, Chape 4, MIT Pe, 4 Saple Sac and Populaon Paaee A Scheac Depcon

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

A multiple-relaxation-time lattice Boltzmann model for simulating. incompressible axisymmetric thermal flows in porous media

A multiple-relaxation-time lattice Boltzmann model for simulating. incompressible axisymmetric thermal flows in porous media A mulple-elaxaon-me lace Bolmann model fo smulang ncompessble axsymmec hemal flows n poous meda Qng Lu a, Ya-Lng He a, Qng L b a Key Laboaoy of Themo-Flud Scence and Engneeng of Mnsy of Educaon, School

More information

Department of Economics University of Toronto

Department of Economics University of Toronto Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of

More information

Variability Aware Network Utility Maximization

Variability Aware Network Utility Maximization aably Awae Newok ly Maxmzaon nay Joseph and Gusavo de ecana Depamen of Eleccal and Compue Engneeng, he nvesy of exas a Ausn axv:378v3 [cssy] 3 Ap 0 Absac Newok ly Maxmzaon NM povdes he key concepual famewok

More information

APPROXIMATIONS FOR AND CONVEXITY OF PROBABILISTICALLY CONSTRAINED PROBLEMS WITH RANDOM RIGHT-HAND SIDES

APPROXIMATIONS FOR AND CONVEXITY OF PROBABILISTICALLY CONSTRAINED PROBLEMS WITH RANDOM RIGHT-HAND SIDES R U C O R R E S E A R C H R E P O R APPROXIMAIONS FOR AND CONVEXIY OF PROBABILISICALLY CONSRAINED PROBLEMS WIH RANDOM RIGH-HAND SIDES M.A. Lejeune a A. PREKOPA b RRR 7-005, JUNE 005 RUCOR Ruges Cene fo

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

IDENTIFICATION AND ANALYSIS OF PROXIMATE EVENTS IN HIGH DENSITY ENROUTE AIRSPACES

IDENTIFICATION AND ANALYSIS OF PROXIMATE EVENTS IN HIGH DENSITY ENROUTE AIRSPACES IDENTIFICATION AND ANALYSIS OF PROXIMATE EVENTS IN IG DENSITY ENROUTE AIRSPACES Eduado José Gacía González INECO Madd Span Fancsco Jae Sáez Neo Polyechnc Unesy of Madd Madd Span Maa Isabel Izquedo EUROCONTROL

More information

An Automatic Door Sensor Using Image Processing

An Automatic Door Sensor Using Image Processing An Auomaic Doo Senso Using Image Pocessing Depamen o Elecical and Eleconic Engineeing Faculy o Engineeing Tooi Univesiy MENDEL 2004 -Insiue o Auomaion and Compue Science- in BRNO CZECH REPUBLIC 1. Inoducion

More information

Course Outline. 1. MATLAB tutorial 2. Motion of systems that can be idealized as particles

Course Outline. 1. MATLAB tutorial 2. Motion of systems that can be idealized as particles Couse Oulne. MATLAB uoal. Moon of syses ha can be dealzed as pacles Descpon of oon, coodnae syses; Newon s laws; Calculang foces equed o nduce pescbed oon; Deng and solng equaons of oon 3. Conseaon laws

More information

Chapter Fifiteen. Surfaces Revisited

Chapter Fifiteen. Surfaces Revisited Chapte Ffteen ufaces Revsted 15.1 Vecto Descpton of ufaces We look now at the vey specal case of functons : D R 3, whee D R s a nce subset of the plane. We suppose s a nce functon. As the pont ( s, t)

More information

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes]

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes] ENGI 44 Avance alculus fo Engineeing Faculy of Engineeing an Applie cience Poblem e 9 oluions [Theoems of Gauss an okes]. A fla aea A is boune by he iangle whose veices ae he poins P(,, ), Q(,, ) an R(,,

More information

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example C 188: Aificial Inelligence Fall 2007 epesening Knowledge ecue 17: ayes Nes III 10/25/2007 an Klein UC ekeley Popeies of Ns Independence? ayes nes: pecify complex join disibuions using simple local condiional

More information

Natural Lighting System Combined With a Prismatic Canopy

Natural Lighting System Combined With a Prismatic Canopy Naual Lghng Ssem Combned Wh a Psmac Canop Shh-Chuan Yeh 1, Ju-Ln Lu 2 Absac Psmac elemens ae fequenl usng o edec dalgh n buldngs fo comfo and eneg savng. Ths pape analzed he llumnaon effcenc of an open

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information