Comparison between LETKF and EnVAR with observation localization
|
|
- Kevin Dorsey
- 5 years ago
- Views:
Transcription
1 Comprson beeen LETKF nd EnVAR h observon loclzon * Sho Yoo Msru Kun Kzums Aonsh Se Orguch Le Duc Tuy Kb 3 Tdsh Tsuyu Meeorologcl Reserch Insue JAMSTEC 3 Meeorologcl College 6..7 D Assmlon Semnr n RIKEN/AICS
2 Bcground covrnce o o solve o o clcule 3DVAR4DVAR Ssc Implcly Wh don o M nd EnKF Ensemble-bsed Eplcly Ensemble ppromon ybrd-4dvar Ensemble-bsed Implcly Wh don o M nd EnVAR Ensemble-bsed Implcly Ensemble ppromon Ensemble-bsed vronl d ssmlon Inroducon T T M M J y R y B T M M J J y R B Cos Funcon Byesn ssmlon provdes Anlyss rom Frs guess nd Observon y. es mmum lelhood vlue hen cos uncon J s mnmum ( J=). Severl mehods re clssed usng ho o solve J=. Bcground erm Observon erm Grden EnVAR provdes nlyss mplcly hou don models /33
3 Inroducon Why s J= solved mplcly? 3/33 Cos Funcon J Bcground erm T B M Observon erm T y R M y J b J o Qudrc uncon o - Qudrc uncon o (M ( )) (M ( ))-y Gussn ppromon Observon (M ( )) erm s srcly s ppromed reed o qudrc uncon o by ssumng lner (M ( )) J E Im Implc mehod s beer becuse observon operor re srcly reed J= s chnged cn be solved by solvng eplcly J= e.g. mplcly EnKF
4 Inroducon Prevous sudes bou EnVAR 4/33 Zupns (5) Zupns e l. (8) EnVAR mehod s suggesed Lu e l. (8 9) Buehner (3) 4D-EnVAR s compred o oher mehods Gussn ppromon Splly loclzed bcground covrnce un e l. (4) 4D-EnKF s suggesed Any me nlyss n ssmlon ndo s provded Assmlon ndo Lu e l. (8) 3D-EnKFEnVAR 4D-EnKFEnVAR n- me n n- me In hs sudy 4D-EnVAR s compred o 4D-EnKF (LETKF) n
5 Conens 5/33. Inroducon. Formulon o EnVAR h observon loclzon 3. Comprson beeen LETKF nd EnVAR. Sngle-observon ssmlon. OSSEs h SPEEDY model 3. Rel observon d ssmlon h JMANM
6 OSSE Observon sysem smulon epermens h SPEEDY model Number o members: Assmlon ndo: 6 hours Loclzon rdus: σ =(m) σ V =.(sgm) Observons: U V T R Ps Inlon: Mulplcve (=.) Anlyss me: Cener o he ndo (=3h) Observon me: =h 3h 5h Posons o observons 6/33 Bs o specc humdy 6-hour orecs (g/g) o nure run RMSE o specc humdy 6-hour orecs (g/g) o nure run Number o orecs-nlyss cycles (every 6 hour) Bs nd RMSE o EnVAR re smller hn hose o LETKF
7 Formulon 7/33 EnVAR ormulon Mulplcve nlon Bcground Anlyss error covrnce B T L M : nlyss pons : observon pons : me slos : ensemble members Anlyss vluble s rnsormed rom o J Grden o J Then M Loclzon Perurbon o L R y Observon error vrnce - Observon loclzon - Derved rom Bcground loclzon - Globlly dened J Observon operor (non-lner) Smlr o LETKF Deren rom LETKF
8 EnVAR hou loclzon Formulon Weghed summon o ensemble perurbons s dded o rs guess ) ( y R M J C: Zupns e l. (8) T T J y R y B y R J B y R M J M B 8/33 Cos uncon: Componens ormulon: Appromon o B usng ensemble ere Solve or nd gn : grd pons(-n) : obs. pons(-k) : me slos(-t) : members(-m)
9 EnVAR h observon loclzon Formulon M B l l l L / ~ L M B l l l L L L / / l l y R M J ~ ~ ~ ~ ~ l l l y R M J l: grd pons(-n) : obs. pons(-k) : me slos(-t) : members(-m) The number o ensemble members s usully oo smll o me nlyss h lrge degree o reedom. Loclzon requred or ncresng degree o reedom o Then cos uncon: Grden o cos uncon: Loclzon cor (I grd s r rom grd s smll or.) o s hs clculed? 9/33 Mulplcve nlon prmeer
10 EnVAR h observon loclzon Formulon ~ ~ ~ l l l y R M J N N N l N N l l N N l l l L L L / / / ~ ~ /33 l: grd pons(-n) : obs. pons(-k) : me slos(-t) : members(-m) ere Usng ollong equon l l l L / ~ I nd re compleely on he sme pon s sme s. (Then depends only on he vluble on he grd.) / L l
11 Formulon o o solve J= /33 ~ J ~ l J M ~ l R y / l Ll L l / l L / l ~ J ~ J M l ~ No ndependen or nlyss pons l M L R Independenly clculed or every nlyss pons R L y y Usng ollong equon / / L L l L l l L / l ~ l l Observon loclzon Appromed ne cos uncon s dened l: grd pons(-n) : obs. pons(-k) : me slos(-t) : members(-m)
12 Formulon Summry o EnVAR ormulon /33 Anlyss J J T M M L y R Observon error vrnce L y R Anlyss perurbon J M T M δ / U Loclzon cer U L R Observon operor U Egenvlue decomposon o essn mr o J Mnmzon o globlly dened J h observon loclzon U : nlyss pons : observon pons : me slos : ensemble members
13 Formulon [] (Locl or Globl) LETKF Derence beeen LETKF nd EnVAR EnVAR EnVAR clcules drecly I s lner nd nlyss pon s sme s observon pon EnVAR=LETKF LETKF EnVAR Clculed h nlyss pon Independenly or ech nlyss [] Grden o (round Frs guess or Anlyss) δ n EnVAR s round nlyss I s lner EnVAR=LETKF 3/33 Clculed h observon pon or ll nlyss Observon pon Anlyss pon : nlyss pons : observon pons : me slos : ensemble members
14 4/33 Sngle-observon ssmlon Comprson o LETKF (Lner cse) Number o members: Loclzon rdus: σ=(m) σv=.(sgm) Observons: σ=.835 Frs guess o relve humdy nd horzonl nd U=-9.8 m s- Anlyss-Frs guess (LETKF) U=-.9 m s- Anlyss-Frs guess (EnVAR) U=-.9 m s- Incremen o EnVAR s sme s LETKF observon pon bu smller r rom observon pon ( severer loclzon)
15 Sngle-observon ssmlon Why s EnVAR loclzon severer hn LETKF? 5/33 Observon loclzon o hs EnVAR s derved rom bcground loclzon bu h o LETKF s no. e.g. In o nlyss pons nd one observon K y nd Bcground loclzon K K Then B B R LB B R K (Greybush e l. ) K K Observon loclzon o LETKF K K B B B R B R B LB B R / L LB R B B B R B R Bloclzon Rloclzon K Thereore bcground L loclzon s severer
16 6/33 Sngle-observon ssmlon Comprson o LETKF (Non-lner cse) Number o members: Loclzon rdus: σ=(m) σv=.(sgm) Observons: σ=.835 Frs guess o relve humdy nd horzonl nd R=53. % Anlyss-Frs guess (LETKF) R=44. % Anlyss-Frs guess (EnVAR) R=39.6 % EnVAR nlyss s closer o observon hn LETKF
17 OSSE Observon sysem smulon epermens h SPEEDY model Number o members: Assmlon ndo: 6 hours Loclzon rdus: σ =(m) σ V =.(sgm) Observons: U V T R Ps Inlon: Mulplcve (=.) Anlyss me: cener o he ndo Observon me: =h 3h 5h Posons o observons 7/33 Bs o specc humdy 6-hour orecs (g/g) o nure run RMSE o specc humdy 6-hour orecs (g/g) o nure run Number o orecs-nlyss cycles (every 6 hour) EnVAR s beer cused by derence o ho o clcule nd δ
18 OSSE O-F sgrm Observon-Forecs (O-F) hsogrm n ll EnVAR nlyss 8/33 Specc humdy ssmlon: Lner bu non-gussn probbly dsrbuon
19 OSSE Specc humdy ssmlon 9/33 LETKF EnVAR Bs o specc humdy 6-hour orecs (g/g) o nure run Thc: relve humdy ssmlon (CTL) Thn: specc humdy ssmlon RMSE o specc humdy 6-hour orecs (g/g) o nure run EnVAR s beer hn LETKF nd relve humdy ssmlon s beer hn specc humdy ssmlon
20 OSSE EnVAR h loclly dened cos uncon Locl J s mnmzed mplcly Bs o specc humdy 6-hour orecs (g/g) o nure run Clclon me (sec) /33 > 6 RMSE o specc humdy 6-hour orecs (g/g) o nure run Blc: LETKF Red: EnVAR h Globl J Blue: EnVAR h Locl J Globlly dened clculed h observon pon Loclly dened clculed h nlyss pon Smlr o LETKF Globl J hs good mpc
21 OSSE EnVAR h he specc number o eron /33 Bs o specc humdy 6-hour orecs (g/g) o nure run -3 RMSE o specc humdy 6-hour orecs (g/g) o nure run Blc: LETKF Red: EnVAR (CTL) Green: EnVAR (sop er 5 erons) Blue: EnVAR (sop er erons) EnVAR h 5 erons s beer hn LETKF (clculon me s -3 mes s long s LETKF)
22 Summry o OSSEs /33 We developed EnVAR h observon loclzon nd compred o LETKF EnVAR nlyss s closer o rue vlue hn LETKF becuse globlly dened cos uncon s mnmzed Severl mes longer clculon me hn LETKF Non-lner observon operor s srcly reed n EnVAR (Gussn ppromon should no be used) Observon loclzon o hs EnVAR s sme s bcground loclzon ( severer hn loclzon o LETKF) Is EnVAR lso beer hn LETKF n rel obs. d ssmlon?
23 Rel d ssmlon 3/33 Locl Rnll on 8 July 3 5 6JST 6 7JST 7 8JST 8 9JST 9 JST JST Anlyzed precpon MSM (nl: 5JST) - To precpon sysems ere genered. - Accure orecs s dcul. (even hough he nl condon ncluded rnll) MSM (nl: 8JST) Dense observons re epeced o mprove orecss
24 Rel d ssmlon Assmled Dense Observons 4/33 Observon Elemens Frequency Surce (JMA Surce observon nd AMeDAS) U V T every mnues GNSS PWV every mnues Rdr Rdl nd every mnues Ksh ned Nr Rdosonde U V T R every 3 hours Tsuub Ur Yoosu Ryou Mru Seng orzonl loclzon: m Vercl loclzon:. lnp (PWV s no loclzed verclly) Mulplcve nlon prmeer:. Observon error: U V: m/s T: K R: % PWV: 5 g/m Rdl nd: 3 m/s Surce nd (m/s) 7/8 8JST GNSS PWV (g/m ) 7/8 8JST :Rdr :Sonde
25 Rel d ssmlon Flo o Assmlon Epermens 5/33 Boundry condon: JMA GSM Forecs + Weely Ensemble Perurbon 376 9JST JST 378 6JST 378 9JST 378 JST 378 5JST 378 8JST Ouer Grd nervl: m Grd number: Ensemble sze: 5 Anlyss ndo: 3 hour (Operonl Observons used n JMA Meso-DA every 3 mnues) Donsclng 9JST- 5 Members Boundry Condon every 3 mnues Inner Grd nervl: m Grd number: 6 Ensemble sze: 5 Anlyss ndo: hour 5JST 6JST 7JST 8JST Eended Forecs : Ensemble Forecss : Anlyss (LETKF or EnVAR) Domn Trge: Locl rn ner Toyo n JST
26 Rel d ssmlon 9JST Comprson o -h Rnll n 8-9 JST 8JST 9JST 6/33 EnVAR EnVAR-NPWV (/o PWV d) EnVAR-NSONDE (/o Sonde d) LETKF NDA (/o ny d) Anlyzed precpon Good mpcs o PWV nd Sonde d ssmlon Domn o clcule he score Trge o sensvy
27 Rel d ssmlon Are Frcons Sll Scores mproved? 7/33 FSS O F O F 9JST- 5JST 6JST 7JST 8JST O F : number densy o observed rnll n -h rcon : number densy o orecs rnll n -h rcon All our orecss rom EnVAR nlyses re beer hn NDA srong rn ghresoluon rn poson
28 Rel d ssmlon Impc o Dense Observons 8/33 Rnll n 8-9 JST EnVAR EnVAR-NPWV (/o PWV d) EnVAR-NSONDE (/o Sonde d) [EnVAR] [NDA] [EnVAR] [EnVAR-NPWV] - PWV d grely mproved rnll orecss. - Rdosonde d lso mproved e rn orecss. [EnVAR] [EnVAR-NSONDE] Boh PWV nd rdosonde d could mprove rnll orecss
29 Rel d ssmlon EnVAR v.s. LETKF Rnll n 8-9 JST EnVAR LETKF [EnVAR] [NDA] 9/33 - Derence beeen EnVAR nd LETKF s smll Tme seres o RMS o (O A) nd (O F) o PWV n he orecs-nlyss cycles [EnVAR] [LETKF] In EnVAR srong rnll (> 5 mm/hr) orecss re slghly beer hn h o LETKF
30 Rel d ssmlon Correlon beeen Rnll nd Inl Ses Correlon beeen J nd n CORR( ) m J m m J m( ) m( ) J J ( ) ( ) m m m m 3/33 m er vpor nd nds o EnVAR nlyss : grd number m: ensemble member Lrge grden J m m : -h rnll (8 9JST) verged n hs re ( ) : vrbles n m hegh 8JST I nds pon o he drecon o vecors n hs gure rnll becomes sronger Lo-level convergence s correled o rnll nensy Convergence Posve correlon o er vpor Correlon beeen rnll nd m er vpor nd nds clculed by 5-member EnVAR
31 Rel d ssmlon 3/33 Derence o Lo-level vrbles [EnVAR] [EnVAR-NSONDE] [EnVAR] [EnVAR-NPWV] m er vpor nd nds o EnVAR nlyss Convergence Posve ncremen o er vpor [EnVAR] [LETKF] Locl ron? Derence o m er vpor nd nds Convergence Convergence Incremen o lo-level er vpor nd convergence mes rnll sronger Posve correlon o er vpor Correlon beeen rnll nd m er vpor nd nds clculed by 5-member EnVAR
32 Summry o Rel D Assmlon 3/33 We ssmled dense obs. or he locl rnll ner Toyo Impc o dense PWV nd Rdosonde obs. PWV mproved rnll orecs hrough correcng lo-level er vpor Sonde obs. mproved rnll orecs hrough correcng lo-level nds Comprson beeen LETKF nd EnVAR EnVAR cn me he nlyss hch s closer o obs. hn LETKF Improvemen o rnll orecs by usng EnVAR s smll Correlon o rnll bsed on ensemble orecss Lo-level er vpor nd convergence mde locl rnll sronger Are hese mpcs generl? Vercon n longer perod requres
33 Summry (EnVAR v.s. LETKF) Formulon Non-lner observon operor s more srcly reed n EnVAR Observon loclzon o EnVAR s sme s bcground loclzon ( severer hn loclzon o LETKF) OSSEs Anlyss o hs EnVAR lzon re more ccure hn LETKF becuse globlly dened cos uncon s mnmzed Rel observon d ssmlon PWV ssmlon n boh EnVAR nd LETKF mproved rnll orecs EnVAR nlyss s closer o obs. hn LETKF Improvemen o rnll orecs by usng EnVAR s smll n hs cse 33/33 Our reserch s suppored n pr by Sregc Progrm or Innovve Reserch (SPIRE) Feld 3 (proposl number: hp4 nd hp54) nd Toyo Meropoln Are Convecon Sudy or Ereme Weher Reslen Ces (TOMACS). SPEEDY-LETKF (hps://code.google.com/p/myosh/) nd he source code developed by Numercl Predcon Dvson n JMA re used n hs sudy. GNSS d ere provded rom he nd Lborory Meeorologcl Selle nd Observon Sysem Reserch Deprmen n MRI. Rdosonde observons ere conduced s pr o TOMACS progrm. The oher observon d ere rom JMA.
Data assimilation for local rainfall near Tokyo on 18 July 2013 using EnVAR with observation space localization
Daa assimilaion for local rainfall near Tokyo on 18 July 2013 using EnVAR wih observaion space localizaion *1 Sho Yokoa, 1 Masaru Kunii, 1 Kazumasa Aonashi, 1 Seiji Origuchi, 2,1 Le Duc, 1 Takuya Kawabaa,
More informationPhysics 201 Lecture 2
Physcs 1 Lecure Lecure Chper.1-. Dene Poson, Dsplcemen & Dsnce Dsngush Tme nd Tme Inerl Dene Velocy (Aerge nd Insnneous), Speed Dene Acceleron Undersnd lgebrclly, hrough ecors, nd grphclly he relonshps
More informationParameter estimation method using an extended Kalman Filter
Unvers o Wollongong Reserch Onlne cul o Engneerng nd Inormon cences - Ppers: Pr A cul o Engneerng nd Inormon cences 007 Prmeer esmon mehod usng n eended lmn ler Emmnuel D. Blnchrd Unvers o Wollongong eblnch@uow.edu.u
More informationDecompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)
. Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.
More information4.8 Improper Integrals
4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls
More informationChapter 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 informationMultivariate Time Series Analysis
Mulvre me Sere Anl Le { : } be Mulvre me ere. Denon: () = men vlue uncon o { : } = E[ ] or. (,) = Lgged covrnce mr o { : } = E{[ - ()][ - ()]'} or, Denon: e me ere { : } onr e jon drbuon o,,, e me e jon
More informationFINANCIAL ECONOMETRICS
FINANCIAL ECONOMETRICS SPRING 07 WEEK IV NONLINEAR MODELS Prof. Dr. Burç ÜLENGİN Nonlner NONLINEARITY EXISTS IN FINANCIAL TIME SERIES ESPECIALLY IN VOLATILITY AND HIGH FREQUENCY DATA LINEAR MODEL IS DEFINED
More informationA Kalman filtering simulation
A Klmn filering simulion The performnce of Klmn filering hs been esed on he bsis of wo differen dynmicl models, ssuming eiher moion wih consn elociy or wih consn ccelerion. The former is epeced o beer
More informationChapter 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 informationSeptember 20 Homework Solutions
College of Engineering nd Compuer Science Mechnicl Engineering Deprmen Mechnicl Engineering A Seminr in Engineering Anlysis Fll 7 Number 66 Insrucor: Lrry Creo Sepember Homework Soluions Find he specrum
More informationPHYS 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 informationLump Solutions to a Jimbo-Miwa Like Equations
Lump Soluons o Jmbo-Mw Lke Equons Hrun-Or-Roshd * M. Zulkr Al b Deprmen o Mhemcs Pbn Unvers o Scence nd Technolog Bngldesh b Deprmen o Mhemcs Rjshh Unvers Bngldesh * Eml: hrunorroshdmd@gml.com Absrc A
More informationPrinciple 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 informationLecture VI Regression
Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M
More informatione t dt e t dt = lim e t dt T (1 e T ) = 1
Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie
More informationLecture 6: Learning for Control (Generalised Linear Regression)
Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson
More informationBackground and Motivation: Importance of Pressure Measurements
Imornce of Pressre Mesremens: Pressre s rmry concern for mny engneerng lcons e.g. lf nd form drg. Cvon : Pressre s of fndmenl mornce n ndersndng nd modelng cvon. Trblence: Velocy-Pressre-Grden ensor whch
More informationUnscented Transformation Unscented Kalman Filter
Usceed rsformo Usceed Klm Fler Usceed rcle Fler Flerg roblem Geerl roblem Seme where s he se d s he observo Flerg s he problem of sequell esmg he ses (prmeers or hdde vrbles) of ssem s se of observos become
More information1.B Appendix to Chapter 1
Secon.B.B Append o Chper.B. The Ordnr Clcl Here re led ome mporn concep rom he ordnr clcl. The Dervve Conder ncon o one ndependen vrble. The dervve o dened b d d lm lm.b. where he ncremen n de o n ncremen
More informationChapter 2 Linear Mo on
Chper Lner M n .1 Aerge Velcy The erge elcy prcle s dened s The erge elcy depends nly n he nl nd he nl psns he prcle. Ths mens h prcle srs rm pn nd reurn bck he sme pn, s dsplcemen, nd s s erge elcy s
More informationORDINARY DIFFERENTIAL EQUATIONS
6 ORDINARY DIFFERENTIAL EQUATIONS Introducton Runge-Kutt Metods Mult-step Metods Sstem o Equtons Boundr Vlue Problems Crcterstc Vlue Problems Cpter 6 Ordnr Derentl Equtons / 6. Introducton In mn engneerng
More information22.615, MHD Theory of Fusion Systems Prof. Freidberg Lecture 9: The High Beta Tokamak
.65, MHD Theory of Fusion Sysems Prof. Freidberg Lecure 9: The High e Tokmk Summry of he Properies of n Ohmic Tokmk. Advnges:. good euilibrium (smll shif) b. good sbiliy ( ) c. good confinemen ( τ nr )
More informationII The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions
II The Z Trnsfor Tocs o e covered. Inroducon. The Z rnsfor 3. Z rnsfors of eleenry funcons 4. Proeres nd Theory of rnsfor 5. The nverse rnsfor 6. Z rnsfor for solvng dfference equons II. Inroducon The
More informationAverage & instantaneous velocity and acceleration Motion with constant acceleration
Physics 7: Lecure Reminders Discussion nd Lb secions sr meeing ne week Fill ou Pink dd/drop form if you need o swich o differen secion h is FULL. Do i TODAY. Homework Ch. : 5, 7,, 3,, nd 6 Ch.: 6,, 3 Submission
More informationT-Match: Matching Techniques For Driving Yagi-Uda Antennas: T-Match. 2a s. Z in. (Sections 9.5 & 9.7 of Balanis)
3/0/018 _mch.doc Pge 1 of 6 T-Mch: Mching Techniques For Driving Ygi-Ud Anenns: T-Mch (Secions 9.5 & 9.7 of Blnis) l s l / l / in The T-Mch is shun-mching echnique h cn be used o feed he driven elemen
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationAdvanced Electromechanical Systems (ELE 847)
(ELE 847) Dr. Smr ouro-rener Topc 1.4: DC moor speed conrol Torono, 2009 Moor Speed Conrol (open loop conrol) Consder he followng crcu dgrm n V n V bn T1 T 5 T3 V dc r L AA e r f L FF f o V f V cn T 4
More informationIncluding the ordinary differential of distance with time as velocity makes a system of ordinary differential equations.
Soluons o Ordnary Derenal Equaons An ordnary derenal equaon has only one ndependen varable. A sysem o ordnary derenal equaons consss o several derenal equaons each wh he same ndependen varable. An eample
More information4. Runge-Kutta Formula For Differential Equations. A. Euler Formula B. Runge-Kutta Formula C. An Example for Fourth-Order Runge-Kutta Formula
NCTU Deprme o Elecrcl d Compuer Egeerg Seor Course By Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos A. Euler Formul B. Ruge-Ku Formul C. A Emple or Four-Order Ruge-Ku Formul
More informationNotes 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 informationCHAPTER 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 informationReview: Transformations. Transformations - Viewing. Transformations - Modeling. world CAMERA OBJECT WORLD CSE 681 CSE 681 CSE 681 CSE 681
Revew: Trnsforons Trnsforons Modelng rnsforons buld cople odels b posonng (rnsforng sple coponens relve o ech oher ewng rnsforons plcng vrul cer n he world rnsforon fro world coordnes o cer coordnes Perspecve
More informationANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES DATA
Tn Corn DOSESCU Ph D Dre Cner Chrsn Unversy Buchres Consnn RAISCHI PhD Depren of Mhecs The Buchres Acdey of Econoc Sudes ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES
More informationEEM 486: Computer Architecture
EEM 486: Compuer Archecure Lecure 4 ALU EEM 486 MIPS Arhmec Insrucons R-ype I-ype Insrucon Exmpe Menng Commen dd dd $,$2,$3 $ = $2 + $3 sub sub $,$2,$3 $ = $2 - $3 3 opernds; overfow deeced 3 opernds;
More information4. Runge-Kutta Formula For Differential Equations
NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul
More informationGAUSS 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 informationSequential methods for ocean data assimilation. From theory to practical implementations (I)
Sequenl ehods or ocen d sslon Fro heory o rccl leenons I P. BRASSEUR CNRS/LEGI, Grenoble, Frnce Perre.Brsseur@hg.ng.r J. Bllbrer, L. Berlne, F. Brol, J.M. Brnkr, G. Broque, V. Crlle, F. Csrucco, F. Debos,
More informationRelative controllability of nonlinear systems with delays in control
Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.
More informationHidden Markov Model. a ij. Observation : O1,O2,... States in time : q1, q2,... All states : s1, s2,..., sn
Hdden Mrkov Model S S servon : 2... Ses n me : 2... All ses : s s2... s 2 3 2 3 2 Hdden Mrkov Model Con d Dscree Mrkov Model 2 z k s s s s s s Degree Mrkov Model Hdden Mrkov Model Con d : rnson roly from
More informationJordan Journal of Physics
Volume, Number, 00. pp. 47-54 RTICLE Jordn Journl of Physcs Frconl Cnoncl Qunzon of he Free Elecromgnec Lgrngn ensy E. K. Jrd, R. S. w b nd J. M. Khlfeh eprmen of Physcs, Unversy of Jordn, 94 mmn, Jordn.
More informationBLOWUPS IN GAUGE AND CONSTRAINT MODES. Bernd Reimann, AEI in collaboration with M. Alcubierre, ICN (Mexico)
BLOWUPS IN GAUGE AND CONSTRAINT MODES Bernd Remnn, AEI n ollboron M. Aluberre, ICN (Mexo) Jen, Jnury 30, 006 1 Tops Pologes ( soks nd bloups ) n sysems of PDEs Te soure rer for vodng bloups Evoluon Sysem:
More informationAn object moving with speed v around a point at distance r, has an angular velocity. m/s m
Roion The mosphere roes wih he erh n moions wihin he mosphere clerly follow cure phs (cyclones, nicyclones, hurricnes, ornoes ec.) We nee o epress roion quniiely. For soli objec or ny mss h oes no isor
More informationInterval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X
ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce
More informationDEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL
DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA
More informationMinimum Squared Error
Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > for ll smples y i solve sysem of liner inequliies MSE procedure y i = i for ll smples
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationCalculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( )
Clculu 4, econ Lm/Connuy & Devve/Inel noe y Tm Plchow, wh domn o el Wh we hve o : veco-vlued uncon, ( ) ( ) ( ) j ( ) nume nd ne o veco The uncon, nd A w done wh eul uncon ( x) nd connuy e he componen
More informationMinimum Squared Error
Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > 0 for ll smples y i solve sysem of liner inequliies MSE procedure y i i for ll smples
More informationContraction Mapping Principle Approach to Differential Equations
epl Journl of Science echnology 0 (009) 49-53 Conrcion pping Principle pproch o Differenil Equions Bishnu P. Dhungn Deprmen of hemics, hendr Rn Cmpus ribhuvn Universiy, Khmu epl bsrc Using n eension of
More informationScattering at an Interface: Oblique Incidence
Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may
More informationS Radio transmission and network access Exercise 1-2
S-7.330 Rdio rnsmission nd nework ccess Exercise 1 - P1 In four-symbol digil sysem wih eqully probble symbols he pulses in he figure re used in rnsmission over AWGN-chnnel. s () s () s () s () 1 3 4 )
More informationCH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC
CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal
More informationSimplified Variance Estimation for Three-Stage Random Sampling
Deprmen of ppled Sscs Johnnes Kepler Unversy Lnz IFS Reserch Pper Seres 04-67 Smplfed rnce Esmon for Three-Sge Rndom Smplng ndres Quember Ocober 04 Smplfed rnce Esmon for Three-Sge Rndom Smplng ndres Quember
More informationUse 10 m/s 2 for the acceleration due to gravity.
ANSWERS Prjecle mn s he ecrl sum w ndependen elces, hrznl cmpnen nd ercl cmpnen. The hrznl cmpnen elcy s cnsn hrughu he mn whle he ercl cmpnen elcy s dencl ree ll. The cul r nsnneus elcy ny pn lng he prblc
More informationMODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB
MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB P. Florn, P. Vrání, R. Čermá Fculy of Mechncl Engneerng, Unversy of Wes Bohem Asrc The frs pr of hs pper s devoed o mhemcl modellng
More informationGo 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 informationChapters 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 informationSupporting information How to concatenate the local attractors of subnetworks in the HPFP
n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced
More informationSoftware Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags
Sofwre Relbly Growh Models Incorporng Ful Dependency wh Vrous Debuggng Tme Lgs Chn-Yu Hung 1 Chu-T Ln 1 Sy-Yen Kuo Mchel R. Lyu 3 nd Chun-Chng Sue 4 1 Deprmen of Compuer Scence Nonl Tsng Hu Unversy Hsnchu
More informationReinforcement Learning for a New Piano Mover s Problem
Renforcemen Lernng for New Pno Mover s Problem Yuko ISHIWAKA Hkode Nonl College of Technology, Hkode, Hokkdo, Jpn Tomohro YOSHIDA Murorn Insue of Technology, Murorn, Hokkdo, Jpn nd Yuknor KAKAZU Reserch
More informationElectromagnetic Transient Simulation of Large Power Transformer Internal Fault
Inernonl Conference on Advnces n Energy nd Envronmenl Scence (ICAEES 5) Elecromgnec Trnsen Smulon of rge Power Trnsformer Inernl Ful Jun u,, Shwu Xo,, Qngsen Sun,c, Huxng Wng,d nd e Yng,e School of Elecrcl
More informationMotion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration.
Moion Accelerion Pr : Consn Accelerion Accelerion Accelerion Accelerion is he re of chnge of velociy. = v - vo = Δv Δ ccelerion = = v - vo chnge of velociy elpsed ime Accelerion is vecor, lhough in one-dimensionl
More informationINVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES
Brodogrdnj/Shpuldng Volume 65 Numer 3, 214 Ferd Ckc Muhsn Aydn ISSN 7-215X eissn 1845-5859 INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES UDC 629.5(5) Professonl pper Summry
More informationIntroduction 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 informationHEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD
Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,
More informationEFFECT OF DISTANCES, SPACING AND NUMBER OF DOWELS IN A ROW ON THE LOAD CARRYING CAPACITY OF CONNECTIONS WITH DOWELS FAILING BY SPLITTING
CIB-W8/5-7-7 INTERNATIONAL COUNCIL FOR RESEARCH AND INNOVATION IN BUILDIN AND CONSTRUCTION WORKIN COMMISSION W8 - TIMBER STRUCTURES EFFECT OF DISTANCES, SPACIN AND NUMBER OF DOWELS IN A ROW ON THE LOAD
More informationPrivacy-Preserving Bayesian Network Parameter Learning
4h WSEAS In. Conf. on COMUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS nd CYBERNETICS Mm, Flord, USA, November 7-9, 005 pp46-5) rvcy-reservng Byesn Nework rmeer Lernng JIANJIE MA. SIVAUMAR School of EECS,
More informationDepartment 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 informationRobustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationResponse of MDOF systems
Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss
More informationSolution 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 information12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer
d Model Cvl and Surveyng Soware Dranage Analyss Module Deenon/Reenon Basns Owen Thornon BE (Mech), d Model Programmer owen.hornon@d.com 4 January 007 Revsed: 04 Aprl 007 9 February 008 (8Cp) Ths documen
More informationMODEL SOLUTIONS TO IIT JEE ADVANCED 2014
MODEL SOLUTIONS TO IIT JEE ADVANCED Pper II Code PART I 6 7 8 9 B A A C D B D C C B 6 C B D D C A 7 8 9 C A B D. Rhc(Z ). Cu M. ZM Secon I K Z 8 Cu hc W mu hc 8 W + KE hc W + KE W + KE W + KE W + KE (KE
More informationII. Light is a Ray (Geometrical Optics)
II Lgh s a Ray (Geomercal Opcs) IIB Reflecon and Refracon Hero s Prncple of Leas Dsance Law of Reflecon Hero of Aleandra, who lved n he 2 nd cenury BC, posulaed he followng prncple: Prncple of Leas Dsance:
More informationAppendix H: Rarefaction and extrapolation of Hill numbers for incidence data
Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs
More informationCS286.2 Lecture 14: Quantum de Finetti Theorems II
CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2
More informationChapter Direct Method of Interpolation
Chper 5. Direc Mehod of Inerpolion Afer reding his chper, you should be ble o:. pply he direc mehod of inerpolion,. sole problems using he direc mehod of inerpolion, nd. use he direc mehod inerpolns o
More informationObtaining the Optimal Order Quantities Through Asymptotic Distributions of the Stockout Duration and Demand
he Seond Inernonl Symposum on Sohs Models n Relbly Engneerng Lfe Sene nd Operons Mngemen Obnng he Opml Order unes hrough Asympo Dsrbuons of he Sokou Duron nd Demnd Ann V Kev Nonl Reserh omsk Se Unversy
More informationPower Series Solutions for Nonlinear Systems. of Partial Differential Equations
Appled Mhemcl Scences, Vol. 6, 1, no. 14, 5147-5159 Power Seres Soluons for Nonlner Sysems of Prl Dfferenl Equons Amen S. Nuser Jordn Unversy of Scence nd Technology P. O. Bo 33, Irbd, 11, Jordn nuser@us.edu.o
More informationVariants 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 information5.1-The Initial-Value Problems For Ordinary Differential Equations
5.-The Iniil-Vlue Problems For Ordinry Differenil Equions Consider solving iniil-vlue problems for ordinry differenil equions: (*) y f, y, b, y. If we know he generl soluion y of he ordinry differenil
More informationRL for Large State Spaces: Policy Gradient. Alan Fern
RL for Lrge Se Spce: Polcy Grden Aln Fern RL v Polcy Grden Serch So fr ll of our RL echnque hve red o lern n ec or pprome uly funcon or Q-funcon Lern opml vlue of beng n e or kng n con from e. Vlue funcon
More informationNormal Random Variable and its discriminant functions
Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The
More informationChapter 6: AC Circuits
Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.
More informationComputing Relevance, Similarity: The Vector Space Model
Compung Relevance, Smlary: The Vecor Space Model Based on Larson and Hears s sldes a UC-Bereley hp://.sms.bereley.edu/courses/s0/f00/ aabase Managemen Sysems, R. Ramarshnan ocumen Vecors v ocumens are
More informationIsotropic Non-Heisenberg Magnet for Spin S=1
Ierol Jourl of Physcs d Applcos. IN 974- Volume, Number (, pp. 7-4 Ierol Reserch Publco House hp://www.rphouse.com Isoropc No-Heseberg Mge for p = Y. Yousef d Kh. Kh. Mumov.U. Umrov Physcl-Techcl Isue
More informationDefinition 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 informationJackson 2.26 Homework Problem Solution Dr. Christopher S. Baird University of Massachusetts Lowell
Jckson 2.26 Homework Problem Solution Dr. Christopher S. Bird University of Msschusetts Lowell PROBLEM: The two-dimensionl region, ρ, φ β, is bounded by conducting surfces t φ =, ρ =, nd φ = β held t zero
More informationDisplacement, Velocity, and Acceleration. (WHERE and WHEN?)
Dsplacemen, Velocy, and Acceleraon (WHERE and WHEN?) Mah resources Append A n your book! Symbols and meanng Algebra Geomery (olumes, ec.) Trgonomery Append A Logarhms Remnder You wll do well n hs class
More informationOrigin Destination Transportation Models: Methods
In Jr. of Mhemcl Scences & Applcons Vol. 2, No. 2, My 2012 Copyrgh Mnd Reder Publcons ISSN No: 2230-9888 www.journlshub.com Orgn Desnon rnsporon Models: Mehods Jyo Gup nd 1 N H. Shh Deprmen of Mhemcs,
More information5.2 Volumes: Disks and Washers
4 pplictions of definite integrls 5. Volumes: Disks nd Wshers In the previous section, we computed volumes of solids for which we could determine the re of cross-section or slice. In this section, we restrict
More informationprinciples, scales and applications
Ippen Lecure Kul Lumpur 207 Cellulr Auom n ecohydrulcs modelng: prncples scles nd pplcons Quwen Chen Nnng Hydrulc Reserch Insue Chn PhD Progrm 935 2009 Rver Hydrulcs Por cos nd offshore. Wer resources
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary
More informationMath 128b Project. Jude Yuen
Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally
More informationMechanics 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 information5.7 Improper Integrals
458 pplictions of definite integrls 5.7 Improper Integrls In Section 5.4, we computed the work required to lift pylod of mss m from the surfce of moon of mss nd rdius R to height H bove the surfce of the
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae
More informationGMM parameter estimation. Xiaoye Lu CMPS290c Final Project
GMM paraeer esaon Xaoye Lu M290c Fnal rojec GMM nroducon Gaussan ure Model obnaon of several gaussan coponens Noaon: For each Gaussan dsrbuon:, s he ean and covarance ar. A GMM h ures(coponens): p ( 2π
More information