Accuracy of TEXTOR He-beam diagnostics
|
|
- Maria Johnson
- 5 years ago
- Views:
Transcription
1 Accuracy of TEXTOR He-beam diagostics O. Schmitz, I.L.Beigma *, L.A. Vaishtei *, A. Pospieszczyk, B. Schweer, M. Krychoviak, U. Samm ad the TEXTOR team Forschugszetrum Jülich,, Jülich, Germay *Lebedev Physical Istitut, Moscow, RF Itroductio stadard He-beam diagostics o TEXTOR Atomic processes ivolved Evaluatio procedures Atomic data Results - stadard, ew - compariso of itesity profile fits compariso for differet atomic data compariso for differet discharge coditios compariso of differet diagostics Summary & coclusios
2 Stadard TEXTOR He-beam diagostics imagig fibre guide image itesifier Li chael He chaels iterferece filters photo objectives f=25mm ijectio system lier ALTlimiter lie emissio collisios ijected He & Li atoms vacuum vessel i / 1 costat relaxatio time τ r sufficietly small temporal resolutio: t=τ r (max) spatial resolutio: x = t v Strahl o p & d collisios 18-3 /10 m e T/eV e liie itesity ratios: siglet : siglet I(668m) / I(728m) e depedet siglet : triplet I(728m) / I(706m) T e depedet M.Brix (2000)
3 bdx d i = Trilateral Euregio Cluster pop. processes + σ e,jiv j,j i Atomic processes e + σ p,jiv ioj j,j i + A j depop.processes σ e,ij v i,j i σ p,ijv i,j i ji j j, j> i j, j< i e A ij i iois, i io i σ v σ v σ p cx, i iois, i v io e i e io i i i electro collisio excitatio io collisio excitatio spotaeous emissio electro collisio ioisatio io collisio ioisatio charge exchage
4 Evaluatio procedures For kow T e (r) ad e (r) -> lie itesity profiles I ( r) = λ k A ki I ( r) = k A ki However, we eed for kow -> T e (r) ad e (r) λ M. Brosda ad M.Brix have already show that it is possible to restore T e ad e profiles from lie profiles I 1 (r); I 2 (r); I 3 (r) Possible approaches: o-statioary (NS): direct solutio of the geeral (time / space) depedet equatio ad fit I (r) = A to the measured oe. quasi-statioary (QS): approx. solutio of the NS equatios: o the right the St-solutio of the balace equatios for rel. populatios k / 1 of the excited states is iserted. λ k ki statioary (St): the derivatives are eglected assumig that the time costat of the measured processes are larger tha the relaxatio times τ for the trasitios used.
5 Atomic data - levels The followig atomic levels of He I have bee icluded i the model: (1): 1s 2 1 S; 1sl 1 L; 1sl 3 L; = 2; 3; 4, l = 0; 1; 2 (1i):1s4f 1 F; 3 F (2): 1s_1 (siglets), 1s_3 (triplets), = 5; 6; 7 summed over all l (3): 1s; = 8; 9 summed over all l ad S (c): c = 1s groud state of He II "effective" levels have bee itroduced, which describe group of levels summed over some quatum umbers (decreases drastically the dimesios of the statistical matrix). group (1): SL couplig is a good approximatio. group (1i) ad ay levels with l > 2: deviatio from the SL couplig is sigificat. The matrix of the eige-vectors i itermediate couplig was obtaied for the states (1i) usig the GRASP92-code (Drake). For the levels 1s; = 8; 9 summed over all l ad S the type of couplig is ot importat. For the levels 1s_1 (siglets), 1s_3 (triplets), = 5; 6; 7 a effective mixig due to deviatio from the SL couplig was itroduced.
6 Atomic data eergies, radiatio, collisio with e The followig atomic levels of He I have bee icluded i the model: (1): 1s 2 1 S; 1sl 1 L; 1sl 3 L; = 2; 3; 4, l = 0; 1; 2 (1i):1s4f 1 F; 3 F (2): 1s_1 (siglets), 1s_3 (triplets), = 5; 6; 7 summed over all l (3): 1s; = 8; 9 summed over all l ad S (c): c = 1s groud state of He II Eergy levels: from NIST A for 1s2p 1,3 P 1s 2 1 S from Wiese (NSRDS-N.B.S.) A for groups (1), (1i) from ATOM, itermediate couplig for 1s4f 1s3d, SL for others A for trasitios iside ad betwee groups (2), (3): Kramers formula <σv> ex for group (1): CCC-89 cross sectios (Bray) <σv> ex from group (1) to groups (1i),(2),(3),c: ATOM code (Norm.BA) <σv> ex iside groups (1),(2),(3): semiclassical method (Beigma) trasitios to group (1i): itermediate couplig
7 Atomic data collisios with p,d, CEX The followig atomic levels of He I have bee icluded i the model: (1): 1s 2 1 S; 1sl 1 L; 1sl 3 L; = 2; 3; 4, l = 0; 1; 2 (1i):1s4f 1 F; 3 F (2): 1s_1 (siglets), 1s_3 (triplets), = 5; 6; 7 summed over all l (3): 1s; = 8; 9 summed over all l ad S (c): c = 1s groud state of He II Collisios with heavy particles at thermal eergies may oly cotribute for trasitios with = 0 <σv> p, d for the most importat 3l 3l : from CC-method (code ATCC (Borodi)) for all other trasitios: Norm. Bor approximatio (Borodi) <σv> CEX from the metastable state 1s2s from code CAPT (Shevelko) with cross sectios for 4 from (Jaev) Normalized Bor Approximatio Close couplig method
8 Results Compariso of differet models ST w/o p ST w p Ti =T e STs ST w/o p ST w p T i =T e ST w p T i =2T e ST w p T i =2T e STs proto collisios do ot seem to play a strog role some effect is of course expected at high desities T i effects are still weak
9 Results T e & e diagram (ew) ST w/o p ST w p T i =T e ST w p T i =2T e /10 m e evaluatio rages could be exteded proto collisios reduce the electro desities icrease the electro temperatures 0 0 T/eV e
10 Results Compariso for itesity profile fits (Ohmic discharge) (# 96705) 667m 706m 728m NS for large radii: τ r is too large ST STs for small radii: stroger impact of higher l-mixig (ot yet cosidered)
11 Results Compariso for Ohmic discharges (# 98026) NS ST STs for large radii: τ r is too large for small radii: stroger impact of higher l-mixig (ot yet cosidered) For Ohmic discharges: The deviatio betwee Brix's CRM ad the ew oe accouts to 10% for T e ad is egligible for e. The impact of proto collisios as well as of the ew atomic processes (CXRS ad a ew ioisatio rate coefficiet) seems to have a mior effect.
12 esults Compariso for itesity profile fits (small NBI-heatig) (# 95907, 300kW) 667m 706m 728m T e & e strog deviatios (especially for T e ) ST STs NS for all models at small radii Ifluece from higher levels!?
13 Results discharges with small NB-heatig (# 95896, 300kW) ST NS STs T e & e much better coicidece ow
14 Results discharges with strog NB-heatig (# 96710, 1200kW) NS ST STs T e & e agai much better coicidece ow, NS allows extesio ito the cofied plasma
15 Results compariso with differet diagostics e He- & Li-beam data extrapolate well ito the HCN data TS seems too small (calibratio error?) Zoom T e He-data extrapolate well ito the HCN & TS data
16 Summary & Coclusio A exteded model (to Brix 2000) with proto collisios ad CEX processes has bee tested o TEXTOR discharges. Oly margial deviatios => impact of the proto collisios is weak uder the target plasma coditios ivestigated. Evaluatio with the o-statioary approach (NS) ehaces the radial extesio of the profile ad cacels relaxatio pheomea. A method to apply the NS method as stadard evaluatio is uder developmet. For the measuremet errors the rage of T e = 30% ad e = 10% give by Brix 2000 is still valid. Proto collisios (witht i =T e ) do ot exceed these margis. Compariso of the e (r) ad T e (r) profiles obtaied with other edge are i reasoable agreemet with those from He. BES o thermal He should be a reliable method for measuremets withi m -3 < e < m -3 ad 20 ev < T e < 300eV.
Hydrogen (atoms, molecules) in external fields. Static electric and magnetic fields Oscyllating electromagnetic fields
Hydroge (atoms, molecules) i exteral fields Static electric ad magetic fields Oscyllatig electromagetic fields Everythig said up to ow has to be modified more or less strogly if we cosider atoms (ad ios)
More information1 Adiabatic and diabatic representations
1 Adiabatic ad diabatic represetatios 1.1 Bor-Oppeheimer approximatio The time-idepedet Schrödiger equatio for both electroic ad uclear degrees of freedom is Ĥ Ψ(r, R) = E Ψ(r, R), (1) where the full molecular
More informationStatistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons
Statistical Aalysis o Ucertaity for Autocorrelated Measuremets ad its Applicatios to Key Comparisos Nie Fa Zhag Natioal Istitute of Stadards ad Techology Gaithersburg, MD 0899, USA Outlies. Itroductio.
More informationExercises and Problems
HW Chapter 4: Oe-Dimesioal Quatum Mechaics Coceptual Questios 4.. Five. 4.4.. is idepedet of. a b c mu ( E). a b m( ev 5 ev) c m(6 ev ev) Exercises ad Problems 4.. Model: Model the electro as a particle
More informationPHYS-3301 Lecture 7. CHAPTER 4 Structure of the Atom. Rutherford Scattering. Sep. 18, 2018
CHAPTER 4 Structure of the Atom PHYS-3301 Lecture 7 4.1 The Atomic Models of Thomso ad Rutherford 4.2 Rutherford Scatterig 4.3 The Classic Atomic Model 4.4 The Bohr Model of the Hydroge Atom 4.5 Successes
More informationMeasurement uncertainty of the sound absorption
Measuremet ucertaity of the soud absorptio coefficiet Aa Izewska Buildig Research Istitute, Filtrowa Str., 00-6 Warsaw, Polad a.izewska@itb.pl 6887 The stadard ISO/IEC 705:005 o the competece of testig
More informationProvläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE
TECHNICAL REPORT CISPR 16-4-3 2004 AMENDMENT 1 2006-10 INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE Amedmet 1 Specificatio for radio disturbace ad immuity measurig apparatus ad methods Part 4-3:
More informationKinetics of Complex Reactions
Kietics of Complex Reactios by Flick Colema Departmet of Chemistry Wellesley College Wellesley MA 28 wcolema@wellesley.edu Copyright Flick Colema 996. All rights reserved. You are welcome to use this documet
More informationThe improvement of the volume ratio measurement method in static expansion vacuum system
Available olie at www.sciecedirect.com Physics Procedia 32 (22 ) 492 497 8 th Iteratioal Vacuum Cogress The improvemet of the volume ratio measuremet method i static expasio vacuum system Yu Hogya*, Wag
More informationThe target reliability and design working life
Safety ad Security Egieerig IV 161 The target reliability ad desig workig life M. Holický Kloker Istitute, CTU i Prague, Czech Republic Abstract Desig workig life ad target reliability levels recommeded
More informationMiscellaneous Notes. Lecture 19, p 1
Miscellaeous Notes The ed is ear do t get behid. All Excuses must be take to 233 Loomis before oo, Thur, Apr. 25. The PHYS 213 fial exam times are * 8-10 AM, Moday, May 6 * 1:30-3:30 PM, Wed, May 8 The
More informationDiffusivity and Mobility Quantization. in Quantum Electrical Semi-Ballistic. Quasi-One-Dimensional Conductors
Advaces i Applied Physics, Vol., 014, o. 1, 9-13 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.1988/aap.014.3110 Diffusivity ad Mobility Quatizatio i Quatum Electrical Semi-Ballistic Quasi-Oe-Dimesioal
More informationExam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234
STA 291 Lecture 19 Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Locatio CB 234 STA 291 - Lecture 19 1 Exam II Covers Chapter 9 10.1; 10.2; 10.3; 10.4; 10.6
More informationSOLUTIONS: ECE 606 Homework Week 7 Mark Lundstrom Purdue University (revised 3/27/13) e E i E T
SOUIONS: ECE 606 Homework Week 7 Mark udstrom Purdue Uiversity (revised 3/27/13) 1) Cosider a - type semicoductor for which the oly states i the badgap are door levels (i.e. ( E = E D ). Begi with the
More informationTrue Nature of Potential Energy of a Hydrogen Atom
True Nature of Potetial Eergy of a Hydroge Atom Koshu Suto Key words: Bohr Radius, Potetial Eergy, Rest Mass Eergy, Classical Electro Radius PACS codes: 365Sq, 365-w, 33+p Abstract I cosiderig the potetial
More informationDiscrete Mathematics for CS Spring 2008 David Wagner Note 22
CS 70 Discrete Mathematics for CS Sprig 2008 David Wager Note 22 I.I.D. Radom Variables Estimatig the bias of a coi Questio: We wat to estimate the proportio p of Democrats i the US populatio, by takig
More informationWorksheet 23 ( ) Introduction to Simple Linear Regression (continued)
Worksheet 3 ( 11.5-11.8) Itroductio to Simple Liear Regressio (cotiued) This worksheet is a cotiuatio of Discussio Sheet 3; please complete that discussio sheet first if you have ot already doe so. This
More informationQuantum Annealing for Heisenberg Spin Chains
LA-UR # - Quatum Aealig for Heiseberg Spi Chais G.P. Berma, V.N. Gorshkov,, ad V.I.Tsifriovich Theoretical Divisio, Los Alamos Natioal Laboratory, Los Alamos, NM Istitute of Physics, Natioal Academy of
More informationDETERMINATION OF MECHANICAL PROPERTIES OF A NON- UNIFORM BEAM USING THE MEASUREMENT OF THE EXCITED LONGITUDINAL ELASTIC VIBRATIONS.
ICSV4 Cairs Australia 9- July 7 DTRMINATION OF MCHANICAL PROPRTIS OF A NON- UNIFORM BAM USING TH MASURMNT OF TH XCITD LONGITUDINAL LASTIC VIBRATIONS Pavel Aokhi ad Vladimir Gordo Departmet of the mathematics
More informationRay Optics Theory and Mode Theory. Dr. Mohammad Faisal Dept. of EEE, BUET
Ray Optics Theory ad Mode Theory Dr. Mohammad Faisal Dept. of, BUT Optical Fiber WG For light to be trasmitted through fiber core, i.e., for total iteral reflectio i medium, > Ray Theory Trasmissio Ray
More informationLinear Regression Demystified
Liear Regressio Demystified Liear regressio is a importat subject i statistics. I elemetary statistics courses, formulae related to liear regressio are ofte stated without derivatio. This ote iteds to
More informationMATH/STAT 352: Lecture 15
MATH/STAT 352: Lecture 15 Sectios 5.2 ad 5.3. Large sample CI for a proportio ad small sample CI for a mea. 1 5.2: Cofidece Iterval for a Proportio Estimatig proportio of successes i a biomial experimet
More informationModeling of Plasmas and Neutrals Including Plasma-Wall Interaction for Long Term Tokamak Operation
odelig of Plasmas ad Neutrals Icludig Plasma-Wall Iteractio for Log Term Tokamak Operatio Akiyoshi Hatayama 1, Kousuke Okamoto 1, Ryoko Tatsumi 1, Kazuhiro. Abe 1, ad Kazuaki Haada 2 1. Backgroud/otivatio
More informationChapter 8: Estimating with Confidence
Chapter 8: Estimatig with Cofidece Sectio 8.2 The Practice of Statistics, 4 th editio For AP* STARNES, YATES, MOORE Chapter 8 Estimatig with Cofidece 8.1 Cofidece Itervals: The Basics 8.2 8.3 Estimatig
More informationTHE NUMERICAL SOLUTION OF THE NEWTONIAN FLUIDS FLOW DUE TO A STRETCHING CYLINDER BY SOR ITERATIVE PROCEDURE ABSTRACT
Europea Joural of Egieerig ad Techology Vol. 3 No., 5 ISSN 56-586 THE NUMERICAL SOLUTION OF THE NEWTONIAN FLUIDS FLOW DUE TO A STRETCHING CYLINDER BY SOR ITERATIVE PROCEDURE Atif Nazir, Tahir Mahmood ad
More informationBecause it tests for differences between multiple pairs of means in one test, it is called an omnibus test.
Math 308 Sprig 018 Classes 19 ad 0: Aalysis of Variace (ANOVA) Page 1 of 6 Itroductio ANOVA is a statistical procedure for determiig whether three or more sample meas were draw from populatios with equal
More informationPhysics Oct Reading
Physics 301 21-Oct-2002 17-1 Readig Fiish K&K chapter 7 ad start o chapter 8. Also, I m passig out several Physics Today articles. The first is by Graham P. Collis, August, 1995, vol. 48, o. 8, p. 17,
More informationBIOS 4110: Introduction to Biostatistics. Breheny. Lab #9
BIOS 4110: Itroductio to Biostatistics Brehey Lab #9 The Cetral Limit Theorem is very importat i the realm of statistics, ad today's lab will explore the applicatio of it i both categorical ad cotiuous
More information( θ. sup θ Θ f X (x θ) = L. sup Pr (Λ (X) < c) = α. x : Λ (x) = sup θ H 0. sup θ Θ f X (x θ) = ) < c. NH : θ 1 = θ 2 against AH : θ 1 θ 2
82 CHAPTER 4. MAXIMUM IKEIHOOD ESTIMATION Defiitio: et X be a radom sample with joit p.m/d.f. f X x θ. The geeralised likelihood ratio test g.l.r.t. of the NH : θ H 0 agaist the alterative AH : θ H 1,
More informationAll Excuses must be taken to 233 Loomis before 4:15, Monday, April 30.
Miscellaeous Notes The ed is ear do t get behid. All Excuses must be take to 233 Loomis before 4:15, Moday, April 30. The PYS 213 fial exam times are * 8-10 AM, Moday, May 7 * 8-10 AM, Tuesday, May 8 ad
More informationUnit 5. Gases (Answers)
Uit 5. Gases (Aswers) Upo successful completio of this uit, the studets should be able to: 5. Describe what is meat by gas pressure.. The ca had a small amout of water o the bottom to begi with. Upo heatig
More informationEstimation of the Mean and the ACVF
Chapter 5 Estimatio of the Mea ad the ACVF A statioary process {X t } is characterized by its mea ad its autocovariace fuctio γ ), ad so by the autocorrelatio fuctio ρ ) I this chapter we preset the estimators
More informationInvestigating a new estimator of the serial correlation coefficient
Ivestigatig a ew estimator of the serial correlatio coefficiet Mafred Mudelsee Istitute of Mathematics ad Statistics Uiversity of Ket at Caterbury Caterbury Ket CT2 7NF Uited Kigdom 8 May 1998 1 Abstract
More informationHypothesis Testing. Evaluation of Performance of Learned h. Issues. Trade-off Between Bias and Variance
Hypothesis Testig Empirically evaluatig accuracy of hypotheses: importat activity i ML. Three questios: Give observed accuracy over a sample set, how well does this estimate apply over additioal samples?
More informationMark Lundstrom Spring SOLUTIONS: ECE 305 Homework: Week 5. Mark Lundstrom Purdue University
Mark udstrom Sprig 2015 SOUTIONS: ECE 305 Homework: Week 5 Mark udstrom Purdue Uiversity The followig problems cocer the Miority Carrier Diffusio Equatio (MCDE) for electros: Δ t = D Δ + G For all the
More informationTR/46 OCTOBER THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION A. TALBOT
TR/46 OCTOBER 974 THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION by A. TALBOT .. Itroductio. A problem i approximatio theory o which I have recetly worked [] required for its solutio a proof that the
More informationChapter 10: Power Series
Chapter : Power Series 57 Chapter Overview: Power Series The reaso series are part of a Calculus course is that there are fuctios which caot be itegrated. All power series, though, ca be itegrated because
More information6.3 Testing Series With Positive Terms
6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial
More informationEECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1
EECS564 Estimatio, Filterig, ad Detectio Hwk 2 Sols. Witer 25 4. Let Z be a sigle observatio havig desity fuctio where. p (z) = (2z + ), z (a) Assumig that is a oradom parameter, fid ad plot the maximum
More informationExample: Find the SD of the set {x j } = {2, 4, 5, 8, 5, 11, 7}.
1 (*) If a lot of the data is far from the mea, the may of the (x j x) 2 terms will be quite large, so the mea of these terms will be large ad the SD of the data will be large. (*) I particular, outliers
More informationNonequilibrium Excess Carriers in Semiconductors
Lecture 8 Semicoductor Physics VI Noequilibrium Excess Carriers i Semicoductors Noequilibrium coditios. Excess electros i the coductio bad ad excess holes i the valece bad Ambiolar trasort : Excess electros
More informationa b c d e f g h Supplementary Information
Supplemetary Iformatio a b c d e f g h Supplemetary Figure S STM images show that Dark patters are frequetly preset ad ted to accumulate. (a) mv, pa, m ; (b) mv, pa, m ; (c) mv, pa, m ; (d) mv, pa, m ;
More informationLecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting
Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would
More information1. Hydrogen Atom: 3p State
7633A QUANTUM MECHANICS I - solutio set - autum. Hydroge Atom: 3p State Let us assume that a hydroge atom is i a 3p state. Show that the radial part of its wave fuctio is r u 3(r) = 4 8 6 e r 3 r(6 r).
More informationANALYSIS OF EXPERIMENTAL ERRORS
ANALYSIS OF EXPERIMENTAL ERRORS All physical measuremets ecoutered i the verificatio of physics theories ad cocepts are subject to ucertaities that deped o the measurig istrumets used ad the coditios uder
More informationDevelopment of QM. What do we know from classical physics? 1. Energy can take any continuous value.
Developmet of QM 1-1 What do we kow from classical physics? 1. Eergy ca take ay cotiuous value.. Electromagetic radiatio is a electric field oscillatig perpedicular to the directio of propagatio. 3. Ay
More informationTHE KALMAN FILTER RAUL ROJAS
THE KALMAN FILTER RAUL ROJAS Abstract. This paper provides a getle itroductio to the Kalma filter, a umerical method that ca be used for sesor fusio or for calculatio of trajectories. First, we cosider
More information10.6 ALTERNATING SERIES
0.6 Alteratig Series Cotemporary Calculus 0.6 ALTERNATING SERIES I the last two sectios we cosidered tests for the covergece of series whose terms were all positive. I this sectio we examie series whose
More informationSession 5. (1) Principal component analysis and Karhunen-Loève transformation
200 Autum semester Patter Iformatio Processig Topic 2 Image compressio by orthogoal trasformatio Sessio 5 () Pricipal compoet aalysis ad Karhue-Loève trasformatio Topic 2 of this course explais the image
More informationTHE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS
R775 Philips Res. Repts 26,414-423, 1971' THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS by H. W. HANNEMAN Abstract Usig the law of propagatio of errors, approximated
More informationThe Poisson Distribution
MATH 382 The Poisso Distributio Dr. Neal, WKU Oe of the importat distributios i probabilistic modelig is the Poisso Process X t that couts the umber of occurreces over a period of t uits of time. This
More informationFACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals
More informationCSE 527, Additional notes on MLE & EM
CSE 57 Lecture Notes: MLE & EM CSE 57, Additioal otes o MLE & EM Based o earlier otes by C. Grat & M. Narasimha Itroductio Last lecture we bega a examiatio of model based clusterig. This lecture will be
More information11 Correlation and Regression
11 Correlatio Regressio 11.1 Multivariate Data Ofte we look at data where several variables are recorded for the same idividuals or samplig uits. For example, at a coastal weather statio, we might record
More informationNUMERICAL METHODS FOR SOLVING EQUATIONS
Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:
More informationHolistic Approach to the Periodic System of Elements
Holistic Approach to the Periodic System of Elemets N.N.Truov * D.I.Medeleyev Istitute for Metrology Russia, St.Peterburg. 190005 Moskovsky pr. 19 (Dated: February 20, 2009) Abstract: For studyig the objectivity
More informationLecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting
Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would
More informationModified Decomposition Method by Adomian and. Rach for Solving Nonlinear Volterra Integro- Differential Equations
Noliear Aalysis ad Differetial Equatios, Vol. 5, 27, o. 4, 57-7 HIKARI Ltd, www.m-hikari.com https://doi.org/.2988/ade.27.62 Modified Decompositio Method by Adomia ad Rach for Solvig Noliear Volterra Itegro-
More informationStatistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.
Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized
More informationANALYSIS OF DAMPING EFFECT ON BEAM VIBRATION
Molecular ad Quatum Acoustics vol. 7, (6) 79 ANALYSIS OF DAMPING EFFECT ON BEAM VIBRATION Jerzy FILIPIAK 1, Lech SOLARZ, Korad ZUBKO 1 Istitute of Electroic ad Cotrol Systems, Techical Uiversity of Czestochowa,
More informationOn a Smarandache problem concerning the prime gaps
O a Smaradache problem cocerig the prime gaps Felice Russo Via A. Ifate 7 6705 Avezzao (Aq) Italy felice.russo@katamail.com Abstract I this paper, a problem posed i [] by Smaradache cocerig the prime gaps
More informationAnalysis of Experimental Data
Aalysis of Experimetal Data 6544597.0479 ± 0.000005 g Quatitative Ucertaity Accuracy vs. Precisio Whe we make a measuremet i the laboratory, we eed to kow how good it is. We wat our measuremets to be both
More informationSeries III. Chapter Alternating Series
Chapter 9 Series III With the exceptio of the Null Sequece Test, all the tests for series covergece ad divergece that we have cosidered so far have dealt oly with series of oegative terms. Series with
More informationHot electrons and curves of constant gain in long wavelength quantum well lasers
Hot electros ad curves of costat gai i log wavelegth quatum well lasers Vera Gorfikel, Mikhail Kisi ad Serge Luryi Electrical Egieerig Departmet State Uiversity of New York at Stoy Brook Stoy Brook, NY
More informationRecent Experimental Results in ADITYA Tokamak
Recet Experimetal Results i ADITYA Tokamak R. Jha ad the ADITYA Team Istitute for Plasma Research, Bhat, Gadhiagar-382 428, INDIA e-mail:rjha@ipr.res.i Abstract. Recet studies o measuremets of edge turbulece
More informationSection 11.8: Power Series
Sectio 11.8: Power Series 1. Power Series I this sectio, we cosider geeralizig the cocept of a series. Recall that a series is a ifiite sum of umbers a. We ca talk about whether or ot it coverges ad i
More informationResponse Variable denoted by y it is the variable that is to be predicted measure of the outcome of an experiment also called the dependent variable
Statistics Chapter 4 Correlatio ad Regressio If we have two (or more) variables we are usually iterested i the relatioship betwee the variables. Associatio betwee Variables Two variables are associated
More informationOPTIMAL ALGORITHMS -- SUPPLEMENTAL NOTES
OPTIMAL ALGORITHMS -- SUPPLEMENTAL NOTES Peter M. Maurer Why Hashig is θ(). As i biary search, hashig assumes that keys are stored i a array which is idexed by a iteger. However, hashig attempts to bypass
More informationPaired Data and Linear Correlation
Paired Data ad Liear Correlatio Example. A group of calculus studets has take two quizzes. These are their scores: Studet st Quiz Score ( data) d Quiz Score ( data) 7 5 5 0 3 0 3 4 0 5 5 5 5 6 0 8 7 0
More informationChapter 4. Fourier Series
Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,
More informationNumber of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day
LECTURE # 8 Mea Deviatio, Stadard Deviatio ad Variace & Coefficiet of variatio Mea Deviatio Stadard Deviatio ad Variace Coefficiet of variatio First, we will discuss it for the case of raw data, ad the
More informationThe Born-Oppenheimer approximation
The Bor-Oppeheimer approximatio 1 Re-writig the Schrödiger equatio We will begi from the full time-idepedet Schrödiger equatio for the eigestates of a molecular system: [ P 2 + ( Pm 2 + e2 1 1 2m 2m m
More informationDefinitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients.
Defiitios ad Theorems Remember the scalar form of the liear programmig problem, Miimize, Subject to, f(x) = c i x i a 1i x i = b 1 a mi x i = b m x i 0 i = 1,2,, where x are the decisio variables. c, b,
More informationA statistical method to determine sample size to estimate characteristic value of soil parameters
A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig
More informationTRACEABILITY SYSTEM OF ROCKWELL HARDNESS C SCALE IN JAPAN
HARDMEKO 004 Hardess Measuremets Theory ad Applicatio i Laboratories ad Idustries - November, 004, Washigto, D.C., USA TRACEABILITY SYSTEM OF ROCKWELL HARDNESS C SCALE IN JAPAN Koichiro HATTORI, Satoshi
More informationThe Riemann Zeta Function
Physics 6A Witer 6 The Riema Zeta Fuctio I this ote, I will sketch some of the mai properties of the Riema zeta fuctio, ζ(x). For x >, we defie ζ(x) =, x >. () x = For x, this sum diverges. However, we
More informationChapter 5 Vibrational Motion
Fall 4 Chapter 5 Vibratioal Motio... 65 Potetial Eergy Surfaces, Rotatios ad Vibratios... 65 Harmoic Oscillator... 67 Geeral Solutio for H.O.: Operator Techique... 68 Vibratioal Selectio Rules... 7 Polyatomic
More informationStochastic Matrices in a Finite Field
Stochastic Matrices i a Fiite Field Abstract: I this project we will explore the properties of stochastic matrices i both the real ad the fiite fields. We first explore what properties 2 2 stochastic matrices
More informationActivity 3: Length Measurements with the Four-Sided Meter Stick
Activity 3: Legth Measuremets with the Four-Sided Meter Stick OBJECTIVE: The purpose of this experimet is to study errors ad the propagatio of errors whe experimetal data derived usig a four-sided meter
More information(all terms are scalars).the minimization is clearer in sum notation:
7 Multiple liear regressio: with predictors) Depedet data set: y i i = 1, oe predictad, predictors x i,k i = 1,, k = 1, ' The forecast equatio is ŷ i = b + Use matrix otatio: k =1 b k x ik Y = y 1 y 1
More informationPolynomials with Rational Roots that Differ by a Non-zero Constant. Generalities
Polyomials with Ratioal Roots that Differ by a No-zero Costat Philip Gibbs The problem of fidig two polyomials P(x) ad Q(x) of a give degree i a sigle variable x that have all ratioal roots ad differ by
More informationENGI Series Page 6-01
ENGI 3425 6 Series Page 6-01 6. Series Cotets: 6.01 Sequeces; geeral term, limits, covergece 6.02 Series; summatio otatio, covergece, divergece test 6.03 Stadard Series; telescopig series, geometric series,
More informationLast time: Moments of the Poisson distribution from its generating function. Example: Using telescope to measure intensity of an object
6.3 Stochastic Estimatio ad Cotrol, Fall 004 Lecture 7 Last time: Momets of the Poisso distributio from its geeratig fuctio. Gs () e dg µ e ds dg µ ( s) µ ( s) µ ( s) µ e ds dg X µ ds X s dg dg + ds ds
More informationACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER / Statistics
ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER 1 018/019 DR. ANTHONY BROWN 8. Statistics 8.1. Measures of Cetre: Mea, Media ad Mode. If we have a series of umbers the
More informationJaynes-Cummings Model
Jayes-Cummigs Model The JC model plays a importat role i atom-field iteractio. It is a fully quatized model ad yet aalytically solvable. It describes the iteractio betwee a two-level atom ad a quatized
More informationChimica Inorganica 3
himica Iorgaica Irreducible Represetatios ad haracter Tables Rather tha usig geometrical operatios, it is ofte much more coveiet to employ a ew set of group elemets which are matrices ad to make the rule
More informationHE ATOM & APPROXIMATION METHODS MORE GENERAL VARIATIONAL TREATMENT. Examples:
5.6 4 Lecture #3-4 page HE ATOM & APPROXIMATION METHODS MORE GENERAL VARIATIONAL TREATMENT Do t restrict the wavefuctio to a sigle term! Could be a liear combiatio of several wavefuctios e.g. two terms:
More informationOverview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions
Chapter 9 Slide Ifereces from Two Samples 9- Overview 9- Ifereces about Two Proportios 9- Ifereces about Two Meas: Idepedet Samples 9-4 Ifereces about Matched Pairs 9-5 Comparig Variatio i Two Samples
More informationP1 Chapter 8 :: Binomial Expansion
P Chapter 8 :: Biomial Expasio jfrost@tiffi.kigsto.sch.uk www.drfrostmaths.com @DrFrostMaths Last modified: 6 th August 7 Use of DrFrostMaths for practice Register for free at: www.drfrostmaths.com/homework
More informationComparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading
Topic 15 - Two Sample Iferece I STAT 511 Professor Bruce Craig Comparig Two Populatios Research ofte ivolves the compariso of two or more samples from differet populatios Graphical summaries provide visual
More informationComparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series
Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series
More informationAndrei Tokmakoff, MIT Department of Chemistry, 5/19/
drei Tokmakoff, MT Departmet of Chemistry, 5/9/5 4-9 Rate of bsorptio ad Stimulated Emissio The rate of absorptio iduced by the field is E k " (" (" $% ˆ µ # (" &" k k (4. The rate is clearly depedet o
More informationFormation of A Supergain Array and Its Application in Radar
Formatio of A Supergai Array ad ts Applicatio i Radar Tra Cao Quye, Do Trug Kie ad Bach Gia Duog. Research Ceter for Electroic ad Telecommuicatios, College of Techology (Coltech, Vietam atioal Uiversity,
More informationProbability, Expectation Value and Uncertainty
Chapter 1 Probability, Expectatio Value ad Ucertaity We have see that the physically observable properties of a quatum system are represeted by Hermitea operators (also referred to as observables ) such
More informationENGI 4421 Confidence Intervals (Two Samples) Page 12-01
ENGI 44 Cofidece Itervals (Two Samples) Page -0 Two Sample Cofidece Iterval for a Differece i Populatio Meas [Navidi sectios 5.4-5.7; Devore chapter 9] From the cetral limit theorem, we kow that, for sufficietly
More informationOrthogonal Gaussian Filters for Signal Processing
Orthogoal Gaussia Filters for Sigal Processig Mark Mackezie ad Kiet Tieu Mechaical Egieerig Uiversity of Wollogog.S.W. Australia Abstract A Gaussia filter usig the Hermite orthoormal series of fuctios
More informationConfidence Intervals for the Population Proportion p
Cofidece Itervals for the Populatio Proportio p The cocept of cofidece itervals for the populatio proportio p is the same as the oe for, the samplig distributio of the mea, x. The structure is idetical:
More informationNernst Equation. Nernst Equation. Electric Work and Gibb's Free Energy. Skills to develop. Electric Work. Gibb's Free Energy
Nerst Equatio Skills to develop Eplai ad distiguish the cell potetial ad stadard cell potetial. Calculate cell potetials from kow coditios (Nerst Equatio). Calculate the equilibrium costat from cell potetials.
More informationRAINFALL PREDICTION BY WAVELET DECOMPOSITION
RAIFALL PREDICTIO BY WAVELET DECOMPOSITIO A. W. JAYAWARDEA Departmet of Civil Egieerig, The Uiversit of Hog Kog, Hog Kog, Chia P. C. XU Academ of Mathematics ad Sstem Scieces, Chiese Academ of Scieces,
More informationChapter 6 Sampling Distributions
Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to
More information