Estimation of Thomsen s anisotropy parameter, delta, using CSP gathers.

Size: px
Start display at page:

Download "Estimation of Thomsen s anisotropy parameter, delta, using CSP gathers."

Transcription

1 Estmaton of delta Estmaton of Thomsen s ansotropy parameter, delta, usng CSP gathers. Pavan Elapavulur and John C. Bancroft ABSTRACT In order to extend the sesmc processng technques to ansotropc meda, a measure of the dfferent parameters of ansotropy s requred. The purpose of ths study s to estmate the Thomsen s parameter, (delta), for transversely sotropc (TI) meda. Thomsen s ansotropc normal moveout equaton (NMO) s used to determne (delta). It s also shown that estmated usng common Scatter Pont (CSP) gathers s more accurate than those estmated from common md pont (CMP) gathers. INTRODUCTION Earth s fundamentally ansotropc but most of the processng algorthms assume the deal condton of sotropy, ths faulty assumpton leads to erroneous magng and thus faulty nterpretatons. Backus (1962) stated that f the layered earth s probed wth an elastc wave of wavelength much longer than the typcal layer thckness the wave propagates through ths medum as t were n a homogenous and ansotropc medum. Alkalfah and Tsvankn (1994) used an nverson scheme over the nonhyperbolc equaton to estmate Thomsen s ansotropc parameters. Haase(1998) showed that the non hyperbolc moveout observed wth the flat reflectors n the plans data s due to the transverse sotropy(even though the ndvdual layers are sotorpc). He also estmated Thomsen s ansotropy parameters n the plans data. In ths study we estmate Thomsen s parameter usng the Thomsen s ansotropc NMO equaton, wth the assumpton of weak ansotropy. The man dea s to use veloctes estmated from the common scatter pont (CSP) gather and the veloctes from the sonc log run n a well, nearest to the sesmc lne to estmate the value of. THEORY The most common measure of P wave ansotropy s the rato between the horzontal and vertcal P wave veloctes, typcally between 1.05 to 1.1 and s often as large as 1.2 (Sherff 1991). Thomsen (1986) ntroduced a more effectve and a scentfc measure of ansotropy. He ntroduced the constants ε, γ, and as effectve parameters of measure of ansotropy. Accordng to Thomsen, s the most crtcal measure of ansotropy and t doesn t nvolve the horzontal velocty at all n ts defnton. Therefore measurng s very mportant for processes lke depth magng. Accordng to Told et.al (1999) The depth effects are carred by the parameter whch must therefore be measured wth help of well control. CREWES Research Report Volume 13 (2001) 479

2 Elapavulur and Bancroft Usng week ansotropy approxmaton and Thomsen s parameters, the phase velocty, V can be wrtten as equaton 1. It s a functon of phase angle θ. (Thomsen, 1986). ( θ ) ( 1 sn 2 θ cos 2 θ ε sn 4 θ ) V = V + + (1) 0 Under the short spread assumpton (the half offset s smaller than the depth of the reflector, typcally the moveout s hyperbolc n hyperbolc even n ansotropc meda at shorter offsets), the NMO velocty s controlled by only the Thomsen s parameter. (Thomsen, 1986). Ths s shown n equaton 2 (Thomsen, 1986) ( ) V p = V p + (2) ( ) Common Shot Pont (CSP) gathers A common scatter pont gather s a pre-stack mgraton gather that collects all the nput traces that contan energy from a vertcal array of scatter ponts. The dstance from the surface locaton of the scatter pont to the source and recever defnes the offsets n a CSP gather, but not the source recever offset. The CSP gather s smlar n appearance to a common md pont (CMP) gather. They both defne a subsurface locaton, and are sorted by offsets. Hence all the traces n the prestack mgraton aperture, regardless of the source or recever poston, may be used to form a CSP gather. The offsets n a CSP gather are defned as gven by equaton 3 (Bancroft, et.al, 1998) 4xh e = + (3) 2 2 tvrms h x h Where x s dstance between CMP and the CSP gathers locaton and h s half the source recever offset, Vrms s the RMS velocty. Advantages of CSP gather A CSP gather s characterzed by ts very hgh fold, ncreasng the SNR, whch makes velocty pckng more accurate. Generally CSP gathers have larger offsets when compared to CMP gathers. The semblance plot of the CSP gather shows tghter clusterng of energy, whch enables (and requres) more accurate pckng of veloctes. It has been shown by Bancroft (1996) that NMO veloctes estmated usng a CSP gather are more accurate than veloctes estmated over a CMP gather. 480 CREWES Research Report Volume 13 (2001)

3 Estmaton of delta METHOD Calculaton of V V s the nterval NMO velocty estmated from the semblance analyss usng the procedure descrbed below. The short spread normal moveout(nmo) of an N layered transverse sotropc (TI) medum as gven by Alkhalfah and Tsvankn n 1995 can be wrtten as equaton (4) 2 [ ] t N 2 1 V ( ) p V ( p) 0 = 1 = τ (4) whereτ s the 2-way zero-offset traveltme to the bottom of the N th layer, t o s the 2 way zero offset traveltme for an ndvdual layer and V ( p) s the root-meansquare velocty of the NMO velocty n each layer taken at the ray parameter p (Alkhalfah and Tsvankn, 1995). In the case of a flat layered earth the equaton (4) reduces to the Dx equaton. In order to obtan the NMO velocty n any layer (ncludng the one mmedately above the reflector), we need to apply the Dx formula (Dx, 1955) to the NMO veloctes from the top V ( 1) and the bottom V () of the layer (Alkhalfah and Tsvankn, 1995). 2 2 t0 () V () t0 ( 1) V ( 1) [ V ] = (5) t0 () t0 ( 1) t o s the 2 way zero offset traveltme for an ndvdual layer. V () s estmated from the velocty analyss over the CSP/CDP gathers formed at the CDP pont, whch s closest to the well locaton. Calculaton of V 0 V 0 s the nterval vertcal velocty. Ths can be estmated from well logs or VSP measurements. In ths study Veloctes were estmated from the sonc log recorded at the well located nearest to the sesmc lne. Calculaton of Usng both of the veloctes V and V 0 the value of can be estmated at each tme step usng equaton (2), whch can be wrtten as equaton (6). CREWES Research Report Volume 13 (2001) 481

4 Elapavulur and Bancroft 2 ( V ) ( ) 2 V0 1 = 1 (6) 2 where V s the nterval velocty and V s the vertcal velocty. 0 CASE STUDY Ths technque wll now be tested over a synthetc ansotropc data generated usng NORSAR2D software. Norsar 2D s ray tracng program based on the ray theory by (Cerveny, 1985). A layered geologc model (fgure 1) was bult wth 9 flat layers n t. The model s bult n the depth doman and s 6kms deep. The thnnest layer s of thckness 0.5 km and ts velocty s 1000m/s. A 40hz zero-phase Rcker wavelet was used for the generaton of sesmograms wthout volatng the raytracng assumptons. There are 101 shots n total and 300 recevers per shot. The shot spacng was 40m and recever spacng was 20m. Fgure 1 shows the layered model wth table (1) showng the values of the materal propertes vz. P-wave velocty S-wave velocty Densty ε and Interface P-velocty S velocty Densty ε Table 1. Materal propertes of the model used. 482 CREWES Research Report Volume 13 (2001)

5 Estmaton of delta FIG. 1. The Geologcal model. Ths model data was used to test the above method. The algorthm can be descrbed as follows: Perform semblance analyss on the gathers to yeld V. Use equaton (5) to estmate the nterval NMO veloctes Determne the value of V 0, the vertcal velocty, from the VSP data/sonc logs. Use equaton (6) to calculate. Semblance analyss was performed on both common md pont (CMP) and common scatter pont (CSP) gathers (Fgures 2 &3). The values of were calculated usng the NMO veloctes from both the CMP and CSP gathers. Tables (2 and 3) show the values of calculated usng CMP and CSP gathers respectvely. CREWES Research Report Volume 13 (2001) 483

6 Elapavulur and Bancroft FIG. 2. The semblance plot over a CDP gather. FIG. 3. The semblance plot over a CSP gather. 484 CREWES Research Report Volume 13 (2001)

7 Estmaton of delta V (CMP) V 0, (model) (estmated) Table 2. s calculated usng CMP gathers. V (CSP) V 0, (model) (estmated) Table 3. s calculated usng CSP gathers. It s evdent from the tables (2 and 3) that the values of estmated usng CSP gathers are much more accurate than those calculated usng CMP gathers. CREWES Research Report Volume 13 (2001) 485

8 Elapavulur and Bancroft Error analyss The values of estmated are heavly dependent on the veloctes estmated from the semblance analyss. We dd a smple error analyss by ntroducng an error nto the NMO veloctes estmated from the CSP gather. The results are shown n the Fgure 4. For a 0-10% error n veloctes we encountered around % error n the values of estmated. As the veloctes estmated from a CSP gather are more accurate, s can be estmated wth better confdence usng the CSP veloctes. % error Error plot of deltas estmated ntervals where delta s estmated 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% FIG. 4. The error plot. Feld data Ths method was appled over the sesmc data collected over the Blackfoot feld. Blackfoot feld s near Strathmore, Alberta and s operated by PanCanadan petroleum. A 3C 3D data was acqured by CREWES n The Blackfoot Feld s located n Townshp 23, Range 23, West of 4 th merdan, n south central Alberta. Geology The geology of Blackfoot feld has been dscussed n detal by Mller et. al (1995). Ths s a very bref revew of the lthology of the formatons of nterest to ths work. The reservor rocks n ths feld are Glaucontc ncsed valleys n Lower Manvlle group of lower Cretaceous. Coals, Vkng formaton and Base of fsh scales shales overle these reservor rocks. A lne numbered 20M vertcal that has a well (#09-08) located very near to t was chosen to test ths method. The fgure 5 shows the poston of the well on the CDP plot. Fgure 6 shows the poston of the well on the stacked secton. CMP and CSP gathers are the formed at CDP 149, whch s nearest to the well locaton. Semblance analyss was performed on both CSP and CDP gathers formed at ths locaton. The veloctes estmated were used n the algorthm dscussed above to estmate the values of. 486 CREWES Research Report Volume 13 (2001)

9 Estmaton of delta The poston of the well FIG. 5 Poston of the well on the CDP plot. The poston of the well FIG. 6 Poston of the well on the stacked secton. The followng fgures (7 and 8) show the semblances plots of CDP and CSP gathers at CDP 149 respectvely. CREWES Research Report Volume 13 (2001) 487

10 Elapavulur and Bancroft FIG. 7 Semblance plot over a CDP gather. FIG. 8 Semblance plot over a CSP gather. The veloctes estmated from the semblance analyss over CMP and CSP gathers were used to estmate the values of for the formatons of nterest. Table 4 shows the formaton namng conventon used here. Tables 5 and 6 show the values of estmated from CMP and CSP gathers respectvely. 488 CREWES Research Report Volume 13 (2001)

11 Estmaton of delta Abbrevaton Unt Name BFS Base of Fsh Scales Zone MANN Blarmore- Upper Mannvlle COAL Coal Layer GLCTOP Glaucontc Channel porous Sandstone unt MISS Shunda Mssssppan Table 4. Formaton namng conventons. Formaton V (CMP) V 0, (estmated) BFS MANN COAL GLCTOP MISS Table 5. s calculated usng CMP gathers. Formaton V (CSP) V 0, (estmated) BFS MANN COAL GLCTOP MISS Table 6. s calculated usng CSP gathers. CREWES Research Report Volume 13 (2001) 489

12 Elapavulur and Bancroft DISCUSSION AND CONCLUSIONS Estmaton of ansotropy parameters s an mportant aspect n sesmc analyss. Thomsen s ansotropc parameter has an effect on the depth calculatons. The estmaton s hghly dependent on the estmaton NMO velocty. The error analyss of s estmated proves how mportant s estmaton of accurate NMO veloctes s. It has also been showed that estmated usng CSP gathers are more accurate than that of those estmated from CMP gathers. Extendng ths analyss to the real feld data, we found that the shales and coals show very sgnfcant ansotropy. The vertcal veloctes estmated from a sonc log were used n ths study. The veloctes from sonc log are greater than the sesmc veloctes. Vertcal nterval veloctes estmated from VSP data would gve more accurate estmates n ths area. ACKNOWLEDGEMENTS We acknowledge the CREWES sponsors for ther contnued support. We thank Ian Watson for helpng wth the nterpretaton of Blackfoot data. REFERENCES Alkhalfah, T. and Tsvankn, I., 1995, Velocty analyss for transversely sotropc meda: Geophyscs, Soc. Of Expl. Geophys., 60, Backus, G. E., 1962, Long-wave elastc ansotropy produced by horzontal layerng: J.Geophys Res., 70, Bancroft, J.C., 1996, Velocty senstvty for equvalent offset prestack mgraton, Ann. Mtg: Can. Soc. Of Expl Geophys. Bancroft, J.C., Geger, H.D. and Margrave, G.F., 1998, The equvalent offset method of prestack tme mgraton: Geophyscs, Soc. Of Expl. Geophys., 63, Cerveny, V., The applcaton of numercal modellng of sesmc wavefelds n complex structure. Sesmc shear waves, Part A: Theory (ed. G Dhor). Pp Geophyscal press, London. Dx, C.H., 1955, Sesmc veloctes from surface measurements: Geophyscs, Soc. Of Expl. Geophys., 20, Haase, Armn. B., 1998, Non hyperbolc moveout n plans data and the ansotropy queston: Recorder, Can. Soc. of Expl Geophys. Nov, 1998, Mller, S.L.M., Aydemr, E.O. and Margrave, G.F., 1995, Prelmnary nterpretaton of P-P and P-S sesmc data from Blackfoot broad-band survey: Crewes Research Report 1995, Ch 42. Sherff, R. E., 1991, Encyclopedc Dctonary of Exploraton Geophyscs, Encyclopedc dctonary of exploraton geophyscs: Soc. of Expl. Geophys., 384. Thomsen, L., 1986, Weak elastc ansotropy: Geophyscs, Soc. Of Expl. Geophys., 51, Told, J., Alkhalfah, T., Berthet, P., Arnaud, J., Wllamson, P. and Conche, B., 1999, Case study of estmaton of ansotropy: The Leadng Edge, 18, no. 5, CREWES Research Report Volume 13 (2001)

Chapter 4. Velocity analysis

Chapter 4. Velocity analysis 1 Chapter 4 Velocty analyss Introducton The objectve of velocty analyss s to determne the sesmc veloctes of layers n the subsurface. Sesmc veloctes are used n many processng and nterpretaton stages such

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

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

More information

Generalized form of reflection coefficients in terms of impedance matrices of qp-qp and qp-qs waves in TI media

Generalized form of reflection coefficients in terms of impedance matrices of qp-qp and qp-qs waves in TI media Generalzed form of reflecton coeffcents n terms of mpedance matrces of q-q and q-q waves n TI meda Feng Zhang and Xangyang L CNC Geophyscal KeyLab, Chna Unversty of etroleum, Bejng, Chna ummary Reflecton

More information

Mud-rock line estimation via robust locally weighted scattering smoothing method

Mud-rock line estimation via robust locally weighted scattering smoothing method Mud-roc lne va LOESS Mud-roc lne estmaton va robust locally weghted scatterng smoothng method A. Nassr Saeed, Laurence R. Lnes, and Gary F. Margrave ABSTRACT The robust locally weghted scatterng smoothng

More information

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information

Indeterminate pin-jointed frames (trusses)

Indeterminate pin-jointed frames (trusses) Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

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

More information

Main Menu. characterization using surface reflection seismic data and sonic logs. Summary

Main Menu. characterization using surface reflection seismic data and sonic logs. Summary Stochastc Sesmc Inverson usng both Waveform and Traveltme Data and Its Applcaton to Tme-lapse Montorng Youl Quan* and Jerry M. Harrs, Geophyscs Department, Stanford Unversty Summary A stochastc approach

More information

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

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

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

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

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

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

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

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

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

C-wave event automated registration using a nonlinear global search method

C-wave event automated registration using a nonlinear global search method C-wave event automated regstraton usng a nonlnear global search method Shuangquan Chen*,1, Xang-Yang L 1,2 and Xaomng L 1 1 CNPC Keylab of Geophyscal Prospectng, Chna Unversty of Petroleum, Bejng, 102249,

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

a. (All your answers should be in the letter!

a. (All your answers should be in the letter! Econ 301 Blkent Unversty Taskn Econometrcs Department of Economcs Md Term Exam I November 8, 015 Name For each hypothess testng n the exam complete the followng steps: Indcate the test statstc, ts crtcal

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Lecture 3 Stat102, Spring 2007

Lecture 3 Stat102, Spring 2007 Lecture 3 Stat0, Sprng 007 Chapter 3. 3.: Introducton to regresson analyss Lnear regresson as a descrptve technque The least-squares equatons Chapter 3.3 Samplng dstrbuton of b 0, b. Contnued n net lecture

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

( ) ( ) Summary. Introduction

( ) ( ) Summary. Introduction A mgraton velocty updatng method based on the shot ndex common mage gather and fnte-frequency senstvty kernel Xao- Xe* and Hu Yang, nsttute of Geophyscs and Planetary Physcs, Unversty of Calforna, Santa

More information

Assessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion

Assessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,

More information

Chapter 11: Simple Linear Regression and Correlation

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

More information

Definition. Measures of Dispersion. Measures of Dispersion. Definition. The Range. Measures of Dispersion 3/24/2014

Definition. Measures of Dispersion. Measures of Dispersion. Definition. The Range. Measures of Dispersion 3/24/2014 Measures of Dsperson Defenton Range Interquartle Range Varance and Standard Devaton Defnton Measures of dsperson are descrptve statstcs that descrbe how smlar a set of scores are to each other The more

More information

AVO polarization and hodograms: AVO strength and polarization product

AVO polarization and hodograms: AVO strength and polarization product GEOPHYSICS, VOL. 68, NO. 3 (MAY-JUNE 2003); P. 849 862, 20 FIGS., 2 TABLES. 10.1190/1.1581037 AVO polarzaton and hodograms: AVO strength and polarzaton product Patrce Nsoga Mahob and John P. Castagna ABSTRACT

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Linear Regression Analysis: Terminology and Notation

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

More information

Introduction to Antennas & Arrays

Introduction to Antennas & Arrays Introducton to Antennas & Arrays Antenna transton regon (structure) between guded eaves (.e. coaxal cable) and free space waves. On transmsson, antenna accepts energy from TL and radates t nto space. J.D.

More information

EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES

EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES Manuel J. C. Mnhoto Polytechnc Insttute of Bragança, Bragança, Portugal E-mal: mnhoto@pb.pt Paulo A. A. Perera and Jorge

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

More information

SIO 224. m(r) =(ρ(r),k s (r),µ(r))

SIO 224. m(r) =(ρ(r),k s (r),µ(r)) SIO 224 1. A bref look at resoluton analyss Here s some background for the Masters and Gubbns resoluton paper. Global Earth models are usually found teratvely by assumng a startng model and fndng small

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

G xx ; is Green s. ) are the displacement and. Main Menu

G xx ; is Green s. ) are the displacement and. Main Menu for strong-contrast meda and ts applcaton to subsalt mgraton Ru Yan 1* Ru-han Wu 1 Xao-B Xe 1 and Dave Walraven 1 GPP Earth and Planetary cences Department Unversty of Calforna anta Cru; Anadarko petroleum

More information

Appendix B. The Finite Difference Scheme

Appendix B. The Finite Difference Scheme 140 APPENDIXES Appendx B. The Fnte Dfference Scheme In ths appendx we present numercal technques whch are used to approxmate solutons of system 3.1 3.3. A comprehensve treatment of theoretcal and mplementaton

More information

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

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

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

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

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

More information

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11)

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11) Gravtatonal Acceleraton: A case of constant acceleraton (approx. hr.) (6/7/11) Introducton The gravtatonal force s one of the fundamental forces of nature. Under the nfluence of ths force all objects havng

More information

A Hybrid Variational Iteration Method for Blasius Equation

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

More information

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise. Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the

More information

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD Ákos Jósef Lengyel, István Ecsed Assstant Lecturer, Professor of Mechancs, Insttute of Appled Mechancs, Unversty of Mskolc, Mskolc-Egyetemváros,

More information

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown

More information

p 1 c 2 + p 2 c 2 + p 3 c p m c 2

p 1 c 2 + p 2 c 2 + p 3 c p m c 2 Where to put a faclty? Gven locatons p 1,..., p m n R n of m houses, want to choose a locaton c n R n for the fre staton. Want c to be as close as possble to all the house. We know how to measure dstance

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam APPENDIX F A DISPACEMENT-BASED BEAM EEMENT WITH SHEAR DEFORMATIONS Never use a Cubc Functon Approxmaton for a Non-Prsmatc Beam F. INTRODUCTION { XE "Shearng Deformatons" }In ths appendx a unque development

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

Formulas for the Determinant

Formulas for the Determinant page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

Second Order Analysis

Second Order Analysis Second Order Analyss In the prevous classes we looked at a method that determnes the load correspondng to a state of bfurcaton equlbrum of a perfect frame by egenvalye analyss The system was assumed to

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

ORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp.

ORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp. Clck to Vew Mathcad Document 2011 Knovel Corp. Buldng Structural Desgn. homas P. Magner, P.E. 2011 Parametrc echnology Corp. Chapter 3: Renforced Concrete Slabs and Beams 3.2 Renforced Concrete Beams -

More information

GEO-SLOPE International Ltd, Calgary, Alberta, Canada Vibrating Beam

GEO-SLOPE International Ltd, Calgary, Alberta, Canada   Vibrating Beam GEO-SLOPE Internatonal Ltd, Calgary, Alberta, Canada www.geo-slope.com Introducton Vbratng Beam Ths example looks at the dynamc response of a cantlever beam n response to a cyclc force at the free end.

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

Case A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k.

Case A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k. THE CELLULAR METHOD In ths lecture, we ntroduce the cellular method as an approach to ncdence geometry theorems lke the Szemeréd-Trotter theorem. The method was ntroduced n the paper Combnatoral complexty

More information

x i1 =1 for all i (the constant ).

x i1 =1 for all i (the constant ). Chapter 5 The Multple Regresson Model Consder an economc model where the dependent varable s a functon of K explanatory varables. The economc model has the form: y = f ( x,x,..., ) xk Approxmate ths by

More information

Constitutive Modelling of Superplastic AA-5083

Constitutive Modelling of Superplastic AA-5083 TECHNISCHE MECHANIK, 3, -5, (01, 1-6 submtted: September 19, 011 Consttutve Modellng of Superplastc AA-5083 G. Gulano In ths study a fast procedure for determnng the constants of superplastc 5083 Al alloy

More information

Answers Problem Set 2 Chem 314A Williamsen Spring 2000

Answers Problem Set 2 Chem 314A Williamsen Spring 2000 Answers Problem Set Chem 314A Wllamsen Sprng 000 1) Gve me the followng crtcal values from the statstcal tables. a) z-statstc,-sded test, 99.7% confdence lmt ±3 b) t-statstc (Case I), 1-sded test, 95%

More information

Global Sensitivity. Tuesday 20 th February, 2018

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

More information

Physics 181. Particle Systems

Physics 181. Particle Systems Physcs 181 Partcle Systems Overvew In these notes we dscuss the varables approprate to the descrpton of systems of partcles, ther defntons, ther relatons, and ther conservatons laws. We consder a system

More information

Supplemental Material: Causal Entropic Forces

Supplemental Material: Causal Entropic Forces Supplemental Materal: Causal Entropc Forces A. D. Wssner-Gross 1, 2, and C. E. Freer 3 1 Insttute for Appled Computatonal Scence, Harvard Unversty, Cambrdge, Massachusetts 02138, USA 2 The Meda Laboratory,

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

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

More information

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski EPR Paradox and the Physcal Meanng of an Experment n Quantum Mechancs Vesseln C Nonnsk vesselnnonnsk@verzonnet Abstract It s shown that there s one purely determnstc outcome when measurement s made on

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

CHAPTER 14 GENERAL PERTURBATION THEORY

CHAPTER 14 GENERAL PERTURBATION THEORY CHAPTER 4 GENERAL PERTURBATION THEORY 4 Introducton A partcle n orbt around a pont mass or a sphercally symmetrc mass dstrbuton s movng n a gravtatonal potental of the form GM / r In ths potental t moves

More information

Irregular vibrations in multi-mass discrete-continuous systems torsionally deformed

Irregular vibrations in multi-mass discrete-continuous systems torsionally deformed (2) 4 48 Irregular vbratons n mult-mass dscrete-contnuous systems torsonally deformed Abstract In the paper rregular vbratons of dscrete-contnuous systems consstng of an arbtrary number rgd bodes connected

More information

Statistics MINITAB - Lab 2

Statistics MINITAB - Lab 2 Statstcs 20080 MINITAB - Lab 2 1. Smple Lnear Regresson In smple lnear regresson we attempt to model a lnear relatonshp between two varables wth a straght lne and make statstcal nferences concernng that

More information

Pressure Measurements Laboratory

Pressure Measurements Laboratory Lab # Pressure Measurements Laboratory Objectves:. To get hands-on experences on how to make pressure (surface pressure, statc pressure and total pressure nsde flow) measurements usng conventonal pressuremeasurng

More information

DO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED.

DO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED. EE 539 Homeworks Sprng 08 Updated: Tuesday, Aprl 7, 08 DO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED. For full credt, show all work. Some problems requre hand calculatons.

More information

Time lapse view of the Blackfoot AVO anomaly

Time lapse view of the Blackfoot AVO anomaly Time lapse view of the Blackfoot AVO anomaly Han-xing Lu, Gary F. Margrave and Colin C. Potter Time lapse view of the Blackfoot AVO SUMMARY In the Blackfoot field, southeast of Calgary there is an incised

More information

Supplementary Notes for Chapter 9 Mixture Thermodynamics

Supplementary Notes for Chapter 9 Mixture Thermodynamics Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

Computer Based Porosity Design by Multi Phase Topology Optimization

Computer Based Porosity Design by Multi Phase Topology Optimization Computer Based Porosty Desgn by Mult Phase Topology Optmzaton Andreas Burbles and Matthas Busse Fraunhofer-Insttut für Fertgungstechnk und Angewandte Materalforschung - IFAM Wener Str. 12, 28359 Bremen,

More information

Statistics for Business and Economics

Statistics for Business and Economics Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 11-1 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

Lecture 3. Ax x i a i. i i

Lecture 3. Ax x i a i. i i 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

Chapter 8 Indicator Variables

Chapter 8 Indicator Variables Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n

More information

Fuzzy Boundaries of Sample Selection Model

Fuzzy Boundaries of Sample Selection Model Proceedngs of the 9th WSES Internatonal Conference on ppled Mathematcs, Istanbul, Turkey, May 7-9, 006 (pp309-34) Fuzzy Boundares of Sample Selecton Model L. MUHMD SFIIH, NTON BDULBSH KMIL, M. T. BU OSMN

More information

18.1 Introduction and Recap

18.1 Introduction and Recap CS787: Advanced Algorthms Scrbe: Pryananda Shenoy and Shjn Kong Lecturer: Shuch Chawla Topc: Streamng Algorthmscontnued) Date: 0/26/2007 We contnue talng about streamng algorthms n ths lecture, ncludng

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

9.2 Seismic Loads Using ASCE Standard 7-93

9.2 Seismic Loads Using ASCE Standard 7-93 CHAPER 9: Wnd and Sesmc Loads on Buldngs 9.2 Sesmc Loads Usng ASCE Standard 7-93 Descrpton A major porton of the Unted States s beleved to be subject to sesmc actvty suffcent to cause sgnfcant structural

More information

Projective change between two Special (α, β)- Finsler Metrics

Projective change between two Special (α, β)- Finsler Metrics Internatonal Journal of Trend n Research and Development, Volume 2(6), ISSN 2394-9333 www.jtrd.com Projectve change between two Specal (, β)- Fnsler Metrcs Gayathr.K 1 and Narasmhamurthy.S.K 2 1 Assstant

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics ECOOMICS 35*-A Md-Term Exam -- Fall Term 000 Page of 3 pages QUEE'S UIVERSITY AT KIGSTO Department of Economcs ECOOMICS 35* - Secton A Introductory Econometrcs Fall Term 000 MID-TERM EAM ASWERS MG Abbott

More information

Physics 5153 Classical Mechanics. Principle of Virtual Work-1

Physics 5153 Classical Mechanics. Principle of Virtual Work-1 P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal

More information

PY2101 Classical Mechanics Dr. Síle Nic Chormaic, Room 215 D Kane Bldg

PY2101 Classical Mechanics Dr. Síle Nic Chormaic, Room 215 D Kane Bldg PY2101 Classcal Mechancs Dr. Síle Nc Chormac, Room 215 D Kane Bldg s.ncchormac@ucc.e Lectures stll some ssues to resolve. Slots shared between PY2101 and PY2104. Hope to have t fnalsed by tomorrow. Mondays

More information

AS-Level Maths: Statistics 1 for Edexcel

AS-Level Maths: Statistics 1 for Edexcel 1 of 6 AS-Level Maths: Statstcs 1 for Edecel S1. Calculatng means and standard devatons Ths con ndcates the slde contans actvtes created n Flash. These actvtes are not edtable. For more detaled nstructons,

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

e i is a random error

e i is a random error Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown

More information

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013 ISSN: 2277-375 Constructon of Trend Free Run Orders for Orthogonal rrays Usng Codes bstract: Sometmes when the expermental runs are carred out n a tme order sequence, the response can depend on the run

More information

Joint Statistical Meetings - Biopharmaceutical Section

Joint Statistical Meetings - Biopharmaceutical Section Iteratve Ch-Square Test for Equvalence of Multple Treatment Groups Te-Hua Ng*, U.S. Food and Drug Admnstraton 1401 Rockvlle Pke, #200S, HFM-217, Rockvlle, MD 20852-1448 Key Words: Equvalence Testng; Actve

More information

Review of Taylor Series. Read Section 1.2

Review of Taylor Series. Read Section 1.2 Revew of Taylor Seres Read Secton 1.2 1 Power Seres A power seres about c s an nfnte seres of the form k = 0 k a ( x c) = a + a ( x c) + a ( x c) + a ( x c) k 2 3 0 1 2 3 + In many cases, c = 0, and the

More information

Measurement Uncertainties Reference

Measurement Uncertainties Reference Measurement Uncertantes Reference Introducton We all ntutvely now that no epermental measurement can be perfect. It s possble to mae ths dea quanttatve. It can be stated ths way: the result of an ndvdual

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information