Measurement of Positional Deviation of Numerically Controlled Axes

Size: px
Start display at page:

Download "Measurement of Positional Deviation of Numerically Controlled Axes"

Transcription

1 XXXII. Semnar ASR '7 Instruments and Control, Farana, Smutný, Kočí & Babuch (eds) 7, VŠB-TUO, Ostrava, ISBN Measurement of Postonal Devaton of Numercally Controlled Axes KUREKOVÁ, Eva, HAAJ, Martn, OEB, Tomáš & TVRDOŇOVÁ, Martna doc. Ing., PhD., Katedra automatzáce, nformačnej a prístrojovej technky, SjF STU, nám Slobody 7, Bratslava, 8 3, Slovenská republka, eva.kurekova@stuba.sk doc. Ing., PhD., martn.halaj@stuba.sk Ing., PaedDr., tomas.loebl@stuba.sk martna.tvrdonova@stuba.sk Abstract: Nowadays many modern producton machnes requre postonng n thousandths of mllmeter. Therefore a bg attenton must be pad to prepare such a controllng software that drves ndvdual axes of the producton machne whch mnmses devatons between the desred poston and actually reached poston. The so called postonal devaton (dfference between the actual and target poston) belongs to the mportant crtera that descrbe the performance of numercally controlled axes. The procedure for determnaton of such devaton s descrbed n the nternatonal standard ISO 3-:997. Ths standard provdes calculaton of the postonal devaton only n several dscrete (measurng) ponts. Moreover t does not consder effects of the measurng nstrument on the obtaned results. Thus the new methodology must be adopted that enables estmaton of the postonal devaton n any pont of the axs travel, together wth the uncertanty of such estmate. Obtaned results can be ncorporated nto a control system n the form of correctons enhancng postonng possbltes of ndvdual axes. Keywords: postonal devaton, measured data, standard ISO 3-:997, numercally controlled axs Introducton Testng of the postonal devaton of the numercally controlled axs (ether rotary or longtudnal) s ruled by the nternatonal standard ISO 3-:997 []. Ths standard provdes gude for desgn of the test, testng condtons and also evaluaton procedure for processng the measured data. In general, the testng procedure s based on repeated measurements of the actual poston of the tested axs n several dscrete ponts (target postons), located equally along the axs travel (see Fgure ). The several parameters dealng wth postonal devaton can be measured and calculated accordng to such a scheme. The evaluaton of measured data accordng to the standard gves just estmaton of the devce performance n several dscrete ponts (measurement ponts P ). But the course of ndvdual parameters among the measurement ponts s just roughly estmated accordng to the standard, gvng no warranty on correctness of the results n between ponts. 9

2 Fgure Scheme of the measurement n ndvdual ponts Related terms Terms connected wth measurement of postonng numercally controlled axes, as ntroduced by the standard [] (for sake of clarty only undrectonal approach s consdered): - axs travel maxmum travel, lnear of rotary, over whch the movng component can move under numercal control (par..), - measurement travel part of the axs travel whch s used for data capture, selected so that the frst and the last target postons may be approached bdrectonally (par..), - target poston - poston to whch the movng part s programmed to move (par..3). Desgnaton P ( = to m), - actual poston measured poston reached by the movng part on the j-th approach to the -th target poston (par..4). Desgnaton P j ( = to m, j = to n), - devaton of poston (postonal devaton) actual poston reached by the movng part mnus the target poston (par..5). Desgnaton x j, magntude calculated as x j = P j P, - mean undrectonal postonal devaton arthmetc mean of the postonal devatons obtaned by a seres of n undrectonal approaches to a poston P (par..). Desgnaton x, magntude calculated as x = n x j n j= 3

3 - estmator of the undrectonal standard uncertanty of postonng at a poston estmator of the standard uncertanty of the postonal devatons (par..5). Desgnaton s, magntude calculated as n ( xj x ) s = n j = - undrectonal systematc postonal devaton of an axs the dfference between the algebrac maxmum and mnmum of the mean undrectonal postonal devatons at any poston P (par..9). Desgnaton E, - undrectonal accuracy of postonng of an axs (par..). Desgnaton A, magntude calculated as A = max [ x + s ] mn [ x s ]. 3 Evaluaton of measured data accordng to the standard The above mentoned standard ntroduces evaluaton of the measured data that s amed namely at determnng the maxmum postonal devaton over the whole axs (measurement) travel. The evaluaton of results covers calculaton of the parameters related to the postonal devaton (see part ) n each of the measurement ponts P, covered also by the devaton boundares x +3 s ; x - s (respectvely x + s ; x -3 s n reversal drecton), see Fgure. Fgure Example of the graphcal presentaton of evaluated postonal devaton accordng to the standard [] (results plotted for undrectonal postonng) Note that the devaton boundares are calculated only from measured data, consderng ther varaton as caused only by a tested machne and not nfluenced by the measurement nstrument tself. Ths s vald only when the accuracy of measurement nstrument s much better than expected postonal devatons. But for modern numercally controlled axes, operatng wth resolutons of. mm, ths s not always the case and the used measurng nstrument tself can sgnfcantly affect the measurement results. As you can observe from Fgure, the standard assumes lnear course of the postonal devaton among the measurement (testng pont), as well as lnear course of the devaton boundares. 3

4 4 Evaluaton of the postonal devaton n any pont of the axs travel When consderng the above presented measurement scheme, followng postonal devatons are measured n ndvdual measurement ponts: P : Δ, Δ,..., Δ j,..., Δ n P : Δ, Δ,..., Δ j,..., Δ n () M P m : Δ m, Δ m,..., Δ mj,..., Δ mn where to m s the runnng number of the measurement pont (see Fgure ), Δ j are postonal devatons* (see part ), j to n s the runnng number of the measurement of postonal devaton n a gven measurement pont. It s assumed that the same number n of measurements of the postonal devaton s performed n each measurement pont. (*Remark: the postonal devaton that s desgnated n the standard [] as x j, s for sake of better clarty and understandablty desgnated by dfferent symbol Δ j ). If we want to obtan the estmates of the postonal devatons also n other ponts than the measurement ones, we must approxmate course of estmates. The least squares method s sutable for such approxmaton. The curve n a form of polynomal of the thrd order wll be placed over the ponts (P, Δ ), (P, Δ ),..., (P, Δ ),..., (P m, Δ m ): Δ = a + b P + c P + d P 3 () where Δ s the postonal devaton of the target poston and actual poston n any pont P and P <P ; P m >, a, b, c, d are unknown parameters of the polynomal. Besdes that we want to determne also expanded uncertanty U of estmate of the postonal devaton Δ n any pont P. To be able to do ths, we must determne the estmates of polynomal parameters a, b, c and d, ther uncertantes and covarances among them. Thus we leave the evaluaton accordng to the Fgure and we get evaluaton provdng results accordng to the Fgure 3. To do so, we need to ntroduce a completely new approach to evaluaton of the measured data. The postonal devaton for each measurement j = to n n each partcular pont P, wth = to m, can be calculated as the dfference between the target poston and the measured actual poston (see part above) []: ' Δ = P j P (3) j where P s the target (programmed) poston, ' P j s the actual (measured) poston. The actual (measured) poston P ' j j mer. ' P j comprses two components: = P + δ (4) where P j δ mer. s the poston ndcated by the measurng nstrument, s the measurement error n the partcular pont, n our case estmated as the maxmum permssble error of the measurng nstrument [8]. Ths means that the error remans constant n any pont. 3

5 Fgure 3 Estmaton of the postonal devaton n any pont of the axs travel Accordng to the prevous calculatons of estmates of the postonal devaton (as the arthmetc means), the set of equatons for j = to n postonal devatons n m ponts (target postons) can be expressed as [5]: Δ + δ mer. = P P Δ = P P + δ mer (5) M Δ m = Pm P + δ mer. m where P s the estmate (obtaned as an arthmetc mean) of the actual (measured) postons n any gven pont P. The set of equatons descrbng the postonal devatons (5) can be wrtten n a matrx form: x = P - P + δ mer (6) where x P P x = ( Δ Δ, ) T,, Δm T ( P, P,..., P m ) P = P = (P, P,..., P m ) T s the vector of the estmates of ndvdual postonal devatons (dmenson m), s the vector of the estmates of measured actual postons (dmenson m), s the vector of target postons (dmenson m), s the unt vector (dmenson m) and When takng the matrx notaton nto account, the covarance matrx U(x) can be wrtten n a form (assumng P as non-random vector, P and δ are ndependent): U(x) = U( P ) - U(P) + u (δ) T (7) 33

6 The uncertanty of the target poston s zero n our case (no nfluence on value of the target poston) so that the covarance matrx U(x) gets the form [4]: U(x) = U( P ) + u (δ) T (8) After specfyng the ndvdual terms (because P a P j are ndependent): u P U(x) = u = ( ) u ( P ) u ( P ) m ( P ) + u ( δ ) u ( δ ) u ( δ ) u ( δ ) u ( P ) + u ( δ ) u ( δ ) u + u (δ) T = ( δ ) u ( δ ) u ( P ) + u ( δ ) The uncertantes u(p ), where =,,..., m, n the matrx (8) are evaluated by the type A method form measured data: u( P ) n = ( P, j P ) n( n ) j= Ths procedure yelds to the estmates of unknown parameters a, b, c, d, uncertantes of those estmates and covarances among them. 5 The experment The expermental measurement of postonal devaton was performed at the laboratores of Department of Producton Technology, Faculty of Mechancal Engneerng, Slovak Unversty of Technology n Bratslava [3]. The prototype of the unversal plasma cuttng head [] was employed for measurement of ndvdual postonal devatons (see Fgure 4., - Fgure s courtesy of the Department of Producton Technology, Faculty of Mechancal Engneerng, Slovak Unversty of Technology n Bratslava). As the plasma cuttng head contans totally 6 numercally controlled axs, the experment was restrcted to one axs and the others were n standstll. The schematc representaton of the measured axs together wth the precse lnear encoder (used as a reference measurng nstrument) s shown n Fgure 5. m (9) () Fgure 4 Unversal plasma cuttng head Fgure 5 Schematc representaton of the measured axs 34

7 The prelmnary results, evaluated accordng to the standard so far, are shown n Fgures 6 to 8. One can observe that the course of approach does not sgnfcantly affect the obtaned results. Due to a lack of tme the evaluaton accordng to a new methodology has not been performed yet but t s expected n a short tme. The presented methodology s adapted from the methods used for calbraton of measurng nstruments; therefore ts correctness has already been proved [6], [7]. Approach n a postve sense Approach n a negatve sense,6,5 Postonal devaton n mm,4,3,, Runnng number of measurement Fgure 6 Postonal devaton measured when approachng to ndvdual target ponts from both senses of movement Non-senstvty,,5 Non-senstvty n mm,, ,5 -, -,5 Runnng number of measurement Fgure 7 Non-senstvty of the CNC axs 6 Conclusons The presented methodology gves the opportunty to estmate the postonal devaton n any pont of the axs travel, no matter whether rotatonal or longtudnal. Moreover t provdes the estmate of the postonal devaton wth the respectve uncertanty of such estmate. Ths gves the desgner or programmer the possblty to buld approprate correctons nto the control program or the adequate desgn correctons can be performed n the desgn of the machne. The presented evaluaton of measured data accordng to the standard shows smlar behavor of the controlled axes when approachng to the desred poston from both sdes (postve and negatve). 35

8 Postonal devaton n a postve sense x+3s Postonal devaton n a negatve sense x+3s Postonal devaton n a postve sense x-3s Postonal devaton n a negatve sense x-3s,6 Postonal devaton n mm,5,4,3,, Runnng number of measurement Fgure 8 Boundares of the postonal devaton n both senses of moton Acknowledgements: The study was supported by the project 6 th FP EU BoSm and Slovak Mnstry of Educaton. 7 References [] EN ISO 93. Weldng and related processes. Bratslava s. (n Slovak). [] ISO 3-. Test code for machne tools Part : Determnaton of accuracy and repeatablty of postonng of numercally controlled axes pp. (n Englsh). [3] GROS, P. 5. An advanced plasma cuttng unt for both-sde bevellng on metal parts. In: Strojnícky časops = Journal of Mechancal engneerng. Vol. 56, No. 6. 5, pp ISSN [4] KUBÁČEK,. Foundatons of Estmaton Theory. The st edton. Amsterdam, Oxford, New York, Tokyo: Elsever, 988. pp. 38. ISBN [5] PAENČÁR, R., GROS, P., HAAJ, M. 6. Evaluaton of the postonal devaton of numercally controlled axes. In: Strojnícky časops = Journal of Mechancal engneerng. Vol. 57, No.. 6. pp. -. ISSN [6] TEREK, M., HRNČIAROVÁ, Ľ. 4. Statstcal qualty control. The st edton, Bratslava: IURA EDITION, 4. pp. 34. ISBN (n Slovak). [7] VDOEČEK, F. 4. Automaton and dagnostcs. In Proceedngs of the nternatonal conference Strojné nžnerstvo 4. The st edton, Bratslava: Vydavateľstvo STU. 4. pp. S-97 to S-4. ISBN (n Czech). [8] WIMMER, G., PAENČÁR, R., WITKOVSKÝ, V.. Stochastcké modely merana. The st edton. Bratslava : Grafcké štúdo - Jurga,. pp. 5. ISBN X. 36

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

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

More information

Color Rendering Uncertainty

Color Rendering Uncertainty Australan Journal of Basc and Appled Scences 4(10): 4601-4608 010 ISSN 1991-8178 Color Renderng Uncertanty 1 A.el Bally M.M. El-Ganany 3 A. Al-amel 1 Physcs Department Photometry department- NIS Abstract:

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

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

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS Unversty of Oulu Student Laboratory n Physcs Laboratory Exercses n Physcs 1 1 APPEDIX FITTIG A STRAIGHT LIE TO OBSERVATIOS In the physcal measurements we often make a seres of measurements of the dependent

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

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

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

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

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3 Transmtted by the expert from France Informal Document No. GRB-51-14 (67 th GRB, 15 17 February 2010, agenda tem 7) Regulaton No. 117 (Tyres rollng nose and wet grp adheson) Proposal for amendments to

More information

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00 ONE IMENSIONAL TRIANGULAR FIN EXPERIMENT Techncal Advsor: r..c. Look, Jr. Verson: /3/ 7. GENERAL OJECTIVES a) To understand a one-dmensonal epermental appromaton. b) To understand the art of epermental

More information

Uncertainty as the Overlap of Alternate Conditional Distributions

Uncertainty as the Overlap of Alternate Conditional Distributions Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant

More information

Experiment 1 Mass, volume and density

Experiment 1 Mass, volume and density Experment 1 Mass, volume and densty Purpose 1. Famlarze wth basc measurement tools such as verner calper, mcrometer, and laboratory balance. 2. Learn how to use the concepts of sgnfcant fgures, expermental

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

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

Note 10. Modeling and Simulation of Dynamic Systems

Note 10. Modeling and Simulation of Dynamic Systems Lecture Notes of ME 475: Introducton to Mechatroncs Note 0 Modelng and Smulaton of Dynamc Systems Department of Mechancal Engneerng, Unversty Of Saskatchewan, 57 Campus Drve, Saskatoon, SK S7N 5A9, Canada

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

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

6.3.4 Modified Euler s method of integration

6.3.4 Modified Euler s method of integration 6.3.4 Modfed Euler s method of ntegraton Before dscussng the applcaton of Euler s method for solvng the swng equatons, let us frst revew the basc Euler s method of numercal ntegraton. Let the general from

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Numerical Transient Heat Conduction Experiment

Numerical Transient Heat Conduction Experiment Numercal ransent Heat Conducton Experment OBJECIVE 1. o demonstrate the basc prncples of conducton heat transfer.. o show how the thermal conductvty of a sold can be measured. 3. o demonstrate the use

More information

Multi degree of freedom measurement of machine tool movements. W. Knapp, S.Weikert Institute for Machine Tools and Manufacturing (IWF), Swiss Federal

Multi degree of freedom measurement of machine tool movements. W. Knapp, S.Weikert Institute for Machine Tools and Manufacturing (IWF), Swiss Federal Mult degree of freedom measurement of machne tool movements W. Knapp, S.Wekert Insttute for Machne Tools and Manufacturng (IWF), Swss Federal \ ^r Az. ca, we z'tz rf @ z w/: Agpr. graz. ca Abstract The

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

Laboratory 1c: Method of Least Squares

Laboratory 1c: Method of Least Squares Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly

More information

THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD

THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD Journal of Appled Mathematcs and Computatonal Mechancs 7, 6(3), 7- www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.3. e-issn 353-588 THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS

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

Developing a Data Validation Tool Based on Mendelian Sampling Deviations

Developing a Data Validation Tool Based on Mendelian Sampling Deviations Developng a Data Valdaton Tool Based on Mendelan Samplng Devatons Flppo Bscarn, Stefano Bffan and Fabola Canaves A.N.A.F.I. Italan Holsten Breeders Assocaton Va Bergamo, 292 Cremona, ITALY Abstract A more

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

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

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

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

An Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors

An Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors An Algorthm to Solve the Inverse Knematcs Problem of a Robotc Manpulator Based on Rotaton Vectors Mohamad Z. Al-az*, Mazn Z. Othman**, and Baker B. Al-Bahr* *AL-Nahran Unversty, Computer Eng. Dep., Baghdad,

More information

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites IOP Conference Seres: Materals Scence and Engneerng PAPER OPE ACCESS An dentfcaton algorthm of model knetc parameters of the nterfacal layer growth n fber compostes o cte ths artcle: V Zubov et al 216

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

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

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

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

AGC Introduction

AGC Introduction . Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero

More information

Construction of Serendipity Shape Functions by Geometrical Probability

Construction of Serendipity Shape Functions by Geometrical Probability J. Basc. Appl. Sc. Res., ()56-56, 0 0, TextRoad Publcaton ISS 00-0 Journal of Basc and Appled Scentfc Research www.textroad.com Constructon of Serendpty Shape Functons by Geometrcal Probablty Kamal Al-Dawoud

More information

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:

More information

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING N. Phanthuna 1,2, F. Cheevasuvt 2 and S. Chtwong 2 1 Department of Electrcal Engneerng, Faculty of Engneerng Rajamangala

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

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH Proceedngs, XVII IMEKO World Congress, June 7, 3, Dubrovn, Croata Proceedngs, XVII IMEKO World Congress, June 7, 3, Dubrovn, Croata TC XVII IMEKO World Congress Metrology n the 3rd Mllennum June 7, 3,

More information

Influence Of Operating Conditions To The Effectiveness Of Extractive Distillation Columns

Influence Of Operating Conditions To The Effectiveness Of Extractive Distillation Columns Influence Of Operatng Condtons To The Effectveness Of Extractve Dstllaton Columns N.A. Vyazmna Moscov State Unversty Of Envrnmental Engneerng, Department Of Chemcal Engneerng Ul. Staraya Basmannaya 21/4,

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Example: (13320, 22140) =? Solution #1: The divisors of are 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 18, 20, 27, 30, 36, 41,

Example: (13320, 22140) =? Solution #1: The divisors of are 1, 2, 3, 4, 5, 6, 9, 10, 12, 15, 18, 20, 27, 30, 36, 41, The greatest common dvsor of two ntegers a and b (not both zero) s the largest nteger whch s a common factor of both a and b. We denote ths number by gcd(a, b), or smply (a, b) when there s no confuson

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

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

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

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

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

Convexity preserving interpolation by splines of arbitrary degree

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

More information

Statistics II Final Exam 26/6/18

Statistics II Final Exam 26/6/18 Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the

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

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

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

METHOD OF NETWORK RELIABILITY ANALYSIS BASED ON ACCURACY CHARACTERISTICS

METHOD OF NETWORK RELIABILITY ANALYSIS BASED ON ACCURACY CHARACTERISTICS METHOD OF NETWOK ELIABILITY ANALYI BAED ON ACCUACY CHAACTEITIC ławomr Łapńsk hd tudent Faculty of Geodesy and Cartography Warsaw Unversty of Technology ABTACT Measurements of structures must be precse

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

Exercises of Chapter 2

Exercises of Chapter 2 Exercses of Chapter Chuang-Cheh Ln Department of Computer Scence and Informaton Engneerng, Natonal Chung Cheng Unversty, Mng-Hsung, Chay 61, Tawan. Exercse.6. Suppose that we ndependently roll two standard

More information

STUDY OF A THREE-AXIS PIEZORESISTIVE ACCELEROMETER WITH UNIFORM AXIAL SENSITIVITIES

STUDY OF A THREE-AXIS PIEZORESISTIVE ACCELEROMETER WITH UNIFORM AXIAL SENSITIVITIES STUDY OF A THREE-AXIS PIEZORESISTIVE ACCELEROMETER WITH UNIFORM AXIAL SENSITIVITIES Abdelkader Benchou, PhD Canddate Nasreddne Benmoussa, PhD Kherreddne Ghaffour, PhD Unversty of Tlemcen/Unt of Materals

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

Finite Element Modelling of truss/cable structures

Finite Element Modelling of truss/cable structures Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures

More information

APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE

APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE DEFLECTION E. J. Chern Martn Maretta Laboratores 1450 South Rollng Road Baltmore, MD 21227 INTRODUCTION Tubes are a vtal component

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

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

DETERMINATION OF ERRANT RUN-OUT OF THE AXIS OF ROTATION OF OBJECTS, PERFORMING ACCURATE ROTATIONAL MOVEMENTS

DETERMINATION OF ERRANT RUN-OUT OF THE AXIS OF ROTATION OF OBJECTS, PERFORMING ACCURATE ROTATIONAL MOVEMENTS DETERMINATION OF ERRANT RUN-OUT OF THE AXIS OF ROTATION OF OBJECTS, PERFORMING ACCURATE ROTATIONAL MOVEMENTS Hrsto K. RADEV, Vassl J. BOGEV, Velzar A. VASSILEV Techncal Unversty of Sofa, Bulgara Abstract.

More information

Chapter Newton s Method

Chapter Newton s Method Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve

More information

Uncertainty and auto-correlation in. Measurement

Uncertainty and auto-correlation in. Measurement Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at

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

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

Journal of Universal Computer Science, vol. 1, no. 7 (1995), submitted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Springer Pub. Co.

Journal of Universal Computer Science, vol. 1, no. 7 (1995), submitted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Springer Pub. Co. Journal of Unversal Computer Scence, vol. 1, no. 7 (1995), 469-483 submtted: 15/12/94, accepted: 26/6/95, appeared: 28/7/95 Sprnger Pub. Co. Round-o error propagaton n the soluton of the heat equaton by

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy Comparatve Studes of Law of Conservaton of Energy and Law Clusters of Conservaton of Generalzed Energy No.3 of Comparatve Physcs Seres Papers Fu Yuhua (CNOOC Research Insttute, E-mal:fuyh1945@sna.com)

More information

Chapter 12 Analysis of Covariance

Chapter 12 Analysis of Covariance Chapter Analyss of Covarance Any scentfc experment s performed to know somethng that s unknown about a group of treatments and to test certan hypothess about the correspondng treatment effect When varablty

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Laboratory 3: Method of Least Squares

Laboratory 3: Method of Least Squares Laboratory 3: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly they are correlated wth

More information

Chapter 5 Multilevel Models

Chapter 5 Multilevel Models Chapter 5 Multlevel Models 5.1 Cross-sectonal multlevel models 5.1.1 Two-level models 5.1.2 Multple level models 5.1.3 Multple level modelng n other felds 5.2 Longtudnal multlevel models 5.2.1 Two-level

More information

A New Evolutionary Computation Based Approach for Learning Bayesian Network

A New Evolutionary Computation Based Approach for Learning Bayesian Network Avalable onlne at www.scencedrect.com Proceda Engneerng 15 (2011) 4026 4030 Advanced n Control Engneerng and Informaton Scence A New Evolutonary Computaton Based Approach for Learnng Bayesan Network Yungang

More information

Uncertainty evaluation in field measurements of airborne sound insulation

Uncertainty evaluation in field measurements of airborne sound insulation Uncertanty evaluaton n feld measurements of arborne sound nsulaton R. L. X. N. Mchalsk, D. Ferrera, M. Nabuco and P. Massaran Inmetro / CNPq, Av. N. S. das Graças, 50, Xerém, Duque de Caxas, 25250-020

More information

The equation of motion of a dynamical system is given by a set of differential equations. That is (1)

The equation of motion of a dynamical system is given by a set of differential equations. That is (1) Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force.

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force. Secton 1. Dynamcs (Newton s Laws of Moton) Two approaches: 1) Gven all the forces actng on a body, predct the subsequent (changes n) moton. 2) Gven the (changes n) moton of a body, nfer what forces act

More information

Chapter 6. Supplemental Text Material

Chapter 6. Supplemental Text Material Chapter 6. Supplemental Text Materal S6-. actor Effect Estmates are Least Squares Estmates We have gven heurstc or ntutve explanatons of how the estmates of the factor effects are obtaned n the textboo.

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

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

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

Mathematical Preparations

Mathematical Preparations 1 Introducton Mathematcal Preparatons The theory of relatvty was developed to explan experments whch studed the propagaton of electromagnetc radaton n movng coordnate systems. Wthn expermental error the

More information

Statistical Inference. 2.3 Summary Statistics Measures of Center and Spread. parameters ( population characteristics )

Statistical Inference. 2.3 Summary Statistics Measures of Center and Spread. parameters ( population characteristics ) Ismor Fscher, 8//008 Stat 54 / -8.3 Summary Statstcs Measures of Center and Spread Dstrbuton of dscrete contnuous POPULATION Random Varable, numercal True center =??? True spread =???? parameters ( populaton

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

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

Module 3: The Whole-Process Perspective for Thermochemical Hydrogen

Module 3: The Whole-Process Perspective for Thermochemical Hydrogen "Thermodynamc Analyss of Processes for Hydrogen Generaton by Decomposton of Water" by John P. O'Connell Department of Chemcal Engneerng Unversty of Vrgna Charlottesvlle, VA 2294-4741 A Set of Energy Educaton

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

Investigation of Uncertainty Sources in the External Dosimetry Laboratory

Investigation of Uncertainty Sources in the External Dosimetry Laboratory Investgaton of Uncertanty Sources n the External Dosmetry Laboratory Specfcaton.1.1. Analyss of uncertantes Methods for calculatng the overall uncertanty from ndvdual measured uncertantes are gven n the

More information

SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko

SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko SGNose and AGDas - tools for processng of superconductng and absolute gravty data Vojtech Pálnkáš and Mloš Vaľko 1 Research Insttute of Geodesy, Topography and Cartography, Czech Republc SGNose Web tool

More information