Power supplies for parallel operation Power supplies

Size: px
Start display at page:

Download "Power supplies for parallel operation Power supplies"

Transcription

1 Power supplies Power supplies for parallel operatio U 1 2 U Parallel operatio of switchmode power supplies Techical details for passive curret-sharig The aim of operatig switchmode power supplies (SMPS) i parallel is to icrease output power by icreasig the output curret. How this ca best be achieved by usig the passive curret-sharig method is described i the followig article. By Marti Rosebaum U 1 = U + U E1 U 2 = U + U E2 U = U + U E a N a1 Fig. Bild Characteristic Keliie ( weiche lies ( soft Keliie ) characteristics ) vo parallelgeschaltete of switchmode power Schaltetzteile. supplies operated i parallel. U E SMPS SNT 11 SMPS SNT 22 SMPS SNT a1 U 1 a2 U 2 a U a2 There are two methods of realisig parallel operatios that coform with today s stateof-the-art techology. Firstly, usig active curret-sharig (cotrolled load-sharig) ad secodly, usig passive curret-sharig. Active curret-sharig measures the output curret of each power supply ad adjusts the output voltage of each idividual SMPS to produce uiform curret distributio. This method has the advatage of esurig very exact cur- SMPS SNT 1 1 SMPS SNT 2 2 SMPS SNT Bild Fig. 2. Symmetric Symmetrische cablig Verkabelug is a basic requiremet ist eie Voraussetzug for usig für die Parallelschaltug switchmode power vo supplies Schaltetzteile. i parallel. a Verbraucher Load ret-sharig ad costat loadig of the SMPSs operated i parallel. The disadvatages are the eed for additioal compoets ad higher costs. By compariso, i the case of passive curret-shar ig, the curret is distributed as uiformly as possible via a "soft output cha racteristic (Fig.1) for each SMPS. This approach has the advatages of eedig less switchig techology ad allowig for parallel use of a almost ulimited umber of SMPSs. The somewhat more iaccurate curret distributio must be see as a disadvatage. Passive curret distributio -the basics Some importat details o passive curret-sharig follow, based o the assumptio that the followig requiremets have bee met. R i + 2 x a U Fig. Bild Equivalet Ersatzschaltug circuit der diagram parallelgeschaltete use Schaltetzteile of switchmode power for parallel supplies. idetical switchmode power supplies are used i parallel. Each switchmode power supply is symmetrically cabled to the load (Fig. 2) 1. 1 = U + U E1 (R i + 2 ) a = U + U E2 (R i + 2 ) a = U + U E3 (R i + 2 ) a3.... = U + U E (R i + 2 ) a The followig equatios for the switchmode power supplies form the basis for calculatig parallel operatio. Requiremets for parallel operatio of switchmode power supplies: 1 = 2 = 3 =... = ad a = a1 + a2 + a a This gives rise to the followig equatio system for the SMPSs operated i parallel.

2 Power supplies for parallel operatio Power supplies P amax(rl) 132 W Fig. 4. Maximum load output i relatio to wire (The diagrams show i Figures are available upo request for all switchmode power supplies with passive curret-sharig desiged for parallel operatio purchased for example from the compay MGV Stromversorguge ( mω 5 = U + U E1 (R i + 2 ) a1 = U + U E2 (R i + 2 ) a2 = U + U E3 (R i + 2 ) a3... = U + U E (R i + 2 ) a After a umber of simplifi catios, various equatios for the SMPSs operated i parallel ca be deduced with the aid of this equatio system. But fi rst of all, the defi itio of the mea value of the settig toleraces: = 1 i=1 U Ei Voltage at load: = U + R i + 2 a (1) Based o equatio (1), the equivalet circuit diagram for the SMPSs used i parallel ca be costructed as show i Fig. 3. t is amazig that this results i such a simple equivalet circuit diagram (ECD) for a parallel coectio. The ECD cosists of oly three elemets. O the oe had, it is made up of two voltage sources with parameters that are easy to ascertai. Added to these is a resistace, which ca also be simply calculated based o the power supply s iteral resistace, the wire resistace (load wire) ad the umber of switchmode power supplies i parallel coectio. By goig a step further ad addig together the two voltage sources, oe arrives at a equivalet circuit diagram for a real equivalet voltage source cosistig of a ideal voltage source ad iteral Curret at the kth switchmode power supply (1 < k < ): ak UEk UEM 1 = + R + 2 R i L Accuracy of the curret-share at the kth power supply (1 < k < ): a (2) 6 A Fig. 5. Maximum available curret that ca be draw from the parallel series i relatio to wire amax(rl) mω 5

3 Power supplies Power supplies for parallel operatio 24,2 A 24 23,8 Fig. 6. Load voltage i relatio to load curret at Ua(a, 18 mω) 23,6 23,4 23, , A 6 a ak UEk UEM = a ( R i + 2 ) (3) Aalysig equatio (2) more closely, it will be oted that the secod part of the sum represets the share of curret i the case of equal curret distributio across the parallel coectio. t is therefore logical that the fi rst part of the sum represets the deviatio from this uiform curret distributio. By calculatig the deviatio as a proportio of the curret produced by a SMPS i the case of uiform curret-sharig, oe arrives at the deviatio i percetage terms (equatio (3)). t is iterestig to ote the factors affectig this percetage deviatio. For istace, the curret share becomes more exact as the load curret (see Fig. 7), iteral resistace ad wire resistace icrease. Furthermore, the accuracy of the curret share also icreases, the earer the settig tolerace moves towards the mea value of the tolerace of adjustmet. The maximum curret that ca be draw from the parallel coectio (assumig U E1 > U E ad a1 = 1N) is: a max = N ( U E1 ) (4) R i + 2 The maximum curret that ca be draw from the parallel series is reached whe the switchmode power supply with the highest oload-operatio (i this case, SMPS 1 is assumed to have the highest o-load-operatio) draws the rated curret. The total output curret of all other SMPSs is therefore always lower, or ot more tha the rated curret of oe SMPS (Fig. 1). The maximum load output i relatio to the wire resistace whe drawig the maximum curret amouts to: [ ( )] ( ) P max = U + U R + 2 R a E1 N i L UE UEM N 1 Ri + R 2 L (5) Calculatio example demostratig applicatio of the derivative formulae The followig switchmode power-supply data are assumed for the purpose of this example: Nomial voltage: U = 24V Rated curret of oe SMPS: N = 2A teral resistace of the SMPS: R i = 17.5mΩ Switchmode power supplies used i parallel: = 3 Settig toleraces of the three power supplies: U E1 = 5mV, U E2 = -1mV, U E3 = - 3mV Mea value of settig tolerace: = 1/3 (5mV 1mV - -3mV) = 3.33mV

4 Power supplies for parallel operatio Power supplies 5 % a1 1 ak a2 a A 6 a Fig. 7. Percetage curret-share i relatio to the load curret at Determiatig the level of wire resistace based o the maximum available output that ca be draw off by the load, applyig cocrete values (see also Fig. 4) P a max = [ ( )] = ,5 V 2 A 17, mω+2 14 mv 6 A ,5 mω+2 (5a) The resistace ca be read off o the diagram show uder Fig. 4 at the poit where the output reaches its maximum. the case of this sample calculatio, resistace is reached at about 18mΩ. All further calculatios will be based o this optimal wire Maximum curret which ca be draw from the parallel coectio, applyig cocrete values: amax = 14 mv 6 A ,5 mω + 2 R Accordig to Fig. 5, the maximum curret that ca be draw from the parallel supplies, takig ito accout the resistace, amouts to about 57.5A. This fi gure is equivalet to 95.8% of the 6A that are theoretically possible. The load voltage i relatio to the load curret at a resistace of = 18mΩ (Fig. 6) is calculated as the result of: L (5b) 4 mm2 3 Fig. 8. Cross-sectio of the wire i relatio to its legth for A(l) m 3

5 Power supplies Power supplies for parallel operatio Abbreviatios used A Wire cross-sectio R i teral resistace of the SMPS ECD Equivalet circuit diagram Load-wire resistace (go-adi dex umber retur lead) ak Percetage deviatio of the SMPS Switchmode power supply curret share at the kth Output voltage of the SMPSs SMPS operated i parallel a Load curret Output voltage of the th amax Maximum available load SMPS curret U E put voltage a Output curret of the th SMPS Mea value of the settig usig parallel operatio toleraces N Rated output curret of a U Nomial ope-circuit voltage SMPS U Actual ope-circuit voltage of κ Electrical coductivity the th SMPS k dex umber U E Settig tolerace of the output l Wire legth voltage for the th SMPS Number of SMPSs operated i (calculated as the ope-circuit parallel voltage mius rated voltage) P amax Maximum load output Marti Rosebaum Qualified egieer was bor i Coburg. After traiig as a power-plat electroics egieer ad gaiig two years skilled experiece i this field, he studied electrical egieerig at Coburg Polytechic, specialisig i Electrical Power-Techology. After completig his studies, he supervised a research project at Coburg Polytechic o the subject of verterfed asychroos machie. Sice October 1997, he has bee active for the compay MGV Stromversorguge, both developig stadard switchmode power supplies ad realisig customised solutios. marti.rosebaum@mgv.de = 24.33V 17.83mΩ. a Percetage curret-share (based o a/) of the three power supplies i parallel series i relatio to the load curret at resistace: a1 = 2.617A/a a2 =.748A/a a3 = 1.869A/a Fig. 7 illustrates clearly the iterrelatio betwee the curret share ad the load curret. The zero-percet lie o the vertical axis of the diagram correspods with a uiform curret share, meaig that each switchmode power supply is providig the same curret. Very small load currets lead to extremely ufavourable curret distributio. As a rule, this does ot however preset a problem, as i such case the idividual SMPSs are operated far below their rated output. From a load curret of about 25A upwards, the divergece i the curret shares is uder 1% ad at the maximum level of available curret, eve lower tha 5%. As far as the user is cocered, it is ot so much the resistace that is of iterest as the cross-sectio measuremet of the wire that should be used i relatio to its legth i order to realise a This ca be calculated usig the well-kow formula for wire resistace: = 1 (6) κ. A Preparig a diagram based o this equatio ca help to determie relatively quickly ad simply the required cross-sectio of the wire at a give legth, based o the assumptio of maximum available output. The diagram show i Fig. 8 must be qualifi ed by poitig out that the cross-sectio of the wire is subject to a miimum value (curret carryig capacity of cables i accordace with the relevat regulatios). view of the miimal cross-sectio measuremets, it will ot be possible to realise resistace i the case of every applicatio. However, care should by all meas be take to esure symmetrical cablig of the idividual switchmode power supplies.

Determining the sample size necessary to pass the tentative final monograph pre-operative skin preparation study requirements

Determining the sample size necessary to pass the tentative final monograph pre-operative skin preparation study requirements Iteratioal Joural of Cliical Trials Paulso DS. It J Cli Trials. 016 Nov;3(4):169-173 http://www.ijcliicaltrials.com pissn 349-340 eissn 349-359 Editorial DOI: http://dx.doi.org/10.1803/349-359.ijct016395

More information

DESCRIPTION OF THE SYSTEM

DESCRIPTION OF THE SYSTEM Sychroous-Serial Iterface for absolute Ecoders SSI 1060 BE 10 / 01 DESCRIPTION OF THE SYSTEM TWK-ELEKTRONIK GmbH D-001 Düsseldorf PB 1006 Heirichstr. Tel +9/11/6067 Fax +9/11/6770 e-mail: ifo@twk.de Page

More information

Data Analysis and Statistical Methods Statistics 651

Data Analysis and Statistical Methods Statistics 651 Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasii/teachig.html Suhasii Subba Rao Review of testig: Example The admistrator of a ursig home wats to do a time ad motio

More information

Sample Size Determination (Two or More Samples)

Sample Size Determination (Two or More Samples) Sample Sie Determiatio (Two or More Samples) STATGRAPHICS Rev. 963 Summary... Data Iput... Aalysis Summary... 5 Power Curve... 5 Calculatios... 6 Summary This procedure determies a suitable sample sie

More information

STA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to:

STA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to: STA 2023 Module 10 Comparig Two Proportios Learig Objectives Upo completig this module, you should be able to: 1. Perform large-sample ifereces (hypothesis test ad cofidece itervals) to compare two populatio

More information

THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS

THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS R775 Philips Res. Repts 26,414-423, 1971' THE SYSTEMATIC AND THE RANDOM. ERRORS - DUE TO ELEMENT TOLERANCES OF ELECTRICAL NETWORKS by H. W. HANNEMAN Abstract Usig the law of propagatio of errors, approximated

More information

AP Statistics Review Ch. 8

AP Statistics Review Ch. 8 AP Statistics Review Ch. 8 Name 1. Each figure below displays the samplig distributio of a statistic used to estimate a parameter. The true value of the populatio parameter is marked o each samplig distributio.

More information

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals 7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses

More information

C. Complex Numbers. x 6x + 2 = 0. This equation was known to have three real roots, given by simple combinations of the expressions

C. Complex Numbers. x 6x + 2 = 0. This equation was known to have three real roots, given by simple combinations of the expressions C. Complex Numbers. Complex arithmetic. Most people thik that complex umbers arose from attempts to solve quadratic equatios, but actually it was i coectio with cubic equatios they first appeared. Everyoe

More information

Simulation. Two Rule For Inverting A Distribution Function

Simulation. Two Rule For Inverting A Distribution Function Simulatio Two Rule For Ivertig A Distributio Fuctio Rule 1. If F(x) = u is costat o a iterval [x 1, x 2 ), the the uiform value u is mapped oto x 2 through the iversio process. Rule 2. If there is a jump

More information

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015 ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

A proposed discrete distribution for the statistical modeling of

A proposed discrete distribution for the statistical modeling of It. Statistical Ist.: Proc. 58th World Statistical Cogress, 0, Dubli (Sessio CPS047) p.5059 A proposed discrete distributio for the statistical modelig of Likert data Kidd, Marti Cetre for Statistical

More information

Design Data 16. Partial Flow Conditions For Culverts. x S o Q F = C 1 = (3) A F V F Q = x A x R2/3 x S o n 1/2 (2) 1 DD 16 (07/09)

Design Data 16. Partial Flow Conditions For Culverts. x S o Q F = C 1 = (3) A F V F Q = x A x R2/3 x S o n 1/2 (2) 1 DD 16 (07/09) Desig Data 16 Partial Flow Coditios For Culverts Sewers, both saitary ad storm, are desiged to carry a peak flow based o aticipated lad developmet. The hydraulic capacity of sewers or culverts costructed

More information

6.3 Testing Series With Positive Terms

6.3 Testing Series With Positive Terms 6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial

More information

Course Outline. Designing Control Systems. Proportional Controller. Amme 3500 : System Dynamics and Control. Root Locus. Dr. Stefan B.

Course Outline. Designing Control Systems. Proportional Controller. Amme 3500 : System Dynamics and Control. Root Locus. Dr. Stefan B. Amme 3500 : System Dyamics ad Cotrol Root Locus Course Outlie Week Date Cotet Assigmet Notes Mar Itroductio 8 Mar Frequecy Domai Modellig 3 5 Mar Trasiet Performace ad the s-plae 4 Mar Block Diagrams Assig

More information

Statistics 511 Additional Materials

Statistics 511 Additional Materials Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability

More information

1 Hash tables. 1.1 Implementation

1 Hash tables. 1.1 Implementation Lecture 8 Hash Tables, Uiversal Hash Fuctios, Balls ad Bis Scribes: Luke Johsto, Moses Charikar, G. Valiat Date: Oct 18, 2017 Adapted From Virgiia Williams lecture otes 1 Hash tables A hash table is a

More information

ANALYSIS OF EXPERIMENTAL ERRORS

ANALYSIS OF EXPERIMENTAL ERRORS ANALYSIS OF EXPERIMENTAL ERRORS All physical measuremets ecoutered i the verificatio of physics theories ad cocepts are subject to ucertaities that deped o the measurig istrumets used ad the coditios uder

More information

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01 ENGI 44 Cofidece Itervals (Two Samples) Page -0 Two Sample Cofidece Iterval for a Differece i Populatio Meas [Navidi sectios 5.4-5.7; Devore chapter 9] From the cetral limit theorem, we kow that, for sufficietly

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

MA131 - Analysis 1. Workbook 3 Sequences II

MA131 - Analysis 1. Workbook 3 Sequences II MA3 - Aalysis Workbook 3 Sequeces II Autum 2004 Cotets 2.8 Coverget Sequeces........................ 2.9 Algebra of Limits......................... 2 2.0 Further Useful Results........................

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

Statistical Intervals for a Single Sample

Statistical Intervals for a Single Sample 3/5/06 Applied Statistics ad Probability for Egieers Sixth Editio Douglas C. Motgomery George C. Ruger Chapter 8 Statistical Itervals for a Sigle Sample 8 CHAPTER OUTLINE 8- Cofidece Iterval o the Mea

More information

WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? ABSTRACT

WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? ABSTRACT WHAT IS THE PROBABILITY FUNCTION FOR LARGE TSUNAMI WAVES? Harold G. Loomis Hoolulu, HI ABSTRACT Most coastal locatios have few if ay records of tsuami wave heights obtaied over various time periods. Still

More information

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010 Pearso Educatio, Ic. Comparig Two Proportios Comparisos betwee two percetages are much more commo tha questios about isolated percetages. Ad they are more

More information

Linear Regression Demystified

Linear Regression Demystified Liear Regressio Demystified Liear regressio is a importat subject i statistics. I elemetary statistics courses, formulae related to liear regressio are ofte stated without derivatio. This ote iteds to

More information

Number of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day

Number of fatalities X Sunday 4 Monday 6 Tuesday 2 Wednesday 0 Thursday 3 Friday 5 Saturday 8 Total 28. Day LECTURE # 8 Mea Deviatio, Stadard Deviatio ad Variace & Coefficiet of variatio Mea Deviatio Stadard Deviatio ad Variace Coefficiet of variatio First, we will discuss it for the case of raw data, ad the

More information

BIOS 4110: Introduction to Biostatistics. Breheny. Lab #9

BIOS 4110: Introduction to Biostatistics. Breheny. Lab #9 BIOS 4110: Itroductio to Biostatistics Brehey Lab #9 The Cetral Limit Theorem is very importat i the realm of statistics, ad today's lab will explore the applicatio of it i both categorical ad cotiuous

More information

Principle Of Superposition

Principle Of Superposition ecture 5: PREIMINRY CONCEP O RUCUR NYI Priciple Of uperpositio Mathematically, the priciple of superpositio is stated as ( a ) G( a ) G( ) G a a or for a liear structural system, the respose at a give

More information

1 Inferential Methods for Correlation and Regression Analysis

1 Inferential Methods for Correlation and Regression Analysis 1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet

More information

Sample Size Estimation in the Proportional Hazards Model for K-sample or Regression Settings Scott S. Emerson, M.D., Ph.D.

Sample Size Estimation in the Proportional Hazards Model for K-sample or Regression Settings Scott S. Emerson, M.D., Ph.D. ample ie Estimatio i the Proportioal Haards Model for K-sample or Regressio ettigs cott. Emerso, M.D., Ph.D. ample ie Formula for a Normally Distributed tatistic uppose a statistic is kow to be ormally

More information

Confidence Intervals for the Population Proportion p

Confidence Intervals for the Population Proportion p Cofidece Itervals for the Populatio Proportio p The cocept of cofidece itervals for the populatio proportio p is the same as the oe for, the samplig distributio of the mea, x. The structure is idetical:

More information

Analysis of Experimental Measurements

Analysis of Experimental Measurements Aalysis of Experimetal Measuremets Thik carefully about the process of makig a measuremet. A measuremet is a compariso betwee some ukow physical quatity ad a stadard of that physical quatity. As a example,

More information

A statistical method to determine sample size to estimate characteristic value of soil parameters

A statistical method to determine sample size to estimate characteristic value of soil parameters A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig

More information

Reliability and Queueing

Reliability and Queueing Copyright 999 Uiversity of Califoria Reliability ad Queueig by David G. Messerschmitt Supplemetary sectio for Uderstadig Networked Applicatios: A First Course, Morga Kaufma, 999. Copyright otice: Permissio

More information

Discrete Mathematics for CS Spring 2008 David Wagner Note 22

Discrete Mathematics for CS Spring 2008 David Wagner Note 22 CS 70 Discrete Mathematics for CS Sprig 2008 David Wager Note 22 I.I.D. Radom Variables Estimatig the bias of a coi Questio: We wat to estimate the proportio p of Democrats i the US populatio, by takig

More information

Quadratic Functions. Before we start looking at polynomials, we should know some common terminology.

Quadratic Functions. Before we start looking at polynomials, we should know some common terminology. Quadratic Fuctios I this sectio we begi the study of fuctios defied by polyomial expressios. Polyomial ad ratioal fuctios are the most commo fuctios used to model data, ad are used extesively i mathematical

More information

Class 27. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Class 27. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700 Class 7 Daiel B. Rowe, Ph.D. Departmet of Mathematics, Statistics, ad Computer Sciece Copyright 013 by D.B. Rowe 1 Ageda: Skip Recap Chapter 10.5 ad 10.6 Lecture Chapter 11.1-11. Review Chapters 9 ad 10

More information

ELE B7 Power Systems Engineering. Symmetrical Components

ELE B7 Power Systems Engineering. Symmetrical Components ELE B7 Power Systems Egieerig Symmetrical Compoets Aalysis of Ubalaced Systems Except for the balaced three-phase fault, faults result i a ubalaced system. The most commo types of faults are sigle liegroud

More information

Disjoint set (Union-Find)

Disjoint set (Union-Find) CS124 Lecture 7 Fall 2018 Disjoit set (Uio-Fid) For Kruskal s algorithm for the miimum spaig tree problem, we foud that we eeded a data structure for maitaiig a collectio of disjoit sets. That is, we eed

More information

Probability, Expectation Value and Uncertainty

Probability, Expectation Value and Uncertainty Chapter 1 Probability, Expectatio Value ad Ucertaity We have see that the physically observable properties of a quatum system are represeted by Hermitea operators (also referred to as observables ) such

More information

Homework 5 Solutions

Homework 5 Solutions Homework 5 Solutios p329 # 12 No. To estimate the chace you eed the expected value ad stadard error. To do get the expected value you eed the average of the box ad to get the stadard error you eed the

More information

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence Chapter 3 Strog covergece As poited out i the Chapter 2, there are multiple ways to defie the otio of covergece of a sequece of radom variables. That chapter defied covergece i probability, covergece i

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals

More information

Switching, Part I. Circuit-Switching. Switching. Chapter 5: Space-Division. David Larrabeiti José Félix Kukielka Piotr Pacyna Mónica Cortés

Switching, Part I. Circuit-Switching. Switching. Chapter 5: Space-Division. David Larrabeiti José Félix Kukielka Piotr Pacyna Mónica Cortés Switchig, Part I Circuit-Switchig Chapter 5: Space-Divisio Switchig David Larrabeiti José Félix Kukielka Piotr Pacya Móica Cortés Space-Divisio Switches Basic Scheme: Rectagular x M matrix of crosspoits

More information

Chapter 12 EM algorithms The Expectation-Maximization (EM) algorithm is a maximum likelihood method for models that have hidden variables eg. Gaussian

Chapter 12 EM algorithms The Expectation-Maximization (EM) algorithm is a maximum likelihood method for models that have hidden variables eg. Gaussian Chapter 2 EM algorithms The Expectatio-Maximizatio (EM) algorithm is a maximum likelihood method for models that have hidde variables eg. Gaussia Mixture Models (GMMs), Liear Dyamic Systems (LDSs) ad Hidde

More information

Math 257: Finite difference methods

Math 257: Finite difference methods Math 257: Fiite differece methods 1 Fiite Differeces Remember the defiitio of a derivative f f(x + ) f(x) (x) = lim 0 Also recall Taylor s formula: (1) f(x + ) = f(x) + f (x) + 2 f (x) + 3 f (3) (x) +...

More information

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc. Chapter 22 Comparig Two Proportios Copyright 2010, 2007, 2004 Pearso Educatio, Ic. Comparig Two Proportios Read the first two paragraphs of pg 504. Comparisos betwee two percetages are much more commo

More information

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ STATISTICAL INFERENCE INTRODUCTION Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I oesample testig, we essetially

More information

OBJECTIVES. Chapter 1 INTRODUCTION TO INSTRUMENTATION FUNCTION AND ADVANTAGES INTRODUCTION. At the end of this chapter, students should be able to:

OBJECTIVES. Chapter 1 INTRODUCTION TO INSTRUMENTATION FUNCTION AND ADVANTAGES INTRODUCTION. At the end of this chapter, students should be able to: OBJECTIVES Chapter 1 INTRODUCTION TO INSTRUMENTATION At the ed of this chapter, studets should be able to: 1. Explai the static ad dyamic characteristics of a istrumet. 2. Calculate ad aalyze the measuremet

More information

Chapter 12 - Quality Cotrol Example: The process of llig 12 ouce cas of Dr. Pepper is beig moitored. The compay does ot wat to uderll the cas. Hece, a target llig rate of 12.1-12.5 ouces was established.

More information

Live Line Measuring the Parameters of 220 kv Transmission Lines with Mutual Inductance in Hainan Power Grid

Live Line Measuring the Parameters of 220 kv Transmission Lines with Mutual Inductance in Hainan Power Grid Egieerig, 213, 5, 146-151 doi:1.4236/eg.213.51b27 Published Olie Jauary 213 (http://www.scirp.org/joural/eg) Live Lie Measurig the Parameters of 22 kv Trasmissio Lies with Mutual Iductace i Haia Power

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

CS / MCS 401 Homework 3 grader solutions

CS / MCS 401 Homework 3 grader solutions CS / MCS 401 Homework 3 grader solutios assigmet due July 6, 016 writte by Jāis Lazovskis maximum poits: 33 Some questios from CLRS. Questios marked with a asterisk were ot graded. 1 Use the defiitio of

More information

INFINITE SEQUENCES AND SERIES

INFINITE SEQUENCES AND SERIES 11 INFINITE SEQUENCES AND SERIES INFINITE SEQUENCES AND SERIES 11.4 The Compariso Tests I this sectio, we will lear: How to fid the value of a series by comparig it with a kow series. COMPARISON TESTS

More information

THE APPEARANCE OF FIBONACCI AND LUCAS NUMBERS IN THE SIMULATION OF ELECTRICAL POWER LINES SUPPLIED BY TWO SIDES

THE APPEARANCE OF FIBONACCI AND LUCAS NUMBERS IN THE SIMULATION OF ELECTRICAL POWER LINES SUPPLIED BY TWO SIDES THE APPEARANCE OF FIBONACCI AND LUCAS NUMBERS IN THE SIMULATION OF ELECTRICAL POWER LINES SUPPLIED BY TWO SIDES Giuseppe Ferri Dipartimeto di Ihgegeria Elettrica-FacoM di Igegeria, Uiversita di L'Aquila

More information

The Poisson Distribution

The Poisson Distribution MATH 382 The Poisso Distributio Dr. Neal, WKU Oe of the importat distributios i probabilistic modelig is the Poisso Process X t that couts the umber of occurreces over a period of t uits of time. This

More information

Chapter 5. Inequalities. 5.1 The Markov and Chebyshev inequalities

Chapter 5. Inequalities. 5.1 The Markov and Chebyshev inequalities Chapter 5 Iequalities 5.1 The Markov ad Chebyshev iequalities As you have probably see o today s frot page: every perso i the upper teth percetile ears at least 1 times more tha the average salary. I other

More information

Statistical Fundamentals and Control Charts

Statistical Fundamentals and Control Charts Statistical Fudametals ad Cotrol Charts 1. Statistical Process Cotrol Basics Chace causes of variatio uavoidable causes of variatios Assigable causes of variatio large variatios related to machies, materials,

More information

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion Poit Estimatio Poit estimatio is the rather simplistic (ad obvious) process of usig the kow value of a sample statistic as a approximatio to the ukow value of a populatio parameter. So we could for example

More information

Chandrasekhar Type Algorithms. for the Riccati Equation of Lainiotis Filter

Chandrasekhar Type Algorithms. for the Riccati Equation of Lainiotis Filter Cotemporary Egieerig Scieces, Vol. 3, 00, o. 4, 9-00 Chadrasekhar ype Algorithms for the Riccati Equatio of Laiiotis Filter Nicholas Assimakis Departmet of Electroics echological Educatioal Istitute of

More information

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234 STA 291 Lecture 19 Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Locatio CB 234 STA 291 - Lecture 19 1 Exam II Covers Chapter 9 10.1; 10.2; 10.3; 10.4; 10.6

More information

Optimally Sparse SVMs

Optimally Sparse SVMs A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but

More information

Formation of A Supergain Array and Its Application in Radar

Formation of A Supergain Array and Its Application in Radar Formatio of A Supergai Array ad ts Applicatio i Radar Tra Cao Quye, Do Trug Kie ad Bach Gia Duog. Research Ceter for Electroic ad Telecommuicatios, College of Techology (Coltech, Vietam atioal Uiversity,

More information

Parasitic Resistance L R W. Polysilicon gate. Drain. contact L D. V GS,eff R S R D. Drain

Parasitic Resistance L R W. Polysilicon gate. Drain. contact L D. V GS,eff R S R D. Drain Parasitic Resistace G Polysilico gate rai cotact V GS,eff S R S R S, R S, R + R C rai Short Chael Effects Chael-egth Modulatio Equatio k ( V V ) GS T suggests that the trasistor i the saturatio mode acts

More information

Economics Spring 2015

Economics Spring 2015 1 Ecoomics 400 -- Sprig 015 /17/015 pp. 30-38; Ch. 7.1.4-7. New Stata Assigmet ad ew MyStatlab assigmet, both due Feb 4th Midterm Exam Thursday Feb 6th, Chapters 1-7 of Groeber text ad all relevat lectures

More information

R is a scalar defined as follows:

R is a scalar defined as follows: Math 8. Notes o Dot Product, Cross Product, Plaes, Area, ad Volumes This lecture focuses primarily o the dot product ad its may applicatios, especially i the measuremet of agles ad scalar projectio ad

More information

Anna Janicka Mathematical Statistics 2018/2019 Lecture 1, Parts 1 & 2

Anna Janicka Mathematical Statistics 2018/2019 Lecture 1, Parts 1 & 2 Aa Jaicka Mathematical Statistics 18/19 Lecture 1, Parts 1 & 1. Descriptive Statistics By the term descriptive statistics we will mea the tools used for quatitative descriptio of the properties of a sample

More information

Chapter 2 Feedback Control Theory Continued

Chapter 2 Feedback Control Theory Continued Chapter Feedback Cotrol Theor Cotiued. Itroductio I the previous chapter, the respose characteristic of simple first ad secod order trasfer fuctios were studied. It was show that first order trasfer fuctio,

More information

This is an introductory course in Analysis of Variance and Design of Experiments.

This is an introductory course in Analysis of Variance and Design of Experiments. 1 Notes for M 384E, Wedesday, Jauary 21, 2009 (Please ote: I will ot pass out hard-copy class otes i future classes. If there are writte class otes, they will be posted o the web by the ight before class

More information

Chapter 3: Resistive Circuits

Chapter 3: Resistive Circuits Chapter 3: esistive Circuits KICHHOFF S LAWS Kirchhoff s laws are the goverig laws of electric circuits. All techiques are derived from Kirchhoff s laws (VD, CD, Node ad Mesh equatios) ad Ohm s law. I

More information

Econ 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara

Econ 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara Poit Estimator Eco 325 Notes o Poit Estimator ad Cofidece Iterval 1 By Hiro Kasahara Parameter, Estimator, ad Estimate The ormal probability desity fuctio is fully characterized by two costats: populatio

More information

TEACHER CERTIFICATION STUDY GUIDE

TEACHER CERTIFICATION STUDY GUIDE COMPETENCY 1. ALGEBRA SKILL 1.1 1.1a. ALGEBRAIC STRUCTURES Kow why the real ad complex umbers are each a field, ad that particular rigs are ot fields (e.g., itegers, polyomial rigs, matrix rigs) Algebra

More information

Problem of Technical Losses in Natural Gas Distribution and Transportation Systems of Georgia

Problem of Technical Losses in Natural Gas Distribution and Transportation Systems of Georgia Problem of Techical osses i Natural Gas Distributio ad Trasportatio Systems of Georgia `Accordig to the law of Georgia Cocerig Power ad Natural Gas the Commissio, withi the bouds of its authority passes

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Regression and correlation

Regression and correlation Cotets 43 Regressio ad correlatio 1. Regressio. Correlatio Learig outcomes You will lear how to explore relatioships betwee variables ad how to measure the stregth of such relatioships. You should ote

More information

A CONFINEMENT MODEL OF HIGH STRENGTH CONCRETE

A CONFINEMENT MODEL OF HIGH STRENGTH CONCRETE 3 th World Coferece o Earthquake Egieerig Vacouver, B.C., Caada August -6, 24 Paper No. 873 A CONFINEMENT MODEL OF HIGH STRENGTH CONCRETE Nobutaka NAKAZAWA, Kazuhiko KAWASHIMA 2, Gakuho WATANABE 3, Ju-ichi

More information

Element sampling: Part 2

Element sampling: Part 2 Chapter 4 Elemet samplig: Part 2 4.1 Itroductio We ow cosider uequal probability samplig desigs which is very popular i practice. I the uequal probability samplig, we ca improve the efficiecy of the resultig

More information

Introduction to Probability and Statistics Twelfth Edition

Introduction to Probability and Statistics Twelfth Edition Itroductio to Probability ad Statistics Twelfth Editio Robert J. Beaver Barbara M. Beaver William Medehall Presetatio desiged ad writte by: Barbara M. Beaver Itroductio to Probability ad Statistics Twelfth

More information

Statistical Inference Based on Extremum Estimators

Statistical Inference Based on Extremum Estimators T. Rotheberg Fall, 2007 Statistical Iferece Based o Extremum Estimators Itroductio Suppose 0, the true value of a p-dimesioal parameter, is kow to lie i some subset S R p : Ofte we choose to estimate 0

More information

NCSS Statistical Software. Tolerance Intervals

NCSS Statistical Software. Tolerance Intervals Chapter 585 Itroductio This procedure calculates oe-, ad two-, sided tolerace itervals based o either a distributio-free (oparametric) method or a method based o a ormality assumptio (parametric). A two-sided

More information

Statisticians use the word population to refer the total number of (potential) observations under consideration

Statisticians use the word population to refer the total number of (potential) observations under consideration 6 Samplig Distributios Statisticias use the word populatio to refer the total umber of (potetial) observatios uder cosideratio The populatio is just the set of all possible outcomes i our sample space

More information

Roberto s Notes on Infinite Series Chapter 1: Sequences and series Section 3. Geometric series

Roberto s Notes on Infinite Series Chapter 1: Sequences and series Section 3. Geometric series Roberto s Notes o Ifiite Series Chapter 1: Sequeces ad series Sectio Geometric series What you eed to kow already: What a ifiite series is. The divergece test. What you ca le here: Everythig there is to

More information

Average-Case Analysis of QuickSort

Average-Case Analysis of QuickSort Average-Case Aalysis of QuickSort Comp 363 Fall Semester 003 October 3, 003 The purpose of this documet is to itroduce the idea of usig recurrece relatios to do average-case aalysis. The average-case ruig

More information

Example: Find the SD of the set {x j } = {2, 4, 5, 8, 5, 11, 7}.

Example: Find the SD of the set {x j } = {2, 4, 5, 8, 5, 11, 7}. 1 (*) If a lot of the data is far from the mea, the may of the (x j x) 2 terms will be quite large, so the mea of these terms will be large ad the SD of the data will be large. (*) I particular, outliers

More information

Department of Civil Engineering-I.I.T. Delhi CEL 899: Environmental Risk Assessment HW5 Solution

Department of Civil Engineering-I.I.T. Delhi CEL 899: Environmental Risk Assessment HW5 Solution Departmet of Civil Egieerig-I.I.T. Delhi CEL 899: Evirometal Risk Assessmet HW5 Solutio Note: Assume missig data (if ay) ad metio the same. Q. Suppose X has a ormal distributio defied as N (mea=5, variace=

More information

Problem Set 4 Due Oct, 12

Problem Set 4 Due Oct, 12 EE226: Radom Processes i Systems Lecturer: Jea C. Walrad Problem Set 4 Due Oct, 12 Fall 06 GSI: Assae Gueye This problem set essetially reviews detectio theory ad hypothesis testig ad some basic otios

More information

ANALYSIS OF DAMPING EFFECT ON BEAM VIBRATION

ANALYSIS OF DAMPING EFFECT ON BEAM VIBRATION Molecular ad Quatum Acoustics vol. 7, (6) 79 ANALYSIS OF DAMPING EFFECT ON BEAM VIBRATION Jerzy FILIPIAK 1, Lech SOLARZ, Korad ZUBKO 1 Istitute of Electroic ad Cotrol Systems, Techical Uiversity of Czestochowa,

More information

A quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population

A quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population A quick activity - Cetral Limit Theorem ad Proportios Lecture 21: Testig Proportios Statistics 10 Coli Rudel Flip a coi 30 times this is goig to get loud! Record the umber of heads you obtaied ad calculate

More information

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series

More information

4.3 Growth Rates of Solutions to Recurrences

4.3 Growth Rates of Solutions to Recurrences 4.3. GROWTH RATES OF SOLUTIONS TO RECURRENCES 81 4.3 Growth Rates of Solutios to Recurreces 4.3.1 Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer.

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

4.1 SIGMA NOTATION AND RIEMANN SUMS

4.1 SIGMA NOTATION AND RIEMANN SUMS .1 Sigma Notatio ad Riema Sums Cotemporary Calculus 1.1 SIGMA NOTATION AND RIEMANN SUMS Oe strategy for calculatig the area of a regio is to cut the regio ito simple shapes, calculate the area of each

More information

Provläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE

Provläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE TECHNICAL REPORT CISPR 16-4-3 2004 AMENDMENT 1 2006-10 INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE Amedmet 1 Specificatio for radio disturbace ad immuity measurig apparatus ad methods Part 4-3:

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 9

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 9 Hypothesis testig PSYCHOLOGICAL RESEARCH (PYC 34-C Lecture 9 Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I

More information

Statistical inference: example 1. Inferential Statistics

Statistical inference: example 1. Inferential Statistics Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either

More information

Intro to Learning Theory

Intro to Learning Theory Lecture 1, October 18, 2016 Itro to Learig Theory Ruth Urer 1 Machie Learig ad Learig Theory Comig soo 2 Formal Framework 21 Basic otios I our formal model for machie learig, the istaces to be classified

More information

Bounds of Herfindahl-Hirschman index of banks in the European Union

Bounds of Herfindahl-Hirschman index of banks in the European Union MPRA Muich Persoal RePEc Archive Bouds of Herfidahl-Hirschma idex of bas i the Europea Uio József Tóth Kig Sigismud Busiess School, Hugary 4 February 2016 Olie at https://mpra.ub.ui-mueche.de/72922/ MPRA

More information