Research on structural optimization design for shield beam of hydraulic support. based on response surface method

Size: px
Start display at page:

Download "Research on structural optimization design for shield beam of hydraulic support. based on response surface method"

Transcription

1 APCOM & ISCM -4 th December, 03, Sgapore Reearch o tructural optmzato deg for held beam of hydraulc upport Abtract baed o repoe urface method *Dogche Q, Huyu L, Zhul Lu, ad Jagy Che School of Mechacal Egeerg, Zhegzhou Uverty, Cha *Correpodg author: dcq@zzu.edu.c The held beam the ma load-bearg compoet of the hydraulc upport. The tructural optmzato deg of oe held beam fulflled by the repoe urface method. Ug the weght a the objectve fucto, the tructural optmzato mathematcal model of held beam et up. The expermetal deg performed the ANSYS oftware ad uform deg. The maxmum tree of held beam are gotte the dfferet ze. The repoe urface model of deg parameter ad maxmum tree are ftted by the leat quare method. The tructural optmzato deg of held beam completed by the radom drecto method. Th reearch mplemet the tructural optmzato deg of hydraulc upport held beam a moder deg method, ad provde a valuable gudace for the hydraulc upport reearch ad developmet. Keyword: hydraulc upport, held beam, tructural optmzato deg, repoe urface method. Itroducto The held beam oe of the ma compoet of the hydraulc upport to bear load. Optmzg the deg of the held beam tructure ca reduce t weght, whch play a mportat role for the utaable developmet of coal machery eterpre. Tradtoal tructural optmzato of the held beam tructure ca be roughly dvded to two type.oe that, accordg to the theory of materal mechac or mechacal formula, the repoe of the tructure ca be computed ad the deg varable ad objectve fucto ca be choe, the to optmze the deg ug effcet optmzato algorthm (Lu, 007). For umerou mplfcato to model, th method would yeld the optmzato reult morecoervatve outcome.whle the other that, the repoe of the tructure obtaed by ug fte elemet oftware ad choe a the cotrat codto, the to elect the utable optmzato trategy for more accurate tructure optmzato deg (Yao, 0a).Th approach, where each terato wll be made durg the optmzato proce wth fte elemet calculato, rug lowly. To overcome the uffcecy of above tradtoal optmal deg method, by ug the repoe urface methodology, th reearch ha realzed the tructure optmzato of held beam for a certa type hydraulc upport. Wth ecto dmeo of the held beam a varable, ANSYS ued to calculate the tre of the held beam uder partal load, ad the repoe urface method of uform deg expermet appled to obta the fuctoal relatohp betwee tre value ad ecto dmeo of the held beam. For the weght reducto purpoe, the tructure optmzato deg of held beam performed uder the cotrat of tructure tregth ad geometrcal dmeo. It proved that the optmzato method feable ad effectve by fte elemet aaly ad valdato reult. Optmzato Baed o Repoe Surface Method The optmzato baed o repoe urface method geerally clude uch certa tep a, expermet deg, repoe urface model, ad earchg for the optmal pot (Lag, etc.,00). Expermet deg for the ake of cetfc ad reaoable arragemet of tet cheme wth fewer expermet to get more properte of deg pace (Kleje, 005a). A cetfc expermet deg ca arrage varou expermetal factor reaoably ad aalye tet data effectvely, thu

2 realzg more rch ad relable data obtaed wth ug le reource. Commoly ued expermetal deg method are full factoral orthogoal deg of expermet ad uform deg expermetato, etc. The uform deg expermetato, whch wll dtrbute deg pot evely wth the deg pace, choe th reearch. Compared wth other method, the uform deg expermetato ca requre le expermet tme, ad mprove the preco of repoe urface to a certa extet (L, etc.,005b). Ug th approach, everal umercal mulato tet are carred out to obta a ere of deg pot, whoe umber ad locato are determed. O th ba, the fucto relatohp betwee cotrol varable ad target varable etablhed wth regreo method, amely, repoe urface model. The repoe urface model reflect the fucto relatohp betwee target varable (depedet varable) ad everal cotrol varable (depedet varable). A th fucto relatohp geerally curve or curved urface, whch called a repoe urface model. Becaue the repoe urface model baed o ere of regreo of tet data, the qualty of regreo aaly drectly determe the accuracy of repoe urface model (Todorok, A. ad Ihkawa, T., 004). I the feld of tructural mechac, the repoe urface fucto model ofte adopt the quadratc polyomal form, uch a 0 Y( X) = a + ax + ajxx j = = j= where, a 0, a ad aj are udetermed coeffcet, x (=,, ) are bac varable. I order to mplfy the calculato ad avod applcato rage retrcto for the repoe urface, the cotat term, frt-order term ad ecod-order quared are remaed, ad the ecod-order cro term eglected, amely, the mplfed form Y( X) = a + ax + ax 0 = = Searchg for the optmal pot the geerated repoe urface model typcally clude to elect the deg objectve, cotrat ad the optmal algorthm. For dfferet tuato, the mathematcal cotrat are appeded to the model, the deg objectve ad a ere of the earch algorthm for the optmal pot are provded, uch a gradet algorthm, radom drecto method, pealty fucto method, etc. The radom drecto method, whch poee the eay procedure ad fat coverget rate, adopted here. The Repoe Surface Model Etablhmet Sheld Beam Statc Aaly The held beam of a certa type hydraulc upport taked a the reearch object. Fte elemet model for the held beam etablhed uder ANSYS evromet, the the held beam mehed freely wth SOLID87 elemet. Accordg to the techcal pecfcato ad load-bearg tuato of hydraulc upport uder partal load (Q, etc.,0b), the correpodg boudary codto ad load are appled, ad ANSYS tructural tatc aaly performed to obta the tre dtrbuto ad deformato codto for the held beam, a how Fg. ad. The fgure how the held beam' tre tuato ad deformato uder partal load, where the maxmum tre value of the held beam MPa, ad the maxmum deformato 8.96 mm. To verfy the relablty of the fte elemet aaly, the real phycal prototype tre tet for the held beam alo made. Accordg to the charactertc of tre dtrbuto of the held beam, pate poto of the fol tra gauge are determed for the phycal tre tet, ad the tre value of the tet pot are obtaed. The locato of the tetg pot are how Fg. 3. At the ame tme, the correpodg 6 poto at fte elemet model of held beam are elected too, where the average tre reult are recorded. A comparo of fte elemet calculato reult ad meaured value how Table. The fte elemet reult ad the expermetal reult are ot cotet to a certa degree, whch caued by the tet error ad calculato error. The log-tme expermet reult utable () ()

3 workg evromet of the tra gauge, poble dfferet charactertc of each tra gauge, ad agular devato ad poto devato for the gauge patch, all thee factor wll lead to tet error. Whe fte elemet model beg mulated, parameter ettg, grd dvo ad the dfferece amog the cotrat boudary codto wll caue calculato error. Therefore, t poble to caue larger relatve error of ome dvdual pot. Fgure. Sheld beam' tre dtrbuto Fgure. Sheld beam' deformato Number of meaurg pot Fgure 3. Locato of tet pot Table. Expermetal verfcato Fte elemet reult/mpa Meaured reult/mpa Relatve error/% Uform Deg Expermetal Aaly A held beam tructure complex, there are more parameter affectg the compoet tregth. Sce the dtace betwee the held beam frot ad back hged pot already determed durg overall deg of hydraulc upport, the lghtet weght wll be treated a the optmzato objectve of held beam. I other word, the mmum ectoal area wll be regarded a optmzato objectve (Zhu, etc., 0). The held beam, made up of upper ad lower cover plate ad vertcal rb, a box welded tructure wth a cro ecto of 5 cavte, whch how Fg. 4. Fgure4. The held beam cro ecto 3

4 Where, x the dtace betwee the frt cavty ad mddle plae, x the wdth of the ecod ad thrd cavte, x 3 the heght of the cavty, x 4 ad x 5 are the thcke of upper ad lower cover plate ad the thcke of the vertcal rb, eparately. I th paper, the uform deg expermetato ued to carry out repoe urface expermet. Through the parameterzed modelg to realze the chage of ze of thcke, ad the fte elemet aaly of each group of dmeoal data, we ca obta the maxmum tre value of the held beam. The quadratc polyomal wthout cro term take a the repoe urface equato whch cota 5 parameter ad ukow coeffcet (that equal to +, the umber of parameter). So, 6 tme orthogoal tet, amely cludg 6 level 5 parameter, ca be determed ad performed. The tet reult are how Table. Table. Uform tet table Tme x /mm x /mm x 3 /mm x 4 /mm x 5 /mm σ max /MPa Ug the leat-quare method to ft the repoe urface fucto, y e = x + x 6.754x.3887x3.69x x x + x where y e a repoe value of the maxmum tre x x Accordg to the evaluato formula of multple correlato coeffcet (equato (4)), we ca evaluate the fttg degree ad get R for every repoe urface fucto. The relatve hgh evaluato dex (R =0.9664) for equato (3) prove that the ftted repoe urface fucto utable, whch mea that the repoe urface expermet well wth repect to practcal mulato, ad t wll provde a good foudato for the ext tep of tructure optmzato. R SSE = = SST = = ( y yˆ ) ( y y ) 5 (3) (4) The Optmzato Deg Optmal Deg Mathematc Model 4

5 The lghtet weght of the held beam, equvalet to the mmum cover of cro ecto area of the held beam. From the Fg., the objectve fucto ca be made a F( X) = x (x + 4x + 6 x ) + 6x x (5) The cotrat codto of the tructural optmzato o held beam are dvded to the followg kd: Stregth codto: It eured that the the maxmum tre value of the held beam uder partal load mut ot exceed the allowable tre. There y e σ / (6) σ the materal' yeld lmt (MPa); allowable afety factor. The thcke retrcto of the held beam: Coderg the factor uch a the vetlato ecto, the ga emo, pedetra ad the overall effect of the upport, a thcke rage of held beam ofte pecfed the deg. There T x + x T m 3 4 max (7) T m the mmum thcke of the held beam; T max the maxmum thcke of the held beam (mm). The overall thcke retrcto of the abdome: Coderg that the held beam of hydraulc upport ha certa tffe, the abdome deg hould defe a mmum thcke. There c m + + (8) ( x x 3 x5) cm lower boud of the total thcke of the abdome (mm). Boudary codto: The value of the parameter retrcted by varou pecfcato of the plate, alo by the overall or partal tffe ad deformato. Therefore, the deg varable are wth a certa rage. There l x u =,,,5 l the lower boud of the varable (mm); u the upper boud of the varable (mm). So the mathematcal model ca be ummarzed a follow: M F( X) = x4(x+ 4x + 6 x5) + 6x3x 5 X=[x,x,x 3,x 4,x 5 ].t. ye σ / Tm x3 + x4 Tmax ( x + x + 3 x ) c l x u =,,, 5 5 m (9) Optmzato ad Valdato Reult I th paper, the radom drecto method programmed wth MATLAB to olve optmzato model. Combg wth actual producto requremet, the optmal reult of deg varable for egeerg proce ca be obtaed a how Table 3. 5

6 Before optmzato, the cro ectoal area of the held beam m, ad through optmzato, the cro ectoal area of the held beam decreae to m. That mea, the held beam themelve weght wll be reduced by.8%. Accordg to the above optmzato, the held beam ha to be modeled aga. Uder the ulateral loadg codto of top beam, the fte elemet aaly for the held beam performed aga. The cotour of tre ad dplacemet of the optmzed held beam are how Fg. 5 ad 6. Parameter Table3. Deg varable optmzato reult Upper Lower Orgal lmt lmt value/mm value/mm value/mm Optmal value/mm x x x x x Fgure 5. Optmzed tre dtrbuto of the held beam Cocluo Fgure 6. Optmzed deformato of the held beam () Baed o the repoe urface method, the tructural optmzato o held beam propoed. Ad the repoe urface method ad the fte elemet aaly are appled for held beam tructure optmzato. The held beam ectoal dmeo of a certa type Hydraulc Support optmzed to verfy the practcalty of the method. () The depedet varable of repoe urface fucto are choe accordg to ectoal dmeo of held beam, ad the expermet deg carred out by ug uform expermetal method. The leat-quare ued to ft the repoe urface fucto, whch ca approxmately reflect the relato betwee the ectoal dmeo ad the maxmum tre. 6

7 (3) Sce the repoe urface fucto fttg depedet of the pecfc tructure hape, the method ha a certa uveralty ad ca be appled to other tructure optmzato for hydraulc upport. Referece Lu, W.W. (007), A mechacal aaly ad the deg of the ma tructural o the ma top coal hydraulc powered upport.the mater the of Laog Techcal Uverty. Yao, X.Y. (0a), A reeearch for tructural optmzato deg of hydraulc upport' held beam. The mater the of Zhegzhou Uverty. Lag, J.,Xu, J.S., Xe, J. etc. (00),Hull le automatc optmzato baed o deg pace explorato. Joural of Shp Mechac, 7, pp Kleje, P.C. (005a), A overvew of the deg ad aaly of mulato expermet for etvty aaly. Europea Joural of Operatoal Reearch, 64, pp L, X., L, W.J. ad Peg, C.Y. (005b), Repoe urface methodology baed o uform deg ad t applcato to complex egeerg ytem optmzato. Mechacal Scece ad Techology, 4, pp Akra Todorok ad Tetuya Ihkawa. (004), Deg of expermet for tackg equece optmzato wth geetc algorthm ug repoe urface approxmato. Compote Structure, 64, pp Q, D.C.,Yao, X.Y., Wu, H.X.,etc. (0b), Fte elemet aaly of hydraulc upport cavg held baed o Pro/E ad ANSYS.Coal Me Machery, 3, pp Zhu, Q.,Q, D.C. ad Yao, X.Y. (0), Study o tructural optmzato of hydraulc upport cavg held ug ANSYS.Coal Me Machery, 33, pp

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4

Reaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4 CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The

More information

r y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation

r y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation Maatee Klled Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6.11 A Smple Regreo Problem 1 I there relato betwee umber of power boat the area ad umber of maatee klled?

More information

Simple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation.

Simple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation. Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6. A Smple Regreo Problem I there relato betwee umber of power boat the area ad umber of maatee klled? Year NPB( )

More information

Simple Linear Regression Analysis

Simple Linear Regression Analysis LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

A Result of Convergence about Weighted Sum for Exchangeable Random Variable Sequence in the Errors-in-Variables Model

A Result of Convergence about Weighted Sum for Exchangeable Random Variable Sequence in the Errors-in-Variables Model AMSE JOURNALS-AMSE IIETA publcato-17-sere: Advace A; Vol. 54; N ; pp 3-33 Submtted Mar. 31, 17; Reved Ju. 11, 17, Accepted Ju. 18, 17 A Reult of Covergece about Weghted Sum for Exchageable Radom Varable

More information

Linear Approximating to Integer Addition

Linear Approximating to Integer Addition Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for

More information

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1

CS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1 CS473-Algorthm I Lecture b Dyamc Table CS 473 Lecture X Why Dyamc Table? I ome applcato: We do't kow how may object wll be tored a table. We may allocate pace for a table But, later we may fd out that

More information

Temperature Memory Effect in Amorphous Shape Memory Polymers. Kai Yu 1, H. Jerry Qi 1, *

Temperature Memory Effect in Amorphous Shape Memory Polymers. Kai Yu 1, H. Jerry Qi 1, * Electroc Supplemetary Materal (ESI) for Soft Matter. h joural he Royal Socety of Chemtry 214 Supplemetary Materal for: emperature Memory Effect Amorphou Shape Memory Polymer Ka Yu 1, H. Jerry Q 1, * 1

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Linear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption?

Linear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption? Iter-erupto Tme Weght Correlato & Regreo 1 1 Lear Regreo 0 80 70 80 Heght 1 Ca heght formato be ued to predct weght of a dvdual? How log hould ou wat tll et erupto? Weght: Repoe varable (Outcome, Depedet)

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology It J Pure Appl Sc Techol, () (00), pp 79-86 Iteratoal Joural of Pure ad Appled Scece ad Techology ISSN 9-607 Avalable ole at wwwjopaaat Reearch Paper Some Stroger Chaotc Feature of the Geeralzed Shft Map

More information

8 The independence problem

8 The independence problem Noparam Stat 46/55 Jame Kwo 8 The depedece problem 8.. Example (Tua qualty) ## Hollader & Wolfe (973), p. 87f. ## Aemet of tua qualty. We compare the Huter L meaure of ## lghte to the average of coumer

More information

Summarizing Bivariate Data. Correlation. Scatter Plot. Pearson s Sample Correlation. Summarizing Bivariate Data SBD - 1

Summarizing Bivariate Data. Correlation. Scatter Plot. Pearson s Sample Correlation. Summarizing Bivariate Data SBD - 1 Summarzg Bvarate Data Summarzg Bvarate Data - Eamg relato betwee two quattatve varable I there relato betwee umber of hadgu regtered the area ad umber of people klled? Ct NGR ) Nkll ) 447 3 4 3 48 4 4

More information

T-DOF PID Controller Design using Characteristic Ratio Assignment Method for Quadruple Tank Process

T-DOF PID Controller Design using Characteristic Ratio Assignment Method for Quadruple Tank Process World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Electrcal ad Iformato Egeerg Vol:, No:, 7 T-DOF PID Cotroller Deg ug Charactertc Rato Agmet Method for Quadruple Tak Proce Tacha Sukr, U-tha

More information

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN

European Journal of Mathematics and Computer Science Vol. 5 No. 2, 2018 ISSN Europea Joural of Mathematc ad Computer Scece Vol. 5 o., 018 ISS 059-9951 APPLICATIO OF ASYMPTOTIC DISTRIBUTIO OF MA-HITEY STATISTIC TO DETERMIE THE DIFFERECE BETEE THE SYSTOLIC BLOOD PRESSURE OF ME AD

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Predicting the eruption time after observed an eruption of 4 minutes in duration.

Predicting the eruption time after observed an eruption of 4 minutes in duration. Lear Regreo ad Correlato 00 Predctg the erupto tme after oberved a erupto of 4 mute durato. 90 80 70 Iter-erupto Tme.5.0.5 3.0 3.5 4.0 4.5 5.0 5.5 Durato A ample of tererupto tme wa take durg Augut -8,

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information

Decision Science Letters

Decision Science Letters Deco Scece etter 4 (5) 47 48 Cotet lt avalable at GrowgScece Deco Scece etter homepage: www.growgscece.com/dl Optmum hape deg of tructural model wth mprece coeffcet by parametrc geometrc programmg Samr

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

System Reliability-Based Design Optimization Using the MPP-Based Dimension Reduction Method

System Reliability-Based Design Optimization Using the MPP-Based Dimension Reduction Method Sytem Relablty-Baed Deg Optmzato Ug the M-Baed Dmeo Reducto Method I Lee ad KK Cho Departmet of Mechacal & Idutral Egeerg College of Egeerg, The Uverty of Iowa Iowa Cty, IA 54 ad Davd Gorch 3 US Army RDECOM/TARDEC,

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Simple Linear Regression

Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

Research Article A Fuzzy Multi-attribute Decision Making Method for Sensory Evaluation of Tea Liquor

Research Article A Fuzzy Multi-attribute Decision Making Method for Sensory Evaluation of Tea Liquor Advace Joural of Food Scece ad Techology 9(2: 87-9, 205 DOI: 0.9026/aft.9.99 ISSN: 202-868; e-issn: 202-876 205 Maxwell Scetfc Publcato Corp. Submtted: October 05, 20 Accepted: March 20, 205 Publhed: Augut

More information

EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleneck Model. Part mix Mix of the various part or product styles produced by the system

EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleneck Model. Part mix Mix of the various part or product styles produced by the system Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleeck Model Provde tartg etmate of FMS deg arameter uch a roducto rate ad umber of worktato Bottleeck

More information

KR20 & Coefficient Alpha Their equivalence for binary scored items

KR20 & Coefficient Alpha Their equivalence for binary scored items KR0 & Coeffcet Alpha Ther equvalece for bary cored tem Jue, 007 http://www.pbarrett.et/techpaper/r0.pdf f of 7 Iteral Cotecy Relablty for Dchotomou Item KR 0 & Alpha There apparet cofuo wth ome dvdual

More information

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14)

Quiz 1- Linear Regression Analysis (Based on Lectures 1-14) Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,

More information

Reliability evaluation of distribution network based on improved non. sequential Monte Carlo method

Reliability evaluation of distribution network based on improved non. sequential Monte Carlo method 3rd Iteratoal Coferece o Mecatrocs, Robotcs ad Automato (ICMRA 205) Relablty evaluato of dstrbuto etwork based o mproved o sequetal Mote Carlo metod Je Zu, a, Cao L, b, Aog Tag, c Scool of Automato, Wua

More information

Research on SVM Prediction Model Based on Chaos Theory

Research on SVM Prediction Model Based on Chaos Theory Advaced Scece ad Techology Letters Vol.3 (SoftTech 06, pp.59-63 http://dx.do.org/0.457/astl.06.3.3 Research o SVM Predcto Model Based o Chaos Theory Sog Lagog, Wu Hux, Zhag Zezhog 3, College of Iformato

More information

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model 1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s). CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

A note on testing the covariance matrix for large dimension

A note on testing the covariance matrix for large dimension A ote o tetg the covarace matrx for large dmeo Melae Brke Ruhr-Uvertät Bochum Fakultät für Mathematk 44780 Bochum, Germay e-mal: melae.brke@ruhr-u-bochum.de Holger ette Ruhr-Uvertät Bochum Fakultät für

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Trignometric Inequations and Fuzzy Information Theory

Trignometric Inequations and Fuzzy Information Theory Iteratoal Joural of Scetfc ad Iovatve Mathematcal Reearch (IJSIMR) Volume, Iue, Jauary - 0, PP 00-07 ISSN 7-07X (Prt) & ISSN 7- (Ole) www.arcjoural.org Trgometrc Iequato ad Fuzzy Iformato Theory P.K. Sharma,

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

INEQUALITIES USING CONVEX COMBINATION CENTERS AND SET BARYCENTERS

INEQUALITIES USING CONVEX COMBINATION CENTERS AND SET BARYCENTERS Joural of Mathematcal Scece: Advace ad Alcato Volume 24, 23, Page 29-46 INEQUALITIES USING CONVEX COMBINATION CENTERS AND SET BARYCENTERS ZLATKO PAVIĆ Mechacal Egeerg Faculty Slavok Brod Uverty of Ojek

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

An Introduction to. Support Vector Machine

An Introduction to. Support Vector Machine A Itroducto to Support Vector Mache Support Vector Mache (SVM) A classfer derved from statstcal learg theory by Vapk, et al. 99 SVM became famous whe, usg mages as put, t gave accuracy comparable to eural-etwork

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging

On a Truncated Erlang Queuing System. with Bulk Arrivals, Balking and Reneging Appled Mathematcal Scece Vol. 3 9 o. 3 3-3 O a Trucated Erlag Queug Sytem wth Bul Arrval Balg ad Reegg M. S. El-aoumy ad M. M. Imal Departmet of Stattc Faculty Of ommerce Al- Azhar Uverty. Grl Brach Egypt

More information

Aitken delta-squared generalized Juncgk-type iterative procedure

Aitken delta-squared generalized Juncgk-type iterative procedure Atke delta-squared geeralzed Jucgk-type teratve procedure M. De la Se Isttute of Research ad Developmet of Processes. Uversty of Basque Coutry Campus of Leoa (Bzkaa) PO Box. 644- Blbao, 488- Blbao. SPAIN

More information

On the energy of complement of regular line graphs

On the energy of complement of regular line graphs MATCH Coucato Matheatcal ad Coputer Chetry MATCH Cou Math Coput Che 60 008) 47-434 ISSN 0340-653 O the eergy of copleet of regular le graph Fateeh Alaghpour a, Baha Ahad b a Uverty of Tehra, Tehra, Ira

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise

( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise OISE Thermal oe ktb (T abolute temperature kelv, B badwdth, k Boltzama cotat) 3 k.38 0 joule / kelv ( joule /ecod watt) ( ) v ( freq) 4kTB Thermal oe refer to the ketc eergy of a body of partcle a a reult

More information

Nargozy T. Danayev*, Darkhan Zh. Akhmed-Zaki* THE USAGE OF MATHEMATICAL MLT MODEL FOR THE CALCULATION OF THERMAL FILTRATION

Nargozy T. Danayev*, Darkhan Zh. Akhmed-Zaki* THE USAGE OF MATHEMATICAL MLT MODEL FOR THE CALCULATION OF THERMAL FILTRATION WIERTNICTWO NAFTA GAZ TOM 3/ 6 Nargozy T. Daayev*, Darka Z. Akmed-Zak* THE USAGE OF MATHEMATICAL MLT MODEL FOR THE CALCULATION OF THERMAL FILTRATION Durg te reearc we ued a well-kow matematcal MLT model

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

AXIOMATIC APPROACH IN THE ANALYSIS OF DATA FOR DESCRIBING COMPLEX SHAPES

AXIOMATIC APPROACH IN THE ANALYSIS OF DATA FOR DESCRIBING COMPLEX SHAPES Proceedg of ICAD004 ICAD-004-43 AXIOMAIC APPROACH I HE AALYSIS OF DAA FOR DESCRIBIG COMPLEX SHAPES Mchele Pappalardo mpappalardo@ua.t Departmet of Mechacal Egeerg Uverty of Salero Va Pote Do Melllo, 84084,

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder

Collapsing to Sample and Remainder Means. Ed Stanek. In order to collapse the expanded random variables to weighted sample and remainder Collapg to Saple ad Reader Mea Ed Staek Collapg to Saple ad Reader Average order to collape the expaded rado varable to weghted aple ad reader average, we pre-ultpled by ( M C C ( ( M C ( M M M ( M M M,

More information

1. Linear second-order circuits

1. Linear second-order circuits ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of

More information

Some distances and sequences in a weighted graph

Some distances and sequences in a weighted graph IOSR Joural of Mathematc (IOSR-JM) e-issn: 78-578 p-issn: 19 765X PP 7-15 wwworjouralorg Some dtace ad equece a weghted graph Jll K Mathew 1, Sul Mathew Departmet of Mathematc Federal Ittute of Scece ad

More information

Bezier curve and its application

Bezier curve and its application , 49-55 Receved: 2014-11-12 Accepted: 2015-02-06 Ole publshed: 2015-11-16 DOI: http://dx.do.org/10.15414/meraa.2015.01.02.49-55 Orgal paper Bezer curve ad ts applcato Duša Páleš, Jozef Rédl Slovak Uversty

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Correlation and Simple Linear Regression

Correlation and Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uverst Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

Acoustics Field and Active Structural Acoustic Control Modeling in ANSYS

Acoustics Field and Active Structural Acoustic Control Modeling in ANSYS Acoutc Feld ad Actve Structural Acoutc Cotrol Modelg ANSYS M. S. Kha, C. Ca ad K. C. Hug Ittute of Hgh erformace Computg 89-C Scece ark Drve #0-/, he Rutherford Sgapore Scece ark, Sgapore 86 Abtract: h

More information

Chapter Two. An Introduction to Regression ( )

Chapter Two. An Introduction to Regression ( ) ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool for the

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

Scheduling Jobs with a Common Due Date via Cooperative Game Theory

Scheduling Jobs with a Common Due Date via Cooperative Game Theory Amerca Joural of Operato Reearch, 203, 3, 439-443 http://dx.do.org/0.4236/ajor.203.35042 Publhed Ole eptember 203 (http://www.crp.org/joural/ajor) chedulg Job wth a Commo Due Date va Cooperatve Game Theory

More information

Application of Global Sensitivity Indices for measuring the effectiveness of Quasi-Monte Carlo methods and parameter estimation. parameter.

Application of Global Sensitivity Indices for measuring the effectiveness of Quasi-Monte Carlo methods and parameter estimation. parameter. Applcato of Global estvty Idces for measurg the effectveess of Quas-Mote Carlo methods ad parameter estmato parameter estmato Kuchereko Emal: skuchereko@cacuk Outle Advatages ad dsadvatages of Mote Carlo

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post

1. a. Houston Chronicle, Des Moines Register, Chicago Tribune, Washington Post Homework Soluto. Houto Chrocle, De Moe Regter, Chcago Trbue, Wahgto Pot b. Captal Oe, Campbell Soup, Merrll Lych, Pultzer c. Bll Japer, Kay Reke, Hele Ford, Davd Meedez d..78,.44, 3.5, 3.04 5. No, the

More information

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,

More information

Progressive failure of masonry shear walls a distinct element approach *

Progressive failure of masonry shear walls a distinct element approach * Joural of Appled Mathematc ad Phyc, 2016, *, *-* http://www.crp.org/joural/jamp ISSN Ole: 2327-4379 ISSN Prt: 2327-4352 Progreve falure of maory hear wall a dtct elemet approach * (Afflato): School of

More information

ANOVA with Summary Statistics: A STATA Macro

ANOVA with Summary Statistics: A STATA Macro ANOVA wth Summary Stattc: A STATA Macro Nadeem Shafque Butt Departmet of Socal ad Prevetve Pedatrc Kg Edward Medcal College, Lahore, Pata Shahd Kamal Ittute of Stattc, Uverty of the Puab Lahore, Pata Muhammad

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two Overvew of the weghtg costats ad the pots where we evaluate the fucto for The Gaussa quadrature Project two By Ashraf Marzouk ChE 505 Fall 005 Departmet of Mechacal Egeerg Uversty of Teessee Koxvlle, TN

More information

ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014

ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014 ECE 4/599 Electrc Eergy Systems 7 Optmal Dspatch of Geerato Istructor: Ka Su Fall 04 Backgroud I a practcal power system, the costs of geeratg ad delverg electrcty from power plats are dfferet (due to

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

EFFECT OF MODAL TRUNCATION IN MULTIPLE SUPPORT RESPONSE SPECTRUM ANALYSIS OF BRIDGES.

EFFECT OF MODAL TRUNCATION IN MULTIPLE SUPPORT RESPONSE SPECTRUM ANALYSIS OF BRIDGES. he 4 th World Coferece o Earthquae Egeerg October -7, 008, Bejg, Cha EFFEC OF MODAL RUNCAION IN MULIPLE SUPPOR RESPONSE SPECRUM ANALYSIS OF BRIDGES K. Koal ad A. Der Kuregha Doctoral Studet, Dept. of Cvl

More information

260 I-1 INTRODUCTION PERCENT ERROR AND PERCENT DIFFERENCE

260 I-1 INTRODUCTION PERCENT ERROR AND PERCENT DIFFERENCE 60 I- INTRODUCTION PERCENT ERROR AND PERCENT DIFFERENCE A percet error hould be calculated whe a eperetal value E copared to a tadard or accepted value S of the ae quatt. We epre the dfferece betwee E

More information

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K

ROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu

More information

G S Power Flow Solution

G S Power Flow Solution G S Power Flow Soluto P Q I y y * 0 1, Y y Y 0 y Y Y 1, P Q ( k) ( k) * ( k 1) 1, Y Y PQ buses * 1 P Q Y ( k1) *( k) ( k) Q Im[ Y ] 1 P buses & Slack bus ( k 1) *( k) ( k) Y 1 P Re[ ] Slack bus 17 Calculato

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Lecture 25 Highlights Phys 402

Lecture 25 Highlights Phys 402 Lecture 5 Hhlht Phy 40 e are ow o to coder the tattcal mechac of quatum ytem. I partcular we hall tudy the macrocopc properte of a collecto of may (N ~ 0 detcal ad dtuhable Fermo ad Boo wth overlapp wavefucto.

More information