Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes

Size: px
Start display at page:

Download "Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes"

Transcription

1 Covero of No-Lear Stregth Evelope to Geeralzed Hoek-Brow Evelope Itroducto The power curve crtero commoly ued lmt-equlbrum lope tablty aaly to defe a o-lear tregth evelope (relatohp betwee hear tre, τ, ad ormal tre, ) for ol. I the Rocece lope tablty program Slde the crtero ha the form: τ b = a( + d) + c+ ta( θ w), () where a, b ad c are parameter typcally obtaed from a leat-quare regreo ft of data obtaed from mall-cale hear tet. The d parameter repreet the tele tregth of a materal, whle θ w kow a the wave agle. Aother popular tregth model ued lope tablty aaly the hear /ormal fucto. It cot of par of hear ad ormal tre value that defe arbtrary, o-lear hear/ormal tregth evelope for materal. Becaue o flow rule have bee derved or defed for the power curve ad hear/ormal fucto crtera, t curretly mpoble to ue them elato-platc fte elemet aaly. A a reult, whe uch a tregth model ext a Slde fle that mported to Phae 2, t coverted to a equvalet Geeralzed Hoek-Brow model. The Geeralzed Hoek-Brow crtero the mot wdely ued model for characterzg the tregth of rock mae, ad ha a well-defed platc flow rule. The ext ecto wll preet the equato of the Geeralzed Hoek-Brow crtero, ad wll outle the procedure for determg a Geeralzed Hoek-Brow crtero equvalet to a power curve or hear/ormal tregth model. The Geeralzed Hoek-Brow tregth crtero The o-lear Geeralzed Hoek-Brow crtero [] for rock mae defe materal tregth term of major ad mor prpal tree a: a = + m b + where the uaxal compreve tregth of the tact rock materal, whle GSI 00 = mexp 28 4 D, GSI 00 = exp 9 D, ad ( GSI /5 20/ a e e ) = (2)

2 m a tact rock materal property, GSI kow a the geologcal tregth dex, whle D termed the dturbace factor []. Ug relatohp developed by Balmer [, 2], a hear-ormal tre evelope equvalet to the Geeralzed Hoek-Brow prpal tre evelope ca be determed. The hear tre (τ ) ad ormal tre ( ) par correpodg to a pot o a prpal tre evelope ca be determed from the equato τ = d d d + d d ( ) ( ) 2 2 d + d = + d (). (4) For the Geeralzed Hoek-Brow crtero, the followg equato relate ad τ to ad : τ = + am b + a a 2 + am b + (5) a a + a = am b + (6) For a gve et of Geeralzed Hoek-Brow parameter ad a pefed value, ca be determed from Equato (5) through replacemet of wth the defto of the crtero (Equato ()).

3 Etmatg the parameter of a Geeralzed Hoek-Brow evelope equvalet to a Power Curve power GHB Fgure 2 how a power curve evelope, τ, ad a ew Geeralzed Hoek-Brow, τ, that approxmate the power curve. Both evelope are draw hear-ormal pace. For ay gve value, the quare of the error betwee the reduced ad approxmated evelope defed by the equato: 2 power GHB 2 ε = τ τ. (7) Orgal power hear evelope Approxmate GHB hear evelope 0.06 Dfferece (error) betwee the curve t max Fgure 2. Approxmato of a power curve wth a equvalet Geeralzed Hoek-Brow evelope hear-ormal pace. Notce the rego of error or dfferece betwee the two curve. The total error of the ft of fucto: GHB τ to power τ ca be obtaed through tegrato of the quared error

4 max t 2 Total error = (8) ε d over the rage t (the tele tregth) to a maxmum ormal tre value, max. Becaue the quared error fucto doe ot expltly relate to τ, the tegrato bet performed ug a umercal approach uch a gaua quadrature. The parameter of the bet-ft Geeralzed Hoek-Brow evelope to the power curve tregth evelope ca be obtaed through mmzato of the total quared error. Phae 2 doe th mmzato the Smplex techque, whch doe ot requre dervatve of the fucto beg mmzed. Procedure for computg equvalet Geeralzed Hoek-Brow parameter To reduce the uer of parameter to be determed, the curve-fttg procedure aume the dturbace parameter D = 0, ad etmate bet-ft value for the three parameter, m ad GSI. Th becaue, a ee from the equato that defe the Geeralzed Hoek-Brow crtero, the parameter,, ad a ca be calculated ug m ad GSI. Aumg D = 0 mplfe calculato ubtatally wth practcally o pealty to the accuracy of the curvefttg procedure. The tep for etmatg the Geeralzed Hoek-Brow parameter equvalet to a power curve evelope are the a follow: () Etablh the rage of mor prpal tree actg a lope. Sce the mmum tre take to be the tele tregth, t, t oly eceary to determe the maxmum value the lope. () Determe the correpodg value of ormal tre, max, ug Equato (5). () Mmze the quared error fucto over the rage [ t, max] ug a techque uch a the Smplex method. (The tegrato the quared error fucto performed ug the umercal gaua quadrature method.) The varable of the fucto are, m ad GSI. D aumed to have a fxed value of zero. (v) Ue m ad GSI to calculate the parameter m,, ad a. b

5 Determato of equvalet Geeralzed Hoek-Brow curve for Shear- Normal fucto The procedure for determg a Geeralzed Hoek-Brow curve that bet ft a hear-ormal fucto very mlar to thoe decrbed above for the power curve model. The prmary dfferece le the quared error fucto. Sce the hear-ormal fucto defed by a dcrete uer m of data pot, the quared error fucto tead of havg a tegral ue the ummato: m = 2, Total error = ε. (9) REFERENCES. Hoek E., C. Carraza-Torre, ad B. Corkum Hoek-Brow crtero 2002 edto. I Proceedg of the 5th North Amerca Rock Mechac Sympoum ad the 7th Tuellg Aoato of Caada: NARMS-TAC 2002, Toroto, Caada, ed. R.E. Hammah et al, Vol., pp Balmer G A geeral aalytcal oluto for Mohr evelope. Amerca Soety for Tetg ad Materal, vol. 52, pp

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX III THE ENVELOPE PROPERTY Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there

More information

Data Analysis Toolkit #10: Simple linear regression Page 1

Data Analysis Toolkit #10: Simple linear regression Page 1 Data Aaly Toolkt #0: mple lear regreo Page mple lear regreo the mot commoly ued techque f determg how oe varable of teret the repoe varable affected by chage aother varable the explaaty varable. The term

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev

The Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has

More information

3.6. Metal-Semiconductor Field Effect Transistor (MESFETs)

3.6. Metal-Semiconductor Field Effect Transistor (MESFETs) .6. Metal-Semcouctor Fel Effect rator (MESFE he Metal-Semcouctor-Fel-Effect-rator (MESFE cot of a couctg chael potoe betwee a ource a ra cotact rego a how the Fgure.6.1. he carrer flow from ource to ra

More information

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute

More information

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts Optmal replacemet ad overhaul decsos wth mperfect mateace ad warraty cotracts R. Pascual Departmet of Mechacal Egeerg, Uversdad de Chle, Caslla 2777, Satago, Chle Phoe: +56-2-6784591 Fax:+56-2-689657 rpascual@g.uchle.cl

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

Basic statistics formulas

Basic statistics formulas Wth complmet of tattcmetor.com, the te for ole tattc help Set De Morga Law Bac tattc formula Meaure of Locato Sample mea (AUB) c A c B c Commutatvty & (A B) c A c U B c A U B B U A ad A B B A Aocatvty

More information

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID

CH. V ME256 STATICS Center of Gravity, Centroid, and Moment of Inertia CENTER OF GRAVITY AND CENTROID CH. ME56 STTICS Ceter of Gravt, Cetrod, ad Momet of Ierta CENTE OF GITY ND CENTOID 5. CENTE OF GITY ND CENTE OF MSS FO SYSTEM OF PTICES Ceter of Gravt. The ceter of gravt G s a pot whch locates the resultat

More information

Load and Resistance Factor Design (LRFD)

Load and Resistance Factor Design (LRFD) 53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo

More information

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis

6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis 6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,

More information

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. yayli@science.ankara.edu.tr. evrenziplar@yahoo.

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. yayli@science.ankara.edu.tr. evrenziplar@yahoo. ON SLANT HELICES AND ENERAL HELICES IN EUCLIDEAN -SPACE Yusuf YAYLI Evre ZIPLAR Departmet of Mathematcs Faculty of Scece Uversty of Akara Tadoğa Akara Turkey yayl@sceceakaraedutr Departmet of Mathematcs

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk

The Analysis of Development of Insurance Contract Premiums of General Liability Insurance in the Business Insurance Risk The Aalyss of Developmet of Isurace Cotract Premums of Geeral Lablty Isurace the Busess Isurace Rsk the Frame of the Czech Isurace Market 1998 011 Scetfc Coferece Jue, 10. - 14. 013 Pavla Kubová Departmet

More information

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,

More information

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve

More information

A general sectional volume equation for classical geometries of tree stem

A general sectional volume equation for classical geometries of tree stem Madera y Boque 6 (2), 2:89-94 89 NOTA TÉCNICA A geeral ectoal volume equato for clacal geometre of tree tem Ua ecuacó geeral para el volume de la eccó de la geometría cláca del troco de lo árbole Gldardo

More information

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN

ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl

More information

Network dimensioning for elastic traffic based on flow-level QoS

Network dimensioning for elastic traffic based on flow-level QoS Network dmesog for elastc traffc based o flow-level QoS 1(10) Network dmesog for elastc traffc based o flow-level QoS Pas Lassla ad Jorma Vrtamo Networkg Laboratory Helsk Uversty of Techology Itroducto

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

Constrained Cubic Spline Interpolation for Chemical Engineering Applications

Constrained Cubic Spline Interpolation for Chemical Engineering Applications Costraed Cubc Sple Iterpolato or Chemcal Egeerg Applcatos b CJC Kruger Summar Cubc sple terpolato s a useul techque to terpolate betwee kow data pots due to ts stable ad smooth characterstcs. Uortuatel

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

Analysis of one-dimensional consolidation of soft soils with non-darcian flow caused by non-newtonian liquid

Analysis of one-dimensional consolidation of soft soils with non-darcian flow caused by non-newtonian liquid Joural of Rock Mechacs ad Geotechcal Egeerg., 4 (3): 5 57 Aalyss of oe-dmesoal cosoldato of soft sols wth o-darca flow caused by o-newtoa lqud Kaghe Xe, Chuaxu L, *, Xgwag Lu 3, Yul Wag Isttute of Geotechcal

More information

Curve Fitting and Solution of Equation

Curve Fitting and Solution of Equation UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

More information

Performance Attribution. Methodology Overview

Performance Attribution. Methodology Overview erformace Attrbuto Methodology Overvew Faba SUAREZ March 2004 erformace Attrbuto Methodology 1.1 Itroducto erformace Attrbuto s a set of techques that performace aalysts use to expla why a portfolo's performace

More information

Report 52 Fixed Maturity EUR Industrial Bond Funds

Report 52 Fixed Maturity EUR Industrial Bond Funds Rep52, Computed & Prted: 17/06/2015 11:53 Report 52 Fxed Maturty EUR Idustral Bod Fuds From Dec 2008 to Dec 2014 31/12/2008 31 December 1999 31/12/2014 Bechmark Noe Defto of the frm ad geeral formato:

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom. UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.

More information

The simple linear Regression Model

The simple linear Regression Model The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are : Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of

More information

OPTIMAL KNOWLEDGE FLOW ON THE INTERNET

OPTIMAL KNOWLEDGE FLOW ON THE INTERNET İstabul Tcaret Üverstes Fe Blmler Dergs Yıl: 5 Sayı:0 Güz 006/ s. - OPTIMAL KNOWLEDGE FLOW ON THE INTERNET Bura ORDİN *, Urfat NURİYEV ** ABSTRACT The flow roblem ad the mmum sag tree roblem are both fudametal

More information

Online Appendix: Measured Aggregate Gains from International Trade

Online Appendix: Measured Aggregate Gains from International Trade Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,

More information

CHAPTER 2. Time Value of Money 6-1

CHAPTER 2. Time Value of Money 6-1 CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK

Fractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag

More information

ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS. Janne Peisa Ericsson Research 02420 Jorvas, Finland. Michael Meyer Ericsson Research, Germany

ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS. Janne Peisa Ericsson Research 02420 Jorvas, Finland. Michael Meyer Ericsson Research, Germany ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS Jae Pesa Erco Research 4 Jorvas, Flad Mchael Meyer Erco Research, Germay Abstract Ths paper proposes a farly complex model to aalyze the performace of

More information

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT

DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa

More information

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1

Banking (Early Repayment of Housing Loans) Order, 5762 2002 1 akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of

More information

Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.

Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. Proceedgs of the 21 Wter Smulato Coferece B. Johasso, S. Ja, J. Motoya-Torres, J. Huga, ad E. Yücesa, eds. EMPIRICAL METHODS OR TWO-ECHELON INVENTORY MANAGEMENT WITH SERVICE LEVEL CONSTRAINTS BASED ON

More information

A Hybrid Data-Model Fusion Approach to Calibrate a Flush Air Data Sensing System

A Hybrid Data-Model Fusion Approach to Calibrate a Flush Air Data Sensing System AIAA Ifotech@Aerospace - Aprl, Atlata, Georga AIAA -3347 A Hybrd Data-Model Fuso Approach to Calbrate a Flush Ar Data Sesg System Akur Srvastava Rce Uversty, Housto, Texas, 775 Adrew J. Meade Rce Uversty,

More information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

b g (17) c n m 1 [see (1)], and by (2) and (3) s 2 and s are both zero since standard error = s (16) JUNE 1999 THE AUSTRALIAN SURVEYOR Vol. 44 No.

b g (17) c n m 1 [see (1)], and by (2) and (3) s 2 and s are both zero since standard error = s (16) JUNE 1999 THE AUSTRALIAN SURVEYOR Vol. 44 No. JUE 999 A OTE O STADARD DEVATO AD RMS R.E.Deak D.G.ldea RMT Uverty GPO Box 476V MELBOURE VC 3 AUSTRALA TE AUSTRALA SURVEYOR Vol. 44 o. TRODUCTO The am of th paper to clarfy ome cofug omeclature of ome

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

Online Modern Philosophy for Stability Detection Based on Critical Energy of Individual Machines

Online Modern Philosophy for Stability Detection Based on Critical Energy of Individual Machines Iteratoal Joural of Scetfc Reearch Egeerg Techology Volume 1, Iue 4, July-2015, ISSN (Ole): 2395-566X Ole oder Phloophy for Stablty Detecto Baed o Crtcal Eergy of Idvdual ache Wagdy. ANSOUR Emal: wagdy_brahm2010@yahoo.com

More information

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree , pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal

More information

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems Fto: A Fater, Permutable Icremetal Gradet Method or Bg ata Problem Aaro J. eazo Tbéro S. Caetao Jut omke NICTA ad Autrala Natoal Uverty AARON.FAZIO@ANU.U.AU TIBRIO.CATANO@NICTA.COM.AU JUSTIN.OMK@NICTA.COM.AU

More information

On Error Detection with Block Codes

On Error Detection with Block Codes BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 3 Sofa 2009 O Error Detecto wth Block Codes Rostza Doduekova Chalmers Uversty of Techology ad the Uversty of Gotheburg,

More information

FEASIBILITY OF AN ADJOINT MONTE CARLO PULSE HEIGHT SPECTRUM CALCULATION

FEASIBILITY OF AN ADJOINT MONTE CARLO PULSE HEIGHT SPECTRUM CALCULATION Nuclear Mathematcal ad Computatoal Scece: A Cetury Revew, A Cetury Aew Gatlburg, Teeee, Aprl 6-, 23, o CD-ROM, Amerca Nuclear Socety, LaGrage Park, IL (23) FEASIBILITY OF AN ADJOINT MONTE CARLO PULSE HEIGHT

More information

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable

More information

An Effectiveness of Integrated Portfolio in Bancassurance

An Effectiveness of Integrated Portfolio in Bancassurance A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the

More information

hal-00092005, version 2-12 Mar 2008

hal-00092005, version 2-12 Mar 2008 Author maucrpt, publhed "6th IFAC Sympoum o Fault Detecto, Supervo ad Safety of Techcal Procee, Safeproce'06, Beg : Cha (2006)" ODDS ALGORITHM -BASED OPPORTUNITY-TRIGGERED PREVENTIVE MAINTENANCE WITH PRODUCTION

More information

10.5 Future Value and Present Value of a General Annuity Due

10.5 Future Value and Present Value of a General Annuity Due Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the

More information

Online Tuning of Two Degrees of Freedom Fractional Order Control Loops

Online Tuning of Two Degrees of Freedom Fractional Order Control Loops DOI:.7694/bajece.49 Ole Tug of Two Degree of Freedom Fractoal Order Cotrol Loop A. Ate, ad C. Yeroglu Abtract Th paper preet ole tug of Two Degree of Freedom cotrol loop wth fractoal order proportoaltegraldervatve

More information

Supply Chain Management Chapter 5: Application of ILP. Unified optimization methodology. Beun de Haas

Supply Chain Management Chapter 5: Application of ILP. Unified optimization methodology. Beun de Haas Supply Cha Maagemet Chapter 5: Ufed Optmzato Methodology for Operatoal Plag Problem What to do whe ILP take too much computato tme? Applcato of ILP Tmetable Dutch Ralway (NS) Bu ad drver chedulg at Coeo,

More information

Maximization of Data Gathering in Clustered Wireless Sensor Networks

Maximization of Data Gathering in Clustered Wireless Sensor Networks Maxmzato of Data Gatherg Clustere Wreless Sesor Networks Taq Wag Stuet Member I We Hezelma Seor Member I a Alreza Seye Member I Abstract I ths paper we vestgate the maxmzato of the amout of gathere ata

More information

Confidence Intervals for Linear Regression Slope

Confidence Intervals for Linear Regression Slope Chapter 856 Cofidece Iterval for Liear Regreio Slope Itroductio Thi routie calculate the ample ize eceary to achieve a pecified ditace from the lope to the cofidece limit at a tated cofidece level for

More information

M. Salahi, F. Mehrdoust, F. Piri. CVaR Robust Mean-CVaR Portfolio Optimization

M. Salahi, F. Mehrdoust, F. Piri. CVaR Robust Mean-CVaR Portfolio Optimization M. Salah, F. Mehrdoust, F. Pr Uversty of Gula, Rasht, Ira CVaR Robust Mea-CVaR Portfolo Optmzato Abstract: Oe of the most mportat problems faced by every vestor s asset allocato. A vestor durg makg vestmet

More information

Speeding up k-means Clustering by Bootstrap Averaging

Speeding up k-means Clustering by Bootstrap Averaging Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg

More information

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting MANAGEMENT SCIENCE Vol. 52, No., Jauary 26, pp. 95 ss 25-99 ess 526-55 6 52 95 forms do.287/msc.5.447 26 INFORMS Prcg Amerca-Style Dervatves wth Europea Call Optos Scott B. Laprse BAE Systems, Advaced

More information

Measuring the Quality of Credit Scoring Models

Measuring the Quality of Credit Scoring Models Measur the Qualty of Credt cor Models Mart Řezáč Dept. of Matheatcs ad tatstcs, Faculty of cece, Masaryk Uversty CCC XI, Edurh Auust 009 Cotet. Itroducto 3. Good/ad clet defto 4 3. Measur the qualty 6

More information

In the UC problem, we went a step further in assuming we could even remove a unit at any time if that would lower cost.

In the UC problem, we went a step further in assuming we could even remove a unit at any time if that would lower cost. uel Schedulg (Chapter 6 of W&W.0 Itroducto I ecoomc dpatch we aumed the oly lmtato were o the output of the geerator: m g. h aumed that we could et ge to ay value we dered wth the rage, at ay tme, to acheve

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet gerry@me.pdx.edu Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato

More information

Vibration and Speedy Transportation

Vibration and Speedy Transportation Research Paper EAEF (3) : 8-5, 9 Path Plag of Tomato Cluster Harvestg Robot for Realzg Low Vbrato ad Speedy Trasportato Naosh KONDO *, Koch TANIHARA *, Tomowo SHIIGI *, Hrosh SHIMIZU *, Mtsutaka KURITA

More information

Report 05 Global Fixed Income

Report 05 Global Fixed Income Report 05 Global Fxed Icome From Dec 1999 to Dec 2014 31/12/1999 31 December 1999 31/12/2014 Rep05, Computed & Prted: 17/06/2015 11:24 New Performace Idcator (01/01/12) 100% Barclays Aggregate Global Credt

More information

THE EQUILIBRIUM MODELS IN OLIGOPOLY ELECTRICITY MARKET

THE EQUILIBRIUM MODELS IN OLIGOPOLY ELECTRICITY MARKET Iteratoal Coferee The Euroea Eletrty Market EEM-4 etember -, 4, Lodz, Polad Proeedg Volume,. 35-4 THE EQUILIBRIUM MODEL IN OLIGOPOLY ELECTRICITY MARKET Agezka Wyłomańka Wrolaw Uverty of Tehology Wrolaw

More information

Swarm Based Truck-Shovel Dispatching System in Open Pit Mine Operations

Swarm Based Truck-Shovel Dispatching System in Open Pit Mine Operations Swarm Baed Truck-Shovel Dpatchg Sytem Ope Pt Me Operato Yaah Br, W. Scott Dubar ad Alla Hall Departmet of Mg ad Meral Proce Egeerg Uverty of Brth Columba, Vacouver, B.C., Caada Emal: br@mg.ubc.ca Abtract

More information

On formula to compute primes and the n th prime

On formula to compute primes and the n th prime Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal

More information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected

More information

Fundamentals of Mass Transfer

Fundamentals of Mass Transfer Chapter Fudametals of Mass Trasfer Whe a sgle phase system cotas two or more speces whose cocetratos are ot uform, mass s trasferred to mmze the cocetrato dffereces wth the system. I a mult-phase system

More information

A Parallel Transmission Remote Backup System

A Parallel Transmission Remote Backup System 2012 2d Iteratoal Coferece o Idustral Techology ad Maagemet (ICITM 2012) IPCSIT vol 49 (2012) (2012) IACSIT Press, Sgapore DOI: 107763/IPCSIT2012V495 2 A Parallel Trasmsso Remote Backup System Che Yu College

More information

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh

More information

Statistical Decision Theory: Concepts, Methods and Applications. (Special topics in Probabilistic Graphical Models)

Statistical Decision Theory: Concepts, Methods and Applications. (Special topics in Probabilistic Graphical Models) Statstcal Decso Theory: Cocepts, Methods ad Applcatos (Specal topcs Probablstc Graphcal Models) FIRST COMPLETE DRAFT November 30, 003 Supervsor: Professor J. Rosethal STA4000Y Aal Mazumder 9506380 Part

More information

FINANCIAL MATHEMATICS 12 MARCH 2014

FINANCIAL MATHEMATICS 12 MARCH 2014 FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.

More information

Report 19 Euroland Corporate Bonds

Report 19 Euroland Corporate Bonds Rep19, Computed & Prted: 17/06/2015 11:38 Report 19 Eurolad Corporate Bods From Dec 1999 to Dec 2014 31/12/1999 31 December 1999 31/12/2014 Bechmark 100% IBOXX Euro Corp All Mats. TR Defto of the frm ad

More information

Report 06 Global High Yield Bonds

Report 06 Global High Yield Bonds Rep06, Computed & Prted: 17/06/2015 11:25 Report 06 Global Hgh Yeld Bods From Dec 2000 to Dec 2014 31/12/2000 31 December 1999 31/12/2014 New Bechmark (01/01/13) 80% Barclays Euro HY Ex Facals 3% Capped

More information

Design of Experiments

Design of Experiments Chapter 4 Desg of Expermets 4. Itroducto I Chapter 3 we have cosdered the locato of the data pots fxed ad studed how to pass a good respose surface through the gve data. However, the choce of pots where

More information

Borehole breakout and drilling-induced fracture analysis from image logs

Borehole breakout and drilling-induced fracture analysis from image logs Borehole breakout ad drllg-duced fracture aalyss from mage logs M. Tgay, J. Reecker, ad B. Müller Itroducto Borehole breakouts ad drllg-duced fractures (DIFs) are mportat dcators of horzotal stress oretato,

More information

MDM 4U PRACTICE EXAMINATION

MDM 4U PRACTICE EXAMINATION MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths

More information

Introduction to Maintainability

Introduction to Maintainability Itroducto to Mataablty The cocept of mataablty ecompasses: A operatoal measure of effectveess A characterstc of desg A egeerg specalty that supports desg A cost drver A plaed actvty each stage of product

More information

Three Dimensional Interpolation of Video Signals

Three Dimensional Interpolation of Video Signals Three Dmesoal Iterpolato of Vdeo Sgals Elham Shahfard March 0 th 006 Outle A Bref reve of prevous tals Dgtal Iterpolato Bascs Upsamplg D Flter Desg Issues Ifte Impulse Respose Fte Impulse Respose Desged

More information

Mathematics of Finance

Mathematics of Finance CATE Mathematcs of ace.. TODUCTO ths chapter we wll dscuss mathematcal methods ad formulae whch are helpful busess ad persoal face. Oe of the fudametal cocepts the mathematcs of face s the tme value of

More information

INF 4300 Digital Image Analysis REPETITION

INF 4300 Digital Image Analysis REPETITION INF 4300 Dgtal Image Aaly REEIION Ae Solberg 805 INF 4300 Repetto -Eroo of a bary mage Smplfed otato o compute the eroo of pel,y mage f wth the tructurg elemet S: place the tructurg elemet uch that t orgo

More information

GRADUATION PROJECT REPORT

GRADUATION PROJECT REPORT SPAM Flter School of Publc Admtrato Computer Stude Program GRADUATION PROJECT REPORT 2007-I-A02 SPAM Flter Project group leader: Project group member: Supervor: Aeor: Academc year (emeter): MCCS390 Graduato

More information

Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases

Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases Locally Adaptve Dmesoalty educto for Idexg Large Tme Seres Databases Kaushk Chakrabart Eamo Keogh Sharad Mehrotra Mchael Pazza Mcrosoft esearch Uv. of Calfora Uv. of Calfora Uv. of Calfora edmod, WA 985

More information

Sequences and Series

Sequences and Series Secto 9. Sequeces d Seres You c thk of sequece s fucto whose dom s the set of postve tegers. f ( ), f (), f (),... f ( ),... Defto of Sequece A fte sequece s fucto whose dom s the set of postve tegers.

More information

where p is the centroid of the neighbors of p. Consider the eigenvector problem

where p is the centroid of the neighbors of p. Consider the eigenvector problem Vrtual avgato of teror structures by ldar Yogja X a, Xaolg L a, Ye Dua a, Norbert Maerz b a Uversty of Mssour at Columba b Mssour Uversty of Scece ad Techology ABSTRACT I ths project, we propose to develop

More information

Regression Analysis. 1. Introduction

Regression Analysis. 1. Introduction . Itroducto Regresso aalyss s a statstcal methodology that utlzes the relato betwee two or more quattatve varables so that oe varable ca be predcted from the other, or others. Ths methodology s wdely used

More information

Session 4: Descriptive statistics and exporting Stata results

Session 4: Descriptive statistics and exporting Stata results Itrduct t Stata Jrd Muñz (UAB) Sess 4: Descrptve statstcs ad exprtg Stata results I ths sess we are gg t wrk wth descrptve statstcs Stata. Frst, we preset a shrt trduct t the very basc statstcal ctets

More information

Finite Difference Method

Finite Difference Method Fte Dfferece Method MEL 87 Computatoa Heat rasfer --4) Dr. Praba audar Assstat Professor Departmet of Mechaca Egeerg II Deh Dscretzato Methods Requred to covert the geera trasport equato to set of agebrac

More information

Bayesian Network Representation

Bayesian Network Representation Readgs: K&F 3., 3.2, 3.3, 3.4. Bayesa Network Represetato Lecture 2 Mar 30, 20 CSE 55, Statstcal Methods, Sprg 20 Istructor: Su-I Lee Uversty of Washgto, Seattle Last tme & today Last tme Probablty theory

More information

Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center

Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center 200 IEEE 3rd Iteratoal Coferece o Cloud Computg Dyamc Provsog Modelg for Vrtualzed Mult-ter Applcatos Cloud Data Ceter Jg B 3 Zhlag Zhu 2 Ruxog Ta 3 Qgbo Wag 3 School of Iformato Scece ad Egeerg College

More information

A particle Swarm Optimization-based Framework for Agile Software Effort Estimation

A particle Swarm Optimization-based Framework for Agile Software Effort Estimation The Iteratoal Joural Of Egeerg Ad Scece (IJES) olume 3 Issue 6 Pages 30-36 204 ISSN (e): 239 83 ISSN (p): 239 805 A partcle Swarm Optmzato-based Framework for Agle Software Effort Estmato Maga I, & 2 Blamah

More information

Integrating Production Scheduling and Maintenance: Practical Implications

Integrating Production Scheduling and Maintenance: Practical Implications Proceedgs of the 2012 Iteratoal Coferece o Idustral Egeerg ad Operatos Maagemet Istabul, Turkey, uly 3 6, 2012 Itegratg Producto Schedulg ad Mateace: Practcal Implcatos Lath A. Hadd ad Umar M. Al-Turk

More information

Reinsurance and the distribution of term insurance claims

Reinsurance and the distribution of term insurance claims Resurace ad the dstrbuto of term surace clams By Rchard Bruyel FIAA, FNZSA Preseted to the NZ Socety of Actuares Coferece Queestow - November 006 1 1 Itroducto Ths paper vestgates the effect of resurace

More information

Settlement Prediction by Spatial-temporal Random Process

Settlement Prediction by Spatial-temporal Random Process Safety, Relablty ad Rs of Structures, Ifrastructures ad Egeerg Systems Furuta, Fragopol & Shozua (eds Taylor & Fracs Group, Lodo, ISBN 978---77- Settlemet Predcto by Spatal-temporal Radom Process P. Rugbaapha

More information