DISTANCE MEASURE FOR ORDINAL DATA *

Size: px
Start display at page:

Download "DISTANCE MEASURE FOR ORDINAL DATA *"

Transcription

1 ARGUMENTA OECONOMICA No (8) 999 PL ISSN Mre Wes DISTANCE MEASURE FOR ORDINAL DATA * The study cosders the proe of costructo esures of srty for ord dt. The ord chrcter of the dt requred the ppcto of specfc esure of the oect s dstce. Wes (993 p ) gves the propos of ew esure of oects srty whch c e pped the stuto whe vres descrg oects re esured o the ord sce. Ths esure ws used order to evute the srtes of oects whch were sed o uers of retos equ to greter th d ser th. The dstce esure tes cre of vres wth equ weghts. We sh descre sght geersto of ths esure so coverg dfferet weghts of vres. The stregths d weesses of the proposed dstce esure re dscussed.. INTRODUCTION Cssfcto utdeso scg d er orderg ethods re portt d frequety pped toos of utvrte sttstc yss. The ppcto of these ethods requres forsto of the ter srty of oects. The use of prtcur costructo of srty esure depeds o the sce o whch the vres re esured. I the esureet theory four sc sces re dstgushed: o ord terv d rto. These were troduced y Steves (959). Aog the four sces of esureet the o s cosdered the weest. It s foowed y the ord sce the terv sce d the rto sce whch s the strogest. The choce of srty esures s rther spe whe the vres descrg eed oects re esured o the se sce. Lterture presets pety of dfferet wys of srty esureet whch c e dopted to vres esured o the sce: rto terv d (or) rto o ( cudg ry vres). A wde rge of srty esures hs ee show : Corc (97); Adererg (973); Evertt (974); Kuf d Rousseeuw (990); Co d Co (994 p. 0 ); Wede d Kur (998 p. 47). * Deprtet of Ecooetrcs d Coputer Scece Wrocłw Uversty of Ecoocs e-: [email protected].

2 M. WALESIAK 68 Wes (993 p ) gves the propos of ew esure of oects srty whch c e pped stuto whe vres descrg those oects re esured oy o the ord sce (see: so Wes Dzechcrz d Bą 998 p ). If we hve set A of oects descred y ord vres the coutg of evets s the oy posse rthetc operto whch c e perfored o these oects. The proposed esure s gve y the foowg foru: d () where: r p r p r p r p f f 0 f for p = ; r = ; =... uer of oect =... uer of ord vre ) ( th ( th th) oservto o th ord vre oect h oservedfor ser t h d retos greter t uer of. oect h oservedfor ser t h d retos greter t uer of Epe. Appcto of dstce () to copute the dstces of oects fro the ptter (de pot). The output resut s vector of dstces.

3 DISTANCE MEASURE FOR ORDINAL DATA 69 Te Dt No. Noteoo Effcecy Equpet Quty Ergoocs Docuetto Cfor Access Cfor Access Cevo Mtsu P-96-3R Cevo Mtsu P-98R Copq Ard 590DT De Lttude CP 66ST Dgt HNote VP Dgt HNote Utr Euroco Futsu LfeBoo 675CDT Futsu LfeBoo 765TCDT Futsu LfeBoo 985CDT GerCo Overdose Epre 8500T Hyud HN IBM ThPd TP380ED Po Tosh Stete Pro 480CDT Tosh Tecr 750DVD Tup Moto Le d 5/ Twhed Arsto FT-9000 DSC Twhed Arsto FT-9000 TFT Twhed Arsto FT-9300T Vos HS LeBoo Advce 66 DSC Vos HS LeBoo Advce 00 TFT Ptter Weghts Source: CHIP 998 o. 4. Te The dstces of oects fro the ptter (de pot) Posto Noteoo Dstce () Posto Noteoo Dstce () Source: ow reserch.

4 70 M. WALESIAK. MODIFICATION OF DISTANCE MEASURE d The dstce esure () tes cre of vres wth equ weghts. We sh descre sght geersto of ths esure so coverg dfferet weghts of vres. Suppose vre weghts w ( =... ) stsfy codtos: w (0; ). () w Three or ethods of vre weghtg hve ee deveoped: pror sed o epert opos procedures sed o forto cuded the dt d coto of these two ethods. Grńs (99) Mg (989) Arhowcz d Ząc (986) d Borys (984) dscuss the proe of vre weghtg utvrte sttstc yss. The proe of whether or ot to weght vres hs cused cotroversy. Ws sys (see: Adederfer d Bshfed 984 p. ) tht weghtg s spy the puto of vue of vre. Seth d So (973) suggest tht the pproprte wy to esure srty s to gve vres equ weght. If vre weghts re ot ufor the dstce esure s defed s (3). w d (3) w w w w Whe vre weghts re equ the foru (3) ecoes dstce esure (). Epe. Appcto of dstce (3) to copute the dstces of oects fro the ptter (de pot). The output resut s vector of dstces. Te 3 w Weghts for vres sed o CHIP epert opos Vre Effcecy Equpet Quty Ergoocs Docuetto Weghts Source: CHIP 998 o. 4.

5 DISTANCE MEASURE FOR ORDINAL DATA 7 Te 4 The dstces of oects fro the ptter (de pot) Posto Noteoo Dstce (3) Posto Noteoo Dstce (3) Source: ow reserch. 3. THE STRENGTHS AND WEAKNESSES OF THE DISTANCE MEASURE d Dstce esure d : c e pped stuto whe vres descrg oects re esured oy o the ord sce eeds t est oe pr of o-detc oects A ot to hve zero the deotor Ked s de of correto coeffcet for ord vres ws used for the esure d costructo (see: Ked 955 p. 9) dstce d ssues vues fro the [0; ] terv. Vue 0 dctes tht for the copred oects etwee correspodg oservtos of ord vres oy retos equ to te pce. Vue dctes tht for the copred oects etwee correspodg oservtos o ord vres retos greter th te pce or retos greter th d retos equ to f they re hed for other oects (.e. oects uered =... ; where ) dstce d stsfes codtos: d 0 d 0 d d (for =... ) suto yss proves tht dstce d ot wys stsfes the trge equty trsforto of ord dt y y strcty cresg fucto does ot chge the vue of d dstce.

6 7 M. WALESIAK 4. CONCLUDING REMARKS The use of vres esured o ord sce s retvey rre the terture. Specfc ytc toos re eeded for such forto. The proposed dstce esures () d (3) re pproprte such stutos. Whe vre weghts re equ foru (3) ecoes dstce esure (). The ddto resut of ths study s coputer progr whch ows coputg dstces etwee oects (see Apped). APPENDIX The coputer code the C++ guge coputg the vue of esure (3) of the dstce cosdered s ve t Wrocłw Uversty of Ecoocs the Dept of Ecooetrcs d Coputer Scece Ths verso of the progr ows to copute dstces etwee oects (the output resut s syetrc dstce tr) d so ccuto of the dstces of oects fro the ode or de pot (the output resut s vector of dstces). Ths tr y e used the herrchc ggoertve ethods of the cssfcto for the dvso of set of oects to csses. Ths tr c so e used for further coputtos the SPSS for Wdows pcge. Acowedgeets: The reserch preseted the pper ws supported y the proect KBN H0B 0 6. REFERENCES Arhowcz M. Ząc K. (986): Metod wże zeych w tsoo uerycze procedurch porządow owego [Vre Weghtg Agorth Nuerc Tooy d Ler Orderg Procedures]. Wrocłw Uversty of Ecoocs Reserch Ppers o. 38 pp Adederfer M. S. Bshfed R. K. (984): Custer Ayss Sge Bevery Hs. Adererg M. R. (973): Custer Ayss for Appctos. Acdec Press New Yor S Frcsco Lodo. Borys T. (984): Ktegor ośc w sttystycze ze porówwcze [Ctegory of Quty Sttstc Coprtve Ayss]. Wrocłw Uversty of Ecoocs Reserch Ppers o. 84. Corc R. M. (97): A Revew of Cssfcto (wth Dscusso) Jour of the Roy Sttstc Socety seres: A (3) pp Co T.F. Co M.A. A. (994): Mutdeso Scg Chp d H Lodo. Evertt B. S. (974): Custer Ayss Hee Lodo. Grńs T. (99): Metody tsooetr [Tooetrc Methods] Crcow Uversty of Ecoocs Krów.

7 DISTANCE MEASURE FOR ORDINAL DATA 73 Kuf L. Rousseeuw P. J. (990): Fdg Groups Dt: Itroducto to Custer Ayss Wey New Yor. Ked G. (955): R Correto Methods Grff Lodo. Mg G. W. (989): A Vdto Study of Vre Weghtg Agorth for Custer Ayss Jour of Cssfcto o. pp Seth P. H. A. So R. R. (973): Nuerc Tooy W.H. Free d Co. S Frcsco. Steves S. S. (959): Mesureet Psychophyscs d Utty : Church C. W. d Rtoosh P. (eds.): Mesureet; Deftos d Theores. Wey New Yor. Wes M. (993): Sttystycz z weowyrow w dch retgowych [Mutvrte Sttstc Ayss Mretg Reserch]. Wrocłw Uversty of Ecoocs Reserch Ppers o Wes M. (996): Metody zy dych retgowych [Methods of Mretg Dt Ayss]. PWN Wrszw. Wes M. Dzechcrz J. Bą A. (998): Ord Vres the Segetto of Advertseet Recevers : Rzz A. Vch N. Boc H. H.: Advces Dt Scece d Cssfcto Proc. 6th Cof. Iterto Federto of Cssfcto Socetes Roe. Sprger Hedeerg pp Wede M. Kur W. A. (998): Mret Segetto. Coceptu d Methodoogc Foudtos Kuwer Bosto Dordrecht Lodo. Receved: ; revsed verso

Generalized solutions for the joint replenishment problem with correction factor

Generalized solutions for the joint replenishment problem with correction factor Geerzed soutos for the ot repeshet proe wth correcto fctor Astrct Erc Porrs, Roert Deer Ecooetrc Isttute, erge Isttute, Ersus Uversty Rotterd, P.O. Box 73, 3 DR Rotterd, he etherds Ecooetrc Isttute Report

More information

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

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

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral

More information

Master Thesis Mathematical Modeling and Simulation On Fuzzy linear programming problems solved with Fuzzy decisive set method

Master Thesis Mathematical Modeling and Simulation On Fuzzy linear programming problems solved with Fuzzy decisive set method 008:05 Mster Thess Mthemt Modeg d Smuto O Fuzzy er progrmmg proems soved wth Fuzzy desve set method Author shd Mehmood Thess for the degree Mster of Mthemt Modeg d Smuto 5 redt pots 5 ECTS redts 08 009

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

Lecture 7. Norms and Condition Numbers

Lecture 7. Norms and Condition Numbers Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three

More information

16. Mean Square Estimation

16. Mean Square Estimation 6 Me Sque stmto Gve some fomto tht s elted to uow qutty of teest the poblem s to obt good estmte fo the uow tems of the obseved dt Suppose epeset sequece of dom vbles bout whom oe set of obsevtos e vlble

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

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

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

Fuzzy Task Assignment Model of Web Services Supplier in Collaborative Development Environment

Fuzzy Task Assignment Model of Web Services Supplier in Collaborative Development Environment , pp.199-210 http://dx.do.org/10.14257/uesst.2015.8.6.19 Fuzzy Task Assget Model of Web Servces Suppler Collaboratve Developet Evroet Su Ja 1,2, Peg Xu-ya 1, *, Xu Yg 1,3, Wag Pe-e 2 ad Ma Na- 4,2 1. College

More information

A MODEL FOR AIRLINE PASSENGER AND CARGO FLIGHT SCHEDULING

A MODEL FOR AIRLINE PASSENGER AND CARGO FLIGHT SCHEDULING A MODEL FOR AIRLINE PASSENGER AND CARGO FLIGHT SCHEDULING Shgyo YAN Yu-Hsu CHEN Professor Mster Deprtet of Cvl Egeerg Deprtet of Cvl Egeerg Ntol Cetrl Uversty Ntol Cetrl Uversty No300, Jhogd Rd, Jhogl

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

Generalized Difference Sequence Space On Seminormed Space By Orlicz Function

Generalized Difference Sequence Space On Seminormed Space By Orlicz Function Ieaoa Joa of Scece ad Eee Reeach IJSER Vo Ie Decembe -4 5687 568X Geeazed Dffeece Seece Sace O Semomed Sace B Ocz Fco A.Sahaaa Aa ofeo G Ie of TechooCombaoeIda. Abac I h aewe defe he eece ace o emomed

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 [email protected],

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

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

An SVR-Based Data Farming Technique for Web Application

An SVR-Based Data Farming Technique for Web Application A SVR-Based Data Farmg Techque for Web Appcato Ja L 1 ad Mjg Peg 2 1 Schoo of Ecoomcs ad Maagemet, Behag Uversty 100083 Bejg, P.R. Cha [email protected] 2 Isttute of Systems Scece ad Techoogy, Wuy Uversty, Jagme

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

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

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,

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

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

Developing a Fuzzy Search Engine Based on Fuzzy Ontology and Semantic Search

Developing a Fuzzy Search Engine Based on Fuzzy Ontology and Semantic Search 0 IEEE Iteratoal Coferece o Fuzzy Systes Jue 7-30, 0, Tape, Tawa Developg a Fuzzy Search Ege Based o Fuzzy Otology ad Seatc Search Le-Fu La Chao-Ch Wu Pe-Yg L Dept. of Coputer Scece ad Iforato Egeerg Natoal

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

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

Projection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li

Projection model for Computer Network Security Evaluation with interval-valued intuitionistic fuzzy information. Qingxiang Li Iteratoal Joural of Scece Vol No7 05 ISSN: 83-4890 Proecto model for Computer Network Securty Evaluato wth terval-valued tutostc fuzzy formato Qgxag L School of Software Egeerg Chogqg Uversty of rts ad

More information

EXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26

EXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26 EXAMLE... A. Edowme... B. ure edowme d Term surce... 4 C. Reseres... 8. Bruo premum d reseres... EXAMLE 2... 4 A. Whoe fe... 4 B. Reseres of Whoe fe... 6 C. Bruo Whoe fe... 7 EXAMLE 3... 8 A.ure edowme...

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

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

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

FUZZY PERT FOR PROJECT MANAGEMENT

FUZZY PERT FOR PROJECT MANAGEMENT Itertol Jourl of dvces Egeerg & Techology Sept. 04. IJET ISSN: 96 FUZZY PERT FOR PROJECT MNGEMENT Ther hed Sdoo l S Rd M. Ro l Brhe ssst. Prof ssstt Lecturer College of dstrto d Ecoocs Mgeet Iforto Systes

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

Fr ag m e n tac i ó n y c o m p l e j i da d: a n á l i s i s d e l c a m b i o

Fr ag m e n tac i ó n y c o m p l e j i da d: a n á l i s i s d e l c a m b i o ss: 57606 Fr e o d Copey: y S r u c u r Che he Chco Reo Ecooy Fr e c ó y c o p e d d: á s s d e c o esrucur e ecooí de reó de Chco sdoro Roero Uversdd de Sev [email protected] Er Deecher Uversy o Groe & Uversy

More information

Optimal Packetization Interval for VoIP Applications Over IEEE 802.16 Networks

Optimal Packetization Interval for VoIP Applications Over IEEE 802.16 Networks Optmal Packetzato Iterval for VoIP Applcatos Over IEEE 802.16 Networks Sheha Perera Harsha Srsea Krzysztof Pawlkowsk Departmet of Electrcal & Computer Egeerg Uversty of Caterbury New Zealad [email protected]

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

Green Master based on MapReduce Cluster

Green Master based on MapReduce Cluster Gree Master based o MapReduce Cluster Mg-Zh Wu, Yu-Chag L, We-Tsog Lee, Yu-Su L, Fog-Hao Lu Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of

More information

Randomized Load Balancing by Joining and Splitting Bins

Randomized Load Balancing by Joining and Splitting Bins Radomzed Load Baacg by Jog ad Spttg Bs James Aspes Ytog Y 1 Itoducto Cosde the foowg oad baacg sceao: a ceta amout of wo oad s dstbuted amog a set of maches that may chage ove tme as maches o ad eave the

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

CSSE463: Image Recognition Day 27

CSSE463: Image Recognition Day 27 CSSE463: Image Recogto Da 27 Ths week Toda: Alcatos of PCA Suda ght: roject las ad relm work due Questos? Prcal Comoets Aalss weght grth c ( )( ) ( )( ( )( ) ) heght sze Gve a set of samles, fd the drecto(s)

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

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

A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining

A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining A Fast Clusterg Algorth to Cluster Very Large Categorcal Data Sets Data Mg Zhexue Huag * Cooperatve Research Cetre for Advaced Coputatoal Systes CSIRO Matheatcal ad Iforato Sceces GPO Box 664, Caberra

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

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

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

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

More information

Present and Future Value Formulae for Uneven Cash Flows Based on Performance of a Business

Present and Future Value Formulae for Uneven Cash Flows Based on Performance of a Business Itertol Jourl of Bkg d Fce Volue 8 Issue Artcle 0--0 reset d Future Vlue Forule for Ueve Csh Flows Bsed o erforce of Busess Aeh Tefer Tesse Costructo d Busess Bk, Etho, [email protected] Follow ths d ddtol

More information

Classic Problems at a Glance using the TVM Solver

Classic Problems at a Glance using the TVM Solver C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the

More information

Automated Event Registration System in Corporation

Automated Event Registration System in Corporation teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee

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

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

Near Neighbor Distribution in Sets of Fractal Nature

Near Neighbor Distribution in Sets of Fractal Nature Iteratoal Joural of Computer Iformato Systems ad Idustral Maagemet Applcatos. ISS 250-7988 Volume 5 (202) 3 pp. 59-66 MIR Labs, www.mrlabs.et/jcsm/dex.html ear eghbor Dstrbuto Sets of Fractal ature Marcel

More information

Software Size Estimation in Incremental Software Development Based On Improved Pairwise Comparison Matrices

Software Size Estimation in Incremental Software Development Based On Improved Pairwise Comparison Matrices Computer Scece Systems Bology Reserch Artcle Artcle Ocheg d Mwg, 204, 7:3 http://d.do.org/0.472/csb.0004 Ope Ope Access Softwre Sze Estmto Icremetl Softwre Developmet Bsed O Improved Prwse Comprso Mtrces

More information

Maintenance Scheduling of Distribution System with Optimal Economy and Reliability

Maintenance Scheduling of Distribution System with Optimal Economy and Reliability Egeerg, 203, 5, 4-8 http://dx.do.org/0.4236/eg.203.59b003 Publshed Ole September 203 (http://www.scrp.org/joural/eg) Mateace Schedulg of Dstrbuto System wth Optmal Ecoomy ad Relablty Syua Hog, Hafeg L,

More information

Decision Science Letters

Decision Science Letters Decso Scece Letters 1 (2012) 11 22 Cotets sts avaabe at GrowgScece Decso Scece Letters hoepage: www.growgscece.co/ds A Fuzzy-MOORA approach for ERP syste seecto Prasad Karade a ad Shakar Chakraborty b*

More information

An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment

An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment 31 Itertol Jourl of Cotrol, Yog-Shk Automto, Km d Keum-Shk d Systems, Hog vol. 2, o. 3, pp. 31-318, September 24 A IMM Algorthm for Trckg Meuverg Vehcles Adptve Cruse Cotrol Evromet Yog-Shk Km d Keum-Shk

More information

Polyphase Filters. Section 12.4 Porat 1/39

Polyphase Filters. Section 12.4 Porat 1/39 Polyphase Flters Secto.4 Porat /39 .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful

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: [email protected] amh Abdul-Nab Lebaese Iteratoal

More information

Project 3 Weight analysis

Project 3 Weight analysis The Faculty of Power ad Aeroautcal Egeerg Arcraft Desg Departet Project 3 Weght aalyss Ths project cossts of two parts. Frst part cludes fuselage teror (cockpt) coceptual desg. Secod part cludes etoed

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 [email protected] Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato

More information

STANDARDISATION OF DATA SET UNDER DIFFERENT MEASUREMENT SCALES. 1 The measurement scales of variables

STANDARDISATION OF DATA SET UNDER DIFFERENT MEASUREMENT SCALES. 1 The measurement scales of variables STANDARDISATION OF DATA SET UNDER DIFFERENT MEASUREMENT SCALES Krzysztof Jajuga 1, Marek Walesiak 1 1 Wroc law University of Economics, Komandorska 118/120, 53-345 Wroc law, Poland Abstract: Standardisation

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

Fast, Secure Encryption for Indexing in a Column-Oriented DBMS

Fast, Secure Encryption for Indexing in a Column-Oriented DBMS Fast, Secure Ecrypto for Idexg a Colum-Oreted DBMS Tgja Ge, Sta Zdok Brow Uversty {tge, sbz}@cs.brow.edu Abstract Networked formato systems requre strog securty guaratees because of the ew threats that

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

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

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

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

Common p-belief: The General Case

Common p-belief: The General Case GAMES AND ECONOMIC BEHAVIOR 8, 738 997 ARTICLE NO. GA97053 Commo p-belef: The Geeral Case Atsush Kaj* ad Stephe Morrs Departmet of Ecoomcs, Uersty of Pesylaa Receved February, 995 We develop belef operators

More information

Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines

Approximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines (ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005

More information

Automated Alignment and Extraction of Bilingual Ontology for Cross-Language Domain-Specific Applications

Automated Alignment and Extraction of Bilingual Ontology for Cross-Language Domain-Specific Applications Automated Agmet ad Extracto of gua Otoogy for Cross-Laguage Doma-Specfc Appcatos Ju-Feg Yeh, Chug-Hse Wu, Mg-Ju Che ad Lag-Chh Yu Departmet of Computer Scece ad Iformato Egeerg Natoa Cheg Kug Uversty,

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

Put the human back in Human Resources.

Put the human back in Human Resources. Put the human back in Human Resources A Co m p l et e Hu m a n Ca p i t a l Ma n a g em en t So l u t i o n t h a t em p o w er s HR p r o f essi o n a l s t o m eet t h ei r co r p o r a t e o b j ect

More information

Application of GA with SVM for Stock Price Prediction in Financial Market

Application of GA with SVM for Stock Price Prediction in Financial Market Iteratoa Joura of Scece ad Research (IJSR) ISSN (Oe): 39-7064 Impact Factor (0): 3.358 Appcato of GA wth SVM for Stock Prce Predcto Faca Market Om Prakash Jea, Dr. Sudarsa Padhy Departmet of Computer Scece

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

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

Redundant Virtual Machine Placement for Fault-tolerant Consolidated Server Clusters

Redundant Virtual Machine Placement for Fault-tolerant Consolidated Server Clusters Redudt Vrtul Mche Plceet for Fult-tolert Cosoldted Server Clusters Fuo Mchd, Mshro Kwto d Yoshhru Meo Servce Pltfors Reserch Lbortores, NEC Cororto 753, Shoube, Nkhr-ku, Kwsk, Kgw 2-8666, J {h-chd@b, -kwto@,

More information

We will begin this chapter with a quick refresher of what an exponent is.

We will begin this chapter with a quick refresher of what an exponent is. .1 Exoets We will egi this chter with quick refresher of wht exoet is. Recll: So, exoet is how we rereset reeted ultilictio. We wt to tke closer look t the exoet. We will egi with wht the roerties re for

More information

Numerical Comparisons of Quality Control Charts for Variables

Numerical Comparisons of Quality Control Charts for Variables Global Vrtual Coferece Aprl, 8. - 2. 203 Nuercal Coparsos of Qualty Cotrol Charts for Varables J.F. Muñoz-Rosas, M.N. Pérez-Aróstegu Uversty of Graada Facultad de Cecas Ecoócas y Epresarales Graada, pa

More information

n Using the formula we get a confidence interval of 80±1.64

n Using the formula we get a confidence interval of 80±1.64 9.52 The professor of sttistics oticed tht the rks i his course re orlly distributed. He hs lso oticed tht his orig clss verge is 73% with stdrd devitio of 12% o their fil exs. His fteroo clsses verge

More information

Efficient Traceback of DoS Attacks using Small Worlds in MANET

Efficient Traceback of DoS Attacks using Small Worlds in MANET Effcet Traceback of DoS Attacks usg Small Worlds MANET Yog Km, Vshal Sakhla, Ahmed Helmy Departmet. of Electrcal Egeerg, Uversty of Souther Calfora, U.S.A {yogkm, sakhla, helmy}@ceg.usc.edu Abstract Moble

More information

Fault Tree Analysis of Software Reliability Allocation

Fault Tree Analysis of Software Reliability Allocation Fault Tree Aalyss of Software Relablty Allocato Jawe XIANG, Kokch FUTATSUGI School of Iformato Scece, Japa Advaced Isttute of Scece ad Techology - Asahda, Tatsuokuch, Ishkawa, 92-292 Japa ad Yaxag HE Computer

More information

of the relationship between time and the value of money.

of the relationship between time and the value of money. TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp

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

The Time Value of Money

The Time Value of Money The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto

More information

Analyzing and Evaluating Query Reformulation Strategies in Web Search Logs

Analyzing and Evaluating Query Reformulation Strategies in Web Search Logs Alyg d Evlutg Query Reformulto Strteges We Serch Logs Jeff Hug Uversty of Wshgto Iformto School [email protected] Efthms N. Efthmds Uversty of Wshgto Iformto School [email protected] ABSTRACT Users frequetly

More information

Study on prediction of network security situation based on fuzzy neutral network

Study on prediction of network security situation based on fuzzy neutral network Avalable ole www.ocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(6):00-06 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 Study o predcto of etwork securty stuato based o fuzzy eutral etwork

More information

ON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. [email protected]. 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

How To Make A Profit From A Website

How To Make A Profit From A Website Mg Koledge-Shrg Stes for Vrl Mretg Mtthe Rchrdso d edro Dogos Deprtet of Coputer Scece d Egeerg Uversty of Wshgto Box 3535 Settle, WA 9895-35 {ttr,pedrod}@cs.shgto.edu ABSTRACT Vrl retg tes dvtge of etors

More information