Monitoring of Network Traffic based on Queuing Theory

Size: px
Start display at page:

Download "http://www.ejournalofscience.org Monitoring of Network Traffic based on Queuing Theory"

Transcription

1 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. hp:// Moiorig of Newor Traffic base o Queuig Theory S. Saha Ray,. Sahoo Naioal Isiue of Techology Deparme of Mahemaics Rourela 7698, Iia saausaharay@yahoo.com, saharays@irl.ac.i ABSTRAT Newor raffic moiorig is a impora way for ewor performace aalysis a moior. The prese aricle explores how o buil he basic moel of ewor raffic aalysis base o Queuig Theory. I he prese wor, wo ueuig moels M/M/: +/FFS a M/M/: +/FFS have bee applie o eermie he forecas way for he sable cogesio rae of he ewor raffic. Usig his we ca obai he ewor raffic forecasig ways a he sable cogesio rae formula. ombiig he geeral ewor raffic moior parameers, we ca realize he esimaio a moior process for he ewor raffic raioally. Keywors: Newor raffic, Queuig Theory, sable cogesio rae. INTRODUTION Newor raffic moiorig is a impora way for ewor performace aalysis a moior. The prese aalysis sees o explore how o buil he basic moel of ewor raffic aalysis base o Queuig Theory []. Usig his, we ca obai he ewor raffic forecasig ways a he sable cogesio rae formula, combiig he geeral ewor raffic moior parameers. oseuely we ca realize he esimaio a moiio process for he ewor raffic raioally. Queuig Theory, also calle raom service heory, is a brach of Operaio Research i he fiel of Applie Mahemaics. I is a subjec which aalyze he raom regulaio of ueuig pheomeo, a buils up he mahemaical moel by aalyzig he ae of he ewor. Through he preicio of he sysem, we ca reveal he regulaio abou he ueuig probabiliy a choose he opimal meho for he sysem. Aopig Queuig Theory o esimae he ewor raffic, i becomes he impora ways of ewor performace preicio, aalysis a esimaio a, hrough his way, we ca imiae he rue ewor, i is useful a reliable for orgaizig, moiorig a efeig he ewor.. THE MATHEMATIAL MODEL OF THE QUEUING THEORY I ewor commuicaio, from seig, rasferrig o receivig aa a he proceeig of he aa coig, ecoig a seig o he higher layer, i all hese process, we ca fi a simple ueuig moel. Accorig o he Queuig Theory, his correspo proceure ca be absrace as Queuig heory moel [], lie fig.. osierig his i of simple aa rasmiig sysem saisfies he ueue moel []. N T s λ' T N λ T J T D T Figure : The absrac moel of commuicaio process

2 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. From he above fig., : Seig rae of he seer. T N : Trasporaio elay ime. : Arrivig spee of he aa paces N : Quaiy of aa paces sore i he buffer emporary sorage. : aces rae which have misae i seig from receiver i.e., los rae of he receiver. T s : Service ime of aa paces i he server where T s =T J +T D +T T J : Decoig ime T D : Dispachig ime T : alculaig ime or evaluaig ime or halig ime.. Moel-: The Queuig moel wih oe server M/M/:+/FFS hp:// I moel M/M/, he wo M represe he seig process of he seer a he receivig process of he receiver separaely. They boh follow he Marov rocess [], also eep o oisso Disribuio, while he umber sas for he chael. Le N= be he legh of he ueue a he mome of. So he probabiliy of he ueue whose legh is, be =rob [N= ] I his moel, = Rae of arrival io he sae µ =Rae of eparure from he sae We have he rasiio rae iagram as follows The sysem of iffereial ifferece euaio is Figure : Sae rasiio iagram Here, λ is cosiere as he arrival rae while μ as he service rae. { } I he seay sae euaio, for L A for a L { } = I moel M/M/, we le Hece, from es. a whe we ge A Where λ a µ are cosas. for 5 The es. a reuces o a This implies for From e.5 whe =, we ge A for Therefore, =

3 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. I geeral, or, where Here, is calle server uilizaio facor or raffic hp:// N Usig he Lile s law we have Also T s a iesiy. We ow, Therefore, Also, or, oseuely,, where < Hece,, =,,,. Suppose, L sas for he legh of he ueue uer he seay sae coiio. I iclues he average volume of all he aa paces which eer he processig moule a sore i he buffer. If L Also Hece L 7 L Sice, N eoes he average volume of he buffers aa paces he L N 8 9 Usig e.9, e.8 reuces o N This implies Ts N Ts Or, T T N N, ' The above euaio e. provies he relaio bewee followig parameers T s = Service ime Seig rae N 6 Quaiy of aa paces sore i he buffer If we ow ay wo variables, i is easy o gai he umerical value of he hir oe. So, hese hree variables are ey parameers for measurig he performace of he rasmissio sysem.. QUEUING THEORY AND THE NETWORK TRAFFI MONITOR..Forecasig he ewor raffic usig Queuig Theory The ewor raffic is very commo [5]. The sysem will be i worse coiio, whe he raffic becomes uer exreme siuaio, i which leas o he ewor cogesio [6]. There are a grea eal of research abou moiorig he cogesio a prese,besies, he ocumes which mae use of Queuig Theory o research he raffic rae appear more a more. For forecasig he raffic rae, we ofe es he aa isposal fucio of he rouer use i he ewor. osierig a rouer s arrival rae of aa flow i groups is, a he average ime which he rouers use o ispose each group is, he buffer of he rouers is, if a cerai group arrives, he waiig legh of he ueue i groups has alreay reache, so he group has o be los. Whe he arrivig ime of group imeous, he group has o rese. Suppose, he group s average s s

4 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. waiig ime is. We ieify i o be he arrival probabiliy of he ueue legh for he rouers group a he mome of, supposig he ueue legh is i: =,,...,, i =,,...,+. hp:// The he ueuig sysem of he rouer s ae groups saisfies simple Marov rocess [7], accorig o Marov rocess, we ca fi he iversio sregh of marix of moel as follow: Q. Newor ogesio Rae Newor cogesio rae is chagig all he ime [8]. The isaaeous cogesio rae a he sable cogesio rae are ofe use o aalysis he ewor raffic i ewor moior. The isaaeous rae A is he cogesio rae a he mome of. The A ca be obaie by solvig he sysem legh of he ueue s probabiliy isribuig, which is calle. Le, =,,...,+ o be he arrival probabiliy of he ueue legh for he rouers group a he mome of by cosierig he ueue legh is. The, he ueuig sysem of he rouer s ae groups saisfies simple Marov rocess. Accorig o Marov rocess, saisfies he followig sysem of iffereial ifferece euaios. Le, = rob { umber of aa paces prese i he sysem i ime } a = rob { umber of aa paces prese i he sysem i ime + } ase : For = rob { umber of aa paces prese i he sysem a ime } rob { o aa pace arrival i ime } rob { o aa pace eparure i ime } + rob { - umber of aa paces prese i he sysem a ime } rob { aa pace arrival i ime } rob { o aa pace eparure i ime } + rob { + umber of aa paces prese i he sysem a ime } rob {o aa pace arrival i ime } rob { aa pace eparure i ime }+ { o }{ o } { o }{ o } + o { o }{ o } + o Diviig boh sies by a aig limi as { } o, sice lim

5 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. Here, i sae, aa paces arrival is i.e. Also, i sae, aa pace eparure is i.e. Hece, e. reuces o { } { } where =,,, hp:// = rob { + o. of aa paces prese i he sysem a ime + } = rob { o. of aa paces prese i ime } prob { aa pace arrival i ime } rob { o aa pace eparure i ime } + rob { + o of aa paces prese i ime } rob { o aa pace eparure i ime }+ { o }{ o } { o } o o ase : For =, we have + = rob { o aa pace prese i he sysem a ime + } = rob { o aa pace prese i ime } + rob { o aa pace arrival i ime } + rob {oe aa pace prese i ime } rob { o aa pace arrival i ime } rob { oe aa pace eparure i ime } Diviig boh sies by we ge sice a aig limi as { } { }, By solvig his iffereial euaio sysem, we ge he isaaeous cogesio rae A as = { o } { o }{ o } o o Diviig boh sies by a aig limi as, we obai A e The isaaeous cogesio rae ca o be use o measure he sable operaig coiio of he sysem, so we mus obai he sable cogesio rae of he sysem. The so-calle sable cogesio rae meas, i will o chage wih he ime chagig, whe he sysem wors i a sable operaig coiio. The efiiio of he sable cogesio rae is A lim A } { ase : For =+, we have sice, a osierig, lim as he isribuig of he sable legh of he ueue a as he buffer of he rouer, he sable cogesio rae ca be obaie i wo ways: firsly, we obai he isaaeous cogesio rae, he fi is limi. Accorig o is efiiio, i ca be obaie wih he isribuig of he legh of he ueue. Secoly, accorig o he Marov rocess, we ow ha he isribuig of he sable legh of ueue 5

6 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. ca be obaie hrough sysem of seay sae euaios. From e., e. a e., we have he sysem of iffereial ifferece euaios as follows { } { } 5 for =,,,, { } for = 6 { } for =+ 7 Accorig o some properies of Marov process, we ow ha i i=,,,,+ saisfies he above iffereial euaio. Here, [,,..., ],,,..., [,,..., ] For seay sae euaio, lim a lim Uer seay sae coiio, es.5,6 a 7 rasform o followig balace euaios. { } for =,,,, 8 for = 9 for =+ The above sysem of seay sae euaios ca be wrie i marix from as hp:// where,,..., a i a Q For =, From e.9, we have i Q Also, Solvig a we ge Hece, A For = Also, From e., we ge Therefore,, From e.5, we have, sice,

7 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. hp:// 7 Usig e.6, we obai ] [ From e.7 yiels.. Hece, A For =, 8 9 Also, From e.8, we obai a From e.9, we have From e., we have From e., we have Hece, A For =, yielig From e.6, we obai a From e. From e. From e.7, we have Also, Hece, A

8 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. O he aalogy of his, we coclue ha, he sable cogesio rae is A A, for { } A hp:// A 5. THE QUEUING MODEL WITH ADDITIONAL ONE SERVER M/M/ : +/FFS There will be o ueue. Therefore - server will remai ile a he combie service rae will be, ase- For The, all he servers will be busy. So, maximum - umber of aa paces prese i he ueue. The combie service rae will be, I his moel, umber of servers or chaels is wo a hese are arrage i parallel. Here, arrival isribuio is oisso isribuio wih mea rae per ui ime. The service ime is expoeioal wih mea rae per ui ime. Each server is ieical i.e. each server gives ieically service wih mea rae per ui ime. The overall service rae ca be obaie i wo siuaios. If here are umbers of aa paces are prese i he sysem. Hece, combiig ase- a ase-, we have for all for,,, ase- For < Figure : Sae rasiio rae iagram The seay sae euaios are for = 8 for 9 { } for for The above sysem of seay sae balace euaios ca be wrie i marix form as Q 8

9 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. hp:// 9 a i i where,...,, a Q From e.9, we obai sice,, From e.5, we ge, Usig he value of Sice, or, ] [ Hece A For = From e.5, we have a From e.5 From e.55, we ge

10 VOL., NO., November ISSN XXXX-XXXX ARN Joural of Sciece a Techology - ARN Jourals. All righs reserve. [ ] From e.57 8 hp:// he ewor raffic hrough ueuig heory moels. I he prese wor wo ueuig moels M/M/: +/FFS a M/M/:+/FFS have bee applie. These wo moels are use o eermie he forecas way for he sable cogesio rae of he ewor raffic. Usig he Queuig Theory moels, i is coveie a simple way for calculaig a moiorig he ewor raffic properly i he ewor commuicaio sysem. We ca moior he ewor efficiely, i he view of he ormal, opimal a or eve for he high overhea ewor maageme, by moiorig a aalyzig he ewor raffic rae. Fially, we ca say ha ewor raffic rae ca have a impora role i he ewor commuicaio sysem. REFERENES Also, [ ] O he aalogy of his, we coclue ha, he sable cogesio rae is A A { } A A, for 6. ONLUSION This research paper cies he aalysis of he ewor raffic moel hrough Queuig Theory. I he prese aalysis, we escribe ha how we ca mae a ueuig moel o he basis of ueuig heory a subseuely we erive he esimaio afer aalyzig [] Joh N. Daigle, 5, Queueig Theory wih Applicaios o ace Telecommuicaio, ISBN: , Spriger, Boso, USA. [] Ver axso, Sally Floy, 997, Why We Do Kow How To Simulae The Iere. I roceeigs of he 997 Wier Simulaio oferece, e. S. Araóir, K. J. Healy, D. H. Wihers, a B. L. Nelso, USA:AM. [] Re Xiagcai, Xiog Qibag,, A Applicaio of Mobile Age for I Newor Traffic Maageme, ompuer Egieerig, -. [] Li Da-Qi, She Ju-Yi, a Zhou Jiag-liag, 7, Hece, Queuig Theory Supervisig K-Meas luserig Algorihm a ITS Appllicaio i Opimize A Desig of TT Newor, Joural of Asroauics, 8 8, pp [5] Wag ei-fa, Zhag Shi-wei, Li Ju, 5, The Applicaio a Achieveme of SVG i Newor Neflow Moior Fiel, hiese Joural of Semicoucors,, pp [6] Wag Tig, Wag Yu, 7, Survey o a Queue Theory Base Haover Scheme for UAVS ommuicaio Newor, hiese Joural of Sesors a Acuaors, 7,. [7] Guher, N., 998, The racical erformace Aalys, McGraw-Hill Ic., New Yor. [8] Ha Jig, Guo Fag, Shi Ji-Hua, 7, Research o he raffic moiorig of he isribue ewor base o huma immue algorihm, Microcompuer Iformaio, 7-8.

The Monitoring of The Network Traffic Based on Queuing Theory

The Monitoring of The Network Traffic Based on Queuing Theory The Moitorig of The Networ Traffic Based o Queuig Theory A roject Thesis Submitted by alash Sahoo Roll No: 49MA7 I partial fulfillmet of the requiremets For the award of the degree Of MASTER OF SIENE IN

More information

A Queuing Model of the N-design Multi-skill Call Center with Impatient Customers

A Queuing Model of the N-design Multi-skill Call Center with Impatient Customers Ieraioal Joural of u- ad e- ervice, ciece ad Techology Vol.8, o., pp.- hp://dx.doi.org/./ijuess..8.. A Queuig Model of he -desig Muli-skill Call Ceer wih Impaie Cusomers Chuya Li, ad Deua Yue Yasha Uiversiy,

More information

Bullwhip Effect Measure When Supply Chain Demand is Forecasting

Bullwhip Effect Measure When Supply Chain Demand is Forecasting J. Basic. Appl. Sci. Res., (4)47-43, 01 01, TexRoad Publicaio ISSN 090-4304 Joural of Basic ad Applied Scieific Research www.exroad.com Bullwhip Effec Measure Whe Supply Chai emad is Forecasig Ayub Rahimzadeh

More information

FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND

FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND FORECASTING MODEL FOR AUTOMOBILE SALES IN THAILAND by Wachareepor Chaimogkol Naioal Isiue of Developme Admiisraio, Bagkok, Thailad Email: wachare@as.ida.ac.h ad Chuaip Tasahi Kig Mogku's Isiue of Techology

More information

Optimal Combination of International and Inter-temporal Diversification of Disaster Risk: Role of Government. Tao YE, Muneta YOKOMATSU and Norio OKADA

Optimal Combination of International and Inter-temporal Diversification of Disaster Risk: Role of Government. Tao YE, Muneta YOKOMATSU and Norio OKADA 京 都 大 学 防 災 研 究 所 年 報 第 5 号 B 平 成 9 年 4 月 Auals of Disas. Prev. Res. Is., Kyoo Uiv., No. 5 B, 27 Opimal Combiaio of Ieraioal a Ier-emporal Diversificaio of Disaser Risk: Role of Goverme Tao YE, Muea YOKOMATSUaNorio

More information

Why we use compounding and discounting approaches

Why we use compounding and discounting approaches Comoudig, Discouig, ad ubiased Growh Raes Near Deb s school i Souher Colorado. A examle of slow growh. Coyrigh 000-04, Gary R. Evas. May be used for o-rofi isrucioal uroses oly wihou ermissio of he auhor.

More information

UNDERWRITING AND EXTRA RISKS IN LIFE INSURANCE Katarína Sakálová

UNDERWRITING AND EXTRA RISKS IN LIFE INSURANCE Katarína Sakálová The process of uderwriig UNDERWRITING AND EXTRA RISKS IN LIFE INSURANCE Kaaría Sakálová Uderwriig is he process by which a life isurace compay decides which people o accep for isurace ad o wha erms Life

More information

Managing Learning and Turnover in Employee Staffing*

Managing Learning and Turnover in Employee Staffing* Maagig Learig ad Turover i Employee Saffig* Yog-Pi Zhou Uiversiy of Washigo Busiess School Coauhor: Noah Gas, Wharo School, UPe * Suppored by Wharo Fiacial Isiuios Ceer ad he Sloa Foudaio Call Ceer Operaios

More information

A formulation for measuring the bullwhip effect with spreadsheets Una formulación para medir el efecto bullwhip con hojas de cálculo

A formulation for measuring the bullwhip effect with spreadsheets Una formulación para medir el efecto bullwhip con hojas de cálculo irecció y rgaizació 48 (01) 9-33 9 www.revisadyo.com A formulaio for measurig he bullwhip effec wih spreadshees Ua formulació para medir el efeco bullwhip co hojas de cálculo Javier Parra-Pea 1, Josefa

More information

COLLECTIVE RISK MODEL IN NON-LIFE INSURANCE

COLLECTIVE RISK MODEL IN NON-LIFE INSURANCE Ecoomic Horizos, May - Augus 203, Volume 5, Number 2, 67-75 Faculy of Ecoomics, Uiversiy of Kragujevac UDC: 33 eissn 227-9232 www. ekfak.kg.ac.rs Review paper UDC: 005.334:368.025.6 ; 347.426.6 doi: 0.5937/ekohor30263D

More information

CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING

CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING Q.1 Defie a lease. How does i differ from a hire purchase ad isalme sale? Wha are he cash flow cosequeces of a lease? Illusrae.

More information

Introduction to Statistical Analysis of Time Series Richard A. Davis Department of Statistics

Introduction to Statistical Analysis of Time Series Richard A. Davis Department of Statistics Iroduio o Saisial Aalysis of Time Series Rihard A. Davis Deparme of Saisis Oulie Modelig obeives i ime series Geeral feaures of eologial/eviromeal ime series Compoes of a ime series Frequey domai aalysis-he

More information

A panel data approach for fashion sales forecasting

A panel data approach for fashion sales forecasting A pael daa approach for fashio sales forecasig Shuyu Re(shuyu_shara@live.c), Tsa-Mig Choi, Na Liu Busiess Divisio, Isiue of Texiles ad Clohig, The Hog Kog Polyechic Uiversiy, Hug Hom, Kowloo, Hog Kog Absrac:

More information

1/22/2007 EECS 723 intro 2/3

1/22/2007 EECS 723 intro 2/3 1/22/2007 EES 723 iro 2/3 eraily, all elecrical egieers kow of liear sysems heory. Bu, i is helpful o firs review hese coceps o make sure ha we all udersad wha his heory is, why i works, ad how i is useful.

More information

Mathematical Modeling of Life Insurance Policies

Mathematical Modeling of Life Insurance Policies Europea Joura of Busiess a Maageme ISSN -905 (Paper) ISSN -839 (Oie) o 3, No.4, 0 Mahemaica Moeig of Life Isurace Poicies Ui M. Kuub eparme of Mahemaics, Uiversiy of haka, Bagaesh kuubu9@gmai.com Isam

More information

Combining Adaptive Filtering and IF Flows to Detect DDoS Attacks within a Router

Combining Adaptive Filtering and IF Flows to Detect DDoS Attacks within a Router KSII RANSAIONS ON INERNE AN INFORMAION SYSEMS VOL. 4, NO. 3, Jue 2 428 opyrigh c 2 KSII ombiig Adapive Filerig ad IF Flows o eec os Aacks wihi a Rouer Ruoyu Ya,2, Qighua Zheg ad Haifei Li 3 eparme of ompuer

More information

REVISTA INVESTIGACION OPERACIONAL VOL. 31, No.2, 159-170, 2010

REVISTA INVESTIGACION OPERACIONAL VOL. 31, No.2, 159-170, 2010 REVISTA INVESTIGACION OPERACIONAL VOL. 3, No., 59-70, 00 AN ALGORITHM TO OBTAIN AN OPTIMAL STRATEGY FOR THE MARKOV DECISION PROCESSES, WITH PROBABILITY DISTRIBUTION FOR THE PLANNING HORIZON. Gouliois E.

More information

APPLICATIONS OF GEOMETRIC

APPLICATIONS OF GEOMETRIC APPLICATIONS OF GEOMETRIC SEQUENCES AND SERIES TO FINANCIAL MATHS The mos powerful force i he world is compoud ieres (Alber Eisei) Page of 52 Fiacial Mahs Coes Loas ad ivesmes - erms ad examples... 3 Derivaio

More information

Hilbert Transform Relations

Hilbert Transform Relations BULGARIAN ACADEMY OF SCIENCES CYBERNEICS AND INFORMAION ECHNOLOGIES Volume 5, No Sofia 5 Hilber rasform Relaios Each coiuous problem (differeial equaio) has may discree approximaios (differece equaios)

More information

Modelling Time Series of Counts

Modelling Time Series of Counts Modellig ime Series of Cous Richard A. Davis Colorado Sae Uiversiy William Dusmuir Uiversiy of New Souh Wales Yig Wag Colorado Sae Uiversiy /3/00 Modellig ime Series of Cous wo ypes of Models for Poisso

More information

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary Random Walk in -D Random walks appear in many cones: diffusion is a random walk process undersanding buffering, waiing imes, queuing more generally he heory of sochasic processes gambling choosing he bes

More information

Reaction Rates. Example. Chemical Kinetics. Chemical Kinetics Chapter 12. Example Concentration Data. Page 1

Reaction Rates. Example. Chemical Kinetics. Chemical Kinetics Chapter 12. Example Concentration Data. Page 1 Page Chemical Kieics Chaper O decomposiio i a isec O decomposiio caalyzed by MO Chemical Kieics I is o eough o udersad he soichiomery ad hermodyamics of a reacio; we also mus udersad he facors ha gover

More information

Studies in sport sciences have addressed a wide

Studies in sport sciences have addressed a wide REVIEW ARTICLE TRENDS i Spor Scieces 014; 1(1: 19-5. ISSN 99-9590 The eed o repor effec size esimaes revisied. A overview of some recommeded measures of effec size MACIEJ TOMCZAK 1, EWA TOMCZAK Rece years

More information

Mechanical Vibrations Chapter 4

Mechanical Vibrations Chapter 4 Mechaical Vibraios Chaper 4 Peer Aviabile Mechaical Egieerig Deparme Uiversiy of Massachuses Lowell 22.457 Mechaical Vibraios - Chaper 4 1 Dr. Peer Aviabile Modal Aalysis & Corols Laboraory Impulse Exciaio

More information

A Bayesian Based Search and Classification System for Product. Information of Agricultural Logistics Information Technology

A Bayesian Based Search and Classification System for Product. Information of Agricultural Logistics Information Technology A Bayesia Based Searh ad Classifiaio Sysem for Produ Iformaio of Agriulural Logisis Iformaio Tehology Dada Li 1,Daoliag Li 1,3, Yigyi Che 1,3, Li Li 1, Xiagyag Qi 3, Yogu Zheg 1, * 1 Chia Agriulural Uiversiy,

More information

Study on the application of the software phase-locked loop in tracking and filtering of pulse signal

Study on the application of the software phase-locked loop in tracking and filtering of pulse signal Advaced Sciece ad Techology Letters, pp.31-35 http://dx.doi.org/10.14257/astl.2014.78.06 Study o the applicatio of the software phase-locked loop i trackig ad filterig of pulse sigal Sog Wei Xia 1 (College

More information

A Way of Hedging Mortality Rate Risks in Life Insurance Product Development

A Way of Hedging Mortality Rate Risks in Life Insurance Product Development A Way of Hegig Moraliy ae iss i Life Isurace Prouc Develome Chagi Kim Absrac Forecasig moraliy imrovemes i he fuure is imora a ecessary for isurace busiess. A ieresig observaio is ha moraliy raes for a

More information

Ranking of mutually exclusive investment projects how cash flow differences can solve the ranking problem

Ranking of mutually exclusive investment projects how cash flow differences can solve the ranking problem Chrisia Kalhoefer (Egyp) Ivesme Maageme ad Fiacial Iovaios, Volume 7, Issue 2, 2 Rakig of muually exclusive ivesme projecs how cash flow differeces ca solve he rakig problem bsrac The discussio abou he

More information

Predicting Indian Stock Market Using Artificial Neural Network Model. Abstract

Predicting Indian Stock Market Using Artificial Neural Network Model. Abstract Predicig Idia Sock Marke Usig Arificial Neural Nework Model Absrac The sudy has aemped o predic he moveme of sock marke price (S&P CNX Nify) by usig ANN model. Seve years hisorical daa from 1 s Jauary

More information

Localization Techniques in Wireless Sensor Networks using Measurement of Received Signal Strength Indicator

Localization Techniques in Wireless Sensor Networks using Measurement of Received Signal Strength Indicator ELERONIS, VOL. 5, NO., JUNE 0 67 Localizaio echiqes i Wireless Sesor Neworks sig Measreme o Receive Sigal Sregh Iicaor M. Srbiovska, V. Dimcev,. Gavrovski a Z. Kokolaski Absrac he presee paper escribes

More information

Financial Data Mining Using Genetic Algorithms Technique: Application to KOSPI 200

Financial Data Mining Using Genetic Algorithms Technique: Application to KOSPI 200 Fiacial Daa Miig Usig Geeic Algorihms Techique: Applicaio o KOSPI 200 Kyug-shik Shi *, Kyoug-jae Kim * ad Igoo Ha Absrac This sudy ieds o mie reasoable radig rules usig geeic algorihms for Korea Sock Price

More information

Queuing Systems: Lecture 1. Amedeo R. Odoni October 10, 2001

Queuing Systems: Lecture 1. Amedeo R. Odoni October 10, 2001 Queuig Systems: Lecture Amedeo R. Odoi October, 2 Topics i Queuig Theory 9. Itroductio to Queues; Little s Law; M/M/. Markovia Birth-ad-Death Queues. The M/G/ Queue ad Extesios 2. riority Queues; State

More information

Abstract. 1. Introduction. 1.1 Notation. 1.2 Parameters

Abstract. 1. Introduction. 1.1 Notation. 1.2 Parameters 1 Mdels, Predici, ad Esimai f Oubreaks f Ifecius Disease Peer J. Csa James P. Duyak Mjdeh Mhashemi {pjcsa@mire.rg, jduyak@mire.rg, mjdeh@mire.rg} he MIRE Crprai 202 Burlig Rad Bedfrd, MA 01730 1420 Absrac

More information

Modeling the Nigerian Inflation Rates Using Periodogram and Fourier Series Analysis

Modeling the Nigerian Inflation Rates Using Periodogram and Fourier Series Analysis CBN Joural of Applied Saisics Vol. 4 No.2 (December, 2013) 51 Modelig he Nigeria Iflaio Raes Usig Periodogram ad Fourier Series Aalysis 1 Chukwuemeka O. Omekara, Emmauel J. Ekpeyog ad Michael P. Ekeree

More information

ACCOUNTING TURNOVER RATIOS AND CASH CONVERSION CYCLE

ACCOUNTING TURNOVER RATIOS AND CASH CONVERSION CYCLE Problems ad Persecives of Maageme, 24 Absrac ACCOUNTING TURNOVER RATIOS AND CASH CONVERSION CYCLE Pedro Orí-Ágel, Diego Prior Fiacial saemes, ad esecially accouig raios, are usually used o evaluae acual

More information

On Motion of Robot End-effector Using The Curvature Theory of Timelike Ruled Surfaces With Timelike Ruling

On Motion of Robot End-effector Using The Curvature Theory of Timelike Ruled Surfaces With Timelike Ruling O Moio of obo Ed-effecor Usig he Curvaure heory of imelike uled Surfaces Wih imelike ulig Cumali Ekici¹, Yasi Ülüürk¹, Musafa Dede¹ B. S. yuh² ¹ Eskişehir Osmagazi Uiversiy Deparme of Mahemaics, 6480-UKEY

More information

Research Article Dynamic Pricing of a Web Service in an Advance Selling Environment

Research Article Dynamic Pricing of a Web Service in an Advance Selling Environment Hidawi Publishig Corporaio Mahemaical Problems i Egieerig Volume 215, Aricle ID 783149, 21 pages hp://dx.doi.org/1.1155/215/783149 Research Aricle Dyamic Pricig of a Web Service i a Advace Sellig Evirome

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

HYPERBOLIC DISCOUNTING IS RATIONAL: VALUING THE FAR FUTURE WITH UNCERTAIN DISCOUNT RATES. J. Doyne Farmer and John Geanakoplos.

HYPERBOLIC DISCOUNTING IS RATIONAL: VALUING THE FAR FUTURE WITH UNCERTAIN DISCOUNT RATES. J. Doyne Farmer and John Geanakoplos. HYPERBOLIC DISCOUNTING IS RATIONAL: VALUING THE FAR FUTURE WITH UNCERTAIN DISCOUNT RATES By J. Doye Farmer ad Joh Geaakoplos Augus 2009 COWLES FOUNDATION DISCUSSION PAPER NO. 1719 COWLES FOUNDATION FOR

More information

A Heavy Traffic Approach to Modeling Large Life Insurance Portfolios

A Heavy Traffic Approach to Modeling Large Life Insurance Portfolios A Heavy Traffic Approach o Modelig Large Life Isurace Porfolios Jose Blache ad Hery Lam Absrac We explore a ew framework o approximae life isurace risk processes i he sceario of pleiful policyholders,

More information

Teaching Bond Valuation: A Differential Approach Demonstrating Duration and Convexity

Teaching Bond Valuation: A Differential Approach Demonstrating Duration and Convexity JOURNAL OF EONOMIS AND FINANE EDUATION olume Number 2 Wier 2008 3 Teachig Bod aluaio: A Differeial Approach Demosraig Duraio ad ovexi TeWah Hah, David Lage ABSTRAT A radiioal bod pricig scheme used i iroducor

More information

The Term Structure of Interest Rates

The Term Structure of Interest Rates The Term Srucure of Ieres Raes Wha is i? The relaioship amog ieres raes over differe imehorizos, as viewed from oday, = 0. A cocep closely relaed o his: The Yield Curve Plos he effecive aual yield agais

More information

Capital Budgeting: a Tax Shields Mirage?

Capital Budgeting: a Tax Shields Mirage? Theoreical ad Applied Ecoomics Volume XVIII (211), No. 3(556), pp. 31-4 Capial Budgeig: a Tax Shields Mirage? Vicor DRAGOTĂ Buchares Academy of Ecoomic Sudies vicor.dragoa@fi.ase.ro Lucia ŢÂŢU Buchares

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

PERFORMANCE COMPARISON OF TIME SERIES DATA USING PREDICTIVE DATA MINING TECHNIQUES

PERFORMANCE COMPARISON OF TIME SERIES DATA USING PREDICTIVE DATA MINING TECHNIQUES , pp.-57-66. Available olie a hp://www.bioifo.i/coes.php?id=32 PERFORMANCE COMPARISON OF TIME SERIES DATA USING PREDICTIVE DATA MINING TECHNIQUES SAIGAL S. 1 * AND MEHROTRA D. 2 1Deparme of Compuer Sciece,

More information

THE IMPACT OF FINANCING POLICY ON THE COMPANY S VALUE

THE IMPACT OF FINANCING POLICY ON THE COMPANY S VALUE THE IMPACT OF FINANCING POLICY ON THE COMPANY S ALUE Pirea Marile Wes Uiversiy of Timişoara, Faculy of Ecoomics ad Busiess Admiisraio Boțoc Claudiu Wes Uiversiy of Timişoara, Faculy of Ecoomics ad Busiess

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006

UC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006 Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Estimating Non-Maturity Deposits

Estimating Non-Maturity Deposits Proceedigs of he 9h WSEAS Ieraioal Coferece o SIMULATION, MODELLING AND OPTIMIZATION Esimaig No-Mauriy Deposis ELENA CORINA CIPU Uiversiy Poliehica Buchares Faculy of Applied Scieces Deparme of Mahemaics,

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Introduction to Hypothesis Testing

Introduction to Hypothesis Testing Iroducio o Hyohei Teig Iroducio o Hyohei Teig Scieific Mehod. Sae a reearch hyohei or oe a queio.. Gaher daa or evidece (obervaioal or eerimeal) o awer he queio. 3. Summarize daa ad e he hyohei. 4. Draw

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.

Repeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern. 5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers

More information

COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S 2 CONTROL CHART FOR THE CHANGES IN A PROCESS

COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S 2 CONTROL CHART FOR THE CHANGES IN A PROCESS COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S CONTROL CHART FOR THE CHANGES IN A PROCESS Supraee Lisawadi Departmet of Mathematics ad Statistics, Faculty of Sciece ad Techoology, Thammasat

More information

Derivative Securities: Lecture 7 Further applications of Black-Scholes and Arbitrage Pricing Theory. Sources: J. Hull Avellaneda and Laurence

Derivative Securities: Lecture 7 Further applications of Black-Scholes and Arbitrage Pricing Theory. Sources: J. Hull Avellaneda and Laurence Deivaive ecuiies: Lecue 7 uhe applicaios o Black-choles ad Abiage Picig heoy ouces: J. Hull Avellaeda ad Lauece Black s omula omeimes is easie o hik i ems o owad pices. Recallig ha i Black-choles imilaly

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

The Application of Multi Shifts and Break Windows in Employees Scheduling

The Application of Multi Shifts and Break Windows in Employees Scheduling The Applicaion of Muli Shifs and Brea Windows in Employees Scheduling Evy Herowai Indusrial Engineering Deparmen, Universiy of Surabaya, Indonesia Absrac. One mehod for increasing company s performance

More information

4. Levered and Unlevered Cost of Capital. Tax Shield. Capital Structure

4. Levered and Unlevered Cost of Capital. Tax Shield. Capital Structure 4. Levered ad levered Cos Capial. ax hield. Capial rucure. Levered ad levered Cos Capial Levered compay ad CAP he cos equiy is equal o he reur expeced by sockholders. he cos equiy ca be compued usi he

More information

Quantitative Computer Architecture

Quantitative Computer Architecture Performace Measuremet ad Aalysis i Computer Quatitative Computer Measuremet Model Iovatio Proposed How to measure, aalyze, ad specify computer system performace or My computer is faster tha your computer!

More information

Data Protection and Privacy- Technologies in Focus. Rashmi Chandrashekar, Accenture

Data Protection and Privacy- Technologies in Focus. Rashmi Chandrashekar, Accenture Daa Proeio ad Privay- Tehologies i Fous Rashmi Chadrashekar, Aeure Sesiive Creai Daa Lifeyle o Busiess sesiive daa proeio is o a sigle eve. Adequae proeio o mus be provided appropriaely hroughou Mai he

More information

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical

More information

A BitTorrent Proxy for Green Internet File Sharing: Design and Experimental Evaluation

A BitTorrent Proxy for Green Internet File Sharing: Design and Experimental Evaluation A BitTorret roxy for Gree Iteret File Sharig: Desig a Experimetal Evaluatio GIUSEE ANASTASI, IARIA GIANNETTI, ANDREA ASSAREA 2 Dept. of Iformatio Egieerig, Uiversity of isa, via Diotisalvi 2, 5622 isa,

More information

Characterizing End-to-End Packet Delay and Loss in the Internet

Characterizing End-to-End Packet Delay and Loss in the Internet Characterizig Ed-to-Ed Packet Delay ad Loss i the Iteret Jea-Chrysostome Bolot Xiyu Sog Preseted by Swaroop Sigh Layout Itroductio Data Collectio Data Aalysis Strategy Aalysis of packet delay Aalysis of

More information

A GLOSSARY OF MAIN TERMS

A GLOSSARY OF MAIN TERMS he aedix o his glossary gives he mai aggregae umber formulae used for cosumer rice (CI) uroses ad also exlais he ierrelaioshis bewee hem. Acquisiios aroach Addiiviy Aggregae Aggregaio Axiomaic, or es aroach

More information

12. Spur Gear Design and selection. Standard proportions. Forces on spur gear teeth. Forces on spur gear teeth. Specifications for standard gear teeth

12. Spur Gear Design and selection. Standard proportions. Forces on spur gear teeth. Forces on spur gear teeth. Specifications for standard gear teeth . Spur Gear Desig ad selecio Objecives Apply priciples leared i Chaper 11 o acual desig ad selecio of spur gear sysems. Calculae forces o eeh of spur gears, icludig impac forces associaed wih velociy ad

More information

Task is a schedulable entity, i.e., a thread

Task is a schedulable entity, i.e., a thread Real-Time Scheduling Sysem Model Task is a schedulable eniy, i.e., a hread Time consrains of periodic ask T: - s: saring poin - e: processing ime of T - d: deadline of T - p: period of T Periodic ask T

More information

Distributed Containment Control with Multiple Dynamic Leaders for Double-Integrator Dynamics Using Only Position Measurements

Distributed Containment Control with Multiple Dynamic Leaders for Double-Integrator Dynamics Using Only Position Measurements IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 57, NO. 6, JUNE 22 553 Disribued Coaime Corol wih Muliple Dyamic Leaders for Double-Iegraor Dyamics Usig Oly Posiio Measuremes Jiazhe Li, Wei Re, Member, IEEE,

More information

Fuzzy Task Assignment Model of Web Services Supplier

Fuzzy Task Assignment Model of Web Services Supplier Advaed Siee ad Tehology eers Vol.78 (Mulrab 2014),.43-48 h://dx.doi.org/10.14257/asl.2014.78.08 Fuzzy Task Assige Model of Web Servies Sulier Su Jia 1,2,Peg Xiu-ya 1, *, Xu Yig 1,3, Wag Pei-lei 2, Ma Na-ji

More information

REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ),

REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ), REVISTA INVESTIGACION OPERACIONAL Vol 25, No, 24 RECURRENCE AND DIRECT FORMULAS FOR TE AL & LA NUMBERS Eduardo Pza Volo Cero de Ivesgacó e Maemáca Pura y Aplcada (CIMPA), Uversdad de Cosa Rca ABSTRACT

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer) Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions

More information

TRANSPORT ECONOMICS, POLICY AND POVERTY THEMATIC GROUP

TRANSPORT ECONOMICS, POLICY AND POVERTY THEMATIC GROUP TRANSPORT NOTES TRANSPORT ECONOMICS, POLICY AND POVERTY THEMATIC GROUP THE WORLD BANK, WASHINGTON, DC Traspor Noe No. TRN-6 Jauary 2005 Noes o he Ecoomic Evaluaio of Traspor Projecs I respose o may requess

More information

Procedia - Social and Behavioral Sciences 109 ( 2014 ) 590 594

Procedia - Social and Behavioral Sciences 109 ( 2014 ) 590 594 Available olie a www.scieceirec.com ScieceDirec Proceia - Social a Behavioral Scieces 109 ( 2014 ) 590 594 2 Worl Coferece O Busiess, Ecoomics A Maageme - WCBEM2013 Aalyzig he applicaios of cusomer lifeime

More information

Ranking Optimization with Constraints

Ranking Optimization with Constraints Rakig Opimizaio wih Cosrais Fagzhao Wu, Ju Xu, Hag Li, Xi Jiag Tsighua Naioal Laboraory for Iformaio Sciece ad Techology, Deparme of Elecroic Egieerig, Tsighua Uiversiy, Beijig, Chia Noah s Ark Lab, Huawei

More information

Experience and Innovation

Experience and Innovation AC Servo Drives Sigma-5 Large Capaciy EN DE Coe 2 Abou YASKAWA Experiece ad Iovaio 3 Powerful ad Smar 4 Applicaios Efficie High Performace Applicaios 5 Easy Seup 6 Feaures Experiece ad Iovaio Ousadig Expadabiliy

More information

The monitoring of the network traffic based on queuing theory

The monitoring of the network traffic based on queuing theory The 7th International Symposium on Operations Research and Its Applications (ISORA 08) Lijiang, China, October 31 Novemver 3, 2008 Copyright 2008 ORSC & APORC, pp. 60 65 The monitoring of the network traffic

More information

Dynamic Isoline Extraction for Visualization of Streaming Data

Dynamic Isoline Extraction for Visualization of Streaming Data Dyamic Isolie Extractio for Visualizatio of Streamig Data Dia Goli 1, Huaya Gao Uiversity of Coecticut, Storrs, CT USA {qg,ghy}@egr.uco.eu Abstract. Queries over streamig ata offer the potetial to provie

More information

ON THE RISK-NEUTRAL VALUATION OF LIFE INSURANCE CONTRACTS WITH NUMERICAL METHODS IN VIEW ABSTRACT KEYWORDS 1. INTRODUCTION

ON THE RISK-NEUTRAL VALUATION OF LIFE INSURANCE CONTRACTS WITH NUMERICAL METHODS IN VIEW ABSTRACT KEYWORDS 1. INTRODUCTION ON THE RISK-NEUTRAL VALUATION OF LIFE INSURANCE CONTRACTS WITH NUMERICAL METHODS IN VIEW BY DANIEL BAUER, DANIELA BERGMANN AND RÜDIGER KIESEL ABSTRACT I rece years, marke-cosise valuaio approaches have

More information

A New Hybrid Network Traffic Prediction Method

A New Hybrid Network Traffic Prediction Method This full ex paper was peer reviewed a he direcio of IEEE Couicaios Sociey subjec aer expers for publicaio i he IEEE Globeco proceedigs. A New Hybrid Nework Traffic Predicio Mehod Li Xiag, Xiao-Hu Ge,

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

TACTICAL PLANNING OF THE OIL SUPPLY CHAIN: OPTIMIZATION UNDER UNCERTAINTY

TACTICAL PLANNING OF THE OIL SUPPLY CHAIN: OPTIMIZATION UNDER UNCERTAINTY TACTICAL PLANNING OF THE OIL SUPPLY CHAIN: OPTIMIZATION UNDER UNCERTAINTY Gabriela Ribas Idusrial Egieerig Deparme Poifical Caholic Uiversiy of Rio de Jaeiro PUC-Rio, CP38097, 22453-900 Rio de Jaeiro Brazil

More information

Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument

Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument Chaper 4 Mulple-Degree-of-Freedom (MDOF Sysems Eamples: Pacg of a srume Number of degrees of freedom Number of masses he sysem X Number of possble ypes of moo of each mass Mehods: Newo s Law ad Lagrage

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Kyoung-jae Kim * and Ingoo Han. Abstract

Kyoung-jae Kim * and Ingoo Han. Abstract Simulaeous opimizaio mehod of feaure rasformaio ad weighig for arificial eural eworks usig geeic algorihm : Applicaio o Korea sock marke Kyoug-jae Kim * ad Igoo Ha Absrac I his paper, we propose a ew hybrid

More information

Robust Disturbance Rejection with Time Domain Specifications in Control Systems Design

Robust Disturbance Rejection with Time Domain Specifications in Control Systems Design Robust Disturbace Rejectio with Time Domai Speciicatios i Cotrol Systems Desig Fabrizio Leoari Mauá Istitute o Techology, Electrical Egieerig Departmet Praça Mauá, o.1 CEP 09580900 São Caetao o Sul SP

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Unsteady State Molecular Diffusion

Unsteady State Molecular Diffusion Chaper. Differeial Mass Balae Useady Sae Moleular Diffusio Whe he ieral oeraio gradie is o egligible or Bi

More information

Optimal Stock Selling/Buying Strategy with reference to the Ultimate Average

Optimal Stock Selling/Buying Strategy with reference to the Ultimate Average Opimal Sock Selling/Buying Sraegy wih reference o he Ulimae Average Min Dai Dep of Mah, Naional Universiy of Singapore, Singapore Yifei Zhong Dep of Mah, Naional Universiy of Singapore, Singapore July

More information

Overview on S-Box Design Principles

Overview on S-Box Design Principles Overview o S-Box Desig Priciples Debdeep Mukhopadhyay Assistat Professor Departmet of Computer Sciece ad Egieerig Idia Istitute of Techology Kharagpur INDIA -721302 What is a S-Box? S-Boxes are Boolea

More information

Analysis of Non-Stationary Time Series using Wavelet Decomposition

Analysis of Non-Stationary Time Series using Wavelet Decomposition Naure an Science ;8() Analysis of Non-Saionary Time Series using Wavele Decomposiion Lineesh M C *, C Jessy John Deparmen of Mahemaics, Naional Insiue of Technology Calicu, NIT Campus P O 673 6, Calicu,

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

Domain 1 - Describe Cisco VoIP Implementations

Domain 1 - Describe Cisco VoIP Implementations Maual ONT (642-8) 1-800-418-6789 Domai 1 - Describe Cisco VoIP Implemetatios Advatages of VoIP Over Traditioal Switches Voice over IP etworks have may advatages over traditioal circuit switched voice etworks.

More information

Online Banking. Internet of Things

Online Banking. Internet of Things Olie Bakig & The Iteret of Thigs Our icreasigly iteretcoected future will mea better bakig ad added security resposibilities for all of us. FROM DESKTOPS TO SMARTWATCHS Just a few years ago, Americas coducted

More information

The Transport Equation

The Transport Equation The Transpor Equaion Consider a fluid, flowing wih velociy, V, in a hin sraigh ube whose cross secion will be denoed by A. Suppose he fluid conains a conaminan whose concenraion a posiion a ime will be

More information

Inductance and Transient Circuits

Inductance and Transient Circuits Chaper H Inducance and Transien Circuis Blinn College - Physics 2426 - Terry Honan As a consequence of Faraday's law a changing curren hrough one coil induces an EMF in anoher coil; his is known as muual

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Using Kalman Filter to Extract and Test for Common Stochastic Trends 1

Using Kalman Filter to Extract and Test for Common Stochastic Trends 1 Usig Kalma Filer o Exrac ad Tes for Commo Sochasic Treds Yoosoo Chag 2, Bibo Jiag 3 ad Joo Y. Park 4 Absrac This paper cosiders a sae space model wih iegraed lae variables. The model provides a effecive

More information

THE FOREIGN EXCHANGE EXPOSURE OF CHINESE BANKS

THE FOREIGN EXCHANGE EXPOSURE OF CHINESE BANKS Workig Paper 07/2008 Jue 2008 THE FOREIGN ECHANGE EPOSURE OF CHINESE BANKS Prepared by Eric Wog, Jim Wog ad Phyllis Leug 1 Research Deparme Absrac Usig he Capial Marke Approach ad equiy-price daa of 14

More information

Circularity and the Undervaluation of Privatised Companies

Circularity and the Undervaluation of Privatised Companies CMPO Workig Paper Series No. 1/39 Circulariy ad he Udervaluaio of Privaised Compaies Paul Grou 1 ad a Zalewska 2 1 Leverhulme Cere for Marke ad Public Orgaisaio, Uiversiy of Brisol 2 Limburg Isiue of Fiacial

More information

MODELLING OF TWO STRATEGIES IN INVENTORY CONTROL SYSTEM WITH RANDOM LEAD TIME AND DEMAND

MODELLING OF TWO STRATEGIES IN INVENTORY CONTROL SYSTEM WITH RANDOM LEAD TIME AND DEMAND art I. robobabilystic Moels Computer Moelling an New echnologies 27 Vol. No. 2-3 ransport an elecommunication Institute omonosova iga V-9 atvia MOEING OF WO AEGIE IN INVENOY CONO YEM WIH ANOM EA IME AN

More information