Importance of Sources using the Repeated Fusion Method and the Proportional Conflict Redistribution Rules #5 and #6
|
|
- Linette Blankenship
- 5 years ago
- Views:
Transcription
1 dvances and pplications of DST for Inforation Fusion. Collected Works. Volue 4 Iportance of Sources using the Repeated Fusion Method and the Proportional Conflict Redistribution Rules #5 and #6 Florentin Sarandache Jean Dezert Originally published as a scientific note 00 in HL archive. hal.archives-ouvertes.fr/hal Printed with perission. bstract. We present in this paper soe exaples of how to copute by hand the fusion rule for three sources, so the reader will better understand its echanis. We also take into consideration the iportance of sources, which is different fro the classical discounting of sources.. Introduction. Discounting of Sources. Discounting a source (.) with the coefficient 0 α and a source (.) with a coefficient 0 β (because we are not very confident in the), eans to adjust the to (.) and (.) such that: () = α () for (total ignorance), and ( ) = α ( ) -α, and () = β () for (total ignorance), and ( ) = β ( ) - β. Iportance of Sources using Repeated Fusion. But if a source is ore iportant than another one (since a such source coes fro a ore iportant person with a decision power, let s say an executive director), for exaple if source (.) is twice ore iportant than source (.), then we can cobine (.) with (.) and with (.), so we repeated (.) twice. Doing this procedure, the source which is repeated (cobined) ore ties than another source attracts the result towards its asses see an exaple below. Jean Dezert has criticized this ethod since if a source is repeated say 4 ties and other source is repeated 6 ties, then cobining 4 ties (.) with 6 ties (.) will give a result different fro cobining ties (.) with 3 ties (.), although 4/6 = /3. In order to avoid this, we take the siplified fraction n/p, where gcd(n, p) =, where gcd is the greatest coon divisor of the natural nubers n and p. This ethod is still controversial since after a large nuber of cobining n ties (.) with p ties (.) for np sufficiently large, the result is not uch different fro a previous one which cobines n ties (.) with p ties (.) for n p sufficiently large but a little less than np, so the ethod is not well responding for large nubers. 47
2 dvances and pplications of DST for Inforation Fusion. Collected Works. Volue 4 ore efficacy ethod of iportance of sources consists in taking into consideration the discounting on the epty set and then the noralization (see especially paper [] and also[]).. Using for 3 Sources. Exaple calculated by hand for cobining three sources using fusion rule. ; therefore we fusion (.), (.), (.). Let s say that ( ) is ties ore iportant than ( ). B B B= Φ x B B = = = = x = y B = z B = x B B = = = = = x = yb = z B = x3 3B 3 B = = = = x y3b z3 B x4 4B 4 B (0.4)(0.)(0.) = = = = = x y4b z4 B
3 dvances and pplications of DST for Inforation Fusion. Collected Works. Volue 4 x5 5B 5 B = = = = x y5b z5 B x6 6B 6 B = = = = x y6b z6 B x7 y7b (0.)(0.)(0.) 0.00 = = = 0. (0.)(0.) x y6b x8 y8b (0.4)(0.7)(0.) = = = = 0.4 (0.7)(0.) x y8b x = x y = y B 8B x0 y0b (0.)(0.4)(0.) = = = = = (0.)(0.4) x y8 B x = x y = y B 0B 49
4 dvances and pplications of DST for Inforation Fusion. Collected Works. Volue 4 x yb (0.4)(0.4)(0.7) 0.. = = = = (0.)(0.4) x yb B B If we didn t double (.) in the fusion rule, we d get a different result. Let s suppose we only fusion (.) with (.): B B B= Φ nd now we copare the fusion results: B B three sources(sec ond source doubled ); iportance of sources considered; two sources; iportance of sources not considered. The ore ties we repeat (.) the closer... () ()=0.4,... (B) (B)=0., and... ( B) ( B)=0.5. Therefore, doubling, tripling, etc. a source, the ass of each eleent in the frae of discernent tends towards the ass value of that eleent in the repeated source (since that source is considered to have ore iportance than the others). For the readers who want to do the previous calculation with a coputer, here it is the PCR 5 Forula for 3 Sources: ( ) ( X) 3( Y) ( ) = 3 XY, G ( ) ( X) 3( Y ) X Y X Y=Φ Y 3 X X Y 3 Y X X Y
5 dvances and pplications of DST for Inforation Fusion. Collected Works. Volue 4 X 3 X X 3 X X X 3 X 3 X X 3 X X X 3 3 X X 3 X 3 3 X X 3 X 3 3. Siilarly, let s see the PCR6 Forula for 3 Sources: ( ) ( ) 3( ) X Y PCR6( ) = 3 X Y XY, G 3 X Y X Y=Φ Y 3 X X Y 3 Y X X Y 3 3 X 3 X X 3 X X X 3 X 3 X X 3 X X X 3 ( ) ( ) ( X) X X ( X) ( ) 3( ) ( X) ( ) 3( ) ( X) ( ) 3( ) ( ) ( X) ( ) 3 3 X X 3 4. General Forula for PCR 6 for s Sources. s ( ) = ( ) ( )... ( ) PCR6... s i i ik X, X,..., Xs G k= ( i, i,..., is ) P(,,..., s) Xi, i {,,..., s } s Xi =Φ i= i ( ) ( )... ( ) ( i i )... ( ) k i X k i X s s k ( ) ( )... ( ) ( X )... ( X ) i i ik ik is s k where P(,,, s) is the set of all perutations of the eleents {,,, s}. 5
6 dvances and pplications of DST for Inforation Fusion. Collected Works. Volue 4 It should be observed that X, X,, X s- ay be different fro each other, or soe of the equal and others different, etc. We wrote this PCR6 general forula in the style of, different fro rnaud Martin & Christophe Oswald s notations, but actually doing the sae thing. In order not to coplicate the forula of PCR6, we did not use ore suations or products after the third Siga. s a particular case: ( ) = PCR6 3 i ( )... ( ) ( )... ( ) ( i )... ( 3 ) k i i k i X k i X ( )... ( ) ( X )... ( X ) X, k= ( i, i, i3) P(,,3) i ik ik i3 X, X X X =Φ where P(,, 3) is the set of perutations of the eleents {,, 3 }. It should also be observed that X ay be different fro or equal to X. Conclusion. The ai of this paper was to show how to anually copute for 3 sources on soe exaples, thus better understanding its essence. nd also how to take into consideration the iportance of sources doing the Repeated Fusion Method. We did not present the Method of Discounting to the Epty Set in order to ephasize the iportance of sources, which is better than the first one, since the second ethod was the ain topic of paper []. We also presented the forula for 3 sources (a particular case when n=3), and the general forula for PCR6 in a different way but yet equivalent to Martin-Oswald s PCR6 forula. References:. Dezert J., Tacnet J.-M., Batton-Hubert M., Sarandache F., Multi-criteria Decision Making Based on DST-HP, in Proceedings of Workshop on the Theory of Belief Functions, pril -, 00, Brest, France (available at Sarandache F., Dezert J., Tacnet J.-M., Fusion of Sources of Evidence with Different Iportances and Reliabilities, subitted to Fusion 00, International Conference, Edinburgh, U.K., July Sarandache Florentin, Dezert Jean, Li Xinde, DS Field and Linear lgebra of Refined Labels (FLRL), in the book dvances and pplications of DST for Inforation Fusion,. Res. Press, Rehoboth, US, Chapter (pages 75-84), 009; online at: 4. Sarandache F., Dezert J., dvances and pplications of DST for Inforation Fusion, Vols. -3,. Res. Press, Rehoboth, 004, 006, 009; 5
Importance of Sources using the Repeated Fusion Method and the Proportional Conflict Redistribution Rules #5 and #6
Iportance of Sources using the Repeated Fusion Method and the Proportional Conflict Redistribution Rules #5 and #6 Florentin Sarandache Math & Sciences Departent University of New Mexico, Gallup Capus,
More informationAn In-Depth Look at Information Fusion Rules and the Unification of Fusion Theories
n In-Depth Look at Inforation Fusion Rules and the Unification of Fusion Theories Dr. Florentin Sarandache The University of New Mexico 00 College Road Gallup NM 8730 US sarand@un.edu www.gallup.un.edu/~sarandache/dst.ht
More informationPolygonal Designs: Existence and Construction
Polygonal Designs: Existence and Construction John Hegean Departent of Matheatics, Stanford University, Stanford, CA 9405 Jeff Langford Departent of Matheatics, Drake University, Des Moines, IA 5011 G
More informationFeature Extraction Techniques
Feature Extraction Techniques Unsupervised Learning II Feature Extraction Unsupervised ethods can also be used to find features which can be useful for categorization. There are unsupervised ethods that
More informationPart I: How Dense Is It? Fundamental Question: What is matter, and how do we identify it?
Part I: How Dense Is It? Fundaental Question: What is atter, and how do we identify it? 1. What is the definition of atter? 2. What do you think the ter ass per unit volue eans? 3. Do you think that a
More informationPattern Recognition and Machine Learning. Learning and Evaluation for Pattern Recognition
Pattern Recognition and Machine Learning Jaes L. Crowley ENSIMAG 3 - MMIS Fall Seester 2017 Lesson 1 4 October 2017 Outline Learning and Evaluation for Pattern Recognition Notation...2 1. The Pattern Recognition
More informationThe Weierstrass Approximation Theorem
36 The Weierstrass Approxiation Theore Recall that the fundaental idea underlying the construction of the real nubers is approxiation by the sipler rational nubers. Firstly, nubers are often deterined
More informationUnits conversion is often necessary in calculations
Easy Units Conversion Methodology Igathinathane Cannayen, Departent of Agricultural and Biosystes Engineering, NDSU, Fargo, ND Units conversion is often necessary in culations as any types of units were
More informationFast Montgomery-like Square Root Computation over GF(2 m ) for All Trinomials
Fast Montgoery-like Square Root Coputation over GF( ) for All Trinoials Yin Li a, Yu Zhang a, a Departent of Coputer Science and Technology, Xinyang Noral University, Henan, P.R.China Abstract This letter
More informationBlock designs and statistics
Bloc designs and statistics Notes for Math 447 May 3, 2011 The ain paraeters of a bloc design are nuber of varieties v, bloc size, nuber of blocs b. A design is built on a set of v eleents. Each eleent
More informationClosed-form evaluations of Fibonacci Lucas reciprocal sums with three factors
Notes on Nuber Theory Discrete Matheatics Print ISSN 30-32 Online ISSN 2367-827 Vol. 23 207 No. 2 04 6 Closed-for evaluations of Fibonacci Lucas reciprocal sus with three factors Robert Frontczak Lesbank
More informationSoft Computing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis
Soft Coputing Techniques Help Assign Weights to Different Factors in Vulnerability Analysis Beverly Rivera 1,2, Irbis Gallegos 1, and Vladik Kreinovich 2 1 Regional Cyber and Energy Security Center RCES
More informationA Quantum Observable for the Graph Isomorphism Problem
A Quantu Observable for the Graph Isoorphis Proble Mark Ettinger Los Alaos National Laboratory Peter Høyer BRICS Abstract Suppose we are given two graphs on n vertices. We define an observable in the Hilbert
More informationLecture 21 Principle of Inclusion and Exclusion
Lecture 21 Principle of Inclusion and Exclusion Holden Lee and Yoni Miller 5/6/11 1 Introduction and first exaples We start off with an exaple Exaple 11: At Sunnydale High School there are 28 students
More informationModule #1: Units and Vectors Revisited. Introduction. Units Revisited EXAMPLE 1.1. A sample of iron has a mass of mg. How many kg is that?
Module #1: Units and Vectors Revisited Introduction There are probably no concepts ore iportant in physics than the two listed in the title of this odule. In your first-year physics course, I a sure that
More informationFault Tree Modeling Using CBHRA and SAF Method. Korea Atomic Energy Research Institute Hyun Gook Kang
Fault Tree Modeling Using CBHRA and SAF Method Korea Atoic Energy Research Institute Hyun Goo Kang Contents 1 2 Introduction Siplified Alpha Factor Method 3 Condition-based HRA Method Case Study 5 Conclusions
More informationON THE INTEGER PART OF A POSITIVE INTEGER S K-TH ROOT
ON THE INTEGER PART OF A POSITIVE INTEGER S K-TH ROOT Yang Hai Research Center for Basic Science, Xi an Jiaotong University, Xi an, Shaanxi, P.R.China Fu Ruiqin School of Science, Xi an Shiyou University,
More information2.003 Engineering Dynamics Problem Set 2 Solutions
.003 Engineering Dynaics Proble Set Solutions This proble set is priarily eant to give the student practice in describing otion. This is the subject of kineatics. It is strongly recoended that you study
More information1. INTRODUCTION AND RESULTS
SOME IDENTITIES INVOLVING THE FIBONACCI NUMBERS AND LUCAS NUMBERS Wenpeng Zhang Research Center for Basic Science, Xi an Jiaotong University Xi an Shaanxi, People s Republic of China (Subitted August 00
More informationThe Fundamental Basis Theorem of Geometry from an algebraic point of view
Journal of Physics: Conference Series PAPER OPEN ACCESS The Fundaental Basis Theore of Geoetry fro an algebraic point of view To cite this article: U Bekbaev 2017 J Phys: Conf Ser 819 012013 View the article
More informationANALYSIS OF HALL-EFFECT THRUSTERS AND ION ENGINES FOR EARTH-TO-MOON TRANSFER
IEPC 003-0034 ANALYSIS OF HALL-EFFECT THRUSTERS AND ION ENGINES FOR EARTH-TO-MOON TRANSFER A. Bober, M. Guelan Asher Space Research Institute, Technion-Israel Institute of Technology, 3000 Haifa, Israel
More informationGeneral Properties of Radiation Detectors Supplements
Phys. 649: Nuclear Techniques Physics Departent Yarouk University Chapter 4: General Properties of Radiation Detectors Suppleents Dr. Nidal M. Ershaidat Overview Phys. 649: Nuclear Techniques Physics Departent
More informationAbout the definition of parameters and regimes of active two-port networks with variable loads on the basis of projective geometry
About the definition of paraeters and regies of active two-port networks with variable loads on the basis of projective geoetry PENN ALEXANDR nstitute of Electronic Engineering and Nanotechnologies "D
More informationBALLISTIC PENDULUM. EXPERIMENT: Measuring the Projectile Speed Consider a steel ball of mass
BALLISTIC PENDULUM INTRODUCTION: In this experient you will use the principles of conservation of oentu and energy to deterine the speed of a horizontally projected ball and use this speed to predict the
More informationEXPLICIT CONGRUENCES FOR EULER POLYNOMIALS
EXPLICIT CONGRUENCES FOR EULER POLYNOMIALS Zhi-Wei Sun Departent of Matheatics, Nanjing University Nanjing 10093, People s Republic of China zwsun@nju.edu.cn Abstract In this paper we establish soe explicit
More informationCOS 424: Interacting with Data. Written Exercises
COS 424: Interacting with Data Hoework #4 Spring 2007 Regression Due: Wednesday, April 18 Written Exercises See the course website for iportant inforation about collaboration and late policies, as well
More informationNational 5 Summary Notes
North Berwick High School Departent of Physics National 5 Suary Notes Unit 3 Energy National 5 Physics: Electricity and Energy 1 Throughout the Course, appropriate attention should be given to units, prefixes
More informationHandwriting Detection Model Based on Four-Dimensional Vector Space Model
Journal of Matheatics Research; Vol. 10, No. 4; August 2018 ISSN 1916-9795 E-ISSN 1916-9809 Published by Canadian Center of Science and Education Handwriting Detection Model Based on Four-Diensional Vector
More informationLesson 24: Newton's Second Law (Motion)
Lesson 24: Newton's Second Law (Motion) To really appreciate Newton s Laws, it soeties helps to see how they build on each other. The First Law describes what will happen if there is no net force. The
More informationChaotic Coupled Map Lattices
Chaotic Coupled Map Lattices Author: Dustin Keys Advisors: Dr. Robert Indik, Dr. Kevin Lin 1 Introduction When a syste of chaotic aps is coupled in a way that allows the to share inforation about each
More informationPoly-Bernoulli Numbers and Eulerian Numbers
1 2 3 47 6 23 11 Journal of Integer Sequences, Vol. 21 (2018, Article 18.6.1 Poly-Bernoulli Nubers and Eulerian Nubers Beáta Bényi Faculty of Water Sciences National University of Public Service H-1441
More informationarxiv: v2 [math.co] 8 Mar 2018
Restricted lonesu atrices arxiv:1711.10178v2 [ath.co] 8 Mar 2018 Beáta Bényi Faculty of Water Sciences, National University of Public Service, Budapest beata.benyi@gail.co March 9, 2018 Keywords: enueration,
More informationMachine Learning Basics: Estimators, Bias and Variance
Machine Learning Basics: Estiators, Bias and Variance Sargur N. srihari@cedar.buffalo.edu This is part of lecture slides on Deep Learning: http://www.cedar.buffalo.edu/~srihari/cse676 1 Topics in Basics
More informationDefect-Aware SOC Test Scheduling
Defect-Aware SOC Test Scheduling Erik Larsson +, Julien Pouget*, and Zebo Peng + Ebedded Systes Laboratory + LIRMM* Departent of Coputer Science Montpellier 2 University Linköpings universitet CNRS Sweden
More informationSupport Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization
Recent Researches in Coputer Science Support Vector Machine Classification of Uncertain and Ibalanced data using Robust Optiization RAGHAV PAT, THEODORE B. TRAFALIS, KASH BARKER School of Industrial Engineering
More informationThe accelerated expansion of the universe is explained by quantum field theory.
The accelerated expansion of the universe is explained by quantu field theory. Abstract. Forulas describing interactions, in fact, use the liiting speed of inforation transfer, and not the speed of light.
More informationMULTIPLAYER ROCK-PAPER-SCISSORS
MULTIPLAYER ROCK-PAPER-SCISSORS CHARLOTTE ATEN Contents 1. Introduction 1 2. RPS Magas 3 3. Ites as a Function of Players and Vice Versa 5 4. Algebraic Properties of RPS Magas 6 References 6 1. Introduction
More informationNumerical Studies of a Nonlinear Heat Equation with Square Root Reaction Term
Nuerical Studies of a Nonlinear Heat Equation with Square Root Reaction Ter Ron Bucire, 1 Karl McMurtry, 1 Ronald E. Micens 2 1 Matheatics Departent, Occidental College, Los Angeles, California 90041 2
More informationIntelligent Systems: Reasoning and Recognition. Artificial Neural Networks
Intelligent Systes: Reasoning and Recognition Jaes L. Crowley MOSIG M1 Winter Seester 2018 Lesson 7 1 March 2018 Outline Artificial Neural Networks Notation...2 Introduction...3 Key Equations... 3 Artificial
More informationNote-A-Rific: Mechanical
Note-A-Rific: Mechanical Kinetic You ve probably heard of inetic energy in previous courses using the following definition and forula Any object that is oving has inetic energy. E ½ v 2 E inetic energy
More informationCurious Bounds for Floor Function Sums
1 47 6 11 Journal of Integer Sequences, Vol. 1 (018), Article 18.1.8 Curious Bounds for Floor Function Sus Thotsaporn Thanatipanonda and Elaine Wong 1 Science Division Mahidol University International
More informationPattern Recognition and Machine Learning. Artificial Neural networks
Pattern Recognition and Machine Learning Jaes L. Crowley ENSIMAG 3 - MMIS Fall Seester 2017 Lessons 7 20 Dec 2017 Outline Artificial Neural networks Notation...2 Introduction...3 Key Equations... 3 Artificial
More information#A52 INTEGERS 10 (2010), COMBINATORIAL INTERPRETATIONS OF BINOMIAL COEFFICIENT ANALOGUES RELATED TO LUCAS SEQUENCES
#A5 INTEGERS 10 (010), 697-703 COMBINATORIAL INTERPRETATIONS OF BINOMIAL COEFFICIENT ANALOGUES RELATED TO LUCAS SEQUENCES Bruce E Sagan 1 Departent of Matheatics, Michigan State University, East Lansing,
More informationEvidential Reasoning for Multi-Criteria Analysis Based on DSmT-AHP
Evidential Reasoning for Multi-Criteria Analysis Based on DSmT-AHP Jean Dezert Jean-Marc Tacnet Originally published as Dezert J., Tacnet J.-M., Evidential Reasoning for Multi-Criteria Analysis based on
More informationOBJECTIVES INTRODUCTION
M7 Chapter 3 Section 1 OBJECTIVES Suarize data using easures of central tendency, such as the ean, edian, ode, and idrange. Describe data using the easures of variation, such as the range, variance, and
More informationMoment of Inertia. Terminology. Definitions Moment of inertia of a body with mass, m, about the x axis: Transfer Theorem - 1. ( )dm. = y 2 + z 2.
Terinology Moent of Inertia ME 202 Moent of inertia (MOI) = second ass oent Instead of ultiplying ass by distance to the first power (which gives the first ass oent), we ultiply it by distance to the second
More informationA Simplified Analytical Approach for Efficiency Evaluation of the Weaving Machines with Automatic Filling Repair
Proceedings of the 6th SEAS International Conference on Siulation, Modelling and Optiization, Lisbon, Portugal, Septeber -4, 006 0 A Siplified Analytical Approach for Efficiency Evaluation of the eaving
More information13 Harmonic oscillator revisited: Dirac s approach and introduction to Second Quantization
3 Haronic oscillator revisited: Dirac s approach and introduction to Second Quantization. Dirac cae up with a ore elegant way to solve the haronic oscillator proble. We will now study this approach. The
More informationDerivative at a point
Roberto s Notes on Differential Calculus Capter : Definition of derivative Section Derivative at a point Wat you need to know already: Te concept of liit and basic etods for coputing liits. Wat you can
More informationModel Fitting. CURM Background Material, Fall 2014 Dr. Doreen De Leon
Model Fitting CURM Background Material, Fall 014 Dr. Doreen De Leon 1 Introduction Given a set of data points, we often want to fit a selected odel or type to the data (e.g., we suspect an exponential
More informationHomework 3 Solutions CSE 101 Summer 2017
Hoework 3 Solutions CSE 0 Suer 207. Scheduling algoriths The following n = 2 jobs with given processing ties have to be scheduled on = 3 parallel and identical processors with the objective of iniizing
More informationBayes Theorem & Diagnostic Tests Screening Tests
Bayes heore & Diagnostic ests Screening ests Box contains 2 red balls and blue ball Box 2 contains red ball and 3 blue balls A coin is tossed. If Head turns up a ball is drawn fro Box, and if ail turns
More informationLinear recurrences and asymptotic behavior of exponential sums of symmetric boolean functions
Linear recurrences and asyptotic behavior of exponential sus of syetric boolean functions Francis N. Castro Departent of Matheatics University of Puerto Rico, San Juan, PR 00931 francis.castro@upr.edu
More informationApplication of Evidence Theory to Construction Projects
Application of Evidence Theory to Construction Projects Desmond Adair, University of Tasmania, Australia Martin Jaeger, University of Tasmania, Australia Abstract: Crucial decisions are necessary throughout
More informationOptical Properties of Plasmas of High-Z Elements
Forschungszentru Karlsruhe Techni und Uwelt Wissenschaftlishe Berichte FZK Optical Properties of Plasas of High-Z Eleents V.Tolach 1, G.Miloshevsy 1, H.Würz Project Kernfusion 1 Heat and Mass Transfer
More informationSampling How Big a Sample?
C. G. G. Aitken, 1 Ph.D. Sapling How Big a Saple? REFERENCE: Aitken CGG. Sapling how big a saple? J Forensic Sci 1999;44(4):750 760. ABSTRACT: It is thought that, in a consignent of discrete units, a certain
More informationMultiple Testing Issues & K-Means Clustering. Definitions related to the significance level (or type I error) of multiple tests
StatsM254 Statistical Methods in Coputational Biology Lecture 3-04/08/204 Multiple Testing Issues & K-Means Clustering Lecturer: Jingyi Jessica Li Scribe: Arturo Rairez Multiple Testing Issues When trying
More informationConstruction of the Electronic Angular Wave Functions and Probability Distributions of the Hydrogen Atom
Construction of the Electronic Angular Wave Functions and Probability Distributions of the Hydrogen Ato Thoas S. Kuntzlean Mark Ellison John Tippin Departent of Cheistry Departent of Cheistry Departent
More informationarxiv: v1 [math.co] 19 Apr 2017
PROOF OF CHAPOTON S CONJECTURE ON NEWTON POLYTOPES OF q-ehrhart POLYNOMIALS arxiv:1704.0561v1 [ath.co] 19 Apr 017 JANG SOO KIM AND U-KEUN SONG Abstract. Recently, Chapoton found a q-analog of Ehrhart polynoials,
More informationChapter 6 1-D Continuous Groups
Chapter 6 1-D Continuous Groups Continuous groups consist of group eleents labelled by one or ore continuous variables, say a 1, a 2,, a r, where each variable has a well- defined range. This chapter explores:
More informationAlgebraic Approach for Performance Bound Calculus on Transportation Networks
Algebraic Approach for Perforance Bound Calculus on Transportation Networks (Road Network Calculus) Nadir Farhi, Habib Haj-Sale & Jean-Patrick Lebacque Université Paris-Est, IFSTTAR, GRETTIA, F-93166 Noisy-le-Grand,
More informationMA304 Differential Geometry
MA304 Differential Geoetry Hoework 4 solutions Spring 018 6% of the final ark 1. The paraeterised curve αt = t cosh t for t R is called the catenary. Find the curvature of αt. Solution. Fro hoework question
More informationSeismic Analysis of Structures by TK Dutta, Civil Department, IIT Delhi, New Delhi.
Seisic Analysis of Structures by K Dutta, Civil Departent, II Delhi, New Delhi. Module 5: Response Spectru Method of Analysis Exercise Probles : 5.8. or the stick odel of a building shear frae shown in
More informationRisk & Safety in Engineering. Dr. Jochen Köhler
Risk & afety in Engineering Dr. Jochen Köhler Contents of Today's Lecture Introduction to Classical Reliability Theory tructural Reliability The fundaental case afety argin Introduction to Classical Reliability
More informationOn the Communication Complexity of Lipschitzian Optimization for the Coordinated Model of Computation
journal of coplexity 6, 459473 (2000) doi:0.006jco.2000.0544, available online at http:www.idealibrary.co on On the Counication Coplexity of Lipschitzian Optiization for the Coordinated Model of Coputation
More informationESTIMATING AND FORMING CONFIDENCE INTERVALS FOR EXTREMA OF RANDOM POLYNOMIALS. A Thesis. Presented to. The Faculty of the Department of Mathematics
ESTIMATING AND FORMING CONFIDENCE INTERVALS FOR EXTREMA OF RANDOM POLYNOMIALS A Thesis Presented to The Faculty of the Departent of Matheatics San Jose State University In Partial Fulfillent of the Requireents
More informationResearch in Area of Longevity of Sylphon Scraies
IOP Conference Series: Earth and Environental Science PAPER OPEN ACCESS Research in Area of Longevity of Sylphon Scraies To cite this article: Natalia Y Golovina and Svetlana Y Krivosheeva 2018 IOP Conf.
More informationSolution of the VBIED problem using Dezert-Smarandache Theory (DSmT)
Solution of the VBIED problem using Dezert-Smarandache Theory (DSmT) Dr. Jean Dezert The French Aerospace Lab. - ONERA 29 Av. de la Division Leclerc 92320 Châtillon, France jean.dezert@onera.fr Dr. Florentin
More informationSome Simplified Forms of Reasoning with Distance-Based Entailments
Soe Siplified Fors of Reasoning with Distance-Based Entailents Ofer Arieli 1 and Anna Zaansky 2 1 Departent of Coputer Science, The Acadeic College of Tel-Aviv, Israel. oarieli@ta.ac.il 2 Departent of
More informationThe Universe of Symmetry Breaking Tasks
The Universe of Syetry Breaking Tasks Daien Ibs, Sergio Rajsbau, Michel Raynal To cite this version: Daien Ibs, Sergio Rajsbau, Michel Raynal. The Universe of Syetry Breaking Tasks. [Research Report] PI-1965,
More informationA Model for the Selection of Internet Service Providers
ISSN 0146-4116, Autoatic Control and Coputer Sciences, 2008, Vol. 42, No. 5, pp. 249 254. Allerton Press, Inc., 2008. Original Russian Text I.M. Aliev, 2008, published in Avtoatika i Vychislitel naya Tekhnika,
More informationLATTICE POINT SOLUTION OF THE GENERALIZED PROBLEM OF TERQUEi. AND AN EXTENSION OF FIBONACCI NUMBERS.
i LATTICE POINT SOLUTION OF THE GENERALIZED PROBLEM OF TERQUEi. AND AN EXTENSION OF FIBONACCI NUMBERS. C. A. CHURCH, Jr. and H. W. GOULD, W. Virginia University, Morgantown, W. V a. In this paper we give
More informationAssessing the Overall Sufficiency of Safety Arguments
University of Pennsylvania ScholarlyCoons Departental Papers (CIS) Departent of Coputer & Inforation Science 2-203 Assessing the Overall Sufficiency of Safety Arguents Anaheed Ayoub University of Pennsylvania,
More informationForecasting Financial Indices: The Baltic Dry Indices
International Journal of Maritie, Trade & Econoic Issues pp. 109-130 Volue I, Issue (1), 2013 Forecasting Financial Indices: The Baltic Dry Indices Eleftherios I. Thalassinos 1, Mike P. Hanias 2, Panayiotis
More informationThis model assumes that the probability of a gap has size i is proportional to 1/i. i.e., i log m e. j=1. E[gap size] = i P r(i) = N f t.
CS 493: Algoriths for Massive Data Sets Feb 2, 2002 Local Models, Bloo Filter Scribe: Qin Lv Local Models In global odels, every inverted file entry is copressed with the sae odel. This work wells when
More informationE0 370 Statistical Learning Theory Lecture 6 (Aug 30, 2011) Margin Analysis
E0 370 tatistical Learning Theory Lecture 6 (Aug 30, 20) Margin Analysis Lecturer: hivani Agarwal cribe: Narasihan R Introduction In the last few lectures we have seen how to obtain high confidence bounds
More informationEVALUATION OF A SIMPLIFIED METHOD FOR THE DETERMINATION OF THE NON LINEAR SEISMIC RESPONSE OF RC FRAMES
EVALUATIO OF A SIMPLIFIED METHOD FOR THE DETERMIATIO OF THE O LIEAR SEISMIC RESPOSE OF RC FRAMES 9 Misael REQUEA And A. Gustavo AYALA SUMMARY In this paper a siplified ethod is developed for the evaluation
More informationAnton Bourdine. 1. Introduction. and approximate propagation constants by following simple ratio (Equation (32.22) in [1]):
Matheatical Probles in Engineering olue 5, Article ID 843, pages http://dx.doi.org/.55/5/843 Research Article Fast and Siple Method for Evaluation of Polarization Correction to Propagation Constant of
More informationA Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine. (1900 words)
1 A Self-Organizing Model for Logical Regression Jerry Farlow 1 University of Maine (1900 words) Contact: Jerry Farlow Dept of Matheatics Univeristy of Maine Orono, ME 04469 Tel (07) 866-3540 Eail: farlow@ath.uaine.edu
More informationAlgorithms for parallel processor scheduling with distinct due windows and unit-time jobs
BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES Vol. 57, No. 3, 2009 Algoriths for parallel processor scheduling with distinct due windows and unit-tie obs A. JANIAK 1, W.A. JANIAK 2, and
More informationBiostatistics Department Technical Report
Biostatistics Departent Technical Report BST006-00 Estiation of Prevalence by Pool Screening With Equal Sized Pools and a egative Binoial Sapling Model Charles R. Katholi, Ph.D. Eeritus Professor Departent
More informationEstimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples
Open Journal of Statistics, 4, 4, 64-649 Published Online Septeber 4 in SciRes http//wwwscirporg/ournal/os http//ddoiorg/436/os4486 Estiation of the Mean of the Eponential Distribution Using Maiu Ranked
More informationUniform Approximation and Bernstein Polynomials with Coefficients in the Unit Interval
Unifor Approxiation and Bernstein Polynoials with Coefficients in the Unit Interval Weiang Qian and Marc D. Riedel Electrical and Coputer Engineering, University of Minnesota 200 Union St. S.E. Minneapolis,
More informationRising, Setting and Twilight
Rising, Setting and Twilight The rotation of e ear on its own axis causes e phenoenon of e rising and setting of all celestial bodies. Generally, e celestial bodies see to appear at e eastern horizon,
More informationPHY 171. Lecture 14. (February 16, 2012)
PHY 171 Lecture 14 (February 16, 212) In the last lecture, we looked at a quantitative connection between acroscopic and icroscopic quantities by deriving an expression for pressure based on the assuptions
More informationSequence Analysis, WS 14/15, D. Huson & R. Neher (this part by D. Huson) February 5,
Sequence Analysis, WS 14/15, D. Huson & R. Neher (this part by D. Huson) February 5, 2015 31 11 Motif Finding Sources for this section: Rouchka, 1997, A Brief Overview of Gibbs Sapling. J. Buhler, M. Topa:
More informationEnsemble Based on Data Envelopment Analysis
Enseble Based on Data Envelopent Analysis So Young Sohn & Hong Choi Departent of Coputer Science & Industrial Systes Engineering, Yonsei University, Seoul, Korea Tel) 82-2-223-404, Fax) 82-2- 364-7807
More informationA class of fusion rules based on the belief redistribution to subsets or complements
Chapter 5 A class of fusion rules based on the belief redistribution to subsets or complements Florentin Smarandache Chair of Math. & Sciences Dept., Univ. of New Mexico, 200 College Road, Gallup, NM 87301,
More informationCourse Notes for EE227C (Spring 2018): Convex Optimization and Approximation
Course Notes for EE227C (Spring 2018): Convex Optiization and Approxiation Instructor: Moritz Hardt Eail: hardt+ee227c@berkeley.edu Graduate Instructor: Max Sichowitz Eail: sichow+ee227c@berkeley.edu October
More informationStudy on the Interactive Decision-Making in Warship Cannon Choosing
78 Study on the Interactive Decision-Maing in Warship Cannon Choosing Dong Peng,2, Luo Zhaohui, Yang Chao 2 Departent of Manageent Science, Naval University of Engineeringy, Wuhan, P.R.China, 4333 2 School
More informationPh 20.3 Numerical Solution of Ordinary Differential Equations
Ph 20.3 Nuerical Solution of Ordinary Differential Equations Due: Week 5 -v20170314- This Assignent So far, your assignents have tried to failiarize you with the hardware and software in the Physics Coputing
More informationC na (1) a=l. c = CO + Clm + CZ TWO-STAGE SAMPLE DESIGN WITH SMALL CLUSTERS. 1. Introduction
TWO-STGE SMPLE DESIGN WITH SMLL CLUSTERS Robert G. Clark and David G. Steel School of Matheatics and pplied Statistics, University of Wollongong, NSW 5 ustralia. (robert.clark@abs.gov.au) Key Words: saple
More informationCautious OWA and Evidential Reasoning for Decision Making under Uncertainty
Cautious OWA and Evidential Reasoning for Decision Making under Uncertainty Jean-Marc Tacnet Cemagref -ETGR 2, rue de la papèterie - B.P. 76 F-38402 Saint Martin d Hères Cedex, France Email: jean-marc.tacnet@cemagref.fr
More informationarxiv: v1 [math.nt] 14 Sep 2014
ROTATION REMAINDERS P. JAMESON GRABER, WASHINGTON AND LEE UNIVERSITY 08 arxiv:1409.411v1 [ath.nt] 14 Sep 014 Abstract. We study properties of an array of nubers, called the triangle, in which each row
More informationIN modern society that various systems have become more
Developent of Reliability Function in -Coponent Standby Redundant Syste with Priority Based on Maxiu Entropy Principle Ryosuke Hirata, Ikuo Arizono, Ryosuke Toohiro, Satoshi Oigawa, and Yasuhiko Takeoto
More informationNon-Parametric Non-Line-of-Sight Identification 1
Non-Paraetric Non-Line-of-Sight Identification Sinan Gezici, Hisashi Kobayashi and H. Vincent Poor Departent of Electrical Engineering School of Engineering and Applied Science Princeton University, Princeton,
More informationHighway Disaster Alignment Decision-making Model Under the Fragile Environment Condition in Mountain Area
Send Orders for Reprints to reprints@benthascience.ae The Open Civil Engineering Journal, 2015, 9, 115-119 115 Open Access Highway Disaster Alignent Decision-aking Model Under the Fragile Environent Condition
More informationA GENERAL FORM FOR THE ELECTRIC FIELD LINES EQUATION CONCERNING AN AXIALLY SYMMETRIC CONTINUOUS CHARGE DISTRIBUTION
A GENEAL FOM FO THE ELECTIC FIELD LINES EQUATION CONCENING AN AXIALLY SYMMETIC CONTINUOUS CHAGE DISTIBUTION BY MUGU B. ăuţ Abstract..By using an unexpected approach it results a general for for the electric
More informationHyperbolic Horn Helical Mass Spectrometer (3HMS) James G. Hagerman Hagerman Technology LLC & Pacific Environmental Technologies April 2005
Hyperbolic Horn Helical Mass Spectroeter (3HMS) Jaes G Hageran Hageran Technology LLC & Pacific Environental Technologies April 5 ABSTRACT This paper describes a new type of ass filter based on the REFIMS
More informationExperimental Design For Model Discrimination And Precise Parameter Estimation In WDS Analysis
City University of New York (CUNY) CUNY Acadeic Works International Conference on Hydroinforatics 8-1-2014 Experiental Design For Model Discriination And Precise Paraeter Estiation In WDS Analysis Giovanna
More information