REAL-TIME DETERMINATION OF INDOOR CONTAMINANT SOURCE LOCATION AND STRENGTH, PART II: WITH TWO SENSORS. Beijing , China,
|
|
- Damian Heath
- 5 years ago
- Views:
Transcription
1 REAL-TIME DETERMIATIO OF IDOOR COTAMIAT SOURCE LOCATIO AD STREGTH, PART II: WITH TWO SESORS Hao Ca,, Xantng L, Wedng Long 3 Department of Buldng Scence, School of Archtecture, Tsnghua Unversty Bejng 84, Chna, cahaohvac@63.com Engneerng Insttuton of Engneerng Corps, PLA Unversty of Scence & Technology, anjng 7, Chna, cahaohvac@63.com 3 Sno-German College of Appled Scences, Tongj Unversty, Shangha 84, Chna, wednglong63@63.com ABSTRACT In the precedng companon paper (Part I), a method wth one sensor that could dentfy the ndoor contamnant source locaton strength n short tme was presented. On the bass of further theoretcal study, a method wth two sensors s presented n ths paper to dentfy contamnant source wth hgher accuracy. Ths paper demonstrates how to use the method wth two sensors to fnd the locaton of contamnant source n a threedmensonal room. In addton, the accuracy of two types of methods was compared. The correctness probablty, whch are used to evaluate the accuracy of source locatng, of the method wth two sensors the method wth one sensor are 83.3% 94.8% respectvely. The results show that the method wth two sensors wors better for locatng contamnant source than that wth one sensor. In practce, the method wth two sensors may be more applcable for the stuatons where the accuracy of source dentfcaton s crucal whle the costs of sensors are not of great concern. KEYWORDS Contamnant source, Identfcaton method, Computatonal flud dynamcs (CFD), Sensor, Indoor envronment ITRODUCTIO In Part I of ths companon papers, we presents a method wth one sensor (Method I for short) to dentfy the contamnant source locaton strength n real tme. The results of case study show that Method I could dentfy the contamnant source locatons wth acceptable accuracy. Although the dentfcatons of source strength are not very accurate, the predctons actual values are n the same order of magntude (Ca et al, 7). One major purpose of the above study s to explore whether we could dentfy the contamnant source wth mnmum sensors. If the answer s postve, the costs of sensors wll be saved sgnfcantly, so that the technque of source dentfcaton wll be more applcable for the stuatons where the costs of sensors are of great concern. Fortunately, we get the postve answer. evertheless, problems are stll far from settled. Let us consder another nd of stuatons where the accuracy of source dentfcaton s crucal whle the costs of sensors are not of great concern. Under such crcumstances, t may be of sgnfcance to explore the problem that whether we can mprove the accuracy of dentfcaton wth more sensors. Ths paper ams to presents a new method wth two sensors (Method II for short) to mprove the accuracy of Method I. For the purpose of demonstraton comparson, ths new method wll be appled to the dentcal room presented n the prevous paper (Part I). METHODOLOGY The premses of present method are same wth that of Method I. They are brefly summarzed n the followng,. Indoor arflow s nown s n steady state;. Only one pont source s released at a steady rate n the room; 3. The sensors employed can measure the contamant under any condtons; 4. The release the measurements tae place smultaneously. Suppose that two sensors are placed n the room at ponts β. Unnowns to the problem nclude locaton α strength S of the source CS. The overall framewor of Method II s dvded nto two stages just as Method I. In the pre-event stage, we dvde the room nto control volumes form a set A = { α, α, L, α } wth ther centers, construct a vector X ( ) composed of ndex X, ) whch represents the closeness degree between α α, run CFD
2 smulatons to get the concentraton of contamnant at ponts β for every possble scenaros, store the smulatons n the database for next stage. As dscrbed n Part I, ths stage s not tme-crtcal not dffcult to acheve by vrtue of fast personal computers nexpensve data storage devces. In the second stage, we solve the vector X ( ) wth the smulatons stored n the database the measurements from the two sensors, fnd the closest pont α to α n the set A usng the vector X ( ). As dscussed n Part I, ths stage s mathematcally smple can be executed n real tme durng a release event. Although the premses overall framewor of Method II s smlar to that of Method I, Method II s dstnct from Method I n one mportant feature, that s, the specfc form of ndex X, ). In order to mae full use of the measurements from two sensors mprove the accuracy of dentfcaton, we should develop a new ndex n ths paper. In the followng, we wll present how to derve ths new ndex. Frst, we redefne the smlarty characterstc ndex as follows, SC, ) = log C, t)d t C ( β, t)dt C, t)d t C, t)dt () where SC (, ) α s a non-dmensonal parameter, whch ndcates the smlarty characterstc of α α at tme ; C, t) C, t) are the measurements from two sensors at tme t, at pont β, respectvely, when the unnown source CS s released n the room; C, t) C, t) are the contamnant concentratons at tme t, at pont β, respectvely, when the source CS s released at pont α wth steady strength S, whch are obtaned by smulatons stored n the database; the subscrpt represents that two sensors are employed. Snce the ndoor flow feld s nown, the equaton of contamnant transport s a lner equaton, where Superposton Theorem can be appled. Thus f S S are constant, when α, we get C, t)dt S C (, )d S β t t α () From Eqs. () (), we obtan that (, ) log SC α = as α α, n other words, the varaton curve of SC (, ) α wth approaches a horzontal lne f ( ) =. Based on SC (, ) α, we rewrtten the absolute smlarty ndex n the followng, { } ASD ( α, ) = SC ( α, t)dt (3) where ASD (, ) α s a non-dmensonal parameter whle ASD (, ) α n Part I s dmensonal. From the Eq. (3), we can see that ASD (, ) α s characterzed by the followng propertes: ) ASD (, ) α ; ) The more the curve of g( ) = SC, ) approaches the horzontal lne f ( ) =, the greater the value of ASD (, ) α ; 3) As α α, ASD (, ) α. Accordng to ASD (, ) α, the relatve smlarty nex s gven further, MGS ( α, ) = ASD ( α, ) ASD ( α, ) (4) = where MGS (, ) α s a non-dmensonal ndex has major propertes as follows: ) MGS, ) ; ) MGS, ) = ; = 3) As α α, (, ) MGS α. Wth the above propertes, MGS (, ) α s employed here to represent the closeness degree between α α. As there are control volumes, we construct a vector as, MGS ( ) = [ MGS( α, ), MGS( α, ), L, MGS ( α, )] T (5) In the above vector, the value of each element represents the probablty or menbershp grade of whch α s at pont α. The larger the dffferences between these elements, the easer more accurate the dentfcaton wll be, vce versa. If we fnd the center α whch s correspondng to the bggest element n MGS ( ), then the contamnant source locaton could be dentfed
3 After we have clarfed how to locate the contamnant source wth Method II the major dfferences between two methods, t s nessecary to determne whether Method II can locate source wth hgher accuracy than Method I. Therefore, an evaluaton system s needed. The correctness probablty ndex η correspondng evaluaton stard, whch are proposed n Part I, wll be adopted n the next secton for the purpose of comparson between two methods. Based on the dentfcaton of source locaton, we move on to dscuss how to fnd ts strength further. In ths paper, we stll use the method presented n the Part I to dentfy the source strength, although ths method needs to be mproved. Despte ths, f source locatng s more accuracte wth Method II, we can also dentfy the strength of source more accurately. For example, f the source locatng s proved to be wrong wth Method I, we could hardly solve the source strength wth hgh accuracy. However, f the souce locatng s rght wth Method II, the soluton of strength may be easer more accurate. CASE STUDY In the followng, we apply Method II to the room, whch s dentcal wth that presented n Part I, for the purpose of demonstraton comparson. Case descrpton The room s 5 m long(x), 3m hgh(y) 5m wde (Z) as shown n Fg.. Detals of t can be referred to that n Part I. SA S Fgure Schematc of the room (SA supply ar dffuser, RA return ar grll, S Contamnant source locatons to be dentfed) Sxteen possble locatons of source are set up n the example (see Fg. ), whch constuct the set A = { α, α, L, α6} to be determnated. These unnown locatons correspond wth the volume centers one by one, that s α = α ( =,, K,6). Two sensor are placed at the pont β, X, Y, Z =.5,.,.5 m,, X, Y, Z = 3.75,., 3.5 RA m. The plan of unnown sources, sensors, control volumes are shown n Fgure n whch sources zones at lower level n Y drecton are labeled n the bracet. Fgure Plan of the locatons of sources two sensors, the volumes Results dscusson Snce two nds of methods presented n ths companon papers decouple the pre-event smulatons from source dentfcaton durng the event stage, we can qucly obtan the results wth Method II, need not re-execute the tmeconsumng pre-event stage of the smulatons. The decoupled nature of these methods together wth the correctness problty ndex also allow easy quc evaluaton of alternatve dentfcaton methods samplng plans wthout repeatng the computatonally ntensve CFD smulatons. In the precedng paper (Part I), we solved the concentraton feld of contamnant by Arpa (Fluent Inc. ) n order to obtan the measures from the sensor durng the event. In each smulaton, the contamnant source to be dentfed was placed at the locatons, α ( =,, L,6), released n steady strength, S =. g/s. In ths study, what we only need to do s to read the samples of C, t) C, t) wth a constant tme step s from the solved concentraton feld. By calculatng MGS ( ), we dentfy the source locatons qucly n the second stage. Table summarzes the results of the dentfcatons by two methods. The samplng duraton of sensors was = 3s, that s, the samplng of sensor ended after 3s from the release. From Table, t s shown that Method II s more accurate than Method I for source locatng. In ths case, all the sxteen locatons are dentfed correctly by vrtue of the new method, whch mproves the accuracy of source locatng by - 8 -
4 properly tang advantage of the measurements from two sensors. By the evaluaton method presented n the prevous companon paper, we got the correctness probablty of Method II s 94.8%. Snce the correctness probablty of method I s 83.3%. A comparson between Method II Method I shows that the mprovement s qute obvous. Fgure 3 shows the nfluence of sensor samplng duraton on the locatng of dfferent sources usng the Method II. The probablty of correctness η decreases wth the ncrease of samplng duraton as shown, whch s just smlar to that of Method I. Ths smlar feature ndcates that t s possble for us to dentfy the soure wth hgh accuracy after short tme from the release usng Method II. The decoupled nature of the methods proposed n ths seres (Parts I II) together wth the evlauton stard smlar to the gradng method of shootng competton also allow easy quc evaluaton of alternatve dentfcaton methods samplng plans wthout repeatng the computatonally ntensve CFD smulatons. It could be helpful for us to further study the mprovement of dentfcaton methods the optmzaton of sensors layout n the future. ACKOWLEDGEMET Ths study s supported by atonal atural Scence Foundaton of Chna (Grant o ). REFERECES umber of Score s 6s s 8s Score Ca H., L X.T., Long W.D., et al. 7. Real-tme determnaton of ndoor contamnant source locaton strength, part I: wth one sensor, The th Internatonal Buldng Performance Smulaton Assocaton Conference Exhbton, Bejng (Submtted) Fluent Inc.. Arpa. User s Gude, Fluent Inc. Samplng Duraton, Fgure 3. Locatng results wth dfferent samplng duraton usng the method wth two sensors Moreover, snce all the sxteen locatons are dentfed correctly by Method II whle only thrteen locatons by Method I, the dentfcaton of strength may be more accurate by Method II. COCLUSIO Based on the prevous companon paper (Part I), ths paper presented a new method wth two sensors for dentfyng source locaton strength n real tme. Method II proposed n ths paper nhert the advantages of Method I mae full use of the measurements from two sensors. By applyng Method II to the room dentcal wth that presented n Part I, the followng conclusons could be drawn,. Method II could be more accurate than Method I for source locatng. As a result, Method II may be an alternatve way for mprovng the accuracy of the dentfcaton of source strength.. Method II also has the nature that the accuracy of source locatng decreases wth the ncrease of samplng duraton. 3. Method II may be more applcable for the stuatons where the accuracy of source dentfcaton s crucal whle the costs of sensors are not very mportant
5 Table Comparson of the contamnant source dentfcatons by two methods METHOD WITH OE SESOR METHOD WITH TWO SESORS (,3) DETERMIATIO MGS α OF LOCATIO DETERMIATIO MGS α O. OF LOCATIO VALUE RAKIG RIGHT? MARK VALUE RAKIG RIGHT? (Y/) (Y/) MARK.33 Y 5.69 Y 6.58 Y 6.78 Y Y Y Y 5.7 Y Y 6.3 Y Y 6.66 Y Y 6.6 Y Y 5.43 Y 5.75 Y 6.4 Y 5.4 Y 5.77 Y 6.83 Y Y 6.84 Y Y 6.8 Y Y 5.36 Y Y 6.77 Y 6 Correctness probablty η (%) 83.3 Correctness probablty η (%)
REAL-TIME DETERMINATION OF INDOOR CONTAMINANT SOURCE LOCATION AND STRENGTH, PART I: WITH ONE SENSOR. Beijing , China,
REAL-TIME DETERMIATIO OF IDOOR COTAMIAT SOURCE LOCATIO AD STREGTH, PART I: WITH OE SESOR Hao Ca, 2, Xantng L, Wedng Long 3, and Xbn Ma 2 Department of Buldng Scence, School of Archtecture, Tsnghua Unversty
More informationAir Age Equation Parameterized by Ventilation Grouped Time WU Wen-zhong
Appled Mechancs and Materals Submtted: 2014-05-07 ISSN: 1662-7482, Vols. 587-589, pp 449-452 Accepted: 2014-05-10 do:10.4028/www.scentfc.net/amm.587-589.449 Onlne: 2014-07-04 2014 Trans Tech Publcatons,
More informationTurbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH
Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant
More informationSimulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests
Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationAdiabatic Sorption of Ammonia-Water System and Depicting in p-t-x Diagram
Adabatc Sorpton of Ammona-Water System and Depctng n p-t-x Dagram J. POSPISIL, Z. SKALA Faculty of Mechancal Engneerng Brno Unversty of Technology Techncka 2, Brno 61669 CZECH REPUBLIC Abstract: - Absorpton
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationThis column is a continuation of our previous column
Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard
More informationGlobal Sensitivity. Tuesday 20 th February, 2018
Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationValuated Binary Tree: A New Approach in Study of Integers
Internatonal Journal of Scentfc Innovatve Mathematcal Research (IJSIMR) Volume 4, Issue 3, March 6, PP 63-67 ISS 347-37X (Prnt) & ISS 347-34 (Onlne) wwwarcournalsorg Valuated Bnary Tree: A ew Approach
More informationx = , so that calculated
Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to
More informationGraph Reconstruction by Permutations
Graph Reconstructon by Permutatons Perre Ille and Wllam Kocay* Insttut de Mathémathques de Lumny CNRS UMR 6206 163 avenue de Lumny, Case 907 13288 Marselle Cedex 9, France e-mal: lle@ml.unv-mrs.fr Computer
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationGrover s Algorithm + Quantum Zeno Effect + Vaidman
Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the
More informationChapter - 2. Distribution System Power Flow Analysis
Chapter - 2 Dstrbuton System Power Flow Analyss CHAPTER - 2 Radal Dstrbuton System Load Flow 2.1 Introducton Load flow s an mportant tool [66] for analyzng electrcal power system network performance. Load
More informationA Robust Method for Calculating the Correlation Coefficient
A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal
More informationSupplementary Notes for Chapter 9 Mixture Thermodynamics
Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects
More informationAn Improved multiple fractal algorithm
Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton
More informationNumerical Heat and Mass Transfer
Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationA new Approach for Solving Linear Ordinary Differential Equations
, ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of
More informationOrientation Model of Elite Education and Mass Education
Proceedngs of the 8th Internatonal Conference on Innovaton & Management 723 Orentaton Model of Elte Educaton and Mass Educaton Ye Peng Huanggang Normal Unversty, Huanggang, P.R.Chna, 438 (E-mal: yepeng@hgnc.edu.cn)
More informationAP Physics 1 & 2 Summer Assignment
AP Physcs 1 & 2 Summer Assgnment AP Physcs 1 requres an exceptonal profcency n algebra, trgonometry, and geometry. It was desgned by a select group of college professors and hgh school scence teachers
More informationAssessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion
Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,
More information/ n ) are compared. The logic is: if the two
STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence
More information1 GSW Iterative Techniques for y = Ax
1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn
More informationPower law and dimension of the maximum value for belief distribution with the max Deng entropy
Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng
More informationOne-sided finite-difference approximations suitable for use with Richardson extrapolation
Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,
More information2016 Wiley. Study Session 2: Ethical and Professional Standards Application
6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton
More informationAGC Introduction
. Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero
More informationThe Study of Teaching-learning-based Optimization Algorithm
Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute
More informationOpen Systems: Chemical Potential and Partial Molar Quantities Chemical Potential
Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,
More informationA Hybrid Variational Iteration Method for Blasius Equation
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method
More informationERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY
Proceedngs: Indoor Ar 2005 ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY S Lu, J Lu *, N Zhu School of Envronmental Scence and Technology, Tanjn
More informationUncertainty and auto-correlation in. Measurement
Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at
More informationOperating conditions of a mine fan under conditions of variable resistance
Paper No. 11 ISMS 216 Operatng condtons of a mne fan under condtons of varable resstance Zhang Ynghua a, Chen L a, b, Huang Zhan a, *, Gao Yukun a a State Key Laboratory of Hgh-Effcent Mnng and Safety
More informationIndeterminate pin-jointed frames (trusses)
Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all
More informationCSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography
CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationInternational Power, Electronics and Materials Engineering Conference (IPEMEC 2015)
Internatonal Power, Electroncs and Materals Engneerng Conference (IPEMEC 2015) Dynamc Model of Wnd Speed Dstrbuton n Wnd Farm Consderng the Impact of Wnd Drecton and Interference Effects Zhe Dong 1, a,
More informationAdaptive Consensus Control of Multi-Agent Systems with Large Uncertainty and Time Delays *
Journal of Robotcs, etworkng and Artfcal Lfe, Vol., o. (September 04), 5-9 Adaptve Consensus Control of Mult-Agent Systems wth Large Uncertanty and me Delays * L Lu School of Mechancal Engneerng Unversty
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More information829. An adaptive method for inertia force identification in cantilever under moving mass
89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,
More informationEVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES
EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES Manuel J. C. Mnhoto Polytechnc Insttute of Bragança, Bragança, Portugal E-mal: mnhoto@pb.pt Paulo A. A. Perera and Jorge
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More informationThe optimal delay of the second test is therefore approximately 210 hours earlier than =2.
THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple
More informationCHAPTER IV RESEARCH FINDING AND ANALYSIS
CHAPTER IV REEARCH FINDING AND ANALYI A. Descrpton of Research Fndngs To fnd out the dfference between the students who were taught by usng Mme Game and the students who were not taught by usng Mme Game
More informationThe equation of motion of a dynamical system is given by a set of differential equations. That is (1)
Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence
More informationComparison of Regression Lines
STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence
More informationTHE SUMMATION NOTATION Ʃ
Sngle Subscrpt otaton THE SUMMATIO OTATIO Ʃ Most of the calculatons we perform n statstcs are repettve operatons on lsts of numbers. For example, we compute the sum of a set of numbers, or the sum of the
More informationLecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES
COMPUTATIONAL FLUID DYNAMICS: FDM: Appromaton of Second Order Dervatves Lecture APPROXIMATION OF SECOMD ORDER DERIVATIVES. APPROXIMATION OF SECOND ORDER DERIVATIVES Second order dervatves appear n dffusve
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationCorrelation and Regression. Correlation 9.1. Correlation. Chapter 9
Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,
More informationAnswers Problem Set 2 Chem 314A Williamsen Spring 2000
Answers Problem Set Chem 314A Wllamsen Sprng 000 1) Gve me the followng crtcal values from the statstcal tables. a) z-statstc,-sded test, 99.7% confdence lmt ±3 b) t-statstc (Case I), 1-sded test, 95%
More information5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016) Star image identification uninfluenced by rotation
5th Internatonal Conference on Measurement, Instrumentaton and Automaton (ICMIA 2016) Star mage dentfcaton unnfluenced by rotaton Jang D1,a, Zhang Ke1, Lv Mebo1 1 Nortwestern Polytechncal Unversty, X an,
More informationIntroduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:
CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and
More informationONE-DIMENSIONAL COLLISIONS
Purpose Theory ONE-DIMENSIONAL COLLISIONS a. To very the law o conservaton o lnear momentum n one-dmensonal collsons. b. To study conservaton o energy and lnear momentum n both elastc and nelastc onedmensonal
More informationAn identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites
IOP Conference Seres: Materals Scence and Engneerng PAPER OPE ACCESS An dentfcaton algorthm of model knetc parameters of the nterfacal layer growth n fber compostes o cte ths artcle: V Zubov et al 216
More informationCalculating the Quasi-static Pressures of Confined Explosions Considering Chemical Reactions under the Constant Entropy Assumption
Appled Mechancs and Materals Onlne: 202-04-20 ISS: 662-7482, ol. 64, pp 396-400 do:0.4028/www.scentfc.net/amm.64.396 202 Trans Tech Publcatons, Swtzerland Calculatng the Quas-statc Pressures of Confned
More informationAPPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS
Unversty of Oulu Student Laboratory n Physcs Laboratory Exercses n Physcs 1 1 APPEDIX FITTIG A STRAIGHT LIE TO OBSERVATIOS In the physcal measurements we often make a seres of measurements of the dependent
More informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationSpeeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem
H.K. Pathak et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Speedng up Computaton of Scalar Multplcaton n Ellptc Curve Cryptosystem H. K. Pathak Manju Sangh S.o.S n Computer scence
More informationModal Strain Energy Decomposition Method for Damage Detection of an Offshore Structure Using Modal Testing Information
Thrd Chnese-German Jont Symposum on Coastal and Ocean Engneerng Natonal Cheng Kung Unversty, Tanan November 8-16, 2006 Modal Stran Energy Decomposton Method for Damage Detecton of an Offshore Structure
More informationChapter 8. Potential Energy and Conservation of Energy
Chapter 8 Potental Energy and Conservaton of Energy In ths chapter we wll ntroduce the followng concepts: Potental Energy Conservatve and non-conservatve forces Mechancal Energy Conservaton of Mechancal
More informationLab 2e Thermal System Response and Effective Heat Transfer Coefficient
58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),
More informationThe Order Relation and Trace Inequalities for. Hermitian Operators
Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence
More informationTransfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
More informationUncertainty as the Overlap of Alternate Conditional Distributions
Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant
More informationFoundations of Arithmetic
Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an
More informationOn the Interval Zoro Symmetric Single-step Procedure for Simultaneous Finding of Polynomial Zeros
Appled Mathematcal Scences, Vol. 5, 2011, no. 75, 3693-3706 On the Interval Zoro Symmetrc Sngle-step Procedure for Smultaneous Fndng of Polynomal Zeros S. F. M. Rusl, M. Mons, M. A. Hassan and W. J. Leong
More informationOn the correction of the h-index for career length
1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat
More informationTemperature. Chapter Heat Engine
Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the
More informationUSE OF DOUBLE SAMPLING SCHEME IN ESTIMATING THE MEAN OF STRATIFIED POPULATION UNDER NON-RESPONSE
STATISTICA, anno LXXV, n. 4, 015 USE OF DOUBLE SAMPLING SCHEME IN ESTIMATING THE MEAN OF STRATIFIED POPULATION UNDER NON-RESPONSE Manoj K. Chaudhary 1 Department of Statstcs, Banaras Hndu Unversty, Varanas,
More informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationChapter Newton s Method
Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve
More informationCurve Fitting with the Least Square Method
WIKI Document Number 5 Interpolaton wth Least Squares Curve Fttng wth the Least Square Method Mattheu Bultelle Department of Bo-Engneerng Imperal College, London Context We wsh to model the postve feedback
More informationIdentification of Wind Turbine Model for Controller Design
Identfcaton of Wnd Turbne Model for Controller Desgn M. Jelavć *, N. Perć *, I. Petrovć * * Unversty of Zagreb / Faculty of Electrcal Engneerng and Computng, Zagreb, Croata Abstract Wnd power ncreases
More informationResource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis
Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques
More informationThe internal structure of natural numbers and one method for the definition of large prime numbers
The nternal structure of natural numbers and one method for the defnton of large prme numbers Emmanul Manousos APM Insttute for the Advancement of Physcs and Mathematcs 3 Poulou str. 53 Athens Greece Abstract
More informationAvailable online Journal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research 4 6(5):7-76 Research Artcle ISSN : 975-7384 CODEN(USA) : JCPRC5 Stud on relatonshp between nvestment n scence and technolog and
More informationMarkov Chain Monte Carlo Lecture 6
where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways
More informationDesign and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm
Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationPhysics 5153 Classical Mechanics. Principle of Virtual Work-1
P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal
More informationAn Interactive Optimisation Tool for Allocation Problems
An Interactve Optmsaton ool for Allocaton Problems Fredr Bonäs, Joam Westerlund and apo Westerlund Process Desgn Laboratory, Faculty of echnology, Åbo Aadem Unversty, uru 20500, Fnland hs paper presents
More informationPhysics 181. Particle Systems
Physcs 181 Partcle Systems Overvew In these notes we dscuss the varables approprate to the descrpton of systems of partcles, ther defntons, ther relatons, and ther conservatons laws. We consder a system
More informationUsing the estimated penetrances to determine the range of the underlying genetic model in casecontrol
Georgetown Unversty From the SelectedWorks of Mark J Meyer 8 Usng the estmated penetrances to determne the range of the underlyng genetc model n casecontrol desgn Mark J Meyer Neal Jeffres Gang Zheng Avalable
More informationSupplemental document
Electronc Supplementary Materal (ESI) for Physcal Chemstry Chemcal Physcs. Ths journal s the Owner Socetes 01 Supplemental document Behnam Nkoobakht School of Chemstry, The Unversty of Sydney, Sydney,
More informationStatistical Evaluation of WATFLOOD
tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
More informationA PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
HCMC Unversty of Pedagogy Thong Nguyen Huu et al. A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Thong Nguyen Huu and Hao Tran Van Department of mathematcs-nformaton,
More informationSTAT 511 FINAL EXAM NAME Spring 2001
STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte
More informationColor Rendering Uncertainty
Australan Journal of Basc and Appled Scences 4(10): 4601-4608 010 ISSN 1991-8178 Color Renderng Uncertanty 1 A.el Bally M.M. El-Ganany 3 A. Al-amel 1 Physcs Department Photometry department- NIS Abstract:
More informationReport on Image warping
Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.
More informationA new construction of 3-separable matrices via an improved decoding of Macula s construction
Dscrete Optmzaton 5 008 700 704 Contents lsts avalable at ScenceDrect Dscrete Optmzaton journal homepage: wwwelsevercom/locate/dsopt A new constructon of 3-separable matrces va an mproved decodng of Macula
More informationSIMPLE LINEAR REGRESSION
Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two
More informationFormulas for the Determinant
page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use
More informationExpected Value and Variance
MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or
More informationBayesian predictive Configural Frequency Analysis
Psychologcal Test and Assessment Modelng, Volume 54, 2012 (3), 285-292 Bayesan predctve Confgural Frequency Analyss Eduardo Gutérrez-Peña 1 Abstract Confgural Frequency Analyss s a method for cell-wse
More information