Organic Electronic Devices
|
|
- Theodore Egbert Hancock
- 5 years ago
- Views:
Transcription
1 Orgac Electroc Devces Week 1: Semcoductor Sythess ad Characterzato Lecture 1.1: A Itroducto to Orgac Electroc aterals Brya W. Boudours Chemcal Egeerg Purdue Uversty 1
2 Lecture Overvew ad Learg Objectves Cocepts to be Covered ths Lecture Segmet Chemcal Structures of Orgac Semcoductors Nomeclature of Commo Orgac Semcoductg aterals Desg Cosderatos for Small olecule ad acromolecular Orgac Semcoductg aterals olecular Weght Characterzato of Polymer Semcoductors Learg Objectves By the Cocluso of ths Presetato, You Should be Able to: 1. Draw the chemcal structure of a commo orgac semcoductor gve the ame ad/or recte the ame of a orgac semcoductor gve the chemcal structure of the materal. 2. Predct the relatve propertes of two orgac semcoductors gve the chemcal structure of the two materals. 3. Calculate the umber-average molecular weght, weght-average molecular weght, ad dspersty of a semcoductg polymer.
3 Uderstadg Devce Operato Requres Kowledge of aterals Orgac Lght-emttg Devce (OLED) Dsplays Th ad Lghtweght Flexble Trasparet Soy Samsug Orgac Photovoltac (OPV) Devces Polytro Large Area Producto Portable Applcatos Coformal Coverage Koarka Koarka Koarka
4 Geeral Characterstcs of Orgac Semcoductors I ths course, a orgac semcoductor has the followg propertes. 1. The materal s composed prmarly of carbo, hydroge, ad oxyge. Other atoms may be preset the materal, but the majorty (> 9%) of the mass these materals wll be hydrocarbo-based. 2. I geeral, the orgac semcoductors wll cota a great deal of alteratg sgle ad double bods (.e., they are π-cojugated materals). 3. Orgac semcoductors are va der Waals solds that have covalet bods betwee the atoms of the materals. Sgle Crystals Semcrystalle Nearly Amorphous Podzorov Research Group, podzorov/dex.php Toazz, I.; et al. Bophys. J. 21, 98, 284. Scale Bar = 1 µm va Djke, J. G.; Fleschauer,. D.; Brett,. J. J. ater. Chem. 211, 21, 113.
5 Commoly-used Small olecule Orgac Semcoductors Prmarly Hole Trasportg (p-type) Orgac Semcoductors Petacee etal (e.g., Cu or Z) TIPS-Petacee Rubree Phthalocyaes (Pc) Prmarly Electro Trasportg (-type) Orgac Semcoductors Buckmsterfulleree (C 6 ) PCB PTCBI Further Readg: shra, A.; Bäuerle, P. Agew. Chem. It. Ed. 212, 51, 22.
6 Commoly-used Polymerc Orgac Semcoductors Prmarly Hole Trasportg (p-type) Polymer Semcoductors EH-PPV P3AT PBDTTT-C PDTP-DFBT PEDOT:PSS Prmarly Electro Trasportg (-type) Polymer Semcoductors CN-EH-PPV BBL P(NDI2OD-T2) PT1 Further Readg: Boudours, B. W. Curr. Op. Chem. Eg. 213, 2, 294.
7 Desg Cosderatos for Polymer Electroc aterals Icreases Polymer Backboe Cojugato ad Th Flm Crystallty Teds to Improve the Charge Trasport Ablty Hgher olecular Weght Leads to Hgher Degrees of Crystallty Narrow olecular Weght Dstrbutos Lead to Hgher Degrees of Crystallty Fused Rgs Add to the Degree of Cojugato of the Polymer. Ths Leads to ore Charge Delocalzato ad, Geerally, To a Better Ablty to Trasport Charge Sde Chas Are Used to Icrease Solublty But Ca Have Secodary Effects wth Respect to Th Flm Structure Brached Sde Chas Help Icrease the Solublty of the Orgac Electroc aterals Greatly Sde Chas Ca Impact the Thermal, Structural, ad Optoelectroc Propertes of the Polymers by Chagg the Sold State Packg
8 Case Study: Poly(3-alkylthophees) (P3ATs) Powder WAXS Patters UV-Vs Absorpto Spectra μ h ~ 1-4 cm 2 V -1 s -1 μ h ~ 6 x 1-4 cm 2 V -1 s -1 μ h ~1-3 cm 2 V -1 s -1 Polymers ~1 μ chloroform solutos Spu-coat from chloroform for a Fal flm thckess of ~8 m Further Readg: Ho, V.; Boudours, B. W.; Segalma, R. A. acromolecules 21, 43, 7895.
9 Determato of the Number-average olecular Weght ( ) Polymers Cota a xture of acromolecular Szes 6-mer 1-mer 16-mer = 1 g mol -1 olar ass of a Repeat Ut: olecular Weght of a -mer wth umber of repeat uts: ole Fracto of a -mer: x = Number-average olecular Weght: = = x = = (1 6) + (2 1) + (1 16) = = ( 1 ) 1 1 g mol = 1, g mol
10 Determato of the Weght-average olecular Weght ( w ) Polymers Cota a xture of acromolecular Szes 6-mer 1-mer 16-mer = 1 g mol -1 olar ass of a Repeat Ut: olecular Weght of a -mer wth umber of repeat uts: Weght Fracto of a -mer: w Weght-average olecular Weght: = w = = w = = ( ) (1 6) + (2 1) + (1 16) w = = 1 g mol = 1,171 g mol (1 6) + (2 1) + (1 16)
11 Dspersty (Ð) ad the Impact o Orgac Electroc Devces Dspersty s a easure of the olecular Weght Dstrbuto Dspersty of a Polymer: Ð w, The: Ð Because: 1 w Dspersty Ca Be Thought of Terms of the Stadard Devato from the Average: 1 2 w σ = 1 = Ð 1 1 [ ] 2 Narrowg the Dspersty (.e., mzg the Stadard Devato ) of the Polymer Chas, Icreases the Ablty of the Polymer to Acheve a Hgher Degree of Crystallty. Ths, tur, Icreases the Charge Trasport Ablty of the Polymer the Sold State.
12 Summary ad Prevew of the Next Lecture Orgac electroc materals are molecular solds that cota covalet bods ad are composed maly of carbo, hydroge, ad oxyge. They cota a hgh degree of π-cojugato alog the ma cha of the molecules They ca form crystalle domas o the order of mllmeters, mcrometers, or aometers. The structure of the molecule dctates ts optoelectroc propertes. Orgac semcoductors The materals ca ether be small molecules or polymerc, ad they ca preferetally trasport holes (p-type) or electros (-type). The selecto of the fuctoal groups alog the polymer backboe ad the degree of cojugato affect the optoelectroc propertes of the materal. Furthermore, sde chas geerally are used to crease the solublty of the semcoductor soluto; however, they ca mpact the optoelectroc propertes as well. The umberaverage molecular weght, weght-average molecular weght, ad the dspersty of a polymer ca mpact the crystallty ad optoelectroc propertes of the materals. Next Tme: The Sythess of Oft-Used Polymer Semcoductors
Organic Electronic Devices
Orgac Electroc Devces Week 1: Semcoductor Sythess ad Characterzato Tutoral 1.1: Homework Solutos Brya W. Boudours Chemcal Egeerg Purdue Uversty 1 Problem Statemet Solutos to Problem 1 Draw the chemcal
More informationBlock-Based Compact Thermal Modeling of Semiconductor Integrated Circuits
Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud
More informationF A. Review1 7/1/2014. How to prepare for exams. Chapter 10 - GASES PRESSURE IS THE FORCE ACTING ON AN OBJECT PER UNIT AREA MEASUREMENT OF PRESSURE
How to prepare for exams 1. Uderstad EXAMLES chapter(s). Work RACICE EXERCISES 3. Work oe problem from each class of problems at ed of chapter 4. Aswer as may questos as tme permts from text web: www.prehall.com/brow
More informationCarbonyl Groups. University of Chemical Technology, Beijing , PR China;
Electroc Supplemetary Materal (ESI) for Physcal Chemstry Chemcal Physcs Ths joural s The Ower Socetes 0 Supportg Iformato A Theoretcal Study of Structure-Solublty Correlatos of Carbo Doxde Polymers Cotag
More informationAbsorption in Solar Atmosphere
Absorpto Solar Atmosphere A black body spectrum emtted from solar surface causes exctato processes o atoms the solar atmosphere. Ths tur causes absorpto of characterstc wavelegths correspodg to those atoms
More informationOrganic Electronic Devices
Organic Electronic Devices Week 5: Organic Light-Emitting Devices and Emerging Technologies Lecture 5.5: Course Review and Summary Bryan W. Boudouris Chemical Engineering Purdue University 1 Understanding
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationChapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:
Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:
More informationDepartment of Mechanical Engineering ME 322 Mechanical Engineering Thermodynamics. Ideal Gas Mixtures. Lecture 31
Departet of echacal Egeerg E 322 echacal Egeerg Therodyacs Ideal Gas xtures Lecture 31 xtures Egeerg Applcatos atural gas ethae, ethae, propae, butae, troge, hydroge, carbo doxde, ad others Refrgerats
More informationEllipsometry Overview
llpsometry Overvew ~ R Δ p ρ = ta( Ψ) e = ~ Rs ñ(λ) = (λ) + k(λ) ε = ñ 2 p-plae s-plae p-plae plae of cdece s-plae llpsometry buldg-blocks Lght ad Polarzato Materals / Optcal Costats Iteracto of Lght wth
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More informationModule 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law
Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationThe E vs k diagrams are in general a function of the k -space direction in a crystal
vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal
More informationECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013
ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport
More informationn -dimensional vectors follow naturally from the one
B. Vectors ad sets B. Vectors Ecoomsts study ecoomc pheomea by buldg hghly stylzed models. Uderstadg ad makg use of almost all such models requres a hgh comfort level wth some key mathematcal sklls. I
More information1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.
Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationLECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR
amplg Theory MODULE II LECTURE - 4 IMPLE RADOM AMPLIG DR. HALABH DEPARTMET OF MATHEMATIC AD TATITIC IDIA ITITUTE OF TECHOLOGY KAPUR Estmato of populato mea ad populato varace Oe of the ma objectves after
More informationPeriodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon
EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc
More informationStationary states of atoms and molecules
Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationMean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationSolid State Device Fundamentals
Sold State Devce Fudametals 9 polar jucto trasstor Sold State Devce Fudametals 9. polar Jucto Trasstor NS 345 Lecture ourse by Alexader M. Zatsev alexader.zatsev@cs.cuy.edu Tel: 718 98 81 4N101b Departmet
More informationOvercoming Limitations of Sampling for Aggregation Queries
CIS 6930 Approxmate Quer Processg Paper Presetato Sprg 2004 - Istructor: Dr Al Dobra Overcomg Lmtatos of Samplg for Aggregato Queres Authors: Surajt Chaudhur, Gautam Das, Maur Datar, Rajeev Motwa, ad Vvek
More informationQuantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.?
Ch 4. Statstcs 4.1 Quattatve aalyss requres : soud kowledge of chemstry : possblty of terfereces WHY do we eed to use STATISTICS Aal. Chem.? ucertaty ests. wll we accept ucertaty always? f ot, from how
More informationAnalyzing Two-Dimensional Data. Analyzing Two-Dimensional Data
/7/06 Aalzg Two-Dmesoal Data The most commo aaltcal measuremets volve the determato of a ukow cocetrato based o the respose of a aaltcal procedure (usuall strumetal). Such a measuremet requres calbrato,
More informationSection l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58
Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488
More informationMulti Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.
It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationSpreadsheet Problem Solving
1550 1500 CO Emmssos for the US, 1989 000 Class meetg #6 Moday, Sept 14 th CO Emssos (MMT Carbo) y = 1.3x 41090.17 1450 1400 1350 1300 1989 1990 1991 199 1993 1994 1995 1996 1997 1998 1999 000 Year GEEN
More informationIS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model
IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software
More informationStatistics MINITAB - Lab 5
Statstcs 10010 MINITAB - Lab 5 PART I: The Correlato Coeffcet Qute ofte statstcs we are preseted wth data that suggests that a lear relatoshp exsts betwee two varables. For example the plot below s of
More informationCHEMICAL EQUILIBRIA BETWEEN THE ION EXCHANGER AND GAS PHASE. Vladimir Soldatov and Eugeny Kosandrovich
CHEMICAL EQUILIBRIA BETWEEN THE ION EXCHANGER AND GAS PHASE Vladmr Soldatov ad Eugey Kosadrovch Isttute of Physcal Orgac Chemstry Natoal Academy of Sceces of Belarus, 13, Surgaov St, Msk 2272, Rep. of
More informationTransforming Numerical Methods Education for the STEM Undergraduate Torque (N-m)
Regresso Trasformg Numercal Methods Educato for the STEM udergraduate Applcatos Mousetrap Car Torsoal Stffess of a Mousetrap Sprg 0.4 Torque (N-m 0.3 0. T k 0 k1 θ 0.1 0.5 1 1.5 θ (radas 1 Stress vs Stra
More informationWe have already referred to a certain reaction, which takes place at high temperature after rich combustion.
ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a
More information= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality
UCLA STAT Itroducto to Statstcal Methods for the Lfe ad Health Sceces Istructor: Ivo Dov, Asst. Prof. of Statstcs ad Neurology Teachg Assstats: Fred Phoa, Krste Johso, Mg Zheg & Matlda Hseh Uversty of
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationKernel-based Methods and Support Vector Machines
Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg
More informationChemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
Chemstry 163B Itroducto to Multcompoet Systems ad Partal Molar Quattes 1 the problem of partal mmolar quattes mx: 10 moles ethaol C H 5 OH (580 ml) wth 1 mole water H O (18 ml) get (580+18)=598 ml of soluto?
More informationChapter -2 Simple Random Sampling
Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal
More informationChapter -2 Simple Random Sampling
Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationUNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES. Midterm I
UNIVERSITY OF CALIFORNIA, BERKELEY EPARTMENT OF ELECTRICAL ENGINEERING AN COMPUTER SCIENCES EECS 130 Professor Chemg Hu Fall 009 Mdterm I Name: Closed book. Oe sheet of otes s allowed. There are 8 pages
More informationSome Notes on the Probability Space of Statistical Surveys
Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty
More informationCOV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.
c Pogsa Porchawseskul, Faculty of Ecoomcs, Chulalogkor Uversty olato of costat varace of s but they are stll depedet. C,, he error term s sad to be heteroscedastc. c Pogsa Porchawseskul, Faculty of Ecoomcs,
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationChemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
Chemstry 163 Itroducto to Multcompoet Systems ad Partal Molar Quattes 1 the problem of partal mmolar quattes mx: 10 moles ethaol C H 5 OH (580 ml) wth 1 mole water H O (18 ml) get (580+18)=598 ml of soluto?
More informationSoft condensed matter
oft codesed matter aterals whch are easly deformable by exteral stresses, electrc or magetc felds, or eve by thermal fluctuatos; typcally possess structures whch are much larger tha atomc or molecular
More informationLecture #13. Diode Current due to Generation
Lecture #13 Juctos OUTLINE reverse bas curret devatos from deal behavor small-sgal model Readg: Chaters 6. & 7 EE13 Lecture 13, Slde 1 Dode Curret due to Geerato If a electro-hole ar s geerated (e.g. by
More informationRecall MLR 5 Homskedasticity error u has the same variance given any values of the explanatory variables Var(u x1,...,xk) = 2 or E(UU ) = 2 I
Chapter 8 Heterosedastcty Recall MLR 5 Homsedastcty error u has the same varace gve ay values of the eplaatory varables Varu,..., = or EUU = I Suppose other GM assumptos hold but have heterosedastcty.
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More informationThe theoretical background of
he theoretcal backgroud of -echologes he theoretcal backgroud of FactSage he followg sldes gve a abrdged overvew of the ajor uderlyg prcples of the calculatoal odules of FactSage. -echologes he bbs Eergy
More informationOPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK
23rd World Gas Coferece, Amsterdam 2006 OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK Ma author Tg-zhe, Ne CHINA ABSTRACT I cha, there are lots of gas ppele etwork eeded to be desged ad costructed owadays.
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationCan we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?
Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationThe conformations of linear polymers
The coformatos of lear polymers Marc R. Roussel Departmet of Chemstry ad Bochemstry Uversty of Lethbrdge February 19, 9 Polymer scece s a rch source of problems appled statstcs ad statstcal mechacs. I
More information2C09 Design for seismic and climate changes
2C09 Desg for sesmc ad clmate chages Lecture 08: Sesmc aalyss of elastc MDOF systems Aurel Strata, Poltehca Uversty of Tmsoara 06/04/2017 Europea Erasmus Mudus Master Course Sustaable Costructos uder atural
More informationX-ray vortices from nonlinear inverse Thomson scattering
JLab semar 8/7/6 X-ray vortces from olear verse Thomso scatterg Yoshtaka Tara Natoal Isttute of Advaced Idustral Scece ad Techology (AIST) Vstg scetst: Msssspp State Uversty ad Jefferso Lab. Optcal vortex
More informationBayes (Naïve or not) Classifiers: Generative Approach
Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg
More informationLecture 2 - What are component and system reliability and how it can be improved?
Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected
More informationUnsupervised Learning and Other Neural Networks
CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all
More informationLecture 3. Sampling, sampling distributions, and parameter estimation
Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called
More informationLecture 8: Linear Regression
Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE
More informationCloud formation by condensation
Cloud formato by codesato U ( r) SV r m RT r: radus of droplet : surface teso of lqud ( ) - 80 - N/m : desty of lqud ( ) (r): vapor pressure over covex surface e Clouds develop from codesato of water vapor
More informationArithmetic Mean Suppose there is only a finite number N of items in the system of interest. Then the population arithmetic mean is
Topc : Probablty Theory Module : Descrptve Statstcs Measures of Locato Descrptve statstcs are measures of locato ad shape that perta to probablty dstrbutos The prmary measures of locato are the arthmetc
More informationParameter, Statistic and Random Samples
Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose
More informationChapter 3 Sampling For Proportions and Percentages
Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys
More informationLecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1,
Lecture (Part II) Materals Covered Ths Lecture: Chapter 2 (2.6 --- 2.0) The umber of ways of parttog dstct obects to dstct groups cotag, 2,, obects, respectvely, where each obect appears exactly oe group
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationCentroids & Moments of Inertia of Beam Sections
RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol
More informationSystematic Selection of Parameters in the development of Feedforward Artificial Neural Network Models through Conventional and Intelligent Algorithms
THALES Project No. 65/3 Systematc Selecto of Parameters the developmet of Feedforward Artfcal Neural Network Models through Covetoal ad Itellget Algorthms Research Team G.-C. Vosakos, T. Gaakaks, A. Krmpes,
More informationExample: Multiple linear regression. Least squares regression. Repetition: Simple linear regression. Tron Anders Moger
Example: Multple lear regresso 5000,00 4000,00 Tro Aders Moger 0.0.007 brthweght 3000,00 000,00 000,00 0,00 50,00 00,00 50,00 00,00 50,00 weght pouds Repetto: Smple lear regresso We defe a model Y = β0
More informationStudy on a Fire Detection System Based on Support Vector Machine
Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 Sesors & Trasducers 04 by IFSA Publshg, S. L. http://www.sesorsportal.com Study o a Fre Detecto System Based o Support Vector Mache Ye Xaotg, Wu
More informationTHE TRUNCATED RANDIĆ-TYPE INDICES
Kragujeac J Sc 3 (00 47-5 UDC 547:54 THE TUNCATED ANDIĆ-TYPE INDICES odjtaba horba, a ohaad Al Hossezadeh, b Ia uta c a Departet of atheatcs, Faculty of Scece, Shahd ajae Teacher Trag Uersty, Tehra, 785-3,
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationLinear Regression with One Regressor
Lear Regresso wth Oe Regressor AIM QA.7. Expla how regresso aalyss ecoometrcs measures the relatoshp betwee depedet ad depedet varables. A regresso aalyss has the goal of measurg how chages oe varable,
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationChapter 11 The Analysis of Variance
Chapter The Aalyss of Varace. Oe Factor Aalyss of Varace. Radomzed Bloc Desgs (ot for ths course) NIPRL . Oe Factor Aalyss of Varace.. Oe Factor Layouts (/4) Suppose that a expermeter s terested populatos
More informationChapter 8. Inferences about More Than Two Population Central Values
Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha
More informationMULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov
Iteratoal Boo Seres "Iformato Scece ad Computg" 97 MULTIIMNSIONAL HTROGNOUS VARIABL PRICTION BAS ON PRTS STATMNTS Geady Lbov Maxm Gerasmov Abstract: I the wors [ ] we proposed a approach of formg a cosesus
More informationGeometric Suffix Tree: A New Index Structure for Protein 3-D Structures
CPM 2006 Geometrc Suffx ree: A New Idex Structure for Prote 3-D Structures etsuo Shbuya Huma Geome Ceter, Isttute of Medcal Scece, Uversty of okyo oday's alk Backgrouds Prote structures Suffx rees Geometrc
More informationSTK3100 and STK4100 Autumn 2017
SK3 ad SK4 Autum 7 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Sectos 4..5, 4.3.5, 4.3.6, 4.4., 4.4., ad 4.4.3 Sectos 5.., 5.., ad 5.5. Ørulf Borga Deartmet of Mathematcs
More informationLecture 17. Membrane Separations [Ch. 14]
Lecture 17. embrane Separatons [Ch. 14] embrane Separaton embrane aterals embrane odules Transport n embranes -Bulk flow - Lqud dffuson n pores - Gas dffuson - onporous membranes embrane Separaton Separaton
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationLecture 4 Sep 9, 2015
CS 388R: Radomzed Algorthms Fall 205 Prof. Erc Prce Lecture 4 Sep 9, 205 Scrbe: Xagru Huag & Chad Voegele Overvew I prevous lectures, we troduced some basc probablty, the Cheroff boud, the coupo collector
More informationChapter Statistics Background of Regression Analysis
Chapter 06.0 Statstcs Backgroud of Regresso Aalyss After readg ths chapter, you should be able to:. revew the statstcs backgroud eeded for learg regresso, ad. kow a bref hstory of regresso. Revew of Statstcal
More informationMedian as a Weighted Arithmetic Mean of All Sample Observations
Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of
More informationEconomic drivers. Input and output prices Adjustment under ITQs
Ecoomc drvers Iput ad output prces Adjustmet uder ITQs Outle Questo beg examed How are fshers lely to adjust ther fshg operatos uder ITQs? Methodologes to loo at the ssue Cost fuctos Proft fuctos Case
More informationTRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM.
Abbas Iraq Joural of SceceVol 53No 12012 Pp. 125-129 TRIANGULAR MEMBERSHIP FUNCTIONS FOR SOLVING SINGLE AND MULTIOBJECTIVE FUZZY LINEAR PROGRAMMING PROBLEM. Iraq Tarq Abbas Departemet of Mathematc College
More informationSimple Linear Regression
Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationSemiconductor Device Physics
1 Semcoductor evce Physcs Lecture 7 htt://ztomul.wordress.com 0 1 3 Semcoductor evce Physcs Chater 6 Jucto odes: I-V Characterstcs 3 Chater 6 Jucto odes: I-V Characterstcs Qualtatve ervato Majorty carrers
More informationA Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies
ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for
More informationAnalysis of Variance with Weibull Data
Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad
More informationPREDICTION OF VAPOR-LIQUID EQUILIBRIA OF BINARY MIXTURES USING QUANTUM CALCULATIONS AND ACTIVITY COEFFICIENT MODELS
Joural of Chemstry, Vol. 47 (5), P. 547-55, 9 PREDICTIO OF VAPOR-LIQUID EQUILIBRIA OF BIARY MIXTURES USIG QUATUM CALCULATIOS AD ACTIVITY COEFFICIET MODELS Receved May 8 PHAM VA TAT Departmet of Chemstry,
More informationProf. YoginderVerma. Prof. Pankaj Madan Dean- FMS Gurukul Kangri Vishwavidyalaya, Haridwar
Paper:5, Quattatve Techques or aagemet Decsos odule:5 easures o Cetral Tedecy: athematcal Averages (A, G, H) Prcpal Ivestgator Co-Prcpal Ivestgator Paper Coordator Cotet Wrter Pro. S P Basal Vce Chacellor
More information