Parameterisation of Cumulus Convection

Size: px
Start display at page:

Download "Parameterisation of Cumulus Convection"

Transcription

1 Parametersaton of Cmls Convecton Dmtr V. Mronov German Weather Servce, Research and Development, FE14, Offenbach am Man, Germany COSMO-CLM Tranng Corse, Langen, Germany, Febrary 2012

2 Otlne Cmls convecton and the need for parametersatons Convecton parametersaton schemes Mass-flx schemes The COSMO-model convecton parametersaton scheme(s) Crtcal Isses

3 Phenomenology Deep Cmls (ITCZ) A great varety of convectve clods far and wde Shallow Cmls (Trade wnds) Strats, stratocmls (Sb-tropcs) P B L

4 Phenomenology (cont d) Strats Broken stratocmls Cmls Berln-St. Petersbrg, 28 Agst 2007.

5 Phenomenology (cont d) Stratocmls Cmls Berln-St. Petersbrg, 28 Agst 2007.

6 Phenomenology (cont d) Spercell near Alvo, Nebraska, USA, 13 Jne (

7 Phenomenology (cont d) Spercell off Brlegh, Astrala, 31 December (

8 The Need for a Parametersaton Convecton s a sb-grd scale phenomenon. It cannot be explctly compted (resolved) by an atmospherc model. Hence, t shold be parametersed. x y

9 Recall... what a convecton parametersaton shold do (t s not a mystery, t s jst a model) Transport eqaton for a generc qantty ( ) x S x x t =... SGS flx dvergence Sorce terms Splttng of the SGS flx dvergence and of the sorce term ( ) other x conv x other conv S S x x x t =...

10 What a convecton parametersaton shold do (cont d) Temperatre and specfc-hmdty eqatons T t ( T ) x T = x conv T x trb R x rad L( c e) conv L( c e) grd scale q t ( q) x q = x conv q x trb ( e c) conv ( e c) grdscale Here, L s the specfc heat of vaporsaton, e s the rate of evaporaton, and c s the rate of condensaton. Apart from mxng (redstrbton of heat and mostre), convecton prodces precptaton

11 Convecton Parametersaton Schemes Mostre convergence schemes (e.g. Ko 1965, 1974) Convectve adjstment schemes (e.g. Betts 1986, Betts and Mller 1986) Mass-flx schemes (e.g. Arakawa and Schbert 1974; Bogealt 1985; Tedtke 1989; Gregory and Rowntree 1990; Kan and Frtsch, 1990, 1993, Kan 2004; Emanel 2001; Bechtold et al. 2001, 2004)

12 Mass-Flx Schemes. Basc Featres A trple top-hat decomposton, =1, = e d e e d d a a a a a a, d and e refer to the pdraght, downdraght and the envronment, respectvely, and a s the fractonal area coverage. In terms of the probabltes (δ s the Drac delta fncton) Vertcal flx of a flctatng qantty ). ( ) ( ) ( ) (, e e d d e e d d P P P P P P P = = δ δ δ ), ( ) ( ) ( ) )( ( ) )( ( ) )( ( M M M w w a w w a w w a w e e d d e e e d d d = = ρ ρ ρ ρ s the pdraght mass flx (smlarly for the downdraght and for the envronment). ( w) w a M = ρ

13 Mass-Flx Schemes. Basc Featres (cont d) A top-hat representaton of a flctatng qantty Updraght Only coherent top-hat part of the sgnal s acconted for Envronment After M. Köhler (2005)

14 Mass-Flx Schemes. Basc Featres (cont d) Assmpton 1: a mean over the envronment s eqal to to a horzontal mean (over a grd box), 1. 1, << << = d e a and a Assmpton 2: convecton s n a qas-steady state, ( ). 0, 0 = = a z w t a z w t Then, vertcal flx of a flctatng qantty n mass-flx approxmaton s gven by [ ] M M M M w d d d ) ( 1 = ρ

15 Mass-Flx Schemes. Basc Featres (cont d) The eqatons for convectve pdraghts ( ) ( ) ( ),,,, p G c D l z l M c D q q E z q M c L D s s E z s M D E z M ρ ρ ρ ρ = = = = where s s the dry statc energy, q s the specfc hmdty, l s the specfc clod condensate content, E and D are the rates of mass entranment and detranment per nt length, c s the rate of condensaton n the pdraghts, and G p s the rate of converson from clod condensate to precptaton.

16 The COSMO-Model Convecton Parametersaton Schemes Basc Namelst settng: lphys=.true., lconv=.true. Namelst settng: type_conv=0. Tedtke (1989) mass-flx scheme, defalt n COSMO-EU (called every 4th tme step,.e. every 264 s). Namelst settng: type_conv=1. Kan and Frtsch (1990) mass-flx scheme, optonal n COSMO-EU. Namelst settng: type_conv=3. Shallow convecton scheme [bascally, a smplfed Tedtke (1989) scheme that treats shallow non-precptaton convecton only and ncorporates a nmber of rather crde assmptons, e.g. on the convecton vertcal extent], defalt n COSMO-DE (called every 10th tme step,.e. every 250 s). The ECMWF-IFS scheme (Bechtold 2010) s mplemented nto GME (c/o Krstna Fröhlch); ths opton (type_conv=2) s not yet avalable n the COSMO model (Peter Brockhas et al., Ulrch Schättler).

17 The Tedtke (1989) Mass-Flx Convecton Scheme A set of ordnary dfferental eqatons (n z) for convectve pdraghts and downdraghts s solved (entranng-detranng plme model) Shallow, penetratve and md-level convecton are dscrmnated Trblent and organsed entranment and detranment are consdered Trblent entranment and detranment: E =εm and D = δm, ε and δ beng constants that are dfferent for dfferent types of convecton (smlarly for downdraghts) Organsed entranment s proportonal to the large-scale mostre convergence (dv of resolved scale mostre flx) and s appled n the lower part of convectve clod p to the level of strongest vertcal ascent Organsed detranment s appled above the clod top, where clod condensate evaporates nstantaneosly (snce Jly 2008, detraned clod condensate s collected and passed to other COSMO-model rotnes for frther processng) Convectve clod base and convectve clod top are determned sng the parcel method, a test parcel pertrbed wth respect to ts boyancy orgnates near the srface Updraght mass flx at the clod base M b s lnked to the sb-clod layer mostre convergence (dv of the SGS and resolved scale mostre flxes ntegrated from the srface to the clod base) Downdraght mass flx at the level of free snkng (where the downdraght orgnates) s proportonal to M b No mxed phase clod condensate s ether water or ce dependng on whether the temperatre s above or below the freezng pont (mxed phase s ntrodced n Jly 2008) Hghly smplfed mcrophyscs: G p l (the rate of converson from clod condensate to precptaton s proportonal to the amont of clod condensate) Evaporaton of convectve precptaton n the sb-clod layer s consdered Fnally, convectve tendences n T, q, and v, and convectve precptaton rate are compted

18 The Tedtke (1989) Mass-Flx Convecton Scheme (cont d) Organsed detranment of clod ar Converson of clod condensate to precptaton Organsed entranment of envronment ar de to mostre convergence Trblent detranment of clod ar Trblent entranment of envronment ar Assmptons of the T89 scheme are many and vared! Mostre convergence n the sb-clod layer Evaporaton of precptaton n the sb-clod layer

19 Crtcal Isses Possble doble-contng of energy-contanng scales Drnal cycle of convecton Coplng of cmls convecton scheme wth other physcal parametersaton schemes of the COSMO model...

20 Possble doble-contng of energy-contanng scales Convectve precptaton: model vs. observatons Convectve precptaton Possble doble contng de to the assmpton a <<1 Precptaton over Germany, mean over Aprl COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton.

21 Possble doble-contng of energy-contanng scales (cont d) SON 2007 DJF Mxed phase ntrodced MAM 2008 JJA 2008 Precptaton over Germany, September 2007 throgh Agst COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton. [Mxed-phase snce Jly 2008.]

22 Possble doble-contng of energy-contanng scales (cont d) SON 2008 DJF MAM 2009 JJA 2009 Precptaton over Germany, September 2008 throgh Agst COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton.

23 Possble doble-contng of energy-contanng scales (cont d) SON 2009 DJF MAM 2010 JJA 2010 Precptaton over Germany, September 2009 throgh Agst COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton.

24 Possble doble-contng of energy-contanng scales (cont d) SON 2010 DJF MAM 2011 JJA 2011 Precptaton over Germany, September 2010 throgh Agst COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton.

25 Drnal cycle of convecton Srface latent heat flx Srface sensble heat flx Precptaton Typcal daly evolton of srface latent (thck sold lne) and sensble (thn sold lne) heat flx, and precptaton (dotted lne) drng a mdlattde or tropcal smmer day over reasonably hmd land (from Bechtold 2010)

26 Drnal cycle of convecton (cont d) Observatons precptaton (mm/h) GME Maxmm of convectve actvty (precptaton) closely follows srface flxes and occrs too early, possbly de to d/dt=0 ( beng a qantty treated by convecton scheme) local tme (h) Drnal cycle of precptaton n the Rondôna area n Febrary. GME forecasts vs. LBA 1999 observatonal data (Slva Das et al. 2002). The model crves show area-mean vales, emprcal crve shows pont measrements. Both nmercal and emprcal crves represent monthly-mean vales.

27 Coplng of convecton scheme wth other parametersaton schemes Inconsstent treatment of fractonal clod cover, convectve clod cover s nsenstve to mxng rate Trblence Dvergence of SGS flxes (mxng), fractonal clod cover sng statstcal clod scheme No nteracton between trblent and convectve mxng, no resolton senstvty of convectve mxng SGS Clod Cover Fractonal clod cover sng relatve hmdty scheme Cmls Convecton Dvergence of SGS flxes (mxng), convectve precptaton, fractonal cover of convectve clods Mcrophyscs Grd-scale precptaton, resolved scale amont of clod condensate Althogh a feedback of evaporaton/condensaton de to convecton on the resolved scale amont of clod condensate s now ntrodced, a flly consstent treatment s not yet acheved. Grd-Scale Satraton Adjstment Evaporaton/condensaton sng resolved scale qanttes No nteracton between grd-scale and convectve precptaton

28 Otlook Exstng convecton schemes s dffclt to mprove... However, a better coplng of cmls convecton scheme wth other parametersaton schemes of the COSMO model shold be attempted Longer term prospects Relax crcal assmptons of the exstng mass-flx schemes (no tme-rate-of-change terms, small area fracton of convectve clods); the model of Lappen and Randall (2001) holds promse Acheve a nfed descrpton of shallow-convecton and trblence (see Mronov 2009, for dscsson) EU COST Acton ES0905 Basc Concepts for Convecton Parameterzaton n Weather Forecast and Clmate Models (

29 References Arakawa, A., 2004: The cmls parameterzaton problem: past, present, and ftre. J. Clmate, 17, Bechtold, P., 2010: Atmospherc most convecton. ECMWF Lectre Notes, 77 pp. ( Emanel, K. A., 1994: Atmospherc Convecton. Oxford Unv. Press, Oxford, 580 pp. Fedorovch, E., R. Rotnno, and B. Stevens (Eds.), 2004: Atmospherc Trblence and Mesoscale Meteorology. Cambrdge Unv. Press, Cambrdge, 280 pp. Frank, W. M., 1983: The cmls parameterzaton problem. Mon. Weather Rev., 111, Hoze, R. A., 1993: Clod Dynamcs. Academc Press, San Dego, etc., 573 pp. Mronov, D. V., 2009: Trblence n the lower troposphere: second-order closre and mass-flx modellng frameworks. Interdscplnary Aspects of Trblence, Lect. Notes Phys., 756, W. Hllebrandt and F. Kpka, Eds., Sprnger-Verlag, Berln, Hedelberg, Plant, R. S., 2010: A revew of the theoretcal bass for blk mass flx convectve parameterzaton. Atmos. Chem. Phys., 10, Smth, R. K., 2000: The role of cmls convecton n hrrcanes and ts representaton n hrrcane models. Rev. Geophys., 38, Stensrd, D. J., 2007: Parameterzaton Schemes: Keys to Understandng Nmercal Weather Predcton Models. Cambrdge Unv. Press, Cambrdge, 478 pp. Stevens, B., 2005: Atmospherc most convecton. Ann. Rev. Earth Planet. Sc., 33, Tedtke, M., 1988: The Parameterzaton of Most Processes. Part 2: Parameterzaton of Cmls Convecton. Meteorologcal Tranng Corse, Lectre Seres, Eropean Centre for Medm-Range Weather Forecasts, Readng, U.K., 78 pp.

30 References (cont d) Arakawa, A., and W. H. Schbert, 1974: Interacton of a cmls clod ensemble wth the large-scale envronment, Part I. J. Atmos. Sc., 31, Bechtold, P., E. Bazle, F. Gchard, P. Mascart, and E. Rchard, 2001: A mass-flx convecton scheme for regonal and global models. Qart. J. Roy. Meteorol. Soc., 127, Bechtold, P., J.-P. Chaborea, A. Beljaars, A. K. Betts, M. Köhler, M. Mller, and J.-L. Redelsperger, 2004: The smlaton of the drnal cycle of convectve precptaton over land n a global model. Qart. J. Roy. Meteorol. Soc., 130, Betts, A. K., 1986: A new convectve adjstment scheme. Part I: Observatonal and theoretcal bass. Non-precptatng cmls convecton and ts parameterzaton. Qart. J. Roy. Meteorol. Soc., 112, Betts, A. K., and M. J. Mller, 1986: A new convectve adjstment scheme. Part II: Sngle colmn tests sng GATE wave, BOME, ATE and arctc ar-mass data sets. Qart. J. Roy. Meteorol. Soc., 112, Bogealt, P., 1985: A smple parameterzaton of the large-scale effects of cmls convecton. Mon. Weather Rev., 113, Emanel, K. A., 2001: A scheme for representng cmls convecton n large-scale models. J. Atmos. Sc., 48, Gregory, D., and P. R. Rowntree, 1990: A mass flx convecton scheme wth representaton of clod ensemble characterstcs and stablty-dependent closre. Mon. Weather Rev., 118, Kan, J. S., 2004: The Kan-Frtsch convecton parameterzaton: an pdate. J. Appl. Meteorol., 43, Kan, J. S., and J. M. Frtsch, 1990: A one-dmensonal entranng/detranng plme model and ts applcaton n convectve parameterzaton. J. Atmos. Sc., 47, Kan, J. S., and J. M. Frtsch, 1993: Convectve parameterzaton for mesoscale models: the Kan-Frtsch scheme. The Representaton of Cmls Convecton n Nmercal Models, Meteorol. Monogr. No. 24, Amer. Meteor. Soc., Ko, H. L., 1965: On formaton and ntensfcaton of tropcal cyclones throgh latent heat release by cmls convecton. J. Atmos. Sc., 22, Ko, H. L., 1974: Frther stdes of the parameterzaton of the nflence of cmls convecton on large-scale flow. J. Atmos. Sc., 31, Tedtke, M., 1989: A comprehensve mass flx scheme for cmls parameterzaton n large-scale models. Mon. Weather Rev., 117,

31 Thanks for yor attenton! COSMO-CLM Tranng Corse, Langen, Germany, Febrary 2012

32 Geändertes Tedtke-Konvektonsschema Detraned-Wolkenwasser nd Detraned-Wolkenes werden als Tendenzen von q_c nd q_ den anderen Parametrserngsschemata übergeben Wasser-Es Mschng exstert m Temperatrberech zwschen 0 C nd -23 C Verbesserte Kopplng des Konvektonsschemas mt den anderen Parametrserngsschemata Hochrechende Konvekton wrd etwas gebremst

33 Possble doble-contng of energy-contanng scales (cont d) SON 2006 DJF MAM 2007 JJA 2007 Precptaton over Germany, September 2006 throgh Agst COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton.

34 Possble doble-contng of energy-contanng scales (cont d) SON 2008 DJF Precptaton over Germany, September 2008 throgh Febrary COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton.

35 Possble doble-contng of energy-contanng scales (cont d) JJA 2007 JJA 2008 Precptaton over Germany, JJA 2007 verss JJA COSMO-EU (ca. 7 km mesh sze) vs. observatons. Lnes - total precptaton, hatched areas - convectve precptaton. In JJA 2008 Mod < Obs? (*) Changes were ntrodced nto the T89 scheme n Jly (*) Smmer 2008 was dry.

36 Parametersaton of Cmls Convecton Dmtr V. Mronov German Weather Servce, Research and Development, FE14, Offenbach am Man, Germany COSMO-CLM Tranng Corse Langen, Germany, Febrary 2012

37 Thanks for yor attenton!

How To Understand The Results Of The German Meris Cloud And Water Vapour Product

How To Understand The Results Of The German Meris Cloud And Water Vapour Product Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

More information

Chapter 11 CLOUD DYNAMICS AND CHEMISTRY

Chapter 11 CLOUD DYNAMICS AND CHEMISTRY Chapter 11 CLOUD DYNAMICS AND CHEMISTRY Shawn J. Roselle * and Francs S. Bnkowsk ** Atmospherc Modelng Dvson Natonal Exposure Research Laboratory U.S. Envronmental Protecton Agency Research Trangle Park,

More information

1. Introduction to CFD

1. Introduction to CFD . Introdcton to CFD Ths s a qck ntrodcton to the basc concepts nderlyng CFD. The concepts are llstrated by applyng them to a smple D example. We dscss the followng topcs brefly:. Applcatons of CFD. The

More information

The difference between voltage and potential difference

The difference between voltage and potential difference Slavko Vjevć 1, Tonć Modrć 1 and Dno Lovrć 1 1 Unversty of Splt, Faclty of electrcal engneerng, mechancal engneerng and naval archtectre Splt, Croata The dfference between voltage and potental dfference

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation of Deutsch's Algorithm Using Mathcad Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"

More information

Lecture 2: Single Layer Perceptrons Kevin Swingler

Lecture 2: Single Layer Perceptrons Kevin Swingler Lecture 2: Sngle Layer Perceptrons Kevn Sngler [email protected] Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses

More information

Optimal Control Approach to Production Systems. with Inventory-Level-Dependent Demand

Optimal Control Approach to Production Systems. with Inventory-Level-Dependent Demand Optmal Control Approach to Prodcton Systems wth Inventory-Level-Dependent Demand Egene Khmelntsky and Ygal Gerchak Department of Indstral Engneerng, Tel-Avv Unversty, Tel-Avv 69978, Israel Fax: 97--647669,

More information

Finite difference method

Finite difference method grd ponts x = mesh sze = X NÜÆ Fnte dfference method Prncple: dervatves n the partal dfferental eqaton are approxmated by lnear combnatons of fncton vales at the grd ponts 1D: Ω = (0, X), (x ), = 0,1,...,

More information

Frank and Charles Cohen Department of Meteorology The Pennsylvania State University University Park, PA, 16801 -U.S.A.

Frank and Charles Cohen Department of Meteorology The Pennsylvania State University University Park, PA, 16801 -U.S.A. 376 THE SIMULATION OF TROPICAL CONVECTIVE SYSTEMS William M. Frank and Charles Cohen Department of Meteorology The Pennsylvania State University University Park, PA, 16801 -U.S.A. ABSTRACT IN NUMERICAL

More information

So far circuit analysis has been performed on single-

So far circuit analysis has been performed on single- Three phase systems ntrdctn S far crct analyss has been perfrmed n sngle- phase crcts,.e. there has been ne crct wth a nmber f dfferent vltage and crrent srces whch were nt synchrnsed n any prpsefl way.

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Extending Probabilistic Dynamic Epistemic Logic

Extending Probabilistic Dynamic Epistemic Logic Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

Multiple stage amplifiers

Multiple stage amplifiers Multple stage amplfers Ams: Examne a few common 2-transstor amplfers: -- Dfferental amplfers -- Cascode amplfers -- Darlngton pars -- current mrrors Introduce formal methods for exactly analysng multple

More information

For example, you might want to capture security group membership changes. A quick web search may lead you to the 632 event.

For example, you might want to capture security group membership changes. A quick web search may lead you to the 632 event. Audtng Wndows & Actve Drectory Changes va Wndows Event Logs Ths document takes a lghtweght look at the steps and consderatons nvolved n settng up Wndows and/or Actve Drectory event log audtng. Settng up

More information

RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.

RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo. ICSV4 Carns Australa 9- July, 007 RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) [email protected] Abstract

More information

A Review on the Uses of Cloud-(System-)Resolving Models

A Review on the Uses of Cloud-(System-)Resolving Models A Review on the Uses of Cloud-(System-)Resolving Models Jeffrey D. Duda Since their advent into the meteorological modeling world, cloud-(system)-resolving models (CRMs or CSRMs) have become very important

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Air Quality Monitoring Using Model: A Review

Air Quality Monitoring Using Model: A Review Internatonal Journal of Scence and Research (IJSR), Inda Onlne ISSN: 39-7064 Ar ualt Montorng Usng Model: A Revew Ukagwe, Sandra A., Osoka, Emmanuel C., Department of Chemcal Engneerng, Federal Unverst

More information

Traffic State Estimation in the Traffic Management Center of Berlin

Traffic State Estimation in the Traffic Management Center of Berlin Traffc State Estmaton n the Traffc Management Center of Berln Authors: Peter Vortsch, PTV AG, Stumpfstrasse, D-763 Karlsruhe, Germany phone ++49/72/965/35, emal [email protected] Peter Möhl, PTV AG,

More information

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching) Face Recognton Problem Face Verfcaton Problem Face Verfcaton (1:1 matchng) Querymage face query Face Recognton (1:N matchng) database Applcaton: Access Control www.vsage.com www.vsoncs.com Bometrc Authentcaton

More information

Conversion between the vector and raster data structures using Fuzzy Geographical Entities

Conversion between the vector and raster data structures using Fuzzy Geographical Entities Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye [email protected] [email protected] [email protected] Abstract - Stock market s one of the most complcated systems

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Calculating the high frequency transmission line parameters of power cables

Calculating the high frequency transmission line parameters of power cables < ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,

More information

Warsaw University of Technology. Faculty of Electrical Engineering

Warsaw University of Technology. Faculty of Electrical Engineering Warsaw Unversty of Technology Faclty of Electrcal Engneerng Insttte of Control and Indstral Electroncs Ph.D. Thess M. Sc. Marsz Cchowlas! Thess spervsor Prof. Dr Sc. Maran P. Kamerkowsk Warsaw, Poland

More information

Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform

Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform Usng Mean-Shft Trackng Algorthms for Real-Tme Trackng of Movng Images on an Atonomos Vehcle Testbed Platform Benjamn Gorry, Zezh Chen, Kevn Hammond 2, Andy Wallace 3, and Greg Mchaelson () Dept. of Compter

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao

More information

Sketching Sampled Data Streams

Sketching Sampled Data Streams Sketchng Sampled Data Streams Florn Rusu, Aln Dobra CISE Department Unversty of Florda Ganesvlle, FL, USA [email protected] [email protected] Abstract Samplng s used as a unversal method to reduce the

More information

Frequency Selective IQ Phase and IQ Amplitude Imbalance Adjustments for OFDM Direct Conversion Transmitters

Frequency Selective IQ Phase and IQ Amplitude Imbalance Adjustments for OFDM Direct Conversion Transmitters Frequency Selectve IQ Phase and IQ Ampltude Imbalance Adjustments for OFDM Drect Converson ransmtters Edmund Coersmeer, Ernst Zelnsk Noka, Meesmannstrasse 103, 44807 Bochum, Germany [email protected],

More information

the Manual on the global data processing and forecasting system (GDPFS) (WMO-No.485; available at http://www.wmo.int/pages/prog/www/manuals.

the Manual on the global data processing and forecasting system (GDPFS) (WMO-No.485; available at http://www.wmo.int/pages/prog/www/manuals. Gudelne on the exchange and use of EPS verfcaton results Update date: 30 November 202. Introducton World Meteorologcal Organzaton (WMO) CBS-XIII (2005) recommended that the general responsbltes for a Lead

More information

Damage detection in composite laminates using coin-tap method

Damage detection in composite laminates using coin-tap method Damage detecton n composte lamnates usng con-tap method S.J. Km Korea Aerospace Research Insttute, 45 Eoeun-Dong, Youseong-Gu, 35-333 Daejeon, Republc of Korea [email protected] 45 The con-tap test has the

More information

iavenue iavenue i i i iavenue iavenue iavenue

iavenue iavenue i i i iavenue iavenue iavenue Saratoga Systems' enterprse-wde Avenue CRM system s a comprehensve web-enabled software soluton. Ths next generaton system enables you to effectvely manage and enhance your customer relatonshps n both

More information

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA )

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA ) February 17, 2011 Andrew J. Hatnay [email protected] Dear Sr/Madam: Re: Re: Hollnger Canadan Publshng Holdngs Co. ( HCPH ) proceedng under the Companes Credtors Arrangement Act ( CCAA ) Update on CCAA Proceedngs

More information

IMPACT ANALYSIS OF A CELLULAR PHONE

IMPACT ANALYSIS OF A CELLULAR PHONE 4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng

More information

Statistical Methods to Develop Rating Models

Statistical Methods to Develop Rating Models Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

Imperial College London

Imperial College London F. Fang 1, C.C. Pan 1, I.M. Navon 2, M.D. Pggott 1, G.J. Gorman 1, P.A. Allson 1 and A.J.H. Goddard 1 1 Appled Modellng and Computaton Group Department of Earth Scence and Engneerng Imperal College London,

More information

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,

More information

Spatial development of urban road network traffic gridlock

Spatial development of urban road network traffic gridlock Transportaton Spatal deelopent of rban road networ traffc grdloc H.S. Q, Y.Y, Dan Ha Wang, *, Y.M. Be Receed: October 3, Resed: Febrary 4, Accepted: Jne 4 Abstract Abstract: Grdloc s an extree traffc state

More information

A Performance Analysis of View Maintenance Techniques for Data Warehouses

A Performance Analysis of View Maintenance Techniques for Data Warehouses A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao

More information

A Dynamic Load Balancing for Massive Multiplayer Online Game Server

A Dynamic Load Balancing for Massive Multiplayer Online Game Server A Dynamc Load Balancng for Massve Multplayer Onlne Game Server Jungyoul Lm, Jaeyong Chung, Jnryong Km and Kwanghyun Shm Dgtal Content Research Dvson Electroncs and Telecommuncatons Research Insttute Daejeon,

More information

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process Dsadvantages of cyclc TDDB47 Real Tme Systems Manual scheduler constructon Cannot deal wth any runtme changes What happens f we add a task to the set? Real-Tme Systems Laboratory Department of Computer

More information

A Crossplatform ECG Compression Library for Mobile HealthCare Services

A Crossplatform ECG Compression Library for Mobile HealthCare Services A Crossplatform ECG Compresson Lbrary for Moble HealthCare Servces Alexander Borodn, Yulya Zavyalova Department of Computer Scence Petrozavodsk State Unversty Petrozavodsk, Russa {aborod, yzavyalo}@cs.petrsu.ru

More information

Investigations on COSMO 2.8Km precipitation forecast

Investigations on COSMO 2.8Km precipitation forecast Investigations on COSMO 2.8Km precipitation forecast Federico Grazzini, ARPA-SIMC Emilia-Romagna Coordinator of physical aspects group of COSMO Outline Brief description of the COSMO-HR operational suites

More information

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features On-Lne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: [email protected]

More information

Inner core mantle gravitational locking and the super-rotation of the inner core

Inner core mantle gravitational locking and the super-rotation of the inner core Geophys. J. Int. (2010) 181, 806 817 do: 10.1111/j.1365-246X.2010.04563.x Inner core mantle gravtatonal lockng and the super-rotaton of the nner core Matheu Dumberry 1 and Jon Mound 2 1 Department of Physcs,

More information

Regression Models for a Binary Response Using EXCEL and JMP

Regression Models for a Binary Response Using EXCEL and JMP SEMATECH 997 Statstcal Methods Symposum Austn Regresson Models for a Bnary Response Usng EXCEL and JMP Davd C. Trndade, Ph.D. STAT-TECH Consultng and Tranng n Appled Statstcs San Jose, CA Topcs Practcal

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

An economical scale-aware parameterization for representing subgrid-scale clouds and turbulence in cloud-resolving models and global models

An economical scale-aware parameterization for representing subgrid-scale clouds and turbulence in cloud-resolving models and global models An economical scale-aware parameterization for representing subgrid-scale clouds and turbulence in cloud-resolving models and global models Steven Krueger1 and Peter Bogenschutz2 1University of Utah, 2National

More information

An RFID Distance Bounding Protocol

An RFID Distance Bounding Protocol An RFID Dstance Boundng Protocol Gerhard P. Hancke and Markus G. Kuhn May 22, 2006 An RFID Dstance Boundng Protocol p. 1 Dstance boundng Verfer d Prover Places an upper bound on physcal dstance Does not

More information

8.4. Annuities: Future Value. INVESTIGATE the Math. 504 8.4 Annuities: Future Value

8.4. Annuities: Future Value. INVESTIGATE the Math. 504 8.4 Annuities: Future Value 8. Annutes: Future Value YOU WILL NEED graphng calculator spreadsheet software GOAL Determne the future value of an annuty earnng compound nterest. INVESTIGATE the Math Chrstne decdes to nvest $000 at

More information

Section C2: BJT Structure and Operational Modes

Section C2: BJT Structure and Operational Modes Secton 2: JT Structure and Operatonal Modes Recall that the semconductor dode s smply a pn juncton. Dependng on how the juncton s based, current may easly flow between the dode termnals (forward bas, v

More information

RELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT

RELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT Kolowrock Krzysztof Joanna oszynska MODELLING ENVIRONMENT AND INFRATRUCTURE INFLUENCE ON RELIABILITY AND OPERATION RT&A # () (Vol.) March RELIABILITY RIK AND AVAILABILITY ANLYI OF A CONTAINER GANTRY CRANE

More information

TECHNICAL NOTES 8 GRINDING. R. P. King

TECHNICAL NOTES 8 GRINDING. R. P. King TECHNICAL NOTES 8 GRINDING R. P. Kng Copyrght R P kng 000 8. Grndng 8.. Grndng acton Industral grndng machnes used n the mneral processng ndustres are mostly of the tumblng mll type. These mlls exst n

More information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa

More information

A Spatial Model of the Impact of Bankruptcy Law on Entrepreneurship 1. Aparna Mathur 2 Department of Economics, University of Maryland at College Park

A Spatial Model of the Impact of Bankruptcy Law on Entrepreneurship 1. Aparna Mathur 2 Department of Economics, University of Maryland at College Park A Spatal Model of the Impact of Bankrptcy Law on Entreprenershp 1 Aparna Mathr 2 Department of Economcs, Unversty of Maryland at College Park Abstract Ths s the frst paper that hghlghts the role of spatal

More information

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications CMSC828G Prncples of Data Mnng Lecture #9 Today s Readng: HMS, chapter 9 Today s Lecture: Descrptve Modelng Clusterng Algorthms Descrptve Models model presents the man features of the data, a global summary

More information

An Empirical Study of Search Engine Advertising Effectiveness

An Empirical Study of Search Engine Advertising Effectiveness An Emprcal Study of Search Engne Advertsng Effectveness Sanjog Msra, Smon School of Busness Unversty of Rochester Edeal Pnker, Smon School of Busness Unversty of Rochester Alan Rmm-Kaufman, Rmm-Kaufman

More information

An Event-Based Approach to Visualization

An Event-Based Approach to Visualization An Event-Based Approach to Vsualzaton Chrstan Tomns Hedrun Schumann Insttute for Computer Scence Unversty of Rostoc, Germany {ct,schumann}@nformat.un-rostoc.de Abstract Vsualzaton of large data sets s

More information

Self-Consistent Proteomic Field Theory of Stochastic Gene Switches

Self-Consistent Proteomic Field Theory of Stochastic Gene Switches 88 Bophyscal Journal Volume 88 February 005 88 850 Self-Consstent Proteomc Feld Theory of Stochastc Gene Swtches Aleksandra M. Walczak,* Masak Sasa, y and Peter G. Wolynes* z *Department of Physcs, Center

More information

Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data

Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data Kate Thayer-Calder and Dave Randall Colorado State University October 24, 2012 NOAA's 37th Climate Diagnostics and Prediction Workshop Convective

More information

FLUISTCOM Fluid Structure Interaction for Combustion Systems ( MRTN-CT-2003-504183) Combustion Instability Effects and Aero-Thermal Near-Wall Response

FLUISTCOM Fluid Structure Interaction for Combustion Systems ( MRTN-CT-2003-504183) Combustion Instability Effects and Aero-Thermal Near-Wall Response Detsches Zentrm für Lft- nd Ramfahrt e.v. FLISTCOM Fld Strctre Interacton for Combston Systems ( MRTN-CT-003-504183) Combston Instablty Effects and Aero-Thermal Near-Wall Response Agenda 1-Months Progress

More information

FORMAL ANALYSIS FOR REAL-TIME SCHEDULING

FORMAL ANALYSIS FOR REAL-TIME SCHEDULING FORMAL ANALYSIS FOR REAL-TIME SCHEDULING Bruno Dutertre and Vctora Stavrdou, SRI Internatonal, Menlo Park, CA Introducton In modern avoncs archtectures, applcaton software ncreasngly reles on servces provded

More information

Cloud simulation. www.vterrain.org/atmosphere. Course SS 06 Simulation and Animation Prof. Dr. R. Westermann Computer Graphics and Visualization Group

Cloud simulation. www.vterrain.org/atmosphere. Course SS 06 Simulation and Animation Prof. Dr. R. Westermann Computer Graphics and Visualization Group Smulaton and Anmaton Clouds Vsual smulaton of clouds - Cloud smulaton and renderng Stochastc fractals Book by Ebert et al. Texturng and Modelng Dobash et al. A Smple, Effcent Method for Realstc Anmaton

More information

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important

More information

The Safety Board recommends that the Penn Central Transportation. Company and the American Railway Engineering Association revise

The Safety Board recommends that the Penn Central Transportation. Company and the American Railway Engineering Association revise V. RECOWNDATONS 4.! The Safety Board recommends that the Penn Central Transportaton Company and the Amercan Ralway Engneerng Assocaton revse ther track nspecton and mantenance standards or recommended

More information

Simulating injection moulding of microfeatured components

Simulating injection moulding of microfeatured components Smulatng njecton mouldng of mcrofeatured components T. Tofteberg 1 * and E. Andreassen 1 1 SINTEF Materals and Chemstry, Oslo, Norway [email protected]; [email protected] Numercal smulaton

More information

Vembu StoreGrid Windows Client Installation Guide

Vembu StoreGrid Windows Client Installation Guide Ser v cepr ov dered t on Cl enti nst al l at ongu de W ndows Vembu StoreGrd Wndows Clent Installaton Gude Download the Wndows nstaller, VembuStoreGrd_4_2_0_SP_Clent_Only.exe To nstall StoreGrd clent on

More information

A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION

A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION JHENG-LONG WU, PEI-CHANN CHANG, KAI-TING CHANG Department of Informaton Management,

More information

Architectures and competitive models in fibre networks

Architectures and competitive models in fibre networks WIK-Consult Report Study for Vodafone Archtectures and compettve models n fbre networks Authors: Prof. Dr. Steffen Hoerng Stephan Jay Dr. Karl-Henz Neumann Prof. Dr. Martn Petz Dr. Thomas Plückebaum Prof.

More information

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIIOUS AFFILIATION AND PARTICIPATION Danny Cohen-Zada Department of Economcs, Ben-uron Unversty, Beer-Sheva 84105, Israel Wllam Sander Department of Economcs, DePaul

More information

An Integrated Semantically Correct 2.5D Object Oriented TIN. Andreas Koch

An Integrated Semantically Correct 2.5D Object Oriented TIN. Andreas Koch An Integrated Semantcally Correct 2.5D Object Orented TIN Andreas Koch Unverstät Hannover Insttut für Photogrammetre und GeoInformaton Contents Introducton Integraton of a DTM and 2D GIS data Semantcs

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada [email protected] Abstract Ths s a note to explan support vector machnes.

More information

A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS

A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS Shanthy Menezes 1 and S. Venkatesan 2 1 Department of Computer Scence, Unversty of Texas at Dallas, Rchardson, TX, USA 1 [email protected]

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry [email protected] www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information