Detection of Outliers in the Adjustment of Accurate Geodetic Measurements
|
|
- Alfred Carr
- 5 years ago
- Views:
Transcription
1 105 Detecton of Outlers n the Adjustment of Accurate Geodetc Measurements asák, P. and Štroner, M. Department of Specal Geodesy, Faculty of Cvl Engneerng, CU n Prague, hákurova 7, Prague, Czech Republc, Web ste: el.: , E-mal: pavel.trasak@fsv.cvut.cz, martn.stroner@fsv.cutv.cz Abstract he paper deals wth the possbltes of automatc detecton of outlers n processng (adjustment) of accurate terrestral geodetc measurements. hese measurements are obtaned n the determnaton of hghly accurate terrestral geodetc networks, n whch have been repeatedly measured lengths, horzontal drectons and zenth angles and n whch s known a large number of redundant measurements. For automatc detecton of outlers of measurements authors use the robust statstcal methods, namely robust estmates based on the maxmum lkelhood method, so-called M-estmates. hs paper descrbes orgnally desgned experment based on a model of geodetc network wth deal desgned terrestral geodetc measurements. Measurements of ths deal model (measurements fully correspond wth the normal probablty dstrbuton) are frst processed by least square method. In the next phase of the experment the deal measurements are gradually dsturbed by contamnaton of outlers (outlers of lengths, horzontal drectons and zenth angles) and for ther followng processng (adjustment by least square method) are appled selected ndvdual robust M-estmates. hese selected M-estmates are assessed accordng to ther ablty to detect outlers,.e. accordng to ther usablty to automatc detecton of outlers n adjustment of terrestral geodetc measurements by least square method. he result of the present paper s to propose a procedure for processng (adjustment) of geodetc measurements of hgh accurate engneerng-geodetc networks when are exposed by outlers. Key words: outlers, adjustment of geodetc measurements, geodetc networks 1 INRODUCION he prncpal objectve of the paper s the descrpton and testng of a detecton technque of outlers n a set of values of measured geodetc varables. he proposed technque s appled for the adjustment of hghly accurate engneerng geodetc networks where large numbers of repeatedly measured values of geodetc varables are presumed,.e. large numbers of redundant measurements are consdered. S 3 Poster Presentaton INGEO th Internatonal Conference on Engneerng Surveyng Brjun, Croata, September 22-24, 2011
2 106 INGEO EXPERIMEN PROPOSAL In order to detect outlers n a set of hghly accurate engneerng geodetc measurements the authors have appled a technque whose effcency s assessed usng the experment descrbed below. 2.1 DEECION ECHNIQUE OF OULIERS he proposal s based on the classc adjustment of geodetc measurements usng the Method of Least Squares beng subdvded nto two basc steps. 1. Detecton of the robust estmaton of measured varables. In the frst phase of the geodetc data processng, the Robust M-Estmator (Huber, 1981) s used to make the estmaton of measured varables. he robust M-estmator applcaton s based on the modfcaton of a commonly used technque for the adjustment of geodetc measurements usng the Method of Least Squares (MLS). Unlke MLS the appled Robust M-Estmator s much less senstve to the fulflment of the assumpton of the normalty of processed data beng, therefore, to a certan extent, resstant to the nfluence of outlers. he resultng robust estmaton s not sgnfcantly affected by outlers. 2. Elmnaton of outlers of measured varables. Outlers are removed from the data set usng the selected rejecton rule. he detecton of outlyng values s performed on the bass of the assessment of ther dstance from the computed robust estmaton. 2.2 DESCRIPION OF HE PROPOSED EXPERIMEN he usablty of the proposed technque for the detecton of outlers n a set of geodetc measurements s assessed on the bass of the evaluaton of expermental data. he proposed experment may be splt nto the basc phases below. 1. Creaton of a model of geodetc measurements wth accurately defned parameters. 2. Artfcal ntroducton of outlers nto the model set of geodetc measurements. 3. Detecton of outlers usng the proposed technque. 4. Assessment of the effcency of the selected technque by comparng the numbers of ntroduced and successvely detected outlers. 3 MODEL OF GEODEIC MEASUREMEN he descrbed experment s based on the applcaton of proposed methods and technques onto accurately desgned artfcally modelled geodetc data. he reason for the modellng of geodetc data s the necessty of perfect knowledge of a pror accuraces of generated geodetc measurements whch are necessary for the objectve assessment of the results of the outputs acheved. 3.1 GEODEIC NEWORK he model geodetc network s desgned as a standard spatal medum-sze surveyng network shaped lke an rregular pentagon wth a sx standponts. Whle creatng the model,
3 asák, P. et al.: Detecton of Outlers n the Adjustment of 107 only the rough shape of the network was set and the detaled spatal poston of ndvdual ponts was randomly selected. he maxmum horzontal dstance between the ponts n the network s m, and the maxmum heght dfference equals 6.728m. A detaled dstrbuton of ndvdual ponts n the network (together wth ther spatal rectangular coordnates) s shown n Fgure 1. Fgure 1 Layout of the geodetc network 3.2 GEODEIC MEASUREMEN he model set of geodetc measurements smulates the classc output of a hghly accurate terrestral geodetc measurement obtaned wth the use of the total staton and a set of reflectng prsms. he set contans a large number of repeatedly dentfed horzontal drectons, zenth angles and slope dstances measured between ponts of the network. All values of the above-measured varables wthn the entre set have been arranged nto ndvdual sets of horzontal drectons, zenth angles z and slope dstances d measured n one set (.e. n two faces of the telescope). he set of geodetc measurements was modelled n the followng steps: 1. Determnaton of the number of values of measured varables n a model network. 6 sets (one set n each pont) are consdered wthn the model network, whch represents the total of 85 values of geodetc varables measured n one set (30 horzontal drectons, 30 zenth angles, 25 slope dstances), producng the total of 304% of redundant measurements. 2. Modellng of the set of measurements for each face of the telescope. Usng the pseudorandom number generator (asák et al., 2010) a set (random sample) of values of a standard normal dstrbuton N(0,1) was generated, whch was subsequently transformed nto a set wth a non-standard normal dstrbuton (X 0, 1 2 ) where X 0 s the true value of the measured varable ( 0, z 0, d 0 computed from modelled coordnates of the ponts n the network) and 1 the standard devaton of the measured varable n one face of the telescope ( 1, 1z, 1d measured n one face of the telescope). he standard devatons of measured varables correspond to the accuracy of total statons n a hgher accuracy class, such as (rmble, 2011) (numercal values of standard devatons are lsted n ab. 1).
4 108 INGEO Processng of measurements wthn one set (2 faces of the telescope). he fnal phase of the model data set creaton ncluded the computaton of the values of measured varables determned n two faces of the telescope: a) Averagng of 2 respectve values of horzontal drectons dfferng by 200gon. b) Correcton of the zenth angle elmnatng the ndex error. c) Averagng of 2 respectve values of slope dstances. Correspondng standard devatons were assgned to the resultng values of measured varables (see ab. 1). he obtaned values were used for subsequent processng,.e. for the adjustment of geodetc network measurements. ab. 1 Standard devatons of measured varables horzontal drecton 1 face of telescope mgon 1 set mgon (2 faces of telescope) 2 zenth angle z 1z z z mgon mgon slope dstance d 1 d 2. 0mm d d mm 4 GEODEIC NEWORK ADJUSMEN USING MLS he method of the free spatal geodetc network adjustment was used for the adjustment of the model of geodetc measurements. In ths case, the geodetc network s not frmly bound to any pont of the network. he adjustment (estmaton) of measured varables results n the adjustment (estmaton) of the coordnates of all ponts n the network. As the geodetc network s not frmly bound to any pont, ts general localzaton n space must be ensured. For ths reason, the soluton of the free network problem apples the method of the adjustment of observaton equatons wth condton equatons Böhm (1990) whch wll ensure ths localzaton. Intermedate varables consdered n the soluton of the model network were drectly measured varables whch, therefore, allow a drect expresson of the relaton l f (x), (1) where l s the vector of adjusted measured varables (the vector of estmatons of measured varables l) and x s the vector of adjusted unknown varables (the vector of estmatons of unknown varables x). Furthermore, we may state that l v f ( x 0 dx), (2) where v s the vector of resduals of measured varables, x 0 s the vector of approxmate values of unknown varables and dx s the vector of dfferences to approxmate values of unknown varables. Followng the lnearzaton of the above expresson t holds true that v Adx l, (3) l f x ) l, (4) ( 0 where l s the vector of reduced observatons and A s the matrx of lnearzed expressons among measured and unknown varables (the matrx of partal dervatons of functons of measured varables accordng to ndvdual unknowns). By expressng the necessary condton of the network localzaton for unknown varables as ( x) O, (5)
5 asák, P. et al.: Detecton of Outlers n the Adjustment of 109 and ts lnearzaton Bdx b O, (6) b ( x 0 ), (7) and, further, by ntroducng the condton of the Method of Least Squares v Pv mn, (8) the Method of Lagrange Multplers (Böhm, 1990) s used for the defnton of a system of normal equatons and the formula for the calculaton of the sought vector of dfferences to approxmate values of unknown varables dx A PA k B B O 1 A Pl, (9) b where k s the auxlary vector of Lagrange multplers (correlate), B s the matrx of lnearzed condtons of unknown varables (the matrx of partal dervatons of ndvdual condtons accordng to ndvdual unknowns), b stands for the vector of condtons of unknown varables expressed by means of ther approxmate values x 0, O s the zero matrx and P the weght coeffcent matrx of measured varables l. In a model geodetc network contanng measured horzontal drectons, zenth angles z and slope dstances d, the vector of measured varables l(m,1) takes the form l ( 1,, m, z,, zm, d,, dm ) , (10) 3 where m 1, m 2, m 3 are the numbers of measured horzontal drectons, zenth angles and slope dstances and m = m 1 + m 2 + m 3 s the total number of measured varables. he selected unknown varables are spatal rectangular coordnates of all ponts of the network (X, Y, Z) and also orentaton shfts of ndvdual sets of horzontal drectons at ndvdual standponts o. Hence, the vector of unknown varables x(n,1) takes the form x ( X 1, Y1, Z1,, X n, Yn, Z n, o,, on ) , (11) 3 where n 1 s the number of ponts n the network, n 2 s the number of orentaton shfts and n = n 1 + n 2 s the total number of unknown varables. o be able to plot the vector of measured varables l and the vector of unknown varables x, ndvdual elements of the matrx of lnearzed expressons of measured and unknown varables wth a total magntude A(m,n) are defned as partal dervatons f ( x) A, j, (12) x xx0 where f(x) s the functon of unknown varables x expressng the measured varable l. In order to localze the geodetc network n space, Helmert s transformaton condton (Koch, 1999) was selected for the model network at all ponts of the network,.e. the squares of coordnate dfferences of approxmate and adjusted ponts of the network were mnmzed dx dx mn. (13) Provded all three types of varables (horzontal drectons, zenth angles and slope dstances) are measured n a spatal network, t holds true for ths condton that,1 o b 4, (14) B, n B (4,3) Bn (4,3) O(4, ) 4 1 n 1 2, (15) where the submatrx
6 110 INGEO 2011 Y0 X B (16) he weght coeffcent matrx of measured varables s composed as the dagonal matrx P dag( p 1,, p ), (17) m where the weght coeffcent of ndvdual measured varables s expressed by the relaton p, (18) where 0 s the a pror unt standard devaton and stands for the standard devaton of the measured varable. In the case of the model network, the a pror unt standard devaton selected was 0 = 1, whle the standard devatons of measured varables were defned by the modellng parameters (see ab. 1). 5 ROBUS SAISICAL MEHODS As already stated, ths paper s focused on the assessment of potental detecton of outlers of measured varables by means of robust statstcal estmatons, usng one group of robust estmatons n partcular, so-called M-estmators. As the paper solely deals wth the assessment of potental applcatons of such estmatons, deeper theoretcal nsght s not ncluded n t, but only a bref descrpton of the ntroducton of the robust M-estmator nto the adjustment of a free geodetc network usng MLS. A detaled descrpton of robust estmatons appled here s n (asák et al., 2011). he prncple of the robust M-estmator applcaton s based on a gradual teratve adjustment of geodetc measurements usng the Method of Least Squares under the condton of a gradual change n the weght coeffcent of ndvdual measurements n relaton to the development of the magntude of ther normed resduals determned by the adjustment. In ths way, outlers of measured varables are gradually elmnated. In each teraton step, therefore, the robust weght coeffcent of each measured varable s computed w f (vˆ), (19) and the matrx of robust weght coeffcents s defned W dag( w, w2, w ). (20) 1 m In the zero teraton step, the robust weght coeffcents of all measurements are set as w (0) = 1 (W = E(m,m)), the weght coeffcent s not ntroduced and the estmaton of measured varables s computed usng the non-robust Method of Least Squares ( 1 0) (0) (0) dx A W PA B A W Pl A PA k B O b B Normed resduals of measured varables are computed v Adx p 0 B O 1 A Pl. (21) b l, (22) vˆ v, (23) and the computatonal model s adjusted
7 asák, P. et al.: Detecton of Outlers n the Adjustment of 111 x x dx. (24) 0 In the next step, the computatonal model s made more accurate x ( j1) 0 x A, B, l. (25) New robust weght coeffcents are computed usng normed resduals of measured varables w ( j1) f ( ) vˆ j, (26) ( j1) ( j1) ( j1) W dag( w,, w ). (27) 1 m and a new estmaton of measured varables s defned 1 ( j1) ( j1) ( j1) dx A W PA B A W Pl. (28) k B O b he convergence of ths teratve computaton s proved n (Huber, 1981). 5.1 USED M-ESIMAORS he totals of 12 dfferent formulae for the robust weght coeffcent computaton were used n the soluton of the descrbed experment (19). he lst of M-estmators used s dsplayed n ab. 2. he descrpton of all used estmatons together wth the formulae for ther weght functons are n (asák et al., 2011). ab. 2 Lst of M-estmators used Cauchy dstrbuton 1 Huber estmator 5 estmator Modfed Huber ukey s bweght 2 6 estmator estmator Geman McClure 3 Hampel estmator 7 estmator 4 alwar estmator 8 Andrews estmator 12 9 Welsch estmator 10 Far estmator 11 L1 standard Hybrd L1/L2 standard 6 DEECION OF OULIERS IN MEASUREMENS he proposed prncple of the detecton of outlers n a set of measured varables s based on the assessment of the magntude of resduals of ndvdual measurements. hese resduals are obtaned from the results of the adjustment of geodetc measurements applyng the robust M-estmator. By usng the robust M-estmator n the adjustment of geodetc networks the effect of measured outlyng values s reduced thus obtanng an estmaton of measured varables ndependent of outlers. Assumng that the obtaned estmaton of the measured varable X approaches ts true value X 0 and that the set of values (random sample) of the measured varable comes from the normal probablty dstrbuton N(X 0, 1 2 ), the technque below may be used for the detecton of outlyng values. Followng suffcent stablzaton of the teratve computaton of the adjustment of geodetc measurements, by fulfllng the condton
8 112 INGEO 2011 ( ) ( j1) max( w j w ), (29) where s the maxmum tolerated change n the robust weght (ths lmt was set as = n the experment), the resduals of measured values v are computed n accordance wth (3) and ther lmt values,.e. the lmt resduals of measured values, are further detected as v, (30) M u p v where u p s the standard normal probablty dstrbuton value set for the sgnfcance level (u p = 1.96 for = 0.05 was selected for the experment) and v s the standard devaton of the resdual of the measured value l, whch s defned by the formula, (31) v 0 Q v, v where Q v,v s the dagonal element of the covarance matrx of resduals of measured varables Q 1 1 v, v P A( A W PA) A. (32) A comparson s subsequently made and provded t holds true that v v M, (33) the value of the measured varable v s declared as outlyng and s elmnated from the set of measurements. After the elmnaton of all outlers the reduced set of measured varables should fulfl the condton of normalty of measured data, and the non-robust MLS may then be used for the computaton of the best objectve estmaton of measured varables. 7 ESING HE MEHOD OF DEECION OF OULIERS IN MEASUREMENS he testng of the proposed technque of the detecton of outlers of measured varables s based on repettve processng (adjustments) of an artfcally generated model set of geodetc measurements wth a gradual ntroducton of dfferent quanttes of dfferently outlyng values of measured varable (, z, d). he result of the testng s the determnaton of the effcency of the detecton of outlers by all methods presented here (see ab. 2) n relaton to specfc confguratons of the model set (.e. the number and magntude of outlers present n the set). 7.1 INRODUCION OF OULIERS Outlyng values were ntroduced nto the model set n a completely random way regardless of the type of measurement (, z, d). o make the nterpretaton of the results smpler, the set was always contamnated wth numbers of outlers of the same magntude. he magntude of outlers was defned by the coeffcent h of the standard devaton of the measured varable j l j, l h j, j, z, d, (34) he used coeffcents of standard devatons h are lsted n ab. 3. o smplfy the nterpretaton of the results, the outlers are subdvded nto 3 groups by magntude. he quantty of outlers s expressed relatvely n relaton to the total number of measurements n the model set ( ab. 4). he used quanttes are splt nto two nterpretaton groups.
9 asák, P. et al.: Detecton of Outlers n the Adjustment of 113 ab. 3 Magntude of ntroduced outlers coeffcents of standard devatons of measured varables Outlyng values More extreme outlyng Gross errors of medum sze values 2 2,5 3 3, ab. 4 Quanttes of ntroduced outlers Smaller numbers of outlers [%] Larger numbers of outlers [%] RESULS OF HE EXPERIMEN As already mentoned above, the respectve experment results n the determnaton of the effcency of the detecton of outlers. hs effcency s descrbed by two values: the number of correctly detected ntroduced outlers a (the number of values of measured varables whch are correctly consdered as outlyng by the method; expressed as the percentage of the number of outlers ntroduced nto the set) and the number of wrongly detected non-ntroduced outlers b (the number of values of measured varables whch are ncorrectly consdered as outlyng by the method; expressed as the percentage of the total number of values n the set). For the reason of reducng the large number of outputs, the acheved effcency rates of ndvdual methods were arranged nto nterpretaton domans (see ab. 5). he acheved effcency rate was averaged wthn ndvdual domans and each doman s, therefore, represented by only two values: the average number of correctly and wrongly detected outlers n the set, whch are dsplayed n ab. 6 (ndvdual types of M-estmators are numbered accordng to ab. 2). Effcency rates (a, b) are presented only for Huber s M-estmator technque. For the other technques, dfferences ( a, b ) related to Huber s M- estmator are descrbed. Fgure 2 further hypsometrcally dsplays the effcency of Huber s M-estmator (wthout the subdvson of the results nto nterpretaton groups). o ncrease the relablty of the acheved results the whole experment was repeated 10 tmes and the presented values represent the average of all repettons. ab. 5 Interpretaton domans of the magntude and quantty of ntroduced outlers Name of nterpretaton doman Magntude Quantty [h] [%] A Doman of a smaller number of medum-sze outlers B Doman of a larger number of medum-sze outlers C Doman of a smaller number of more extreme outlers D Doman of a larger number of more extreme outlers E Doman of a smaller number of gross errors F Doman of a larger number of gross errors
10 114 INGEO 2011 ab. 6 Numbers of correctly ([%], a [%]) and wrongly ([%], b [%]) detected outlers a b a b a b a b a b a b A B C D E F a b a b a b a b a b a b A B C D E F
11 asák, P. et al.: Detecton of Outlers n the Adjustment of 115 Fgure 2 Huber s M-estmator - numbers of correctly and wrongly detected outlers 7.3 ASSESSMEN OF ACHIEVED RESULS he presented results mply that the effcency of ndvdual robust M-estmators s comparable. he values n felds E and F are consdered as completely tentatve and not much weght should be attrbuted to them n the total assessment. In these cases, the set was contamnated wth gross errors whch can easly be removed n advance durng the processng of data from accurate geodetc networks. he best effcency results have been acheved by usng Huber s M-estmator (the drawbacks n the effcency of the other estmatons of the robust estmaton type are lsted n ab. 6). Huber s M-estmator technque s paradoxcally the oldest estmaton technque, and the other robust estmaton technques were desgned later wth the am of mprovng ths estmaton technque. he effcency of the correct dentfcaton of outlers n a set of geodetc measurements (a) s drectly proportonal to the magntude of the error of outlyng measurements (expressed by the coeffcent of the standard devaton h n the experment). ogether wth the growng magntude of the error of outlers the growng amount of detecton of wrongly dentfed nonoutlers s negatvely manfested (b). As compared to the effect of the growng magntude of the error of outlers, the effect of the growng number of these errors s much less sgnfcant. A detaled effcency pattern (correct and wrong detecton of ntroduced outlers) for Huber s M-estmator technque s dsplayed n Fgure 2. 8 CONCLUSION he experment resulted n the determnaton of the usablty of robust M-estmator technques for the detecton of outlers of geodetc varables measured n hghly accurate engneerng geodetc networks. he applcaton of robust estmatons for the detecton of outlers s a hghly effcent method whose feasblty, however, s condtonal upon numerous factors and nput condtons. o acheve adequate results, a suffcent number of redundant measurements must be provded (correspondng to the number of redundant measurements appled n a commonly measured hghly accurate engneerng geodetc network), and the absence of extreme gross measurement errors must be ensured (whch may be elmnated by checkng the data before the adjustment of the measurement). Unlke the commonly used technques of the detecton of outlers whose prncple s based on repettve rejectons of ndvdual measurements wth large resduals aganst the MLS estmaton (thus resemblng the tral and error method), the applcaton of robust estmatons s fully automatc and the decson on the rejecton of all measured outlers may be made all at once and not gradually by assessng ndvdual measurements. he weak pont of the descrbed technque s ts dependence on the classc adjustment technque of geodetc measurements (MLS adjustment), whch n some cases, wth the presence of extreme gross errors, tends to be computatonally unstable, and once the computaton of the estmaton of measured varables by means of MLS has faled, the whole technque of the detecton of measured outlers fals, too. Acknowledgement
12 116 INGEO 2011 he artcle was wrtten wth support from the nternal grant SGS10/153/OHK1/2/11 Complex software processng of measurements n engneerng surveyng REFERENCES HUBER, P. J.: Robust Statstcs. New York, John Wley and Sons, ASÁK, P. - ŠRONER, M.: estng of Generators of Pseudorandom Numbers from the Normal Dstrbuton for Use n Smulaton of Geodetc Measurements - Part 1 (n Czech). Stavební obzor. 2010, vol. 19, no. 2, pp ISSN RIMBLE: Corporate lterature for rmble S6 nstrument (n Czech). t_d=4&category_d=15&opton=com_vrtuemart&itemd= BÖHM, J. RADOUCH, V. HAMPACHER, M.: heory of Errors and Adjustment Calculus (n Czech). 2nd ed., Praha, Geodetcký a kartografcký podnk ISBN KOCH, K. R.: Parameter Estmaton and Hypothess estng n Lnear Models. Berln Hedelberg New York, Sprnger Verlag, 1999, ISBN ASÁK, P. - ŠRONER, M.: Robust Adjustment Methods (n Czech). Geodetcký a kartografcký obzor. 2011, vol. 57, no. 7, ISSN
Proper Choice of Data Used for the Estimation of Datum Transformation Parameters
Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and
More informationX- Chart Using ANOM Approach
ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationR s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes
SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationy and the total sum of
Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton
More information2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements
Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationReading. 14. Subdivision curves. Recommended:
eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton
More informationAngle-Independent 3D Reconstruction. Ji Zhang Mireille Boutin Daniel Aliaga
Angle-Independent 3D Reconstructon J Zhang Mrelle Boutn Danel Alaga Goal: Structure from Moton To reconstruct the 3D geometry of a scene from a set of pctures (e.g. a move of the scene pont reconstructon
More informationSome Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.
Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,
More informationAPPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT
3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ
More informationLecture 4: Principal components
/3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness
More informationSteps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices
Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between
More informationSURFACE PROFILE EVALUATION BY FRACTAL DIMENSION AND STATISTIC TOOLS USING MATLAB
SURFACE PROFILE EVALUATION BY FRACTAL DIMENSION AND STATISTIC TOOLS USING MATLAB V. Hotař, A. Hotař Techncal Unversty of Lberec, Department of Glass Producng Machnes and Robotcs, Department of Materal
More informationContent Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers
IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationTECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS. Muradaliyev A.Z.
TECHNIQUE OF FORMATION HOMOGENEOUS SAMPLE SAME OBJECTS Muradalyev AZ Azerbajan Scentfc-Research and Desgn-Prospectng Insttute of Energetc AZ1012, Ave HZardab-94 E-mal:aydn_murad@yahoocom Importance of
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More informationS.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?
S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert
More informationWishing you all a Total Quality New Year!
Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma
More informationSubspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;
Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features
More informationAPPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA
RFr"W/FZD JAN 2 4 1995 OST control # 1385 John J Q U ~ M Argonne Natonal Laboratory Argonne, L 60439 Tel: 708-252-5357, Fax: 708-252-3 611 APPLCATON OF A COMPUTATONALLY EFFCENT GEOSTATSTCAL APPROACH TO
More informationThe Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole
Appled Mathematcs, 04, 5, 37-3 Publshed Onlne May 04 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/0.436/am.04.584 The Research of Ellpse Parameter Fttng Algorthm of Ultrasonc Imagng Loggng
More informationA Robust Method for Estimating the Fundamental Matrix
Proc. VIIth Dgtal Image Computng: Technques and Applcatons, Sun C., Talbot H., Ourseln S. and Adraansen T. (Eds.), 0- Dec. 003, Sydney A Robust Method for Estmatng the Fundamental Matrx C.L. Feng and Y.S.
More informationFEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur
FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents
More informationOutlier Detection in GPS Networks with Fuzzy Logic and Conventional Methods
Outler Detecton n GPS Networks wth Fuzzy Logc and Conventonal ethods Ertan GÖALP and Yüksel BOZ, urkey ey Words: Outler, Conventonal ethod, Fuzzy Logc, Statstcal est, GPS SUARY It s assumed that the geodetc
More informationMathematics 256 a course in differential equations for engineering students
Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the
More informationSum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints
Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationA MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS
Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationAn Entropy-Based Approach to Integrated Information Needs Assessment
Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology
More informationAn Image Fusion Approach Based on Segmentation Region
Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua
More informationResearch on laser tracker measurement accuracy and data processing. Liang Jing IHEP,CHINA
Research on laser tracker measurement accuracy and data processng Lang Jng IHEP,CHINA 214.1 1 Outlne 1. Accuracy test experment for laser tracker In the transversal drecton In the longtudnal drecton In
More informationMOTION BLUR ESTIMATION AT CORNERS
Gacomo Boracch and Vncenzo Caglot Dpartmento d Elettronca e Informazone, Poltecnco d Mlano, Va Ponzo, 34/5-20133 MILANO boracch@elet.polm.t, caglot@elet.polm.t Keywords: Abstract: Pont Spread Functon Parameter
More informationNAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics
Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson
More informationHigh resolution 3D Tau-p transform by matching pursuit Weiping Cao* and Warren S. Ross, Shearwater GeoServices
Hgh resoluton 3D Tau-p transform by matchng pursut Wepng Cao* and Warren S. Ross, Shearwater GeoServces Summary The 3D Tau-p transform s of vtal sgnfcance for processng sesmc data acqured wth modern wde
More informationImproved Methods for Lithography Model Calibration
Improved Methods for Lthography Model Calbraton Chrs Mack www.lthoguru.com, Austn, Texas Abstract Lthography models, ncludng rgorous frst prncple models and fast approxmate models used for OPC, requre
More informationParameter estimation for incomplete bivariate longitudinal data in clinical trials
Parameter estmaton for ncomplete bvarate longtudnal data n clncal trals Naum M. Khutoryansky Novo Nordsk Pharmaceutcals, Inc., Prnceton, NJ ABSTRACT Bvarate models are useful when analyzng longtudnal data
More informationExercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005
Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationSkew Angle Estimation and Correction of Hand Written, Textual and Large areas of Non-Textual Document Images: A Novel Approach
Angle Estmaton and Correcton of Hand Wrtten, Textual and Large areas of Non-Textual Document Images: A Novel Approach D.R.Ramesh Babu Pyush M Kumat Mahesh D Dhannawat PES Insttute of Technology Research
More informationSimulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010
Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement
More informationREFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water.
Purpose Theory REFRACTION a. To study the refracton of lght from plane surfaces. b. To determne the ndex of refracton for Acrylc and Water. When a ray of lght passes from one medum nto another one of dfferent
More informationA Fast Visual Tracking Algorithm Based on Circle Pixels Matching
A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng
More informationHierarchical clustering for gene expression data analysis
Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally
More informationBioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented
More informationFinite Element Analysis of Rubber Sealing Ring Resilience Behavior Qu Jia 1,a, Chen Geng 1,b and Yang Yuwei 2,c
Advanced Materals Research Onlne: 03-06-3 ISSN: 66-8985, Vol. 705, pp 40-44 do:0.408/www.scentfc.net/amr.705.40 03 Trans Tech Publcatons, Swtzerland Fnte Element Analyss of Rubber Sealng Rng Reslence Behavor
More informationEstimating Regression Coefficients using Weighted Bootstrap with Probability
Norazan M R, Habshah Md, A H M R Imon Estmatng Regresson Coeffcents usng Weghted Bootstrap wth Probablty NORAZAN M R, HABSHAH MIDI AND A H M R IMON Faculty of Computer and Mathematcal Scences, Unversty
More informationMethodology of optimal sampling planning based on VoI for soil contamination investigation
Japanese Geotechncal Socety Specal Publcaton The 5th Asan Regonal Conference on Sol echancs and Geotechncal Engneerng ethodology of optmal samplng plannng based on VoI for sol contamnaton nvestgaton Iumasa
More informationModular PCA Face Recognition Based on Weighted Average
odern Appled Scence odular PCA Face Recognton Based on Weghted Average Chengmao Han (Correspondng author) Department of athematcs, Lny Normal Unversty Lny 76005, Chna E-mal: hanchengmao@163.com Abstract
More informationAn Image Compression Algorithm based on Wavelet Transform and LZW
An Image Compresson Algorthm based on Wavelet Transform and LZW Png Luo a, Janyong Yu b School of Chongqng Unversty of Posts and Telecommuncatons, Chongqng, 400065, Chna Abstract a cylpng@63.com, b y27769864@sna.cn
More informationHermite Splines in Lie Groups as Products of Geodesics
Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the
More informationAn Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed
More informationA Semi-parametric Regression Model to Estimate Variability of NO 2
Envronment and Polluton; Vol. 2, No. 1; 2013 ISSN 1927-0909 E-ISSN 1927-0917 Publshed by Canadan Center of Scence and Educaton A Sem-parametrc Regresson Model to Estmate Varablty of NO 2 Meczysław Szyszkowcz
More informationQuality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation
Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on
More informationAccounting for the Use of Different Length Scale Factors in x, y and z Directions
1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,
More informationMachine Learning: Algorithms and Applications
14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationProblem Definitions and Evaluation Criteria for Computational Expensive Optimization
Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty
More informationLife Tables (Times) Summary. Sample StatFolio: lifetable times.sgp
Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables
More informationA Robust LS-SVM Regression
PROCEEDIGS OF WORLD ACADEMY OF SCIECE, EGIEERIG AD ECHOLOGY VOLUME 7 AUGUS 5 ISS 37- A Robust LS-SVM Regresson József Valyon, and Gábor Horváth Abstract In comparson to the orgnal SVM, whch nvolves a quadratc
More informationVISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES
UbCC 2011, Volume 6, 5002981-x manuscrpts OPEN ACCES UbCC Journal ISSN 1992-8424 www.ubcc.org VISUAL SELECTION OF SURFACE FEATURES DURING THEIR GEOMETRIC SIMULATION WITH THE HELP OF COMPUTER TECHNOLOGIES
More informationA Statistical Model Selection Strategy Applied to Neural Networks
A Statstcal Model Selecton Strategy Appled to Neural Networks Joaquín Pzarro Elsa Guerrero Pedro L. Galndo joaqun.pzarro@uca.es elsa.guerrero@uca.es pedro.galndo@uca.es Dpto Lenguajes y Sstemas Informátcos
More informationTerm Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task
Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto
More informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationClassifying Acoustic Transient Signals Using Artificial Intelligence
Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)
More informationWavefront Reconstructor
A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen 14-12-201212 2012 Delft Unversty of Technology Contents Introducton Wavefront reconstructon usng Smplex B-Splnes
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationRobust data analysis in innovation project portfolio management
MATEC Web of Conferences 70, 007 (08) SPbWOSCE-07 https://do.org/0.05/matecconf/0870007 Robust data analyss n nnovaton project portfolo management Bors Ttarenko,*, Amr Hasnaou, Roman Ttarenko 3 and Llya
More informationComputer Animation and Visualisation. Lecture 4. Rigging / Skinning
Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume
More informationA Deflected Grid-based Algorithm for Clustering Analysis
A Deflected Grd-based Algorthm for Clusterng Analyss NANCY P. LIN, CHUNG-I CHANG, HAO-EN CHUEH, HUNG-JEN CHEN, WEI-HUA HAO Department of Computer Scence and Informaton Engneerng Tamkang Unversty 5 Yng-chuan
More informationParallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)
Technsche Unverstät München WSe 6/7 Insttut für Informatk Prof. Dr. Thomas Huckle Dpl.-Math. Benjamn Uekermann Parallel Numercs Exercse : Prevous Exam Questons Precondtonng & Iteratve Solvers (From 6)
More informationSix-Band HDTV Camera System for Color Reproduction Based on Spectral Information
IS&T's 23 PICS Conference Sx-Band HDTV Camera System for Color Reproducton Based on Spectral Informaton Kenro Ohsawa )4), Hroyuk Fukuda ), Takeyuk Ajto 2),Yasuhro Komya 2), Hdeak Hanesh 3), Masahro Yamaguch
More informationWe Two Seismic Interference Attenuation Methods Based on Automatic Detection of Seismic Interference Moveout
We 14 15 Two Sesmc Interference Attenuaton Methods Based on Automatc Detecton of Sesmc Interference Moveout S. Jansen* (Unversty of Oslo), T. Elboth (CGG) & C. Sanchs (CGG) SUMMARY The need for effcent
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationLearning-Based Top-N Selection Query Evaluation over Relational Databases
Learnng-Based Top-N Selecton Query Evaluaton over Relatonal Databases Lang Zhu *, Wey Meng ** * School of Mathematcs and Computer Scence, Hebe Unversty, Baodng, Hebe 071002, Chna, zhu@mal.hbu.edu.cn **
More informationEmpirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap
Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*
More informationSCALABLE AND VISUALIZATION-ORIENTED CLUSTERING FOR EXPLORATORY SPATIAL ANALYSIS
SCALABLE AND VISUALIZATION-ORIENTED CLUSTERING FOR EXPLORATORY SPATIAL ANALYSIS J.H.Guan, F.B.Zhu, F.L.Ban a School of Computer, Spatal Informaton & Dgtal Engneerng Center, Wuhan Unversty, Wuhan, 430079,
More informationCompiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz
Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster
More informationData Mining: Model Evaluation
Data Mnng: Model Evaluaton Aprl 16, 2013 1 Issues: Evaluatng Classfcaton Methods Accurac classfer accurac: predctng class label predctor accurac: guessng value of predcted attrbutes Speed tme to construct
More informationAir Transport Demand. Ta-Hui Yang Associate Professor Department of Logistics Management National Kaohsiung First Univ. of Sci. & Tech.
Ar Transport Demand Ta-Hu Yang Assocate Professor Department of Logstcs Management Natonal Kaohsung Frst Unv. of Sc. & Tech. 1 Ar Transport Demand Demand for ar transport between two ctes or two regons
More informationMULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION
MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and
More informationTHE THEORY OF REGIONALIZED VARIABLES
CHAPTER 4 THE THEORY OF REGIONALIZED VARIABLES 4.1 Introducton It s ponted out by Armstrong (1998 : 16) that Matheron (1963b), realzng the sgnfcance of the spatal aspect of geostatstcal data, coned the
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationCircuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)
Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,
More informationIMPROVING AND EXTENDING THE INFORMATION ON PRINCIPAL COMPONENT ANALYSIS FOR LOCAL NEIGHBORHOODS IN 3D POINT CLOUDS
IMPROVING AND EXTENDING THE INFORMATION ON PRINCIPAL COMPONENT ANALYSIS FOR LOCAL NEIGHBORHOODS IN 3D POINT CLOUDS Davd Belton Cooperatve Research Centre for Spatal Informaton (CRC-SI) The Insttute for
More informationA Simple and Efficient Goal Programming Model for Computing of Fuzzy Linear Regression Parameters with Considering Outliers
62626262621 Journal of Uncertan Systems Vol.5, No.1, pp.62-71, 211 Onlne at: www.us.org.u A Smple and Effcent Goal Programmng Model for Computng of Fuzzy Lnear Regresson Parameters wth Consderng Outlers
More informationUser Authentication Based On Behavioral Mouse Dynamics Biometrics
User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA
More informationA new segmentation algorithm for medical volume image based on K-means clustering
Avalable onlne www.jocpr.com Journal of Chemcal and harmaceutcal Research, 2013, 5(12):113-117 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCRC5 A new segmentaton algorthm for medcal volume mage based
More informationA mathematical programming approach to the analysis, design and scheduling of offshore oilfields
17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and
More informationA New Approach For the Ranking of Fuzzy Sets With Different Heights
New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays
More informationDirect Projection An Efficient Approach for Datum Transformation of Plane Co-ordinates
Drect Projecton An Effcent Approach for Datum Transformaton of Plane Co-ordnates Lars E. ENGBERG and Mkael LILJE, Sweden Key words: datum transformaton, map-grd co-ordnates. SUMMARY Lantmäteret has decded
More information