AUTOMATED METHOD FOR STATISTICAL PROCESSING OF AE TESTING DATA
|
|
- Julian Nicholson
- 5 years ago
- Views:
Transcription
1 AUTOMATED METHOD FOR STATISTICAL PROCESSING OF AE TESTING DATA V. A. Barat and A. L. Alyakrtsky Research Dept, Interuns Ltd., bld. 24, corp 3-4, Myasntskaya str., Moscow, 0000, Russa Keywords: sgnal processng, AE data classfcaton Abstract Acoustc emsson (AE) as a nondestructve testng method allows estmatng the condton of sophstcated ndustral objects, detectng defects at ther ntaton, and preventng development of such defects. The relablty of detecton and the accuracy of determnaton of AE source locaton depend on the correct nterpretaton of the AE testng results. Testng procedures of dfferent ndustral equpment are ntended for estmatng condton of the objects on the bass of AE parameters, whle the AE sgnals proper are not used n analyss as the prmary dagnostc data. We have developed the statstcal method of analyss to study the whole complex of measured data, both the AE parameters and sgnal waveforms. Ths method s based on the two-level clusterng of data, such as AE sgnals from separate channels, and "groups of sgnals" formed on the bass of the prmary clusterng. The algorthm allows the determnaton of the quantty of AE sources, and estmates the degree of ther danger wthout resortng to the prelmnary locaton. The zonal locaton s carred out after the determnaton of AE sources. It avods the so-called false locatons, whch consderably complcate the locaton map. On the bass of classfcaton results, one can correct the AE sgnal arrval tme, and such correcton makes t possble to mprove the accuracy of the AE source locaton. The characterstc property of the developed method s data processng n an automatc mode, wth mnmal operator nterventon. In ths case there s no need to employ a hgh-sklled operator. Snce the AE sgnals themselves are used for analyss, the obtaned results prove to be more relable than those obtaned when workng wth the AE parameters. The algorthm bult on the developed method has been successfully appled for processng data obtaned as a result of laboratory research of the corroson development, for studyng renforced concrete structures, and also as an addtonal nstrument for processng data of the ndustral AE testng. Introducton Each AE event defnes a unque process occurred at a certan pont of the test object. When AE sgnals propagate from the pont of emsson to a sensor, the waveforms are complcated due to conversons to dfferent modes of waves, multplcty of dstrbuton paths, and due to wave velocty dsperson by frequency. AE sgnals contan nformaton not only about the AE source event for ths sgnal, but n the hghest degree about parameters of acoustc and electrc path. In ths connecton constructon of the analytcal or even numercal dagnoss model for nterpretaton of AE sgnals appears to be an ntrcate and nontrval problem, the soluton of whch cannot be generalzed for the test equpment of dfferent types. Whle the data nterpretaton based on accurate dagnostc models appears to be dffcult, the statstcal analyss s a reasonable choce for carryng out the data nterpretaton and classfcaton wth the hgh certanty. At present the statstcal methods of data analyss are wdely appled both n ndustral AE systems and n laboratory research, and the applcatons are vared: data clusterng, correlaton analyss, and check of dfferent statstcal hypotheses. In ths paper the statstcal method of analyss enablng the automatc clusterng of AE testng data s presented. Ths method makes t possble to process bulk data obtaned as a result of the AE testng or durng the laboratory research n the absence of a pror nformaton. As a consequence of processng, the data are structured and organzed; each cluster formed as a result of analyss characterzes an AE source at a defnte stage of development EWGAE, Cracow UT
2 Method Descrpton The clusterng of AE data s used rather frequently. However, dfferent methods of data processng pursue dfferent ams, and have specal features of mplementaton [4,5,7,8]. The key features of a gven method s, frstly, the possblty for data processng n an automatc mode wth mnmum operator s nvolvement and a mnmum number of settngs, and, secondly, the possblty to carry out the analyss of the heterogeneous dagnoss nformaton. The ntal data for algorthm realzng the gven method can be both the AE sgnals and also ther parameters computed under data acquston n on-lne mode. Even f the AE count rates are such that on techncal grounds an acquston of prmary dagnoss nformaton n full capacty s mpossble, more than half the AE sgnals can be substtuted for values of AE parameters (arrval tme, rse tme, energy, etc.) wthout loss of the processng accuracy. Start Formaton of classes of sgnals Groupng of sgnals relatng to the same AE event Formaton of classes of groups Are all AE-sgnals recorded? Calculaton of features for each class of groups Classfcaton of AE sgnals specfed by parameters Refnement of sgnals arrval tme correcton of locaton maps End Fg.. Flowchart of automated method of statstcal processng of AE data. Fgure shows the flowchart of the proposed method. The data are processed n two stages. At the frst stage the clusterng of AE sgnals takes place. The data recorded by each measurng channel are analyzed ndvdually. The correlaton coeffcent s used as a measure of smlarty of each par of sgnals. The classes of sgnals are formed at ths frst stage. Next, the classfed sgnals are combned n groups n such a way that one group wll nclude the sgnals of dfferent measurng channels, whch belong to the same AE event. The next stage of algorthm s the formaton of classes of groups ; to the same class of groups assgned are the groups wheren the sgnals recorded by one and the same class of sgnals correspond to the same classes of sgnals. The quantty and parameters of AE sources are estmated by the results of classfcaton of sgnal groups. When a part of AE sgnals s defned only by parameters, and waveforms are absent, the classes of sgnals and classes of groups are formed on the bass of ncomplete dagnoss nformaton. For classfcaton of AE sgnals, specfed only by ther parameters, each class of groups s characterzed by a set of features. The classfcaton n ths case s accomplshed on the bass of multdmensonal emprcal dstrbuton functon bult for the calculated features. Clusterng of Acoustc Emsson Sgnals For AE sgnal clusterng, t s necessary to determne a dstance measure [3]. In order that an estmaton error of AE parameters does not nfluence on the classfcaton result, the correlaton coeffcent r was used as a measure of smlarty of two sgnals; see Eq. (). The prelmnary analyss has shown that sgnals relevant to the same AE source and recorded by the same 77
3 measurng channel have a hgh correlaton coeffcent, whose value vares from 0.70 to 0.99 dependng on the nature of the AE generatng process. r = N! = N! = ( x " x) ( x " x)( y 2 N! = " y) ( y " y) 2 Usng the correlaton coeffcent as the dstance measure of AE sgnals, an automatc correcton of arrval tmes of sgnals, whch belong to the same cluster, can be carred out, because the cross-correlaton functon reaches ts peak at the tme correspondng to a dfference of arrval tmes of AE sgnals. When processng bulk data, the calculaton of correlaton coeffcents for all pars of sgnals requres a long tme; to speed up the data processng, a wavelet decomposton may be used, namely, a sgnal decomposton by wavelet packets [2]. The decomposton by wavelet packets s one of varety of the mult-resoluton analyss, and t s the sgnal decomposton on the bass of wavelets (n a general case by the Rss bass) specfed for a sequence of subspaces embedded to each other. w 2 n( x) = 2! h( k) wn (2t " k) w ( 2 n+ x) = 2! g( k) wn (2t " k), (2) k k where g( k) = (! ) h(! k) s defned by the type of wavelet functon. Under a dscrete wavelet transform the sgnal s decomposed nto a low-frequency component - approxmaton and a hgh-frequency component - detalng. At the next level both the approxmaton and the detalng are subject to further decomposton. So, at j-level of decomposton the 2 j coeffcents are calculated. In ths case the coeffcent (j, k) localzes energy wthn the frequency range (Eq. 3), where Ω 0 s the frequency correspondng to a half of the sample rate frequency & ' 0 ' 0 # (' = $ k, ( k + )!. (3) j j % 2 2 " As a rule, the AE sgnal energy s non-unformly dstrbuted on the spectrum, and often 3 or 5 coeffcents of cluster decomposton are suffcent for locaton of 95-99% of energy. Moreover, the dmenson of nformatonal components, as a rule, s at least four tmes smaller than the orgnal sgnal dmenson. The research has shown that the precse estmate of correlaton coeffcent based on the weghted sum of correlaton coeffcents of wavelet-packet decomposton components s 85%. Wth the clusterng of AE sgnals, the quantty of clusters s to correspond to the quantty of the assumed AE sources. In desgnng the clusterng algorthm, a partcular attenton was gven to the true determnaton of clusters quantty. For llustratng ths am, we suggested a two-stage clusterng dagram, as shown n Fg. 2; that s, the flowchart of clusterng algorthm. At the frst stage, a herarchcal agglomeratve algorthm wth complete lnk clusterng s appled. At the second step an teraton analyss s appled, the dstances between centers of mass of the clusters formed are calculated, and two clusters havng the maxmum and above-threshold dstance between the clusters centers are merged. Thereafter, the re-clusterng s accomplshed by the method of k-averages. When usng ths flowchart of clusterng algorthm, the clusterng s flexble and controllable; t uses two settngs the threshold value for sgnal addton to the cluster (at herarchcal clusterng) and the threshold value for the nter-cluster dstance. The settng values are selected on the bass of nformaton about the test object and the testng condtons. Clusterng of Groups of Acoustc Emsson Sgnals At the next stage of algorthm, the groups of sgnals relevant to the same AE event are analyzed. For formaton of such the groups analyzed are the arrval tmes of AE sgnals, the dffer- k () 78
4 Start Herarchcal clusterng wth clusters mergng as per the complete lnk rule No Is the mnmum dstance between centers of clusters less than the threshold one? Yes Clusters mergng Reclusterng by the method of k-average Fg. 2. Flowchart of two-stage clusterng algorthm of AE sgnals. ence of recordng tme of the frst and last sgnals n the group should not exceed the specfed value, whch s determned by the rato of ts maxmum overall dmenson and mnmum velocty of acoustc wave propagaton expermentally measured for the gven test object. An AE sgnal beyond the tme wndow s ntal AE sgnal for the next group. To the same class of groups assgned are the groups wheren the sgnals recorded by the same measurng channels correspondng to the same classes of sgnals. The study of algorthm has shown that for assgnng the group of sgnals to one or other class t s suffcent that the classes of sgnals concde at least for two channels n the group. The membershp of classes of groups can be also defned for AE sgnals, even wth no nformaton about the AE sgnal waveform. For ths purpose, each class of groups obtaned s characterzed by the values of features lsted below representng a medan of dstrbuton for each class. Numbers of three channels wth mnmum tme of sgnal arrval {n_t, n_t 2, n_t 3 } Numbers of three channels wth maxmum sgnal ampltude {n_a, n_a 2, n_a 3 } Maxmum value of sgnal ampltude n the group A max Rato of ampltudes of sgnals recorded by channels n_t 2 and n_t, and also n_t 3 and n_t {A 2 & A 3 } The sgnal group belongng to one or other class s defned smlarly to the k-averages method, by the mnmum dstance between the features, characterzng each class of groups and the group of features to be analyzed. The classes of groups obtaned by such a manner characterze the potental AE sources wth a hgh degree of probablty. The quantty of classes of groups corresponds to the quantty of AE sources; the numbers of channels, by whch the sgnals wth the mnmum arrval tme are recorded, defne the number of locaton zone. The quantty of groups n each class and the energy of AE sgnals ncluded nto the group can defne the degree of danger of AE source. Results and Dscusson The practcal examples can vsually confrm the effectveness of the present method. To research regulartes of AE n concrete, a seres of experments was carred out. On a concrete cube wth a -m sde length, eght AE sensors were placed, one at each vertex. The AE sensors were used both for AE measurement mode and as a pulser for the smulaton of AE sources. To llustrate advantages of the present method, two of the experments were selected. Experment llustrates the case when the AE sources are not found because of mechancal nose and mpacts. There are two AE sources, a pulser that smulates a growng defect, and a hammer that smulates mechancal nose of equpment. Fgure 3а shows the result of volume locaton of 79
5 the AE sources. Indcatons dstrbuted over the whole feld of locaton are generated by the mechancal nose source or hammer blows. As the hammer mpacts are made manually, the relevant sgnals have dfferent waveforms. In ths case, determnaton of the arrval tme by the threshold method entals the dfference n arrval tme errors. The errors of arrval tme determnaton dffer n ther meanng and sgn for the sgnals wth dfferent waveforms. Ths s one of the possble reasons for the dstrbuton of source locatons. Applcaton of the statstcal method of data analyss allows not only formng the class of groups of AE mpulses emtted by the smulator, but also pontng out as an ndependent class the sgnals relevant to the hammer blows. Table а shows the results of operaton; the attrbutes gven n ths table conform to the lst of class of group features. The names of features n Table comply wth the prevous lst of features. Each lne of the table characterzes one "class of groups". а) b) Fg. 3 Locaton results. a) Experment, b) Experment 2. Table а AE class data 2 classes Class Q-ty of A max n_t n_t 2 n_t 3 No. Class Elements Table b AE class data 6 classes Class Q-ty of A max n_t n_t 2 n_t 3 No. Class Elements In Experment 2, there are sx AE sources n all. Two types of acoustc waves were generated at three postons on the concrete cube surface by means of a pulser and a Hsu-Nelsen source. Fgure 3b shows the three locatons of the AE sources. Durng the statstcal processng of data, applcaton of the present method can gve sx classes of events correspondng to two dfferent sources of AE at three ponts on the test object surface, as shown n Table b. There are two "classes of groups" n each locaton zone, whch s defned by the channels number {n_t, n_t 2, n_t 3 }. One more useful mplementaton of the present method s structurng and data compresson. The data structurng s realzed due to replacement of the AE sgnals beng analyzed by the "classes of sgnals" and "classes of groups". Thus, the quantty of the nformaton under analyss s reduced consderably. For example, durng expermental AE studes from pttng corroson growth, several hundred thousand sgnals were recorded; after the statstcal analyss of data n an auto-mode wthout pre-processng about 20 representatve classes of "groups of pulses" descrbng dfferent stages of corroson damage development were defned [6]. In practcal AE applcatons, t s effectve to use automated method of statstcal analyss for processng of the AE montorng data, especally when t s necessary to analyze changes n the
6 structure of AE sgnals recorded for any length of tme, and also n the case of testng sophstcated ndustral facltes, for whch the constructon of an acceptable locaton scheme appears to be dffcult. Concluson In ths paper descrbed s an automated statstcal analyss method of AE data, whch makes t possble to structure the AE test data through parttonng nto dfferent clusters or the groups of sgnals, characterzng the dfferent AE sources. The key features of the gven method are, frstly, the possblty to process data n an automatc mode wth mnmum nvolvement of operator and a mnmum number of settngs, and, secondly, the possblty to carry out the analyss of the heterogeneous dagnoss nformaton. Based on the results of statstcal analyss, t s possble to specfy the quantty of AE sources, to carry out ts zone locaton, and to get addtonal evaluaton of the danger crteron wthout resortng to the prelmnary locaton. When usng the correlaton coeffcent as a measure of proxmty under cluster analyss of AE sgnals, we can carry out an automatc correcton of sgnal arrval tmes belongng to the same clusters, because the cross-correlaton functon reaches ts peak at the pont of tme correspondng to the dfference of arrval tmes of AE sgnals. References. V.A. Barat, A.L. Alyakrtsky. Statstcal method for processng of AE sgnals and ther parameters for ncreasng of relablty of the testng results. Proceedng of the 7th Russan- Internatonal Scentfc-Technologcal Conference on Non-Destructve Testng and Dagnostcs. Ekaternburg, (on the CD-ROM) 2. Ingrd Daubeches, Ten lectures on wavelets, SIAM, Phladelpha, Ajvazyan S.A., Buhshtaber V.M., Enjukov I.S., Meshalkn L.D., Appled statstcs n 3 parts, 989. (n Russan) 4. L.N. Stepanova, A.E. Kareev. Development of a method for the dynamc clusterng of AE sgnals for ncrease of accuracy of ther localzaton, Control Dagnostka, 2003, 6, pp (n Russan) 5. A. A. Anastassopoulos, T. P. Phlppds. Clusterng Methodologes for the evaluaton of AE from Compostes, Journal of Acoustc Emsson, 3, (/2), 995, Yu.S. Popkov, A.L. Alyakrtsky, E.Yu. Sorokn, D.A. Terentyev. AE method for determnaton of pttng corroson depth and montorng of defect propagaton rate. the Proceedng of EW- GAE Vallen VsualAE. The standard n Acoustc Emsson software. Vallen-Systeme GmbH AE-Studo. NPF Daton. 8
TECHNIQUE 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 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 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 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 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 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 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 informationTN348: Openlab Module - Colocalization
TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages
More informationLobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide
Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.
More informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
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 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 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 informationDYNAMIC NETWORK OF CONCEPTS FROM WEB-PUBLICATIONS
DYNAMIC NETWORK OF CONCEPTS FROM WEB-PUBLICATIONS Lande D.V. (dwl@vst.net), IC «ELVISTI», NTUU «KPI» Snarsk A.A. (asnarsk@gmal.com), NTUU «KPI» The network, the nodes of whch are concepts (people's names,
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 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 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 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 informationSimulation Based Analysis of FAST TCP using OMNET++
Smulaton Based Analyss of FAST TCP usng OMNET++ Umar ul Hassan 04030038@lums.edu.pk Md Term Report CS678 Topcs n Internet Research Sprng, 2006 Introducton Internet traffc s doublng roughly every 3 months
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 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 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 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 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 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 informationBiostatistics 615/815
The E-M Algorthm Bostatstcs 615/815 Lecture 17 Last Lecture: The Smplex Method General method for optmzaton Makes few assumptons about functon Crawls towards mnmum Some recommendatons Multple startng ponts
More informationUnsupervised Learning
Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS Non-Parametrc Approach Mxture of Denstes Maxmum-Lkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and
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 informationUnsupervised Learning and Clustering
Unsupervsed Learnng and Clusterng Why consder unlabeled samples?. Collectng and labelng large set of samples s costly Gettng recorded speech s free, labelng s tme consumng 2. Classfer could be desgned
More informationProper 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 informationThe Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique
//00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy
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 informationBIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING
An Improved K-means Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College
More informationCLASSIFICATION OF ULTRASONIC SIGNALS
The 8 th Internatonal Conference of the Slovenan Socety for Non-Destructve Testng»Applcaton of Contemporary Non-Destructve Testng n Engneerng«September -3, 5, Portorož, Slovena, pp. 7-33 CLASSIFICATION
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 informationCorrelative features for the classification of textural images
Correlatve features for the classfcaton of textural mages M A Turkova 1 and A V Gadel 1, 1 Samara Natonal Research Unversty, Moskovskoe Shosse 34, Samara, Russa, 443086 Image Processng Systems Insttute
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 informationGraph-based Clustering
Graphbased Clusterng Transform the data nto a graph representaton ertces are the data ponts to be clustered Edges are eghted based on smlarty beteen data ponts Graph parttonng Þ Each connected component
More informationHigh-Boost Mesh Filtering for 3-D Shape Enhancement
Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,
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 informationImperialist Competitive Algorithm with Variable Parameters to Determine the Global Minimum of Functions with Several Arguments
Fourth Internatonal Conference Modellng and Development of Intellgent Systems October 8 - November, 05 Lucan Blaga Unversty Sbu - Romana Imperalst Compettve Algorthm wth Varable Parameters to Determne
More informationSHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE
SHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE Dorna Purcaru Faculty of Automaton, Computers and Electroncs Unersty of Craoa 13 Al. I. Cuza Street, Craoa RO-1100 ROMANIA E-mal: dpurcaru@electroncs.uc.ro
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 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 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 informationDependence of the Color Rendering Index on the Luminance of Light Sources and Munsell Samples
Australan Journal of Basc and Appled Scences, 4(10): 4609-4613, 2010 ISSN 1991-8178 Dependence of the Color Renderng Index on the Lumnance of Lght Sources and Munsell Samples 1 A. EL-Bally (Physcs Department),
More informationVRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,
VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual
More informationKeyword-based Document Clustering
Keyword-based ocument lusterng Seung-Shk Kang School of omputer Scence Kookmn Unversty & AIrc hungnung-dong Songbuk-gu Seoul 36-72 Korea sskang@kookmn.ac.kr Abstract ocument clusterng s an aggregaton of
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 informationPetri Net Based Software Dependability Engineering
Proc. RELECTRONIC 95, Budapest, pp. 181-186; October 1995 Petr Net Based Software Dependablty Engneerng Monka Hener Brandenburg Unversty of Technology Cottbus Computer Scence Insttute Postbox 101344 D-03013
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
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 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 informationDiscrete and Continuous Time High-Order Markov Models for Software Reliability Assessment
Dscrete and Contnuous Tme Hgh-Order Markov Models for Software Relablty Assessment Vtaly Yakovyna and Oksana Nytrebych Software Department, Lvv Polytechnc Natonal Unversty, Lvv, Ukrane vtaly.s.yakovyna@lpnu.ua,
More informationTsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance
Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for
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 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 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 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 informationA PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION
1 THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 4, Number 2/2003, pp.000-000 A PATTERN RECOGNITION APPROACH TO IMAGE SEGMENTATION Tudor BARBU Insttute
More informationDistance Calculation from Single Optical Image
17 Internatonal Conference on Mathematcs, Modellng and Smulaton Technologes and Applcatons (MMSTA 17) ISBN: 978-1-6595-53-8 Dstance Calculaton from Sngle Optcal Image Xao-yng DUAN 1,, Yang-je WEI 1,,*
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
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 informationcos(a, b) = at b a b. To get a distance measure, subtract the cosine similarity from one. dist(a, b) =1 cos(a, b)
8 Clusterng 8.1 Some Clusterng Examples Clusterng comes up n many contexts. For example, one mght want to cluster journal artcles nto clusters of artcles on related topcs. In dong ths, one frst represents
More informationHelsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)
Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute
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 informationCell Count Method on a Network with SANET
CSIS Dscusson Paper No.59 Cell Count Method on a Network wth SANET Atsuyuk Okabe* and Shno Shode** Center for Spatal Informaton Scence, Unversty of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
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 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 informationBRDPHHC: A Balance RDF Data Partitioning Algorithm based on Hybrid Hierarchical Clustering
015 IEEE 17th Internatonal Conference on Hgh Performance Computng and Communcatons (HPCC), 015 IEEE 7th Internatonal Symposum on Cyberspace Safety and Securty (CSS), and 015 IEEE 1th Internatonal Conf
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 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 informationAvailable online at Available online at Advanced in Control Engineering and Information Science
Avalable onlne at wwwscencedrectcom Avalable onlne at wwwscencedrectcom Proceda Proceda Engneerng Engneerng 00 (2011) 15000 000 (2011) 1642 1646 Proceda Engneerng wwwelsevercom/locate/proceda Advanced
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 informationReview of approximation techniques
CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated
More informationLocalization Algorithm for Acoustic Emission
Avalable onlne at www.scencedrect.com Physcs Physcs Proceda 3 (2010) 00 (2009) 863 871 000 000 www.elsever.com/locate/proceda Internatonal Congress on Ultrasoncs, Unversdad de Santago de Chle, January
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 informationMachine Learning. Topic 6: Clustering
Machne Learnng Topc 6: lusterng lusterng Groupng data nto (hopefully useful) sets. Thngs on the left Thngs on the rght Applcatons of lusterng Hypothess Generaton lusters mght suggest natural groups. Hypothess
More informationClustering Algorithm of Similarity Segmentation based on Point Sorting
Internatonal onference on Logstcs Engneerng, Management and omputer Scence (LEMS 2015) lusterng Algorthm of Smlarty Segmentaton based on Pont Sortng Hanbng L, Yan Wang*, Lan Huang, Mngda L, Yng Sun, Hanyuan
More informationObject-Based Techniques for Image Retrieval
54 Zhang, Gao, & Luo Chapter VII Object-Based Technques for Image Retreval Y. J. Zhang, Tsnghua Unversty, Chna Y. Y. Gao, Tsnghua Unversty, Chna Y. Luo, Tsnghua Unversty, Chna ABSTRACT To overcome the
More informationResearch and Application of Fingerprint Recognition Based on MATLAB
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Automaton and Control Systems Journal, 205, 7, 07-07 Open Access Research and Applcaton of Fngerprnt Recognton Based on MATLAB Nng Lu* Department
More informationFuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches
Proceedngs of the Internatonal Conference on Cognton and Recognton Fuzzy Flterng Algorthms for Image Processng: Performance Evaluaton of Varous Approaches Rajoo Pandey and Umesh Ghanekar Department of
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 15
CS434a/541a: Pattern Recognton Prof. Olga Veksler Lecture 15 Today New Topc: Unsupervsed Learnng Supervsed vs. unsupervsed learnng Unsupervsed learnng Net Tme: parametrc unsupervsed learnng Today: nonparametrc
More informationAn Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem
An Effcent Genetc Algorthm wth Fuzzy c-means Clusterng for Travelng Salesman Problem Jong-Won Yoon and Sung-Bae Cho Dept. of Computer Scence Yonse Unversty Seoul, Korea jwyoon@sclab.yonse.ac.r, sbcho@cs.yonse.ac.r
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 informationEECS 730 Introduction to Bioinformatics Sequence Alignment. Luke Huan Electrical Engineering and Computer Science
EECS 730 Introducton to Bonformatcs Sequence Algnment Luke Huan Electrcal Engneerng and Computer Scence http://people.eecs.ku.edu/~huan/ HMM Π s a set of states Transton Probabltes a kl Pr( l 1 k Probablty
More informationOptimal Design of Nonlinear Fuzzy Model by Means of Independent Fuzzy Scatter Partition
Optmal Desgn of onlnear Fuzzy Model by Means of Independent Fuzzy Scatter Partton Keon-Jun Park, Hyung-Kl Kang and Yong-Kab Km *, Department of Informaton and Communcaton Engneerng, Wonkwang Unversty,
More informationCalculation of Sound Ray s Trajectories by the Method of Analogy to Mechanics at Ocean
Open Journal of Acoustcs, 3, 3, -6 http://dx.do.org/.436/oja.3.3 Publshed Onlne March 3 (http://www.scrp.org/journal/oja) Calculaton of Sound Ray s Trajectores by the Method of Analogy to Mechancs at Ocean
More informationComparison Study of Textural Descriptors for Training Neural Network Classifiers
Comparson Study of Textural Descrptors for Tranng Neural Network Classfers G.D. MAGOULAS (1) S.A. KARKANIS (1) D.A. KARRAS () and M.N. VRAHATIS (3) (1) Department of Informatcs Unversty of Athens GR-157.84
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 informationCOMPLETE CALCULATION OF DISCONNECTION PROBABILITY IN PLANAR GRAPHS. G. Tsitsiashvili. IAM, FEB RAS, Vladivostok, Russia s:
G. Tstsashvl COMPLETE CALCULATION OF ISCONNECTION PROBABILITY IN PLANAR GRAPHS RT&A # 0 (24) (Vol.) 202, March COMPLETE CALCULATION OF ISCONNECTION PROBABILITY IN PLANAR GRAPHS G. Tstsashvl IAM, FEB RAS,
More informationLoad-Balanced Anycast Routing
Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance
More informationIMPACT OF RADIO MAP SIMULATION ON POSITIONING IN INDOOR ENVIRONTMENT USING FINGER PRINTING ALGORITHMS
IMPACT OF RADIO MAP SIMULATION ON POSITIONING IN INDOOR ENVIRONTMENT USING FINGER PRINTING ALGORITHMS Jura Macha and Peter Brda Unversty of Zlna, Faculty of Electrcal Engneerng, Department of Telecommuncatons
More informationCOMPARISON OF A MACHINE OF MEASUREMENT WITHOUT CONTACT AND A CMM (1) : OPTIMIZATION OF THE PROCESS OF METROLOGY.
TEHNOMUS - New Technologes and Products n Machne Manufacturng Technologes COMPARISON OF A MACHINE OF MEASUREMENT WITHOUT CONTACT AND A CMM (1) : OPTIMIZATION OF THE PROCESS OF METROLOGY. WOLFF Valery 1,
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 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 informationAn Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 97-735 Volume Issue 9 BoTechnology An Indan Journal FULL PAPER BTAIJ, (9), [333-3] Matlab mult-dmensonal model-based - 3 Chnese football assocaton super league
More informationSynthesizer 1.0. User s Guide. A Varying Coefficient Meta. nalytic Tool. Z. Krizan Employing Microsoft Excel 2007
Syntheszer 1.0 A Varyng Coeffcent Meta Meta-Analytc nalytc Tool Employng Mcrosoft Excel 007.38.17.5 User s Gude Z. Krzan 009 Table of Contents 1. Introducton and Acknowledgments 3. Operatonal Functons
More informationApplication of Clustering Algorithm in Big Data Sample Set Optimization
Applcaton of Clusterng Algorthm n Bg Data Sample Set Optmzaton Yutang Lu 1, Qn Zhang 2 1 Department of Basc Subjects, Henan Insttute of Technology, Xnxang 453002, Chna 2 School of Mathematcs and Informaton
More informationVirtual Machine Migration based on Trust Measurement of Computer Node
Appled Mechancs and Materals Onlne: 2014-04-04 ISSN: 1662-7482, Vols. 536-537, pp 678-682 do:10.4028/www.scentfc.net/amm.536-537.678 2014 Trans Tech Publcatons, Swtzerland Vrtual Machne Mgraton based on
More information