Adaptive Control

Automatic 3D Model Generation based on a Matching of Adaptive Control Pointsthe shape of the arterial is different for each3D model of standardhich is built from a data base that implementedKorean vascular system(Lee et al 2006)e have limited the scope of the main arteries for the 3D model of the standard vessel asope of the database for themodel of the standard vesselTable 1 shows the database of tartery of Lt main(Left Main Coronary Artery),LAD(Left Anterior Descending) and LCX(Left Circumflex artery) information

Thisdatabase consists of 40 people with mixed gender informationLt mainOs distal length Os distal length! Os distal lebelow484459430441+059942380436041705235043303192667554450544+0484+3839033603172+5836+0434+0424689d(male)belo919937±1834±161066233±1531±1414155291328+122139(female)above 60 years of 1707+44 4307 4106 125 479 35*0634805 237333Left Mainold(female)Table 1 Database of the coronary arteryCoronary Arter

Adaptive ControlTo quantify the 3D model of the coronary artery, the angles of the vessel bifurcation areasured with references to LCX, Liegardless of their gender and age were selected randomly for measuring the angles of thegramsstandard deviations of each individual measurement are shown in Table 2RAO30°AO30°P0°LAO60°LAO60°AP0°CAUD30°CRA30°CRA30°CRA30°CAUD30°CAUD30°6917123318401509872022380「51759973739223456772897702120577210071713364,1233253124409713500618751

27418808911984571450928772114716770591431845893701635775445118803450473967523479Average 6867661833216231100836269StandardTable 2 Measured angles of the vessel bifurcation from six angiographieI generation of the standard vessel from30°CAUD( Caudal)30°,RAO30°CRA( CranialAnterior)30°,AP(AntRA( Cranial Anterior)30, LAO (Left EntericOblique60PCRA30°LAO60CAUD30°, APO CAUD30°

Automatic 3D Model Generation based on a Matching of Adaptive Control PointsRAO30°RAO30°AP0°VieCAUD30°CRA30°CRA30°AngiogramModelLAO60°LAO60°AP0°CRA30°CAUD30°CAUD30°3D Model7<g

3 3D model generation of the standard vessel from six angiographieEvaluating the angles of the vessel bifurcation from six angiographiesduce themeasurement error whichhen the angle from a singleeasured

Adaptive Controlvessel into2D projection Fig 4 shows the projected images of the sta2D planethrough the projection The projection result can be view as vertices or poFig 4 Projection result for 2D image of standard vessel3 Matching of the Adaptive Control Pointsorresponding control points(Lee et al, 2006)and (Lee et al 2007) In this paper, weextracted feature points of the vessel automatically and defined as control points(Lee et al006)and (Lee et al, 2007) Feature points mean is referred to the corner points of an objectpared to the surrounding pixels in an image,which are differentiated from other points in an image Such feature points can be defined inny differentParker, 1996)and(Pitas, 2000)

Thets thdo not change in spite of specific transformations Generally feature points can be d) The first onen-linear filter such as theSUSANetector proposed by Smith(Woods et al, 1993)which relates each pixel ton area centered by a pixel In thisit is called the susanall the pixels hael If the center pixel isfeature point is also referred to as a"corner"), SUSANg thexels around it A SUSRosenfeld's method1997) This kind of method needs to extract edges indvance, and then elucidate the feature points using the information on the curvature of thedges The disadvantage of this method is required more needs a complicated computation,processing speed is relatively slow The third method is exploial, 2003) It produthrough an eigenvalues analysis Since it does notneed to use a slide window explicitly, its processing speed is very fast Accordingly, this

Automatic 3D Model Generation based on a Matching of Adaptive Control Pointsper used the Harris corner detector to find the control points of standard and individual2006)and(Lee,2031 Extraction of the Control pointsector is a popular interest point detector due to its strong invariancedcorrelation functiothe local changes of the signal with patches shifted by a smallount in different directions(Derpanis, 2004) However, the Harris corner detector hasFig 5 shows extracted 9 control points in individual vessel by using the Harris corneretector We noticed thatector wdividual vessel than standard vessel Fig 6 shows theion of control points fromindividual飛Fig

5 Extracted 9 control pin individual vessel32 Extrae performed thinning by using the structural characteristics offind thector Lee, 200hows the thinning prfor detection of corner pointsindividual vessel人(a) Segmented vessel)Thinned vesseFig 6 Thinning process for detection of corner points in individual vessevascular treed into a set of elementary compoments, and bifurcation (Wahle et al, 1994) Using this intuit

Adaptive ControlelIn etal, 2001)and (Lee, 200vascular tree of thinned vessel consists of three verticesas the following equation (1) Here, vertices(voint )are comprised a start point (vsrand two end points(vendint(1)IvIf the reference point is a vertex the closest twedefinedthe corner points If the refebifurcationclosest to it after comparing the distances between therol points aredefined as the corner points As shown in Fig

7, if the reference point is the vertex(Vstart point ),vi and 12 become the corner points; if the reference point is the bifurcation(bif),v6, l'i and vis become the corner points(Lee, 2007)Primitives of a vascular net33 Adaptive Itof the Control points between cOnce the contrcorner points are extracted from an individual vessel, anHard vessel is applied For an accurate matching, the control pointsed into the corresponding standard vessel in proportion to thetrol points between theLee,2007)g8 shows theterpolation of the control points Control points of astandard vessel are adaptively interpolated by the distance rate between control point (v3)4)of an individual vessel Fig 8(a)shows the extracted control

Automatic 3D Model Generation based on a Matching of Adaptive Control Pointspoints from an individual vessel, and (b) shows an example of control point interpolatedpoints from(a)imagea)Individual vesselb)Standard vesselFig 8 Interpolation of the control points for a standard vessthe segmented vessel in the individual vessel and an adaptive interpolation of theorresponding the control points in the standard vesselaI Prole下Fig 9 Result of an adaptive interpolation of the corresponding control pointsWe have warped the standard vessel with respect to the individual vessel

Gts of corresponding control points, Ssu and I-iniiz,is), the warping is apphe standard vessel to suit the individu

al vessel Here, S is a set of control points in thetandard vessel and I is a set of one in the individual vessel (Lee et al, 2006)and

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"in9 2009 In-techAdditional copies can be obtained fromPrint published January 2009Adaptive Control, Edited by Kwanho YouAdaptive Control 1 Kwanho You

Preface1950s Since more and more adaptive algorithms are applied in van%eredrtant for practicaltion as itis certain thatant guidance for technology devAlso adaptive control has been believed as a breakthrough for realization of intellisystems Even with the parametric and model uncertainties, adaptive control enableshe time varying changes and manipulate the controllermultivariable systemsith the advent of high-speed microproceis possible to implement the innovative adaptive algorithmn in real timethe booktroduce their recent research results and provide new idea for improved performanceThe book is organized in the following way

Therecussing the issuesof adaptive control application to model generation, acnd feedback, electrical drives, optical communicatiesimulation andimplementation:Chapter One: Automatic 3D Model Generation based on a Matching of AdaptivePointsH Choiapter Two: Adaptive Estimation and ContSystemsParametdUnknown Exosystems, by I Mizumoto and Z IwaiChapter Four: Output Feedback Direct Adaptive Control for a TwRobot Subject to Parameter Changes, by S Ozcelik and E MirandaChapter Five: Discrete Model Matching Adaptive Control for Potentially Inersely Non-Stable Continuous-Time Plants by Using Multirate Sampling, by SChapter Six: Hybrid Schemes for Adaptive Control Strategies, by R Ribeiro and K

Seven: Adaptive Control for Systems with Randomly Missing Measureents in a Network Environment, by Y Shi and HFChapter Eight: Adaptive Control based on Neural Network, by S Wei, Z Lujin, Zinhai, and M sChapter Nine: Adaptive Control of the Electrical Drives with the Elastic Couplinusing Kalman Filter, by K Szabat and T

Orlowska-KowalskaChapter Ten: Adaptive Control of Dynamic Systems with Sandwiched Hysteresibased on Neural Estimator, by Y Tan, R Dong, and X ZhaoChapter Eleven: High-Speed Adaptive Control Technique based on Steepest Decent Method for Adaptive Chromatic Dispersion Compensation in Optical CommunIcations, by K Taa and a hiroseTwelve: Adaptive Control of Piezoelectric Actuators with Unknown Hys-Chapter Thirteen: On the Adaptive Tracking Control of 3-D Overhead Crane Sys-Chapter Fourteen: Adaptive Inverse Optimal Control of aystem, by Y Satoh, H Nakamura, H Katayama, and H NishitaniChapter Fifteen: Adaptive Precision Geolocation Algorithm with Multiple Modncertainties, by w Sung and KChapter Sixteen: Adaptive ControlClass of Non-affine Nonlinear Systema Neural Networks, by Z TongWe expect that the readers haurse in automatic control, linear systems,ms, Thwritten in a self-contained way for bettedustrial engineers, gradd study and the researcherslated in adaptive control field such as electrical, aeronautical, and mechanical enginKwanho you

ContentsNa-Young Lee, JoongJae Lee, Gye Young Kim and Hyung-l Chov3

Adaptivelationwn linear systems wi0654 Output Feedback Direct Adaptive Control for ank Flexible robotDiscrete Model Matching Adaptive Control for Potentially Inversely Nore Plants by Using Multirate SamplingS A/onso- Quesada arDe la senybrid Schemes for Adaptive Control StrategiesRicardo Ribeiro and kurios queir6ang Shi and Huazhen Fangand Miao SiyiOnlowska-KowalskaAdaptive Control of Dynamic Systems with Sandwichonghong Tan, Ruili Dong and Xinlong zhao

VIpeed Adaptive Control Technique Based on Steepest Descent4tical Comicationsaizawa and Akira Hiroseon the Adaptive Tracking Control of 3-D Overhead Crane Systemof a magneyuki Satoh, Hisakazu Nakamura, Hitoshi Katayama and Hirokazu Nishitanicolocation Algorithm with Multiple Model Uncertainties 3236

Adaptive Control for a class of Non-affine Nonlinear Systems via Neural 337NetworksZhao to

Automatic 3D Model Generation based on aMatching of Adaptive Control PointsNa-Young Lee, Joong-Jae Lee Gye-Young Kim and Hyung-ll ChoiRadioisotope Research Division, Korea Atomic Energy ResearchCenter for Cognitive Robotics research, Korea Institute of Sciene and TeSchool of computing soongsil universipublic of Koreavailable, nor can be exactly measured in a 2D image Therefore, highly accurate softwarr a 3D model ofe generatedndard vessel becthe shape of the arterial is different for each individual vessel,where the standard vessel can be adjusted to suit individual vessel In this paper,wepropose a new approach for an automatic 3D model generation basedadaptive control points The proposed method is carried out in three steps First, standarduired

The standard vesseluired by a 3D modelrojection, while the individual vessel of the first segmented vessel bifurcation is obtainedontrol points between the standard and individualessels, where a set of control and corner points are automaticallyd using the Harrisrner detector If control points exist betadaptively interpolated in the corresponding standard vessel whichonal to thethose control points of standard vessel Finally, we apply warping on the standard vessel tosuit the individual vessel using the Tps Thin Plate Spline)interpolation function ForKeywords: Ceangiography, adaptive control point, standard vessel, individualvessel, vessel warpingX-ray angiography is the most frequently used imaging modality to diagnose coronarytheir severity Traditionally, this assessment is performedirectly from the angiograms, and thus, can suffer from viewpoint orientation dependennd lack of precision of quantitativeares due touncertainty

Adaptive Controlger et al 2000),(Lee et al 2006)and (Lee et al 2007 ) 3D model is providedations and aneurysms(Holger et al 2005)

Consequently, accurate software for aof patients It could lead tofast diagnosis and make it more accurate in an ambiguous conditionIn this paper, we present an automatic 3D model generation based on a matchingthree steps: image acquisition, matching of the adaptitrol points and thnage in standard and indire described Section 3 presents the matching of the corresponding control pthe standard and individual vessels Section 4 describes the 3D modelling ofExperimental results of the vessel transformation are given in Section 5 Finally, we pthe condSection 6Fig 1 Overall flow of the system configuration