Alonso, A.

Hidalgo, C.

Klinger, T.

König, R.

Thomsen, H.

English

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Pattern recognition techniques in plasma turbulence imaging

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Pattern recognition techniques in plasma turbulence imagingH. Thomsen, T. Klinger , R. König, A. Alonso†, C. Hidalgo†Max-Planck-Institut für Plasmaphysik, EURATOM Association, Greifswald, Germany†Laboratorio Nacional de Fusión. Asociación EURATOM-CIEMAT, Madrid, SpainIntroductionThe availability of fast framing CCD cameras with up to 1 million frames per second enablesthe study of edge plasma turbulence by means of the Gas Puff Imaging (GPI) technique [1].The data contain a lot of information about the turbulent dynamics in the plasma boundary.Potentially, one could gain deep insight into edge plasma turbulence, however, the data analy-sis is a challenge for several reasons: 1. high complexity, 2. huge amounts of data (2d spatialinformation with good temporal resolution), 3. no established data analysis schemes for suchdata. Typical data rates of todays fast framing cameras are of the order of 100 Mbyte/s. E.g.,the Photron Ultima 40k camera (specs: 40500 frames/s with 64x64 pixel resolution, 8 bit) hasa data rate of 160 Mbyte/s with a recording time of up to 2 s (limited by the amount of cameramemory, only). Taking into account the technical improvements in camera resolution which canbe expected over the next few years, the data storage problemwi l be getting more severe.Figure 1: Sequence of video frames recorded by anUltima 40k fast framing camera with a rate of 40.5kFrames/s. The camera is viewing in toroidal directionand records light from the radial-poloidal plane of theedge plasma of the TJ-II stellarator (discharge #11431).The moving average intensity of each pixel over 100frames is subtracted. Inset: The view of the camera andthe magnetic configuration in TJ-II (top view).Due to the stochastic nature of turbulence,a statistical characterization scheme is ap-propriate. Useful characterizing quantities (ormeta-information) are sizes and life timesof structures as well as propagation veloci-ties. For one spatial and one temporal dimen-sion only, the average values of these quanti-ties can be derived from fits to the 2d-cross-correlation function [2]. This analysis methodis not appropriate for the fast camera datawith two spatial dimensions, since the dimen-sionality in the cross-correlation function isconserved. Here, we describe a different ap-proach for the extraction of meta-informationby means of pattern recognition techniquesand demonstrate the capability of this methodfor preliminary data from turbulence imaging32nd EPS Conference on Plasma Phys. Tarragona, 27 June - 1 July 2005 ECA Vol.29C, P-2.030  (2005)measurements on the TJ-II stellarator. The Photron Ultima 40k camera recorded the turbulentfluctuations in the visible light in a view approximately parallel to the magnetic field. A se-quence of 9 frames is shown in Fig. 1.Region hierarchy and extraction of meta-informationPattern recognition generally consists of three basic steps, namely: segmentation, featureextraction and classification [3]. The segmentation divides the data in regions, which shouldrepresent existent structures in the data. If we consider a single video frame, the interestingsubgroups are the local maxima and minima. For the multi-scale d ta in our case, a watershedalgorithm [3] yields good extraction results. The algorithms used here are programmed in IDL[4]. In Fig. 2, a sample input frame and the detected local maxi are shown. The recursiveFigure 2:(a) Sample input for the segmentation: Objects A–J with different amplitudes and sizes. (b) Extractionresult are the 4 objects, which do not contain more than one sub-object and are larger than the minimum detectedsize. The amplitude is the average over the intensity of the det cted structure size. (c) Region object hierarchy intree representation. The green objects follow the selection rule (see text), the darker green objects are also largerthan the minimum size (rejection of pixel artifacts).algorithm looks for regions with a higher amplitude than a given base-level. In each of theobtained region, all regions with the next-higher level areextracted and so on, until the regionswith the highest level are found. At the end of the segmentation, a hierarchy of region objects(which all know their parent and their children objects) describes each frame (Fig. 2c). Pixelartifacts are suppressed by the definition of a minimal allowable structure size. A useful subsetof these region objects are those regions, which are one level above a fork (that is a parentobject with two or more children objects) and which have no fork in their descendants (cf. Fig.2). This subset maps the basic structure of the region hierarchy. Moreover, the region objectsin the subset have no intersections between each other, which is advantageous for the regiontracking described below.In a next step, the features (as mean intensity, size, position of the centroid, elongation, aspectratio and tilt angle) are extracted from those region objects by principle components analysis.This way, the shapes of the region objects are approximated by ellipsoids. Each region object32nd EPS 2005; H.Thomsen et al. : Pattern recognition techniques in plasma turbulence imaging 2 of 4is represented by a single point in a low-dimensional feature space. Provided that this repre-sentation is sufficient, i.e. the features contain the relevant turbulent structures, the problem ofthe large data amounts to be handled is solved: the typical packing ratio is of the order of 1 /1,000,000. It is of course necessary to check the assumptionof a sufficient approximation bycomparison of raw and meta data. However, as will be shown in the next section, the utilizedsegmentation algorithm is already quite powerful for the stati ical description of the turbulentdynamics.For the classification, sequences of frames are considered,since we are interested in theturbulent dynamics in the spatio-temporal domain (not onlyspatial). This is possible by a simpleobject tracking based on a minimal distance criterion betwen region objects in subsequentframes. The paths of region objects can be followed over several frames and the velocities arecalculated accordingly. One main advantage of the pattern recognition technique, which cannotbe easily accessed by other methods, is the possibility to get the featuredistributionsin contrastto just averages. Histograms are a good representation, e.g. of the size of region objects, life timeand velocity . We note here, that effects like region splitting (which might occur in a shearedflow) will not always be detected in a correct way by this simple approach. In a further step itis planed to incorporate the full hierarchy of region objects for a better matching of features tothe turbulent structures and in order to improve the tracking results.First results of pattern recognition characterizationIn Fig. 3(a) the paths of the basic structures are plotted forframe #7 of Fig. 1. The positivestructure ’1’ is moving from the middle of the left edge towards the lower right corner. Structure’3’ starts higher at the top left edge and then shows a similartrajectory. The negative structure’2’ seems to follow structure ’1’, suggesting a dipole strucure. However, this effect might bean artifact caused by the data preprocessing, since the subtraction of the mean from the originaldata causes a negative ’hole’ for a moving positive structure. In Fig. 3(b) the paths of regionobjects (maxima only) within a sequence of 100 frames are plotted, and the correspondingvelocity field is shown in Fig. 3(d). A similar flow as previously described for the single regionobjects ’1’ and ’3’ is also evident in this averaged data representation. From the velocity fieldit is thus possible to get valuable information on the turbulence dynamics without the need of(the very time-consuming) frame-by-frame evaluation of the video data.Further useful statistics for the turbulence characterization are histograms of the area, meanvalue, life time and velocity of the basic structures (cf. Fig. 3(c,e)). The detection of largecoherent structures (’blobs’) is of particular interest inhe GPI investigations [1], since theseblobs seem to be responsible for a large fraction of the anomalous transport in the edge plasma.32nd EPS 2005; H.Thomsen et al. : Pattern recognition techniques in plasma turbulence imaging 3 of 4Figure 3:(a) Result of the segmentation and feature-extraction (black ellipsoids: the region objects) and of theobject tracking (black crosses: previous positions, yellow ines: path in subsequent frames). Numbers mark regionobjects discussed in the text. (b) Object paths from tracking, connected to the object life time. (d) Velocity field.These blobs are characterized by a relatively large life timand a strong radiation due to theirhigh density. They can easily be detected within the patternr cognition approach by searchingfor region objects with a large product of life time and intensity.ConclusionsPattern recognition techniques for data analysis of turbulence imaging is a very promisingapproach. This method has the potential to significantly simplify the analysis of the huge dataamount collected by fast framing cameras. Combined with object tracking a unique insight intothe turbulent dynamics is possible. It is possible to estimate distributions of feature parameterslike life times of structures, etc. Moreover, the extractedf atures are a good basis for noveltydetection techniques, which statistically classify turbulence data, e.g. for different dischargescenarios.References[1] S. Zweben et al., Nucl. Fusion44, 134 (2004)[2] J. Bleuel et al., New J. Physics4,38 (2002)[3] M. Sonka, V. Hlavac, R. Boyle. Image processing, Analysis and Maschine Vision -2nd ed. (PWS Publishing,Pacific Grove, 1998)[4] http://www.rsinc.com/idl32nd EPS 2005; H.Thomsen et al. : Pattern recognition techniques in plasma turbulence imaging 4 of 4

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Pattern recognition techniques in plasma turbulence imaging

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