Lykov, V. A.

Vengrinovich, V. L.

English

Tomsk State University Repository

МИНИСТЕРСТВО ОБРАЗОВАНИЯ И НАУКИ РФ Национальный исследовательский Томский государственный университет Томский государственный университет систем управления и радиоэлектроники Болгарская Академия наук ООО «ЛИТТ» ИННОВАТИКА-2015 СБОРНИК МАТЕРИАЛОВ XI Международной школы-конференции студентов, аспирантов и молодых ученых  21–23 мая 2015 г. г. Томск, Россия Под ред. проф. А.Н. Солдатова, доц. С.Л. Минькова Томск – 201540 7. Meier, Ralf (2001): Internal binding of the occupational social work. in: Jente among other things (Ed.), S. 25-62, p. 32 following 8. Wagner, Stefan (2001): Internal positions of the occupational social work and cooperation with other functional bearers. In: Jente among other things (Ed.), S. 71-88, p. 72. 9. Jaeppelt, Alexandra/Goercke, Manuela: The new generation of the occupational social work : employee assistance progrief as an innovative stone of enterprise health promotion. Lighted. publishing company; in 2009, p. 116. 10. Wachter, Karin: Impact of occupational social work – possibilities and borders. In: Small, Susanne/Appelt, Hans-Jürgen (Ed.): Practise manual occupational social work. Kröning: Asanger in 2012, S. 31-44. For the addicted help blacksmith provides an interesting calculation model (blacksmith in 2001, p. 191 following, see final mark 2). 11. Jaeppelt, Alexandra/Goercke, Manuela in 2009, p. 3. For the area operational health pro-motion the analysis of the IGA has brought the different studies to the overview which show partly also a Return of investment. The values reach here from 1:2 to 1:10 (cf. effectiveness and use in 2008). 12. Stoll, Bettina: Occupational social work. Duties; meaning; practical conversion. Regens-burg / Berlin: Valhalla in 2001, p. 195. 13 Edgar (2004): Occupational social work with positive cost use balance. Results of a study from Switzerland. Land round letter DBSH Niedersachsen/Hamburg/Saxony-Anhalt 4/2004th Internet: http://www.dbsh-niedersachsen.de/dbsh2/downloads/lrbs/dbsh404.pdf.    FRACTAL ANALYSIS OF DATA IN MULTI SENSOR SYSTEMS V.L. Vengrinovich1,2, V.A. Lykov1 1Laboratory of Computer Diagnostics, Institute of Applied Physics, Belarus Academy of Sciences;  2National Research Tomsk State University e-mail: veng@iaph.bas-net.by  ФРАКТАЛЬНЫЙ АНАЛИЗ МНОГОМЕРНЫХ ДАННЫХ  В МНОГОСЕНСОРНЫХ СИСТЕМАХ  В.Л. Венгринович1,2, В.А. Лыков1 1Институт прикладной физики Национальной Академии наук Беларуси 2Национальный исследовательский Томский государственный университет  The article presents the new fractal approach to data analysis in the output of automatic Multi Sensors Systems (MSS). In addition to the Multi Compo-nent Methods like PCA and ICA the technique displays much better recogni-tion ability of defected zones in the object under observation, than simple technique like thresholding, usually used for damage or pathology prediction. New technique also involves possibility to detect at early stage the defects, 41 incipient in the construction's area free of sensors. Case study includes simu-lated data fractal analysis using PCA and fractal algorithms respectively Keywords: structural health monitoring, pattern recognition, multi sensors data, fractal analysis.  1. Introduction MSS are more widely used in robot technology, Structural Health Monitor-ing (SHM), and a lot of medical diagnosis techniques for the goal of decision making. Usually the decision are taken on the primitive thresholding level: everything what is higher than a threshold is identified as a damage or pathol-ogy. The smart techniques like principle Component Analysis (PCA) or Inde-pendent Component Analysis (ICA), etc. give additional features to the object under observation [1-3]. But as a whole the problems needs further investiga-tion, because there is a deep gap between perceived sensory system signal and its interpretation.  In living systems, the signal is perceived by the sense organs, and analyzed and interpreted by the brain of an animal or human. The separation of sensing and interpreting is due to differences in biophysics of sense organs and brain, respectively. Mathematically speaking, if the effects of external stimuli on the sensors are the forward problems, the problem of reconstructing a pattern of danger and risk assessment given measured signals, is inverse one. In animals, this inverse problem solution occurs at the level of "sensation, perception and representation" (I.P.Pavlov). It is known that the elephants leaved the risk area of tsunami in Tailand in good time, while the number of victim was more than 200 thousands. Full danger prediction systems was activated automatically. In future the smart data processing techniques should not only feel the signals but also estimate risks, recognize patterns and predict the reactions. In technical systems, where the sensor signals are evaluated quantitatively, the restoration of a danger pattern must be performed by solving the inverse problem using a priori information from past experience [4]. From the stand-point of the Zellner theorem [5] of an optimal signal processing, reconstructed patterns could be optimally restored from the corresponding input data using the inverse problem solution in a Bayesian formulation [6,7]. The alternative inverse problem solutions are as well applicable [4]. It is a great temptation to built the technical MSS by analogy with that in animate beings, which can be reduced to the Table below:    Differ ExcitatiperceiveAcousti(ear)  Multi msors) Excitatisweet, bacids ceExcitatismell renerve   In ages, dnals, aa cons 2. BTothe recvectormeasuUsa stateexceedtoring ing damuch withinconsidence between perceiveWhat do we perceivon of cone and rod  color and light respectic vibration from 16Hzuscle tension (skin-likon of four basic taste itter, and sour, as wentres (Taste buds)  on of mucous membranceptors connected to tthis report we consefects and patholos well as risk assesecutive damage, suig Data Analysis  simplify the abovtangular matrix fo , where I is therements by all sensually the threshold  of danger in SHMs some ultimate vaprocess the changenger in any ofsmaller than ultima automatic threshoer arbitrary loaded rA42 d sensory system signahumans e? cells which vely (eye) Return to 28 kHz Returne array sen- Returnvibratibuds: salty, ll as amino Returnes that have he olfactory Returnider the final goalsgy in the entire objsment of direct andch as collapse), givin the output of SHe approach, considrm  wi number of sensorsors at once: Q>>I. analysis is applied system, the last ilue, e.g. stress, givs in matrix's (1) ter the object’s elemte values. Thus theld-like data proceshell (fig.1), split il and its interpretatioHow we interpret the s visual image  voice image  surface shapes, edges,on, roughness, pain etc.  taste sensation of an ob gustatory of an object  of SHM systems: ect, basing on limi indirect consequeen measurement mM system er the output SHMth the column vect, and Q – number   to matrix’s (1) dats being identified en by designer. Dm value even in a ents, free of sensoy are being unnotissing cycle. To exn cells, the internaT a b l e 1. n in animals and ignal?  temperature, ject search for dam-ted sensors sig-nces (the last is ulti sensor data.  system data in or  and row of simultaneous a to values find when the value uring the moni-state of upcom-rs, are usually ced and missed plain this let’s l stress distribu-43 tion in each cell is under evaluation given stress measurements values in the cells, where the sensors are located (shaded cells).                     (1)   The direct Bayesian inversion [6] contains many uncertainties. The alterna-tive way is to consider matrix data in the output of devices, measuring stress values in the shaded cells. Then one could try to find some features in the measured data array, responsible for danger alert in the entire object. As MSS collects measured data continuously during a long time period, in the resultant matrix  , what limits analytical capability [5]. To overcome the limi-tations the fractal analysis is considered below. Let’s consider the part of simulated matrix 10x280 random data array shown in the fig. 2, upper matrix. The initial and final (principal) matrix dis-persion range is 20%. There are two groups of “defected” zones with dispersed by 10% values : the values in the first are higher, while in the second – less than respectively upper and lower threshold of the principal matrix.  The exceeding rows are 1-st to 3-rd beginning from 100-th column, re-duced values are from 7 to 9 rows. The mean values of the initial and corrected matrix are shown in the fig. 3, while the real parts of the first principle compo-nents values are shown in the fig.4.  It is clear that both multi sensors data can not distinguish between initial and corrected data. The same conclusion can be done using first values of ei-genvectors (not shown). The opposite pattern can be shown using as condi-tioned the eigenvalues in the series of the fractals. The result is shown in the fig.5.    1 . . .1 1. . .. . .. . .1 . . .QB BBqQB BI IIQ Fig.1. Loaded shell, split in cells. Stress sensors are in the shaded cells. Stresses in blank cells are under evaluation 44  Fig.2. Elements of simulated matrix's values with two exceeding and decreasing respectively by 10% groups of "defected" zones, comparing to the matrix range of about 20% dispersion..  Conclusion  We introduced here, and illustrated using simulated data, the new fractal approach to multi sensors data analysis, which gives the possibility to automat-ically distinguish between very small differences in the initial measured data. The first eigenvalues and eigenvectors of the measured data appeared to be very sensitive to small peculiarities which can indicate the presence of the damages or pathology in the systems under observation.  The further study should deal with real experimental data in various sys-tems to reduce the application opportunities to some important practical cases.  45  Fig.3. Mean values in the fractals after their continuous shift before (blue) and after (yellow) correction.  Fig.4. Real parts of the principle components before (a) and after (b) corrections.  Fig.5. The first eigenvalues in the series of the fractals (yellow) of initial data (blue).  0 50 100 150 200 25011611812012212412650 100 150 200 25020204050 100 150 200 2500.050.100.1550 100 150 200 250202040a b 46 The research was supported by the grant 4.36 of the “Technical Diagnostics” of the State Scientific Research Program of Belarus Academy of Sciences.  Literature 1. D. L. Donoho, I. M. Johnstone, J. C. Hoch, and A. S. Stern, “Maximum Entropy and the Near Black Object,” J. Roy. Stat. Soc. B 54, (1), pp. 41--81 (1992). 2. Hoon Sohn, Farar C., Froncois M.Hemez et.al. A Review of Structural Health Monitoring Literature. Los Alamos National Laboratory Report, LA-13976-MS, 2004. 3. V.Vengrinovich, S.Klimenko, A.Klimenko, S.Rotkov. Bionics for safety: approach of ani-mate beings applied to the safety of engineering systems (Inv. lect.). Proc. International Conf. CPT_2013, Larnaka, Cyprus, May 13-19, 2013, pp.i-ix.  4. Skilling J. Probabilistic data analysis: an introductory guide. Journal of Microscopy. Vol.190, Pts.1/2 pp.28–36, 1998. 5. Zellner A. Optimal information processing and Bayes theorem. American Statistican, 1998, v.42, N4, рр.278-284. 6. Vengrinovich V.L. Bayesian Image and Pattern Reconstruction from Incomplete and Noisy Data. Tutorial. Int.J., “Pattern Recognition and Image Analysis”, Vol. 22, No. 1, 2012, pp.99-107. 7. V.L. Vengrinovich, Y.B. Denkevich, G.-R. Tillack. Reconstruction of Three_Dimensional Binary Structures from an Extremely Limited Number of Cone_Beam X-ray Projections. Choice of Prior // Journal of Physics. D:Appl. Phys. – 1999. – Vol. 32. – P. 2505–2514.    FRACTAL APPROACH TO MATERIAL AND TISSUE AGING J. Schreiber Fraunhofer Institute National of Ceramic Technology and Systems and  German-Korean Company «NUGA LAB GmbH», Dresden, Germany e-mail: juergen.schreiber@ikts-md.fraunhofer.de  ФРАКТАЛЬНЫЙ ПОДХОД К СТАРЕНИЮ  МАТЕРИАЛОВ И ТКАНЕЙ Ю. Шрайбер Институт Фраунгофера керамических технологий и систем, Германо-Корейская компания «NUGA Lab GmbH», г. Дрезден, Германия  The complex nature of material damage due to fatigue or short overload is the rea-son of ambiguous behaviour of physical characteristics. Therefore most of the present attempts failed to give reliable assessment of the damage process, especially; micro-scopic investigations could not offer a sufficient general and suitable view to that be-haviour. A similar situation is found considering cancer diagnostics in human medicine. Instead of analysing averaged integral parameters or individual microscopic pattern and single molecule features like the lifetime of an excited molecule state it seems to be reasonable to study collective or cooperative phenomena, mainly looking at the scaling 

Фрактальный анализ многомерных данных в многосенсорных системах

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Фрактальный анализ многомерных данных в многосенсорных системах

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