Scientific and Technical Journal


ISSN Print 2221-3937
ISSN Online 2221-3805

In this paper, an analysis of existing automated test systems (ATS) and corresponding information technologies (IT) of parameters processing was carried out.

At the heart of the IT and processing methods (identification, segmentation, clustering, classification) is the optimization of the corresponding functional. The analysis showed that for small-scale production the objective function is multimodal and noisy. In gradient processing techniques in such circumstances there is low noise immunity, methods are sensitive to local extreme and the initial search point, in subgradient methods – high error.

To reduce these shortcomings authors proposed methods of identification, classification, clustering, pre-processing and evaluation of product parameters. They are based on the evaluation of the respective functional extremum coordinates using wavelet transform (WT).

The results showed that the proposed methods have improved the clustering quality and classification reliability under a priori uncertainty of diagnostic parameters. On the basis of these methods is proposed to develop the selection of components for the IT equipment in critical applications in radio equipment manufacturing.

  1. Ruvinova E. Avtomatizirovannyi opticheskiy control pechatnih uzlov [Automated Optical Control of Printed Circuits], (2002),  Electronika: Nauka, Technologiya, Bizness, No.6, pp. 26 – 32  [In Russian].
  2. Belbahir A., Fanni A., Lera M., and Montisci A., (2005), An Automated Optical Inspection System for the Diagnosis of Printed Circuits based on neural Networks, Industry Applications Conference, Vol. 1, pp. 680 – 684 [In English].
  3. Shcherbakova G. Subgradientniy metod klassificacii v prostranstve weivlet preobrazovaniya dlia technicheskoy diagnostiki [Sub-gradient Classification Method in the Wavelet domain for Technical Diagnostics], (2010), Electrotehnicheskie i Kompiuternie Sistemy, No. 01 (77), pp.136 – 142  [In Russian].
  4. Shcherbakova G., Krylov V., Logvinov O., and Pisarenko R., Issledovaniye adaptivnogo subgradientnogo metoda klasterizacii v prostranstve weivlet preobrazovaniya [Investigation of the Adaptive sub-gradient Clustering Method in the Wavelet domain], (2012),  Radioelectronni i Kompiuterni Systemy,  No. 7 (59), pp. 142 – 146 [In Russian].
  5. Krylov V., Shcherbakova G., and  Pisarenko R. Vosstanovlenie signalov posredstvom slepoy deconvolucii na baze multistartovoy optimizacii v prostranstve weivlet preobrazovaniya [Signal Restoration by means of blind Deconvolution based on multi-start Optimization Method in the Wavelet domain], (2014),  Electrotehnicheskie i Kompiuternie Sistemy, No.13 (89), pp.184 – 191 [In Russian].
  6. Shcherbakova G., Krylov V., and  Pisarenko  R., (2013), Information Technology of Parameters Prediction with Adaptive Clustering in the Space of the Wavelet Transform,  Praci Odes. Politehn. Un-tu, No. 1 (40), pp. 104 – 109 [In English].
  7. Strogonov A. Primenenie neyronnih setey dlia otbora partiy IS s povishennoy nadegnostiyu [Neural nets Utilization for IC Batch with high Reliability Selection], (2007),  Komponenty i Tehnologii,  No. 8, pp. 175 – 178 [In Russian].
  8. Gorlov M., and Strogonov A. Otbrakovochnyie ispytaniya kak sredstvo povysheniya nadegnosti partiy IS [Preinstallation Testing for IC batch for his Reliability Improuving],       (2006), Tehnologii v Electronnoy Promyshlennosti, No. 1,  pp. 70 – 75 [In Russian]
  9. Gadnov V., Avdeev D., Kulygin V.,  Polesskiy S., and Tihmenev A. Informacionnaya tehnologiya obespecheniya nadegnosti slognyh elektronnyh sredstv voennogo i specialnogo naznacheniya [Information Technology for Special Military and Complex Electronic Apparatus Reliability Improving ], (2011),  Komponenty i Tehnologii, No. 6, pp. 168 – 174 [In Russian].
  10. Strogonov A. Individualnoye prognosirovanie dolgovechnosti IS s ispolsovaniem ARPSS modeley vremennyh riadov [Individual Prediction of IC Period of work with ARPSS Time Series Utilization], (2006), Komponenty i Tehnologii,  No. 10 [In Russian].
  11. Trang V., and  Yang B., (2009), Machine fault Diagnosis and Prognosis: The State of the Art, The International Journal of Fluid Machinery and Systems (IJFMS), No. 2 (1), pp. 61 – 71 [In English].
  12. Greshilov A., Stakun V., and Stakun A., Matematicheskiye metody postroeniya prognosov [Mathematicals Methods of Prediction Construction], (1997),  Moscow, Russian Federation, Radio i Sviaz,  112 p. [In Russian].
  13. Dorofeyuk Y., and  Dorofeyuk A., Metody strukturno-klassificacionnogo prognosirovaniya mnogomernyh dinamicheskih obiektov [Structure Classification Prediction Method for Multidimensional Dynamic Objects], (2006), Iskusstvenniy Intellect, No.2, pp.138 – 141 [In Russian].
  14. Krylov V., Shcherbakova G., and  Kozina Y., Posicionirovaniye izobrageniy fotoshablonov v sistemah avtomatizirovannogo opticheskogo kontrolia [Photo Masks Images Alignment in Automated Optical Inspection System], (2007), Tehnologiya i Konstruirovanie v Electronnoy Apparature,  No.3 (69), pp. 61 – 64 [In Russian].
  15. Antoshchuk S.,  Krylov V., and  Shcherbakova G., The Integrated Circuits photomasks images Alignment for Automated Optical Inspection System, (2007), DAAAM International Scientific Book, Vienna. Austria, pp. 287 – 294 [In English].
  16. Gonzalez R., Woods R., and  Eddins S.,  (2004), Digital Image processing using MATLAB. – Upper Saddle River, N.J.: Pearson Prentice Hall, 620 p. [In English].
  17. Bellini S., (1986), Bussgang Techniques for blind Equalization, IEEE GLOBECOM Conf. Rec., pp. 1634 – 1640 [In English].
  18. Haykin S., (1986), Adaptive filter theory, Englewood Cliffs, N.J.: Prentice-Hall, 704 p. [In English].
  19. Godard D., (1980), Self-recovering Equalization and carrier Tracking in two-dimensional data Communication Systems, Transactions on Communications, No. 28 (11), pp. 1867 – 1875  [In English].
  20. Treichler J., and Agee B., (1983), A new Approach to Multipath Correction of Constant Modulus Signals, IEEE Transactions on Acoustics, Speech and Signal Processing, No. 31(2),  pp. 459 – 472 [In English].
  21. Godfrey R., and Rocca F., (1981), Zero Memory Nonlinear Deconvolution, Geophys. Prospecting, No. 29, pp. 189 – 228 [In English].
  22. Shcherbakova G., Krylov V.,and  Kuzmenko V., [The Information Technology of Object Search in the solder Joint Automated optical Control Systems with multi-start Optimization aid], Obchisluvalniy Intelekt (rezultaty, problemy, perspectyvy): II Mignar. Naukovo-tehn. Konf. Cherkasy, 14-18 travnia  2013, pp.  449 – 450 [In Russian]. 
Last download:
2017-11-23 06:34:33

[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2014. Any use of information from the site is possible only under the condition that the source link! ]