{"id":2006,"date":"2021-01-18T18:29:36","date_gmt":"2021-01-18T16:29:36","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=2006"},"modified":"2021-04-14T14:17:20","modified_gmt":"2021-04-14T11:17:20","slug":"%d1%88%d1%82%d1%83%d1%87%d0%bd%d0%b0-%d0%bd%d0%b5%d0%b9%d1%80%d0%be%d0%bd%d0%bd%d0%b0-%d0%bc%d0%b5%d1%80%d0%b5%d0%b6%d0%b0-%d0%b4%d0%bb%d1%8f-%d0%b0%d0%b2%d1%82%d0%be%d0%bc%d0%b0%d1%82%d0%b8%d0%b7","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=2006","title":{"rendered":"\u0428\u0442\u0443\u0447\u043d\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430 \u043c\u0435\u0440\u0435\u0436\u0430 \u0434\u043b\u044f \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u043e\u0432\u0430\u043d\u043e\u0433\u043e \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u0441\u043a\u043b\u0430\u0434\u043e\u0432\u0438\u0445 \u043f\u043e\u0445\u0438\u0431\u043e\u043a \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f"},"content":{"rendered":"

\u0428\u0422\u0423\u0427\u041d\u0410 \u041d\u0415\u0419\u0420\u041e\u041d\u041d\u0410 \u041c\u0415\u0420\u0415\u0416\u0410 \u0414\u041b\u042f \u0410\u0412\u0422\u041e\u041c\u0410\u0422\u0418\u0417\u041e\u0412\u0410\u041d\u041e\u0413\u041e \u0420\u041e\u0417\u041f\u0406\u0417\u041d\u0410\u0412\u0410\u041d\u041d\u042f \u0421\u041a\u041b\u0410\u0414\u041e\u0412\u0418\u0425 \u041f\u041e\u0425\u0418\u0411\u041e\u041a \u0412\u0418\u041c\u0406\u0420\u042e\u0412\u0410\u041d\u041d\u042f<\/p>\n

ARTIFICIAL NEURAL NETWORK FOR AUTOMATED RECOGNITION OF CONSTRUCTION MEASUREMENTS<\/p>\n

<\/a> \u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 137-144. \u041d\u043e\u043c\u0435\u0440: \u21161, 2019 (269)<\/strong>
\n\u0410\u0432\u0442\u043e\u0440\u0438:<\/strong>
\n\u0406.\u042e. \u0427\u0415\u0420\u0415\u041f\u0410\u041d\u0421\u042c\u041a\u0410, \u0410.\u042e. \u0421\u0410\u0417\u041e\u041d\u041e\u0412
\n\u0416\u0438\u0442\u043e\u043c\u0438\u0440\u0441\u044c\u043a\u0438\u0439 \u0434\u0435\u0440\u0436\u0430\u0432\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442
\n\u041e.\u041c. \u0411\u0415\u0417\u0412\u0415\u0421\u0406\u041b\u042c\u041d\u0410
\n\u041d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u0423\u043a\u0440\u0430\u0457\u043d\u0438 \u201c\u041a\u0438\u0457\u0432\u0441\u044c\u043a\u0438\u0439 \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0456\u043d\u0441\u0442\u0438\u0442\u0443\u0442 \u0456\u043c\u0435\u043d\u0456 \u0406\u0433\u043e\u0440\u044f \u0421\u0456\u043a\u043e\u0440\u0441\u044c\u043a\u043e\u0433\u043e\u201d
\nI. CHEREPANSKA, A. SAZONOV
\nZhytomyr State Technological University
\nO. BEZVESILNA
\nNational Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d
\nDOI:<\/strong> https:\/\/www.doi.org\/10.31891\/2307-5732-2019-269-1-137-144<\/a>
\n\u0420\u0435\u0446\u0435\u043d\u0437\u0456\u044f\/Peer review :<\/strong> 17.01.2019 \u0440.
\n\u041d\u0430\u0434\u0440\u0443\u043a\u043e\u0432\u0430\u043d\u0430\/Printed :<\/strong> 16.02.2019 \u0440.<\/p>\n

\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u043c\u043e\u0432\u043e\u044e \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u0443<\/strong><\/p>\n

\u0412 \u0441\u0442\u0430\u0442\u0442\u0456 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0430 \u0448\u0442\u0443\u0447\u043d\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430 \u043c\u0435\u0440\u0435\u0436\u0430 (\u0428\u041d\u041c), \u044f\u043a\u0430 \u0434\u043e\u0437\u0432\u043e\u043b\u044f\u0454 \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u0442\u0438 \u0441\u043a\u043b\u0430\u0434\u043e\u0432\u0456 \u043f\u043e\u0445\u0438\u0431\u043e\u043a \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f \u0432 \u0440\u0435\u0436\u0438\u043c\u0456 \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0447\u0430\u0441\u0443, \u043f\u0440\u0438 \u0433\u043e\u043d\u0456\u043e\u043c\u0435\u0442\u0440\u0438\u0447\u043d\u0438\u0445 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f\u0445 \u0432\u0435\u043b\u0438\u0447\u0438\u043d \u043f\u043b\u043e\u0441\u043a\u043e\u0433\u043e \u043a\u0443\u0442\u0430 \u0431\u0430\u0433\u0430\u0442\u043e\u0433\u0440\u0430\u043d\u043d\u0438\u0445 \u043f\u0440\u0438\u0437\u043c. \u0428\u041d\u041c \u0454 \u0430\u043b\u044c\u0442\u0435\u0440\u043d\u0430\u0442\u0438\u0432\u043e\u044e \u0442\u0440\u0430\u0434\u0438\u0446\u0456\u0439\u043d\u043e\u043c\u0443 \u043c\u0435\u0442\u043e\u0434\u0443 \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0439\u043d\u043e\u0433\u043e \u0430\u043d\u0430\u043b\u0456\u0437\u0443 \u0437\u0430 \u043a\u0440\u0438\u0442\u0435\u0440\u0456\u0454\u043c \u0424\u0456\u0448\u0435\u0440\u0430 \u043c\u043d\u043e\u0436\u0438\u043d\u0438 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043b\u044c\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 \u0442\u0430 \u0434\u043e\u0437\u0432\u043e\u043b\u044f\u0454 \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0443\u0432\u0430\u0442\u0438 \u0430\u043d\u0430\u043b\u0456\u0437 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043b\u044c\u043d\u043e\u0457 \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0457 \u043f\u0440\u0438 \u043d\u043e\u0440\u043c\u0443\u0432\u0430\u043d\u043d\u0456 \u043f\u043e\u0445\u0438\u0431\u043e\u043a \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f. \u0426\u0435 \u043f\u0456\u0434\u0432\u0438\u0449\u0443\u0454 \u0442\u043e\u0447\u043d\u0456\u0441\u0442\u044c \u0442\u0430 \u0448\u0432\u0438\u0434\u043a\u0456\u0441\u0442\u044c, \u0430 \u0442\u0430\u043a\u043e\u0436 \u0437\u043c\u0435\u043d\u0448\u0443\u0454 \u0442\u0440\u0443\u0434\u043e\u043c\u0456\u0441\u0442\u043a\u0456\u0441\u0442\u044c \u043e\u0431\u0440\u043e\u0431\u043a\u0438 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043b\u044c\u043d\u043e\u0457 \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0457.
\n\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u0448\u0442\u0443\u0447\u043d\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430 \u043c\u0435\u0440\u0435\u0436\u0430, \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0456\u0439\u043d\u0438\u0439 \u0430\u043d\u0430\u043b\u0456\u0437, \u043a\u0440\u0438\u0442\u0435\u0440\u0456\u0439 \u0424\u0456\u0448\u0435\u0440\u0430, \u043f\u043e\u0445\u0438\u0431\u043a\u0430 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f, \u0432\u0438\u043f\u0430\u0434\u043a\u043e\u0432\u0430 \u0441\u043a\u043b\u0430\u0434\u043e\u0432\u0430 \u043f\u043e\u0445\u0438\u0431\u043a\u0438 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f, \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u043d\u0430 \u0441\u043a\u043b\u0430\u0434\u043e\u0432\u0430 \u043f\u043e\u0445\u0438\u0431\u043a\u0438 \u0432\u0438\u043c\u0456\u0440\u044e\u0432\u0430\u043d\u043d\u044f.<\/p>\n

\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0430\u043d\u0433\u043b\u0456\u0439\u0441\u044c\u043a\u043e\u044e \u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

The purpose of the article is to develop an artificial neural network (ANN), which will allow the recognition of components of measurement errors in real time, with goniometric measurements of the flat angle of multifaceted prisms. An artificial neural network is developed for the automated recognition of components of measurement errors, which allows to perform high-performance (for an example in the article in 2 seconds) the definition of components of measurement errors in an automated mode. Time consumed by ANN for processing information is significantly less than the time spent using Fischer’s dispersion analysis. This greatly reduces the complexity of the work performed, as well as improves the accuracy and speed of the processing of the measurement information. This is confirmed by the fact that for the whole test set of measurements of the magnitude of the flat angle of the 24-faceted prism, which was chosen as an example, the positive results of the work of the CNM were obtained. In this case, the probability of a correct response of ANN is close to 100%, and the average quadratic error of the ANN does not exceed 5%. The use of ANN can be an indisputable alternative to the traditional method of dispersion analysis according to Fisher’s criterion, to automate the analysis of a plurality of measuring information in the normalization of measurement errors, to increase the accuracy and speed of processing of measurement information.
\nKeywords:<\/strong> artificial neural network, dispersion analysis, Fisher’s criterion, measurement error, random component of measurement error, systematic component of measurement error.<\/p>\n

References<\/strong><\/p>\n

    \n
  1. Metrologija intellektual’nyh izmeritel’nyh sistem : monografija \/ [Shevchuk V. P., Kaplja V. I., Zheltonogov A. P., Ljasin D. N. Volgograd]. \u2013 2005. \u2013 210 s.<\/li>\n
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  3. Kas’janik V. V. Primenenie nejrosetevogo podhoda dlja ocenki pogreshnosti odometrov mobil’nogo robota [Elektronnij resurs] \/ Kas’janik V. V., Dunec A. P., Dunec I. P., Shut’ V. N. \u2013 Rezhim dostupu : http:\/\/robotics.bstu.by\/mwiki\/images\/8\/8d\/Neuroinformatika_2012.pdf<\/li>\n
  4. Golygin N. H. Vozmozhnosti povyshenija tochnosti geodezicheskih izmerenij na osnove iskusstvennyh nejrosetej \/ N. H. Golygin, O. B. Hinoeva, H. K. Jambaev \/\/ Izv. VUZov. Geodezija i ajerofotos#emka. \u2013 2005. \u2013 \u2116 5. \u2013 S. 17\u201327.<\/li>\n
  5. Skvorcov M. G. Nejrosetevoj izmeritel’nyj preobrazovatel’ \/ M. G. Skvorcov, E. A. Zrjumov, S. P. Pronin \/\/ Izvestija VolgGTU. \u2013 2011. \u2013 T. 6, \u2116 5. \u2013 S. 88\u201390.<\/li>\n
  6. Cherepanska I. Automated Technological Complex to Precise Angles Measurement Based on Goniometer \/ I. Cherepanska, E. Bezvesilna, A. Sazonov, A. Lavrischev \/\/ Technological Complexes. \u2013 2015. \u2013 \u2116 1\/2 (12). \u2013 P. 38\u201345.<\/li>\n
  7. Cherepanska I. The procedure for determining the number of measurements in the normalization of random error of an information-measuring system with elements of artificial intelligence \/ Irina Cherepanska, Elena Bezvesilna, Artem Sazonov, Sergey Nechai, Tatiana Khylchenko \/\/ Eastern-European journal of enterprise technologies. \u2013 2017. \u2013 \u2116 5\/9 (89). \u2013 R. 58\u201367.<\/li>\n
  8. Analiticheskij paket Deductor Profess\u0456onal : [rukovodstvo pol’zovatelja]. \u2013 BaseGroupLabs, 2002. \u2013 184 s.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    \u0428\u0422\u0423\u0427\u041d\u0410 \u041d\u0415\u0419\u0420\u041e\u041d\u041d\u0410 \u041c\u0415\u0420\u0415\u0416\u0410 \u0414\u041b\u042f \u0410\u0412\u0422\u041e\u041c\u0410\u0422\u0418\u0417\u041e\u0412\u0410\u041d\u041e\u0413\u041e \u0420\u041e\u0417\u041f\u0406\u0417\u041d\u0410\u0412\u0410\u041d\u041d\u042f \u0421\u041a\u041b\u0410\u0414\u041e\u0412\u0418\u0425 \u041f\u041e\u0425\u0418\u0411\u041e\u041a \u0412\u0418\u041c\u0406\u0420\u042e\u0412\u0410\u041d\u041d\u042f ARTIFICIAL NEURAL NETWORK FOR AUTOMATED RECOGNITION OF CONSTRUCTION MEASUREMENTS \u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 137-144. \u041d\u043e\u043c\u0435\u0440: \u21161, 2019 (269) \u0410\u0432\u0442\u043e\u0440\u0438: \u0406.\u042e. \u0427\u0415\u0420\u0415\u041f\u0410\u041d\u0421\u042c\u041a\u0410, \u0410.\u042e. \u0421\u0410\u0417\u041e\u041d\u041e\u0412 \u0416\u0438\u0442\u043e\u043c\u0438\u0440\u0441\u044c\u043a\u0438\u0439 \u0434\u0435\u0440\u0436\u0430\u0432\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u041e.\u041c. \u0411\u0415\u0417\u0412\u0415\u0421\u0406\u041b\u042c\u041d\u0410 \u041d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u0423\u043a\u0440\u0430\u0457\u043d\u0438 \u201c\u041a\u0438\u0457\u0432\u0441\u044c\u043a\u0438\u0439 \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0456\u043d\u0441\u0442\u0438\u0442\u0443\u0442 \u0456\u043c\u0435\u043d\u0456 \u0406\u0433\u043e\u0440\u044f \u0421\u0456\u043a\u043e\u0440\u0441\u044c\u043a\u043e\u0433\u043e\u201d I. CHEREPANSKA, A. SAZONOV Zhytomyr State Technological University O. BEZVESILNA […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[21],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/2006"}],"collection":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2006"}],"version-history":[{"count":5,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/2006\/revisions"}],"predecessor-version":[{"id":5742,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/2006\/revisions\/5742"}],"wp:attachment":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2006"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2006"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2006"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}