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Published June 4, 2024 | Version v1
Journal article Open

TRIKOTAJ TO'QIMALARINING STRUKTURASINI KOMPYUTER KO'RISH TEXNIKASI ASOSIDA TASNIFLASH

  • 1. Namangan Muhandislik texnologiyalari instituti fizika matematika fanlari doktori, professor
  • 2. Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti Farg'ona filiali

Description

An'anaviy tarzda to'qilgan trikotaj to'qimalarining tuzilishi tasnifi to'qimachilik sanoatida qo'lda ishlashga asoslangan. Ushbu maqola uchta to'qilgan matolarni tasniflash uchun avtomatik yondashuvni taklif qiladi: tekis,yumaloq, atlas to'quv. Birinchidan, mato tasvirlarini tahlil qilishni kamaytirish uchun past chastotali pastki tasvirni olish uchun 2-D to'lqinli transformatsiyadan foydalaniladi. Keyin mato tasvirlarini qayta ishlashdan oldin tekstura xususiyatlarini olish uchun kulrang darajali birgalikdagi matritsasi (GLCM) va Gabor to'lqinlari qabul qilinadi. Nihoyat, probabilistik neyron tarmog'i (PNN) uchta asosiy to'qilgan matolarni tasniflash uchun qo'llaniladi. Tajriba natijalari shuni ko'rsatadiki, tavsiya etilgan usul to'qilgan matolarni avtomatik tarzda, samarali tasniflashi va aniq tasniflash natijalarini (93,33%) olishi mumkin

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References

  • A. Baykasoglu, L. Ozbakir, S. Kulluk, Classifying defect factors in fabric production via DIFACONN-miner: A case study, Expert Systems with Applications, 38(9), 2011, 11321-11328
  • T. J. Kang, C. H. Kim, K. W. Oh, Automatic recognition of fabric weave patterns by digital image analysis, Textile Research Journal, 69(2), 1999, 77-83
  • R. M. Haralick, K. Shanmugam, I. H. Dinstein, Textural features for image classification, IEEE Transactions Systems, Man and Cybernetics, 3(6), 1973, 610-621
  • Yun E., Kim S., Yun C. Development of digitized evaluation methods for fabric shrinkage and damage using image analysis //Fashion and Textiles. – 2023. – Т. 10. – №. 1. – С. 23.
  • Fan, J., & Hunter, L. (2009). Engineering Apparel Fabrics and Garments. Woodhead Publishing. Gallen Daniel, F., & Felix, F. (2011). Article 306 / 307 "POKA DOT" Test Fabric for Mechanical Action. EMPA Testmaterialien AG..
  • Hill, M., Kamalakannan, S., Gururajan, A., Sari-Sarraf, H., & Hequet, E. (2011). Dimensional change measurement and stain segmentation in printed fabrics. Textile Research Journal, 81(16), 1655–1672.
  • Cho, Y., Yun, C., & Park, C. H. (2017). The efect of fabric movement on washing performance in a front-loading washer IV: under 3.25-kg laundry load condition. Textile Research Journal, 87(9), 1071–1080..
  • ISO 7772-1. (1998). Assessment of industrial laundry machinery by its efect on textiles—part 1. Washing machines.
  • Jasińska, I. (2019). The algorithms of image processing and analysis in the textile fabrics abrasion assessment. Applied Sciences, 9(18), Article 3791.
  • Yusubjanovich S. N., Muminjonovich K. A. TRIKOTAJ TO 'QIMALARINING SHAKL SAQLASH XUSUSIYATLARINI RAQAMLI BAHOLASH USULLARI //Al-Farg'oniy avlodlari. – 2024. – Т. 1. – №. 1. – С. 57-61.
  • Зулунов Р. М., Каюмов А. М. ИДЕНТИФИКАЦИЯ И СОРТИРОВКА ТЕКСТИЛЯ ДЛЯ АВТОМАТИЗИРОВАННОЙ ОБРАБОТКИ С ПОМОЩЬЮ БЛИЖНЕЙ ИНФРАКРАСНОЙ СПЕКТРОСКОПИИ //Universum: технические науки. – 2024. – Т. 1. – №. 3 (120). – С. 38-41.
  • Kayumov A. Development of mathematical models for detecting defects in fabric on textile machines //Journal of technical research and development. – 2023. – Т. 1. – №. 2.
  • Kayumov A. СОЗДАНИЕ НА ОСНОВЕ ЭКСПЕРТНОЙ СИСТЕМЫ ПРОГРАММЫ ОЦЕНКИ ЭФФЕКТИВНОСТИ ТЕКСТИЛЬНЫХ МАШИН //Потомки Аль-Фаргани. – 2023. – Т. 1. – №. 2. – С. 49-52.
  • Muminjonovich K. A. CREATING MATHEMATICAL MODELS TO IDENTIFY DEFECTS IN TEXTILE MACHINERY FABRIC //Al-Farg'oniy avlodlari. – 2023. – Т. 1. – №. 4. – С. 257-261.
  • Muminjonovich K. A. METHODS OF TECHNOLOGICAL MACHINERY MONITORING AND FAULT DIAGNOSIS. Intent Research Scientific Journal, 2 (10), 11–17. – 2023.