Russian Federation
The paper examines the data used to develop an information system that determines wood defects using a neural network approach. To understand the subject area, we determine what wood defects may be. We take the methodology GOST 2140-81 and GOST 9463-2016 as a basis. Based on them, we are developing a dataset for an information system using computer vision. We train a neural network based on it. After viewing, the neural network itself will classify round coniferous timber by grades.
neural network, GOST 2140-81, GOST 9463-2016, wood defects, dataset, information system
1. Bugaev, Yu. V. Analiz modeley i algoritmov optimizacii raskroya odnomernyh lesomaterialov / Yu. V. Bugaev, L. A. Korobova, I. Yu. Shurupova // Modelirovanie sistem i processov. – 2024. – T. 17, № 4. – S. 23-31. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-4-23-31. – EDN MRHWHP.
2. Gribanov, A. A. Modelirovanie processa kombinirovannogo poliva s ispol'zovaniem elektrohimicheski aktivirovannoy vody v teplicah / A. A. Gribanov, A. A. Mescheryakova, A. V. Starikov // Modelirovanie sistem i processov. – 2024. – T. 17, № 4. – S. 31-42. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-4-31-42. – EDN ZTAVIF.
3. Komp'yuternoe modelirovanie rabotosposobnosti elektricheskoy shemy v sistemah avtomatizacii proektirovaniya / V. K. Zol'nikov, S. V. Stoyanov, E. V. Shmakov, N. N. Litvinov // Modelirovanie sistem i processov. – 2024. – T. 17, № 3. – S. 26-36. – DOIhttps://doi.org/10.12737/2219-0767-2024-24-34. – EDN EJJKJP.
4. Imitacionnoe modelirovanie deformativnyh pokazateley vedomstvennyh avtomobil'nyh dorog, ukreplennyh shlakosilikatnym vyazhuschim / I. A. Vikulin, V. V. Zinov'eva, O. S. Nikul'cheva [i dr.] // Modelirovanie sistem i processov. – 2024. – T. 17, № 2. – S. 24-31. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-2-24-31. – EDN JPHRZY.
5. Minakova, O. V. Povyshenie effektivnosti raboty v proektah open sourse na osnove arhitekturnogo analiza (na primere proekta Sahana) / O. V. Minakova, I. V. Pocebneva, P. Yu. Gusev // Modelirovanie sistem i processov. – 2024. – T. 17, № 1. – S. 84-92. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-1-84-92. – EDN NZNSKM.
6. Epifanov, E. N. Sistemnyy analiz akusticheskih svoystv rechevyh opoveschateley / E. N. Epifanov, V. F. Asminin, S. A. Sazonova // Modelirovanie sistem i processov. – 2024. – T. 17, № 4. – S. 42-53. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-4-42-53. – EDN BMZBYL.
7. Ischenko, E. A. Primenenie tehnologiy elektrodinamicheskogo modelirovaniya dlya opredeleniya effektivnoy ploschadi rasseyaniya malyh ob'ektov / E. A. Ischenko, S. M. Fedorov, D. M. Fedorov // Modelirovanie sistem i processov. – 2024. – T. 17, № 4. – S. 53-59. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-4-53-59. – EDN PPJTCT.
8. Makarenko F.V., Yagodkin A.S., Zol'nikov K.V., Denisova O.A., Poluektov A.V. Obzor logicheskih bazisov i mikroshem pri postroenii kombinacionnogo ustroystva s uchetom nadezhnosti// Modelirovanie sistem i processov. – 2022. – T. 15, № 1. – S. 115-124. DOI: https://doi.org/10.12737/2219-0767-2022-15-1-115-124; EDN: https://elibrary.ru/JKQHOQ
