APPLICATION OF MACHINE LEARNING METHODS FOR THE SELECTION OF FOREST TRACKED VEHICLES

Dolzhikov I.; Khitrov E.; Grigor'ev Igor'

doi:doi:10.58168/E-SEFTFI2024_44-48

Home / Conferences / ENERGY-SAVING AND ENVIRONMENTALLY FRIENDLY TECHNOLOGIES IN THE FORESTRY INDUSTRY / Materials of the International Scientific Conference of Scientists and Students «ENERGY-SAVING AND ENVIRONMENTALLY FRIENDLY TECHNOLOGIES IN THE FORESTRY INDUSTRY»

APPLICATION OF MACHINE LEARNING METHODS FOR THE SELECTION OF FOREST TRACKED VEHICLES

Submit manuscript

To cite

APPLICATION OF MACHINE LEARNING METHODS FOR THE SELECTION OF FOREST TRACKED VEHICLES

Section: GENERAL SECTION

Proceedings: MATERIALS OF THE INTERNATIONAL SCIENTIFIC CONFERENCE OF SCIENTISTS AND STUDENTS «ENERGY-SAVING AND ENVIRONMENTALLY FRIENDLY TECHNOLOGIES IN THE FORESTRY INDUSTRY»

UDK 630 Лесное хозяйство. Лесоводство

Dolzhikov I. ¹

Khitrov E. ²

Grigor'ev Igor' ³

Author and publication information

Authors:

1. St. Petersburg State University of Architecture and Civil Engineering
Russian Federation

2. Peter the Great St. Petersburg Polytechnic University
Russian Federation

3. Arctic State Agrotechnological University (Department of Technology and Equipment of the Forestry Complex, Professor)
Yakutsk, Russian Federation

Type:

Сonference article

DOI:

https://doi.org/10.58168/E-SEFTFI2024_44-48

Published:

28.12.2024

Subject area:

UDK 630 Лесное хозяйство. Лесоводство

Language:

Russian

Keywords:

forest engineering, machine systems, machine learning, tracked forest machines

Funding:

The research was carried out at the expense of a grant from the Russian Science Foundation No. 23-16-00092, https://rscf.ru/project/23-16-00092/.

Abstract and keywords

Abstract (English):
Tracked tractors used in logging and forestry are characterized by great variability in design parameters and operational characteristics. The purpose of this study is to substantiate the classification of tracked tractors, for their effective selection for specific natural production conditions of logging and forestry operations, based on the results of data analysis obtained using machine learning algorithms. The classification is proposed based on the markup of objects (tractor models) obtained using the k-means data clustering method. The work was carried out within the framework of the scientific school "Innovative developments in the field of logging industry and forestry".

Keywords:
forest engineering, machine systems, machine learning, tracked forest machines

References

1. Chemshikova Yu.M. Transportno-tehnologicheskie sistemy dlya lesorazvedeniya na baze gusenichnyh vezdehodov / Yu.M. Chemshikova, A.B. Davtyan, O.I. Grigor'eva // Transportnye i transportno-tehnologicheskie sistemy. Materialy Mezhdunarodnoy nauchno-tehnicheskoy konferencii ; otv. red. N.S. Zaharov. 2020. S. 400-403.

2. Sredoschadyaschie tehnologii razrabotki lesosek v usloviyah Sverdlovskoy oblasti / E.F. Gerc, O.A. Kunickaya, V.A. Makuev, A.S. Dmitriev, E.A. Tihonov, O.I. Grigor'eva // Derevoobrabatyvayuschaya promyshlennost'. 2023. № 1. S. 52-63.

3. O vazhnosti issledovaniy ekologicheskoy effektivnosti processa trelevki / A.V. Kalistratov, O.I. Grigor'eva, G.V. Grigor'ev, I.N. Dmitrieva // Nauka, obrazovanie, innovacii v prigranichnom regione. Materialy respublikanskoy nauchno-prakticheskoy konferencii. Petrozavodskiy gosudarstvennyy universitet. 2015. S. 7-9.

4. Forest preservation techniques in the Urals / E.F. Gerts, O.A. Kunitskaya, E.M. Runova, E.A. Tikhonov, R.S. Timokhov, E. Mikhaylenko, Ju. Chemshikova, P.N. Perfiliev // International Journal of Environmental Studies. 2023. T. 80. № 4. S. 1055-1064.

5. Tehnologicheskie processy sploshnyh i vyborochnyh rubok lesa pri pomoschi universal'nyh lesozagotovitel'nyh mashin / O.A. Kunickaya, A.A. Krivosheev, A.S. Shvecov, M.V. Stepanischeva, S.I. Revyako, V.P. Druz'yanova // Sistemy. Metody. Tehnologii. 2023. № 4 (60). S. 106-112.

Submit manuscript

To cite

Citations:

Confirmation

Регистрация