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Odun Yoğunluğu Tahmini için Veri Madenciliği ve Piksel Dağılımı Yaklaşımı

Yıl 2019, Cilt: 21 Sayı: 2, 386 - 396, 15.08.2019

Öz

Ahşap ekonomik
kalkınmada stratejik bir öneme sahiptir. Yenilikler, tüm endüstrilerde olduğu
gibi ahşap endüstrisinde de ticari başarının temelini oluşturur. Ahşabın
yoğunluğu, ahşabın fiziksel ve mekanik özellikleri hakkında değerli bilgiler
sağlar ve ayrıca orman endüstrisindeki verim ile de doğrudan ilgilidir.
Ahşap yapıların
fiziksel özelliklerini değerlendirmek için birçok tahribatsız test çalışmaları
yapılmıştır. Bu çalışma, gri tonlamalı görüntüdeki piksel sayısı ve veri
madenciliğini kullanarak meşe (Quercus
robur
) ve kayın (Fagus orientalis
L.) ağacının yoğunluğunu tahmin etmek için yapıldı.
Bu amaçla, ahşap
görüntülerden elde edilen piksel yoğunluğu verileri kaydedildi. Bu veriler
yapay sinir ağları ve rastgele orman algoritmalarında tanımlayıcı değişkenler
olarak kullanılmıştır. Tasarlanan yapay sinir ağı ve rastgele orman algoritmaları,
test aşamasında sırasıyla % 95,19 ve          % 96,36 doğrulukla yoğunluk tahmini
sağladı. Sonuç olarak, bu çalışma piksel yoğunluğunun ve veri madenciliğinin
ahşabın yoğunluğunu öngörmede bir araç olarak kullanılma potansiyeline sahip
olduğunu göstermiştir.

Kaynakça

  • 1. Ao Y, Li H, Zhu L, Ali S, Yang Z. (2019). Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm. Journal of Petroleum Science and Engineering, Elsevier, 173, 781–792.
  • 2. Chuchala D, Orlowski KA, Sandak A, Sandak J, Pauliny D, Barański J. (2014). The Effect of Wood Provenance and Density on Cutting Forces While Sawing Scots Pine (Pinus sylvestris L.). BioResources, 9(3), 5349–5361.
  • 3. Diaconu D, Wassenberg M, Spiecker H. (2016). Variability of European beech wood density as influenced by interactions between tree-ring growth and aspect. Forest Ecosystems, 3(1), 6.
  • 4. Eskandarian S, Bahrami P, Kazemi P. (2017). A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking. Journal of Petroleum Science and Engineering, 156, 605–615.
  • 5. Foca G, Masino F, Antonelli A, Ulrici A. (2011). Prediction of compositional and sensory characteristics using RGB digital images and multivariate calibration techniques. Analytica Chimica Acta, 706(2), 238–245.
  • 6. Gogebakan M, Erol H. (2018). A New Semi-Supervised Classification Method Based on Mixture Model Clustering for Classification of Multispectral Data. Journal of the Indian Society of Remote Sensing, 46(8), 1323–31.
  • 7. Simon H. (1999). Neural networks : a comprehensive foundation, Prentice Hall.
  • 8. Hryniewicz P, Banaś W, Gwiazda A, Foit K, Sękala A, Kost G. (2015). Technological process supervising using vision systems cooperating with the LabVIEW vision builder. IOP Conference Series: Materials Science and Engineering, IOP Publishing, 95(1), 012086.
  • 9. Khalid M, Lee E, Yusof R. (2008). Design of an intelligent wood species recognition system. International Journal of Simulation System, Science and Technology, 9(3), 9-19.
  • 10. Komi M, Jun Li, Yongxin Z, Xianguo Z. (2017). Application of data mining methods in diabetes prediction,” in: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), IEEE, 1006–1010.
  • 11. Masanori K, Nakano T. (2004). Artificial weathering of tropical woods. part 2: color change. Holzforschung 58(5), 558–65.
  • 12. Montes S, Hernández RE, Beaulieu J, Weber JC. (2007). Genetic variation in wood color and ıts correlations with tree growth and wood density of calycophyllum spruceanum at an early age in the peruvian amazon. New Forests 35(1), 57–73.
  • 13. Lana MM, Tijskens LMM, van Kooten O. (2006). Effects of storage temperature and stage of ripening on RGB colour aspects of fresh-cut tomato pericarp using video image analysis. Journal of Food Engineering, 77(4), 871–879.
  • 14. Lin CT. Ching T, Lee CSG. (1996). Neural fuzzy systems : a neuro-fuzzy synergism to intelligent systems, Prentice Hall PTR.
  • 15. Luna-Moreno D, Espinosa Sánchez, YM. Ponce de León YR, Noé Arias E, a Garnica Campos G. (2015). Virtual instrumentation in LabVIEW for multiple optical characterizations on the same opto-mechanical system. Optik - International Journal for Light and Electron Optics, 126(19), 1923–1929.
  • 16. Osborne NL, Høibø Ø.A, Maguire DA. (2016). Estimating the density of coast Douglas-fir wood samples at different moisture contents using medical X-ray computed tomography.Computers and Electronics in Agriculture, 127, 50–55.
  • 17. Rapidminer. (n.d.). “Neural Net - RapidMiner Documentation,” <https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/neural_nets/neural_net.html> (Mar. 15, 2018).
  • 18. Rojas MR, Martina AMS. (1996). Manual De Identıfıcacıon De Especıes Forestales De La Subregion Andina. Ministerio de Agricultura, INIA, Instituto Nacional de Investigación Agraria, Organización Internacional de las Maderas Tropicales, OIMT
  • 19. Schinker MG, Hansen N, Spiecker H. (2003). High-frequency densitometry - a new method for the rapid evaluation of wood density variations. IAWA J, 24.
  • 20. Shi C, Teng G, Li Z. (2016). An approach of pig weight estimation using binocular stereo system based on LabVIEW. Computers and Electronics in Agriculture, 129, 37–43.
  • 21. Sozen E, Bardak T, Aydemir D, Bardak S. (2018). Estimation of deformation in nanocomposites using artificial neural networks and deep learning algorithms. Journal of Bartın Faculty of Forestry, 20(2), 223–231.
  • 22. Sun J, Zhong G, Huang K, Dong J. (2018). Banzhaf random forests: Cooperative game theory based random forests with consistency. Neural Networks, 106, 20–29.
  • 23. Tiryaki S, Bardak S, Bardak, T. (2015). Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive. Journal of Adhesion Science and Technology, 29(23), 2521-2536.
  • 24. TS 2472 (1976). Wood - determination of density for physical and mechanical tests, TSE, Ankara.
  • 25. Wadie BS, Badawi AM, Abdelwahed M, Elemabay SM. (2006). Application of artificial neural network in prediction of bladder outlet obstruction: A model based on objective, noninvasive parameters. Urology, Elsevier, 68(6), 1211–1214.
  • 26. Wang C, Shu Q, Wang X, Guo B, Liu P, Li Q. (2019). A random forest classifier based on pixel comparison features for urban lidar data. ISPRS Journal of Photogrammetry and Remote Sensing 148, 75–86.
  • 27. Wang W, Li C, Tollner EW, Rains GC. (2012). Development of software for spectral imaging data acquisition using LabVIEW. Computers and Electronics in Agriculture, 84, 68–75.
  • 28. Wu D, Shi , Wang, S, He Y, Bao Y, Liu K. (2012). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, Elsevier, 726, 57–66.
  • 29. Wu X, Zhang X. (2019). An efficient pixel clustering-based method for mining spatial sequential patterns from serial remote sensing ımages. Computers & Geosciences, 124, 128-139.
  • 30. Zobel BJ, Jett JB. (1995). The Importance of Wood Density (Specific Gravity) and Its Component Parts. Springer, Berlin, Heidelberg, 78–97.
  • 31. Zor M, Sozen E, Bardak T. (2016). Mechanical performances of laminated wood and determination of deformation in the bending test with the aid of ımage analysis method,” Journal of Bartin Faculty of Forestry, 18(2), 126–126.

Data Mining and Pixel Distribution Approach for Wood Density Prediction

Yıl 2019, Cilt: 21 Sayı: 2, 386 - 396, 15.08.2019

Öz

Wood has a strategic importance in economic
development. Innovations are the basic premise of commercial success in the
wood industry, as in all industries. The density of wood provides valuable
information about the physical and mechanical properties of the wood, and it is
also directly related to the productivity in the forest industry. Many
non-destructive test studies have been conducted to evaluate the physical
properties of wood structures. This study was conducted to predict the density
of wood in oak (Quercus robur) and
beech (Fagus orientalis L.) using the
number of pixels in grayscale image and data mining. To this purpose, pixel
density of data were saved from wood images. This data was used as descriptor
variables in artificial neural networks and random forest algorithm. The
designed artificial neural network model and random forest algorithm allowed
the prediction of density with an accuracy of 95.19% and 96.36%, respectively
for the testing phase. As a result, this study showed that pixel density and
data mining have the potential to be used as an instrument for predicting the
density of wood

Kaynakça

  • 1. Ao Y, Li H, Zhu L, Ali S, Yang Z. (2019). Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm. Journal of Petroleum Science and Engineering, Elsevier, 173, 781–792.
  • 2. Chuchala D, Orlowski KA, Sandak A, Sandak J, Pauliny D, Barański J. (2014). The Effect of Wood Provenance and Density on Cutting Forces While Sawing Scots Pine (Pinus sylvestris L.). BioResources, 9(3), 5349–5361.
  • 3. Diaconu D, Wassenberg M, Spiecker H. (2016). Variability of European beech wood density as influenced by interactions between tree-ring growth and aspect. Forest Ecosystems, 3(1), 6.
  • 4. Eskandarian S, Bahrami P, Kazemi P. (2017). A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking. Journal of Petroleum Science and Engineering, 156, 605–615.
  • 5. Foca G, Masino F, Antonelli A, Ulrici A. (2011). Prediction of compositional and sensory characteristics using RGB digital images and multivariate calibration techniques. Analytica Chimica Acta, 706(2), 238–245.
  • 6. Gogebakan M, Erol H. (2018). A New Semi-Supervised Classification Method Based on Mixture Model Clustering for Classification of Multispectral Data. Journal of the Indian Society of Remote Sensing, 46(8), 1323–31.
  • 7. Simon H. (1999). Neural networks : a comprehensive foundation, Prentice Hall.
  • 8. Hryniewicz P, Banaś W, Gwiazda A, Foit K, Sękala A, Kost G. (2015). Technological process supervising using vision systems cooperating with the LabVIEW vision builder. IOP Conference Series: Materials Science and Engineering, IOP Publishing, 95(1), 012086.
  • 9. Khalid M, Lee E, Yusof R. (2008). Design of an intelligent wood species recognition system. International Journal of Simulation System, Science and Technology, 9(3), 9-19.
  • 10. Komi M, Jun Li, Yongxin Z, Xianguo Z. (2017). Application of data mining methods in diabetes prediction,” in: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), IEEE, 1006–1010.
  • 11. Masanori K, Nakano T. (2004). Artificial weathering of tropical woods. part 2: color change. Holzforschung 58(5), 558–65.
  • 12. Montes S, Hernández RE, Beaulieu J, Weber JC. (2007). Genetic variation in wood color and ıts correlations with tree growth and wood density of calycophyllum spruceanum at an early age in the peruvian amazon. New Forests 35(1), 57–73.
  • 13. Lana MM, Tijskens LMM, van Kooten O. (2006). Effects of storage temperature and stage of ripening on RGB colour aspects of fresh-cut tomato pericarp using video image analysis. Journal of Food Engineering, 77(4), 871–879.
  • 14. Lin CT. Ching T, Lee CSG. (1996). Neural fuzzy systems : a neuro-fuzzy synergism to intelligent systems, Prentice Hall PTR.
  • 15. Luna-Moreno D, Espinosa Sánchez, YM. Ponce de León YR, Noé Arias E, a Garnica Campos G. (2015). Virtual instrumentation in LabVIEW for multiple optical characterizations on the same opto-mechanical system. Optik - International Journal for Light and Electron Optics, 126(19), 1923–1929.
  • 16. Osborne NL, Høibø Ø.A, Maguire DA. (2016). Estimating the density of coast Douglas-fir wood samples at different moisture contents using medical X-ray computed tomography.Computers and Electronics in Agriculture, 127, 50–55.
  • 17. Rapidminer. (n.d.). “Neural Net - RapidMiner Documentation,” <https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/neural_nets/neural_net.html> (Mar. 15, 2018).
  • 18. Rojas MR, Martina AMS. (1996). Manual De Identıfıcacıon De Especıes Forestales De La Subregion Andina. Ministerio de Agricultura, INIA, Instituto Nacional de Investigación Agraria, Organización Internacional de las Maderas Tropicales, OIMT
  • 19. Schinker MG, Hansen N, Spiecker H. (2003). High-frequency densitometry - a new method for the rapid evaluation of wood density variations. IAWA J, 24.
  • 20. Shi C, Teng G, Li Z. (2016). An approach of pig weight estimation using binocular stereo system based on LabVIEW. Computers and Electronics in Agriculture, 129, 37–43.
  • 21. Sozen E, Bardak T, Aydemir D, Bardak S. (2018). Estimation of deformation in nanocomposites using artificial neural networks and deep learning algorithms. Journal of Bartın Faculty of Forestry, 20(2), 223–231.
  • 22. Sun J, Zhong G, Huang K, Dong J. (2018). Banzhaf random forests: Cooperative game theory based random forests with consistency. Neural Networks, 106, 20–29.
  • 23. Tiryaki S, Bardak S, Bardak, T. (2015). Experimental investigation and prediction of bonding strength of Oriental beech (Fagus orientalis Lipsky) bonded with polyvinyl acetate adhesive. Journal of Adhesion Science and Technology, 29(23), 2521-2536.
  • 24. TS 2472 (1976). Wood - determination of density for physical and mechanical tests, TSE, Ankara.
  • 25. Wadie BS, Badawi AM, Abdelwahed M, Elemabay SM. (2006). Application of artificial neural network in prediction of bladder outlet obstruction: A model based on objective, noninvasive parameters. Urology, Elsevier, 68(6), 1211–1214.
  • 26. Wang C, Shu Q, Wang X, Guo B, Liu P, Li Q. (2019). A random forest classifier based on pixel comparison features for urban lidar data. ISPRS Journal of Photogrammetry and Remote Sensing 148, 75–86.
  • 27. Wang W, Li C, Tollner EW, Rains GC. (2012). Development of software for spectral imaging data acquisition using LabVIEW. Computers and Electronics in Agriculture, 84, 68–75.
  • 28. Wu D, Shi , Wang, S, He Y, Bao Y, Liu K. (2012). Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Analytica Chimica Acta, Elsevier, 726, 57–66.
  • 29. Wu X, Zhang X. (2019). An efficient pixel clustering-based method for mining spatial sequential patterns from serial remote sensing ımages. Computers & Geosciences, 124, 128-139.
  • 30. Zobel BJ, Jett JB. (1995). The Importance of Wood Density (Specific Gravity) and Its Component Parts. Springer, Berlin, Heidelberg, 78–97.
  • 31. Zor M, Sozen E, Bardak T. (2016). Mechanical performances of laminated wood and determination of deformation in the bending test with the aid of ımage analysis method,” Journal of Bartin Faculty of Forestry, 18(2), 126–126.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kereste, Hamur ve Kağıt
Bölüm Wood Machinary, Occupational Safety and Health, Business Administration
Yazarlar

Timuçin Bardak 0000-0002-1403-1049

Selahattin Bardak 0000-0001-9724-4762

Eser Sözen Bu kişi benim 0000-0003-4798-7124

Yayımlanma Tarihi 15 Ağustos 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 21 Sayı: 2

Kaynak Göster

APA Bardak, T., Bardak, S., & Sözen, E. (2019). Data Mining and Pixel Distribution Approach for Wood Density Prediction. Bartın Orman Fakültesi Dergisi, 21(2), 386-396.


Bartin Orman Fakultesi Dergisi Editorship,

Bartin University, Faculty of Forestry, Dean Floor No:106, Agdaci District, 74100 Bartin-Turkey.

Tel: +90 (378) 223 5094, Fax: +90 (378) 223 5062,

E-mail: bofdergi@gmail.com