Journals / Politeknik Dergisi / 2017 / Cilt: 20 - Sayı: 4

Kırmızı-yeşil-mavi (kym) renk ve bulanık mantık teknikleri kullanılarak odun yoğunluğu tahmini

Prediction of wood density by using red- green-blue (rgb) color and fuzzy logic techniques

Pages
978–984
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Consequently, Fuzzy logic is visable method for the prediction of the wood density.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Density is an important wood property since it correlates to mechanical properties of wood.
📊
Key Findings & Data:Afterwards, the experimental and modeling data results were compared. 98.17% accuracy was observed between the measurement and the fuzzy logic model.
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Conclusion & Impact:Consequently, Fuzzy logic is visable method for the prediction of the wood density.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Density is an important wood property since it correlates to mechanical properties of wood. Fuzzy logic, among the various available Artificial Intelligence techniques, emerges as a good technique in predicting. Digital image analysis is an powerful tool to obtain meaningful data out of an image. In this study, digital image processing based on a red-green-blue (RGB) color examination was practiced to measure the intensity of wood color. Densities of the test samples were measured. Then, a new fuzzy logic model was developed based on these measured values and RGB color intensity of wood. Afterwards, the experimental and modeling data results were compared. 98.17% accuracy was observed between the measurement and the fuzzy logic model. Consequently, Fuzzy logic is visable method for the prediction of the wood density.

Özet

Ahşap malzemenin yoğunluğu ahşabın mekaniksel özelliklerini etkilemesinden dolayı önemlidir. Mevcut yapay zeka teknikleri arasında bulanık mantık tahminlerde iyi bir yöntem olarak ortaya çıkmaktadır. Dijital görüntü tekniği bir görüntüden anlamlı bir bilgi elde etmek için kullanılan güçlü bir yöntemdir. Test örneklerinin yoğunlukları ölçülmüştür. Ayrıca, örneklerin renk yoğunluğunu ölçmek için Kırmızı-Yeşil-Mavi (KYM) renk muayenesine dayanan dijital görüntü analizi uygulanmıştır. Ölçülen değerler ve ahşabın KYM renk yoğunluğu temelinde yeni bir bulanık mantık modeli geliştirilmiştir. Sonrasında deneyler ve model verileri karşılaştırılmıştır. Hazırlanan modelin çıkarımları ile deneysel veriler %98.17 oranında doğruluk göstermiştir. Sonuç olarak, bulanık mantık odun yoğunluğu tahmini için geçerli bir yöntem olduğu tespit edilmiştir.

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Cite This Article

BARDAK, T., BARDAK, S. (2017). Kırmızı-yeşil-mavi (kym) renk ve bulanık mantık teknikleri kullanılarak odun yoğunluğu tahmini. Politeknik Dergisi, Cilt: 20 - Sayı: 4, 978–984. https://ajindex.com/articles/306569

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References (14)

  1. Ors Y. and Keskin H., "Wood materials science (Ağaç malzeme teknolojisi)", Gazi University Publication, No: 2001-352, Ankara, (2008)
  2. Zareiforoush H., Minaei S., Alizadeh M.R. and Banakar A., "A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice", Measurement, 66: 26-34, (2016)
  3. Chen C.L. and C.L. Tai C.L., "Adaptive fuzzy color segmentation with neural network for road detections", Engineering Applications Artificial Intelligence, 23 (3): 400-410, (2010)
  4. Hassan M.A., Yusof Y., Azmi M.A. and Mazli M.N., "Fuzzy Logic Based Intelligent Control of RGB Colour Classification System for Undergraduate Artificial Intelligence Laboratory", The World Congress on Engineering, London, 713-718, (2012)
  5. Tou J.Y., Lau P.Y. and Tay Y.H., "Computer vision- based wood recognition system", International Workshop on Advanced Image Technology (IWAIT), Bangkok, Thailand, 197-202, (2007)
  6. Esteban L.G., Fernández F.G., Palacios P. and Conde M., "Artificial neural networks in variable process control: application in particleboard manufacture", Investıgacıon Agrarıa-Sıstemas Y Recursos Forestales, 18(1): 92-100, (2009)
  7. Khalid M., Lee E.L.Y., Yusof R. and Nadaraj m., "Design of an intelligent wood species recognition system", International Journal of Simulation: Systems, Science and Technology, 9(3): 9-19, (2008)
  8. Ozsahin S., "Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis", European Journal of Wood and Wood Products, 71(6): 769-777, (2013)
  9. Ruz G.A., Estévez P.A., and Perez C.A., "A neurofuzzy color image segmentation method for wood surface defect detection", Forest Products Journal, 55(4): 52-58, (2005)
  10. Cavdarlı M. and Seke E., "Measuring roughness on wood surfaces for detection of defects using multi-frame imaging", The International Symposium on Innovations in Intelligent Systems and Applications, Kayseri, 21-23, (2010)
  11. Cheung W.W.L., Pitcher T.J. and Pauly D., "A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing", Biological Conservation, 124(1): 97-111, (2005)
  12. Uraon K.K. and Kumar S., "Analysis of Defuzzification Method for Rainfall Event", International Journal of Computer Science and Mobile Computing, 5(1): 341- 354, (2016)
  13. URL1,https://en.wikipedia.org/wiki/Fuzzy_set_ operations#Fuzzy_unions. (Accessed 9 Semtember 2016)
  14. URL2,http://www.massey.ac.