Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Identification of Plastic Wastes by Using Fuzzy Radial Basis Function Neural Networks Classifier with Conditional Fuzzy C-Means Clustering

  • Roh, Seok-Beom (Dept. of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
  • Received : 2016.03.07
  • Accepted : 2016.06.13
  • Published : 2016.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

Keywords

References

  1. Scott D. M., "A two color near-infrared sensor for sorting recycled plastic waste," Measurement Science and Technology, vol. 6, pp. 156-159, 1995. https://doi.org/10.1088/0957-0233/6/2/004
  2. Edward, J. and Sommer J. R., "Method and Apparatus for Near Infrared Sorting of Recycled Plastic Waste," Patent in United States, Pub. No.: US 2001/0045518 A1, 2001.
  3. Sorely J. Cocjrane and Jordana Blacksberg, "A Fast Classification Scheme in Raman Spectroscopy for the identification of Mineral Mixtures Using a Large Database with Correlated Predictors," IEEE Trans. On Geoscience and Remote Sensing, vol. 53, No. 8, pp. 4259-4274, Aug. 2015. https://doi.org/10.1109/TGRS.2015.2394377
  4. E. Smith and G. Dent, "Modern Raman Spectroscopy-A Practical Approach," Hoboken, NJ, USA: Wiley, 2005.
  5. L. Zhang, K. Li, H. He, and G. W. Irwin, "A New Discrete-Continuous Algorithm for Radial Basis Function Networks Construction," IEEE Trans. On Neural Networks and Learning Systems, vol. 24, no. 11, pp. 1785-1798, 2013. https://doi.org/10.1109/TNNLS.2013.2264292
  6. W. Pedrycz, R. Al-Hmouz, and A. S. Balamash, "Hierarchical Granular Clustering: An Emergence of Information Granules of Higher Type and Higher Order," IEEE Trans. On Fuzzy Systems, vol. 23, no. 6, pp. 2270-2283, 2015. https://doi.org/10.1109/TFUZZ.2015.2417896
  7. A. Gacek and W. Pedrycz, "Clustering Granular Data and Their Characterization with Information Granules of Higher Type," IEEE Trans. On Fuzzy Systems, vol. 23, no. 4, pp. 850-860, 2015. https://doi.org/10.1109/TFUZZ.2014.2329707
  8. X. Q. Tang and P. Zhu, "Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space," IEEE Trans. On Fuzzy Systems, vol. 21, no. 5, pp. 814-824, 2013. https://doi.org/10.1109/TFUZZ.2012.2230176
  9. W. Pedrycz, "Conditional fuzzy C-Means," Pattern Recognition Letters, vol.17, no,6, pp.625-632, 1996. https://doi.org/10.1016/0167-8655(96)00027-X
  10. M.J. Er, S.Q. Wu, J.W. Lu, and H.L. Toh, "Face recognition with radical basis function (RBF) neural networks," IEEE Transactions on Neural Networks, vol. 13, no. 5, pp. 697-710, 2002. https://doi.org/10.1109/TNN.2002.1000134
  11. X.Y. Jing, Y.F. Yao, D. Zhang, J.Y. Yang, and M. Li, "Face and palm print pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition," Pattern Recognition, vol.40, pp.3209-3224, 2007. https://doi.org/10.1016/j.patcog.2007.01.034
  12. S.-B. Roh, S.-K Oh, and W. Z. Choi, "Design of fuzzy radial basis function neural networks classifier based on conditional fuzzy C-means clustering algorithm," in conference of ICMIT 2015, pp.67-70, 2015.

Cited by

  1. Reinforced rule-based fuzzy models: Design and analysis vol.119, 2017, https://doi.org/10.1016/j.knosys.2016.12.003
  2. Bromine in plastic consumer products – Evidence for the widespread recycling of electronic waste vol.601-602, 2017, https://doi.org/10.1016/j.scitotenv.2017.05.173
  3. Use of laser-induced breakdown spectroscopy for the determination of polycarbonate (PC) and acrylonitrile-butadiene-styrene (ABS) concentrations in PC/ABS plastics from e-waste vol.70, 2017, https://doi.org/10.1016/j.wasman.2017.09.027
  4. Solving Classification Problems Using Projection-Based Learning Algorithm with Fuzzy Radial Basis Function Neural Network vol.17, pp.03, 2018, https://doi.org/10.1142/S146902681850013X
  5. Identification of Black Plastics Based on Fuzzy RBF Neural Networks: Focused on Data Preprocessing Techniques Through Fourier Transform Infrared Radiation vol.14, pp.5, 2018, https://doi.org/10.1109/TII.2017.2771254