Volume 14, Issue 2 (9-2017)                   JSDP 2017, 14(2): 115-130 | Back to browse issues page


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A New Method for Classification of Nano-Structures based on Time Series Analysis and Fuzzy Logic. JSDP 2017; 14 (2) :115-130
URL: http://jsdp.rcisp.ac.ir/article-1-409-en.html
Abstract:   (4770 Views)

Dispersion of nanoparticles in nanostructures is one of the most important indicators designed to verify the effectiveness of proposed methods in the synthesis of nanomaterials. In the recent years, various methods have been suggested for the synthesis of nanostructures in which the Scanning Electron Microscopy (SEM) has been used to show the quality of the nanomaterial. The SEM images of nanoparticles contain structural, chemical and morphological information with high resolution in nanometer scale of nanomaterials.
One of the challenges in the quality of dispersion’s nanostructures is detection of agglomeration degree. In some SEM images of nanoparticles, the particles have speeded uniformly and not aggregately. In some of the other SEM images, their particles are agglomerated. Also, there are a few SEM images of nanoparticles that their particles aren’t very aggregate or diffused. If the SEM images of nanoparticles with their particles speeded uniformly, are called good images, and the images with their aggregate particles are called bad images, and the images with their particle dispersion between good and bad images, are called average images, the nanomaterials could be classified in categories of good, average, and bad images.
In this paper, a new algorithm has been provided to classify nanostructures using SEM images of nanoparticles. For this purpose, these images were transformed to time series at first (the time series extracted are unique for each SEM image of nanoparticles) and their specifications were investigated through time series analysis methods. Then, statistical specifications of these series were extracted. Six statistical specifications have been extracted for classification of nanostructures. These specifications are as follows: standard deviation, first and second kurtosis, interquartile range, the criterion of Pearson, and skewness. The extracted specifications were used as inputs of a fuzzy inference system for classifying microscopic images of nanostructures into three groups: good, average and bad. This algorithm has been tested on 65 nanoparticles microscopic images with identical size and resulted precision above 93 percent indicated validity of this algorithm.
 

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Type of Study: Research | Subject: Paper
Received: 2015/08/25 | Accepted: 2017/03/5 | Published: 2017/10/21 | ePublished: 2017/10/21

References
1. [1] J. Ramsden, Applied nanotechnology: the conversion of research results to products: William Andrew, 2013.
2. [2] G. M. Whitesides, "Nanoscience, nanotechnolo-gy, and chemistry," Small, vol. 1, no. 2, pp. 172-179, 2005. [DOI:10.1002/smll.200400130] [PMID]
3. [3] J. J. Ramsden, and J. Freeman, "The nanoscale," Collegium, vol. 3, 2008.
4. [4] K. Chatterjee, S. Sarkar, K. J. Rao, and S. Paria, "Core/shell nanoparticles in biomedical applica-tions," Advances in colloid and interface scienc-e, vol. 209, pp. 8-39, 2014. [DOI:10.1016/j.cis.2013.12.008] [PMID]
5. [5] A. K. Hussein, "Applications of nanotechnology in renewable energies—A comprehensive overview and understanding," Renewable and Sustainable Energy Reviews, vol. 42, pp. 460-476, 2015. [DOI:10.1016/j.rser.2014.10.027]
6. [6] R. Misra, S. Acharya, and S. K. Sahoo, "Cancer nanotechnology: application of nanotechnology in cancer therapy," Drug Discovery Today, vol. 15, no. 19, pp. 842-850, 2010. [DOI:10.1016/j.drudis.2010.08.006] [PMID]
7. [7] R. Toy, L. Bauer, C. Hoimes, K. B. Ghaghada, and E. Karathanasis, "Targeted nanotechnology for cancer imaging," Advanced drug delivery reviews, vol. 76, pp. 79-97, 2014. [DOI:10.1016/j.addr.2014.08.002] [PMID] [PMCID]
8. [8] H. Han, Z. Huang, and W. Lee, "Metal-assisted chemical etching of silicon and nanotechnology applications," Nano Today, vol. 9, no. 3, pp. 271-304, 2014. [DOI:10.1016/j.nantod.2014.04.013]
9. [9] Y. Sun, and Y. Xia, "Shape-controlled synthesis of gold and silver nanoparticles," Science, vol. 298, no. 5601, pp. 2176-2179, 2002. [DOI:10.1126/science.1077229] [PMID]
10. [10] H. Chen, S. Witharana, Y. Jin, C. Kim, and Y. Ding, "Predicting thermal conductivity of liquid suspensions of nanoparticles (nanofluids) based on rheology," Particuology, vol. 7, no. 2, pp. 151-157, 2009. [DOI:10.1016/j.partic.2009.01.005]
11. [11] V. Pokropivny, and V. Skorokhod, "Classifica-tion of nanostructures by dimension-ality and concept of surface forms engineering in nanomaterial science," Materials Science and Engineering: C, vol. 27, no. 5, pp. 990-993, 2007. [DOI:10.1016/j.msec.2006.09.023]
12. [12] V. Pokropivny, and V. Skorokhod, "New dimensionality classifications of nanostructur-es," Physica E: Low-dimensional Systems and nanostructures, vol. 40, no. 7, pp. 2521-2525, 2008. [DOI:10.1016/j.physe.2007.11.023]
13. [13] E. Kustov, and V. Nefedov, "Nanostructures: Compositions, structure, and classification," Russian Journal of Inorganic Chemistry, vol. 53, no. 14, pp. 2103-2170, 2008. [DOI:10.1134/S0036023608140027]
14. [14] A. A. Al-Mousa, A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic: Santa Clara University, 2010. [PMCID]
15. [15] T.-c. Fu, "A review on time series data mining," Engineering Applications of Artificial Intellige-nce, vol. 24, no. 1, pp. 164-181, 2011. [DOI:10.1016/j.engappai.2010.09.007]
16. [16] O. J. Pereira, L. de Almeida Pacheco, S. S. Barreto, and T. Cavalcante, "Pattern Recogni-tion using Multivariate Time Series for Fault Detection in a Thermoeletric Unit." p. 315.
17. [17] N. Ding, Y. Bésanger, and F. Wurtz, "Next-day MV/LV substation load forecaster using time series method," Electric Power Systems Resea-rch, vol. 119, pp. 345-354, 2015. [DOI:10.1016/j.epsr.2014.10.003]
18. [18] J. A. Brady, "Considering Complexity: Image Matching With Time Series," University of California, Riverside, 2007.
19. [19] A. Elsayed, M. H. A. Hijazi, F. Coenen, M. Garcıa-Finana, V. Sluming, and Y. Zheng, "Image Categorisation Using Time Series Case Based Reasoning." pp. 423-436.
20. [20] M. D. Gupta, and S. Banerjee, "Similarity Based Retrieval in Case Based Reasoning for Analysis of Medical Images," World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 8, no. 3, pp. 539-545, 2015.
21. [21] T. Guyet, and H. Nicolas, "Long term analysis of time series of satellite images," Pattern Recognition Letters, vol. 70, pp. 17-23, 2016. [DOI:10.1016/j.patrec.2015.11.005]
22. [22] L. A. Zadeh, "Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic," Fuzzy sets and systems, vol. 90, no. 2, pp. 111-127, 1997. [DOI:10.1016/S0165-0114(97)00077-8]

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