A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 1
Jan.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Cosimo Ieracitano, Annunziata Paviglianiti, Maurizio Campolo, Amir Hussain, Eros Pasero and Francesco Carlo Morabito, "A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 64-76, Jan. 2021. doi: 10.1109/JAS.2020.1003387
Citation: Cosimo Ieracitano, Annunziata Paviglianiti, Maurizio Campolo, Amir Hussain, Eros Pasero and Francesco Carlo Morabito, "A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 64-76, Jan. 2021. doi: 10.1109/JAS.2020.1003387

A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers

doi: 10.1109/JAS.2020.1003387
Funds:  This work was supported by the European Commission, the European Social Fund and the Calabria Region (C39B18000080002). The authors are the only responsible for this publication and the European Commission and the Region of Calabria decline any responsibility for the use that may be made of the information in it held. This work was also supported by the UK Engineering and Physical Sciences Research Council (EPSRC) (EP/M026981/1, EP/T021063/1, EP/T024917/1)
More Information
  • The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope (SEM) images of the electrospun nanofiber, to ensure that no structural defects are produced. The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology. Hence, the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0. In this paper, a novel automatic classification system for homogenous (anomaly-free) and non-homogenous (with defects) nanofibers is proposed. The inspection procedure aims at avoiding direct processing of the redundant full SEM image. Specifically, the image to be analyzed is first partitioned into sub-images (nanopatches) that are then used as input to a hybrid unsupervised and supervised machine learning system. In the first step, an autoencoder (AE) is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features. Next, a multilayer perceptron (MLP), trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber (NH-NF) and homogenous nanofiber (H-NF) patches. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques, reporting accuracy rate up to 92.5%. In addition, the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks (CNN). The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.


  • loading
  • [1]
    S. Rai and A. Rai, “Nanotechnology-the secret of fifth industrial revolution and the future of next generation,” Nusantara Biosci., vol. 7, no. 2, pp. 61–66, Nov. 2015.
    A. Cappy, D. Stievenard, and D. Vuillaume, “Nanotechnology: The next industrial revolution?” in Proc. Gallium Arsenide Applications Symp., Milano, Italy, 2002.
    R. Vasita and D. S. Katti, “Nanofibers and their applications in tissue engineering,” Int. J. Nanomedicine, vol. 1, no. 1, pp. 15–30, Mar. 2006. doi: 10.2147/nano.2006.1.1.15
    R. Aliaksandra, “Nanomaterials for biosensing and phototherapy applications,” in Proc. Int. Conf. Laser Optics. St. Petersburg, Russia, 2018, pp. 540.
    A. X. Liu, J. S. Gao, and M. Y. Wu, “Effects of nanomaterials on water quality of aquiculture,” in Proc. Third Int. Conf. Intelligent System Design and Engineering Applications, Hong Kong, China, 2013, pp. 688−691.
    K. M. Yun, C. J. Hogan Jr., Y. Matsubayashi, M. Kawabe, F. Iskandar, and K. Okuyama, “Nanoparticle filtration by electrospun polymer fibers,” Chem. Eng. Sci., vol. 62, no. 17, pp. 4751–4759, Sept. 2007. doi: 10.1016/j.ces.2007.06.007
    I. Tlili and T. A. Alkanhal, “Nanotechnology for water purification: Electrospun nanofibrous membrane in water and wastewater treatment,” J. Water Reuse Desal., vol. 9, no. 3, pp. 232–248, Sept. 2019. doi: 10.2166/wrd.2019.057
    G. R. Sun, L. Q. Sun, H. M. Xie, and J. Liu, “Electrospinning of nanofibers for energy applications,” Nanomaterials, vol. 6, no. 7, pp. 129, Jul. 2016. doi: 10.3390/nano6070129
    P. P. Ramesh Kumar, N. Khan, S. Vivekanandhan, N. Satyanarayana, A. K. Mohanty, and M. Misra, “Nanofibers: Effective generation by electrospinning and their applications,” J. Nanosci. Nanotechnol., vol. 12, no. 1, pp. 1–25, Jan. 2012. doi: 10.1166/jnn.2012.5111
    D. Kolberg and D. Zühlke, “Lean automation enabled by industry 4.0 technologies,” IFAC-PapersOnLine, vol. 48, no. 3, pp. 1870–1875, 2015. doi: 10.1016/j.ifacol.2015.06.359
    M. A. Kamarul Bahrin, M. F. Othman, N. H. Nor Azli, and M. F. Talib, “Industry 4.0: A review on industrial automation and robotic,” J. Teknol., vol. 78, no. 6−13, pp. 137–143, Mar. 2016.
    L. Z. Wang and G. Healey, “Using Zernike moments for the illumination and geometry invariant classification of multispectral texture,” IEEE Trans. Image Process., vol. 7, no. 2, pp. 196–203, Feb. 1998. doi: 10.1109/83.660996
    T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002. doi: 10.1109/TPAMI.2002.1017623
    N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst.,Man,Cybern., vol. 9, no. 1, pp. 62–66, Jan. 1979. doi: 10.1109/TSMC.1979.4310076
    X. C. Yuan, L. S. Wu, and Q. J. Peng, “An improved Otsu method using the weighted object variance for defect detection,” Appl. Surf. Sci., vol. 349, pp. 472–484, Sept. 2015. doi: 10.1016/j.apsusc.2015.05.033
    F. Zhou, G. H. Liu, F. Xu, and H. Deng, “A generic automated surface defect detection based on a bilinear model,” Appl. Sci., vol. 9, no. 15, pp. 3159, Aug. 2019. doi: 10.3390/app9153159
    Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015. doi: 10.1038/nature14539
    Y. Bengio, “Deep learning of representations for unsupervised and transfer learning,” in Proc. ICML Workshop on Unsupervised and Transfer Learning, 2012, pp. 17−36.
    C. Ieracitano, A. Adeel, F. C. Morabito, and A. Hussain, “A novel statistical analysis and autoencoder driven intelligent intrusion detection approach,” Neurocomputing, vol. 387, pp. 51–62, Apr. 2020. doi: 10.1016/j.neucom.2019.11.016
    H. Zhang, Y. D. Li, Z. H. Lv, A. K. Sangaiah, and T. Huang, “A real-time and ubiquitous network attack detection based on deep belief network and support vector machine,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 790–799, May 2020. doi: 10.1109/JAS.2020.1003099
    C. Ieracitano, N. Mammone, A. Bramanti, A. Hussain, and F. C. Morabito, “A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings,” Neurocomputing, vol. 323, pp. 96–107, Jan. 2019. doi: 10.1016/j.neucom.2018.09.071
    T. D. Pham, K. Wardell, A. Eklund, and G. Salerud, “Classification of short time series in early Parkinson’s disease with deep learning of fuzzy recurrence plots,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1306–1317, Nov. 2019.
    Y. F. Xia, H. Yu, and F. Y. Wang, “Accurate and robust eye center localization via fully convolutional networks,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1127–1138, Sept. 2019. doi: 10.1109/JAS.2019.1911684
    Z. X. Wang and Z. P. Lin, “Optimal feature selection for learning-based algorithms for sentiment classification,” Cogn. Comput., vol. 12, no. 1, pp. 238–248, Jan. 2020. doi: 10.1007/s12559-019-09669-5
    E. Ragusa, P. Gastaldo, R. Zunino, M. J. Ferrarotti, W. Rocchia, and S. Decherchi, “Cognitive insights into sentic spaces using principal paths,” Cogn. Comput., vol. 11, no. 5, pp. 656–675, Oct. 2019. doi: 10.1007/s12559-019-09651-1
    Y. M. Li, L. Yang, B. Xu, J. Wang, and H. F. Lin, “Improving user attribute classification with text and social network attention,” Cogn. Comput., vol. 11, no. 4, pp. 459–468, Aug. 2019. doi: 10.1007/s12559-019-9624-y
    F. Gao, T. Huang, J. P. Sun, J. Wang, A. Hussain, and E. F. Yang, “A new algorithm for SAR image target recognition based on an improved deep convolutional neural network,” Cogn. Comput., vol. 11, no. 6, pp. 809–824, Dec. 2019. doi: 10.1007/s12559-018-9563-z
    A. Z. Zhang, S. H. Liu, G. Y. Sun, H. Huang, P. Ma, J. Rong, H. Z. Ma, C. Y. Lin, and Z. J. Wang, “Clustering of remote sensing imagery using a social recognition-based multi-objective gravitational search algorithm,” Cogn. Comput., vol. 11, no. 6, pp. 789–798, Dec. 2019. doi: 10.1007/s12559-018-9582-9
    Q. S. Lian, W. F. Yan, X. H. Zhang, and S. Z. Chen, “Single image rain removal using image decomposition and a dense network,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1428–1437, Nov. 2019.
    E. Principi, D. Rossetti, S. Squartini, and F. Piazza, “Unsupervised electric motor fault detection by using deep autoencoders,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 441–451, Mar. 2019. doi: 10.1109/JAS.2019.1911393
    G. Boracchi, D. Carrera, and B. Wohlberg, “Novelty detection in images by sparse representations,” in Proc. IEEE Symp. Intelligent Embedded Systems, Orlando, USA, 2014, pp. 47−54.
    D. Carrera, F. Manganini, G. Boracchi, and E. Lanzarone, “Defect detection in SEM images of nanofibrous materials,” IEEE Trans. Ind. Inform., vol. 13, no. 2, pp. 551–561, Apr. 2017. doi: 10.1109/TII.2016.2641472
    P. Napoletano, F. Piccoli, and R. Schettini, “Anomaly detection in nanofibrous materials by CNN-based self-similarity,” Sensors, vol. 18, no. 1, pp. 209, Jan. 2018. doi: 10.1109/JSEN.2017.2771313
    C. Ieracitano, F. Pantó, N. Mammone, A. Paviglianiti, P. Frontera, and F. C. Morabito, “Toward an automatic classification of SEM images of nanomaterials via a deep learning approach,” in Neural Approaches to Dynamics of Signal Exchanges, A. Esposito, M. Faundez-Zanuy, F. C. Morabito, and E. Pasero, Eds. Singapore, Singapore: Springer, 2020, pp. 61−72.
    C. Ieracitano, A. Paviglianiti, N. Mammone, M. Versaci, E. Pasero, and F. C. Morabito, “SoCNNet: An optimized sobel filter based convolutional neural network for SEM images classification of nanomaterials,” in Progresses in Artificial Intelligence and Neural Systems, A. Esposito, M. Faundez-Zanuy, F. C. Morabito, and E. Pasero, Eds. Singapore, Singapore: Springer, 2021, pp. 103−113.
    Z. M. Huang, Y. Z. Zhang, S. Ramakrishna, and C. T. Lim, “Electrospinning and mechanical characterization of gelatin nanofibers,” Polymer, vol. 45, no. 15, pp. 5361–5368, Jul. 2004. doi: 10.1016/j.polymer.2004.04.005
    S. A. Theron, E. Zussman, and A. L. Yarin, “Experimental investigation of the governing parameters in the electrospinning of polymer solutions,” Polymer, vol. 45, no. 6, pp. 2017–2030, Mar. 2004. doi: 10.1016/j.polymer.2004.01.024
    A. Abutaleb, D. Lolla, A. Aljuhani, H. U. Shin, J. W. Rajala, and G. G. Chase, “Effects of surfactants on the morphology and properties of electrospun polyetherimide fibers,” Fibers, vol. 5, no. 3, pp. 33, Sept. 2017. doi: 10.3390/fib5030033
    M. S. Islam, B. C. Ang, A. Andriyana, and A. M. Afifi, “A review on fabrication of nanofibers via electrospinning and their applications,” SN Appl. Sci., vol. 1, no. 10, pp. 1248, Sept. 2019. doi: 10.1007/s42452-019-1288-4
    J. Fang, H. T. Niu, T. Lin, and X. G. Wang, “Applications of electrospun nanofibers,” Chin. Sci. Bull., vol. 53, no. 15, pp. 2265–2286, Aug. 2008.
    A. K. Gaharwar, S. Sant, M. J. Hancock, and S. A. Hacking, Nanomaterials in Tissue Engineering: Fabrication and Applications. Oxford, UK: Woodhead Publishing, 2013.
    R. S. Bhattarai, R. D. Bachu, S. H. S. Boddu, and S. Bhaduri, “Biomedical applications of electrospun nanofibers: Drug and nanoparticle delivery,” Pharmaceutics, vol. 11, no. 1, pp. 5, 2019.
    P. Gibson, H. Schreuder-Gibson, and D. Rivin, “Transport properties of porous membranes based on electrospun nanofibers,” Coll. Surf. A:Physicochem. Eng. Aspects, vol. 187−188, pp. 469–481, Aug. 2001.
    S. Gee, B. Johnson, and A. L. Smith, “Optimizing electrospinning parameters for piezoelectric PVDF nanofiber membranes,” J. Membr. Sci., vol. 563, pp. 804–812, Oct. 2018. doi: 10.1016/j.memsci.2018.06.050
    R. Tala-Ighil, “Nanomaterials in solar cells,” in Handbook of Nanoelectrochemistry, M. Aliofkhazraei and A. S. H. Makhlouf, Eds. Cham, Germany: Springer, 2016, pp. 1251−1270.
    B. Du, W. Xiong, J. Wu, L. F. Zhang, L. P. Zhang, and D. C. Tao, “Stacked convolutional denoising auto-encoders for feature representation,” IEEE Trans. Cybern., vol. 47, no. 4, pp. 1017–1027, Apr. 2017. doi: 10.1109/TCYB.2016.2536638
    P. Baldi, “Autoencoders, unsupervised learning and deep architectures,” in Proc. Int. Conf. Unsupervised and Transfer Learning Workshop, 2011, pp. 37−50.
    Y. Ollivier, “Auto-encoders: Reconstruction versus compression,” arXiv preprint arXiv: 1403.7752, 2014.
    P. Liu, P. J. Zheng, and Z. Y. Chen, “Deep learning with stacked denoising auto-encoder for short-term electric load forecasting,” Energies, vol. 12, no. 12, pp. 2445, Jun. 2019. doi: 10.3390/en12122445
    P. S. Bradley and O. L. Mangasarian, “Massive data discrimination via linear support vector machines,” Optim. Methods Softw., vol. 13, no. 1, pp. 1–10, 2000. doi: 10.1080/10556780008805771
    S. Balakrishnama and A. Ganapathiraju, “Linear discriminant analysis-a brief tutorial,” Institute for Signal and Information Processing, vol. 18, pp. 1–8, 1998.
    M. Ojala and G. C. Garriga, “Permutation tests for studying classifier performance,” J. Mach. Learn. Res., vol. 11, pp. 1833–1863, Aug. 2010.
    A. Alsarhan, Y. Kilani, A. Al-Dubai, A. Y. Zomaya, and A. Hussain, “Novel fuzzy and game theory based clustering and decision making for VANETs,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 1568–1581, Feb. 2020. doi: 10.1109/TVT.2019.2956228
    S. F. Zhang, K. Z. Huang, R. Zhang, and A. Hussain, “Generalized adversarial training in riemannian space,” in Proc. IEEE Int. Conf. Data Mining, Beijing, China, 2019, pp. 826−835.
    H. Jiang, K. Huang, R. Zhang, and A. Hussain, “Style-neutralized pattern classification based on adversarially trained upgraded U-net,” Cognitive Computation, pp. 1–14, 2019. DOI: 10.1007/s12559-019-09660-0
    M. Mahmud, M. S. Kaiser, A. Hussain, and S. Vassanelli, “Applications of deep learning and reinforcement learning to biological data,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 6, pp. 2063–2079, Jun. 2018. doi: 10.1109/TNNLS.2018.2790388
    X. Yang, K. Z. Huang, R. Zhang, and J. Y. Goulermas, “A novel deep density model for unsupervised learning,” Cogn. Comput., vol. 11, no. 6, pp. 778–788, Dec. 2019. doi: 10.1007/s12559-018-9566-9
    Q. F. Wang, M. Xu, and A. Hussain, “Large-scale ensemble model for customer churn prediction in search ads,” Cogn. Comput., vol. 11, no. 2, pp. 262–270, Apr. 2019. doi: 10.1007/s12559-018-9608-3
    V. Sachnev, S. Suresh, N. Sundararajan, B. S. Mahanand, M. W. Azeem, and S. Saraswathi, “Multi-region risk-sensitive cognitive ensembler for accurate detection of attention-deficit/hyperactivity disorder,” Cogn. Comput., vol. 11, no. 4, pp. 545–559, Aug. 2019. doi: 10.1007/s12559-019-09636-0
    X. Sun and M. Lv, “Facial expression recognition based on a hybrid model combining deep and shallow features,” Cognitive Computation, vol. 11, no. 4, pp. 587–597, 2019.
    F. Z. Xiong, B. Sun, X. Yang, H. Qiao, K. Z. Huang, A. Hussain, and Z. Y. Liu, “Guided policy search for sequential multitask learning,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 49, no. 1, pp. 216–226, Jan. 2019. doi: 10.1109/TSMC.2018.2800040
    A. Zheng, J. Dong, X. Lin, L. Liu, B. Jiang, and B. Luo, “Visual cognition–inspired multi-view vehicle re-identification via Laplacian-regularized correlative sparse ranking,” Cognitive Computation, pp. 1–14, 2019. DOI: 10.1007/s12559-019-09687-3
    X. B. Jia, X. B. Li, Y. Jin, and J. Miao, “Region-enhanced multi-layer extreme learning machine,” Cogn. Comput., vol. 11, no. 1, pp. 101–109, Feb. 2019. doi: 10.1007/s12559-018-9596-3
    W. D. Zhang, Q. Z. Li, Q. M. J. Wu, Y. M. Yang, and M. Li, “A novel ship target detection algorithm based on error self-adjustment extreme learning machine and cascade classifier,” Cogn. Comput., vol. 11, no. 1, pp. 110–124, Feb. 2019. doi: 10.1007/s12559-018-9606-5
    S. C. Gao, M. C. Zhou, Y. R. Wang, J. J. Cheng, H. Yachi, and J. H. Wang, “Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction,” IEEE Trans. Neural Networks Learn. Syst., vol. 30, no. 2, pp. 601–614, Feb. 2019. doi: 10.1109/TNNLS.2018.2846646


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(3)

    Article Metrics

    Article views (1828) PDF downloads(104) Cited by()


    • Anomaly detection system for electrospun nanofibers
    • Decomposition of original SEM images in sub-patches
    • Hybrid unsupervised and supervised machine learning approach
    • Combination of AE and MLP for features extraction and classification


    DownLoad:  Full-Size Img  PowerPoint