Advertisement

Applications of Computational Intelligence in Industrial and Environmental Scenarios

  • Ruggero  Donida Labati
  • Angelo Genovese
  • Enrique Muñoz
  • Vincenzo Piuri
  • Fabio Scotti
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 756)

Abstract

Computational Intelligence (CI) techniques are receiving increasing attention by the industrial and academic communities involved in the design of automatic systems for industrial and environmental monitoring and control applications. CI techniques are able to aggregate inputs from several heterogeneous sensors, adapt themselves to wide ranges of operational and environmental conditions, and cope with incomplete or noise-affected data. With current computing architectures evolving towards smaller size, higher computational power, and more affordable cost, a great number of devices can embed CI techniques to support different kinds of applications. In this paper, we present a survey of the recent CI methods designed for the main processing steps of industrial and environmental monitoring systems.

Notes

Acknowledgements

This work was supported in part by the EC within the H2020 program under grant agreement 644597 (ESCUDO-CLOUD).

References

  1. 1.
    Ahmadi, P., Dincer, I., Rosen, M.A.: Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system. Energy Convers. Manag. 76, 282–300 (2013)CrossRefGoogle Scholar
  2. 2.
    Alaei, H.K., Salahshoor, K., Alaei, H.K.: A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis. Soft Comput. 17(3), 345–362 (2013)CrossRefGoogle Scholar
  3. 3.
    Alexandridis, A.: Evolving RBF neural networks for adaptive soft-sensor design. Int. J. Neural Syst. 23(6), 1350029 (2013)CrossRefGoogle Scholar
  4. 4.
    Alippi, C., Braione, P.: Classification methods and inductive learning rules: what we may learn from theory. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 36(5), 649–655 (2006)Google Scholar
  5. 5.
    Alippi, C., Casagrande, E., Fumagalli, M., Scotti, F., Piuri, V., Valsecchi, L.: An embedded system methodology for real-time analysis of railways track profile. In: Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IMTC), pp. 747–751 (2002)Google Scholar
  6. 6.
    Alippi, C., Casagrande, E., Scotti, F., Piuri, V.: Composite real-time image processing for railways track profile measurement. IEEE Trans. Instrum. Meas. 49(3), 559–564 (2000)CrossRefGoogle Scholar
  7. 7.
    Alippi, C., D’Angelo, G., Matteucci, M., Pasquettaz, G., Piuri, V., Scotti, F.: Composite techniques for quality analysis in automotive laser welding. In: Proceedings of the 2003 IEEE International Symposium on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 72–77 (2003)Google Scholar
  8. 8.
    Alippi, C., Ferrero, A., Piuri, V.: Artificial intelligence for instruments and measurement applications. IEEE Instrum. Meas. Mag. 1(2), 9–17 (1998)CrossRefGoogle Scholar
  9. 9.
    Alippi, C., Roveri, M., Piuri, V., Scotti, F.: Computational intelligence in industrial quality control. In: Proceedings of the 2005 IEEE International Workshop on Intelligent Signal Processing (WISP), pp. 4–9. Faro, Portugal (2005)Google Scholar
  10. 10.
    Amigoni, F., Brandolini, A., Caglioti, V., Di Lecce, V., Guerriero, A., Lazzaroni, M., Lombardi, F., Ottoboni, R., Pasero, E., Piuri, V., Scotti, O., Somenzi, D.: Agencies for perception in environmental monitoring. IEEE Trans. Instrum. Meas. 55(4), 1038–1050 (2006)CrossRefGoogle Scholar
  11. 11.
    Amit, S.N.K.B., Shiraishi, S., Inoshita, T., Aoki, Y.: Analysis of satellite images for disaster detection. In: Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5189–5192 (2016)Google Scholar
  12. 12.
    Azadeh, A., Ghaderi, S.F., Sohrabkhani, S.: Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers. Manag. 49(8), 2272–2278 (2008)CrossRefGoogle Scholar
  13. 13.
    Azar, T.A., Vaidyanathan, S.: Computational intelligence applications in modeling and control. Springer International Publishing (2015)Google Scholar
  14. 14.
    Banerjee, T.P., Das, S.: Multi-sensor data fusion using support vector machine for motor fault detection. Inf. Sci. 217, 96–107 (2012)CrossRefGoogle Scholar
  15. 15.
    Bellocchio, F., Borghese, N.A., Ferrari, S., Piuri, V.: 3D Surface Reconstruction: multi-scale hierarchical approaches. Springer (2013)Google Scholar
  16. 16.
    Braeuer, B., Bauer, K.: A new interpretation of seismic tomography in the southern Dead Sea basin using neural network clustering techniques: interpretation of tomography in the SDSB. Geophys. Res. Lett. 42(22), 9772–9780 (2015)CrossRefGoogle Scholar
  17. 17.
    Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: Can plain neural networks compete with BM3D? In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399 (2012)Google Scholar
  18. 18.
    Cadenas, J.M., Garrido, M.C., Muñoz, E.: Facing dynamic optimization using a cooperative metaheuristic configured via fuzzy logic and SVMs. Appl. Soft Comput. 11(8), 5639–5651 (2011)CrossRefGoogle Scholar
  19. 19.
    Campbell, C.: An introduction to kernel methods. In: Howlett, R.J., Jain, L.C. (eds.) Radial basis function networks: design and applications. Springer, Berlin (2000)Google Scholar
  20. 20.
    Charfi, Y., Wakamiya, N., Murata, M.: Challenging issues in visual sensor networks. IEEE Wirel. Commun. 16(2), 44–49 (2009)CrossRefGoogle Scholar
  21. 21.
    De Capitani di Vimercati, S., Foresti, S., Livraga, G., Samarati, P.: Data privacy: definitions and techniques. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 20(6), 793–817 (2012)Google Scholar
  22. 22.
    De Capitani di Vimercati, S., Genovese, A., Livraga, G., Piuri, V., Scotti, F.: Privacy and security in environmental monitoring systems: issues and solutions. In: Vacca J.R (ed.) Computer and Information Security Handbook, 2nd edn, pp. 835–853. Morgan Kaufmann, Boston (2013)Google Scholar
  23. 23.
    De Capitani di Vimercati, S., Livraga, G., Piuri, V., Scotti, F.: Privacy and security in environmental monitoring systems. In: Proceedings of the 2012 IEEE 1st AESS European Conference on Satellite Telecommunications (ESTEL), pp. 1–6 (2012)Google Scholar
  24. 24.
    Di Natale, C., Davide, F.A.M., D’Amico, A., Göpel, W., Weimar, U.: Sensor arrays calibration with enhanced neural networks. Sens. Actuators B: Chem. 19(1), 654–657 (1994)CrossRefGoogle Scholar
  25. 25.
    Ding, A., Zhang, Q., Zhou, X., Dai, B.: Automatic recognition of landslide based on CNN and texture change detection. In: Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 444–448 (2016)Google Scholar
  26. 26.
    Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)CrossRefGoogle Scholar
  27. 27.
    Donida Labati, R., Genovese, A., Muñoz, E., Piuri, V., Scotti, F., Sforza, G.: Improving OSB wood panel production by vision-based systems for granulometric estimation. In: Proceedings of the 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry (RTSI), pp. 557–562 (2015)Google Scholar
  28. 28.
    Donida Labati, R., Genovese, A., Muñoz, E., Piuri, V., Scotti, F., Sforza, G.: Analyzing images in frequency domain to estimate the quality of wood particles in OSB production. In: Proceedings of the 2016 IEEE Internationa Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). Budapest, Hungary (2016)Google Scholar
  29. 29.
    Donida Labati, R., Genovese, A., Muñoz, E., Piuri, V., Scotti, F., Sforza, G.: Computational intelligence for industrial and environmental applications. In: Proceedings of the 2016 IEEE 8th International Conference on Intelligent Systems (IS), pp. 8–14 (2016)Google Scholar
  30. 30.
    Donida Labati, R., Genovese, A., Piuri, V., Scotti, F.: Low-cost volume estimation by two-view acquisitions: a computational intelligence approach. In: Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2012)Google Scholar
  31. 31.
    Donida Labati, R., Genovese, A., Piuri, V., Scotti, F.: A virtual environment for the simulation of 3D wood strands in multiple view systems for the particle size measurements. In: Proceedings of the 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 162–167 (2013)Google Scholar
  32. 32.
    Donida Labati, R., Genovese, A., Piuri, V., Scotti, F.: Wildfire smoke detection using computational intelligence techniques enhanced with synthetic smoke plume generation. IEEE Trans. Syst. Man Cybern. Syst. 43(4), 1003–1012 (2013)CrossRefGoogle Scholar
  33. 33.
    D’Urso, P., Di Lallo, D., Maharaj, E.A.: Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks. Soft Comput. 17(1), 83–131 (2013)CrossRefGoogle Scholar
  34. 34.
    Dutta, S., Datta, A., Chakladar, N.D., Pal, S.K., Mukhopadhyay, S., Sen, R.: Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique. Precis. Eng. 36(3), 458–466 (2012)CrossRefGoogle Scholar
  35. 35.
    Engelbrecht, A.: Computational Intelligence: an introduction. Wiley (2007)Google Scholar
  36. 36.
    Ferrari, S., Frosio, I., Piuri, V., Borghese, N.A.: Automatic multiscale meshing through HRBF networks. IEEE Trans. Instrum. Meas. 54(4), 1463–1470 (2005)CrossRefGoogle Scholar
  37. 37.
    Ferrari, S., Piuri, V.: Neural networks in intelligent sensors and measurement systems for industrial applications. In: Ablameyko, S., Goras, L., Gori, M., Piuri, V. (eds.) Neural networks for instrumentation, measurement, and related industrial applications, pp. 19–42. IOS Press (2003)Google Scholar
  38. 38.
    Ferrari, S., Piuri, V., Scotti, F.: Image processing for granulometry analysis via neural networks. In: Proceedings of the 2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 28–32 (2008)Google Scholar
  39. 39.
    Foo, P.H., Ng, G.W.: High-level information fusion: an overview. J. Adv. Inf. Fus. 8(1), 33–72 (2013)Google Scholar
  40. 40.
    Fortuna, L., Giannone, P., Graziani, S., Xibilia, M.G.: Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery. IEEE Trans. Instrum. Meas. 56(1), 95–101 (2007)CrossRefGoogle Scholar
  41. 41.
    Fortuna, L., Graziani, S., Xibilia, M.: Soft sensors for product quality monitoring in debutanizer distillation columns. Control Eng. Pract. 13(4), 499–508 (2005)CrossRefGoogle Scholar
  42. 42.
    Fowler, K.: Sensor survey Part 2: Sensors and sensor networks in five years. IEEE Instrum. Meas. Mag. 12(2), 40–44 (2009)CrossRefGoogle Scholar
  43. 43.
    Fuente, M.J., Garcia-Alvarez, D., Sainz-Palmero, G.I., Vega, P.: Fault detection in a wastewater treatment plant based on neural networks and PCA. In: Proceedings of the 2012 20th Mediterranean Conference on Control Automation (MED), pp. 758–763 (2012)Google Scholar
  44. 44.
    Gamassi, M., Piuri, V., Scotti, F., Roveri, M.: Genetic techniques for pattern extraction in particle boards images. In: Proceedings of the 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 129–134 (2006)Google Scholar
  45. 45.
    García Nieto, P.J., Combarro, E.F., del Coz Díaz, J.J., Montañés, E.: A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl. Math. Comput. 219(17), 8923–8937 (2013)Google Scholar
  46. 46.
    GE Oil and Gas: Ca-Zoom® digital PTZ industrial inspection cameras. https://www.gemeasurement.com/inspection-ndt/remote-visual-inspection/ca-zoom-industrial-ptz-cameras
  47. 47.
    Genovese, A., Donida Labati, R., Piuri, V., Scotti, F.: Wildfire smoke detection using computational intelligence techniques. In: Proceedings of the 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 1–6 (2011)Google Scholar
  48. 48.
    Gungor, V.C., Hancke, G.P.: Industrial wireless sensor networks: challenges, design principles, and technical approaches. IEEE Trans. Ind. Electron. 56(10), 4258–4265 (2009)CrossRefGoogle Scholar
  49. 49.
    Haykin, S.: Neural Networks and Learning Machines. v.10. Prentice Hall (2009)Google Scholar
  50. 50.
    Hibert, C., Grandjean, G., Bitri, A., Travelletti, J., Malet, J.P.: Characterizing landslides through geophysical data fusion: example of the La Valette landslide (France). Eng. Geol. 128, 23–29 (2012)CrossRefGoogle Scholar
  51. 51.
    Hossain, M., Rekabdar, B., Louis, S.J., Dascalu, S.: Forecasting the weather of nevada: a deep learning approach. In: Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2015)Google Scholar
  52. 52.
    Hou, L., Bergmann, N.W.: Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Trans. Instrum. Meas. 61(10), 2787–2798 (2012)CrossRefGoogle Scholar
  53. 53.
    Ince, T., Kiranyaz, S., Eren, L., Askar, M., Gabbouj, M.: Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron. 63(11), 7067–7075 (2016)CrossRefGoogle Scholar
  54. 54.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)CrossRefGoogle Scholar
  55. 55.
    Karri, V., Ho, T., Madsen, O.: Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction. Int. J. Hydrog. Energy 33(11), 2857–2867 (2008)CrossRefGoogle Scholar
  56. 56.
    Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)CrossRefGoogle Scholar
  57. 57.
    Khan, S.A., Shahani, D.T., Agarwala, A.K.: Sensor calibration and compensation using artificial neural network. ISA Trans. 42(3), 337–352 (2003)CrossRefGoogle Scholar
  58. 58.
    Kothari, V., Anuradha, J., Shah, S., Mittal, P.: A Survey on Particle Swarm Optimization in Feature Selection, pp. 192–201. Springer, Berlin, Heidelberg (2012)Google Scholar
  59. 59.
    Kreibich, O., Neuzil, J., Smid, R.: Quality-based multiple-sensor fusion in an industrial wireless sensor network for MCM. IEEE Trans. Ind. Electron. 61(9), 4903–4911 (2014)CrossRefGoogle Scholar
  60. 60.
    Krömer, P., Platoš, J., Snášel, V.: Mining multi-class industrial data with evolutionary fuzzy rules. In: Proceedings of the 2013 IEEE International Conference on Cybernetics (CYBCONF), pp. 191–196 (2013)Google Scholar
  61. 61.
    Kulkarni, R.V., Forster, A., Venayagamoorthy, G.K.: Computational intelligence in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 13(1), 68–96 (2011)CrossRefGoogle Scholar
  62. 62.
    Langone, R., Alzate, C., De Ketelaere, B., Vlasselaer, J., Meert, W., Suykens, J.A.K.: LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Eng. Appl. Artif. Intell. 37, 268–278 (2015)CrossRefGoogle Scholar
  63. 63.
    Larios, D.F., Barbancho, J., Rodríguez, G., Sevillano, J.L., Molina, F.J., León, C.: Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring. IET Commun. 6(14), 2189 (2012)CrossRefGoogle Scholar
  64. 64.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  65. 65.
    Lerner, B., Guterman, H., Aladjem, M., Dinstein, I.: A comparative study of neural network based feature extraction paradigms. Pattern Recognit. Lett. 20(1), 7–14 (1999)zbMATHCrossRefGoogle Scholar
  66. 66.
    Li, G., Rong, M., Wang, X., Li, X., Li, Y.: Partial discharge patterns recognition with deep Convolutional Neural Networks. In: Proceedings of the 2016 International Conference on Condition Monitoring and Diagnosis (CMD), pp. 324–327 (2016)Google Scholar
  67. 67.
    Li, P., Li, N., Cao, M.: Meteorology features extraction for transmission line icing process based on Kohonen Self-Organizing Maps. In: Proceedings of the 2010 International Conference on Computer Design and Applications (ICCDA), pp. 430–433 (2010)Google Scholar
  68. 68.
    Liu, Q., Jin, D., Shen, J., Fu, Z., Linge, N.: A WSN-based prediction model of microclimate in a greenhouse using extreme learning approaches. In: Proceedings of the 2016 18th International Conference on Advanced Communication Technology (ICACT), pp. 1–2 (2016)Google Scholar
  69. 69.
    Longbotham, N., Pacifici, F., Glenn, T., Zare, A., Volpi, M., Tuia, D., Christophe, E., Michel, J., Inglada, J., Chanussot, J., Du, Q.: Multi-modal change detection, application to the detection of flooded areas: outcome of the 2009–2010 data fusion contest. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(1), 331–342 (2012)Google Scholar
  70. 70.
    Makhtar, A.K., Yussof, H., Al-Assadi, H., Yee, L.C., Othman, M.F., Shazali, K.: Wireless sensor network applications: A study in environment monitoring system. In: Proceedings of the 2012 International Symposium on Robotics and Intelligent Sensors (IRIS), vol. 41, pp. 1204–1210 (2012)Google Scholar
  71. 71.
    Marco, S., Gutiérrez-Gálvez, A., Lansner, A., Martinez, D., Rospars, J.P., Beccherelli, R., Perera, A., Pearce, T.C., Verschure, P.F.M.J., Persaud, K.: A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation. Microsyst. Technol. 20(4–5), 729–742 (2014)CrossRefGoogle Scholar
  72. 72.
    Marwala, T.: Condition Monitoring using Computational Intelligence Methods: applications in mechanical and electrical systems. Springer Science and Business Media (2012)Google Scholar
  73. 73.
    Marzano, F.S., Rivolta, G., Coppola, E., Tomassetti, B., Verdecchia, M.: Rainfall nowcasting from multisatellite passive-sensor images using a recurrent neural network. IEEE Trans. Geosci. Remote Sens. 45(11), 3800–3812 (2007)CrossRefGoogle Scholar
  74. 74.
    Mohamed, R., Ahmed, A., Eid, A., Farag, A.: Support vector machines for camera calibration problem. In: Proceedings of the 2006 International Conference on Image Processing (ICIP), pp. 1029–1032 (2006)Google Scholar
  75. 75.
    Mourot, G., Bousghiri, S., Kratz, F.: Sensor fault detection using fuzzy logic and neural networks. In: Proceedings of the International Conference on Systems, Man and Cybernetics (SMC), pp. 369–374 (1993)Google Scholar
  76. 76.
    Muñoz, E., Ruspini, E.H.: Simulation of fuzzy queueing systems with a variable number of servers, arrival rate, and service rate. IEEE Trans. Fuzzy Syst. 22(4), 892–903 (2014)CrossRefGoogle Scholar
  77. 77.
    Nakama, T., Muñoz, E., LeBlanc, K., Ruspini, E.: Generalizing and formalizing precisiation language to facilitate human-robot interaction. In: Computational Intelligence, pp. 381–397. Springer (2016)Google Scholar
  78. 78.
    Nor, A.S.M., Faramarzi, M., Yunus, M.A.M., Ibrahim, S.: Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method. IEEE Sens. J. 15(1), 497–504 (2015)CrossRefGoogle Scholar
  79. 79.
    O’Connor, E., Smeaton, A.F., O’Connor, N.E., Regan, F.: A neural network approach to smarter sensor networks for water quality monitoring. Sensors 12(4), 4605 (2012)CrossRefGoogle Scholar
  80. 80.
    Palade, V., Bocaniala, C.D., Jain, L.: Computational Intelligence in Fault Diagnosis. Springer (2006)Google Scholar
  81. 81.
    Paulinas, M., Ušinskas, A.: A survey of genetic algorithms applications for image enhancement and segmentation. Inf. Technol. control 36(3) (2015)Google Scholar
  82. 82.
    phoneArena: A modern smartphone or a vintage supercomputer: which is more powerful? http://www.phonearena.com/news/A-modern-smartphone-or-a-vintage-supercomputer-which-is-more-powerful_id57149 (2014)
  83. 83.
    Piuri, V., Scotti, F., Roveri, M.: Visual inspection of particle boards for quality assessment. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 521–524 (2005)Google Scholar
  84. 84.
    Prieto, M.D., Cirrincione, G., Espinosa, A.G., Ortega, J.A., Henao, H.: Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans. Ind. Electron. 60(8), 3398–3407 (2013)CrossRefGoogle Scholar
  85. 85.
    Qiao, T., Ren, J., Craigie, C., Zabalza, J., Maltin, C., Marshall, S.: Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions. J. Appl. Spectrosc. 82(1), 137–144 (2015)CrossRefGoogle Scholar
  86. 86.
    Ribeiro, B.: Support vector machines for quality monitoring in a plastic injection molding process. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 35(3), 401–410 (2005)Google Scholar
  87. 87.
    Sagheer, A.: Piecewise one dimensional Self Organizing Map for fast feature extraction. In: Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 633–638 (2010)Google Scholar
  88. 88.
    Sammouda, R., Adgaba, N., Touir, A., Al-Ghamdi, A.: Agriculture satellite image segmentation using a modified artificial Hopfield neural network. Comput. Hum. Behav. 30, 436–441 (2014)CrossRefGoogle Scholar
  89. 89.
    Sarcevic, P., Pletl, S., Kincses, Z.: Evolutionary algorithm based 9DOF sensor board calibration. In: Proceedings of the 12th IEEE International Symposiumon Intelligent Systems and Informatics (SISY), pp. 187–192 (2014)Google Scholar
  90. 90.
    Shirvaikar, M.: Trends in automated visual inspection. J. Real Time Image Process. 1(1), 41–43 (2006)CrossRefGoogle Scholar
  91. 91.
    Simon, D.: Evolutionary Optimization Algorithms. Wiley (2013)Google Scholar
  92. 92.
    Singha, S., Bellerby, T.J., Trieschmann, O.: Satellite oil spill detection using artificial neural networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(6), 2355–2363 (2013)Google Scholar
  93. 93.
    Su, I.J., Tsai, C.C., Sung, W.T.: Area temperature system monitoring and computing based on adaptive fuzzy logic in wireless sensor networks. Appl. Soft Comput. 12(5), 1532–1541 (2012)CrossRefGoogle Scholar
  94. 94.
    Szenkovits, A., Gaskó, N., Jahier, E.: Environment-model based testing with differential evolution in an industrial setting, pp. 819–830 (2016)Google Scholar
  95. 95.
    Teti, R., Segreto, T., Simeone, A., Teti, R.: Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion. In: Proceedings of the 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 85–90 (2013)Google Scholar
  96. 96.
    Toh, K.K.V., Isa, N.A.M., Ashidi, N.: Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Process. Lett. 17(3), 281–284 (2010)CrossRefGoogle Scholar
  97. 97.
    Trillas, E., Eciolaza, L.: Fuzzy Logic: an introductory course for engineering students. Springer Publishing Company, Incorporated (2015)CrossRefGoogle Scholar
  98. 98.
    Vaseghi, S.V.: Advanced Digital Signal Processing and Noise Reduction, 4th edn. Wiley (2008)Google Scholar
  99. 99.
    Vos, T.E.J., Baars, A.I., Lindlar, F.F., Windisch, A., Wilmes, B., Gross, H., Kruse, P.M., Wegener, J.: Industrial case studies for evaluating search based structural testing. Int. J. Softw. Eng. Knowl. Eng. 22(08), 1123–1149 (2012)CrossRefGoogle Scholar
  100. 100.
    Vos, T.E.J., Lindlar, F.F., Wilmes, B., Windisch, A., Baars, A.I., Kruse, P.M., Gross, H., Wegener, J.: Evolutionary functional black-box testing in an industrial setting. Softw. Qual. J. 21(2), 259–288 (2013)CrossRefGoogle Scholar
  101. 101.
    Wang, L., Fu, X.: Data mining with computational intelligence. In: Advanced Information and Knowledge Processing. Springer, Berlin, New York (2005)Google Scholar
  102. 102.
    Wang, X.Y., Yang, H.Y., Zhang, Y., Fu, Z.K.: Image denoising using SVM classification in nonsubsampled contourlet transform domain. Inf. Sci. 246, 155–176 (2013)MathSciNetzbMATHCrossRefGoogle Scholar
  103. 103.
    Wijayasekara, D., Linda, O., Manic, M., Rieger, C.: FN-DFE: Fuzzy-neural data fusion engine for enhanced resilient state-awareness of hybrid energy systems. IEEE Trans. Cybern. 44(11), 2065–2075 (2014)CrossRefGoogle Scholar
  104. 104.
    Wu, J.L., Li, I.J.: A SOM-based dimensionality reduction method for KNN classifiers. In: Proceedings of the 2010 International Conference on System Science and Engineering (ICSSE), pp. 173–178 (2010)Google Scholar
  105. 105.
    Xiang, Y., Jiang, L.: Water quality prediction using LS-SVM and Particle Swarm Optimization. In: Proceedings of the 2nd International Workshop on Knowledge Discovery and Data Mining (WKDD), pp. 900–904 (2009)Google Scholar
  106. 106.
    Xie, X., Guo, J., Zhang, H., Jiang, T., Bie, R., Sun, Y.: Neural-network based structural health monitoring with wireless sensor networks. In: Proceedings of the 2013 9th International Conference on Natural Computation (ICNC), pp. 163–167 (2013)Google Scholar
  107. 107.
    Xu, F., Song, X., Wang, X., Su, J.: Neural network model for earthquake prediction using DMETER data and seismic belt information. In: Proceedings of the 2010 2nd WRI Global Congress on Intelligent Systems (GCIS), vol. 3, pp. 180–183 (2010)Google Scholar
  108. 108.
    Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)CrossRefGoogle Scholar
  109. 109.
    Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)CrossRefGoogle Scholar
  110. 110.
    Yao, L., Lu, N., Jiang, S.: Artificial neural network (ANN) for multi-source PM2.5 estimation using surface, MODIS, and meteorological data. In: Proceedings of the 2012 International Conference on Biomedical Engineering and Biotechnology (iCBEB), pp. 1228–1231 (2012)Google Scholar
  111. 111.
    Ye, Y., Ci, S., Katsaggelos, A.K., Liu, Y., Qian, Y.: Wireless video surveillance: a survey. IEEE Access 1, 646–660 (2013)CrossRefGoogle Scholar
  112. 112.
    Yildiz, A.R.: A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl. Soft Comput. 13(3), 1561–1566 (2013)CrossRefGoogle Scholar
  113. 113.
    Yin, S., Ding, S.X., Xie, X., Luo, H.: A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Ind. Electron. 61(11), 6418–6428 (2014)CrossRefGoogle Scholar
  114. 114.
    Zhang, M., Liu, X.: A soft sensor based on adaptive fuzzy neural network and support vector regression for industrial melt index prediction. Chemom. Intell. Lab. Syst. 126, 83–90 (2013)CrossRefGoogle Scholar
  115. 115.
    Zhao, W., Du, S.: Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach. IEEE Trans. Geosci. Remote Sens. 54(8), 4544–4554 (2016)CrossRefGoogle Scholar
  116. 116.
    Zhao, Z., Liu, F.: Industrial monitoring based on moving average PCA and neural network. In: Proceedings of the 30th Annual Conference of IEEE Industrial Electronics Society (IECON), vol. 3, pp. 2168–2171 (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ruggero  Donida Labati
    • 1
  • Angelo Genovese
    • 1
  • Enrique Muñoz
    • 1
  • Vincenzo Piuri
    • 1
  • Fabio Scotti
    • 1
  1. 1.Department of Computer ScienceUniversità Degli Studi di MilanoCremaItaly

Personalised recommendations