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All issues Volume 197 (2020) E3S Web Conf., 197 (2020) 06020 References
Open Access
Issue
E3S Web Conf.
Volume 197, 2020
75th National ATI Congress – #7 Clean Energy for all (ATI 2020)
Article Number 06020
Number of page(s) 15
Section Internal Combustion Engines
DOI https://doi.org/10.1051/e3sconf/202019706020
Published online 22 October 2020
  1. Commission Regulation (EU) 2016/427 (2016) [Google Scholar]
  2. Commission Regulation (EU) 2016/646 (2016) [Google Scholar]
  3. Commission Regulation (EU) 2017/1154 (2017) [Google Scholar]
  4. T. Donateo, M. Giovinazzi, “Some Repeatability and Reproducibility Issues in Real Driving Emission Tests” SAE Technical Paper, 2018-01-5020 (2018) [Google Scholar]
  5. C. L. Myung, W. Jang, S. Kwon, J. Ko, D. Jin, and S. Park, “Evaluation of the real-time de-nox performance characteristics of a lnt-equipped euro-6 diesel passenger car with various vehicle emissions certification cycles,” Energy, vol. 132, pp. 356-369 (2017) [CrossRef] [Google Scholar]
  6. X. Wang, H. Yin, Y. Ge, L. Yu, Z. Xu, C. Yu, X. Shi, and H. Liu, “On-vehicle emission measurement of a light-duty diesel van at various speeds at high altitude,” Atmospheric Environment, vol. 81, pp. 263-269 (2013). [CrossRef] [Google Scholar]
  7. L. Qu, M. Li, D. Chen, K. Lu, T. Jin, and X. Xu, “Multivariate analysis between driving condition and vehicle emission for light duty gasoline vehicles during rush hours,” Atmospheric Environment, vol. 110, pp. 103-110 (2015) [CrossRef] [Google Scholar]
  8. O. V. Lozhkina and V. N. Lozhkin, “Estimation of nitrogen oxides emissions from petrol and diesel passenger cars by means of on-board monitoring: Effect of vehicle speed, vehicle technology, engine type on emission rates,” Transportation Research Part D: Transport and Environment, vol. 47, pp. 251-264 (2016) [CrossRef] [Google Scholar]
  9. R. Jaikumar, S. S. Nagendra, and R. Sivanandan, “Modal analysis of real-time, real world vehicular exhaust emissions under heterogeneous traffic conditions,” Transportation Research Part D: Transport and Environment, vol. 54, pp. 397-409 (2017) [CrossRef] [Google Scholar]
  10. D. Lejri, A. Can, N. Schiper, and L. Leclercq, “Accounting for traffic speed dynamics when calculating copert and phem pollutant emissions at the urban scale,” Transportation Research Part D: Transport and Environment, vol. 63, pp. 588-603 (2018) [CrossRef] [Google Scholar]
  11. M. Fischer, “Transient NOx estimation using artificial neural networks,” IFAC Proceedings Volumes, vol. 46, no. 21, pp. 101-106 (2013). [CrossRef] [Google Scholar]
  12. G. Najafi, B. Ghobadian, A. Moosavian, T. Yusaf, R. Mamat, M. Kettner, and W. Azmi, “Svm and anfis for prediction of performance and exhaust emissions of a si engine with gasoline-ethanol blended fuels,” Applied Thermal Engineering, vol. 95, pp. 186-203 (2016) [CrossRef] [Google Scholar]
  13. H. C. Krijnsen, R. Bakker, W. E. J. van Kooten, H. P. A. Calis, R. P. Verbeek, and C. M. van den Bleek, “Evaluation of fit algorithms for NOx emission prediction for efficient denox control of transient diesel engine exhaust gas,” Industrial & Engineering Chemistry Research, vol. 39, no. 8, pp. 2992-2997 (2000) [CrossRef] [Google Scholar]
  14. J. Ma, F. Xu, K. Huang, and R. Huang, “Improvement on the linear and nonlinear auto-regressive model for predicting the NOx emission of diesel engine,” Neurocomputing, vol. 207, pp. 150-164 (2016) [CrossRef] [Google Scholar]
  15. I. Arsie, D. Marra, C. Pianese, and M. Sorrentino, “Real-time estimation of engine NOx emissions via recurrent neural networks,” IFAC Proceedings Volumes, vol. 43, no. 7, pp. 228-233 (2010) [CrossRef] [Google Scholar]
  16. S. Roy, R. Banerjee, A. K. Das, and P. K. Bose, “Development of an ANN based system identification tool to estimate the performance-emission characteristics of a crdi assisted cng dual fuel diesel engine,” Journal of Natural Gas Science and Engineering, vol. 21, pp. 147-158 (2014) [CrossRef] [Google Scholar]
  17. S. Uslu and M. B. Celik, “Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether,” Engineering Science and Technology, an International Journal, vol. 21, no. 6, pp. 1194201 (2018) [CrossRef] [Google Scholar]
  18. M. Canakci, A. N. Ozsezen, E. Arcaklioglu, and A. Erdil, “Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil,” Expert Systems with Applications, vol. 36, no. 5, pp. 9268-9280 (2009) [CrossRef] [Google Scholar]
  19. L. Guzzella and A. Sciarretta, Vehicle Propulsion Systems: Introduction to Modeling and Optimization. Springer-Verlag Berlin Heidelberg (2007) [Google Scholar]
  20. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980 (2015) [Google Scholar]
  21. A. Langevin and D. Riopel, Logistics Systems: Design and Optimization. Springer US, (2005) [CrossRef] [Google Scholar]
  22. M. A. Gelbart, J. Snoek, and R. P. Adams, “Bayesian optimization with unknown constraints,” ArXiv, vol. abs/1403.5607 (2014) [Google Scholar]
  23. J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” ArXiv, vol. abs/1206.2944 (2012) [Google Scholar]
  24. J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyper-parameter optimization,” in Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS’11, Red Hook, NY, USA, p. 2546-2554 (2011) [Google Scholar]
  25. I. Dewancker, M. McCourt, and S. Clark, “Bayesian optimization primer,” https://bit.ly/3d9LiRn. (2015) [Google Scholar]

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