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S. Zhang, X. Chen, and Y. Yin, "An ELM based online soft sensing approach for alumina concentration detection," Mathematical Problems in Engineering, vol. 2015, Article ID 268132, 8 pages, 2015.
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K. G. Sheela and S. N. Deepa, "Neural network based hybrid computing model for wind speed prediction," Neurocomputing, vol. 122, pp. 425-429, 2013.
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J. Rehrla, A. P. Karttunenb, N. Nicolaic, T. Hormanne, and M. Hornd, "Control of three different continuous pharmaceutical manufacturing processes: use of soft sensors," International Journal of Pharmaceutics, vol. 543, no. 1-2, pp. 60-72, 2018.
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S. Yin, W. Ji, and L. Wang, "A machine learning based energy efficient trajectory planning approach for industrial robots," Procedia CIRP, vol. 81, pp. 429-434, 2019, DOI: 10.1016/j.procir.2019.03.074.
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G. Hussain, M. Jabbar, J.-D. Cho, and S. Bae, "Indoor positioning system: a new approach based on LSTM and two stage activity classification," Electronics, vol. 8, no. 4, p. 375, 2019.
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S. Xingjian, Z. Chen, H. Wang et al., "Convolutional LSTM network: a machine learning approach for precipitation nowcasting," in Proceedings of Advances in Neural Information Processing Systems, pp. 802-810, Montreal, Canada.
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L. Yang, Y. Li, J. Wang, and Z. Tang, "Post text processing of Chinese speech recognition based on bidirectional LSTM networks and CRF," Electronics, vol. 8, no. 11, p. 1248, 2019.
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Z. Ge, "Supervised latent factor analysis for process data regression modeling and soft sensor application," IEEE Transactions on Control Systems Technology, vol. 24, no. 3, pp. 1004-1011, 2015.
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B. Maschler, D. White, and M. Weyrich, "Anwendungsfalle und Methoden der kunstlichen Intelligenz in der anwendungsorientierten Forschung im Kontext von Industrie 4.0," University of Stuttgart, 2020, DOI: 10.18419/opus-10740.
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A. Mayr et al., "Machine Learning in Production - Potentials, Challenges and Exemplary Applications," Procedia CIRP, vol. 86, pp. 49-54, 2019, DOI: 10.1016/j.procir.2020.01.035.
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J. Chen, Q. Jin, and J. Chao, "Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin," Mathematical Problems in Engineering, vol. 2012, Article ID 235929, 16 pages, 2012.
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12 |
S. Gao, J. Yang, and J. Wang, "D-FNN based modeling and BP neural network decoupling control of PVC stripping process," Mathematical Problems in Engineering, vol. 2014, Article ID 681259, 13 pages, 2014.
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J. Liu, A. Shahroudy, D. Xu et al., "Spatio-temporal lstm with trust gates for 3d human action recognition," in Proceedings of European Conference on Computer Vision, pp. 816-833, Amsterdam, Netherlands.
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T. Muller, N. Jazdi, J.-P. Schmidt, and M. Weyrich, "Cyber-Physical Production Systems: enhancement with a self-organized reconfiguration management," Procedia CIRP, 2020 (accepted).
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K. Sun, X. Wu, J. Xue, and F. Ma, "Development of a new multi-layer perceptron based soft sensor for SO2 emissions in power plant," Journal of Process Control, vol. 84, pp. 182-191, 2019.
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Q. Sun and Z. Ge, "Probabilistic sequential network for deep learning of complex process data and soft sensor application," IEEE Transactions on Industrial Informatics, vol. 15, pp. 2700-2709, 2018.
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C. Bennett, J. F. Dunne, S. Trimby, and D. Richardson, "Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks," Mechanical Systems and Signal Processing, vol. 85, pp. 126-145, 2017, DOI: 10.1016/j.ymssp.2016.07.015.
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W. Yan, D. Tang, and Y. Lin, "A data-driven soft sensor modeling method based on deep learning and its application," IEEE Transactions on Industrial Electronics, vol. 64, no. 5, pp. 4237-4245, 2017.
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Z. Ujevic, I. Mohler, and N. Bolf, "Soft sensors for splitter product property estimation in CDU," Chemical Engineering Communications, vol. 198, pp. 1566-1578, 2011.
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K. Sun, J. Liu, J.-L. Kang, S.-S. Jang, D. S.-H. Wong, and D.-S. Chen, "Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote," Journal of Process Control, vol. 24, pp. 1068-1075, 2014.
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A. Nawaz, A. S. Arora, C. M. Yun, H. Cho, S. You, and M. Lee, "Data authorization and forecasting by a proactive soft sensing tool-anammox based process," Industrial & Engineering Chemistry Research, vol. 58, no. 22, pp. 9552-9563, 2019.
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M. Zabadaj, K. Chreptowicz, J. Mierzejewska, and P. Ciosek, "Two-dimensional fluorescence as soft sensor in the monitoring of biotransformation performed by yeast," Biotechnology Progress, vol. 33, no. 2, pp. 299-307, 2017.
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S. Trimby, J. F. Dunne, C. Bennett, and D. Richardson, "Unified approach to engine cylinder pressure reconstruction using time-delay neural networks with crank kinematics or block vibration measurements," International Journal of Engine Research, vol. 18, no. 3, pp. 256-272, 2017, DOI: 10.1177/1468087416655013.
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X. Yuan, L. Li, and Y. Wang, "Nonlinear dynamic soft sensor modeling with supervised long short-term memory network," IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3168-3176, 2019.
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E. Heidari, M. A. Sobati, and S. Movahedirad, "Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN)," Chemometrics and Intelligent Laboratory Systems, vol. 155, pp. 73-85, 2016.
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Y. He, Y. Xu, Z. Geng, and Q. Zhu, "Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network," Chinese Journal of Chemical Engineering, vol. 23, no. 1, pp. 138-145, 2015.
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27 |
R. Johnsson, "Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals," Mechanical Systems and Signal Processing, vol. 20, no. 8, pp. 1923-1940, 2006, DOI: 10.1016/j.ymssp.2005.09.003.
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H. Mrabet, E. Giacoumidis, I. Dayoub, and Aymen Belghith, "A Survey of Applied Machine Learning Techniques for Optical Orthogonal Frequency Division Multiplexing Based Networks", Emerging Telecommunications Technologies, ETT 2022, ISSN: 2161-3915, Wiley, November 2021, DOI: https://doi.org/10.1002/ett.4400.
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Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, "LSTM network: a deep learning approach for short-term traffic forecast," IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68-75, 2017.
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