An intelligent aerator algorithm inspired-by deep learning

Hongjie Deng; Lingxi Peng; Jiajing Zhang; Chunming Tang; Haoliang Fang; Haohuai Liu; Hongjie Deng; Lingxi Peng; Jiajing Zhang; Chunming Tang; Haoliang Fang; Haohuai Liu

doi:10.3934/mbe.2019148

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 4: 2990-3002. doi: 10.3934/mbe.2019148

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Research article Special Issues

An intelligent aerator algorithm inspired-by deep learning

1.
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China
2.
School of Mathematics and information science, Guangzhou University, Guangzhou, 510006, China
3.
School of Chemistry, Guangzhou University, Guangzhou 510006, China

Received: 06 January 2019 Accepted: 21 March 2019 Published: 10 April 2019

Aerator is an indispensable tool in aquaculture, and China is one of the largest aquaculture countries in the world. So, the intelligent control of the aerator is of great significance to energy conservation and environmental protection and the prevention of the deterioration of dissolved oxygen. There is no intelligent aerator related work in practice and research. In this paper, we mainly study the intelligent aerator control based on deep learning, and propose a dissolved oxygen prediction algorithm with long and short term memory network, referred as DopLSTM. The prediction results are used to the intelligent control design of the aerator. As a result, it is proved that the intelligent control of the aerator can effectively reduce the power consumption and prevent the deterioration of dissolved oxygen
- intelligent aerator,
- long term memory network,
- deep learning
Citation: Hongjie Deng, Lingxi Peng, Jiajing Zhang, Chunming Tang, Haoliang Fang, Haohuai Liu. An intelligent aerator algorithm inspired-by deep learning[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2990-3002. doi: 10.3934/mbe.2019148

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Abstract

Aerator is an indispensable tool in aquaculture, and China is one of the largest aquaculture countries in the world. So, the intelligent control of the aerator is of great significance to energy conservation and environmental protection and the prevention of the deterioration of dissolved oxygen. There is no intelligent aerator related work in practice and research. In this paper, we mainly study the intelligent aerator control based on deep learning, and propose a dissolved oxygen prediction algorithm with long and short term memory network, referred as DopLSTM. The prediction results are used to the intelligent control design of the aerator. As a result, it is proved that the intelligent control of the aerator can effectively reduce the power consumption and prevent the deterioration of dissolved oxygen

References

[1]	M. Nie, Experimental analysis of pressure fluctuations behind a bottom aerator, Tsinghua Sci. Technol., 4(1999), 1358–1362.
[2]	H. Gu and Y. Wang, The development status, issues and trends of pond aeration technology in China, Moderniz. Fish, 41(2014), 66–68.
[3]	M. Zhang, X. Yang, X. Yang, et al., Current situation of application of micro-pore aeration, Chin. J. Fish, 29(2016), 48–50.
[4]	J. F. Pérez, J. Llanos, C. Sáez, et al., The pressurized jet aerator: A new aeration system for high-performance H₂O₂ electrolyzers, Electrochem. Communi., 89(2018), 19–22.
[5]	M. Zheng, R. Xia, J. Hu, et al., The intelligent control system of oxygen increasing machine based on cloud platform, Mod. Agric. Equip., 73(2008), 63–66.
[6]	C. Liang and L. Peng, An automated diagnosis system of liver disease using artificial immune and genetic algorithms, J. Med. Syst., 37(2013), 9932.
[7]	L. Peng, W. Chen, W. Zhou, et al., An immune-inspired semi-supervised algorithm for breast cancer diagnosis, Comput. Meth. Prog. Bio., 134(2016), 259–265.
[8]	Y. Xie, Y. Chen and L. Peng, An immune-inspired political boycotts action prediction paradigm, Cluster Comput., 20( 2017), 1379–1386.
[9]	C. Yang, X. Wang, L. Cheng, et al., Neural-learning based telerobot control with guaranteed performance, IEEE Trans. Cybern., 47(2017), 3148–3159.
[10]	Z. Xiao, L. Peng, Y. Chen, et al., The dissolved oxygen prediction method based on neural network, Complexity, 2017(2017), 4967870.
[11]	Z. Zhao, J. Shi, X. Lan, et al., Adaptive neural network control of a flexible string system with non-symmetric dead-zone and output constraint. Neurocomputing, 283(2018), 1–8.
[12]	C. Yang, X. Wang, Z. Li, et al., Teleoperation Control based on Combination of Wave Variable and Neural Networks, IEEE T. Syst. Mancy-s., 47(2017), 2125–2136.
[13]	B. Xu and P. Zhang, Composite learning sliding mode control of flexible-link manipulator, Complexity, 2017(2017), 9430259.
[14]	L. Shao, D. Wu and X. Li, Learning deep and wide: a spectral method for learning deep networks, IEEE T. Neur. Net. Learn., 25(2014), 2303–2308.
[15]	Q. Ding, H. Tai, D. Ma, et al., Development of a smart dissolved oxygen sensor based on ieee1451.2, Sens. Lett., 9(2011), 1049–1054.
[16]	S. Fiori, Auto-regressive moving-average discrete-time dynamical systems and autocorrelation functions on real-valued Riemannian matrix manifolds. Discret. Cont. Dyn-b(DCDS-B), 19(2017), 2785–2808.
[17]	L. R. Michele and P. Cira, Designing neural networks for modeling biological data: A statistical perspective, Math. Biosci. Eng., 11(2014), 331–342.
[18]	Y. Lei, M. Guo, D. Hu, et al., Short-term prediction of UT1-UTC by combination of the grey model and neural networks, Adv. Space Res., 59(2017), 524–531.
[19]	Y. Wang, Y. Wang and Z. Yang, Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context. BMC Syst. Bio., 5(2011), S6.
[20]	F. Gers, J. Schmidhuber and F. Cummins, Learning to forget: Continual prediction with LSTM. Neural Comput., 12(2000), 2451–2471.
[21]	Y. Yuan, L. Mou and X. Lu, Scene recognition by manifold regularized deep learning architecture, IEEE T. Neur. Net. Learn., 26(2015), 2222–2233.
[22]	S. Hochreiter and J. Schmidhuber, Long short-term memory. Neural Comput., 9(1997), 1735–1780.
[23]	A. Graves, S. Fernández and J. Schmidhuber, Bidirectional LSTM networks for improved phoneme classification and recognition. Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications-Volume Part II. 2005, Warsaw, Poland: Springer-Verlag, pp.799–804.
[24]	The detailed formula derivation of LSTM, 2018. Available from: https://blog.csdn.net/u010754290/article/details/47167979?from=singlemessage.
[25]	A. Graves, Supervised Sequence Labeling with Recurrent Neural Networks, Springer-Verlag, New York, 2012.
[26]	Y. Bao and N. Li, Mathematical statistics and MATLAB data processing. Northeastern University press, 2008.

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