Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

Tian, Wenliang; Meng, Fanning; Liu, Li; Liu, Ying; Wang, Fuhui

doi:10.1038/srep40827

Cited by 26 publications

(10 citation statements)

References 35 publications

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“…More recently, high-level ab initio calculations have been used to train artificial neural networks to fit highdimensional interaction models [10][11][12][13][14][15], and to make informed predictions about material properties [16,17]. These approaches have proven to be quite powerful, yielding models trained for specific atomic species or based upon hand-selected geometric features [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Deep learning and the Schrödinger equation

Mills¹,

Spanner²,

Tamblyn³

2017

Phys. Rev. A

159

125

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We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials. On randomly generated potentials, for which there is no analytic form for either the potential or the ground-state energy, the model was able to predict the ground-state energy to within chemical accuracy, with a median absolute error of 1.49 mHa. We also investigate the performance of the model in predicting other quantities such as the kinetic energy and the first excited-state energy. arXiv:1702.01361v3 [cond-mat.mtrl-sci]

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Section: Introductionmentioning

confidence: 99%

Deep learning and the Schrödinger equation

Mills¹,

Spanner²,

Tamblyn³

2017

Phys. Rev. A

159

125

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“…In short, coating adhesion and compactness are two critical parts in failure process of organic coating, which can be used to achieve in-situ evaluation by electrochemical measurement methods. As to the further investigation of electrochemical evaluation, the application of artificial intelligence analysis [49,50] may be an essential trend.…”

Section: Resultsmentioning

confidence: 99%

Electrochemical Evaluation Technologies of Organic Coatings

Meng¹,

Liu²

2019

Coatings and Thin-Film Technologies

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Organic coatings are widely utilized to protect metals from corrosion and inevitably suffer degradation due to exposure to surroundings. Electrochemical technology is suitable for evaluating the protective performance of organic coatings since it has the advantages in rapidity and in-situ measurement. In this chapter, several electrochemical measurement technologies including open circuit potential (OCP), linear polarization resistance (LPR), electrochemical impedance spectroscopy (EIS) as well as electrochemical noise (EN) are introduced as ideal methods for acquiring mechanistic information about the failure behavior of the painted metal. The research status on measuring configurations, choosing data acquisition parameters and analytical methods are also discussed.

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“…There is no mutual connection between the same layers. The neural network training comprises two steps: signal positive spread and error BP [ 30 ]. The sample data are transferred from the input layer to the hidden layer and processed to the output layer.…”

Section: Design Of the Neural Network Modelmentioning

confidence: 99%

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Wen

Zhang

Yin

et al. 2019

Sensors

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Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized.

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Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

Cited by 26 publications

References 35 publications

Deep learning and the Schrödinger equation

Deep learning and the Schrödinger equation

Electrochemical Evaluation Technologies of Organic Coatings

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

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