Short-Term and Long-Term Forecasting for the 3D Point Position Changing by Using Artificial Neural Networks

Abstract Nowadays, artificial intelligence algorithms are regaining visibility mainly due to the increase in computational capability. Among those, artificial neural networks (ANN) are very useful for the regression of highly nonlinear phenomena, such as the dynamic response of offshore structures. Due to the escalating demand in the oil and gas industry, offshore fields have been explored in deeper waters, which leads to more severe environmental conditions. A reliable and efficient evaluation of the long-term response of mooring systems, a crucial element of floating offshore structures, is then imperative. The estimation of the mooring long-term response is usually obtained numerically through the convolution of the short-term responses, based on short-term stationary environmental conditions (typically 3-h). Each of these short-term responses is obtained through a time-domain dynamic structural analysis from which statistical parameters of interest are calculated, such as the mean of the tension maxima sample or the maxima frequency. Such analyses tend to be quite time-consuming and a reliable estimator of these short-term statistical parameters may be of great help. In this paper, an ANN is trained to predict the short-term extreme peak response statistical parameters. The used training datasets include the wave significant height and spectral peak period for both wind sea and swell waves, generated by Importance Sampling Monte Carlo Simulation (ISMCS) method. Fixed directions of wind sea and swell are considered. It is shown that the ANN successfully predicts the short-term response statistical parameters for both cases, which are later used for the evaluation of the long-term N-year mooring response.

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Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

Atmospheric Measurement Techniques ◽

10.5194/amt-9-53-2016 ◽

2016 ◽

Vol 9 (1) ◽

pp. 53-62 ◽

Cited By ~ 13

Author(s):

R. D. García ◽

O. E. García ◽

E. Cuevas ◽

V. E. Cachorro ◽

A. Barreto ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Mineral Dust ◽

Dust Particles ◽

Filter Radiometer ◽

Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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Ion-gating synaptic transistors with long-term synaptic weight modulation

Journal of Materials Chemistry C ◽

10.1039/d1tc00048a ◽

2021 ◽

Vol 9 (16) ◽

pp. 5396-5402

Author(s):

Youngjun Park ◽

Min-Kyu Kim ◽

Jang-Sik Lee

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Synaptic Weight ◽

Recognition Accuracy ◽

Artificial Neural ◽

High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

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