scholarly journals Using artificial neural networks to predict riming from Doppler cloud radar observations

2021 ◽  
Author(s):  
Teresa Vogl ◽  
Maximilian Maahn ◽  
Stefan Kneifel ◽  
Willi Schimmel ◽  
Dmitri Moisseev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Doppler Velocity ◽  
Data Sets ◽  
Radar Observations ◽  
Cloud Radar ◽  
Target Values ◽  
In Situ Observations ◽  
Artificial Neural

Abstract. Riming, i.e. the accretion and freezing of SLW on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest, however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-band cloud radar and in-situ observations of snowfall. We focus on two configurations of input variables: ANN #1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width; SEW), and the skewness as input features. ANN #2 only uses Ze, SEW and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (riming index = 1) and yield low values (riming index ≤ 0.4) for unrimed snow. In general, the predictions of ANN #1 and ANN #2 are very similar, advocating the capability to predict riming without the use of MDV. It is demonstrated that both ANN setups are able to generalize to W-band radar data. The predictions of both ANNs for a wintertime convective cloud fit coinciding in-situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN #2 to an orographic case yields high riming index values coinciding with observations of solid graupel particles at the ground.

scholarly journals Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

Crystals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 663
Author(s):  
Natasha Dropka ◽  
Martin Holena
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crystal Growth ◽  
Real Time ◽  
Electronic Materials ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Approach ◽  
Artificial Neural

In this review, we summarize the results concerning the application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible. This important machine learning approach thus makes it possible to determine optimized parameters for high-quality up-scaled crystals in real time.

scholarly journals Simulation of flood flow in a river system using artificial neural networks

2005 ◽  
Vol 9 (4) ◽  
pp. 313-321 ◽  
Author(s):  
R. R. Shrestha ◽  
S. Theobald ◽  
F. Nestmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Simulation ◽  
Simulation Models ◽  
River System ◽  
Training Data ◽  
Data Sets ◽  
Network Training ◽  
Artificial Neural ◽  
Flood Flow

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

scholarly journals Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Hazem Abdel-Khalek ◽  
Mirko Schäfer ◽  
Raquel Vásquez ◽  
Jan Frederick Unnewehr ◽  
Anke Weidlich
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Degrees Of Freedom ◽  
Western Europe ◽  
Power Transmission ◽  
Cross Border ◽  
Electricity Trading ◽  
Target Values ◽  
Capacity Calculation ◽  
Artificial Neural

Abstract Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.

scholarly journals Artificial Neural Networks Application in Modal Analysis of Tires

2013 ◽  
Vol 13 (5) ◽  
pp. 273-278 ◽  
Author(s):  
P. Koštial ◽  
Z. Jančíková ◽  
D. Bakošová ◽  
J. Valíček ◽  
M. Harničárová ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modal Analysis ◽  
Speckle Pattern ◽  
Point Of View ◽  
Electronic Speckle Pattern Interferometry ◽  
Data Sets ◽  
Artificial Neural

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

Apply Artificial Neural Networks to Estimate Stress Distribution around Holes Edge in Composites

2012 ◽  
Vol 490-495 ◽  
pp. 3105-3108
Author(s):  
Kamran Pazand ◽  
Younes Alizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Composite Laminates ◽  
Composite Plate ◽  
Input Data ◽  
Data Sets ◽  
Fast Determination ◽  
Artificial Neural

The purpose of this paper is to estimate the fast determination of stress distribution around a circular hole in symmetric composite laminates under in-plane loading. For this purpose calculation of stress values in the composite plate around edge holes in different plies position for a finite number of input data sets using the Lekhnitskii expressions and code program. The resulting data would then be used to train artificial neural networks (ANN) which would be able to predict –accurately enough- those quantities throughout the composite plate body for any given input value in any position ply and fore and stress that impose.

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