This 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.
Since a few years ago, there is an increasing interest for utilization of transfer functions (TF) as a reliable method for diagnosing of mechanical faults in transformer structure. However, this paper aims to develop the application of TF method in order to evaluate the drying quality of active part during the manufacturing process of transformer. To reach this goal, the required measurements are carried out on 50 MVA 132 KV/33 KV power transformer when active part is placed in the drying chamber. Two different features extracted from the measured TFs are then used as the inputs to artificial neural network (ANN) to give an estimate for required time in drying process. Results show that this new represented method could well forecast the required time. The results obtained from this method are valid for all the transformers which have the same design.
The aim of this paper is to answer the question: Are the Łódź Hills
useful for electrical energy production from wind energy or not? Due to
access to short-term data related to wind measurements (the period of
2008 and 2009) from a local meteorological station, the measure –
correlate – predict approach have been applied. Long-term (1979‒2016)
reference data were obtained from ECWMF
ERA-40 Reanalysis.
Artificial neural networks were used to calculate predicted wind speed.
The obtained average wind speed and wind power density was 4.21 ms⁻¹
and 70 Wm⁻¹, respectively, at 10 m above ground
level (5.51 ms⁻¹, 170 Wm⁻¹ at 50 m).
From the point of view of Polish wind conditions, Łódź Hills may be
considered useful for wind power engineering.
Effects of infrared power output and sample mass on drying behaviour, colour parameters, ascorbic acid degradation, rehydration characteristics and some sensory scores of spinach leaves were investigated. Within both of the range of the infrared power outputs, 300–500 W, and sample amounts, 15–60 g, moisture content of the leaves was reduced from 6.0 to 0.1±(0.01) kg water/kg dry base value. It was recorded that drying times of the spinach leaves varied between 3.5–10 min for constant sample amount, and 4–16.5 min for constant power output. Experimental drying data obtained were successfully investigated by using artificial neural network methodology. Some changes were recorded in the quality parameters of the dried leaves, and acceptable sensory scores for the dried leaves were observed in all of the experimental conditions.
The artificial neural network method (ANN) is widely used in both
modeling and optimization of manufacturing processes. Determination of
optimum processing parameters plays a key role as far as both cost and
time are concerned within the manufacturing sector. The burnishing
process is simple, easy and cost-effective, and thus it is more common
to replace other surface finishing processes in the manufacturing
sector. This study investigates the effect of burnishing parameters such
as the number of passes, burnishing force, burnishing speed and feed
rate on the surface roughness and microhardness of an AZ91D magnesium
alloy using different artificial neural network models (i.e. the
function fitting neural network (FITNET), generalized regression neural
network (GRNN), cascade-forward neural network (CFNN) and feed-forward
neural network (FFNN). A total of 1440 different estimates were made by
means of ANN methods using different parameters. The best average
performance results for surface roughness and microhardness are obtained
by the FITNET model (i.e. mean square error (MSE): 0.00060608, mean
absolute error (MAE): 0.01556013, multiple correlation coefficient (R):
0.99944545), using the Bayesian regularization process (trainbr)). The
FITNET model is followed by the FFNN (i.e. MAE: 0.01707086, MSE:
0.00072907, R: 0.99932069) and CFNN (i.e. MAE: 0.01759166, MSE:
0.00080154, R: 0.99924845) models with very small differences,
respectively. The GRNN model has noted worse estimation results
(i.e.
MSE: 0.00198232, MAE: 0.02973829, R: 0.99900783) as compared with the
other models. As a result, MSE, MAE and R values show that it is
possible to predict the surface roughness and microhardness results of
the burnishing process with high accuracy using ANN models.
This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.
This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periodsrespectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this tech-nique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.
EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.
In the paper the use of the artificial neural network to the control of the work of heat treating equipment for the long axisymmetric steel
elements with variable diameters is presented. It is assumed that the velocity of the heat source is modified in the process and is in real
time updated according to the current diameter. The measurement of the diameter is performed at a constant distance from the heat source
(∆z = 0). The main task of the model is control the assumed values of temperature at constant parameters of the heat source such as radius
and power. Therefore the parameter of the process controlled by the artificial neural network is the velocity of the heat source. The input
data of the network are the values of temperature and the radius of the heated element. The learning, testing and validation sets were
determined by using the equation of steady heat transfer process with a convective term. To verify the possibilities of the presented
algorithm, based on the solve of the unsteady heat conduction with finite element method, a numerical simulation is performed. The
calculations confirm the effectiveness of use of the presented solution, in order to obtain for example the constant depth of the heat
affected zone for the geometrically variable hardened axisymmetric objects.
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