Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.
Noise control has gained a lot of attention recently. However, presence of nonlinearities in signal paths for some applications can cause significant difficulties in the operation of control algorithms. In particular, this problem is common in structural noise control, which uses a piezoelectric shunt circuit. Not only vibrating structures may exhibit nonlinear characteristics, but also piezoelectric actuators. In this paper, active device casing is addressed. The objective is to minimize the noise coming out of the casing, by controlling vibration of its walls. The shunt technology is applied. The proposed control algorithm is based on algorithms from a group of soft computing. It is verified by means of simulations using data acquired from a real object.
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.
The article presents results of the influence of the GMDH (Group Method of Data Handling) neural network input data preparation method on the results of predicting corrections for the Polish timescale UTC(PL). Prediction of corrections was carried out using two methods, time series analysis and regression. As appropriate to these methods, the input data was prepared based on two time series, ts1 and ts2. The implemented research concerned the designation of the prediction errors on certain days of the forecast and the influence of the quantity of data on the prediction error. The obtained results indicate that in the case of the GMDH neural network the best quality of forecasting for UTC(PL) can be obtained using the time-series analysis method. The prediction errors obtained did not exceed the value of ± 8 ns, which confirms the possibility of maintaining the Polish timescale at a high level of compliance with the UTC.
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.
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 article shows a new model of Continuous Cooling Transformation (CCT) diagrams of structural steels and engineering steels. The modelling used artificial neural networks and a set of experimental data prepared based on 550 CCT diagrams published in the literature. The model of CCT diagrams forms 17 artificial neural networks which solve classification and regression tasks. Neural model is implemented in a computer software that enables calculation of a CCT diagram based on chemical composition of steel and its austenitizing temperature.
Due to fast-paced technical development, companies are forced to modernise and update
their equipment, as well as production planning methods. In the ordering process, the customer
is interested not only in product specifications, but also in the manufacturing lead
time by which the product will be completed. Therefore, companies strive towards setting
an appealing but attainable manufacturing lead date.
Manufacturing lead time depends on many different factors; therefore, it is difficult to predict.
Estimation of manufacturing lead time is usually based on previous experience. In the
following research, manufacturing lead time for tools for aluminium extrusion was estimated
with Artificial Intelligence, more precisely, with Neural Networks.
The research is based on the following input data; number of cavities, tool type, tool category,
order type, number of orders in the last 3 days and tool diameter; while the only output
data are the number of working days that are needed to manufacture the tool. An Artificial
Neural Network (feed-forward neural network) was noted as a sufficiently accurate method
and, therefore, appropriate for implementation in the company.