The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.
The article presents the basic rules for constructing and training neural networks called the Support Vector Machine method as well as possible applications for this kind of network. SVM networks are mainly used for solving tasks of classifying linearly and non-linearly separable data and regression. However, in recent years more applications have been found for them. The networks also solve such problems as the recognition of signals and images as well as speech identification.
In this paper, non-linear SVM networks have been used for classifying linearly separable and non-separable data with a view to formulating a model of displacements of points in a measurement-control network. The points of the measurement-control network were placed on a civil engineering object located on expansive soil (linearly separable data) and represented a mining exploitation area (linearly non-separable data). The task of training SVM networks requires the use of quadratic programming in search of an optimum point of the Lagrangian function in relation to the parameters being optimised. In the case of linearly non-separable data, the SVM method makes it possible to find a hyperplane which classifies objects as correctly as possible, and at the same time is located possibly far away from concentrations typical of each class.
Determination of vertical displacements of engineering objects is closely related to geodesic monitoring. Its purpose is to record the dynamics of changes in the deformation phenomenon. Geodesic monitoring requires the use of appropriate measurement equipment and appropriate methods for processing observation results, which make it possible to determine the correlation between the causes and effects of deformations in engineering objects. Progress in information technology resulted in the appearance of new methods for processing and compressing experimental data which are resistant to noise or interference and enable reduction of the amount of information.
The paper presents a method for statistical analysis of multidimensional data based on PCA (principal component analysis) transformation, implemented with the use of a neural network. PCA transformation, related to the Karhunen–Loeve transformation, is used for processing signals regarded as stochastic processes. This method makes enables reduction of the input data space on the basis of independent principal components with due attention to their significance. It also makes it possible to model changes occurring in both buildings and terrain in glacitectonically disturbed areas.
ART (Adaptive Resonance Theory) networks were invented in the 1990s as a new approach to the problem of image classification and recognition. ART networks belong to the group of resonance networks, which are trained without supervision. The paper presents the basic principles for creating and training ART networks, including the possibility of using this type of network for solving problems of predicting and processing measurement data, especially data obtained from geodesic monitoring. In the first stage of the process of creating a prediction model, a preliminary analysis of measurement data was carried out. It was aimed at detecting outliers because of their strong impact on the quality of the final model. Next, an ART network was used to predict the values of the vertical displacements of points of measurement and control networks stabilized on the inner and outer walls of an engineering object.
The article presents the possibilities of applying geodetic methods to determine the vertical deviation of historical buildings. In particular, the results of measurements obtained for a brick and wood Town Hall Tower located in the town of Nowe Miasteczko have been presented. Geodetic measurements of vertical deviation taken before and after carrying out repairs which were aimed at stopping or eliminating the destructive processes of degradation taking place, especially in the wooden part of the tower. During the renovation works, attention was also given to improving the technical condition of the building, which was reflected by the results of the measurements and calculations.
The article presents the use of an evolutionary algorithm for determining the shape of the guy rope sag of a steel smokestack. The author excludes the analysis of the operation of the rope, and discusses only the problem of determining parameters of the function of the adaption of the rope sag curve into empirical data, obtained by the geodetic method. The estimation of parameters of the curve and the characteristics of the accuracy of its adaption into experimental data were carried out by means of an evolutionary algorithm with the use of an evolutionary strategy (μ+λ). The correctness of the strategy presented in the paper, as an instrument for searching for a global minimum of a criterion function, has been presented using as an example the minimisation of a certain two dimensional function and the estimation of parameters of an ordinary and orthogonal regression function. Previous theoretical analyses have also been used for determining parameters of the guy rope sag of a steel smokestack, which is measured periodically. In addition approximate values of the pull forces in the guy ropes have been calculated.
The article presents the present situation in terms of energy production from renewable energy sources and perspectives for development, based on research on the existing resources and possibilities of using them. The Lubusz Voivodship is not an important energy producer in Poland. In terms of the amount of energy produced it comes twelfth out of sixteen voivodships. The annual energy production from renewable energy sources is 290,9 GWh, which is 11.6% of the total energy produced. At the end of 2014 there were 73 licensed installations producing electrical energy from renewable energy sources in the Lubusz Voivodship with a total capacity of 189 MW. The largest amount of energy is produced by a pumped storage power plant (91,3 MW). The total capacity of the licensed installations using RES in the Lubusz Voivodship rose from 103 MW in 2007 to about 189 MW in 2014. Research on the existing resources indicates that it is possible to develop RES. Preparations are under way to build 66 new wind farms with a total capacity of 1834 MW , 89 photovoltaic power plants with a total capacity of 468 MW, 21 water power plants with a total capacity of about 60 MW, 54 biogas power plants with a total expected capacity of about 67 MW. The total capacity of the RES installations that are planned to be built by 2023 will be 2469 MW. Therefore, in the coming years the installed capacity of RES installations will increase 13 times in the Lubusz Voivodship. There are still no plans to use the energy of deep geothermal waters due to low profitability.