P. Szymczyk

Classification of geological structure using ground penetrating radar and Laplace transform artificial neural networks

Abstract This paper focuses on a new kind of artificial neural networks – the Laplace transform artificial neural networks (LTANN). It is proposed to use the Laplace transform instead of ordinary weights and a linear activation function of an artificial neuron. This extension allows to use artificial neural networks in new areas. The ordinary description of artificial neural networks is a special case of the description proposed in this paper. Three models of different geological structures based on the LTANN are discussed in this paper. Using these models, it is possible to classify an unknown geological structure as the structure without anomaly, the structure with a sinkhole or the structure with a loose zone.

Preprocessing of GPR data

Abstract In this article a set of procedures for data preprocessing of GPR radargrams are presented. Raw data taken from GPR are affected by different noises and instability of equipment. The data in this form, are not suitable for the further analysis. They must undergo a set of transformations in order to obtain indispensable information.

Neural Networks As A Tool For Georadar Data Processing

Abstract In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

Supervised learning Laplace transform artificial neural networks and using it for automatic classification of geological structure

This paper presents a method of learning novel Laplace transform artificial neural network (LTANN) and shows examples of network learning. It also contains a description of the use of the LTANN for searching anomalies in geological structures (loosened zone of river embankments).

Z-transform artificial neural networks

This paper focuses on a new kind of artificial neural networks - the Z-transform artificial neural networks (ZTANNs). It is proposed to use the Z-transform instead of ordinary weights and a linear activation function of an artificial neuron. This extension allows to use artificial neural networks in new areas. The ordinary description of artificial neural networks is a special case of the description proposed in this paper. It also contains a description of the use of the ZTANN for automatic identification of objects in digital control system.

Identification of dynamic object using Z-transform artificial neural network

Abstract The aim of the paper is to present the method of identification of dynamic discreet objects using neural networks with Z-transform. The process of network learning consists of determining approximation of object Z-transform, with increasing precision in each iteration, based on all the previous input and output signals. This method of identification may be used also in non-stationary objects. It allows to modify the Z-transform on line. The proposed method of identification is based on iterative modification of the transmittance of a neural network with Z-transform. The modification is calculated after every step which provides new coefficients. The exact transmittance of the object is defined after evaluation of the last coefficient which has the longest delay time. In the beginning of the article, theoretical solutions describing the identification method have been described. Next part shows the application of those solutions on four examples. The last part presents the results and proposed future development. The article also includes an appendix with Z-transform basics.

Reliability of Cluster System with a Lot of Software Instances

This paper presents model of complex fault – tolerant system with multiple software instances and hardware clusters. We want to show influence of number software and hardware components on overall reliability of the system. Previously, other models have been developed only for software or hardware systems. Our model assumes that failure of each component is statistically independent.