The deformation measurements are performed for the purpose of obtaining information concerning ground movement and objects on the ground within given time intervals. For the purpose of improving conventional models of deformation analysis (CDA) it is desirable to use several different methods and also implement alternative proce dures as a further improvement, such as the concept of robust geodetic networks and strain analysis, aimed at obtaining objective information about the movements. In the present paper, in addition to the CDA methods, we also analyze the robust methods in deformation detecting and the method of the strain analysis based on elasticity theory as a supplement to the conventional geometric deformation methods (CDA). The mentioned methods are applied and analysed for the case of a test example of Fruška Gora in Serbia, for which there exist geological and geophysical studies of recent tectonic movements. The measuring results for two measuring epochs concern the GNSS vectors measured by applying the fast static method within closed polygons over a ten-year interval, where only the horizontal movement component is analysed. The efficiency of the applied CDA and robust methods is measured by applying a mean success rate (MSR) by applying Monte Carlo simulations in order to investigate the efficiency of a given methods for a given control network., Zoran Sušić, Mehmed Batilović, Toša Ninkov, Vladimir Bulatović, Ivan Aleksić and Gojko Nikolić., and Obsahuje bibliografické odkazy
Different approaches have been proposed to determine the possible outliers existing in a dataset. The most widely used consists in the application of the data snooping test over the least squares adjustment results. This strategy is very likely to succeed for the case of zero or one outliers but, contrary to what is often assumed, the same is not valid for the multiple outlier case, even in its iterative application scheme. Robust estimation, computed by iteratively reweighted least squares or a global optimization method, is other alternative approach which often produces good results in the presence of outliers, as is the case of exhaustive search methods that explore elimination of every possible set of observations. General statements, having universal validity, about the best way to compute a geodetic network with multiple outliers are impossible to be given due to the many different factors involved (type of network, number and size of possible errors, available computational force, etc.). However, we see in this paper that some conclusions can be drawn for the case of a leveling network, which has a certain geometrical simplicity compared with planimetric or three-dimensional networks though a usually high number of unknowns and relatively low redundancy. Among other results, we experience the occasional failure in the iterative application of the data snooping test, the relatively successful results obtained by both methods computing the robust estimator, which perform equivalently in this case, and the successful application of the exhaustive search method, for different cases that become increasingly intractable as the number of outliers approaches half the number of degrees of freedom of the network.
The article describes a neural network-based articulatory feature (AF) estimation for the Czech speech. First, the relationship between AFs and a Czech phone inventory is defined, and then the estimation based on the MLP neural networks is done. The usage of several speech representations on the input of the MLP classifiers is proposed with the purpose to obtain a robust AF estimation. The realized experiments have proved that an ANN- based AF estimation works very reliably especially in a low noise environment. Moreover, in case the number of neurons in a hidden layer is increased and if the temporal context DCT-TRAP features are used on the input of the MLP network, the AF classification works accurately also for the signals collected in the environments with a high background noise.