A new way of identification of minerals was suggested. The identification was based on chemometric analysis of measured IR spectra of selected minerals. IR spectra were collected using diffuse reflectance technique. The discriminant analysis and principal component analysis were used as chemometric methods. Five statistical models were created for separation and identification of clay minerals. Up to 60 samples of various mineral standards (clay minerals, feldspars, carbonates, sulphates and quartz) from different localities were selected for the creation of statistical models. The results of this study confirm that the discriminant analysis of IR spectra of minerals could provide a powerful tool for mineral identification. Even differentiation of muscovite from illite and identification of mixed structures of illite-smectite were achieved., Michal Ritz, Lenka Vaculíková and Eva Plevová., and Obsahuje bibliografii
A method for identification of parameters of a non-linear dynamic system, such as an induction motor with saturation effect taken into account, is presented in this paper. Adaptive identifier with structure similar to model of the system performs identification. This identifier can be regarded as a special neural network, therefore its adaptation is based on the gradient descent method and Back-Propagation well known in the neural networks theory. Parameters of electromagnetic subsystems were derived from the values of synaptic weights of the estimator after its adaptation. Testing was performed with simulations taking into account noise in measured quantities. Deviations of identified parameters in case of electrical parameters of the system were up to 1% of real values. Parameters of non-linear magnetizing curve were identified with deviations up to 6% of real values. Identifier was able to follow sudden changes of rotor resistance, load torque and moment of inertia.
This paper evaluates the feasibility of using an Artificial Neural Network (ANN) model for estimating the nominal shear capacity of Reinforced Concrete (RC) beams against diagonal shear failure subjected to shear and flexure. A feedforward back-propagation ANN model was developed utilizing 622 experimental data points of RC beams, which include 111 deep beams data and 20 beams tested for low longitudinal steel ratios. The ANN model was trained on 70% of the data and then it was validated using the remaining 30% data (new data were not used for training). The trained ANN model was compared with three existing approaches, including the American Concrete Institute (ACI) code. The ANN model predictions when compared to the experimental data were very favorable, regarding also the other approaches. The prediction of ANN model was also checked for size effect and deep beams separately. The ANN model was found to be very robust in all situations. The safe form of ANN model was also derived and compared with the design equations of the three methods.
The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.
A new method to detect damages on crates of beverages is investigated. It is based on a pattern-recognition-system by an artificial neural network (ANN) with a feedforward multilayer-perceptron topology. The sorting criterion is obtained by mechanical vibration analysis which provides characteristic frequency spectra for all possible damage cases and crate models. To support the network training, a large number of numerical data-sets is calculated by the finite-elementmethod (FEM). The combination of artificial neural networks with methods of numerical simulation is a powerful instrument to cover the broad range of possible damages. First results are discussed with respect to the influence of modelling inaccuracies of the finite-element-model and the support of the ANN by training-data obtained from numerical simulation. Also the feasibility of neuro-numerical ANN training will be dwelled on.
Classical Russian pendulum seismometer S-5-S was modified for recording of the rotational components of ground motion around the vertical or horizontal axes; the modified sensor is denoted here as S-5-SR. Experimental field testing of the S-5-SR sensor started in December 2010 in the Karvina coal region that is known as an area of intensive mining induced seismicity. First seismic station was installed in Doubrava village characterized by thick sedimentary layers. Next seismic station was installed in Orlova village, in different local geological conditions, i.e. in region without sedimentary layers. More than 200 mining induced seismic events were recorded on each seismic station during the period of six months of seismic monitoring. The recorded wave patterns confirm the existence of rotational ground motion components in this region; the strongest recorded value of this component exceeded 1 mrad.s-1. Analysis of the obtained records is presented in this paper., Zdeněk Kaláb, Jaromír Knejzlík and Markéta Lednická., and Obsahuje bibliografii
The paper presents the preliminary results of the analysis of two archival SAR datasets acquired by ERS-1/2 satellites of the same area of Roznow Lake in Southern Poland. Both datasets cover the same period of 8 years (1992 - 2000) and refers to the same area by the 50% of overlap between the neighbouring satellite tracks. The main purpose of this analysis was to derive the overlapping data about deformation velocity calculated using PSI (Persistent Scatterers Interferometry). The presented PSI results refer to PS (Persistent Scatterers) located on active landslides and therefore representing landslide movement. In Polish Carpathians, due to sparse urbanization, vegetation and rough relief the obtained PS density is usually not very high and generally difficult to interpret. The application of two overlapping datasets, where both of them observe the same phenomena, allow to cross-validate the data by identification of common PS points. For two datasets acquired from different tracks, usually many PS are not common and occur at different locations. Such situation could be explained by the difference between the incidence angles for both acquisitions. In a case of two tracks and therefore different terrain objects might act as PS. By joining the PS point sets from such neighbouring tracks the density of PS could be significantly increased. In order to perform a PSI analysis of Roznow Lake the data acquired from 179 and 408 tracks have been used and a few hundred of PS were obtained from PSI processing. For both tracks similar deformations velocity were obtained within a range of +/- 6 mm/yr. The PS points on active landslides are usually related to the buildings (walls, roofs) and roads affected usually by high risk., Zbigniew Perski, Andrzej Borkowski, Tomasz Wojciechowski and Antoni Wójcik., and Obsahuje bibliografii
Ever since proteomics was proven to be capable of characterizing a large number of differences in both protein quality and quantity, it has been applied in various areas of biomedicine, ranging from the deciphering molecular pathogenesis of diseases to the characterization of novel drug targets and the discovery of potential diagnostic biomarkers. Indeed, the biomarker discovery in human plasma is clearly one of the areas with enormous potential. However, without proper planning and implementation of specific techniques, the efforts and expectations may very easily be hampered. Numerous earlier projects aimed at clinical proteomics, characterized by exaggerated enthusiasm, often underestimated some principal obstacles of plasma biomarker discovery. Consequently, ambiguous and insignificant results soon led to a more critical view in this field. In this article, we critically review the current state of proteomic approaches for biomarker discovery and validation, in order to provide basic information and guidelines for both clinicians and researchers. These need to be closely considered prior to initiation of a project aimed at plasma biomarker discovery. We also present a short overview of recent applications of clinical proteomics in biomarker discovery., V. Tambor ... [et al.]., and Obsahuje bibliografii a bibliografické odkazy
This paper presents the results of geophysical survey performed in the Pilawa River valley in the area of Middle Pomerania (Poland). The resistivity imaging method was applied. Resistivity profile measuring eight hundred metres allowed to investigate the geologic structure to the depth of 150 metres. The resistivity cross section shows the structure of Pleistocene sediments and the depth of Miocene - Pleistocene boundary. The significant lowering of the boundary is related to assumable ice-sheet margin range of Pomeranian phase of North Polish Glaciation. The lowering of the boundary may be a result of sediments compaction and the subglacial tunnel slope as well., Bogdan Żogała, Ryszard Dubiel, Józef Lewandowski, Waclaw M. Zuberek and Grzegorz Gąska., and Obsahuje bibliografické odkazy