Wyniki wyszukiwania - BazTech

1

Performance of bias corrected monthly CMIP6 climate projections with diferent reference period data in Turkey

Oruc Sertac

Acta Geophysica

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2022

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Vol. 70, no 2

777--789

EN

Decisions that are based on the future climate data, and its consequences are significantly important for many sectors such as water, agriculture, built environment, however, the performance of model outputs have direct influence on the accuracy of these decisions. This study has focused on the performance of three bias correction methods, Delta, Quantile Mapping (QM) and Empirical Quantile Mapping (EQM) with two reference data sets (ERA and station-based observations) of precipitation for 5 single CMIP6 GCM models (ACCESS-CM2, CNRM-CM6-1-HR, GFDL-ESM4, MIROC6, MRI-ESM2-0) and ensemble mean approach over Turkey. Performance of model-bias correction method-reference data set combinations was assessed on monthly basis for every single station and regionally. It was shown that performance of GCM models mostly affected by the region and the reference data set. Bias correction methods were not detected as effective as the reference data set over the performance. Moreover, Delta method outperformed among the other bias correction techniques for the computation that used observation as reference data while the difference between bias correction methods was not significant for the ERA based computations. Besides ensemble approach, MIROC6 and MRI-ESM2-0 models were selected as the best performing models over the region. In addition, selection of the reference data sets also found to be a dominant factor for the prediction accuracy, 65% of the consistent performance at the stations achieved by the ERA reference used bias correction approaches.

2

Effective defence method against an adaptive push-styled comand and conrol attack in mobile botnets

Ciołek M., Piotrowski Z.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2017

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nr 8-9

855--859, CD

EN

This article describes a method preventing creation of botnets with the use of push-styled command and control attacks. The defence method was implemented on a mobile Android platform. The mechanisms used by malware for creation of mobile botnets were characterized. A concept and the method for implementation of a protection system were presented, making use of two connected mechanisms: blocking of application ID number during its registration in the phase of initiation of the GCM service, as well as the mechanism of distribution of the sets of restrictions for the selected applications. A mechanism for update, analysis and generation of restrictions by the community of users of the protection system with the use of a dedicated server for distribution of restrictions was suggested. Research results showed that it is possible to effectively defend against the hidden pushstyled command attacks.

PL

W artykule opisano metodę zapobiegania tworzeniu botnetu z wykorzystaniem ataku typu pushstyled command and control. Metoda obrony została zaimplementowana na platformie mobilnej Android. Opisano mechanizmy wykorzystywane przez złośliwe oprogramowanie do tworzenia mobilnych sieci botnet. Przedstawiono koncepcję i sposób implementacji systemu ochronywykorzystującego dwa sprzężone ze sobą mechanizmy: blokownia numeru ID aplikacji podczas jego rejestracji w fazie inicjacji usługi GCM jak również mechanizm dystrybucji zestawów restrykcji dla wybranych aplikacji. Zaproponowano mechanizm aktualizacji, analizy i generowania restrykcji przez społeczność użytkowników systemu ochrony z wykorzystaniem dedykowanego serwera dystrybucji restrykcji. Wyniki badań wykazały, że możliwa jest skuteczna obrona przed skrytymi atakami typu push-styled command.

3

Zastosowanie badań konduktometrycznych z użyciem inwersji 1D, jako narzędzia do kartowania przestrzennego przypowierzchniowych warstw geologicznych

Czarniak P., Pacanowski G., Sobótka P.

Przegląd Geologiczny

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2017

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Vol. 65, nr 10/2

803--810

EN

This article presents the results obtained from geophysical measurements as a supplement to and refinement of geological information obtained from engineering geological studies for the newly constructed highways. The research was conducted using two geophysical methods: Ground Conductivity Meters (GCM) and Electrical Resistivity Tomography (ERT). The above measurements were made on three research fields located along the planned express road S-19 (Kraśnik-Janów Lubelski section). These areas were selected due to the large amount of available archival data and varied geological conditions. The publication focuses primarily on the presentation of the results obtained using the very fast and extremely efficient GCM method. By processing GCM data, using Laterally Constrained Inversion (LCI) and Spatially Constrained Inversion (SCI) algorithms, the values ofelectrical resistivity of the surveyed centers were derived. The results are presented in the form of cross-sections and maps of electrical resistivity from different depths, which were collated and compared with the results from another geophysical method, ERT. These examples show that the use of inversion has a significant impact on the refinement of geological boundaries between layers of different electrical resistivities. Thanks to the correlation of geophysical data with boreholes and engineering geological probes, detailed models of the geological structure of the analyzed areas were elaborated, which are necessary for the selection of appropriate solutions for the construction and modernization of road infrastructure.

4

Granular Computing Based on Gaussian Cloud Transformation

Liu Y., Li D., He W., Wang G.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

385--398

EN

Granular computing is one of the important methods for extracting knowledge from data and has got great achievements. However, it is still a puzzle for granular computing researchers to imitate the human cognition process of choosing reasonable granularities automatically for dealing with difficult problems. In this paper, a Gaussian cloud transformation method is proposed to solve this problem, which is based on Gaussian Mixture Model and Gaussian Cloud Model. Gaussian Mixture Model (GMM) is used to transfer an original data set to a sum of Gaussian distributions, and Gaussian Cloud Model (GCM) is used to represent the extension of a concept and measure its confusion degree. Extensive experiments on data clustering and image segmentation have been done to evaluate this method and the results show its performance and validity.