Wyniki 1-2 spośród 2 dla zapytania: authorDesc:"Kuanysh MUSLIMOV"

A model of destructive processes based on interval fuzzy rough soft sets DOI:10.15199/48.2019.04.23

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Complex systems containing territories with natural and artificial objects as well as a multitude of interacting processes, which evolve in space and time, can be considered as geoecotechnogenic systems (GETS). Some processes arising within GETS are destructive because they give rise to a danger and risk to the certain valuable objects causing their destructions and can often lead to critical situations or emergencies. Solving decision support problems in disaster situation requires real-time geographic information systems (GIS) containing a spatial model of confined space (area of interest, AOI), where the destructive processes take place, as well as adequate models of the destructive processes exposed onto the spatial model. However, the most of the destructive processes are poorly observed and their spreading over the AOI is weakly modeled, so developing decision support systems (DSS) is a complex and nontrivial task, which becomes more complicated due to uncertainty of information, a wide geographically distribution of events and, as usual, a lack of time [1]. The efficiency of decision-making strongly depends on the availability of online disaster monitoring tools aimed at the real-time computation of the most important parameters related to the spreading of the destructive processes. Today, a suite of the most modern methods and techniques, such as remote sensing, GIS, geospatial analysis, unmanned aerial vehicles (UAV), can be synergistically used to build GIS-based DSS. Remote sensing techniques allow generating a full range of data for disaster monitoring [2], which have a form of streams of great volumes that come from sensors on a continuous basis at a high rate and should be analyzed in a real-time [3]. UAVs can effectively perform long-time missions to obtain remote sensing data [4]. However, due to the instrumental inaccuracy and distortions caused by vibrations, remote sensing information obtained from UAVs[...]

Low computational complexity algorithm for recognition highly corrupted QR codes based on Hamming-Lippmann neural network DOI:10.15199/48.2019.04.29

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The beginning of the neural networks history is connected with the "connectionist models" or "parallel distributed processing", that are considered in the publication in 1943 of McCulloch and Pitts. These scientists proposed general approaches and specific mathematical models of the biological neural networks and their components - neurons, that became fundamental in the artificial neural networks theory. These neurons were presented as models of the biological neurons and as conceptual components for circuits, that performed computational tasks. They used threshold elements with two stable states, which are called “McCallock-Pitts neurons" [1]. The task of developing models and systems, which are based on the threshold elements was so unusual and complex, that only in 1956 was appeared the first capable artificial neural network - Rosenblatt perceptron. Its demonstrated the possibility of creating technical patterns, based on the models of the human brain, for image recognition. However, further researches of perceptrons showed, that their usage in the recognition systems is associated with many difficulties. In 1969 Minsky and Papert published their book, in which they described the deficiencies of the perceptron model and showed their fundamental nature. The negative prognosis of the authoritative scientists caused a decline in the interest in neural networks, which lasted more than ten years, but in the 80's after some important theoretical results, the neural networks began to rebound. The renewed interest is reflected in many researches, the amounts of funding, the number of conferences and journals associated with neural networks [2,3]. At the same time, neurocomputing begins to develop, which allows solving problems from different areas of knowledge using neural networks, which are modelled on ordinary computers [4]. The machine interpretation of the neural network came into the world of Computer[...]

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