Remote sensing of the earth and monitoring of various phenomena have been and still remain an important task for solving various problems. One of them is the forest pathology dynamics determining. Assuming its dependence on various factors forest pathology can be either short-term or long-term. Sometimes it is necessary to analyze satellite images within a period of several years in order to determine the dynamics of forest pathology. So it is connected with some special aspects and makes such analysis in manual mode impossible. At the same time automated methods face the problem of identifying a series of suitable images even though they are not covered by clouds, shadows, turbulence and other distortions. Classical methods of nebulosity determination based either on neural network or decision functions do not always give an acceptable result, because the cloud coverage by itself can be either of cirrus intortus type or insignificant within the image, but in case of cloudiness it can be the reason for wrong analysis of the area under examination. The article proposes a new approach for the analysis and selection of images based on key point detectors connected neither with cloudiness determination nor distorted area identification, but with the extraction of suitable images eliminating those that by their characteristics are unfit for forest pathology determination. Experiments have shown that the accuracy of this approach is higher than of currently used method in GIS, which is based on cloud detector.
The article deals with the problem of increasing the efficiency of recruitment of applicants. A formal description of the task of maximally filling budget places and maximizing the average score of applicants entering these places, as well as the decision support software, allows to solve this problem. The recommendations on the use of the presented software are given.
The article deals with the problem of multi-criteria decision-making problems, which are characterized by a large number of options and alternatives. It is proposed to use visual filtering of graphic images describing the corresponding alternatives as one of the stages in decision-making in such tasks. The approaches and requirements for the construction of graphic images of alternatives are considered. Describes the steps and algorithms for constructing visual images of alternatives, based on the radial and pie charts, and include the normalization procedure. It describes software that implements the proposed algorithms, as well as providing interactive interaction with an expert for visual filtering of multi-criteria alternatives. Additionally, the capabilities of the developed software are described, which include filtering alternatives based on threshold values, as well as the possibility of conducting a series of experiments in order to obtain the union or intersection of filtered sets of alternatives. A synthetic test for filtering 201 alternatives is described, each of which is described by 15 criteria. As a result of a series of experiments, this choice set was reduced by about 28 times. A description is also given of an experiment on visual filtering of real alternatives that describe estimates of the accuracy of calculating inviscid flow around a cone using several OpenFoam solvers. Each solver is characterized by 288 criteria, and according to the results of visual filtering, the advantage in the accuracy of the calculations of two solvers over the others is clearly established.
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