Due to the widespread use of sensors and sensor networks in the tasks of territory coverage, the relevant criteria are maximizing coverage and minimizing energy consumption. At the same time, the compliance of the network with these criteria is an urgent problem in the modern technological world. A modification of the method for constructing energy-efficient sensor networks is proposed by introducing an additional criterion for minimizing the number of sensors and limiting the number of sensors used, which allows reducing the energy consumption of sensor networks by 19 %. In the resulting optimization problem, the optimality criteria are the functions of minimizing the area of uncovered territory, the value of energy consumption, and the number of sensors. The optimum solution is formed by pairs of values of the coverage radius and the level of intersection of the coverage areas, which provide maximum coverage while minimizing energy consumption and the number of sensors used. To solve the problem, the parameter convolution method and the genetic algorithm were used. In the case of dynamic sensors, the problem is to find such a trajectory of the sensor that provides the maximum flyby of the territory with a minimum length. A grid algorithm is proposed to find the necessary trajectory. The presented algorithm consists in dividing the territory into nodes and estimating the value of the covered territory by the sensor in this node. After the formation of estimates, the search for a Hamiltonian path was used. The case of a multiply connected territory with the possibility of turning it into a simply connected one is considered. A scheme for finding the parameters of energy-efficient coverage of the territory using static and dynamic sensors is proposed.
The object of research is intelligent decision making support systems. Processing different types of intelligence from a variety of information sources requires significant computational operations with strict time constraints. It leads to the search for new scientific approaches to the processing of various types of geospatial information to increase the efficiency of special purpose systems. This work solves the problem of developing a methodological approach to processing different data types in decision making support systems. During the research, the authors used the main provisions of the queuing theory, the theory of automation, the theory of complex technical systems and general scientific methods of cognition, namely analysis and synthesis. The proposed methodological approach was developed taking into account the practical experience of the authors of this work during the military conflicts of the last decade. The results of the research will be useful in: – development of new algorithms for processing different types of data; – substantiation of recommendations for improving the efficiency of processing various data types; – analysis of the operational situation during the hostilities (operations); – creating promising technologies to increase the efficiency of processing various data types; – assessment of the adequacy, reliability, sensitivity of the scientific and methodological apparatus of processing various data types; – development of new and improvement of existing simulation models of various processing data types. Areas of further research will be aimed at developing a methodology for processing various data types in intelligent decision making support systems.
The object of research is intelligent decision making support systems. Processing different types of intelligence from a variety of information sources requires significant computational operations with strict time constraints. It leads to the search for new scientific approaches to the processing of various types of geospatial information to increase the efficiency of special purpose systems. This work solves the problem of developing a methodological approach to processing different data types in decision making support systems.During the research, the authors used the main provisions of the queuing theory, the theory of automation, the theory of complex technical systems and general scientific methods of cognition, namely analysis and synthesis. The proposed methodological approach was developed taking into account the practical experience of the authors of this work during the military conflicts of the last decade.The results of the research will be useful in:-development of new algorithms for processing different types of data; -substantiation of recommendations for improving the efficiency of processing various data types; -analysis of the operational situation during the hostilities (operations); -creating promising technologies to increase the efficiency of processing various data types; -assessment of the adequacy, reliability, sensitivity of the scientific and methodological apparatus of processing various data types;-development of new and improvement of existing simulation models of various processing data types.Areas of further research will be aimed at developing a methodology for processing various data types in intelligent decision making support systems.
Artificial intelligence has become the backbone of modern decision sup port systems. This is why a complex method for finding solutions for neuro fuzzy expert systems has been deve loped. The proposed complex meth od is based on a mathematical model for the analysis of the operational si tuation. The model makes it pos sible to determine the parameters of the analysis of the operational sit uation, their influence on the qual ity of assessment of the operation al situation and to determine their number with units of measurement. An increase in the efficiency of infor mation processing (error reduction) of the assessment is achieved by the use of evolving neurofuzzy artificial neural networks. Training of evolv ing neurofuzzy artificial neural net works is carried out by training not only synaptic weights of the artifi cial neural network, the type, parame ters of the membership function, but also by applying the procedure for reducing the dimension of the feature space. The efficiency of information processing is also achieved by train ing the architecture of artificial neu ral networks; accounting for the type of uncertainty in the information to be assessed; work with both clear and fuzzy data. We achieved a reduction in computational complexity while mak ing decisions; the absence of errors in training artificial neural networks as a result of processing information entering the input of artificial neural networks. The analysis of the opera tional situation as a whole occurs due to the improved clustering procedure, which allows working with both static and dynamic data. The proposed com plex method was tested on the example of assessing the state of the operatio nal situation. The mentioned example showed an increase in assessment effi ciency at the level of 20-25 % in terms of information processing efficiency
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