The article considers the problem of improving the methods of human identification using biometric features, in particular, the signals of an electroencephalogram. The authors present the results of scientific research into human identification by dynamics of frequency changes of the brain using the known architecture of artificial neural networks: AlexNet and Mobile Net 2. The basic hypothesis is formulated that the waves registered by sensors from head leads are unique for each person. The authors describe the preparation of experimental data on the basis of electroencephalogram signals received as a result of experiments on the formation of steady-state visual evoked potentials in a group of people with the subsequent creation of an applied database. The achievability of the set task was based on assessments of the relevance and representativeness of the obtained data. Frequency-time characteristics were taken as training datasets. Using deep machine learning technology, two classification models are obtained that allow identifying the identity of a person with a probability of 70%. The evaluation of the adequacy of the obtained classification models is carried out with PCA and t-SNE algorithms; the efficiency of their work is evaluated. The results of deep machine learning and machine classification tasks were confirmed by assessments of the adequacy of the obtained models. As a result, the authors confirm the main hypothesis.

The pitch and roll angles of the quadcopter attitude are controlled by torque arising from the difference in thrust of different rotors. Rotational dynamics of the quadcopter as a rigid body is considered. The pitch and roll angles that form the quadcopter tilt angle have similar dynamics for the case of symmetric quadcopter frame, so the pitch angle is studied separately in the paper. The finite frequency identification approach is used to find the transfer function from the experimental flight data. The approach needs data, when sine wave test signals are fed to the system input. In the case of the unstable loop of the quadcopter tilt angle, the closed loop identification procedure is used where the test signal is added to the setpoint of the operating controller. As a result, a more complex model is identified than is commonly used. Moreover, the nonlinear model of the quadcopter pitch angle is considered, taking into account the translation velocity. Parameters of this model are estimated from the same experimental data using the nonlinear grey-box model parameters identification procedure. The transfer function founded from this model confirms the structure of the transfer function obtained via the finite frequency identification procedure.

The dependence of socio-political stability on the emotional expectations of voters is investigated. For this, a model of a socio-political system consisting of a society of voters and a democratically elected politician is considered. The neuropsychological model of the voter takes into account his emotional expectations. The social stability is guaranteed by the expectations of positive emotions of all voters. Socio-political stability means both the social stability and the re-election of politician. One type of voter is a Progressist who seeks to fulfil his desires. It is shown that for the socio-political stability of the Progressist society, is enough a supportive environment. Another type of voter – a Phobic works in a fearful environment. It is shown that for the socio-political stability of the Phobic’s society, regular impacts that support their phobias are sufficient. As an example, emotional expectations, socio-political stability and information management in Eastern Europe are considered. The management of phobias in a weak economy is investigated. It is shown that in the absence of regular impacts supporting phobias a Phobic turns into a Progressist. Then the socio-political system becomes unstable with a weak economy and growth limits. The classification of the main social threats and phobias is given. The socio-political consequences of creating phobias, including corporate psychopaths and toxic leaders, are examined. Ways are proposed to resolve the contradiction between increased consumption and growth limits without the use of phobias, using high humanitarian technologies.

A mixed manna contains alternatives that are the best for some agents, and alternatives that are the never best for all agents. We define a new class of Condorcet domains that are called GF-domains. GF-domains are unique Condorcet domains that are weakly minimally rich, semi-connected and contain a pair of mutually reverse preference orders. GF-domains are single-peaked on a circle that leads to a clear interpretation.

We consider the problem of coalitional manipulation in collective decision making and a probabilistic approach for solving it. We assume that voters have some information about other voters’ preferences from opinion polls held before voting. There are 5 different types of poll information functions. Coalition members are assumed to have identical preferences. We consider the probability that in a randomly chosen preference profile there exists a coalition which has an incentive to manipulate under a given type of poll information. We answer the following questions. How does coalitional manipulability differ from individual? How do different types of poll information affect coalitional manipulability? We answer these questions via both theoretical investigation and computational experiments.