Ensuring the operability of the software for automating the document flow of diploma design

Рассмотрены процессы, связанные с внедрением разработанного программного средства по автоматизации документооборота дипломного проектирования в деятельность кафедры. The processes related to the implementation of the developed software for the automation of the document flow of diploma design in the activities of the department are considered

Quality and risk management at real economy enterprises

The analysis of risk management based on fundamental research by leading scientists has been carried out. It is shown that personnel risk management is a process that begins at the stage of developing a personnel management strategy and covers the entire personnel management system of an organization at all its levels. It is concluded that enterprises in the real sector of the economy should focus on the formation of the competencies of their personnel, since not one of the stages in the development of enterprise management systems could do without new knowledge, without new competencies

Approaches to the formation of the initial requirements of the diploma project on the automation of the department's activities

Рассмотрены процессы, связанные с разработкой в ходе дипломного проектирования программного средства для автоматизации деятельности кафедры. The processes related to the development of a software tool for automating the activities of the department during the diploma design are considered

Modeling a two-level risk reduction of an enterprise in the formation of staff competence

An analysis of the possibility of modeling the risk reduction of an enterprise in the formation of the competence of management personnel, based on fundamental research by leading scientists, was carried out. It is shown that at the enterprise it is possible to distinguish different levels of activity associated with risks in the formation of personnel competencies (current management activities and training of specialists). It is concluded that the modeling of two-level risk reduction can be classified as a non-linear stochastic programming problem due to the clearly non-linear relationships between the model variables and the probabilistic optimality criterion

Hidden attractors in fundamental problems and engineering models

Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered

Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces