Cities today face many challenges, among which energy efficiency is a key requirement. This problem has been the subject of many works both in Russia and abroad. One way to improve the energy efficiency of a Smart city is to manage energy consumption in smart homes. This paper provides an analysis of two ways to improve the energy efficiency of Smart cities using agent training systems and the Internet of things. A comparative analysis of the proposed methods in terms of “The degree of energy consumption reduction” is made.

The article analyzes the array of data about fire reports in the period from 2013 to 2016, as well as the system analysis of time indicators of free fire development. The components of the formula, including new parameters, determining the time of free fire development are presented. The time indicators of the free fire development are assessed according to their significance based on the processed data obtained during the expert survey. Using a system analysis of the data obtained, the most significant time indicators are identified that affect the time of free fire development, as well as its area during the calculation of the necessary forces and means of the fire departments and making decisions by the fire extinguishing manager.

The article presents the results of geological, material and technological features selection of oil well’s efficiency for a regression model that evaluatestheir productivity. The methods of the forward, backward selection of variables and the method based on a random forest are considered. The advantages and disadvantages of the application of these methods in the subject field are revealed, and a comparative analysis is carried out based on an assessment of the model’s accuracy according to the number of the selected features. The adequacy of the obtained regression models is confirmed.

The article analyzes the features of the process for protecting the corporate network from internal and external threats to information security. According to the authors, a particular danger are all kinds of corporate information and personal data leaks arising from the actions of so-called “internal intruders” or insiders, against which the Data Loss Prevention software (DLP systems) is the most effective measure. InfoWatch Traffic Monitor 6.11 is a Russian software product certified by the Federal Service for Technical and Export Control and considered to be one of the world leaders in a given class of software. Based on the license obtained from InfoWatch, in the virtual laboratory of Omsk State Technical University, a specialized stand is created, allowing to configure and verify the DLP system, determine security policies, ensure control of information flows in the company and analyze incidents that have been identified. For these purposes a virtual model for introducing a DLP system into a comprehensive information protection system is developed, where information security objects are identified and appropriate policies are established to prevent data leakage and  the identification of possible incidents, and confidential information is intercepted via controlled communication channels.

The article considers automatic format conversion of legal acts based on custom document templates. Legal acts undergo changes as a result of activities of state authorities, and the task of maintaining the correct edition of a legal act with an increase in the number of revisions becomes more and more difficult. The part of legal acts is stored in an outdated monospace format, and require conversion into a modern format such as Microsoft Word Document. However, many of them cannot be converted automatically. Formatting documents with tables formed by pseudographic characters, whitespaces or other dividers cause a particular difficulty. The approaches to reformatting such documents based on the selection of the structural elements of the document and their transformation using custom templates are presented. The results can be used in regional and federal executive authorities in the preparation of new versions of legal acts in the modern format of Microsoft Word Document.

The article is devoted to the problem of validating business processes when developing solutions based on business process modeling platforms. The article provides a classification of the currently known models of validating data flows in business processes and also considers the types of business processes models: Petri nets, metagraphs, activity-based models, combined models, data flow matrix. The advantages and disadvantages of each model are analyzed, as well as a brief description of the main components of the models. The types of data anomalies and the algorithms used to validate the data flow of a business process are presented. Particular attention is paid to validation methods that use the data flow matrix as the main tool for analyzing data flows. An algorithm for checking anomalies in the absence and redundancy of data using a data flow matrix is given. An example of a model in which a business process works with several domain data models is analyzed. The analyzed example leads to the conclusion that the algorithms built upon the data flow matrix are false when working with several domain data models. The article proposes to find out the possibility of using an ontological model of conceptual objects to identify semantic conflicts between data sets from a variety of domain models.

The article describes the issues of preparation of the expert information for parametric synthesis at the preliminary design of the technical objects under uncertain parameters. Within B. Liu’s uncertainty theory, the algorithms for generating critical variants of the uncertainty distribution functions reflecting the achievement of the “best” and “worst” for a decision-maker (DM) of the numerical characteristics of functions that depend on the uncertain parameters are developed. It is proposed to carry out the formation of uncertainty distribution functions providing a compromise between the expert views, in two stages including the grouping of uncertain values reflecting the views of some experts, and establishment of the compromise functions of uncertainty distribution  for each selected expert group. The algorithm of uncertain values clustering on the fixed number of classes based on the k-means method is developed. An interactive algorithm of uncertain values grouping is suggested. In terms of this algorithm, uncertain values act as graph nodes, whereas the threshold value determined by DM and the distance between uncertain values define the presence or absence of connection between these nodes. Using the proposed expert information processing algorithms, the uncertainty distribution functions are formed, which are the input to algorithms for calculating the numerical characteristics of functions that depend on non-deterministic parameters.

The recognition of types of radar objects by signal characteristics to track their movements in a given area is an important task for ensuring air traffic security. One of the effective tools for solving this problem is the artificial neural networks.
The paper proposes the method for the statistical evaluation of the results of a neural network classification for radar-tracked dynamic objects based on the statistical hypothesis testing. The estimation is carried out by comparing the sample mean of the entire distribution of the results output of the neural network. The results are obtained from a series of observations over objects, with the averaged threshold value of similarity of its output signals, adopted as a hypothetical general average. The increase in the reliability of the neural network classification for a mean of 8% is confirmed during experimental studies of the proposed solutions on the example of the classification of single objects of two types with their Doppler portraits under conditions of significant noise.
The considered method can be used in an independent neural network classification or selection
system for radar-detectable objects and as part of integrated decision support systems for existing
or prospective radar stations.

The article describes spatial filtering that allow improving the quality of images obtained in complex optical conditions. Research methods are based on the provisions of the theory of digital image processing, mathematical analysis and mathematical statistics. The practical implementation of the algorithms is carried out in the MatLab modeling environment.

The article presents a description of the formal optimization problem in terms and notations adopted in the Petri net theory. The statement includes both the objective function and the description of the set of constraints on the varying parameters. The necessity of determining the transition functioning in a network model of statistics is substantiated. A possible way of taking into account the constraints of the problem when performing a simulation experiment, as well as a generalized optimization algorithm based on search procedures, are proposed.