+7 (495) 987 43 74 ext. 3304
Join us -              
Рус   |   Eng

Authors

Puchkov A.

Degree
PhD in Technique, Associate Professor, The Branch of National Research University MPEI in Smolensk
E-mail
putchkov63@mail.ru
Location
Smolensk
Articles

Algorithms for the formation of images of the states of objects for their analysis by deep neural networks

Algorithms of visualization of numerical data characterizing the state of objects and systems of various nature with the aim of finding hidden patterns in them using convolutional neural networks are presented. The algorithms used methods for obtaining images from numerical data on the basis of the discrete Fourier transform of time series fragments, as well as on the basis of the application of visualization using three-component system diagrams, if such a three-component representation of the system is possible. The software implementation of the proposed algorithms was performed in the Linux environment in the Python 3 language using the Keras open neural network library, which is a superstructure above the TensorFlow machine learning framework. For the learning process of the neural network, a Nvidia graphics processor was used that supports the technology of the CUDA parallel computing software and hardware architecture, which significantly reduced the learning time. The proposed approach is the recognition States of the objects according to their visualized data are based on the recognition of no boundaries or forms of the figures in the images and their textures. Also presented is a program that generates sets of images to implement the process of learning and testing convolutional neural networks in order to pre-tune them and assess the quality of the proposed algorithms.Keywords: Internet, Internet security, parental control applications, user security, information security, Internet threats.
Read more...

Preliminary assessment of the pragmatic value of information in the classifiсation problem based on deep neural networks

A method is proposed for preliminary assessment of the pragmatic value of information in the problem of classifying the state of an object based on deep recurrent networks of long short-term memory. The purpose of the study is to develop a method for predicting the state of a controlled object while minimizing the number of used prognostic parameters through a preliminary assessment of the pragmatic value of information. This is an especially urgent task under conditions of processing big data, characterized not only by significant volumes of incoming information, but also by information rate and multiformatness. The generation of big data is now happening in almost all areas of activity due to the widespread introduction of the Internet of Things in them. The method is implemented by a two-level scheme for processing input information. At the first level, a Random Forest machine learning algorithm is used, which has significantly fewer adjustable parameters than a recurrent neural network used at the second level for the final and more accurate classification of the state of the controlled object or process. The choice of Random Forest is due to its ability to assess the importance of variables in regression and classification problems. This is used in determining the pragmatic value of the input information at the first level of the data processing scheme. For this purpose, a parameter is selected that reflects the specified value in some sense, and based on the ranking of the input variables by the level of importance, they are selected to form training datasets for the recurrent network. The algorithm of the proposed data processing method with a preliminary assessment of the pragmatic value of information is implemented in a program in the MatLAB language, and it has shown its efficiency in an experiment on model data. Read more...

Algorithm for predicting the parameters of a system for processing waste apatite-nepheline ores

Within the framework of the concept of a circular economy, research in the field of creating technological systems for recycling waste from mining and processing plants occupies one of the key positions. This is connected, on the one hand, with significant volumes of such waste, reaching tens of millions of tons and posing a significant environmental hazard to air and water basins, human health, and, on the other hand, with their rich chemical and mineralogical composition, which makes it possible to call them accumulations of technogenic deposits. In this regard, the task of creating control systems for technological processes of processing such waste and their information support, including support for all stages of the passage of information processes, is urgent. The novelty of the presented research lies in the proposed structure of an intelligent control system for a complex chemical and energy technological system for processing apatite-nepheline ores, as well as in an algorithm for predicting technological parameters, which is part of the information support of the control system under consideration. The algorithm is based on the use of the apparatus of deep recurrent neural networks and Kalman filtering, which is used at the stage of data preprocessing to train the neural network. The paper describes the proposed algorithm for predicting multidimensional time series, adapted to the considered technological process, presents the software executed in the MatLab environment to demonstrate the efficiency of the specified combination of methods for processing technological parameters. In a model experiment, it has been shown that the use of filtering makes it possible to increase the accuracy of the forecast, which is especially noticeable at its large horizons. The practical significance of the research results is the proposed structure of an intelligent control system for the processing of apatite-nepheline ore waste and software for predicting its parameters, which can be used in various decision support systems. Read more...