Software & Systems

Articles of journal № 1 at 2021 year.

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21. Parallelization in the analysis of physical data of the LHCb experiment [№1 за 2021 год]
Authors: A.V. Egorychev, I.M. Belyaev, Т.A. Ovsiannikova
Visitors: 2873
The general progress in hardware performance since the 1990s has completely expanded the ability to build information systems from ready-made components and made available freely distributed soft-ware tools for designing programming systems, including those that support the organization of parallel processes, if not at the language level itself, then at the level of library components. The paper presents the application results of parallelization in physical data analysis problems of the LHCb experiment. The current realization is implemented in the OSTAP framework based on the ROOT and python packages. The amount of the data obtained in proper time in experiments at the Large Hadron Collider require a high speed of preprocessing, which means high computing perfor-mance. The high processing speed is also a major requirement for analyzing the data obtained in the subsequent stages. Adaptation of the software to modern multi-core and multiprocessor systems makes it possible to achieve the necessary computing power for efficiently solving the data analysis problems in experiments of elementary particle physics. The OSTAP software package has a user-friendly interface which is implemented by using the Py-thon. The Python has also established itself as a powerful tool for developing distributed systems and network programming. The parallel algorithm can be implemented in parts on many different devices with the subsequent combination of the obtained results and obtaining the target result. Multiparadig-matic languages, such as Python, show excellent results in programming network processes for multi-processor systems and attract many supporters.

22. Semantic analysis of scientific texts: Experience in creating a corpus and building language pattern [№1 за 2021 год]
Authors: E.P. Bruches , A.E. Pauls , Batura T.V., V.V. Isachenko, D.R. Shsherbatov
Visitors: 2884
This paper is devoted to the development of methods for named entity recognition (NER) and relation classification (RC) in scientific texts from the information technology domain. Scientific publications provide valuable information about cutting-edge scientific advances, but efficient processing of in-creasing amounts of data is a time-consuming problem. Continuous improvement of automatic methods of such information processing is required. Modern deep learning methods are relatively good at solv-ing these problems with the help of deep computer-aided learning, but in order to achieve outstanding quality on data from specific areas of knowledge, it is necessary to additional training the obtained models on the specially prepared dataset. Such collections of scientific texts are available in English and are actively used by the Russian scientific community, but at present such collections are not pub-licly available in Russian. The paper contains the RuSERRC dataset description, which consists of 1600 unlabeled documents and 80 labeled with entities and semantic relations (6 relation types are considered). Several modifications of the methods for building models for the Russian language are also pro-posed. This is especially important, since most of the existing research is focused on working with data in English and Chinese, and it is not always possible to find high-quality models for the Russian lan-guage in the public domain. The paper includes the results of experiments comparing the vocabulary method, RAKE, and methods based on neural networks. Models and datasets are publicly available, and we hope it can be useful for research purposes and the development of information extraction systems.

23. Geovisualization system of territorial indicators for decision support in situational centers of socio-economic analysis [№1 за 2021 год]
Authors: A.V. Medvedev, E.Yu. Rapp, I.A. Shusharin
Visitors: 1745
One of the critical components of supporting management decision-making in the analysis area, plan-ning and forecasting of territorial social, and economic development is to provide experts the possibil-ity of geovisualization of territorial characteristics of social and economic objects in the operational interaction mode. The paper describes a geovisual system for displaying socio-economic information developed by the authors in the desktop application form, which allows for effective decision-making support in ana-lyzing the socio-economic development of territories. The paper specifies the analytical potential of this system, presents its interface and menu. The authors provide screenshots of the geovisual system operation when it is used in accordance with the current information and analytical center of a higher education institution, illustrating some described possibilities. Analytical processing of coordinates and object characteristic in the proposed geovisual system, in particular, consists in their automated ranking, clustering, representation of objects in different color ranges, in the construction of bar charts and graphs over time, depending on the values of the actual so-cio-economic characteristics of objects stored in a user-friendly format. Object information and its characteristics are automatically read from an Excel file, each sheet of which corresponds to the time of its fixation (observation, recording) in time units selected by the user. The form of information store in the system is a cube with the axes "list of objects", "list of socio-economic characteristics of ob-jects", "moments of data fixation", which allows using the capabilities of OLAP analysis when sorting, ranking, filtering available information about objects. The listed capabilities of the geovisual system are effective when used in the first line support con-ditions for decision making in situational centers and situational rooms in the socio-economic analysis of the functioning of enterprises and territories.

24. Continuous monitoring and quality control system for glassworms production [№1 за 2021 год]
Authors: Matveev, Yu.N., M.M. al-Okabi, Stukalova N.A.
Visitors: 2725
The paper describes the architecture, methods, and tools used to create a system for continuous moni-toring and quality control of glassworms production based on optical technologies and methods of technical vision. Technical vision techniques and optical technologies are often used to check the glass product quality. However, the glassworms production process has its own characteristics that do not al-low the use of standard solutions. The analysis of the technological process made it possible to identify those specific features of production that must be taken into account when using optical technologies and methods of technical vision in the system of continuous monitoring and quality control of glassworms production. The spe-cifics of the technological process include: strong vibration of equipment and glass tube in the process of its movement; the high temperature of the glass tube, which does not allow the optical registration means to be located near the observed object; the need to identify tiny defects, the size of which ranges from tenths to several millimeters, from a long distance; high speed of movement of the glass tube and the need to inspect the glass tube having a round shape from all sides during movement. This is far from the complete list of problems that have been solved in the process of developing a system for con-tinuous monitoring and quality control of glassworms production based on optical technologies and technical vision methods. The paper describes methods and procedures for finding defects and determining their localiza-tion. A procedure for automatic determination of the control area is described, which allows you to keep an object in the camera’s view, despite its vibration. To solve the problem of circular inspection of a moving hot glass tube, a multi-movie camera system was developed that allows it to be inspected from all sides, without its rotation. There are descriptions of the components of an automated monitor-ing system and the glassworms quality control, including a subsystem for collecting and recording vid-eo data, a subsystem for video data preprocessing, a subsystem for deep processing of video data, a control subsystem, and a graphical user interface, as well as their interconnections. There result from preliminary tests of the system in the paper.

25. Method for detecting source noise signals of the radio emission based on fractal analysis [№1 за 2021 год]
Authors: R.R. Mukhamedov, V.V. Utkin , D.S. Voinov
Visitors: 3643
Existing energy detectors can detect a signal at a SNR of at least 20 dB. For energy detectors, the state-ment about the presence of a signal is made by the signal strength. LPI (low-probability-of-inter- cept) – mode, implies the use of signals with a low power level. A decrease in the radiated peak power leads to a decrease in the range of conducting radio surveillance. For radio surveillance stations, it is necessary to provide a detection range of over 174 km, which is not provided by energy detectors for radar stations using these types of signals, therefore, it is neces-sary to develop a detector based not on the signal power, but on other physical principles. To solve this problem, the authors consider the possibility of using fractal analysis of signal spectrograms. To present the results of the fractal analysis of the signal spectrograms, which allows detecting broadband signals with a low power level. The considered version of the broadband signal detector, based on the fractal analysis of spectro-grams, allows detecting signals with a signal-to-noise ratio of less than -5 dB. The results were obtained based on modeling broadband signals in the PyCharm environment in the Python 3.8 programming language, with a low power level, and calculating the fractal dimensions of the spectrograms of these types of signals. According to the Pearson agreement criterion, it is proved that the fractal dimension obeys the normal distribution law, therefore, it is possible to use the Neumann – Pearson detection cri-terion. The probabilities of correct detection of these types of signals are calculated based on the crite-rion. Based on these calculations, it was concluded that with a signal-to-noise ratio of less than -5 dB, the probability of correct detection is over 95%. The decision about the presence of a signal is made based on the calculation of the fractal dimension of the spectrogram of the received signal. The practical significance of this work lies in the fact that the use of fractal analysis of detected sig-nals makes it possible to detect a signal at a greater distance than when using the energy detection method.

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