Software & Systems

Articles of journal № 4 at 2020 year.

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11. Using cloud-based technologies for the typical optimization of logistic processes models [№4 за 2020 год]
Authors: A.A. Levchenko , V.V. Taratukhin
Visitors: 4106
The use of standard models of processes in the enterprise resource planning systems (ERP) implemen-tation can reduce the time and budget of the project. The problem of standard process models optimiz-ing becomes more significant when applying SaaS technologies (Software as a Service). Available standard process models and methods for their optimization don’t take into account the specifics of cloud computing and therefore cannot be applied to new projects using SaaS. The use of system analysis techniques and systems theory has allowed us to formalize the problem of typical process models managing. The problem is formalized for cases of managing the process of implementing the automated process control systems using both the classical implementation method-ology and the methodology for implementing automated process control systems with SaaS technology. To compare the classical approach and the approach using SaaS technologies, a general management theory was applied. When describing the optimization problem, the goal and criteria for the effective-ness of its achievement are determined, and models are built to justify the decision-making. In the models of management systems for the implementation and automated process control sys-tems support, blocks of control, planning, load distribution, and execution were identified; the connec-tion of blocks with each other and with the external environment was described. Recommendations were made to build a model of the simulation of the procurement process, the model was tested, and the implementation of a digital procurement process management system was done. To justify the economic feasibility of the applied method, value management was applied based on standard models of logistics processes by taking into account the regional specifics of Russia and Japan. The developed technology has been successfully tested at the enterprises of the metallurgical indus-try and the high-tech industry.

12. Using decision tables transformations when creating the «Detector» intelligent software module for web applications [№4 за 2020 год]
Author: Yurin A.Yu.
Visitors: 4671
Creating decision-making modules for web applications (which are including knowledge bases) re-quires the development of specialized methods and tools. In this connection, the use of model-driven approaches that implement the principles of transformations, generative and visual programming is promising. This paper describes a new specialization of one of these approaches and its application for creat-ing the Detector intelligent software module. This specialization involves the use of decision tables and conceptual models in the form of UML class diagrams for knowledge formalization and representa-tion; a domain-specific language, namely, Rule Visual Modeling Language for designing logical rules; the Hypertext Preprocessor (PHP) language as a target software platform; Personal Knowledge Base Designer as software that implements the approach. The advantage of this approach is the automated creation of web-based decision-making modules based on transformations of conceptual models and decision tables without direct programming (direct manipulation of programming language constructs). The limitations of the approach are related to a cer-tain class of created systems (PHP web modules), as well as the depth of the implemented logical infer-ence: the decision in the modules is made in one step and does not involve a chain of reasoning. The description of the proposed approach and an example of its application for developing the De-tector module are presented. The Detector is intended for decision making when detecting banned messages and clients violating rules of using a Short Message Service. The applicability of the devel-oped module is shown, as well as the evaluation of the approach based on a time criterion for solving educational (test) problems.

13. The software for the subsystem of quality control of manufactured products using intelligent algorithms [№4 за 2020 год]
Author: Grishin E.S.
Visitors: 3490
This paper solves the problem of reducing the production of welded pipes from stainless steel grades of products of inadequate quality by creating software that functions as part of the plant's process control system and provides quality control of products using intelligent algorithms. The author describes an algorithm for continuous control of product quality, the result of which is a conclusion about the quality of products. On its basis, a subsystem of continuous quality control was developed, which controls the quality of finished products, based on a database of materials and tech-nological maps of manufactured products. This subsystem was developed as part of the creation and implementation of an integrated automat-ed process control system (APCS) and a subsystem for continuous diagnostics and equipment condi-tion monitoring using intelligent algorithms based on machine learning. To implement intelligent ma-chine learning algorithms, the open-source ML.NET cross-platform modeling framework was used, which allows you to get a model based on input data and simplifies the integration of the model into a finished .NET application. If necessary, the framework allows you to train additionally or retrain the model. The subsystem of continuous diagnostics and monitoring of the state of equipment is based on the production model of knowledge representation, which in turn is based on the processing of diag-nostic rules. Diagnostic rules are developed for specific production and unit of equipment by a special-ist in this subject area. The result of the work of the subsystem for quality control of manufactured products is the control of the characteristics of technological equipment that affect the quality of products, control of the characteristics of products based on the data of production flow charts, the issuance of warnings about the tendency for the observed characteristics to leave the range of permissible values and information about incipient defects in products, associated with these characteristics. As a result of the develop-ment of software for the subsystem of quality control of manufactured products using intelligent algo-rithms, the number of products of inadequate quality has been reduced due to the early detection of de-fects (wear) of equipment.

14. Development of a computational environment for the simulation of gas transmission systems regimes based on telemetry data [№4 за 2020 год]
Author: Е.А. Golubyatnikov
Visitors: 3386
The paper discusses the problems of software systems for modeling the regimes of pipeline systems based on telemetry data for dispatch control. The author analyzes the subject area, as well as the fea-tures of the modeling software implementation. As a result, the requirements for such systems are for-mulated. The main requirements are modularity; extensibility and flexibility of integration mechanisms with enterprise information systems and calculation modules; organization of complex and autono-mous computing process; support for distributed component interactions. It is noted that the regime-modeling software operated in the gas oil and gas transportation industry today do not fully meet the stated requirements. Therefore, the paper proposes to develop a specialized distributed computing sim-ulation environment based on telemetry. The paper presents architectural solutions for the computing environment. A microservice approach was chosen as the basis for creating the architecture. According to the ap-proach, the designed system is divided into small, context-sensitive functional blocks. The author pro-poses the way to decompose the developing system into services, describes the roles and functions of each service and methods for service integration. The developed architectural solutions were tested during dispatch control of a real gas transporta-tion system. The paper presents the implementation of the developed architecture. It is integrated with the SCADA-systems of the enterprise for the exchange of telemetry data and simulation results, as well as the Vesta software for solving hydraulic modeling problems. The created software product is used by dispatching personnel on a daily basis and allows solving urgent problems of operational manage-ment: real-time modeling, forecasting the process, calculation of analytical indicators of the system’s functioning.

15. The neural network development for evaluating the technical condition of a hydro turbine using vibration monitoring [№4 за 2020 год]
Authors: А.A. Santalov , Klyachkin, V.N.
Visitors: 4214
The prevention of emergencies at technical facilities is largely provided by the diagnostics of their functioning. One of the important problems is the diagnosis of the technical condition of the hydraulic unit. In the history of hydropower, examples are known where poor quality diagnostics led to serious accidents. To prevent such situations, vibration monitoring of the hydraulic unit is carried out, while the vibration data is sent to the data collection server and transmitted to the control rack, where load adjustments or complete unit shutdown occur. The need for prompt intervention is determined by many indicators that characterize the quality of functioning of the hydraulic unit. This paper explores the effectiveness of the use of neural network methods for vibration diagnos-tics of a hydraulic unit. The resulting sample is divided into three parts: training, control, and test. The training part is designed to build a neural network model - the relationship between the indicators of the functioning of the unit and its states. The control sample is used for the training quality control and helps prevent network retraining. The quality of classification is evaluated by a test sample. When us-ing cross-validation, the original sample is split into several blocks. To assess the diagnosis efficiency, three different quality criteria were used: average error on the test sample, AUC, and F-measure. The practical implementation was carried out using the MATLAB package. For a given set of input data, the best fit configuration was a neural network of three layers with 18 neurons in each layer. As a learning function, it uses the Levenberg-Marquardt algorithm with the backpropagation method of er-ror. The percentage of the average error in recognizing the state of a hydraulic unit using a neural net-work is 4,85 %, AUC is 0,8833, and the F-measure is 0,8282. Analysis of the effectiveness of the ob-tained network configuration compared to the automatically built network using the Statistics and Ma-chine Learning Toolbox library showed an increase in F-measure by 6,7 %.

16. Development of a problem-oriented management system for the construction of geotechnological wells [№4 за 2020 год]
Author: D.N. Moldashi
Visitors: 2959
Characteristic a research object. Geotechnological well support control problem-oriented system is in-tended for remote monitoring and drilling operations dispatching control. In the system being created, control and management activities, such as drilling processes control and drilling equipment according to measuring instruments, working logs maintenance, dispatching control, analysis, and emergency sit-uations monitoring, are automated, performed by technological personnel and expedition management. The main idea a problem-oriented control system creating is to automate the collection and automation of objects information on the current state processing, control, and drilling work control, as well as emergency situations monitoring. The subject area of the problem-oriented control system can be as-signed to automation objects with distributed organizational structure, close information relations, and information, considerable volumes processing discrete character. The aim of the paper is to increase the geological exploration expeditions drilling team’s efficiency on the basis of the problem-oriented system creation for managing the geotechnological wells support, which allows implementing procedures for monitoring the drilling operations parameters at the field, maintaining automated accounting of the drilling equipment operation modes and visualizing various drilling indicators. The novelty of the research is to provide the possibility of drilling team’s production performance in-depth analysis in real-time, drilling parameters control, and solved problems visualization in techno-logical processes continuous nature conditions and drilling rigs territorial distribution. Results of the work. Geotechnological well support control problem-oriented system will allow im-plementing the measurement functions and different drilling modes parameters control and drilling equipment state in real-time. In the system, there is a possibility of drilling parameters indirect meas-urements with subsequent structural relationships calculation, analysis, and corresponding information output. Problem-oriented control system software allows in real-time to receive, process, and visualize data from sensors, to calculate drilling process parameters using mathematical models, and to notify the driller about technological parameters deviation.

17. Development of theoretical bases for classification and clusterization of fuzzy features based on the theory of categories [№4 за 2020 год]
Authors: K.D. Rusakov , D.E. Seliverstov, S.Sh. Hill , S.B. Savilkin
Visitors: 5441
The paper provides a rationale for choosing the measure of uncertainty of information. It describes a modern approach based on the application of fundamental algebraic constructions of category theory. A feature of the set of equivalence relations is the direct establishment of an equivalence relation be-tween an object and a class. The paper shows that at present, there are a number of actual applied problems in the classification field that require a different approach to establishing the equivalence relationship – the use of a cas-cade filter model with intermediate states. To justify the measure of uncertainty about an object, the au-thors proposed to use theoretical propositions based on the mathematical apparatus of the theory of ul-tra-operators. The proposed device also operates with information in terms of definitions of non-elementary information. The characteristics of the proposed device include: the suggestion operate not with information, but with their uncertainties, not considered in the device of ultra-operators; some problems are considered basic information which is a special case in the device of ultra-operators and facilitates the calcula-tions; the scope is narrowed to numbers (i.e., data – sets can only be of numeric nature, compacts, in-cluding multidimensional); operating with numeric sets-information in some cases eliminates the need to explicitly use the grid (and the corresponding scales) concepts, and allow to operate implicitly with infinite lattices. The approach proposed by the authors and the presented mathematical model and measure of in-formation uncertainty is an integral part of the developed "Method of classification and clustering of States of complex systems based on the set-theoretic approach" and allows us to consider the process of obtaining clear classes from the point of view of reducing information entropy using a cascade filter.

18. Implementation of logical conclusion in the production expert system using Rete-network and relational database [№4 за 2020 год]
Authors: L.V. Massel , G.V. An , D.V. Pesterev
Visitors: 5741
One of the areas of artificial intelligence is associated with the development of expert systems (ES). Most often, these systems use a knowledge model in the form of rules, called the Post-production mod-el, such ES are called production expert systems. The classical algorithm for obtaining a solution in an expert system is a sequential logical conclu-sion. With an increase in the volume of rules in the knowledge base, the inference is performed for an unacceptably large time period, which reduces the possibility of obtaining an operational solution. To speed up the output, it is proposed to use the Rete network, a logical inference algorithm for production expert systems proposed by Charles Forgy. The Rete network – the pattern matching algorithm – par-tially solves this problem, but it is desirable to accelerate the conversion of the original rules to the Re-te network. To this goal, the paper proposes the formation and storage of working memory of the logi-cal output system of production expert systems based on Rete network technology using a relational data model. The paper demonstrates the architecture of the data store and knowledge of intelligent systems, de-scribes the implementation of the expert system based on the specification of this architecture, shows the structure of the developed expert system. The approbation has been tested using cognitive models. The cognitive model is one of the types of semantic models that reflects the causal relationship between concepts. It was previously proposed to use the conversion of cognitive models into ES production rules to automate the interpretation of cog-nitive models. The authors illustrate The use of a Rete network for logical inference on products by the example of a cognitive model of one of the threats to energy security: “Underinvestment in the energy sector”. The paper shows that the use of a Rete network and a relational database for storing the working memory of the logical inference system in the developed expert system allows reducing output time with a large number of rules in the knowledge base compared to the naive search algorithm.

19. Implementation of data classification software based on convolutional neural networks and case-based reasoning approach [№4 за 2020 год]
Authors: Varshavskiy P.R., A.V. Kozhevnikov
Visitors: 5547
This paper devotes to the implementation of software for data classification using case-based reasoning (CBR) and convolutional neural network technology (CNN). CBR-methods are widely used to find so-lutions to various problems based on accumulated experience, and CNN are successfully used in solv-ing classification problems by isolating individual elements and forming high-level features using con-volution kernels. One of the necessary conditions for the success of solving the data classification problem is the presence of a correct training dataset. Unfortunately, this condition cannot always be fulfilled (for ex-ample, due to the complexity of the objects under consideration and lack of base information). Due to the ability to accumulate, use, and adapt existing experience, CBR-methods can be used to form a train-ing dataset that can be further used by other methods to solve the data classification problem. Thus, the integration of CNN and CBR improves the efficiency of solving the data classification problem. In addition, CBR-methods can be applied in areas with unpredictable behavior and can be trained in the process of functioning, for example, in the process of training neural networks. This paper proposes the CBR-method for CNN training, managing the process of training, and presentation of iterations of CNN training as a case. The selection of a training step based on precedents improves the performance of the neural network training algorithm. Based on the proposed methods a neural network block using CNN for extending the capability of the CBR-system for data classification is implemented in MS Visual Studio in C# language. To evaluate the effectiveness of the solutions proposed in the work, computational experiments were performed on real data sets.

20. Comparative analysis of DBMS for tourist social network [№4 за 2020 год]
Author: E.F. Feoktistov
Visitors: 4366
Digital technologies are widely used in all spheres of human life, including tourism. In order to book a ready-made tour no longer need to go to a travel agency; Hotel reservations abroad and air travel can also be done without leaving home. Viewing mobile applications with prices for hotels, tickets, and tours has become part of the daily life of a tourist – even when he is not going on a trip, he often opens these applications. So there was a goal to create a mobile application for tourists for daily use. The niche of the tourist social network where tourists could communicate, plan their trips, arrange a cultur-al souvenir exchange, is empty. In this regard, a tourist social network is being developed with a recommendation system based on fairly simple personal data of tourists: lists of souvenirs for exchange (what is what they want); upcom-ing and past trips; the city of residence, and nationality. There is a problem with choosing the database management system necessary for this problem. Firstly, it should be scalable, in view of the possible large influx of users from different countries. Sec-ondly, it must meet modern requirements, be reliable and fast. The paper analyzes various types of non-relational DBMS (database management systems), based on the experience of using them in other social networks. Their advantages and disadvantages are de-scribed for subsequent possible use in a tourist social network. Three graph DBMSs were also tested: Virtuoso, Neo4j, and Sesame to identify the most reliable and fastest DBMS for this development. As a result, on the basis of the data obtained, the best DBMS was revealed, which passed most of the tests with the best time results.

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