Category: AUTOMATICA AND INFORMATICS 3-4, 2020

AUTOMATICA AND INFORMATICS

ISSN 0861-7562 Print,ISSN 2683-1279 Online

Year LІII
No. 3-4/2020

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Contents

CONTENTS

automatica
Intelligent Control of the Wood Thermal Treatment Process under Variable Scheduling. Part 1. Problem Statement and Approaches

saibg

Кey Words: Case-Base Reasoning (CBR); mathematical modeling; operational conditions; scheduling; suboptimal control; Thermal Treatment Process (TTP).

Abstract. An intelligent system for control of the thermal treatment process (TTP) of wood materials addressed toward manufacturing with necessity of often rescheduling is proposed via combination of model-based and data-driven approaches. Using First-principle mathematical model of TTP presented by Partial Differential Equations in 2D space with suboptimal model-based control algorithm and Case-Based Reasoning (CBR) approach an explicit suboptimal control system is investigated in different operational conditions. A set of virtual subspaces for feasible operational situations for variety of objective criteria of value assessment is created using traditional problem-decision representation. As the search spaces are well structured, the search procedure based on traditional K–NN algorithm is strongly simplified. In this way the complicated computer simulation of the TTP at each time step due to the plant’s parameter distribution, nonlinearity and operational or environmental disturbances are fulfilled off-line. On-line are accomplished relatively small part of the calculations connected with the traditional R4–operations in CBR, objective functions estimation, some databased and rule-based control parameter corrections and possible adaptation from charge to charge. Some results of the simulation experiments are presented and analyzed.

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automatica
K. Boshnakov, D. Slavcheva, D. Petkova. Empirical MIMO Model of Biological Wastewater Treatment

saibg

Key Words: Empirical MIMO model; biological wastewater treatment; Wiener model structure; Principal Component Analysis; polynomial approximation; neural networks.

Abstract. The aim of the present work is to develop data based MIMO mathematical model for biological wastewater treatment, designed for real-time work, and a procedure for creating mathematical models of this class. An analysis of the processes of biological wastewater treatment for the purposes of their mathematical modelling is made. The study includes variables that are known to have sensors worldwide or to have software sensors developed. In conducting the research published in the present work, a combination of real and synthetic data is used. The constructive parameters of the considered installation correspond to settlements with an average number of equivalent inhabitants for the country. To develop a MIMO nonlinear dynamic mathematical model, the structure of Wiener model was chosen – series-connected linear dynamic and nonlinear static parts. The procedure for creating the mathematical model includes: processing of incoming data by the principal components method (PCA); to form the nonlinear static part of the model and to compare the predictive abilities, polynomial dependences for each of the intermediate and target variables are derived as a function of the normalized values of the three principal components and two types of neural networks for each variable are trained. In one case the independent variables are the normalized values of the principal components and in the other – the natural values of principal components. In some cases, higher accuracy of approximation is obtained in polynomial dependencies, in others in neural networks. In neural networks, the same approximation accuracy with polynomial models is obtained with a larger number of parameters. Based on simulation studies, the dynamic characteristics of an installation for biological wastewater treatment are derived. A block diagram of the mathematical model for is presented. The created mathematical model can be used on a modular basis with respect to the target variables of interest, regardless of the other target variables.

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automatica
R. Kosturkov. Model-Based Diagnosis of the Pneumatic Systems Condition

saibg

Key Words: Model-based diagnostics; pneumatic systems; pressure drops; time series; coefficient of determination; Pearson correlation coefficient.

Abstract. Faults are adverse events in any industrial production system. Their occurrence affects the efficiency of the system and reduces the competitiveness of production. Early detection and diagnosis of faults in automated systems is important to prevent equipment damage and loss of performance. For this purpose, more and more sophisticated systems for observation and monitoring of basic characteristics in automated processes are being built. A prerequisite for increasing their efficiency is the use of additional sensory information, modeling and intelligent information analysis to detect faults. The paper explores the possibility of diagnosis of unwanted pressure drops in pneumatic systems. These model-based diagnostic methods aim to distinguish the causes of their occurrence or location. The objects of diagnosis are pressure drops in the supply line or those in the branch, main lines. The presented formulation of the problem and task are dictated as a result of inspection and analysis of operating pneumatic systems of industrial enterprises in the country. It is the pressure drops that are defined as the main and most frequently occurring problem in the operation of the system, and for the resource optimization of the distribution network their localization is of special importance. The paper proposes an approach for the use of load diagrams (time series) with two measurable variables – instantaneous flow and pressure. Based on continuous monitoring and a known model relationship between the two quantities, indicators for detection and localization of pressure drops are determined, reducing the efficiency in the components of the pneumatic system – the main line, local stations or the compressor installation. For the purposes of verification of the proposed approach and the performed analysis – in general, real system data from 13 specific production machines were used.

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informatics
Survey of Security Recommendations for Building OPC-UA Applications

saibg

Key Words: IEC-62541 (OPC-UA) standard; Industry 4.0 reference architecture; cyber-attacks; security recommendations; OPC-UA applications.

Abstract. The IEC-62541 (OPC-UA) standard is an important part of the Industry 4.0 reference architecture and is recommended as the only possible communication standard. A particularly important issue that is being addressed is the issue of security. The paper analyzes the vulnerability of cyber-attacks and the main threats that threaten the security of OPC-UA-based applications and defines established and sustainable recommendations for increasing the security of these applications.

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informatics
A. Popov, S. Lekova. System for Monitoring and Analysis of the Environmental Data (Part 1, Review)

saibg

Key Words: Particulate matter PM10 and PM2.5; sensors for РМ; temperature and light; Arduino Nano 3.0.

Abstract. Air pollution causes damage to human health and ecosystems. Large parts of the population do not live in a healthy environment in accordance with current standards. Today, almost every city has problems with particulate matter concentration, especially in time of temperature inversions. This article introduces an automated system for monitoring and analyzing fine particulates in ambient air, temperature and light. It can be used in the workplace, at home, and in particular in student classrooms and laboratories, not only for monitoring, but also for training and refinement involving students, in various disciplines or informal activities.

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informatics
L. Boyanov. Architectures and Tools for Internet of Things Big Data Processing

saibg

Key Words: Internet of Things (IoT); big data; big data processing tools; Hadoop.

Abstract. Internet of Things (IoT) is a modern paradigm referring to interconnected things/objects in the global digital network Internet. This model differs significantly from the traditional approach of connecting computers, laptops and servers to Internet. There is a huge variety of connected devices – ranging from sensors and RFID tags and mobile phones to data centres and supercomputers. They all create, transmit and process digital/digital data in a quantity, variety and unimaginable until recently. All this leads to new requirements for the means and environment for data processing. The paper presents a classification of architectural model, used for data from IoT. They are placed in four groups – such of standardization organization, of commercial organizations, in respect of Industrial Internet of Things and of researchers. A well-known architecture, that distinguishes the data path according to the speed of data processing – Lambda Architecture is also presented. The paper also looks at the most popular products, programs and software libraries for big data processing. A particular attention is given to the Hadoop software library, which allows processing of big sets of data. Other products and tools for ETL (Extract, Transform and Load), distributed event streaming, data storage, data processing and analytics are also presented. The paper describes a simplified architecture, which has been implemented and demonstrated to work on a 40-node cluster. Its software comes from the open source Hadoop environment. The next tasks and future work on this architecture are presented.

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education and qualification
A. Atanasov, D. Pilev. Application of Deep Neural Networks in Online Learning of Students

saibg

Key Words: Online learning; deep neural networks; face recognition; facial emotions recognition; python.

Abstract. This paper presents the application of Deep Convolutional Neural Networks (DNN) in the process of online learning of students, which become very important in the time of Covid pandemic. The pre-trained DNN are analyzed and selected one applied for students’ face recognition and for facial emotions recognition. On the base of face recognition students are admitted to the online lectures, exercises and exams. As well, face recognition used to control students whether they regularly visit the lectures and the exercises. The analysis of students’ facial emotions (positive, neutral or negative) was used for personalization of the study process and for adaptation of the lecture material.

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