Категория: Списание Автоматика и информатика

Key Words: Digital transformation; digitalization; process industries; process automation; simulation.

Abstract. The remarkable evolution of technologies in the last decade enables producers of automaton products, software, and systems to goes further in their solutions. At the same time process industries are challenged by the continuously changing competitive environment, keeping new health and safety regulations, and developing environmental-friendly products. New business models are created, and producers need to fit to be successful in their markets. Digital transformation (DT) is the way how process industries could go further. Process automation systems takes significant part in that transformation using the latest developed technologies. Different studies and research in the automation control theory, especially in the last decades, provides us the available knowledge base for further technology improvement. Normally there is a timeline gap between the theory and the practice due to the complex mathematical models and algorithms which were difficult and expensive to be executed in a real production process. With the development of the new automation hardware, software technologies and edge devices, driven by Industry 4.0, the implementation of theory is more applicable, and the return of investment is more visible. Nowadays, the development of science and production processes go more and more hand in hand from the stage of conceptual design the products production. The leading role has the rapid development of artificial intelligence, supported by the availability of new computer technologies, computational algorithms, and programming languages. The aim of this paper is to show a difference in digital transformation of discrete and process industries. The focus is the DT of process automation using parallel examples from the automation control theory and currently available software and hardware solutions.

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Key Words: SISO; power plant; Machine Learning; PID control; error assessments.

Abstract. The research is focused on possibility of applying the Machine Learning (ML) approach, which is using previous data knowledge for the energy system and monitored process values for control behaviour. The main focus of the research is to make assessments for the quality of the control process by comparing the classical PID Control algorithm and Machine Learning Control (ML) one. The investigation is based on possibility to apply supervisor Machine Learning Control to Single Input Single Output (SISO) Power Plant. In the current research the previous information about the system data (manipulated, control, output variables and system states) are used for forming the training sets which is used for performing the Machine Learning Control. Some simulation results illustrating the behaviour of the power system, which is controlled via ML and PID have been presented. The research gives some assessments about the quality of the control by assumption for presence of low and big system disturbances and uncertainties, which may occur in energy system. In current paper applying the supervisor ML algorithm is only under consideration. The steady describes the basic architecture of developed software system implementing the SISO Plant Control. The research gives assessments for four cases – PID and ML control with low disturbances, PID and ML control with big disturbances. Some assessments about system behavior have been made with possibility to overfeed the training set applying big uncorrelated data. The software system is released with java and weka ML library. Realization of system program modules and architecture of the system has been presented.

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Key Words: Energy efficiency; survey; costs.

Abstract. The object of the study is treated as an integrated system, consisting of a building, systems for ensuring the microclimate, inhabitants and modes of habitation, climatic eff ects of the environment. The source information required for the analysis was gathered from existing documentation and surveying. In solving the task, the following methods and procedures were used and applied: interviews with owners; inspections; modeling and simulation of energy transfer processes; calculations. The source information required for the analysis was collected from: existing documentation; recordings. Solving the task is implemented in the following sequence of actions: analysis of the existing situation; formation of the necessary database for modeling and simulation of energy transfer processes; creating models of real energy consumption; establishment of the main energy characteristics under normal mode of exploitation; simulating the energy transfer processes and revealing the potential possibilities for energy saving; generation of energy-saving measures and technical solutions for their implementation; technical-economic evaluation of prospective measures and their combinations; assessment of saved carbon dioxide emissions as a result of the implementation of measures to increase energy effi ciency. The presented material refl ects only the obtained results without going into detail the implementation of the individual stages.

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Key Words: Deep Neural Networks; Facial Emotions Recognition; Weather Condition Recognition; Python.

Abstract. Emotions are one of the main ways to communicate between people and to express their attitude towards objects, products, services, etc. Emotions are divided into two classes – verbal and non-verbal. Human speech and intonation belong to the first class, and to the second class are facial and body emotions, also known as body language. The subject of this paper is facial emotions and their relationship to the scene in which they occur. A number of studies have established that there is a strong relationship between a person’s emotions and their surroundings. The latter includes meteorological conditions (weather) and other objects, such as other people, landscape, etc. Facial emotions range (FER) from seven basic emotions (joy, anger, surprise, fear, sadness, neutral and disgust and neutral) categorized by P. Ekman through his Facial Action Coding System to 26 emotions represented by Russell through his 3D Valence Arousal Dominance model. Most of the existing deep neural networks for Facial Emotions Recognition recognize mentioned seven emotions. In our previous research, we presented a pre-trained FER model with 69.85% accuracy. Weather conditions are closely related to geographic regions and vary in some cases from sunny to cloudy, or in other cases include some subset of sunny, foggy, snowy, rainy, hot, etc. In this research, we analyze deep learning neural networks, for weather conditions recognition and selected appropriate model. We combined our FER DNN with the selected weather recognition DNN and build a bimodal system, which improves facial emotion recognition to 80-83% especially in the cases when FER model provides contradictory results.

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Key Words: Beehive monitoring system; precision beekeeping.

Abstract. Honey bees are one of the most important insects for human existence. They have a main role in the production of various agricultural crops, as well as in the development of nature itself. Currently, bee colonies are facing many problems related to climate change, diseases, chemical pollution and most of all human activity regarding the development, production and sustainability of the bee colony. The aim of the present development is to provide a solution or at least to partially suppress certain problems of bee families during their development in their active period, with a minimum expenditure of resources. These problems can be summarized as problems related to the health of the hive, problems related to reproduction and problems related to the production of certain bee products (honey, pollen and wax). A system for monitoring of some bee colonies parameters in real time is proposed, based on cloud services and a microprocessor system with a set of sensors. The obtained information can help the beekeeper take timely action with minimal stress to the bees in the colony. The developed system is based on an ESP32 microcontroller and measures basic parameters, such as internal and external temperature, humidity and weight of the beehive. The data is stored in a cloud structure with a good graphical presentation environment for the purpose of subsequent analysis. Attention is paid to energy saving and communication in difficult to reach places and regions. The architecture of the system and its implementation are described in details. The developed system can be used to monitor the production of honey, prevent the process of swarming, as well as to maintain a high health status of the bee colonies. Results of actual data obtained for the observed values during the active period of development of a beehive are presented. Through the technical and economic analysis, information is given about the achieved socio-economic results.

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Key Words: Control praxis; ecology; energy; engineering; industrial automation; innovation.

Abstract. The presented overview represents a general view of the successfully held 30th edition of the International Symposium on Control of Energy, Industrial and Environmental Systems. This was not a narrowly specialized forum, it was a search for a possible way to build a bridge between theoretical achievements and their practical implementation in industrial automation. The main automation engineering companies and the relevant organizational structures in industry and business in Bulgaria have invariably been active participants and sponsors of the symposium. The total number of reports and company presentations published in the proceedings of the symposium exceeded 500. During the three decades of its existence the symposium was constantly evolving following the rapid progress of theoretical achievements, new technical devices and advanced software, emerging innovative technologies based on Big Data and Artificial Intelligence. The symposium was a place for productive discussions, exchange of experience and mutual enrichment of specialists with different profiles. It was a suitable environment for creating new contacts, which in many cases grew into long-term business cooperation. The symposium was a forum for popularizing the scientific and practical achievements of industrial automation in Bulgaria. It was often a place for first appearance and publication of young specialists from scientific circles and industry.

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Key Words: Anaerobic digestion; one-stage anaerobic digestion processes; two-stage anaerobic digestion processes; mathematical modeling; static characteristics; biogas yields; energy yield.

Abstract. Compared to traditional one-stage anaerobic digestion processes (OSAD) with biomethane production, this paper focuses on the study of energy yield of the two-stage anaerobic digestion processes (TSAD), which are able to produce simultaneously biohydrogen and biomethane. In TSAD, relatively fast growing acidogens and H 2-producing bacteria are developed in the first-stage hydrogenic bioreactor and are involved in the production of volatile fatty acides (VFA) and H2. On the other hand, the slow growing acetogens and methanogens are developed in the second-stage methanogenic bioreactor, in which the produced VFA are further converted to CH4 and CO2. This separation allows to optimize physico-chemical parameters for both groups of microorganisms which are not the same. Using mathematical models (mass balance type) developed by our team, theoretical comparative analysis of the energy yield from one-stage and two-stage anaerobic digestion systems is performed. Transforming the differential equations of these balance models, some algebraic equations called static characteristics for both bioreactors were obtained. They represent dependencies of the main process variables from the control variable (dilution rate). On the basis of theese results, the theoretical maximal values of the corresponding enery carriers (hydrogen and methane) yields can be found for different values of the inlet organics (perturbation). The possible maximal biohydrogen and biomethane yields and the overall energy production are calculated by the static characteristics and extremum points of both systems. From the performed analytical and simulation studies, it can be seen that the energy obtained with TSAD is from 32% to 48% greater compared to OSAD under similar conditions (depending on the concentration of the incoming organics).

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Key Words: Online Learning; Deep Neural Networks; Face Recognition; Facial Emotions Recognition; Python.

Abstract. Current study presents development of bimodal system for Facial Emotion Recognition (FER) and Body Gestures Emotion Recognition (BER). The system is based on two Deep Learning Neural Networks (DNN) each one responsible for the recognition of the emotion of the face or the body. The use of the combination of two neural networks has an amplifying synergistic effect, which increases by about 10% the accuracy of the results (recognized emotionс) compared to those of the individual DNN. The selection of pre-trained DNN models for facial and body emotions recognition is based on two authors’ papers, in which detailed analysis of the DNN for FER and BER has been done. Therefore in current study a brief information about selected DNN models is provided, as well information about specific dataset used for training selected DNNs. Verification of the bimodal system is done using our private dataset.

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Key Words: Waste collection; smart city; Internet of Things (IoT); intelligent transportation systems; surveillance systems.

Abstract. The paper presents the structure of an intelligent integrated system for managing the collection of solid waste in urban and suburban environments. The system can automatically maintain the box level and send information to the waste collection truck. The technologies used in the proposed system are good needed to provide real monitoring and management of waste collection processes and to obtain a green environment.

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Key Words: LibreOffice Calc; multi-objective optimization; NLP Solver.

Abstract. There is a common difference between single-objective optimization and multi-objective optimization. In the first case, there is only a single value as a result of the optimization. In the second case, there is a set of solutions called Pareto-optimal solutions. Single-objective solvers are giving only a single value as a result, even for multimodal functions. Because of this single-objective solver is not proper for multi-objective problems. Through additional adaptation, a single-objective solver is possible to start multiple times. Taking the results of multiple starts, the Pareto front is marked. When the solver is a metaheuristic, the front itself is difficult to achieve. With fine-tuning of the solver’s parameters, the solutions can be as close as possible.

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Клуб САИ продължава да е едно от най-привлекателните и популярни мероприятия на САИ. Наред с чисто човешките, колегиални и професионални контакти представянето на фирми и постижения на колеги в областта на автоматиката и информатиката позволява членовете на САИ да се запознаят с актуални теми в науката и реалната икономика в предметната област на Съюза.

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CompSysTech’22 е двадесет и третото издание на Международната конференция по компютърни системи и технологии, организирана от Българското академично дружество по информатика и Съюза по автоматика и информатика (САИ) „Джон Атанасов“ с подкрепата на ФНТС. Конференцията се проведе на 17-18 юни 2022 г. в Русе. Традиционните конференции CompSysTech се провеждат ежегодно от 2000 г. досега.

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1st IFAC Workshop on Control for Smart Cities CSC 2022, 27-30 юни 2022 г., Созопол се проведе във виртуална среда поради ковид пандемията. Това позволи на колеги от страни, отдалечени от България, да участват в CSC 2022. Бяха представени 14 държави. Семинарът е организиран от Съюза по автоматика и информатика 1 2023 37 „Джон Атанасов“ със съдействието на ФНТС.

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Ежегодната международна конференция IEEE International Conference Automatics and Informatics’22 (ICAI’22), продължение на традиционната Международна конференция „Автоматика и информатика“, се проведе в периода 6-8 октомври 2022 г. с домакин ТУ – Варна. Тя е едно от основните мероприятия в Дните на Джон Атанасов, които се провеждат традиционно под патронажа на президента на Република България с цел представяне и популяризиране на резултатите от научните изследвания в областта на автоматиката и информатиката.

Конференцията е с 55-годишна история и има голям принос за развитието на автоматиката и информатиката в България.

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На 10-11 ноември 2022 г. в зала 3 на Дома на науката и техниката, ФНТС, ул. Г. С. Раковски 108 се проведе юбилейното 30-то издание на традиционния ежегоден международен симпозиум „Управление на енергийни, индустриални и екологични системи“, организиран от Съюза по автоматика и информатика „Джон Атанасов“ със съдействието на ФНТС. В 30-годишното си съществуване симпозиумът е поддържал неизменно линията за връзка между специалистите по автоматика и информатика от изследователските групи и индустрията.

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