Volume 70, Issue 3
November 2020

Guest Editor-in-Chief
Cor. member Prof. Petko Petkov, D.Sc.
Preface for Issue 3

DOI: 10.47978/TUS.2020.70.03

Table of Contents

SYNTHESIS OF MATHEMATICAL MODEL FOR ANALYSIS AND EVALUATION OF PARTICULATE MATTER CONCENTRATIONS IN OUTDOOR SPACES Nikolay Stoyanov, Antonia Pandelova, Tzanko Georgiev, Julia Kalapchiiska	PDF
DEVELOPMENT OF MATHEMATICAL MODEL FOR ANALYSIS AND EVALUATION OF PARTICULATE MATTER CONCENTRATIONS IN INDOOR SPACES Nikolay Stoyanov, Antonia Pandelova, Julia Kalapchiiska	PDF
NORMALIZED SITE ATTENUATION MODELING OF INDOOR FACILITIES USED FOR ELECTROMAGNETIC COMPATIBILITY TESTING Mario Gachev, Chavdar Levchev	PDF
DIGITAL AGRICULTURE INDUSTRY – CURRENT SITUATION ON THE BASIS OF EXISTING RESEARCHES AND SHARE FACTS Ilker Yahov, Andrey Elenkov	PDF
H∞ CONTROL DESIGN OF A MULTITANK SYSTEM Andrey Yonchev, Martin Mladenov	PDF
SYSTEM CHARACTERISTICS OF DISTRIBUTED PARAMETER SYSTEMS Kamen Perev	PDF
NEW APPARATUS FOR OPTICAL BIOPSY Asparuh Markovski, Latchezar Avramov	PDF
MODEL-FREE METHOD FOR TIME VARYING DYNAMIC MEASUREMENTS IN CONTROL SYSTEM Miroslava Baraharska, Tsonyo Slavov, Ivan Markovsky	PDF

SYNTHESIS OF MATHEMATICAL MODEL FOR ANALYSIS AND EVALUATION OF PARTICULATE MATTER CONCENTRATIONS IN OUTDOOR SPACES
Nikolay Stoyanov, Antonia Pandelova, Tzanko Georgiev, Julia Kalapchiiska

Abstract
The size of the dust particles, their chemical composition and the elements adsorbed on their surface directly affect the health of people. Different models and systems are used for monitoring and forecasting dust pollution levels. In the presented work a linear multiple regression model is developed to reveal a causal relationship between the concentration of PM10 and the following independent variables: average daily air temperature, average daily solar radiation, wind speed, wind direction, aver-age daily atmospheric pressure. STATGRAPHICS statistical software package was used to perform the necessary analyzes and calculations.

Keywords:
atmospheric air, PM10, mathematical model.

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DOI: 10.47978/TUS.2020.70.03.013

References:
[1] Liping Xia, Yaping Shao, Modelling of traffic flow and air pollution emission with application to Hong Kong Island, Environmental Modelling & Software 20, рр. 1175-1188, 2005
https://doi.org/10.1016/j.envsoft.2004.08.003
[2] Maya P. Stoimenova, Stochastic Modeling of Problematic Air Pollution with Particulate Matter in the City of Pernik, Bulgaria, ECOLOGIA BALKANICA, Vol. 8, Issue 2, pp. 33-41, 2016.

DEVELOPMENT OF MATHEMATICAL MODEL FOR ANALYSIS AND EVALUATION OF PARTICULATE MATTER CONCENTRATIONS IN INDOOR SPACES
Nikolay Stoyanov, Antonia Pandelova, Julia Kalapchiiska

Abstract
People spend more than 80% of their days indoors, with limited air circulation, and this percentage varies depending on the season, age, gender and type of activity performed. Air pollution depends on the structural features of the building, the absence of automated ventilation systems, vehicular traffic in the area, and internal sources of pollution. An experimental model has been developed to analyze the concentration of particulate matter indoors. The least-squares method to determine the relationship between the dependent variable and the independent variables is used. Modeling was performed using the software STATGRAPHICS.

Keywords:
atmospheric air, PM10, mathematical model, indoor spaces, ANOVA.

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DOI: 10.47978/TUS.2020.70.03.014

References:

[1] Aaron Daly, Paolo Zannetti, Air Pollution Modeling - An Overview, Chapter 2, The Arab School for Science and Technology (ASST) and The EnviroComp Institute, 15-28, 2007
[2] B. Naticchiaa, G. Favab, A. Carbonaria and E. Quaqueroc, Preliminary Tests on a Wireless Sensor Network for Pervasive Dust Monitoring in Construction Sites, The Open Environmental Engineering Journal, Vol. 7, 10-18, Italy, 2014. https://doi.org/10.2174/1874829501407010010
[3] Калъпчийска Ю., Панделова А., Стоянов Н., Разработване на сензорна платформа за из-мерване концентрация на прахови частици, Международна конференция "Автоматика 2019", том 69, книга 2, 2019.

NORMALIZED SITE ATTENUATION MODELING OF INDOOR FACILITIES USED FOR ELECTROMAGNETIC COMPATIBILITY TESTING
Mario Gachev, Chavdar Levchev

Abstract
Models of Anechoic and semi anechoic chambers for electromagnetic compatibility tests are proposed. Normalized site attenuations in the frequency range of 30 to 1000 MHz in both cases are determined using full electromagnetic simulation in HFSS software environment. The received results can be used as a reference in the validation tests of anechoic and semi anechoic facilities.

Keywords:
Electromagnetic compatibility, Normalized site attenuation, Anechoic and Semi anechoic chambers.

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DOI: 10.47978/TUS.2020.70.03.015

References:

[1] Electromagnetic compatibility and Radio spectrum Matters (ERM); Normalized Site Attenua-tion (NSA) and validation of a fully lined anechoic chamber up to 40 GHz, ETSI 102321 V1.1.1.
[2] Марио Гачев, Измервания при изпитания на електромагнитната съвместимост, Семинар на Националния Институт по Метрология, 2019.

DIGITAL AGRICULTURE INDUSTRY – CURRENT SITUATION ON THE BASIS OF EXISTING RESEARCHES AND SHARE FACTS
Ilker Yahov, Andrey Elenkov

Abstract
Despite the big and rapid growth of technologies during the 21st century, there is still an industry that is lagging behind with the optimization and launch of its digital transformation. In fact, this is the agricultural sector. Therefore, in recent years, many attempts have been made to develop and implement optimized processes and technologies, in order to increase production and reduce costs while maintaining product quality. The purpose of this publication is to summarize the current situation on the basis of existing researches and share facts.

Keywords:
digital agriculture industry, smart production, automated systems, smart greenhouses, automated plant production, digital transformation, digitalization.

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DOI: 10.47978/TUS.2020.70.03.016

References:

[1] Daniel Azevedo, "Digital agronomists the link between data and crops", https://www.future-farming.com/Smart-farmers/Articles/2019/10/Digital-agronomists-the-link-between-data-and-crops-488987E/
[2] Gartner Glossary, Information Technology, https://www.gartner.com/en/information-technology/glossary/digitalization
[3] Trendov, N. M., Varas, S. & Zeng, M. 2019. "Digital technologies in agriculture and rural areas" - Statusreport. Rome. Licence: cc by-nc-sa 3.0 igo.
[4] Daniel Newman, "Top six digital transformation trends in agriculture" https://www.forbes.com/sites/danielnewman/2018/05/14/top-six-digital-transformation-trends-in-agriculture/
[5] Saha T., M. K. H. Jewel, M. N. Mostakim, N. H. Bhuiyan, M. S. Ali, M. K. Rahman, H. K. Ghosh, Md. Khalid Hossain, "Construction and development of an automated greenhouse system using Arduino Uno", International journal of information engineering and electronic busi-ness(IJIEEB) - Vol.9, No.3, pp.1-8, 2017. DOI: 10.5815/ijieeb.2017.03.01 https://doi.org/10.5815/ijieeb.2017.03.01
[6] Atul, "Smart Greenhouse: The future of agriculture" - https://www.hackster.io/synergy-flynn-9ffb33/smart-greenhouse-the-future-of-agriculture-5d0e68
[7] Jean-Marie Séronie, "The digital revolution, precision agriculture and conservation farming" - - https://www.willagri.com/2020/01/20/the-digital-revolution-precision-agriculture-and-con-servation-farming/?lang=en
[8] Disruptive technologies - "Digital Agriculture - Feeding the future" http://breakthrough.unglobalcompact.org/disruptive-technologies/digital-agriculture/
[9] Suchiradipta Bhattacharjee, Saravanan Raj, "Shaping the future of agricultural extension and advisory services", GFRAS Interest Group on ICT4RAS 2016
[10] Laurens Klerkxa, Emma Jakkub, Pierre Labarthec, "A review of social science on digital agriculture, smart farming andagriculture 4.0": New contributions and a future research agenda, open access article underthe CC BY license (http://creativecommons.org/licenses/BY/4.0/).
[11] Rose DC and Chilvers J (2018) "Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming. Front. Sustain. Food Syst. 2:87. https://doi.org/10.3389/fsufs.2018.00087
[12] 4 Benefits of Digital Agriculture Tools - https://inthefurrow.com/2018/01/29/benefits-preci-sion-agriculture/
[13] Dr Michael Robertson, Dr Andrew Moore, Dr Dave Henry, Dr Simon Barry, "Digital agricul-ture: what's all the fuss about?", AGRICULTURE AND FOOD, csiro.au
[14] Digiteum Team, "Precision Agriculture Technology: The Future of Precision Farming with IoT" www.digiteum.com

H∞ CONTROL DESIGN OF A MULTITANK SYSTEM
Andrey Yonchev, Martin Mladenov

Abstract
This paper considers MATLAB® modelling and simulation of H∞ controller and its realization on the Multitank System. The first task is to study the physical plant the laboratory Multitank System and to apply a given mathematical model for optimal controller design. The general objective of the derived regulator is to reach and stabilize the level in the tanks by an adjustment of the pump operation or/and valves settings. Finally, it is necessary to simulate the obtained closed-loop system and to test its work-ability.

Keywords:
Multitank system, H∞ design, modeling, control, simulation.

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DOI: 10.47978/TUS.2020.70.03.017

References:

[1] Graebe S. F. Goodwin G.C., Control Design and Implementation in Continuous Steel Casting, IEEE Control Systems, August 1995, pp. 64-71. https://doi.org/10.1109/37.408459
[2] Cheung Tak-Fal, Luyben W.L., Liquid Level Control in Single Tanks and Cascade Tanks with Proportional- Only and Proportional -Integral Feedback Controllers, Ing. Eng. Chem Funda-mentals vol. 18, No. 1, 1979, pp. 15-21. https://doi.org/10.1021/i160069a004
[3] Heckenthaler T. Engell S., Approximately Time-Optimal Fuzzy Control of a Two-Tank Sys-tems, IEEE Control Systems, pp.24-30, 1994. https://doi.org/10.1109/37.291460
[4] P. Petkov, M. Konstantinov, Robust Control Systems: analysis and synthesis with MATLAB®, ABC Techniques. Sofia 2002, (in Bulgarian).
[5] INTECO, Multitank System, User's Manual, inteco.com.pl, 2008.
[6] Street R. L., Watters G. Z. Vennard J. K. Elementary Fluid Mechanics, John Wiley&Sons Inc. 1996.
[7] Doyle J.C., K.Glover, P.P.Khargonekar, B.A.Francis (1989). State- Space Solutions to Standard H2 and H∞ Control Problems. IEEE Transactions on Automatic Control 34, pp. 831-847. https://doi.org/10.1109/9.29425

SYSTEM CHARACTERISTICS OF DISTRIBUTED PARAMETER SYSTEMS
Kamen Perev

Abstract
The paper considers the problem of distributed parameter systems modeling. The basic model types are presented, depending on the partial differential equation, which determines the physical processes dynamics. The similarities and the differences with the models described in terms of ordinary differential equations are discussed. A special attention is paid to the problem of heat flow in a rod. The problem set up is demonstrated and the methods of its solution are discussed. The main characteristics from a system point of view are presented, namely the Green function and the transfer function. Different special cases for these characteristics are discussed, depending on the specific partial differential equation, as well as the initial conditions and the boundary conditions.

Keywords:
heat flow equation, the Green function of PDE, transfer function of a distributed parameter system.

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DOI: 10.47978/TUS.2020.70.03.018

References:

[1] Бутковский, А., Характеристики систем с распределенными параметрами, Наука, М., 1979
[2] Мартинсон, Л., Ю. Малов, Дифференциальные уравнения математической физики, Изд. МГТУ им. Баумана, 2006
[3] Curtain, R., K. Morris, "Transfer functions of distributed parameter systems: A tutorial", Automatica, vol. 45, pp. 1101-1116, 2009 https://doi.org/10.1016/j.automatica.2009.01.008
[4] Farlow, S., Partial differential equations for scientists and engineers, John Wiley & sons, N.Y., 1982

NEW APPARATUS FOR OPTICAL BIOPSY
Asparuh Markovski, Latchezar Avramov

Abstract
A newly developed with the participation of the authors device for "optical biopsy" is described. The method involves recording the fluorescent and reflective spectra of human skin for early diagnosis of skin diseases. The diagnosis is based on the different response of diseased and healthy cells to radiation in certain spectral regions. System and computer software for automatic diagnostics based on a neural network, trained on biochemically justified parameters, has been developed.

Keywords:
optical biopsy, skin diseases diagnostics, neural networks.

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DOI: 10.47978/TUS.2020.70.03.019

References:

[1] Borisova E., P., Troyanova, P., Pavlova, L Avramov (2008), Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy, Quantum Electronics 38 (6), 597. https://doi.org/10.1070/QE2008v038n06ABEH013891
[2] Borisova E. L. P., Angelova, E. P. Pavlova, Endogenous and exogenous fluorescence skin cancer diagnostics for clinical applications, IEEE Journal of Selected Topics in Quantum Electron-ics 20 (2), 211-222. https://doi.org/10.1109/JSTQE.2013.2280503

MODEL-FREE METHOD FOR TIME VARYING DYNAMIC MEASUREMENTS IN CONTROL SYSTEM
Miroslava Baraharska, Tsonyo Slavov, Ivan Markovsky

Abstract:
In this paper, a model-free method for time varying dynamic measurements in a control system is presented. As an example, the dynamic mass-measurement process is examined. The method is based on the on-line estimation of time varying parameters of autoregressive model by a recursive least square method with a constant trace of the covariance matrix. The model order selection is performed by Akaike’s information criteria. The performance of the method with respect to the variance of measurement noise is empirically tested by simulation experiments. For the aim of comparison, the Kalman filter for estimation of unknown measurement is designed. The simulation results show the advantage of the model-free method.

Keywords:
Model-free method for dynamic measurements, Kalman filter, dynamic measurements.

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DOI: 10.47978/TUS.2020.70.03.020

References:

[1] S. Eichstadt, C. Elster, T. Esward, and J. Hessling, Deconvolution filters for the analysis of dynamic measurement processes: a tutorial Metrologia, vol. 47, no. 5, pp. 522-533, 2010. https://doi.org/10.1088/0026-1394/47/5/003
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[8] G. Quintana Carapia, I. Markovsky, R. Pintelon, P. Zoltan Csurcsia, and D. Verbeke. Experi-mental validation of a data-driven step input estimation method for dynamic measurements. IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 4843-4851,2020, doi: 10.1109/TIM.2019.2951865. https://doi.org/10.1109/TIM.2019.2951865