Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)

Organized by IEEE Section IEEE Ukraine Section () SP/AP/C/EMC/COM Societies Joint Chapter IEEE Ukraine Section (West) AP/ED/MTT/CPMT/SSC Societies Joint Chapter IT Step University Ukrainian Catholic University Polytechnic National University Kharkiv National University of Radio Electronics

Lviv, Ukraine August 21-25, 2018

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IEEE Conference Operations DSMP’2018 Organizing Committee 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ IT Step University, 08855-1331 USA 83a Zamarstynivs'ka st., 79019, Lviv, Ukraine

E-mail: [email protected]

IEEE Catalog Number: CFP18J13-CDR

ISBN: 978-1-5386-8175-6

DSMP’2018 Conference Committee

Honorary Chairpersons Yuriy Rashkevych, Ukraine Yevgeniy Bodyanskiy, Ukraine

General Chairs Dmytro Peleshko, Ukraine Olena Vynokurova O., Ukraine Yaroslav Prytula, Ukraine

Technical Program Committee Chair Dmytro Peleshko, Ukraine

Publication and Finance Chair Olena Vynokurova O., Ukraine

Technical Program Committee Aizenberg I., USA Nakonechny A., Ukraine Antoshchuk S., Ukraine Petlenkov E., Estonia Babichev S., Czech Republic Qu S.C., China Balasubramaniam J., India Rekik A., Tunisia Berezkiy О., Ukraine Romanyshyn Yu., Ukraine Bidyuk P., Ukraine Rusyn B., Ukraine Bogomolov S., Australian Sachenko A., Ukraine Boyun V., Ukraine Setlak G., Poland Churyumov G., Ukraine Šipeky L., Slovakia Didmanidze I., Georgia Shelevytsky I., Ukraine Du K., China Slipchenko A., Dyvak М., Ukraine Smolarz A., Poland Gabsi M., Snytyuk V., Ukraine Gabsi M., Tunisia Sokolov O., Poland Gozhiy O., Ukraine) Sokolovsky Ya., Ukraine Gryniv R., Ukraine Souii M., Ph.D., Tunisia Hnatushenko V., Ukraine Stepashko V., Ukraine Hu W.B., China Štěpnička M., Czech Republic Kareem Kamal A. Ghany, Egypt Schlesinger M., Ukraine Karlik B., Albania Su J., China Khaled G., Tunisia Szymanski Z., Poland Kharchenko V., Ukraine Temani M., Tunisia Klawonn F., Tkachenko R., Ukraine Kokshenev I., Ph.D., Brazil Tsmots І., Ukraine Krylov V., Ukraine Vassiljeva K., Estonia Lu C.W., China Voloshyn V., Ukraine Lytvynenko V., Ukraine Vorobyov S., Finland Lyubchik L., Ukraine Wójcik W., Poland Lubinets Ya., Ukraine Wu J.Q., China Malyar М., Ukraine Yanovsky F., Ukraine Markov K., Bulgaria Yatsymirskyy М., Poland Mashkov V., Czech Republic Ye Z.W., China Mashtalir V., Ukraine Yegorova E., United Kingdom Mikhalyov O., Ukraine Zhengbing Hu, China Morklyanyk B., Ukraine Zaychenko Yu., Ukraine iii

Local Organizing Committee Chair Taras Rak, Ukraine

PR Manager Maria Shepel, Ukraine

Event Manager Yulia Vasylets, Ukraine

Members of Local Organizing Committee Alekseyev V., Ukraine Miyushkovych Yu.,Ukraine Andriychuk M., Ukraine Molchanovskyi О., Ukraine Batyuk A., Ukraine Mulesa P., Ukraine Berezko O., Ukraine Panchenko T., Ukraine Borzov Yu., Ukraine Perova I., Ukraine Doroshenko A., Ukraine Pichkalov I., Ukraine Didyk O., Ukraine Povkhan I., Ukraine Dumin O., Ukraine Seniuk V., Ukraine Ivanov Yu., Ukraine Shateyev O., Ukraine Figura R., Poland Sviridova T., Ukraine Kostyuk N., Ukraine Sydorenko R., Ukraine Klyuvak A., Ukraine Tsiura N., Ukraine Kyselova A., Ukraine Tyshchenko O., Ukraine Lotoshynska N., Ukraine Veselovsky S., Ukraine Malets I., Ukraine Vysotska V., Ukraine Menshikova O., Ukraine

iv List of Reviewers

Yu. Rashkevych, Ukraine Ye. Bodyanskiy, Ukraine D. Peleshko, Ukraine O. Gozhyj, Ukraine O. Vynokurova, Ukraine I. Aizenberg, USA V. Voloshyn, Ukraine T. Panchenko, Ukraine Ie. Gorovyi, Ukraine R. Ali, Tunisia Yu. Romanyshyn, Ukraine V. Hnatushenko, Ukraine S. Antoshchuk, Ukraine V. Mashtalir, Ukraine Ya. Sokolovskyy, Ukraine V. Alieksieiev, Ukraine I. Perova, Ukraine O. Berezsky, Ukraine O. Didyk, Ukraine E. Yegorova, United Kingdom Ye. Pavlov, Ukraine B. Tiwana, USA N. Kulishova, Ukraine O. Dumin, Ukraine I. Shelevytsky, Ukraine V. Lytvynenko, Ukraine L. Lyubchyk, Ukraine G. Churyumov, Ukraine M. Yatsymirskyy, Poland T.Rak, Ukraine A. Dolotov, Ukraine A. Slipchenko, Netherlands D. Puchala, Poland K. Stokfiszewski, Poland P. Tarasiuk, Poland A. Berko, Ukraine Ie. Burov, Ukraine V. Volkova, Ukraine O. Karabin, Ukraine N. Gandhi, USA G. Kriukova, Ukraine L. Kirichenko, Ukraine N. Lamonova, Ukraine A. Kuzyk, Ukraine V. Lytvyn, Ukraine U. Ozkaya, Turkish Ya. Todorov, Finland R. Upadhyay, Tatarstan G. Ponomaryova, Ukraine O. Menshikova, Ukraine O. Gorokhovatskyi, Ukraine S. Babichev, Czech Republic A. Chernodub, Ukraine

v Partners

Exclusive partner SoftServe www. softserve.ua

Gold partner GlobalLogic www.globallogic.com Perfectial www.perfectial.com

Silver partner ROMB

Partners Lviv City Council http://city-adm.lviv.ua/ Lviv Convetion Bureau http://www.lvivconvention.com.ua/en/ Kyivstar https://kyivstar.ua/ Skhidnytska 118 http://skhidnytska.ua/

The DSMP’2018 is co-supported by program “Scientific Lviv”. vi Welcome Letter

Dear Colleagues,

We would like to personally encourage each of you to join us at IEEE Second International Scientific Conference Data Stream Mining and Processing (DSMP’2018), which is held in Lviv – Kryve Ozero, UKRAINE, 21-25 August, 2018. Our main goal is not only to provide an opportunity for networking and learning recent scientific achievements but also a chance to be involved in real time panel discussions with IT representatives to review and discuss their practical outcomes on real projects.

The DSMP is organized by IEEE Ukraine Section, IEEE Ukraine Section (Kharkiv) SP/AP/C/EMC/COM Societies Joint Chapter, IEEE Ukraine Section (West) AP/ED/MTT/CPMT/SSC Societies Joint Chapter, IT Step University, Ukrainian Catholic University, Lviv Polytechnic National University, and Kharkiv National University of Radio Electronics.

Agenda of the DSMP’2018 is very rich. This year we have nominated a 120 number of accepted papers coming from about 27 countries which makes DSMP a truly international high impact conference. Major highlights of DSMP’2018 are its keynotes speakers. This conference proved to be extremely important given the fruitful dialog and a chance to exchange ideas and sharing valuable hands-on experience.

This year program is based on the following topics: Hybrid Systems of Computational Intelligence, Machine Vision and Pattern Recognition, Dynamic Data Mining & Data Stream Mining, Big Data & Data Science Using Intelligent Approaches and also panel with participation of IT Companies.

We are proud of the fact that DSMP proceedings have been included into the IEEE Xplore Digital Library as well as other Abstracting and Indexing (A&I) databases (Scopus, Web of Science and etc.). High quality of the DSMP program would not be possible without the contribution of authors, keynote speakers, organizers, students, 53 reviewers who devoted a lot of enthusiasm and hard work to prepare papers, presentations, organization infrastructure and carefully review all submissions. We are very grateful for their efforts.

We would like to thank each of your for attending our conference and bringing your expertise to our gathering.

We would like to express our gratitude to our partners and sponsors for being so generous and sponsoring our conference. We wish all participants an excellent conference, fruitful discussions and pleasant stay in Lviv and Conference venue.

Sincerely

Yuriy Rashkevych Yevgeniy Bodyanskiy

vii General List of Topics

Topic #1. Big Data & Data Science Using Intelligent Approaches 1

Topic #2. Dynamic Data Mining & Data Stream Mining 111

Topic #3. Hybrid Systems of Computational Intelligence 303

Topic #4. Machine Vision and Pattern Recognition 453

Panels 615

viii Table of Contents

Topic #1. Big Data & Data Science Using Intelligent Approaches 1

Iryna Perova, Olena Litovchenko, Yevgeniy Bodyanskiy, Yelizaveta Brazhnykova, Igor Zavgorodnii and Pavlo Mulesa. MEDICAL DATA-STREAM MINING IN THE AREA OF ELECTROMAGNETIC RADIATION AND LOW TEMPERATURE INFLUENCE ON BIOLOGICAL OBJECTS 3

Polina Zhernova, Anastasiia Deineko, Yevgeniy Bodyanskiy and Vladyslav Riepin. ADAPTIVE KERNEL DATA STREAMS CLUSTERING BASED ON NEURAL NETWORKS ENSEMBLES IN CONDITIONS OF UNCERTAINTY ABOUT AMOUNT AND SHAPES OF CLUSTERS 7

Ganna Ponomaryova, Igor Nevlydov, Oleksandr Filipenko and Mariya Volkova. MEMS-BASED INERTIAL SENSOR SIGNALS AND MACHINE LEARNING METHODS FOR CLASSIFYING ROBOT MOTION. 13

Dmytro Lande, Valentyna Andrushchenko and Iryna Balagura. DATA SCIENCE IN OPEN-ACCESS RESEARCH ON-LINE RESOURCES 17

Nina Khairova, Svitlana Petrasova and Włodzimierz Lewoniewski. BUILDING THE SEMANTIC SIMILARITY MODEL FOR SOCIAL NETWORK DATA STREAMS 21

Gautam Pal, Gangmin Li and Katie Atkinson. BIG DATA REAL TIME INGESTION AND MACHINE LEARNING 25

Andrii Berko and Vladyslav Alieksieiev. A METHOD TO SOLVE UNCERTAINTY PROBLEM FOR BIG DATA SOURCES. 32

Yuriy Kondratenko and Nina Kondratenko. COMPUTATIONAL LIBRARY OF THE DIRECT ANALYTIC MODELS FOR REAL-TIME FUZZY INFORMATION PROCESSING 38

Oleksandr Gerasin, Yuriy Zaporozhets and Yuriy Kondratenko. MODELS OF MAGNETIC DRIVER INTERACTION WITH FERROMAGNETIC SURFACE AND GEOMETRIC DATA COMPUTING FOR CLAMPING FORCE LOCALIZATION PATCHES 44

Volodymyr Ostakhov, Viktor Morozov and Nadiia Artykulna. MODELS OF IT PROJECTS KPIS AND METRICS 50

Yuliya Kozina, Natalya Volkova and Daniil Horpenko. MOBILE APPLICATION FOR DECISION SUPPORT IN MULTI-CRITERIA PROBLEMS 56

Olena Basalkevych and Olexandr Basalkevych. FUZZY RECONSTRUCTIONS IN LINGUISTICS 60

Mykola Malyar, Oleksey Voloshyn, Volodymyr Polishchuk and Marianna Sharkadi. FUZZY MATHEMATICAL MODELING FINANCIAL RISKS 65

Peter Bidyuk, Aleksandr Gozhyj, Iryna Kalinina, Zdislaw Szymanski and Volodymyr Beglytsia. THE METHODS BAYESIAN ANALYSIS OF THE THRESHOLD STOCHASTIC VOLATILITY MODEL 70

Max Garkavtsev, Natalia Lamonova and Alexander Gostev. СHOSING A PROGRAMMING LANGUAGE FOR A NEW PROJECT FROM A CODE QUALITY PERSPECTIVE 75 ix Viktor Putrenko, Nataliia Pashynska and Sergiy Nazarenko. DATA MINING OF NETWORK EVENTS WITH SPACE-TIME CUBE APPLICATION 79

Vasyl Palchykov and Yurij Holovatch. BIPARTITE GRAPH ANALYSIS AS AN ALTERNATIVE TO REVEAL CLUSTERIZATION IN COMPLEX SYSTEMS 84

Dariusz Puchala, Kamil Stokfiszewski, Kamil Wieloch and Mykhaylo Yatsymirskyy. COMPARATIVE STUDY OF MASSIVELY PARALLEL GPU REALIZATIONS OF WAVELET TRANSFORM COMPUTATION WITH LATTICE STRUCTURE AND MATRIX-BASED APPROACH 88

Vladyslav Alieksieiev. ONE APPROACH OF APPROXIMATION FOR INCOMING DATA STREAM IN IOT BASED MONITORING SYSTEM. 94

Anatoliy Batyuk, Volodymyr Voityshyn and Volodymyr Verhun. SOFTWARE ARCHITECTURE DESIGN OF THE REAL-TIME PROCESSES MONITORING PLATFORM 98

Myroslav Komar, Vladimir Golovko, Anatoliy Sachenko, Vitaliy Dorosh and Pavlo Yakobchuk. DEEP NEURAL NETWORK FOR IMAGE RECOGNITION BASED ON THE CAFFE FRAMEWORK 102

Mansouri Sadek, Mbarek Charhad, Ali Rekik and Mounir Zrigui. A FRAMEWORK FOR SEMANTIC VIDEO CONTENT INDEXING USING TEXTUAL INFORMATION 107

Topic #2. Dynamic Data Mining & Data Stream Mining 111

Olena Vynokurova, Yevgeniy Bodyanskiy, Dmytro Peleshko and Yuriy Rashkevych. THE AUTOENCODER BASED ON GENERALIZED NEO-FUZZY NEURON AND ITS FAST LEARNING FOR DEEP NEURAL NETWORKS 113

Gennady Chuiko, Olga Dvornik and Yevhen Darnapuk. SHAPE EVOLUTIONS OF POINCARÉ PLOTS FOR ELECTROMYOGRAMS IN DATA ACQUISITION DYNAMICS 119

Petro Kravets. GAME MODEL FOR DATA STREAM CLUSTERING 123

Vasyl Lytvyn, Victoria Vysotska, Yevhen Burov and Andriy Demchuk. DEFINING AUTHOR’S STYLE FOR PLAGIARISM DETECTION IN ACADEMIC ENVIRONMENT 128

Volodymyr Yuzevych, Ruslan Skrynkovskyy and Bohdan Koman. INTELLIGENT ANALYSIS OF DATA SYSTEMS FOR DEFECTS IN UNDERGROUND GAS PIPELINE 134

Liliya Chyrun, Iaroslav Kis, Victoria Vysotska and Lyubomyr Chyrun. CONTENT ANALYSIS METHOD FOR CUT FORMATION OF HUMAN PSYCHOLOGICAL STATE 139

Vasyl Lytvyn, Victoria Vysotska, Olga Lozynska, Oksana Oborska and Dmytro Dosyn. METHODS OF BUILDING INTELLIGENT DECISION SUPPORT SYSTEMS BASED ON ADAPTIVE ONTOLOGY 145

Fedir Geche, Oksana Mulesa, Veronika Voloshchuk and Anatoliy Batyuk. ABOUT KERNEL STRUCTURE CONSTRUCTION OF THE GENERALIZED NEURAL FUNCTIONS 151

x Olga Smotr, Nazarii Burak, Yuriy Borzov and Solomija Ljaskovska. IMPLEMENTATION OF INFORMATION TECHNOLOGIES IN THE ORGANIZATION OF FOREST FIRE SUPPRESSION PROCESS 157

Oleg Riznyk, Olexandr Povshuk, Yurii Kynash and Yurii Noga. TRANSFORMATION OF INFORMATION BASED ON NOISY CODES 162

Anna Vergeles, Dmytro Prokopenko, Alexander Khaya and Nataliia Manakova. UNSUPERVISED REAL-TIME STREAM-BASED NOVELTY DETECTION TECHNIQUE 166

Anastasiia Deineko, Polina Zhernova, Boris Gordon, Oleksandr Zayika, Iryna Pliss and Nelya Pabyrivska. DATA STREAM ONLINE CLUSTERING BASED ON FUZZY EXPECTATION-MAXIMIZATION APPROACHING FORMATION ON SUBMISSION 171

Solomija Ljaskovska, Igor Malets, Yevgen Martyn and Oleksandr Prydatko. INFORMATION TECHNOLOGY OF PROCESS MODELING IN THE MULTIPARAMETER SYSTEMS 177

Gennadiy Churyumov, Vladimir Tokarev, Vitalii Tkachov and Stanislav Partyka. SCENARIO OF INTERACTION OF THE MOBILE TECHNICAL OBJECTS IN THE PROCESS OF TRANSMISSION OF DATA STREAMS IN CONDITIONS OF IMPACTING THE POWERFUL ELECTROMAGNETIC FIELD 183

Oleksandr Prydatko, Ivan Solotvinskyy, Yuriy Borzov, Oleksii Didyk and Olga Smotr. INFORMATIONAL SYSTEM OF PROJECT MANAGEMENT IN THE AREAS OF REGIONAL SECURITY SYSTEMS' DEVELOPMENT 187

Leonid Lyubchyk and Galyna Grinberg. ONLINE RANKING LEARNING ON CLUSTERS 193

Vitalii Bulakh, Lyudmyla Kirichenko and Tamara Radivilova. TIME SERIES CLASSIFICATION BASED ON FRACTAL PROPERTIES 198

Olga Zavgorodnia, Ivan Mikheev and Oleksandr Zyma. IDENTIFINING EUROPEAN E-LEARNER PROFILE BY MEANS OF DATA MINING 202

Galyna Kriukova and Mykola Glybovets. HIGH-PERFORMANCE DATA STREAM MINING BY MEANS OF EMBEDDING HIDDEN MARKOV MODEL INTO REPRODUCING KERNEL HIBERT SPACES 207

Daniel Ambach and Oleksandra Ambach. FORECASTING THE OIL PRICE WITH A PERIODIC REGRESSION ARFIMA-GARCH PROCESS 212

Valentyna Volkova, Ivan Deriuga, Vadym Osadchyi and Olga Radyvonenko. IMPROVEMENT OF CHARACTER SEGMENTATION USING RECURRENT NEURAL NETWORKS AND DYNAMIC PROGRAMMING 218

Sergiy Golub and Nataliia Khymytsia. THE METHOD OF CLIОDINAMIK MONITORING 223

Sergii Khlamov, Vadym Savanevych, Olexander Briukhovetskyi, Artem Pohorelov, Vladimir Vlasenko and Eugen Dikov. COLITEC SOFTWARE FOR THE ASTRONOMICAL DATA SETS PROCESSING 227

Anastasiya Doroshenko. PIECEWISE-LINEAR APPROACH TO CLASSIFICATION BASED ON GEOMETRICAL TRANSFORMATION MODEL FOR IMBALANCED DATASET 231

xi Alexey Roenko, Feliks Sirenko, Yevhen Chervoniak and Ievgen Gorovyi. DATA PROCESSING METHODS FOR MOBILE INDOOR NAVIGATION 236

Yurij Holovatch, Ralph Kenna and Olesya Mryglod. DATA MINING IN SCIENTOMETRICS: USAGE ANALYSIS FOR ACADEMIC PUBLICATIONS 241

Hanna Rudakova, Oksana Polyvoda and Anton Omelchuk. USING RECURRENT PROCEDURES TO IDENTIFY THE PARAMETERS OF THE LARGE-SIZED OBJECT MOVING PROCESS MODEL IN REAL TIME 247

Аndrіу Lozynskyy, Іgоr Romanyshyn, Вohdan Rusyn and Volodуmуr Minialo. ROBUST APPROACH TO ESTIMATION OF THE INTENSITY OF NOISY SIGNAL WITH ADDITIVE UNCORRELATED IMPULSE INTERFERENCE 251

Bohdan Pavlyshenko. USING STACKING APPROACHES FOR MACHINE LEARNING MODELS 255

Romanna Malets, Igor Malets, Heorgiy Shynkarenko and Petro Vahin. MODELING OF THERMOVISCOELASTICITY TIME HARMONIC VARIATIONAL PROBLEM FOR A THIN WALL BODY 259

Oleh Suprun, Olena Sipko and Vitaliy Snytyuk. EDUCATIONAL SCHEDULE DEVELOPMENT USING EVOLUTION TECHNOLOGIES 265

Volodymyr Lyubinets, Deon Nicholas and Taras Boiko. AUTOMATED LABELING OF BUGS AND TICKETS USING ATTENTION-BASED MECHANISMS IN RECURRENT NEURAL NETWORKS 271

Yehor Lyebyedyev and Mykola Makhortykh. #EUROMAIDAN: QUANTITATIVE ANALYSIS OF MULTI-LINGUAL FRAMING OF 2013-2014 UKRAINIAN PROTESTS ON TWITTER 276

Serhii Rybalchenko. BIG DATA AUTOMATIC SYSTEM OF ANALYSIS AND TRADING ON FINANCIAL MARKETS 281

Mesbaholdin Salami, Farzad Movahedi Sobhani and Mohammad Sadegh Ghazizadeh. DEVELOPMENT OF A NEW ALGORITHM BASED ON SIMULATION – OPTIMIZATION ALGORITHMS FOR BIG DATA MINING TO IMPROVE PREDICTION OF FUTURE ELECTRICITY PRICES IN THE IRANIAN ELECTRICITY MARKET 286

Topic #3. Hybrid Systems of Computational Intelligence 303

Olena Vynokurova, Dmytro Peleshko, Viktor Voloshyn, Semen Oskerko and Yuriy Borzov. HYBRID MULTIDIMENTIONAL WAVELET-NEURO-SYSTEM AND ITS LEARNING USING CROSS ENTROPY COST FUNCTION FOR PATTERNS RECOGNITION 305

Sergej Korjagin, Pavel Klachek and Irina Liberman. DEVELOPMENT OF HYBRID COMPUTATIONAL INTELLIGENCE BY KNOWLEDGE GENESIS METHOD 310

Igor Aizenberg and Kashifuddin Qazi. CLOUD DATACENTER WORKLOAD PREDICTION USING COMPLEX-VALUED NEURAL NETWORKS 315

Yegor Kovylin and Oleg Volkovsky. COMPUTER SYSTEM OF BUILDING OF THE SEMANTIC MODEL OF THE DOCUMENT INFORMATION ON SUBMISSION 322

xii Alina Shafronenko, Yevgeniy Bodyanskiy, Artem Dolotov and Galina Setlak. FUZZY CLUSTERING OF DISTORTED OBSERVATIONS BASED ON OPTIMAL EXPANSION USING PARTIAL DISTANCES 327

Nataliia Kashpruk, Anna Walaszek-Babiszewska and Marek Rydel. ON THE EQUIVALENCE BETWEEN AR FAMILY TIME SERIES MODELS AND FUZZY MODELS IN SIGNAL PROCESSING 331

Sergii Babichev, Volodymyr Lytvynenko, Maxim Korobchynskyi, Jiři Škvor and Maria Voronenko. INFORMATION TECHNOLOGY OF GENE EXPRESSION PROFILES PROCESSING FOR PURPOSE OF GENE REGULATORY NETWORKS RECONSTRUCTION 336

Ali Rekik and Nissen Masmoudi. A NEW APPROACH FOR FORMING A PROBABILISTIC RISK ASSESSMENT MODEL OF INNOVATIVE PROJECT IMPLEMENTATION UNDER RISK 342

Viktor Morozov, Olena Kalnichenko, Andrii Khrutba, Grigory Steshenko and Iuliia Liubyma. MANAGING OF CHANGE STREAMS IN PROJECTS OF DEVELOPMENT DISTRIBUTED INFORMATION SYSTEM 346

Alexander Vlasenko, Olena Vynokurova, Nataliia Vlasenko and Marta Peleshko. A HYBRID NEURO-FUZZY MODEL FOR STOCK MARKET TIME-SERIES PREDICTION 352

Vladyslav Kotsovsky, Fedir Geche and Anatoliy Batyuk. FINITE GENERALIZATION OF THE OFFLINE SPECTRAL LEARNING 356

Nelya Pabyrivska and Viktor Pabyrivskyy. INVERSE PROBLEM FOR TWO-DIMENSIONAL HEAT EQUATION WITH AN UNKNOWN SOURCE 361

Yuliia Tatarinova. AVIA: AUTOMATIC VULNERABILITY IMPACT ASSESSMENT ON THE TARGET SYSTEM 364

Olexiy Azarov, Leonid Krupelnitsky and Hanna Rakytyanska. A FUZZY MODEL OF TELEVISION RATING CONTROL WITH TREND RULES TUNING BASED ON MONITORING RESULTS 369

Yaroslav Sokolovskyy, Maryana Levkovych, Olha Mokrytska and Vitalij Atamanyuk. MATHEMATICAL MODELING OF TWO-DIMENSIONAL DEFORMATION-RELAXATION PROCESSES IN ENVIRONMENTS WITH FRACTAL STRUCTURE 375

Shashi Bhushan, Raju Pal and Svetlana Antoshchuk. ENERGY EFFICIENT CLUSTERING PROTOCOL FOR HETEROGENEOUS WIRELESS SENSOR NETWORK: A HYBRID APPROACH USING GA AND K-MEANS 381

Pavlo Vitynskyi, Roman Tkachenko, Ivan Izonin and Hakan Kutucu. HYBRIDIZATION OF THE SGTM NEURAL-LIKE STRUCTURE THROUGH INPUTS POLYNOMIAL EXTENSION 386

Igor Aizenberg and Zain Khaliq. ANALYSIS OF EEG USING MULTILAYER NEURAL NETWORK WITH MULTI-VALUED NEURONS 392

Galyna Chornous and Ihor Nikolskyi. BUSINESS-ORIENTED FEATURE SELECTION FOR HYBRID CLASSIFICATION MODEL OF CREDIT SCORING 397

xiii Zhengbing Hu and Oleksii Tyshchenko. A HYBRID NEURO-FUZZY ELEMENT: A NEW STRUCTURAL NODE FOR EVOLVING NEURO- FUZZY SYSTEMS 402

Kostyantyn Kharchenko, Oleksandr Beznosyk and Valeriy Romanov. IMPLEMENTATION OF NEURAL NETWORKS WITH HELP OF A DATA FLOW VIRTUAL MACHINE 407

Viktor Mashkov, Jiří Fišer, Volodymyr Lytvynenko and Maria Voronenko. SELF-DIAGNOSIS OF THE SYSTEMS WITH INTERMITTENTLY FAULTY UNITS 411

Dmytro Chumachenko. ON INTELLIGENT MULTIAGENT APPROACH TO VIRAL HEPATITIS B EPIDEMIC PROCESSES SIMULATION 415

Sergii Kondratiuk and Iurii Krak. DACTYL ALPHABET MODELING AND RECOGNITION USING CROSS PLATFORM SOFTWARE 420

Lukasz Wieczorek and Przemyslaw Ignaciuk. INTELLIGENT SUPPORT FOR RESOURCE DISTRIBUTION IN LOGISTIC NETWORKS USING CONTINUOUS-DOMAIN GENETIC ALGORITHMS 424

Ihor Shelevytsky, Victorya Shelevytska, Vlad Golovko and Bogdan Semenov. SEGMENTATION AND PARAMETRIZATION OF THE PHONOCARDIOGRAM FOR THE HEART CONDITIONS CLASSIFICATION IN NEWBORNS 430

Oleksandr Dumin, Dmytro Shyrokorad, Gennadiy Pochanin, Vadym Plakhtii and Oleksandr Prishchenko. SUBSURFACE OBJECT IDENTIFICATION BY ARTIFICIAL NEURAL NETWORKS AND IMPULSE RADIOLOCATION 434

Ivan Tsmots, Oleksa Skorokhoda, Yurii Tsymbal, Taras Tesluyk and Viktor Khavalko. NEURAL-LIKE MEANS FOR DATA STREAMS ENCRYPTION AND DECRYPTION IN REAL TIME 438

Mykola Dyvak, Iryna Oliynyk, Andriy Pukas and Andriy Melnyk. SELECTION THE “SATURATED” BLOCK FROM INTERVAL SYSTEM OF LINEAR ALGEBRAIC EQUATIONS FOR RECURRENT LARYNGEAL NERVE IDENTIFICATION 444

Paweł Tarasiuk and Mykhaylo Yatsymirskyy. OPTIMIZED CONCISE IMPLEMENTATION OF BATCHER’S ODD-EVEN SORTING 448

Topic #4. Machine Vision and Pattern Recognition 453

Dmytro Peleshko, Oleksii Maksymiv, Taras Rak, Orysia Voloshyn and Bohdan Morklianyk. CORE GENERATOR OF HYPOTHESES FOR REAL-TIME FLAME DETECTING 455

Oleksii Gorokhovatskyi and Olena Peredrii. SHALLOW CONVOLUTIONAL NEURAL NETWORKS FOR PATTERN RECOGNITION PROBLEMS 459

Volodymyr Gorokhovatskyi, Yevgenyi Putyatin, Oleksii Gorokhovatskyi and Olena Peredrii. QUANTIZATION OF THE SPACE OF STRUCTURAL IMAGE FEATURES AS A WAY TO INCREASE RECOGNITION PERFORMANCE 464

Ali Al-Ammouri, Hasan Al-Ammori, Arsen Klochan and Anastasia Degtiarova. LOGIC-MATHEMATICAL MODEL FOR RECOGNITION THE DANGEROUS FLIGHT EVENTS 468 xiv Yevgeniy Bodyanskiy, Nonna Kulishova and Daria Malysheva. THE MULTIDIMENSIONAL EXTENDED NEO-FUZZY SYSTEM AND ITS FAST LEARNING FOR EMOTIONS ONLINE RECOGNITION 473

Nataliya Boyko, Nataliya Shakhovska and Oleg Basystiuk. PERFORMANCE EVALUATION AND COMPARISON OF SOFTWARE FOR FACE RECOGNITION, BASED ON DLIB AND OPENCV LIBRARY 478

Andriy Klyuvak, Oksana Kliuvak and Ruslan Skrynkovskyy. PARTIAL MOTION BLUR REMOVAL 483

Sergei Yelmanov and Yuriy Romanyshyn. A GENERALIZED DESCRIPTION FOR THE PERCEIVED CONTRAST OF IMAGE ELEMENTS 488

Maksym Korobchynskyi, Alexander Mariliv, Mihail Slonov and Serhii Mieshkov. METHOD FOR DETERMINING THE RATIONAL TIME INTERVALS FOR DETECTING OBJECTS BY THERMAL IMAGER 494

Vitaliy Boyun. BIOINSPIRED APPROACHES TO THE SELECTION AND PROCESSING OF VIDEO INFORMATION 498

Vyacheslav Moskalenko, Alona Moskalenko, Artem Korobov, Olha Boiko, Serhii Martynenko and Oleksandr Borovenskyi. MODEL AND TRAINING METHODS OF AUTONOMOUS NAVIGATION SYSTEM FOR COMPACT DRONES 503

Kirill Smelyakov, Dmytro Yeremenko, Vitalii Polezhai, Anton Sakhon and Anastasiya Chupryna. BRAILLE CHARACTER RECOGNITION BASED ON NEURAL NETWORKS 509

Sergey Rassomakhin, Alexandr Kuznetsov, Vladimir Shlokin, Ivan Bilozetsev and Roman Serhiienko. MATHEMATICAL MODEL FOR THE PROBABILISTIC MINUTIA DISTRIBUTION IN BIOMETRIC FINGERPRINT IMAGES 514

Yevgeniy Bodyanskiy, Iryna Pliss, Daria Kopaliani and Olena Boiko. DEEP 2D-NEURAL NETWORK AND ITS FAST LEARNING 519

Andriy Yerokhin, Valerii Semenets, Alina Nechyporenko, Oleksii Turuta and Andrii Babii. F-TRANSFORM 3D POINT CLOUD FILTERING ALGORITHM 524

Petr Hurtik, David Číž, Oto Kaláb, David Musiolek, Petr Kočárek and Martin Tomis. SOFTWARE FOR VISUAL INSECT TRACKING BASED ON F-TRANSFORM PATTERN MATCHING 528

Ievgen Gorovyi, Vitalii Vovk, Maksim Shevchenko, Valerii Zozulia and Dmytro Sharapov. EMBEDDED VISION MODULES FOR TEXT RECOGNITION AND FIDUCIAL MARKERS TRACKING 534

Roman Martsyshyn, Yulia Miyushkovych, Lubomyr Sikora, Natalya Lysa and Rostyslav Tkachuk. TECHNOLOGY OF REMOTE RECOGNITION THE DART-ARROW ON THE TARGET 538

Anatoliy Kovalchuk and Nataliia Lotoshynska. ELEMENTS OF RSA ALGORITHM AND EXTRA NOISING IN A BINARY LINEAR-QUADRATIC TRANSFORMATIONS DURING ENCRYPTION AND DECRYPTION OF IMAGES 542

xv Sergii Mashtalir, Volodymyr Mashtalir and Mykhailo Stolbovyi. REPRESENTATIVE BASED CLUSTERING OF LONG MULTIVARIATE SEQUENCES WITH DIFFERENT LENGTHS 545

Sergii Mashtalir, Olena Mikhnova and Mykhailo Stolbovyi. SEQUENCE MATCHING FOR CONTENT-BASED VIDEO RETRIEVAL 549

Oleh Berezsky, Oleh Pitsun, Natalia Batryn, Kateryna Berezska, Nadiya Savka and Taras Dolynyuk. IMAGE SEGMENTATION METRIC-BASED ADAPTIVE METHOD 554

Igor Malets, Oleksandr Prydatko, Vasyl Popovych and Andriy Dominik. INTERACTIVE COMPUTER SIMULATORS IN RESCUER TRAINING AND RESEARCH OF THEIR OPTIMAL USE INDICATOR 558

Roman Melnyk and Yurii Kalychak. ANALYSIS OF METAL DEFECTS BY CLUSTERING THE SAMPLE AND DISTRIBUTED CUMULATIVE HISTOGRAM 563

Sergei Yelmanov and Yuriy Romanyshyn. IMAGE CONTRAST ENHANCEMENT USING A MODIFIED HISTOGRAM EQUALIZATION 568

Yevhen Zadorozhnii, Yevhenii Tverdokhlib, Tetiana Fedoronchak and Natalia Myronova. DEVELOPMENT AND IMPLEMENTATION OF HUMAN FACE ALIGNMENT AND TRACKING IN VIDEO STREAMS 574

Mariya Nazarkevych, Ivanna Klyujnyk and Hanna Nazarkevych. INVESTIGATION THE ATEB-GABOR FILTER IN BIOMETRIC SECURITY SYSTEMS 580

Bohdan Durnyak, Oleksandr Tymchenko Jr., Oleksandr Tymchenko and Bohdana Havrysh. APPLYING THE NEURONETCHIC METHODOLOGY TO TEXT IMAGES FOR THEIR RECOGNITION 584

Volodymyr Sherstiuk, Marina Zharikova and Igor Sokol. FOREST FIRE MONITORING SYSTEM BASED ON UAV TEAM, REMOTE SENSING, AND IMAGE PROCESSING 590

Yuriy Furgala, Yuriy Mochulsky and Bohdan Rusyn. EVALUATION OF OBJECTS RECOGNITION EFFICIENCY ON MAPES BY VARIOUS METHODS 595

Tetiana Gladkykh, Taras Hnot and Roman Grubnyk. MUSIC CONTENT SELECTION AUTOMATION 599

Galyna Shcherbakova, Victor Krylov, Maksym Gerganov, Svitlana Antoshchuk, Marina Polyakova and Anatoly Sachenko. AREAL MULTISTART METHOD OF OPTIMIZATION FOR IMAGE RECOGNITION 605

Maksym Kovalchuk, Vasyl Koval, Anatoliy Sachenko and Diana Zahorodnia. DEVELOPMENT OF REAL-TIME FACE RECOGNITION SYSTEM USING LOCAL BINARY PATTERNS 609

Panels 615

Author’s Index xvii

xvi IEEE Second International Conference on Data Stream Mining & Processing August 21-25, 2018, Lviv, Ukraine Information Technology of Process Modeling in the Multiparameter Systems

Yevgen Martyn Solomija Ljaskovska Department of Project Management, Information Department of designing and operation of machines Technologies and Telecommunications Lviv Polytechnic National University Lviv State University of Life Safety Lviv, Ukraine Lviv, Ukraine [email protected] [email protected] Igor Malets Oleksandr Prydatko Department of Project Management, Information Department of Project Management, Information Technologies and Telecommunications Technologies and Telecommunications Lviv State University of Life Safety Lviv State University of Life Safety Lviv, Ukraine Lviv, Ukraine [email protected] [email protected]

Abstract— Information graphics technologies of designing diverse purposes, which, using a finite number of models of the processes of multiparameter technical systems assumptions, are reduced to two-parameter systems. To are argued and developed in order to increase the effectiveness them, as an example of IT technology in improving the of determining the influence of many operating parameters on quality of educational processes in the training of rescue their dynamics. The requirements for model designs are workers [3], widely known biological systems, the operating formulated on the basis of the formed numbers of different measurements of multidimensional spaces. Its implementation elements of which are related to the model Lotka-Volterra, is proposed by a complex combination of the mathematical fire and technical systems, which, by a number of description of the parameters interconnection and the use of assumptions, are reduced to dual systems [4], direct or rational geometry. The features of the implementation of alternating current motors when powered from an electric models are shown with the number of possible assumptions network of infinite power [5] and others. Such models reduced. corresponded or sometimes correspond to the operational requirements within the limits of acceptable for engineering Keywords— information technology, modeling, multi- calculations of accuracy. parameter system, processes, applied geometry

dy 1 I. INTRODUCTION =−−();A Ry cx dt a Open systems that surround a person and part of which it (1) dx 1 is, are multiparameter. Their research, the discovery of useful =−()By D features and the creation of more perfect directed intellectual dt b activity of a scientist. The isolation of the characteristic features of the system leads to the identification of its The growth of requirements to systems, primarily to the significant parameters by accepting one or another number of technical, the maintenance of the technological requirements assumptions. As the historical development of information for the accuracy of the parameters prompts the development of modern methods and models, research, and therefore, on technology neglected many factors of influence on the the contrary, reducing the number of assumptions, and as a behavior of the system was reduced to the adoption of a consequence, requires the development and use of new minimum number of significant parameters that distinguish information technology as an environment for the the studied system among others. In the process of creating implementation of modern models, methods, algorithms for and designing target models, it is important to take into calculating the values of parameters of multiparameter consideration the ways in which the parameters of individual objects, systems or processes. For example, research of the units of the studied systems are presented, for example, using dynamics of a mechanism with a DC motor is possible based clearly defined or fuzzy sets [1]. Construction of models of on a mathematical model using (1), which reproduces systems with the ability to study not only static but also processes with sufficient accuracy. However, the higher dynamic characteristics requires the involvement of both harmonics of the power supply system of the thyristor classical and new methods [4]. converter significantly affect the quality of communication. This approach made it possible to create almost identical Research of such influences requires the development and models for different physical entities and apply similar use of other specialized models. research methods to them, in particular, mechanical [12, 6] In the scientific research [14], theoretically based or mechatronic systems [11]. For example, the system of two propositions concerning the modeling and development of differential equations of the first order with defining and certain types of equipment with the use of artificial neural constant parameters aAbBRcD,,,,,, is a basic networks are proposed. mathematical model of many multi-parameter systems of

978-1-5386-2874-4/18/$31.00 ©2018 IEEE 177 The proposed approach to the scientific and practical In the presence of a positively directed axis, for the process of model development is based on the fact that a simulation of the flow of processes in time, space half-space wide class of systems reflects its behavior in universal is used as a space of multiparameter state with the models, which often differ in system variables and parameters of the technical system (Fig. 2 a, c). Projection of parameters [7]. the state space in a direction parallel to its axis, we obtain a phase space (Fig. 2 d) or a phase space with two and The development of models in the classical version interconnected differential equations of essential parameters occurs sequentially from physical representations to the (Fig. 2 b). detection of the method of representing the interconnections of significant parameters with a rational number of assumptions. Due to the change of the philosophy of information technology, the transition to computerized means of scientific research, the development of the research process takes place due to the complexity of models taking into account sufficient for engineering calculations of the number of assumptions regulated by the capabilities of the computer, provided that all the relationships of parameters that are submitted mathematical or geometric means, equal regardless of their importance [14,15].

II. SCIENTIFIC AND APPLIED PRINCIPLES OF CONSTRUCTING а) b) MODELS The process of accumulation of human knowledge is continuous and inexhaustible. One of the means of cognition is simulation. The process of modeling in science is carried out for a thorough study of the properties of the object, the identification of the laws of the mutual influence of the connections of its parameters and the impact on them to optimize the functioning and establishment of useful properties. In modeling, numerical values of parameters, are c) d) presented by graphic dependencies [16] which are used in the analysis of the system [17, 18]. Fig. 2. Spaces of state and phase spaces of multiparameter technical The simulation process consists of two active elements, a systems simulation object and, in fact, its model, which in essence represents a dual system. Formation of an object model can The integral curves (Fig. 2a) and the phase trajectories be given, in particular, as follows (Fig. 1). (Fig. 2b) are presented in the two-dimensional coordinate planes of the multidimensional space of the state of the technical system with measurements of the variables, for example, the time, current and frequency dependencies of the electric motor from time (Fig. 3) .

Fig. 1. The diagram of process for the development model of object МО

The process of constructing an object model begins because of the need to obtain additional knowledge about the object, based on the infinite fluidity of knowledge, which can be given by an infinite-dimensional linear vector space, mutually perpendicular orcs of which are the constituent parts of general knowledge. Such a space as the central in the process of modeling the CP space, interacting with the scope of the parameters of the object PP, forms a modeling space MP. Models of objects MO, configured to conduct research а) b) on the interconnection of their parameters, are represented by real numbers. Consequently, by immersing the modeling Fig. 3. Geometric model and complex drawing for dependencies of engine space MP in the field of real numbers PD, we obtain a model parameters of the object of the study of MO as a common object of the The rotation of the coordinate planes relative to the modeling space MP and the field of real numbers PD. The coordinate axes gives the Cartesian coordinate system (Fig. model of the object of the MO is realized mainly in the 3b) with the combined planes of projections as a complex multidimensional Euclidean space, the dimensionality of drawing of the spatial geometric model of the dependencies which as a derivative of the dimensionality of the central of the engine parameters. space CP is determined by the number of independent essential parameters of the investigated object.

178 The overlap of geometric images in the combined planes planes of a three-dimensional space determine the position of of projections can be avoided using the proposed integrated a three-spatial integral curve: the curves form guides of two- drawings. For a four-dimensional Euclidean space, we map dimensional cylinders, whose intersection is determined by a all the fields in two-dimensional coordinate planes (Fig. 4). one-dimensional multidimensional, linear curve, three- dimensional space. Two of the six coordinate planes of the four-dimensional space pair the links of four parameters, for example, and and. Such connections in the form of plane curves determine the position of guides of three-dimensional cylinders whose intersections in the four-dimensional Euclidean space determine the position of the two- dimensional multidimensional. In addition, there is a change in time only for the parameter z. The minimum number of d multi-species, which determines the completeness of the mapping curves of the dimension of the transition multiparameter process, determine, drawing dependence [10]: Fig. 4. Comprehensive drawing of a four-dimensional Euclidean space d This drawing, provided the smallest number of η =−− (2)  mndi (1), coordinate two-dimensional planes, has the form (Figs. 5a, i=1 b), with a generalization of it on three-dimensional coordinate planes (Fig. 5c, d). For the same measurements of multidimensional cylinders with guides with integral curves, their number is determined from (3):

n −1 d = (3) nm−

In three-dimensional space the number of two- dimensional cylinders of measurements, is. For a four- dimensional space of state, when, we have.

а) b)

c) d)

Fig. 5. Variants of complex drawings for computer visualization

The integral curves of the process of changing the time of two parameters are given by the three-space line of the Euclidean three-dimensional space [8,9]. To form a multi- spatial space line, it is quite sufficient that the change of each of the parameters reflected by the integral curve in the corresponding coordinate plane of the multidimensional state space. Each integral curve represents the direction of a multidimensional cylinder, the intersection of which forms Fig. 6. Formation of the integral curve a in the space of the Oxyzt state the geometric multidimensional space. For its unambiguous definition it is necessary to have a minimal, but sufficient We set the completeness of the task of the integral curve number of integral curves in coordinate planes, which of the four-dimensional space Oxyzt on the basis of (3). We determines the completeness of representation of the will accept three integral curves as guides of three- geometric image. Two integral curves in two coordinate dimensional cylinders with generating two-dimensional

179 planes (Fig. 6). The interconnection of three-dimensional cross-section n −1 - dimensional surface by the next cylinders forms a curve as a one-dimensional four-spatial multidimensional cylinder decreases by one. manifold of this space. Each point of the curve, for example, 3 is formed by the cross section of the corresponding III. IT-IMPLEMENTATION OF GEOMETRIC MODELING OF geometric images. PROCESSES

3D dimensional plane 33ttxtzty 3 3 mГП =3 with the foll 3t on the axis 0t crosses each of three three-dimensional ∏ cylinders of dimensionality mc = 3 in a plane T i =+ −= dimensionality rmmncГП n2 . The dimension of the geometric images rt of the intersection of the hyperplane and each of the guides xxtyytzzt===(), (), () =+ −= rmmtnГП n0 = where mn 1 is the dimension of the guide of the three- dimensional cylinder. These geometric images are given points 3,3,3tx tz ty . The planesТПyz3 і ТПxy3 , ТПxz3 і ТПxy3 ,

ТПxz3 і ТПxz3 intersect in points 3,3txz tyz , and 3txy the set of which defines the position of the intersection of the two- dimensional and three-dimensional hyperplanes. The point

3txz determines the position of the 3y intersection of the two- dimensional plane ТПyz3 and the three-dimensional plane 33ttzТП xy3 The point3txy determines the position of the line 3z of intersection of the two-dimensional plane ТПyz3 and 33ttyТП xz3 the point 3tyz determines the position of the intersection 3x of the two-dimensional plane ТПxy3 and the three-dimensional hyperplane 33ttyТП yz3 . Direct

3,3x y and 3z intersect at point 3, the set of which forms the curve of the four-dimensional space of the Oxyzt state of the technical system, and substantiates the assertion about the completeness of representation of the integral curves of multidimensional phase and space of the state of the technical systems. Trajectories of phase spaces are obtained by projection of integral curves of state space in the direction parallel to the axis Ot of this space, which describe the transition process in a multiparameter system by differential equations of the first order

dxi = f (xx , ,..., x ,..., xt, ). (4) Fig. 7. Diagram of ІТ-modelling dt ijn12

Finally, dimensionality of rkn multi-species as section The integral curve, for example xx= (t) , is a directing 11 n −1- dimensional surface using n - dimensional cylinder is − line of the cylinder with generator n 1 - dimensional rnn=−(1)1 − =, which is the dimensional n - spatial one- subspaces, which are parallel to n −1 - dimensional kn dimensional line n +1 - dimensional space of the system coordinate subspace Ox x... x ... x . The dimension of each 23 in state. Projection of the curve as a guide of the projection =+ − = cylinder is knni 1( 1) . cylinder of dimensionality l = 2 in the subspace of variables Ox x... x ... x as n - dimensional phase space we get a Dimensionality of r section of two arbitrary cylinders 12 in k12 geometric image of dimensionality == + with dimension kkn12 n 1 - dimensional state space Ox x x... x ... x t is 123 in =+ − + =, =+−+=+−−=− qln(n 1) 1 rkknk12 1 2 (1) nnnn 1 1, аnd its section using the third cylinder with dimensionality kn= forms 3 which represents the dimensionality of the phase trajectory multi-species with dimensionality as a projection of the integral state space curve rrkn= +−+=−(1)2 n . The dimensionality of the kk3123 Ox12 x... xin ... x t in the subspace of variables Ox12 x... xin ... x as the phase space of the system.

180 Geometric modeling of processes (Fig. 7) involves the time tspan of the use of options ,,odephas2 and / selection of all stages of the basic stages, which include, in or ,,odephas3 commands options , it is possible to obtain particular, the choice of input parameters and the use for simulation of the numbers of the corresponding solutions as projections of the phase trajectory (5) in two- measurements (real, complex, etc.), the method of geometric dimensional (Fig. 8a), three-dimensional (Fig. 8b) projection planes and their combination (Fig. 8c ). modeling, design and research of the model with presentation of research results in the Euclidean space. IV. PRACTICAL IMPLEMENTATION OF GEOMETRIC SIMULATION OF PROCESS

A. Drive with asynchronous electric fire pump The drive of low-power fire pumps is carried out using asynchronous short-circuited motors. The joint work is investigated by the analysis of so-called dynamic mechanical characteristics MM= ()ω in a two-dimensional space. Such curves represent one of the projections of an integral curve in a two-dimensional plane (Fig. 9) as one of the solutions of the system (6) of differential equations [3]

a)

Fig. 9. Projection of the integral induction motor curve

b) dψ х1 =+−+ukcosγωψωαψωαψ; dt mys01 0х 1 0sr х 2 dψ у1 =−−+uksinγωψωαψωαψ; dt mxsysry01 0 1 0 2 dψ x2 =−−+ψωωωαψωαψ yrxrsx20() 0 2 0k 1; dt (6) c) dψ у2 =−ψωωωαψωαψ() − − + k ; Fig. 8. Projections of the phase trajectory dt xu20201 ry rsy 3 pkω The choice of the calculator determines to a greater M =−02()ψψ ψψ ; σ xy21 xy 12 extent the effectiveness of the research. Taking into account 2 xs the possibility of projection of phase trajectories into two- ω dp=− and three-dimensional phase planes, it is effective to use the ().MMc tools of computer mathematics Matlab. dt I ψψψψ An example of the realization of the projection of the where uuuuuvu11,, 2 , v 2, uvu1122,, , v, phase trajectory of the solution of the differential equation, in particular, of the fifth order rr x2 ωωωα,,,===−=−12 ; σ , σ 10 1kk , 0 ksσσr rs xxsr xx sr dy5432 dy dy dy dy ++++=0 (5) rrxxx,,,, – asynchronous motor parameters. dt5432 dt dt dt dt 120s r M, MS - electromagnetic and moment of loading of indicates the need to reduce its order and to form a asynchronous engine; p - number of pairs of poles of the system of differential equations of the first order. For given asynchronous motor; And - moment of inertia of the system, values of the initial conditions y0 and the integration brought to the shaft of the induction motor.

181 Projections of the phase trajectory of the transition graphic capabilities of IT technologies. Further research process in the asynchronous motor (Fig.10) make it possible relates to IT technologies for geometric modeling of to conduct research of all its determining parameters. processes by reducing the number of equations describing the state of a system with the use of numbers of higher measurements, in particular complex numbers.

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182 Author’s Index

A Chumachenko D., 415 Chupryna A., 509 Aizenberg I., 315, 392 Churyumov G., 183 Al-Ammouri A., 468 Chyrun L., 139 Al-Ammouri H., 468 Chyrun Lu., 139 Ali Rekik, 107, 342 Číž D., 528 Alieksieiev V., 32, 94 Ambach D., 212 D Ambach O., 212 Andrushchenko V., 17 Darnapuk Ye., 119 Antoshchuk S., 381, 605 Degtiarova A., 468 Artykulna N., 50 Deineko A., 7, 171 Atamanyuk V., 375 Demchuk A., 128 Atkinson K., 25 Deriuga I., 218 Azarov O., 369 Didyk O., 187 Dolotov A., 327 B Dolynyuk T., 554 Dominik A., 558 Babichev S., 336 Dorosh V., 102 Babii A., 524 Doroshenko A., 231 Balagura I., 17 Dosyn D., 145 Basalkevych O., 60 Dumin O., 434 Basalkevych O., 60 Durnyak B., 584 Basystiuk O., 478 Dvornik O., 119 Batryn N., 554 Dyvak M., 444 Batyuk A., 98, 151, 356 Beglytsia V., 70 F Berezska K., 554 Berezsky O., 554 Farzad Movahedi Sobhani, 286 Berko A., 32 Fedoronchak T., 574 Beznosyk O., 407 Filipenko O., 13 Bhushan Sh., 381 Fišer J., 411 Bidyuk P., 70 Furgala Yu., 595 Bilozetsev I., 514 Bodyanskiy Ye., 3, 7, 113, 327, 473, 519 G Boiko Olh., 503 Boiko Ol., 519 Garkavtsev M., 75 Boiko T., 271 Geche F., 151, 356 Borovenskyi O., 503 Gerasin O., 44 Borzov Yu., 157, 187, 305 Gerganov M., 605 Boyko N., 478 Gladkykh T., 599 Boyun V., 498 Glybovets M., 207 Brazhnykova Ye., 3 Golovko V., 102 Briukhovetskyi O., 227 Golovko V., 430 Bulakh V., 198 Golub S., 223 Burak N., 157 Gordon B., 171 Burov Ye., 128 Gorokhovatskyi O., 459, 464 Gorokhovatskyi V., 464 C Gorovyi Ie., 236, 534 Gostev A., 75 Charhad M., 107 Gozhyj A., 70 Chervoniak Ye., 236 Grinberg G., 193 Chornous G., 397 Grubnyk R., 599 Chuiko G., 119

xvii H Krylov V., 605 Kulishova N., 473 Havrysh B., 584 Kutucu H., 386 Hnot T., 599 Kuznetsov A., 514 Holovatch Yu., 84, 241 Kynash Yu., 162 Horpenko D., 56 Hu Zh., 402 L Hurtik P., 528 LLamonova N., 75 I Lande D., 17 Levkovych M., 375 Ignaciuk P., 424 Lewoniewski W., 21 Izonin I., 386 Li G., 25 Liberman I., 310 K Litovchenko O., 3 Liubyma Iu., 346 Kaláb O., 528 Ljaskovska S., 157, 177 Kalinina I., 70 Lotoshynska N., 542 Kalnichenko O., 346 Lozynska O., 145 Kalychak Yu., 563 Lozynskyy A., 251 Kashifuddin Qazi, 315 Lyebyedyev Ye., 276 Kashpruk N., 331 Lysa N., 538 Kenna R., 241 Lytvyn V., 128, 145 Khairova N., 21 Lytvynenko V. , 336, 411 Khaliq Z., 392 Lyubchyk L., 193 Kharchenko K., 407 Lyubinets V., 271 Khavalko V., 438 Khaya A., 166 M Khlamov S., 227 Khrutba A., 346 Makhortykh M., 276 Khymytsia N., 223 Maksymiv O., 455 Kirichenko L., 198 Malets I., 177, 259, 558 Kis Ia., 139 Malets R., 259 Klachek P., 310 Malyar M., 65 Kliuvak O., 483 Malysheva D., 473 Klochan A., 468 Manakova N., 166 Klyujnyk I., 580 Mariliv A., 494 Klyuvak A., 483 Martsyshyn R., 538 Kočárek P., 528 Martyn Ye., 177 Koman B., 134 Martynenko S., 503 Komar M., 102 Mashkov V., 411 Kondratenko N., 38 Mashtalir S., 545, 549 Kondratenko Yu., 38, 44 Mashtalir V., 545 Kondratiuk S., 420 Melnyk A., 444 Kopaliani D., 519 Melnyk R., 563 Korjagin S., 310 Mesbaholdin Salami, 286 Korobchynskyi M., 336, 494 Mieshkov S., 494 Korobov A., 503 Mikheev I., 202 Kotsovsky V., 356 Mikhnova O., 549 Koval V., 609 Minialo V., 251 Kovalchuk A., 542 Miyushkovych Y., 538 Kovalchuk M., 609 Mochulsky Yu., 595 Kovylin Ye., 322 Mohammad Sadegh Ghazizadeh, 286 Kozina Yu., 56 Mokrytska O., 375 Krak Iu., 420 Morklianyk B., 455 Kravets P., 123 Morozov V., 50, 346 Kriukova G., 207 Moskalenko A., 503 Krupelnitsky L., 369 Moskalenko V., 503

xviii Mryglod O., 241 Pukas A., 444 Mulesa O., 151 Putrenko V., 79 Mulesa P., 3 Putyatin Ye., 464 Musiolek D., 528 Myronova N., 574 R

N Radivilova T., 198 Radyvonenko O., 218 Nazarenko S., 79 Rak T., 455 Nazarkevych H., 580 Rakytyanska H., 369 Nazarkevych M., 580 Rashkevych Yu., 113 Nechyporenko A., 524 Rassomakhin S., 514 Nevlydov I., 13 Riepin V., 7 Nicholas D., 271 Riznyk O., 162 Nikolskyi I., 397 Roenko A., 236 Nissen Masmoudi, 342 Romanov V., 407 Noga Yu., 162 Romanyshyn I., 251 Romanyshyn Yu., 488, 568 O Rudakova H., 247 Rusyn B., 251, 595 Oborska O., 145 Rybalchenko S., 281 Oliynyk I., 444 Rydel M., 331 Omelchuk A., 247 Osadchyi V., 218 S Oskerko S., 305 Ostakhov V., 50 Sachenko A., 102, 605, 609 Sadek M., 107 P Sakhon A., 509 Savanevych V., 227 Pabyrivska N., 171, 361 Savka N., 554 Pabyrivskyy V., 361 Semenets V., 524 Pal G., 25 Semenov B., 430 Pal R., 381 Serhiienko R., 514 Palchykov V., 84 Setlak G., 327 Partyka S., 183 Shafronenko A., 327 Pashynska N., 79 Shakhovska N., 478 Pavlyshenko B., 255 Sharapov D., 534 Peleshko D., 305, 113, 455 Sharkadi M., 65 Peleshko M., 352 Shcherbakova G., 605 Peredrii O., 459, 464 Shelevytska V., 430 Perova I., 3 Shelevytsky I., 430 Petrasova S., 21 Sherstiuk V., 590 Pitsun O., 554 Shevchenko M., 534 Plakhtii V., 434 Shlokin V., 514 Pliss I., 171, 519 Shynkarenko H., 259 Pochanin G., 434 Shyrokorad D., 434 Pohorelov A., 227 Sikora L., 538 Polezhai V., 509 Sipko O., 265 Polishchuk V., 65 Sirenko F., 236 Polyakova M., 605 Skorokhoda O., 438 Polyvoda O., 247 Skrynkovskyy R., 134, 483 Ponomaryova G., 13 Škvor J., 336 Popovych V., 558 Slonov M., 494 Povshuk O., 162 Smelyakov K., 509 Prishchenko O., 434 Smotr O., 157, 187 Prokopenko D., 166 Snytyuk V., 265 Prydatko O., 177, 187, 558 Sokol I., 590 Puchala D., 88 Sokolovskyy Ya., 375

xix Solotvinskyy I., 187 Volkovsky O., 322 Steshenko G., 346 Voloshchuk V., 151 Stokfiszewski K., 88 Voloshyn Ol., 65 Stolbovyi M., 545, 549 Voloshyn Or., 455 Suprun O., 265 Voloshyn V., 305 Szymanski Z., 70 Voronenko M., 336, 411 Vovk V., 534 T Vynokurova O., 113, 305, 352 Vysotska V., 128, 139, 145 Tarasiuk P., 448 Tatarinova Yu., 364 W Tesluyk T., 438 Tkachenko R., 386 Walaszek-Babiszewska A., 331 Tkachov V., 183 Wieczorek L., 424 Tkachuk R., 538 Wieloch K., 88 Tokarev V., 183 Y Tomis M., 528 Tsmots I., 438 Yakobchuk P., 102 Tsymbal Yu., 438 Yatsymirskyy M., 88, 448 Turuta O., 524 Yelmanov S., 488, 568 Tverdokhlib Ye., 574 Yeremenko D., 509 Tymchenko O., 584 Yerokhin A., 524 Tymchenko O. Jr., 584 Yuzevych V., 134 Tyshchenko O., 402 Z V Zadorozhnii Ye., 574 Vahin P., 259 Zahorodnia D., 609 Vergeles A., 166 Zaporozhets Yu., 44 Verhun V., 98 Zavgorodnia O., 202 Vitynskyi P., 386 Zavgorodnii I., 3 Vlasenko A., 352 Zayika O., 171 Vlasenko N., 352 Zharikova M., 590 Voityshyn V., 98 Zhernova P., 7, 171 Volkova M., 13 Zozulia V., 534 Volkova N., 56 Zrigui M., 107 Volkova V., 218 Zyma O., 202

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Chief Editors

Olena Vynokurova, Dmytro Peleshko

Editorial Board

V. Vysotska, Yu. Borzov

Printing by Publishing House of Lviv Polytechnic National University 2 Kolessa str., Lviv, Ukraine, 79013, [email protected]

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