An intelligent sensor platform with an open architecture for monitoring and controlling cyber-physical

• Autorzy: Rymarczyk Tomasz , Król Krzysztof , Zawadzki Andrzej , Oleszek Michał , Kłosowski Grzegorz

Identyfikatory

DOI

10.15199/48.2021.03.27

Warianty tytułu

PL

Inteligentna platforma sensorowa o architekturze otwartej do monitorowania i sterowania systemów cyber- fizycznych

• Języki publikacji: EN

Abstrakty

EN

The work includes the process of developing intelligent sensors, intelligent mechanisms for monitoring and controlling industrial processes using modern measurement techniques, process tomography, vision systems, motion and temperature sensors, as well as advanced data processing methods.

PL

Praca obejmuje proces opracowania inteligentnych czujników, inteligentnych mechanizmów monitorowania i sterowania procesami przemysłowymi z wykorzystaniem nowoczesnych technik pomiarowych, tomografii procesowej, systemów wizyjnych, czujników ruchu i temperatury, a także zaawansowanych metod przetwarzania danych.

Słowa kluczowe

EN

electrical capacitance tomography; cyber-physical systems sensor; process tomography

PL

elektryczna tomografia pojemnościowa; czujnik systemu cyber-fizycznego; tomografia procesowa

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 3
• Strony: 141--145
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Rymarczyk Tomasz

• University of Economics and Innovation, Projektowa 4, Lublin, Poland
• Research & Development Centre Netrix S.A.

autor

Król Krzysztof

• Research & Development Centre Netrix S.A.

autor

Zawadzki Andrzej

• Research & Development Centre Netrix S.A.

autor

Oleszek Michał

• Research & Development Centre Netrix S.A.

autor

Kłosowski Grzegorz

• Lublin University of Technology, Nadbystrzycka 38, Lublin

Bibliografia

• [1] Charpentier P., Véjar A., From Spatio-Temporal Data to Manufacturing System Model, J Control Autom Electr Syst, vol. 25, no. 5, pp. 557–565, 2014
• [2] Romanowski, A. Contextual Processing of Electrical Capacitance Tomography Measurement Data for Temporal Modeling of Pneumatic Conveying Process. In Proceedings of the 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), Poznan, Poland, 9–12 September 2018; 283–286
• [3] Grudzien, K.; Chaniecki, Z.; Romanowski, A.; Sankowski, D.; Nowakowski, J.; Niedostatkiewicz, M. Application of twin-plane ECT sensor for identification of the internal imperfections inside concrete beams. In Proceedings of the 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Taipei, Taiwan, 23–26 May 2016; 1–6
• [4] Romanowski, A. Big Data-Driven Contextual Processing Methods for Electrical Capacitance Tomography. IEEE Trans. Ind. Informatics, 15 (2019), 1609–1618
• [5] Kryszyn J., Smolik W., Toolbox for 3d modelling and image reconstruction in electrical capacitance tomography, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ), 2017, (1), 137-145
• [6] Dušek J., Hladký D., Mikulka J., Electrical Impedance Tomography Methods and Algorithms Processed with a GPU, In PIERS Proceedings, 2017, 1710-1714
• [7] Rymarczyk T., Characterization of the shape of unknown objects by inverse numerical methods, Przegląd Elektrotechniczny, 88 (2012), No. 7b, 138-140
• [8] Rymarczyk T., Kłosowski G., Tchórzewski P., Cieplak T., Kozłowski E.: Area monitoring using the ERT method with multisensor electrodes, Przegląd Elektrotechniczny, 95 (2019), No. 1, 153-156
• [9] Rymarczyk T., Nita P., Vejar A., Woś M., Stefaniak B., Adamkiewicz P.: Wearable mobile measuring device based on electrical tomography, Przegląd Elektrotechniczny, 95 (219), No. 4, 211-214
• [10] Kłosowski G., Rymarczyk T., Kania K., Świć A., Cieplak T., Maintenance of industrial reactors based on deep learning driven ultrasound tomography, Eksploatacja i Niezawodnosc – Maintenance and Reliability; 22 (2020), No 1, 138–147
• [11] Kłosowski G., Rymarczyk T., Wójcik D., Skowron S., Adamkiewicz P., The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification, Electronics, 9 (2020), No. 9, 1452
• [12] Kłosowski G., Rymarczyk T., Cieplak T., Niderla K., Skowron Ł., Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography, Sensors, 20 (2020), No. 11, 3324
• [13] Koulountzios P., Rymarczyk T., Soleimani M., A quantitative ultrasonic travel-time tomography system for investigation of liquid compounds elaborations in industrial processes, Sensors, 19 (2019), No. 23, 5117
• [14] Korzeniewska, E., Sekulska-Nalewajko, J., Gocawski, J., Droż Dż, T., Kiebasa, P., Analysis of changes in fruit tissue after the pulsed electric field treatment using optical coherence tomography, EPJ Applied Physics, 91 (2020), No.3, 30902
• [15] Sekulska-Nalewajko, J., Gocławski, J., Korzeniewska, E., A method for the assessment of textile pilling tendency using optical coherence tomography, Sensors (Switzerland), 20 (2020), No.13, 1-19, 3687
• [16] Pawłowski, S., Plewako, J., Korzeniewska, E., Field modeling the impact of cracks on the electroconductivity of thin-film textronic structures, Electronics (Switzerland), 9 (2020), No.3, 402
• [17] Kosinski, T.; Obaid, M.; Wozniak, P.W.; Fjeld, M.; Kucharski, J. A fuzzy data-based model for Human-Robot Proxemics. In Proceedings of the 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), New York, NY, USA, 26–31 August 2016; 335–340
• [18] Fraczyk, A.; Kucharski, J. Surface temperature control of a rotating cylinder heated by moving inductors. Appl. Therm. Eng., 125 (2017), 767–779
• [19] Goetzke-Pala A., Hoła A., Sadowski Ł., A non-destructive method of the evaluation of the moisture in saline brick walls using artificial neural networks. Archives of Civil and Mechanical Engineering, 18 (2018), No. 4, 1729-1742
• [20] Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J., Assessment model of cutting tool condition for real-time supervision system, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 21 (2019), No. 4, 679–685
• [21] Kozłowski E., Mazurkiewicz D., Żabiński T., Prucnal S., Sęp J., Machining sensor data management for operation-level predictive model. Expert Systems with Applications 159 (2020), 1-22
• [22] Kozłowski E., Kowalska B., Kowalski D., Mazurkiewicz D., Survival Function in the Analysis of the Factors Influencing the Reliability of Water Wells Operation. Water Resources Management 33 (2019), No. 14, 4909-4921
• [23] Daniewski K., Kosicka E., Mazurkiewicz D., Analysis of the correctness of determination of the effectiveness of maintenance service actions. Management and Production Engineering Review 9 (2018); No. 2, 20-25.
• [24] Kozłowski E., Mazurkiewcz D., Kowalska B., Kowalski D. – Application of multidimensional scaling method to identify the factors influencing on reliability of deep wells. In: Burduk A., Chlebus E., Nowakowski T., Tubis A. (eds) Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, 835 (2018), 56-65

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).