Ústav teoretické a experimentální elektrotechniky

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    Modification of Insulating Properties of Surfaces of Dielectric High-Voltage Devices Using Plasma
    (MDPI, 2024-05-22) Pernica, Roman; Klíma, Miloš; Londák, Pavel; Fiala, Pavel
    Plasma discharges under atmospheric pressure are applicable for modifying the electrical properties of dielectric surfaces. The aim of the plasma discharge treatment of such surfaces is to design a procedure so that its characteristic parameters improve the resulting levels of the breakdown electrical strength Eb when tested under pulsed or alternating electrical voltages. In this research, a set of functional experiments performed by using plasma in samples of two types of materials (thermoset, thermoplastic) were processed and evaluated, and the resulting effect of the magnitude of the breakdown electrical voltage, electrical intensity, and electrical conductivity of the surface were compared. A slit plasma chamber, previously described and parameterized, was employed to treat the surface of the dielectric samples. The surface structure was modified via plasma discharge without precursors, and methodologies were developed to evaluate these modifications with respect to the change in the electrical strength parameters of the insulator surface. Subsequently, the surface strength of the affected and unaffected samples was measured and evaluated as a function of exposure time, and the stability of the modification was assessed. The first methodical test showed that plasma discharge without precursors improved the long-term surface electrical strength of the dielectric surface. The test and its parameters were carried out with respect to feasibility in an industrial environment.
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    Effect of high voltage on the development of the plant tissue
    (Electrotechnical Institute, 2015-05-14) Vlachová Hutová, Eliška; Marcoň, Petr; Bartušek, Karel
    In our experiment the electrical parameters that affect early somatic embryos (ESEs) were investigated. High voltage was generated by a special high voltage generator. High voltages ranging from 5 to 20 kV and frequency of 1Hz were applied longitudinal and transversal directly on the Petri dish with 2 days old ESEs of Picea abies for periods of 3 hours every day. One Petri dish was placed directly on top of the high voltage generator and on the other petri dish were fixed two copper plates for transmission of high voltage. Petri dishes were exposed to high voltage for 14 days. After this time, the influence of high voltage was evaluated.
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    Classification of brain lesions using a machine learning approach with cross-sectional ADC value dynamics
    (Springer Nature, 2023-07-15) Solár, Peter; Valeková, Hana; Marcoň, Petr; Mikulka, Jan; Barák, Martin; Hendrych, Michal; Stránský, Matyáš; Novotná, Kateřina; Kostial, Martin; Holíková, Klára; Brychta, Jindřich; Jančálek, Radim
    Diffusion-weighted imaging (DWI) and its numerical expression via apparent diffusion coefficient (ADC) values are commonly utilized in non-invasive assessment of various brain pathologies. Although numerous studies have confirmed that ADC values could be pathognomic for various ring-enhancing lesions (RELs), their true potential is yet to be exploited in full. The article was designed to introduce an image analysis method allowing REL recognition independently of either absolute ADC values or specifically defined regions of interest within the evaluated image. For this purpose, the line of interest (LOI) was marked on each ADC map to cross all of the RELs’ compartments. Using a machine learning approach, we analyzed the LOI between two representatives of the RELs, namely, brain abscess and glioblastoma (GBM). The diagnostic ability of the selected parameters as predictors for the machine learning algorithms was assessed using two models, the k-NN model and the SVM model with a Gaussian kernel. With the k-NN machine learning method, 80% of the abscesses and 100% of the GBM were classified correctly at high accuracy. Similar results were obtained via the SVM method. The proposed assessment of the LOI offers a new approach for evaluating ADC maps obtained from different RELs and contributing to the standardization of the ADC map assessment.
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    Comparing efficiencies of polypropylene treatment by atmospheric pressure plasma jets
    (WILEY-V C H VERLAG GMBH, 2023-07-11) Polášková, Kateřina; Ozkan, Alp; Klíma, Miloš; Jeníková, Zdeňka; Buddhadasa, Madhuwanthi; Reniers, François; Zajíčková, Lenka
    Plasma treatment of polypropylene (PP) is a well-established method of improving its surface properties. However, the efficiencies of different plasma discharges are seldom compared. Herein, we discuss the differences in PP treated by three arc-based commercial plasma jets working in dry air, Plasmatreat rotating plasma jet (PT-RPJ), AFS PlasmaJet & REG; (AFS-PJ), and SurfaceTreat gliding arc jet (ST-GA), and by the low-temperature RF plasma slit jet (RF-PSJ) working in argon. The AFS-PJ has a significantly different reactive species composition dominated by nitrogen oxides. It induced higher thermal loads leading to surface damage. The other arc-based jets (PT-RPJ and ST-GA) created the PP surface with higher oxygen and nitrogen concentration than the low-temperature RF-PSJ. It induced a higher adhesion strength measured on PP-aluminum joints.
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    Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging
    (MDPI, 2023-06-16) Janoušek, Jiří; Marcoň, Petr; Dohnal, Přemysl; Jambor, Václav; Synková, Hana; Raichl, Petr
    Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.