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23rd International Scientific Conference on Electric Power Engineering, EPE 2023

作者： Krupiarz, Przemyslaw Zygarlicki, Jaroslaw Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland

ISBN: (纸本)9798350335934

This paper presents an initial study of Wireless Power Transfer systems design for Industrial Mobile Robots. Prerequisite for all industrial applications is that it shall be able to perform constantly, whereas currently the Industrial Mobile Robots must stop at fixed location to charge batteries. The time that robots are unutilized therefore should be minimized, to which we propose possible solutions. The designs apply an additional, intermediate energy buffer that increases the maximum applicable current. The proposed systems are split in two categories based on location, the first is a fixed, stationary type and the second is a type of phased array system built-in the plant floor. © 2023 IEEE.

关键词： Mobile robots

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Analysis of selected dielectric parameters of insulating liquids 23

Analysis of selected dielectric parameters of insulating liq...

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23rd International Scientific Conference on Electric Power Engineering, EPE 2023

作者： Pluzek, Aleksandra Nagi, Lukasz Koziol, Michal Zygarlicka, Malgorzata Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Electrical Power and Renewable Energy Opole Poland Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Computer Science Opole Poland

ISBN: (纸本)9798350335934

During the last few years, there has been a growing interest in the topic of using natural or synthetic esters as an alternative to mineral oils in oil transformers due to the easier way to obtain them and their ability to exhibit better electrical properties. In addition, more and more researchers are describing in their work that doping esters with nanoparticles is capable of further improving these properties. Therefore, in the current work, it was decided to see what effect the addition of a hydrophobic carbon shell iron nanoparticle to ORLEN mineral oil and Midel 7131 synthetic ester would have on selected electrical parameters of the insulating fluid, i.e. breakdown voltage and electrical strength. © 2023 IEEE.

关键词： Nanoparticles

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Automated Machine Learning Evaluation of Squat Movement Patterns Using Smartphone and CoreML 12

Automated Machine Learning Evaluation of Squat Movement Patt...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Sokolowska, Marta Podpora, Michal Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole45-758 Poland

ISBN: (纸本)9798350358056

There is a vast diversity of Machine Learning use cases, and both the hardware and the methodology itself are constantly getting better. This article presents a very specific use case - the recognising, classifying, and evaluation of human physical activity by using just a video stream from a smartphone and potentially instructing people how to exercise to get better results. The paper presents two key aspects of the challenge: (1) defining the problem in the physical activity domain and choosing a proper ML approach and tool, and (2) conducting the experiment using consumer-grade hardware (a smartphone) to evaluate if the proposed methodology is applicable using an easy-to-use tool and widely accessible hardware for users with limited (or without) Machine Learning experience. Chapter II presents the process of gathering and preparing the dataset, while the next chapter presents the creation of a Machine Learning model, model training using the acquired dataset, and the model performance. It was possible to obtain the result of 92.4% validation accuracy for the squat movement pattern classification model. The trained model can be used in an ML smartphone application for evaluation of user-provided examples of squats. © 2023 IEEE.

关键词： action classiication Artiicial Intelligence Machine Learning movement patterns pattern recognition video analysis

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APPLICATION OF THE NONLINEAR AUTOREGRESSIVE MODEL WITH EXOGENOUS INPUTS FOR PREDICTING THE GROSS OPERATING PRODUCT BASED ON USALI DATA 38

APPLICATION OF THE NONLINEAR AUTOREGRESSIVE MODEL WITH EXOGE...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Labanowski, Lukasz Fracz, Pawel Wotzka, Daria Foltys, Joachim DeSilva & Mercure Hotels in Opole Poland Faculty of Economics University of Opole Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Poland Humanitas University in Sosnowiec Poland

ISBN: (纸本)9783937436845

This study presents an advanced data analysis for developing a predictive model for the gross operating profit time series, using monthly data from two Polish hotels compliant with the Uniform System of Accounts for the Lodging Industry (USALI). Emphasizing the USALI data, the research utilized the Nonlinear Autoregressive Neural Network with exogenous inputs (NARX) to capture both linear and nonlinear trends. Extensive testing involved eight neural network learning methods across double layer networks with 5 to 30 neurons in the hidden layer and various sizes of delay parameter. Lhe effectiveness of each model was measured by the mean squared error, identifying the model with its lowest value as the most accurate, and by normality of test residuals. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Time series

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Investigation of the temperature impact on the operation of a nonlinear electromagnetic vibration energy harvester

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第3期73卷

作者： Kulik, Marcin Gabor, Rafal Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland

This paper presents a detailed study of the impact of temperature on the performance of a nonlinear electromagnetic vibration energy harvester, which is applied in autonomous power systems. The variability of material properties under the influence of temperature, such as stiffness, damping, and magnetic induction, significantly affects the operational characteristics of the device. A mathematical model accounting for these factors, including linear and quadratic temperature coefficients, optimized using a genetic algorithm, is presented in the article. The simulation results were compared with experimental data, showing good agreement, particularly in the range of frequency bandwidth and output voltage. It was demonstrated that higher temperatures lead to a reduction in mechanical and magnetic stiffness, resulting in decreased energy conversion efficiency. © 2025 The Author(s).

关键词： Vibration analysis

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Analysis of Radio Frequency and Ultraviolet Signals Emitted by Surface Partial Discharges 23

Analysis of Radio Frequency and Ultraviolet Signals Emitted ...

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23rd International Scientific Conference on Electric Power Engineering, EPE 2023

作者： Koziol, Michal Kunicki, Michal Urbaniec, Ireneusz Nagi, Lukasz Kabot, Ondrej Klein, Lukas Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland ENET Centre VSB - Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9798350335934

Partial discharges (PD) occurring in high-voltage insulation systems can cause equipment failures and pose safety hazards. Commonly used techniques for detecting and characterizing PD activity, especially on the surface of insulation systems, include radio frequencies signal analysis and detection of ultraviolet radiation emissions. The aim of the research work was to perform a comparative analysis of the signals emitted by surface partial discharges in radio frequencies and the phenomena accompanying ultraviolet radiation emissions, as well as indicating possible directions for further basic research aimed at building a knowledge base and improving measurement methods. This paper presents an analysis of VHF, UHF and UV signals emitted by surface PD generated under laboratory conditions. The obtained results confirm that radio frequencies and UV signal analysis can provide important information about the activity and surface location of PD, including information on the intensity and nature of PD. © 2023 IEEE.

关键词： Partial discharges

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IMPACT OF THE VOLUME OF DEVELOPER HOUSING UNITS ON REAL ESTATE PRICES IN POLAND: CORRELATION AND COHERENCE ANALYSIS 38

IMPACT OF THE VOLUME OF DEVELOPER HOUSING UNITS ON REAL ESTA...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Wotzka, Daria Mach, Lukasz Fracz, Pawel Chorkowy, Bartosz Stec, Marzena Dabrowski, Ireneusz faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Poland Faculty of Economics University of Opole Poland Narodowy Bank Polski Regional Branch in Opole Poland Collegium of Management and Finance Warsaw School of Economics Poland

This article conducts a comprehensive correlation analysis to explore the relationship between the development volume by developers and average market prices in Poland's housing sector from 2010 to 2023, utilizing quarterly data on the number of apartments released and their average prices. It employs both linear and nonlinear correlation analysis alongside wavelet coherence analysis. Preliminary correlational analysis offered insights into the basic interdependency patterns, highlighting how developer-supplied apartment numbers impact average prices. Wavelet coherence analysis, a more sophisticated approach, decomposed the data across various frequencies to uncover complex, nonlinear relationship patterns potentially missed by conventional correlation methods. The findings of the study highlight significant connections between the variables that vary over time and space, emphasizing the complexity of the housing market and underscoring the necessity for thorough analysis. These results carry important implications for developers, investors, and housing policy formulation, contributing significantly to understanding Poland's residential real estate market dynamics and supporting further research and strategic real estate industry planning. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Correlation methods

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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STUDYING THE INTERRELATIONS BETWEEN USALI PARAMETERS AND GOP THROUGH WAVELET COHERENCE ANALYSIS

STUDYING THE INTERRELATIONS BETWEEN USALI PARAMETERS AND GOP...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Labanowski, Lukasz Frącz, Pawel Wotzka, Daria Dąbrowski, Ireneusz DeSilva & Mercure Hotels in Opole Poland Faculty of Economics University of Opole Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Poland Collegium of Management and Finance Warsaw School of Economics Poland

The article focuses on analysing financial indicators according to the Uniform System of Accounts for the Lodging Industry (USALI) standard, examining metrics across seven selected hotel entities in Poland. It identifies Gross Operating Profit (GOP) as a crucial metric. Through wavelet coherence analysis, the research aims to reveal the temporal relationships and determine the impact of specific USALI components on the GOP within these hotels. Additionally, it seeks to identify whether there are common traits or notable differences among the analysed establishments. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Profitability

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