Conference papers

Displaying 1 - 5 of 511 records

A Report on work: Cardiac MRI CBIR for pathologies detection
(SciTePress, 2023) Michno, Tomasz; Jelonek, Michał; Kielce University of Technology; Center for Cardiovascular Research and Development, American Heart of Poland
The early detection of pathologies in the cardiovascular system is very important. One of the most accurate imaging examinations of human tissues is magnetic resonance imaging (MRI), which is a very precise yet non-invasive test. In order to process MRI images to detect pathologies, one of the most promising methods is Content Based Image Retrieval (CBIR). This paper presents a report on the research on that topic as a result of the Miniatura 5 Grant. The main contributions of the paper are: a review of the state-of-the-art methods, a selection of the most promising image features that may be used to identify pathologies, a description of the proposed system for preparing suggestions for doctors, which takes into consideration also methods for presenting the results, which are most often omitted in other researches. The next step will be incorporating full 3D MRI information into the pipeline.
Bottom-up Efforts for a Low-Carbon Economy: Examples of Local Action Groups Activities in Poland and the Czech Republic
(University of Hradec Králové, 2024-04) Trnková, Gabriela; Furmankiewicz, Marek; Kola-Bezka, Maria; Hewitt, Richard J.; University of Hradec Kralove, Hradec Králové, Czech Republic; Wroclaw University of Environmental and Life Sciences, Wrocław, Poland; Nicolaus Copernicus University in Toruń, Poland; Spanish National Research Council, Madrid, Spain
Actions to counteract and adapt to rapid climate change caused by human activity require large-scale initiatives undertaken by international agencies and central governments as well as changes in the functioning of local economies and communities. In this article, we analyze the possibilities of involving rural territorial partnerships (so-called Local Action Groups; LAGs) in supporting the transformation of the EU local socio-economic systems towards a low-carbon economy (LCE). LAGs operate as associations of local stakeholders from the public, business, social and voluntary sectors and work for local socio-economicdevelopment. They can implement projects supporting energy transformation at three levels: as cooperation projects between LAGs and external institutions, as individual (own) projects, and by supporting grassroots initiatives of local stakeholders. In this paper we present examples of such activities, based on content analysis of LAGs strategic documents and websites. We point out that the potential of LAGs in supporting initiatives towards LCE is currently underused, which may be due to the low social awareness and low financial resources of local communities. However, LAGs have significant potential to support local pro-environmental initiatives using neo-endogenous development mechanisms, in which voluntary local actions are stimulated by external support.
Development of a universal diagnostic system for stator winding faults of induction motor and PMSM based on transfer learning
(IEEE, 2023-10-09) Skowron, Maciej; Skowron, Maciej; Politechnika Wrocławska
This paper deals with the possibility of using transfer learning (TL) of a deep convolutional network (CNN) to develop a universal stator winding fault diagnostic system for the induction motor (IM) and the permanent magnet synchronous motor (PMSM). The proposed diagnostic system uses direct processing of phase current signals to detect and evaluate the degree of stator damage. The TL idea was based on using the CNN structure pre-trained for the IM diagnostic system in a diagnostic application dedicated to IM and PMSM. Experimental verification carried out on real objects confirmed very high precision of steady-state and transient damage detection and classification for both IM and PMSM. Furthermore, the detection time shown in the study for both types of machines did not exceed 0.07 seconds.
Permanent Magnet Synchronous Motor Fault Detection System Based on Transfer Learning Method
(IEEE, 2022-12-09) Skowron, Maciej; Kowalski, Czesław; Skowron, Maciej; Politechnika Wrocławska
Permanent magnet synchronous motors (PMSMs) are increasingly used in industrial and commercial applications. Due to their popularity, the fault detection of these machines constitutes an extremely important issue. Currently used diagnostic tools are mostly developed based on deep or shallow neural structures, and training processes require large learning data packets. This fact results in the need for interference in motor construction to obtain measurable fault symptoms for NN training. To limit physical modelling of damages, mathematical models based on finite element methods (FEM) are mostly used. Nevertheless, the development of neuronal detectors based on simulation results does not provide high accuracy and a short time to implementation in case of changing the diagnostic task. To solve that limitation, the PMSM fault detection system based on the convolutional neural network (CNN) trained according to the transfer learning (TL) method was proposed in the article. The research aims to show the possibility of detecting PMSM faults (partial demagnetization and stator winding fault) in steady and transient states. The CNN was trained using only the phase current signals coming from the FEM model. The experimental verification was carried out on the PMSM motor during changes in drive operating conditions. The results of the experimental research carried out on a specially designed PMSM show the impressive capability of the developed CNN-based diagnostic system obtained using the transfer learning method.
DESTRIERO - a Decision Support Tool for Improved Reconstruction, Recovery and Interopearbility
(Fraunhofer Verlag, 2014) Szklarski, Łukasz; Samp, Krzysztof; Hołubowicz, Witold; Esposito, Christian; Cinquea, Marcello; Cotroneo, Domenico; Carmen, Polcaro; Thomas, Vendieken; ITTI; Consorzio Interuniversitario Nazionale per l’Informatica (CINI), Via Cinthia, Complesso Universitario di M.S. Angelo, Napoli, Italy; Innovalia Association, Bilbao, Spain; Fraunhofer IAO, University of Stuttgart, Stuttgart, Germany

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