48-Hours Hackathon on residue glass fiber material circular use organized by Riga Technical University (Latvia - host of the event) Aarhus University, Herning Campus (Denmark) Lulea University of Technology (Sweden). Join the webinar https://rtucloud1.zoom.us/j/99132779195 at 13:00 CET for more information. The event is partially supported by #IEEE Nordic Countries Nanotechnology Council Chapter, #interreg Baltic Sea "GlassCircle" Project and #Digi NANO/Microfactory Lab Hackathon on circular use of glass fiber production scraps and cut-off materials, is organized by Riga Technical University (Latvia) jointly with project partners from Lulea University of Technology (Sweden) and Aarhus University (Denmark). Hackathon is supported by industrial partners Hitachi Energy and Podcomp AB as well as Valmiera Glass (one of the biggest glass fiber producers in the Baltic Sea Region). Such glass fiber residues are generated daily during the production of glass fiber itself and from manufacturing of sports equipment and different industrial goods (wind turbine blades, reservoirs, insulators used in electrical equipment, bathroom units, housing for equipment or transportation industry, etc.). Current practice is that the residue glass fiber, in spite that it is perfectly useful material, is collected and delivered to landfills as industrial waste. This adds tons of industrial waste and loss of valuable resources. The idea of the hackathon is to bring together students from different subject areas to form teams and generate ideas with the possibility to test ideas, create prototypes and practically work at laboratory and prototyping space. More information available here: https://interreg-baltic.eu/event/hackathon-on-residue-glass-fiber-material-circular-use/ Co-sponsored by: Michail Beliatis Room: 3044, Bldg: BTECH, Birk Centerpark 15, Herning, Arhus Amt, Denmark, 7400, Virtual: https://events.vtools.ieee.org/m/360672

As the continuous consumption of fossil fuels has caused serious diseases, environmental pollution, and distributing the ecological balance, renewable energy sources (RESs) such as solar, wind, hydroelectric, and geothermal energy have started to attract great attention all over the world. The use of renewable and low-carbon energy sources plays a significant role in supplying electrical energy demands for sustainable and environmentally friendly energy production. Photovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. However, losses of electricity production are generally caused by the presence of various anomalies influencing the operation systems in PV plants. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs. In this study, an efficient PV fault detection method is proposed to classify different types of PV module anomalies using thermographic images. The proposed method is designed as a multi-scale convolutional neural network (CNN) with three branches based on the transfer learning strategy. The convolutional branches include multi-scale kernels with levels of visual perception and utilize pre-trained knowledge of the transferred network to improve the representation capability of the network. To overcome the imbalanced class distribution of the raw dataset, the oversampling technique is performed with the offline augmentation method, and the network performance is increased. In the experiments, eleven types of PV module faults such as cracking, diode, hot spot, offline module, and other classes are utilized. The experimental results show that the proposed method gives higher classification accuracy and robustness in PV panel faults and outperforms the pre-trained deep learning methods and existing studies. Speaker(s): Deniz, Bldg: K1, The western Norway University of Applied Science, Bergen, Vestfold, Norway, Virtual: https://events.vtools.ieee.org/m/359414