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1.
6th International Conference En-Re Energy & Responsibility : Book of Extended Abstracts
2024

Abstract: In the context of escalating climate challenges, the EnRe conference is dedicated to exploring pathways to climate neutrality and the sustainable green transition. The conference is focused on the development and implementation of innovations supporting the transformation of energy systems, industrial systems, and living systems, all with the goal of creating a sustainable future with net-zero emissions. The conference brings together experts, researchers, policymakers, and business leaders to share their experiences, research, and visions. The aim of the conference is to foster collaboration and exchange of ideas, and to collectively develop comprehensive approaches and strategies for achieving climate neutrality. This conference is not just a knowledge exchange, but also a platform for encouraging concrete actions that will ensure a greenerand more sustainable future for our next generations to come.
Keywords: alternative energy systems, dynamic tariffing, electrical machines and drives, energy conversions, financing energy projects, nuclear energy, conventional energy systems, climate changes - climate pan, mathematical methods in engineering, micro and nano energy, low-carbon technologies and strategies, renewable energy technologies heating and cooling systems, smart buildings, cities and networks, policies and strategies for renewable energy sources, energy efficiency and the climate
Published in DKUM: 17.05.2024; Views: 61; Downloads: 14
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New suptech tool of the predictive generation for insurance companies : the case of the European market
Timotej Jagrič, Daniel Zdolšek, Robert Horvat, Iztok Kolar, Niko Erker, Jernej Merhar, Vita Jagrič, 2023, original scientific article

Abstract: Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools enable them to discover more and to intervene in a timely manner. We propose an artificial intelligence approach using Kohonen’s self-organizing maps. The dataset used for development and testing included yearly financial statements with 4058 observations for European composite insurance companies from 2012 to 2021. In a novel manner, the model investigates the behaviour of insurers, looking for similarities. The model forms a map. For the obtained groupings of companies from different geographical origins, a common characteristic was discovered regarding their future financial deterioration. A threshold defined using the solvency capital requirement (SCR) ratio being below 130% for the next year is applied to the map. On the test sample, the model correctly identified on average 86% of problematic companies and 79% of unproblematic companies. Changing the SCR ratio level enables differentiation into multiple map sections. The model does not rely on traditional methods, or the use of the SCR ratio as a dependent variable but looks for similarities in the actual insurer’s financial behaviour. The proposed approach offers grounds for a Suptech tool of predictive generation to support early detection of the possible future financial distress of an insurance company.
Keywords: European insurance market, suptech, supervision, financial deterioration identification, neural networks
Published in DKUM: 25.03.2024; Views: 112; Downloads: 9
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4.
Integrating social dimensions into future sustainable energy supply networks
Matevž Obrecht, Yigit Kazancoglu, Matjaž Denac, 2020, original scientific article

Abstract: Environmental protection and sustainable development have become an inevitable trend in many areas, including the energy industry. The development of energy supply networks is strongly correlated with the economics of energy sources as well as ecological and socio-political issues. However, the energy supply network is often distant from the social perspective. This paper therefore combines examination of perceptions and awareness of general public (web-based questionnaire) and top energy experts (a Delphi survey) on the energy supply network and identifies their potential integration in energy supply decision making processes. The results showed that public should be better informed as well as integrated into designing energy supply network as the prosumers gain power and the energy suppliers will no longer dominate the market. Public actors are ready to shape sustainable energy supply and also willing to pay 5.8% more for a sustainable energy supply. The majority are prepared to invest in renewable energy supply network close to their place of residence. Another result is that the public is calling for a shift in priority towards more sustainable and socially friendlier energy supply rather than focusing mainly on the economic and technical perspectives.
Keywords: energy supply, supply networks, sustainable energy, public perception, social integration, supply chain management
Published in DKUM: 19.02.2024; Views: 175; Downloads: 6
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5.
UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning
Milan Bajić, Jr., Božidar Potočnik, 2023, original scientific article

Abstract: A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People's Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 x 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants' detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model's complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems.
Keywords: unmanned aerial vehicle, unexploded ordnance, thermal imaging, UXOTi_NPA dataset, convolutional neural networks, deep learning
Published in DKUM: 12.02.2024; Views: 212; Downloads: 12
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6.
Density-based entropy centrality for community detection in complex networks
Krista Rizman Žalik, Mitja Žalik, 2023, original scientific article

Abstract: One of the most important problems in complex networks is the location of nodes that are essential or play a main role in the network. Nodes with main local roles are the centers of real communities. Communities are sets of nodes of complex networks and are densely connected internally. Choosing the right nodes as seeds of the communities is crucial in determining real communities. We propose a new centrality measure named density-based entropy centrality for the local identification of the most important nodes. It measures the entropy of the sum of the sizes of the maximal cliques to which each node and its neighbor nodes belong. The proposed centrality is a local measure for explaining the local influence of each node, which provides an efficient way to locally identify the most important nodes and for community detection because communities are local structures. It can be computed independently for individual vertices, for large networks, and for not well-specified networks. The use of the proposed density-based entropy centrality for community seed selection and community detection outperforms other centrality measures.
Keywords: networks, undirected graphs, community detection, node centrality, label propagation
Published in DKUM: 06.02.2024; Views: 214; Downloads: 18
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7.
Orchestrating Digital Wallets for On- and Off-Chain Decentralized Identity Management
Vid Keršič, Urban Vidovic, Andraz Vrecko, Martin Domajnko, Muhamed Turkanović, 2023, original scientific article

Abstract: Digital identity is becoming one of the core elements during the digitalization age, when more and more processes and interactions are taking place in the digital sphere. Therefore, current identity management approaches will define how these interactions will look in the future, but different fields and communities often approach management with their own solutions and tools, despite their similarities. This includes decentralized digital identities, where the identity is managed with asymmetric cryptographic keys, and no centralized entity oversees the whole identity system. This paper focuses on managing on- and off-chain decentralized digital identities, with the former being used for blockchain networks and the latter for self-sovereignty and privacy. While both types of decentralized identity build on the same cryptographic and identity primitives, there is no single wallet that handles both. Therefore, this paper proposes an orchestration solution for both wallet types, which enables their convergence to a single universal wallet and validates it with a real-life decentralized identity use case.
Keywords: blockchain networks, current identity management approaches, decentralized digital identities, digital identity, digital sphere, digitalization age, on-and-off-chain decentralized identity management, orchestrating digital wallets, real-life decentralized identity, same cryptographic identity
Published in DKUM: 06.12.2023; Views: 327; Downloads: 17
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8.
Accuracy is not enough: optimizing for a fault detection delay
Matej Šprogar, Domen Verber, 2023, original scientific article

Abstract: This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so.
Keywords: artificial neural networks, deep learning, fault detection, accuracy, multi-objective optimization
Published in DKUM: 30.11.2023; Views: 280; Downloads: 23
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9.
Editorial: combined water and heat integration in the process industries
Elvis Ahmetović, Ignacio E. Grossmann, Zdravko Kravanja, François Marechal, Jiri Klemeš, Luciana E. Savulescu, Dong Hongguang, 2022, preface, editorial, afterword

Abstract: Water and energy are resources that are used in large quantities in different sectors (domestic, agricultural, and industrial). Based on data on global water and energy consumption in the world over the recent past, as well as forecasts for the coming years, a continuous trend of increasing water and energy consumption can be observed. ...
Keywords: water integration, heat integration, systematic methods, pinch analysis, mathematical programming, heat-integrated water networks, optimisation, process industry
Published in DKUM: 18.08.2023; Views: 316; Downloads: 21
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10.
Development of an open-source framework for automatic alignment of KORUZA free-space optical communication system : magistrsko delo
Nejc Klemenčič, 2022, master's thesis

Abstract: This thesis aims to implement an open-source framework for the automatic alignment and tracking of the KORUZA v2 Pro free-space optical solution. Free-space optical systems are explored and current optical alignment and tracking solutions are analyzed. We use Neural Network-based object detection approaches to complement the essential collection of framework functionality. We train a Neural Network to detect KORUZA v2 Pro units with data gathered from currently deployed links. The out-of-the-box solution for automatic alignment and tracking can be freely modified and extended.
Keywords: free-space optics, automatic alignment, neural networks, object detection, open-source framework
Published in DKUM: 14.03.2022; Views: 686; Downloads: 77
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