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1.
Integrating social dimensions into future sustainable energy supply networks
Matevž Obrecht, Yigit Kazancoglu, Matjaž Denac, 2020, izvirni znanstveni članek

Opis: 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.
Ključne besede: energy supply, supply networks, sustainable energy, public perception, social integration, supply chain management
Objavljeno v DKUM: 19.02.2024; Ogledov: 15; Prenosov: 0
.pdf Celotno besedilo (1,60 MB)
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2.
UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning
Milan Bajić, Jr., Božidar Potočnik, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: unmanned aerial vehicle, unexploded ordnance, thermal imaging, UXOTi_NPA dataset, convolutional neural networks, deep learning
Objavljeno v DKUM: 12.02.2024; Ogledov: 56; Prenosov: 3
.pdf Celotno besedilo (16,94 MB)
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3.
Density-based entropy centrality for community detection in complex networks
Krista Rizman Žalik, Mitja Žalik, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: networks, undirected graphs, community detection, node centrality, label propagation
Objavljeno v DKUM: 06.02.2024; Ogledov: 68; Prenosov: 4
.pdf Celotno besedilo (707,65 KB)
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4.
Orchestrating Digital Wallets for On- and Off-Chain Decentralized Identity Management
Vid Keršič, Urban Vidovic, Andraz Vrecko, Martin Domajnko, Muhamed Turkanović, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: 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
Objavljeno v DKUM: 06.12.2023; Ogledov: 210; Prenosov: 13
.pdf Celotno besedilo (3,25 MB)
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5.
Accuracy is not enough: optimizing for a fault detection delay
Matej Šprogar, Domen Verber, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: artificial neural networks, deep learning, fault detection, accuracy, multi-objective optimization
Objavljeno v DKUM: 30.11.2023; Ogledov: 182; Prenosov: 10
.pdf Celotno besedilo (478,93 KB)
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6.
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, predgovor, uvodnik, spremna beseda

Opis: 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. ...
Ključne besede: water integration, heat integration, systematic methods, pinch analysis, mathematical programming, heat-integrated water networks, optimisation, process industry
Objavljeno v DKUM: 18.08.2023; Ogledov: 224; Prenosov: 18
.pdf Celotno besedilo (505,83 KB)
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7.
Development of an open-source framework for automatic alignment of KORUZA free-space optical communication system : magistrsko delo
Nejc Klemenčič, 2022, magistrsko delo

Opis: 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.
Ključne besede: free-space optics, automatic alignment, neural networks, object detection, open-source framework
Objavljeno v DKUM: 14.03.2022; Ogledov: 621; Prenosov: 73
.pdf Celotno besedilo (4,68 MB)

8.
Early spread of COVID-19 in Romania: imported cases from Italy and human-to-human transmission networks
Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, 2020, izvirni znanstveni članek

Opis: We describe the early spread of the novel coronavirus (COVID-19) and the first human-to-human transmission networks, in Romania. We profiled the first 147 cases referring to sex, age, place of residence, probable country of infection, return day to Romania, COVID-19 confirmation date and the probable modes of COVID-19 transmissions. Also, we analysed human-to-human transmission networks and explored their structural features and time dynamics. In Romania, local cycles of transmission were preceded by imported cases, predominantly from Italy. We observed an average of 4.8 days (s.d. = 4.0) between the arrival to a Romanian county and COVID-19 confirmation. Furthermore, among the first 147 COVID-19 patients, 88 were imported cases (64 carriers from Italy), 54 were domestic cases, while for five cases the source of infection was unknown. The early human-to-human transmission networks illustrated a limited geographical dispersion, the presence of super-spreaders and the risk of COVID-19 nosocomial infections. COVID-19 occurred in Romania through case importation from Italy. The largest share of the Romanian diaspora is concentrated especially in the northern parts of Italy, heavily affected by COVID-19. Human mobility (including migration) accounts for the COVID-19 transmission and it should be given consideration while tailoring prevention measures.
Ključne besede: coronavirus, COVID-19, transmission networks, human mobility, migration, nosocomial infections
Objavljeno v DKUM: 16.12.2020; Ogledov: 747; Prenosov: 182
.pdf Celotno besedilo (668,62 KB)
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9.
Nature-inspired algorithms for hyperparameter optimization : magistrsko delo
Filip Glojnarić, 2019, magistrsko delo

Opis: This master thesis is focusing on the utilization of nature-inspired algorithms for hyperparameter optimization, how they work and how to use them. We present some existing methods for hyperparameter optimization as well as propose a novel method that is based on six different nature-inspired algorithms: Firefly algorithm, Grey Wolf Optimizer, Particle Swarm Optimization, Genetic algorithm, Differential Evolution, and Hybrid Bat algorithm. We also show the optimization results (set of hyperparameters) for each algorithm and we present the plots of the accuracy for each combination and handpicked one. In discussion of the results, we provide the answers on our research questions as well as propose ideas for future work.
Ključne besede: artificial intelligence, artificial neural networks, machine learning, nature-inspired algorithms, evolutionary algorithms
Objavljeno v DKUM: 09.12.2019; Ogledov: 1792; Prenosov: 112
.pdf Celotno besedilo (969,13 KB)

10.
FORMULATION, PREPARATION AND CHARACTERIZATION OF NANOEMULSIONS FOR PARENTERAL NUTRITION : doctoral disertation
Dušica Mirković, 2019, doktorska disertacija

Opis: The aim of this doctoral research was to develop and optimize parenteral nanoemulsions as well as the total parenteral nutrition (TPN) admixture containing a nanoemulsion obtained in the course of the optimization process (hereinafter referred to as optimal nanoemulsion), and to examine their physicochemical and biological quality as well. In addition, the quality of the prepared nanoemulsions was compared with the quality of the industrial nanoemulsion (Lipofundin® MCT/LCT 20%), and, in the end, the TPN admixture initially prepared was also compared with the admixture into which the industrial emulsion was incorporated. Parenteral nanoemulsions that were considered in this dissertation were prepared by the high-pressure homogenization method. This method is the most widely applied method for the production of nanoemulsions due to the shortest length of homogenization time, the best-obtained homogeneity of the product and the smallest droplet diameter. For the nanoemulsion formulation, preparation and optimization purposes, by using, firstly, the concept of the computer-generated fractional design, and, after that, the full experimental design, the assessment of both direct effects of different formulation and process parameters (the oil phase type, the emulsifier type and concentration, a number of homogenization cycles and the pressure under which homogenization was carried out) as well as the effects of their interactions on the characteristics of prepared nanoemulsions was performed. Monitoring the nanoemulsion physical and chemical stability parameters was carried out immediately after their preparation, and then after 10, 30 and 60 days. It included the visual inspection, the measurement of the droplet diameter (the mean and volume droplet diameter), the polydispersity index, the ζ-potential, the pH value, the electrical conductivity, and the peroxide number. After the preparation and after 60 days, the biological evaluation (the sterility test and the endotoxic test) of the prepared nanoemulsions was carried out. As far as the characterization of the TPN admixture is concerned, it included practically the same parameters. The dynamics of monitoring the characteristics of the TPN admixture was determined on the basis of practical needs of hospitalized patients (0h, 24h and 72h). The scope and comprehensiveness of this issue indicated the need to divide the doctoral dissertation into three basic stages. The first stage was preliminary. Using the 24-1 fractional factorial design, nanoemulsions for the parenteral nutrition were prepared. They contained either a combination of soybean and fish oil, or a combination of medium chain triglycerides and fish oil. In addition, the type and the amount of an emulsifier used, a number of high-pressure homogenization cycles, and the homogenization pressure, were also varied. The measurement of the above-mentioned parameters for the industrial nanoemulsion was parallely carried out (Lipofundin® MCT/LCT 20%). The objective of this part of the research was to identify critical numerical factors having the most significant effect on the characteristics that define the prepared parenteral nanoemulsions. Parameters that were singled out as the result of this stage of the research (the emulsifier concentration and a number of homogenization cycles) were used as independent variables in the second stage of the research.
Ključne besede: nanoemulsions, total parenteral nutrition admixtures, high pressure homogenization, design of experiments, optimization, analysis of variance, artificial neural networks
Objavljeno v DKUM: 07.06.2019; Ogledov: 11749; Prenosov: 17
.pdf Celotno besedilo (2,82 MB)

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