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Urinary metabolic biomarker profiling for cancer diagnosis by terahertz spectroscopy : review and perspective
Andreja Abina, Tjaša Korošec, Uroš Puc, Mojca Jazbinšek, Aleksander Zidanšek, 2023, pregledni znanstveni članek

Opis: In the last decade, terahertz (THz) technologies have been introduced to the detection, identification, and quantification of biomolecules in various biological samples. This review focuses on substances that represent important biomarkers in the urine associated with various cancers and their treatments. From a diagnostic point of view, urine liquid biopsy is particularly important because it allows the non-invasive and rapid collection of large volumes of samples. In this review, the THz spectral responses of substances considered metabolic biomarkers in urine and obtained in previous studies are collected. In addition, the findings from the relatively small number of prior studies that have already been carried out on urine samples are summarised. In this context, we also present the different THz methods used for urine analysis. Finally, a brief discussion is given, presenting perspectives for future research in this field, interpreted based on the results of previous studies. This work provides important information on the further application of THz techniques in biomedicine for detecting and monitoring urinary biomarkers for various diseases, including cancer.
Ključne besede: terahertz spectroscopy, urinary biomarkers, metabolic biomarkers, cancer diagnostics, biomolecules, non-invasive detection, biomedical detection
Objavljeno v DKUM: 14.03.2024; Ogledov: 139; Prenosov: 20
.pdf Celotno besedilo (12,51 MB)
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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: 258; Prenosov: 19
.pdf Celotno besedilo (707,65 KB)
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A z-axis-tolerant inductive power transfer system using a bipolar double d receiver coil structure
Jure Domajnko, Nataša Prosen, 2023, izvirni znanstveni članek

Opis: This paper presents a solution to a limitation of wireless power transfer that arises when using two D-shaped transmitter and receiver coils. Double D, or DD, coils are well known to have a polar, directional magnetic field, which increases the misalignment tolerance in one of the directions. The misalignment tolerance is nonsymmetric, and it is significantly better in one of the directions, which can also be considered a shortcoming. An additional shortcoming of the DD coil is that it is dependent on the rotation around the z-axis, due to the directional magnetic field. This is not a problem when using classic planar spiral coils, which do not generate a directional magnetic field. Therefore, DD coils are not suitable for applications in which the z-axis orientation is not determined and fixed to specific angle and direction. This paper presents a unique design of a transmitter coil, based on a double DD coil. The transmitter coil consists of two DD coils which are perpendicular to each other. The proposed transmitter structure can excite the receiver DD coil in a way that the efficiency of the power transfer is the highest, regardless of the orientation. The proposed transmitter structure can, therefore, solve the problem with rotation of a single DD coil. The proposed system structure was tested on the small-scale experimental setup
Ključne besede: coil rotation, orientation detection, DD coils, IPT
Objavljeno v DKUM: 20.12.2023; Ogledov: 264; Prenosov: 6
.pdf Celotno besedilo (5,32 MB)
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Simplified method for analyzing the availability of rooftop photovoltaic potential
Primož Mavsar, Klemen Sredenšek, Bojan Štumberger, Miralem Hadžiselimović, Sebastijan Seme, 2019, izvirni znanstveni članek

Opis: This paper presents a new simplified method for analyzing the availability of photovoltaic potential on roofs. Photovoltaic systems on roofs are widespread as they represent a sustainable and safe investment and, therefore, a means of energy self-suffciency. With the growth of photovoltaic systems, it is also crucial to correctly evaluate their global effciency. Thus, this paper presents a comparison between known methods for estimating the photovoltaic potential (as physical, geographic and technical contributions) on a roof and proposes a new simplified method, that takes into account the economic potential of a building that already has installed a photovoltaic system. The measured values of generated electricity of the photovoltaic system were compared with calculated photovoltaic potential. In general, the annual physical, geographic, technical and economic potentials were 1273.7, 1253.8, 14.2 MWh, and 279.1 Wh, respectively. The analysis of all four potentials is essential for further understanding of the sustainable and safe investment in photovoltaic systems.
Ključne besede: photovoltaic system, rooftop photovoltaic potential, economic potential, light detection and ranging, LiDAR
Objavljeno v DKUM: 05.12.2023; Ogledov: 309; Prenosov: 14
.pdf Celotno besedilo (8,24 MB)
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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: 311; Prenosov: 23
.pdf Celotno besedilo (478,93 KB)
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Object detection and graspability analysis for robotic bin-picking application in intralogistics
Primož Bencak, Darko Hercog, Tone Lerher, 2023, objavljeni znanstveni prispevek na konferenci

Opis: Robotics has been gaining attention in intralogistics applications in recent years. Automation of intralogistics processes aims to cope with the rising trends of workforce deficiency, aging, and increasing demands that came with the rise of E-commerce. Many improvements aim at bin-picking applications since order-picking requires most contributions while adding little to the products' value. Robotic bin-pickers are showing promising results; however, they are still subject to many limitations. First, the vision system must correctly determine the object's location and orientation. Second, a correct robotic gripper must be chosen. Lastly, appropriate grasping points that lead to successful picking must be selected. In this paper, we explore the influencing parameters of object detection using a 3D vision system. Second, we analyze an actual bin-picking application to determine the most appropriate selection of the robotic gripper. Based on the experiments, we provide the guidelines for selecting the most appropriate robotic bin-picking configuration.
Ključne besede: intralogistics, robotic bin-picking, detection analysis, graspability analysis
Objavljeno v DKUM: 25.07.2023; Ogledov: 345; Prenosov: 22
.pdf Celotno besedilo (1,89 MB)
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K-vertex: a novel model for the cardinality constraints enforcement in graph databases : doctoral dissertation
Martina Šestak, 2022, doktorska disertacija

Opis: The increasing number of network-shaped domains calls for the use of graph database technology, where there are continuous efforts to develop mechanisms to address domain challenges. Relationships as 'first-class citizens' in graph databases can play an important role in studying the structural and behavioural characteristics of the domain. In this dissertation, we focus on studying the cardinality constraints mechanism, which also exploits the edges of the underlying property graph. The results of our literature review indicate an obvious research gap when it comes to concepts and approaches for specifying and representing complex cardinality constraints for graph databases validated in practice. To address this gap, we present a novel and comprehensive approach called the k-vertex cardinality constraints model for enforcing higher-order cardinality constraints rules on edges, which capture domain-related business rules of varying complexity. In our formal k-vertex cardinality constraint concept definition, we go beyond simple patterns formed between two nodes and employ more complex structures such as hypernodes, which consist of nodes connected by edges. We formally introduce the concept of k-vertex cardinality constraints and their properties as well as the property graph-based model used for their representation. Our k-vertex model includes the k-vertex cardinality constraint specification by following a pre-defined syntax followed by a visual representation through a property graph-based data model and a set of algorithms for the implementation of basic operations relevant for working with k-vertex cardinality constraints. In the practical part of the dissertation, we evaluate the applicability of the k-vertex model on use cases by carrying two separate case studies where we present how the model can be implemented on fraud detection and data classification use cases. We build a set of relevant k-vertex cardinality constraints based on real data and explain how each step of our approach is to be done. The results obtained from the case studies prove that the k-vertex model is entirely suitable to represent complex business rules as cardinality constraints and can be used to enforce these cardinality constraints in real-world business scenarios. Next, we analyze the performance efficiency of our model on inserting new edges into graph databases with varying number of edges and outgoing node degree and compare it against the case when there is no cardinality constraints checking. The results of the statistical analysis confirm a stable performance of the k-vertex model on varying datasets when compared against a case with no cardinality constraints checking. The k-vertex model shows no significant performance effect on property graphs with varying complexity and it is able to serve as a cardinality constraints enforcement mechanism without large effects on the database performance.
Ključne besede: Graph database, K-vertex cardinality constraint, Cardinality, Business rule, Property graph data model, Property graph schema, Hypernode, Performance analysis, Fraud detection, Data classification
Objavljeno v DKUM: 10.08.2022; Ogledov: 694; Prenosov: 77
.pdf Celotno besedilo (3,43 MB)

ARM-Based Video Intercom System with Next-Gen Human Presence Detection using Deep Learning : magistrsko delo
Mario Gavran, 2022, magistrsko delo

Opis: This master's thesis presents an advanced video system with human presence detection based on deep learning and an ARM microcontroller. The objective of the thesis is to develop a system that works as a smart video intercom, which could be installed, e.g. on the entrance door, and autonomously alert the owner that a guest is in front of the door. The main goal is to use an AI algorithm, namely the neural network model on a constrained device, such as an ARM microcontroller, as their main advantage is lower power consumption and cost. The thesis also describes commonly used methods to reduce the power and memory footprint and to implement and accelerate the deep learning algorithms more effectively. Further, the most notable deep learning hardware and some general platforms are described in more detail. The thesis also presents the development of a human presence detection system based on an ARM microcontroller, VGA camera, and LCD, where Tensorflow Lite Micro, an open-source C++ framework for deploying deep learning models to embedded platforms and a pre-trained neural network model for person presence detection are used.
Ključne besede: TensorFlow Lite Micro, Video intercom system, ARM Cortex-M microcontroller, Human presence detection, Neural network
Objavljeno v DKUM: 08.07.2022; Ogledov: 707; Prenosov: 52
.pdf Celotno besedilo (7,86 MB)

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