1. Region-based approach for machining time improvement in robot surface finishingTomaž Pušnik, Aleš Hace, 2024, izvirni znanstveni članek Opis: Traditionally, in robotic surface finishing, the entire workpiece is processed at a uniform speed, predetermined by the operator, which does not account for variations in the machinability across different regions of the workpiece. This conventional approach often leads to inefficiencies, especially given the diverse geometrical characteristics of workpieces that could potentially allow for different machining speeds. Our study introduces a region-based approach, which improves surface finishing machining time by allowing variable speeds and directions tailored to each region’s specific characteristics. This method leverages a task-oriented strategy integrating robot kinematics and workpiece surface geometry, subdivided by the clustering algorithm. Subsequently, methods for optimization algorithms were developed to calculate each region’s optimal machining speeds and directions. The efficacy of this approach was validated through numerical results on two distinct workpieces, demonstrating significant improvements in machining times. The region-based approach yielded up to a 37% reduction in machining time compared to traditional single-direction machining. Further enhancements were achieved by optimizing the workpiece positioning, which, in our case, added up to an additional 16% improvement from the initial position. Validation processes were conducted to ensure the collaborative robot’s joint velocities remained within safe operational limits while executing the region-based surface finishing strategy.
Ključne besede: robot surface finishing, collaborative robot, region-based machining, workpiece optimization, clustering, task-oriented machining, machining time optimization
Objavljeno v DKUM: 25.11.2024; Ogledov: 0; Prenosov: 10
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2. Using machine learning and natural language processing for unveiling similarities between microbial dataLucija Brezočnik, Tanja Žlender, Maja Rupnik, Vili Podgorelec, 2024, izvirni znanstveni članek Opis: Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.
Ključne besede: machine learning, NLP, hierarchical clustering, microbial data, microbiome, n-grame
Objavljeno v DKUM: 04.09.2024; Ogledov: 38; Prenosov: 6
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3. Optimal bus stops' allocation : a school bus routing problem with respect to terrain elevationKlemen Prah, Abolfazl Keshavarzsaleh, Tomaž Kramberger, Borut Jereb, Dejan Dragan, 2018, izvirni znanstveni članek Opis: The paper addresses the optimal bus stops allocation in the Laško municipality. The goal is to achieve a cost reduction by proper re-designing of a mandatory pupils' transportation to their schools. The proposed heuristic optimization algorithm relies on data clustering and Monte Carlo simulation. The number of bus stops should be minimal possible that still assure a maximal service area, while keeping the minimal walking distances children have to go from their homes to the nearest bus stop. The working mechanism of the proposed algorithm is explained. The latter is driven by three-dimensional GIS data to take into account as much realistic dynamic properties of terrain as possible. The results show that the proposed algorithm achieves an optimal solution with only 37 optimal bus stops covering 94.6 % of all treated pupils despite the diversity and wideness of municipality, as well as the problematic characteristics of terrains' elevation. The calculated bus stops will represent important guidelines to their actual physical implementation.
Ključne besede: logistics, maximal covering problems, optimization, data clustering, Monte Carlo simulation, geographic information system (GIS), reduction of transportation costs, Laško, Slovenia
Objavljeno v DKUM: 22.08.2024; Ogledov: 35; Prenosov: 9
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4. Categorisation of open government data literatureAljaž Ferencek, Mirjana Kljajić Borštnar, Ajda Pretnar Žagar, 2022, pregledni znanstveni članek Opis: Background: Due to the emerging global interest in Open Government Data, research papers on various topics in this area have increased.
Objectives: This paper aims to categorise Open government data research.
Methods/Approach: A literature review was conducted to provide a complete overview and classification of open government data research. Hierarchical clustering, a cluster analysis method, was used, and a hierarchy of clusters on selected data sets emerged.
Results: The results of this study suggest that there are two distinct clusters of research, which either focus on government perspectives and policies on OGD, initiatives, and portals or focus on regional studies, adoption of OGD, platforms, and barriers to implementation. Further findings suggest that research gaps could be segmented into many thematic areas, focusing on success factors, best practices, the impact of open government data, barriers/challenges in implementing open government data, etc.
Conclusions: The extension of the paper, which was first presented at the Entrenova conference, provides a comprehensive overview of research to date on the implementation of OGD and points out that this topic has already received research attention, which focuses on specific segments of the phenomenon and signifies in which direction new research should be made.
Ključne besede: open government data, open government data research, hierarchical clustering, OGD classification, OGD literature overview
Objavljeno v DKUM: 12.06.2024; Ogledov: 134; Prenosov: 11
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5. Investigating the impact of COVID-19 on e-learning : country development and COVID-19 responseMirjana Pejić Bach, Božidar Jaković, Ivan Jajić, Maja Meško, 2023, izvirni znanstveni članek Opis: Due to its severity, the outbreak of COVID-19 led to unprecedented levels of social isolation that affected educational institutions, among others. Digital technologies such as cloud computing and video broadcasting helped the adoption of e-learning during the crisis. However, the speed and efficiency of e-learning adoption during the COVID-19 period varied across countries. This paper compares the adoption of e-learning in European countries before and during the COVID-19 pandemic and the relationship between the pandemic, e-learning, and economic development. First, the adoption of e-learning in European countries before and during the pandemic is compared. Second, using fuzzy C-means clustering, homogeneous groups of European countries are formed based on e-learning indicators for the periods before and during the pandemic. Third, GDP per capita is used as an indicator of economic development and severity indices are used as an indicator of the severity of the response to the pandemic to compare the different clusters. The research results show that economically and digitally advanced countries led the adoption of e-learning in both the period before and the period during the pandemic. However, they also responded less strictly to the pandemic. Less-advanced countries responded more strictly to the pandemic, likely due to a lack of healthcare resources, and also fell behind in the adoption of e-learning.
Ključne besede: e-learning, COVID-19, digital technologies, fuzzy clustering
Objavljeno v DKUM: 02.08.2023; Ogledov: 412; Prenosov: 39
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7. Robust clustering of languages across Wikipedia growthKristina Ban, Matjaž Perc, Zoran Levnajić, 2017, izvirni znanstveni članek Opis: Wikipedia is the largest existing knowledge repository that is growing on a genuine crowdsourcing support. While the English Wikipedia is the most extensive and the most researched one with over 5 million articles, comparatively little is known about the behaviour and growth of the remaining 283 smaller Wikipedias, the smallest of which, Afar, has only one article. Here, we use a subset of these data, consisting of 14 962 different articles, each of which exists in 26 different languages, from Arabic to Ukrainian. We study the growth of Wikipedias in these languages over a time span of 15 years. We show that, while an average article follows a random path from one language to another, there exist six well-defined clusters of Wikipedias that share common growth patterns. The make-up of these clusters is remarkably robust against the method used for their determination, as we verify via four different clustering methods. Interestingly, the identified Wikipedia clusters have little correlation with language families and groups. Rather, the growth of Wikipedia across different languages is governed by different factors, ranging from similarities in culture to information literacy.
Ključne besede: Wikipedia, language, growth dynamics, data analysis, clustering
Objavljeno v DKUM: 13.11.2017; Ogledov: 1533; Prenosov: 397
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9. Slovenian Entrepreneurship Observatory 2003Miroslav Rebernik, Dijana Močnik, Jožica Knez-Riedl, Polona Tominc, Karin Širec, Matej Rus, Tadej Krošlin, Silvo Dajčman, končno poročilo o rezultatih raziskav Opis: The monograph Slovenian Entrepreneurship Observatory 2003 consists of several research issues. In the first part, a short review of the current level of entrepreneurship is given, outlined on the basis of economic and statistical data. Understanding what is happening in Slovenian enterprises is important not only in order to pursue an appropriate economic policy but also in order to find the advantages and disadvantages of Slovenian companies in comparison with enterprises in other European countries. If Slovenia wishes to join the most developed European countries, it will have to speed up its economic growth. In the second part of a monograph, a number of topics, based on a survey of a sample of 672 enterprises are dealt with. We studied the relationship between banks and small and medium-sized enterprises (SMEs), female entrepreneurship, clustering, social responsibility of companies and the development of competences.
Ključne besede: Companies Demography, Financing SMEs, Female Entrepreneurship, Clustering, Environmental Responsibility
Objavljeno v DKUM: 18.01.2017; Ogledov: 1891; Prenosov: 460
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10. An efficient k'-means clustering algorithmKrista Rizman Žalik, 2008, izvirni znanstveni članek Opis: This paper introduces k'-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm.
Ključne besede: algorithms, clustering analysis, k-means, cost-function, rival penalized mechanism, datasets
Objavljeno v DKUM: 31.05.2012; Ogledov: 2690; Prenosov: 128
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