1. Razvoj kiberfizičnega sistema za obravnavo načel industrije 4.0Jon Tepeš, 2025, diplomsko delo Opis: Namen diplomskega dela je bil razvoj cenovno dostopnega in modularnega kiberfizičnega sistema za praktično učenje principov Industrije 4.0, saj je dostop do industrijske opreme omejen in drag. Sistem sestavlja fizični model avtomatizirane linije, ki ga uporabnik upravlja preko spletnega vmesnika z interaktivnim digitalnim dvojčkom. Komunikacija med fizičnimi moduli, krmiljenimi z mikrokrmilniki ESP32, in centralnim strežnikom na Raspberry Pi, poteka v realnem času preko protokolov MQTT in Socket.IO.
Rezultat je delujoč prototip, ki uspešno demonstrira cikel sortiranja po barvah in dokazuje, da je z odprtokodnimi tehnologijami mogoče zgraditi učinkovito izobraževalno platformo. Delo tako premošča vrzel med teorijo in prakso na področju industrijske avtomatizacije.
Ključne besede: Industrija 4.0, kiberfizični sistem, digitalni dvojček, internet stvari, izobraževalna platforma
Objavljeno v DKUM: 29.09.2025; Ogledov: 0; Prenosov: 15
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2. Razvoj spletne aplikacije za celovit pregled proizvedene, prejete in oddane energije iz sončnih celicNel Breskvar, 2025, diplomsko delo Opis: Namen diplomske naloge je razviti spletno aplikacijo, ki omogoča pregled nad proizvedeno, oddano, prejeto in porabljeno električno energijo, saj trenutni sistem Moj Elektro ne omogoča vpogleda v proizvodnjo iz sončnih celic. Cilj je integrirati podatke iz Moj Elektro in SolarEdge v enotno platformo. Uporabljene metode vključujejo integracijo API-jev, shranjevanje podatkov v MySQL, uporabo PHP za obdelavo in JavaScript za grafični prikaz podatkov. Implementirani so tudi knjižnica Dotenv za varno shranjevanje občutljivih podatkov in algoritmi za odstranjevanje podvojenih zapisov.
Rešitev je spletna aplikacija, ki omogoča celovit pregled nad energijskimi podatki, vključno s 15-minutnimi povprečji moči, kar omogoča hiter in jasen pregled nad porabo in proizvodnjo električne energije.
Ključne besede: spletna aplikacija, API integracija, energetski podatki, vizualizacija podatkov
Objavljeno v DKUM: 19.09.2025; Ogledov: 0; Prenosov: 6
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3. Predicting wine quality under changing climate : An integrated approach combining machine learning, statistical analysis, and systems thinkingMaja Borlinič Gačnik, Andrej Škraba, Karmen Pažek, Črtomir Rozman, 2025, izvirni znanstveni članek Opis: Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R2 = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding.
Ključne besede: wine quality, machine learning, climate change, viticulture, Slovenia, terroir, statistical analysis, causal loop diagrams, system thinking
Objavljeno v DKUM: 18.08.2025; Ogledov: 0; Prenosov: 10
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4. Razvoj glasovalnega sistema s tehnologijo interneta stvariMatic Magdalenc, 2025, magistrsko delo Opis: Namen naloge je bil razviti glasovalni sistem, ki temelji na tehnologiji interneta stvari (IoT) za varnejše, preglednejše in učinkovitejše glasovanje. Zasnovan sistem omogoča glasovanje preko brezžičnih naprav ESP32, ki so povezane na centralni strežnik prek protokola MQTT. Naloga vključuje zasnovo in razvoj glasovalnih enot in strežnika, ki zagotavlja varno zbiranje glasov ter hitro obdelavo podatkov. Ključna prednost razvitega sistema je zmožnost takojšnjega shranjevanja in prikazovanja rezultatov, s čimer se omogoča hitra in transparentna analiza glasovanj.
Sistem vključuje razvoj programske opreme za ESP32, strežnik z administrativnim spletnim vmesnikom in bazo podatkov SQLite. Glavne prednosti tega sistema so visoka varnost, prilagodljivost in enostavna uporaba. Priporočila za nadaljnji razvoj vključujejo nadgradnjo sistema z možnostjo glasovanja na mobilnih napravah in izboljšanje varnosti z naprednimi tehnologijami, kot je blockchain. Sistem predstavlja pomemben korak v digitalizaciji glasovalnih procesov in ima potencial za široko uporabo v glasovalnih okoljih.
Ključne besede: internet stvari, IoT, ESP32, glasovanje, pametno, volitve
Objavljeno v DKUM: 24.04.2025; Ogledov: 0; Prenosov: 30
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5. 44th International Conference on Organizational Science Development : Human Being, Artificial Intelligence and Organization, Conference Proceedings2025, zbornik recenziranih znanstvenih prispevkov na mednarodni ali tuji konferenci Opis: The 44th International Scientific Conference on the Development of Organisational Science was focused on developing and advancing knowledge in the organisational sciences, with a focus on the contemporary challenges and opportunities of our time. On the one hand, it is humans who have woven the knowledge of organisations and will continue to enrich the knowledge of organisations in the future. On the other hand, we need to take into account the situational factors and the wider environment that are intrinsic to understanding organisations. The title of this year's conference is: Human being, Artificial Intelligence and the Organisation. The society we live in today is going through a period of great change in various areas of our lives. Although our pace sometimes stops, the forces of the environment do not. The pace of change often no longer surprises us. But the pillars of our action, the achievements of human society, are something of which we can be justly proud. Artificial intelligence is one of the forces that has entered our everyday lives in many places in recent times. Where are the opportunities and where are the dangers of artificial intelligence, where is human intelligence still a significant step ahead of artificial intelligence?
Ključne besede: organization, human being, artificial intelligence, changes, organizational development
Objavljeno v DKUM: 20.03.2025; Ogledov: 0; Prenosov: 38
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6. Deep learning predictive models for terminal call rate prediction during the warranty periodAljaž Ferencek, Davorin Kofjač, Andrej Škraba, Blaž Sašek, Mirjana Kljajić Borštnar, 2020, izvirni znanstveni članek Opis: Background: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models.
Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate.
Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance.
Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.
Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.
Ključne besede: manufacturing, product lifecycle, management product failure, machine learning, prediction
Objavljeno v DKUM: 21.01.2025; Ogledov: 0; Prenosov: 6
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7. A system dynamics approach to decision-making tools in farm tourism developmentMaja Borlinič Gačnik, Črtomir Rozman, Andrej Škraba, Boris Prevolšek, 2020, izvirni znanstveni članek Opis: Background: Besides visiting the main tourist attractions in Slovenia, many tourists want to spend their free time in the countryside as well, but the number of farming establishments in Slovenia diminished distinctly in the last years.
Objectives: This paper aims to develop a system dynamics model, with the goal to analyse dynamics of the diversification of agricultural holdings into farm tourism activities in Slovenia.
Methods/Approach: A system dynamics methodology was chosen to model the diversification in farm tourism. First, we present a basic concept of a system dynamics model with a causal loop diagram. Further, a system dynamics model with different scenarios is presented.
Results: The main feedback loops were identified, and the simulation model was used to analyse different simulation scenarios of the transition of farming establishments into farm tourism facilities.
Conclusions: The model provides the answers to the strategic questions about the dynamics of transfer into tourist farms, using several simulation scenarios. The transition mainly relies on subsidies, promotion of diversification and the growth of rural tourism, which provides a relevant direction for the development of future incentives.
Ključne besede: farm tourism, rural tourism, modelling, system dynamics, causal loop diagram, simulation
Objavljeno v DKUM: 13.01.2025; Ogledov: 0; Prenosov: 4
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8. Developing a diversification strategy of non-agricultural activities on farms using system dynamics modelling : a case study of SloveniaMaja Borlinič Gačnik, Boris Prevolšek, Karmen Pažek, Črtomir Rozman, Andrej Škraba, 2022, izvirni znanstveni članek Opis: Purpose: This paper aims to analyse the main variables and causal relationships in the system structure of the diversification of non-agricultural activities on agricultural holdings using system dynamics (SD) modelling. The SD model aims to simulate depictions of the behaviour of the real system while testing the effects of alternative decisions over time.
Design/methodology/approach: An SD methodology was chosen to model diversification in farm tourism.
Findings: A system approach increases the authors’ understanding of the transition of agricultural holdings to farm tourism. The results indicate that the transition to farm tourism depends on the level of tourism development in a certain area. The system is influenced by subsidies allocated by authorities to expand primary agricultural activities. The model describes a situation in which the tourism and agricultural industries have been affected by the COVID-19 pandemic.
Research limitations/implications: The research is limited by the small set of available data due to the limited number of farms in Slovenia. One major problem is the difference in statistical data on the same activity collected from different institutions in Slovenia.
Practical implications: The paper includes implications for understanding the transition process to farm tourism, allowing policymakers to experiment with subsidies and promotion to explore the efficacy and efficiency of proposed policies.
Originality/value: This study provides a structured, systemic view of the diversification of non-agricultural activities on agricultural holdings, where the simulation results are a reliable reflection of the behaviour of the actual system being modelled.
Ključne besede: system dynamics, modelling, simulation, diversification, farm tourism, farm policy, Slovenia, simulation scenarious
Objavljeno v DKUM: 11.12.2024; Ogledov: 0; Prenosov: 11
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9. Nadgradnja ogrevalnega sistema z internetom stvariBranko Zebec, 2024, magistrsko delo Opis: Magistrsko delo vsebuje analizo in razvoj prototipa za nadgradnjo ogrevalnega sistema, pri katerem smo sledili dvema ciljema: povezati ogrevalni sistem v internetno omrežje s pomočjo tehnologije interneta stvari in optimizirati ogrevalni sistema. Za dosego ciljev smo raziskali vse potrebne tehnologije, programska orodja in strojno opremo. Pri implementaciji smo uporabili programsko platformo Home Assistant, s katero smo razvili glavni krmilnik na strojni opremi Raspberry Pi 4. V okviru platforme Home Assistant smo integrirali dodatna programska orodja: Cloudflare za vzpostavitev varne povezave na oblačni strežnik in s tem ustvarjanje varnega tunela do glavnega krmilnika, Node-RED, s katerim smo razvili programsko kodo za povezavo z napravami Modbus in optimizacijo sistema vključno s PID krmilnikom, InfluxDB za bazo podatkov vseh spremenljivk našega ogrevalnega sistema, Grafano za vizualizacijo podatkov iz baze, Google SDK za integracijo Googlovega pomočnika na Home Assistant. Na krmilnik smo povezali naprave prek protokola Modbus TCP/IP (angl. Transmission Control Protocol/Internet Protocol): Daikin krmilnik za dostop do toplotne črpalke Daikin Altherma HT, Siemens sobni termostat. Za spremljanje porabe električne energije smo razvili merilnik porabe na modulu ESP32, ki smo ga povezali na kontakte S0 analognega števca. Programsko kodo C++ smo povzeli in prilagodili ter povezali prek protokola MQTT na Home Assistant. Razvili smo tudi simulacijski model, s pomočjo katerega smo preverjali nastavitve PID krmilnika. S statistično analizo smo preverili, ali so podatki po optimizaciji sistema resnično potrdili pričakovane rezultate glede zmanjšanja porabe električne energije, manjšega vtoka ogrevalne vode ter sprememb sobne temperature. Prototip smo preverjali v celotnem ogrevalnem obdobju, le-ta je deloval zanesljivo brez izpadov. Za oddaljeni dostop smo uporabljali Home Assistant pomočnika in Googlovega pomočnika. Optimizacija ogrevalnega sistema se je izkazala za zelo uspešno, kar smo potrdili z meritvami spremenljivk, predvsem porabe električne energije. Ugotovili smo, da se v prehodnem obdobju ogrevanja poraba znatno zmanjša. Na podlagi tega smo prepričani, da bomo na dolgi rok podaljšali življenjsko dobo toplotne črpalke ter prispevali k trajnostni naravnanosti našega sistema ogrevanja.
Ključne besede: internet stvari (IoT), pametni dom, ogrevanje, Home Assistant, Node-RED, PID.
Objavljeno v DKUM: 22.11.2024; Ogledov: 0; Prenosov: 63
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10. Zasnova spletne aplikacije za naročanje hraneErik Cvetkov, 2024, diplomsko delo Opis: Diplomska naloga obravnava razvoj aplikacije za naročanje hrane. Pri izdelavi aplikacije smo uporabili različne tehnologije, kot so JavaScript, Node.js in Ubuntu Linux strežnik. Interaktivnost je bila realizirana s pomočjo spletnega vtičnika. Razvita aplikacija vključuje vse ključne funkcionalnosti, ki jih pričakujejo uporabniki, vključno s pregledom ponudbe, upravljanjem košarice, dodajanjem naslova dostave in oddajo naročila. Poleg tega aplikacija omogoča restavracijam sprejem naročil v realnem času preko tehnologije WebSocket. Intuitiven uporabniški vmesnik in funkcionalnost, kot je takojšnje posodabljanje stanja naročil, prispevata k izboljšani uporabniški izkušnji tako za stranke kot za restavracije, kar je bilo ključno vodilo pri razvoju aplikacije.
Ključne besede: dostava hrane, prototipni razvoj, Node.js, JavaScript, WebSocket
Objavljeno v DKUM: 30.08.2024; Ogledov: 114; Prenosov: 47
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