Прегледај по Аутор "Brtka, Vladimir"

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    A Model for Working Environment Monitoring in Smart Manufacturing
    (MDPI, 2021) Dobrilovic, Dalibor; Brtka, Vladimir; Stojanov, Zeljko; Jotanovic, Gordana; Perakovic, Dragan; Jausevac, Goran
    The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.
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    Method PPC for Precise Piecewise Correlation after Histogram Segmentation
    (MDPI, 2024) Ognjenovic, Visnja; Stojanov, Jelena; Brtka, Vladimir; Blazic, Marko; Brtka, Eleonora; Berkovic, Ivana
    Correlation, functioning as a symmetric relation, is very powerful indicator of the mutual association between two attributes. The problem of weak correlation indicates a lack of linearity in the observed range. This paper presents the precise piecewise correlation method, which overcomes the problem by determining the segments where the linear association will be present. The determination was achieved using the histogram segmentation method. The conditions of the application and analysis of the method are presented, as well as the application of the method to the representative datasets. The obtained results confirm the existence of stronger linear associations on the segments. Detected correlations reveal the strength and nature of the symmetric association between two attributes on each of the separated segments.
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    Modeling a LoRAWAN Network for Vehicle WildlifeCollision Avoidance System on Rural Roads
    (Springer, 2024) Jotanovic, Gordana; Jausevac, Goran; Perakovic, Dragan; Dobrilovic, Dalibor; Stojanov, Zeljko; Brtka, Vladimir
    The network of rural roads covers different types of terrain, including forest areas, pastures, arable land andsparsely populated areas. The safety of people and animals is a priority in traffi c on these roads. Early detectionof pedestrians, animals and other moving objects along the road can signifi cantly reduce the risk of accidents.As part of this research, a sensor system is being developed that can detect characteristics of living things inmotion, such as unexpectedly crossing the road without clear signs. Such timely detection of moving objectsenables adequate preventive measures and reduces potential traffi c accidents. The consequences of traffi caccidents of this type can cause serious damage to animals and people property, and road infrastructure. Thetopicality of this problem at the spatial and seasonal level is emphasized in studies that identify the hotspots ofthese accidents. Factors such as traffi c characteristics and road infrastructure are key to modeling protectivesystems on rural roads. The presented study investigates the deployment of sensor nodes and LoRAWANgateways for wildlife detection on rural roads, with the aim of reducing the risk of traffi c accidents caused byWildlife-Vehicle Collisions.
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    Software architectures in smart manufacturing: Review and experiences
    (2021) Stojanov, Zeljko; Dobrilovic, Dalibor; Jotanovic, Gordana; Perakovic, Dragan; Jausevac, Goran; Brtka, Vladimir
    Cyber-physical systems based on heterogeneous and distributed devices, applications and services are the core of smart factories. Smart manufacturing systems are highly dependent on software applications and services that enable integration of data from different and heterogeneous sources, as well as support for control and management processes. Development and implementation of specific architecture styles and patterns in smart industrial settings is essential for their performance. Several software architecture styles used in Industry 4.0 environments are discussed and illustrated with examples from literature. Our experience with a prototype of a smart sensor-based layered architecture is presented and discussed. Further work will be directed towards development of service oriented architectures and reengineering method for old fashioned industrial settings.
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    The Cuts Selection Method Based on Histogram Segmentation and Impact on Discretization Algorithms
    (MDPI, 2022) Ognjenovic, Visnja; Brtka, Vladimir; Stojanov, Jelena; Brtka, Eleonora; Berkovic, Ivana
    The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram segmentation, reduct of data and MD algorithm of discretization. An application on the selected database shows that the benefits of a selection of cuts relies on histogram segmentation. The results of the classification were compared with the results of the Naïve Bayes algorithm