Iskanje po katalogu digitalne knjižnice

Opis: This paper investigates emerging technological trends in web commerce and examines user perceptions through a pilot survey. Key innovations include IoT, AI, ML, chatbots, social and voice commerce, as well as immersive technologies like AR and VR. These tools enhance personalization, automate services, and support better consumer decision-making. The pilot study, based on an online questionnaire, explores user behaviour, expectations, and attitudes toward these developments. Results show that while users are familiar with technologies like chatbots and personalization, awareness of AR and VR remains low. Concerns about security and data privacy significantly influence user trust. The findings highlight the need for seamless technological integration, increased user education, and transparent communication to build trust and improve the digital shopping experience.
Ključne besede: web commerce, e-commerce trends, internet of things (IoT), artificial intelligence (AI), machine learning (ML), chatbots, augmented reality (AR), virtual reality (VR), voice commerce, user perception, digital transformation, data privacy
Objavljeno v DKUM: 29.08.2025; Ogledov: 0; Prenosov: 7
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Opis: Purpose – Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery. Design/methodology/approach – The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures. Findings – The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants. Originality/value – The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.
Ključne besede: active building information modeling, BIM, internet of things, IoT, multi-project environment, optimization, ready-mix-concrete delivery, RMC
Objavljeno v DKUM: 11.09.2024; Ogledov: 32; Prenosov: 17
Celotno besedilo (3,94 MB)
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Opis: This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10−6 mol/m2.
Ključne besede: Internet of Things, IoT, remote sensing, sensor integration, machine learning, ensemble method
Objavljeno v DKUM: 22.09.2023; Ogledov: 514; Prenosov: 163
Celotno besedilo (3,72 MB)
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Opis: The number of Internet of Things devices has been increasing rapidly in the recent few years, which opens possibilities for many new use cases. One of those is tracking assets, specifically for the conservation of endangered animals. This is the main goal of the OpenCollar tracking device. Its features and communication technologies are described in this master’s thesis. The device supports a variety of configuration options enabling users to tweak it for their specific usage. The main focus of the thesis was to design, implement, and test a cross-platform mobile application that would simplify the configuration process of such a device. All aspects of the application are described with a focus on the communication protocol. In the end, we present the results and conclude that the implemented application greatly simplifies the management of OpenCollar devices.
Ključne besede: Internet of Things, mobile application, Bluetooth low energy, Long Range
Objavljeno v DKUM: 28.01.2022; Ogledov: 1023; Prenosov: 71
Celotno besedilo (3,08 MB)

Opis: This master thesis describes the development of smart electricity energy meter on the ARM platform. In general, the commercial electrical energy meters are measuring only the total active energy, therefore, users cannot also monitor other important power consumption parameters. The goal of this thesis is to develop a prototype of smart ARM based energy meter, which is capable to measure several power consumption parameters and display them on a webpage in real-time. In the thesis the complete design of the ARM based smart energy meter is presented in detail. Further, the development of user interface for displaying measured data on the webpage in real-time is also presented. These results are presented both in textual and graphical form. Additionally, the theory of electric power consumption measurements and some commercial energy meters are presented.
Ključne besede: energy meter, ARM processor, power consumption, internet of things, calibration, green environment, energy consumption measurement, emoncms
Objavljeno v DKUM: 16.11.2017; Ogledov: 2417; Prenosov: 293
Celotno besedilo (5,10 MB)