Iskanje po katalogu digitalne knjižnice

Opis: Spatio-temporal forecasting is a rapidly evolving field, accelerated by the increasing accessibility of sensoring infrastructure and computational hardware, capable of processing the large amount of sampled data. Applications of spatio-temporal forecasts range from traffic, weather, air pollution forecasting and others. Emerging technologies employ deep learning architectures, such as graph, convolutional, recurrent and transformer neural networks. While the state-of-the-art methods provide accurate time series predictions, they are typically limited to providing forecasts only for the direct locations of sampling, whereas coverage of the entire area is often desired by the applications. In this work, we propose a method that addresses this challenge and improves on the shortcomings of related works, which have already tackled the task. The proposed graph convolutional recurrent neural network based method provides forecasts for arbitrary geolocations without available measurement data, formulating predictions based on contextual information of target geolocations and the time series data of nearby measurement geolocations. We evaluate the method on three real-world datasets from meteorological, traffic and air pollution domains, and gauge its performance against the state-of-the-art spatio-temporal forecasting methods. The proposed method achieves 12.26 %, 66.97 % and 42.89 % improvements in the mean absolute percentage errors on the three aforementioned datasets, compared to the best performing state-of-the-art method GConvGRU.
Ključne besede: spatio-temporal forecasting, graph recurrent neural networks, sparse geospatial sensor networks
Objavljeno v DKUM: 25.07.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (5,19 MB)

Opis: This article presents a cloud-based system for the on-line monitoring of tool conditions in end milling. The novelty of this research is the developed system that connects the IoT (Internet of Things) platform for the monitoring of tool conditions in the cloud to the machine tool and optical system for the detection of cutting chip size. The optical system takes care of the acquisition and transfer of signals regarding chip size to the IoT application, where they are used as an indicator for the determination of tool conditions. In addition, the novelty of the presented approach is in the artificial intelligence integrated into the platform, which monitors a tool’s condition through identification of the current cutting force trend and protects the tool against excessive loading by correcting process parameters. The practical significance of the research is that it is a new system for fast tool condition monitoring, which ensures savings, reduces investment costs due to the use of a more cost-effective sensor, improves machining efficiency and allows remote process monitoring on mobile devices. A machining test was performed to verify the feasibility of the monitoring system. The results show that the developed system with an ANN (artificial neural network) for the recognition of cutting force patterns successfully detects tool damage and stops the process within 35 ms. This article reports a classification accuracy of 85.3% using an ANN with no error in the identification of tool breakage, which verifies the effectiveness and practicality of the approach.
Ključne besede: machining, end milling, tool condition monitoring, chip size detection, cutting force trend identification, visual sensor monitoring, cloud manufacturing technologies
Objavljeno v DKUM: 26.03.2025; Ogledov: 0; Prenosov: 8
Celotno besedilo (5,65 MB)
Gradivo ima več datotek! Več...

Opis: The AA7075-T6 material is widely used in aerospace applications due to its favourable strength-to-weight ratio and cost-effectiveness. The material undergoes a process of cold rolling and subsequent stretching to form metal sheets. This process generates residual compressive stresses on the surface of the material. Surface changes in the material are observed at low stress levels, resulting in variations in residual stresses and surface roughness. This article presents an approach to monitor the surface state changes of AA7075-T6 material during dynamic loading using Fiber Bragg Grating (FBG) sensor. Numerical Finite Element Method (FEM) simulations analyse the transfer of deformations from the damaged surface through the adhesive layer to FBG with different cladding thicknesses. Loading induces microcrack-related intensity changes in the FBG optical spectrum and deformation response. The magnitude of the response is greater, when the cladding thickness of the optical fibre is thinner. Experimental results show that the FBG optical spectrum response varies with cumulative number of dynamic cycles.
Ključne besede: AA7075-T6, dynamic loading, Fiber Bragg Grating (FBG) sensor, surface condition
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 120
Celotno besedilo (1,20 MB)
Gradivo ima več datotek! Več...

Opis: This article presents a short-range fiber-optic quasi-distributed sensing device suitable for strain and temperature measurement. The sensing assembly consists of an fs laser inscribed reference mirror and a sensing array of equidistantly positioned mirrors. Utilization of the reference mirror and proper sensor geometry selection provides the possibility for a high-resolution spectral interrogation of the sensing array while relying on an ordinary, cost-effective distributed feedback (DFB) telecom laser diode. Beside the telecom DFB diode, the entire interrogation system includes only an additional detector, optical coupler, analog interface and a microcontroller. Measurement resolution better than 1 µε was demonstrated experimentally at a sampling rate exceeding 65 samples per second, while utilizing a sensing device with a typical length of 50 mm and spatial resolution of approximately 2 mm. To demonstrate the application potential of the proposed measuring device, a few different packages and sensor configurations were demonstrated and tested, including a system for tactile sensing applications and a short-range quasi-distributed temperature measurement probe.
Ključne besede: optical fiber sensors, short-range quasi-distributed fiber-optic sensor, strain/temperature sensing device, cost-effective interrogation system, tactile sensing, phase subtraction
Objavljeno v DKUM: 03.02.2025; Ogledov: 0; Prenosov: 30
Celotno besedilo (6,44 MB)

Opis: The increasing adoption of 2D/3D laser line sensors in industrial and research applications necessitates accurate and efficient simulation tools for tasks such as surface inspection, dimensional verification, and quality control. This paper presents a novel algorithm developed in MATLAB for simulating the measurements of any 2D/3D laser line sensor on STL CAD models. The algorithm uses a modified fast-ray triangular intersection method, addressing challenges such as overlapping triangles in assembly models and incorporating sensor resolution to ensure realistic simulations. Quantitative analysis shows a significant reduction in computation time, enhancing the practical utility of the algorithm. The simulation results exhibit a mean deviation of 0.42 mm when compared to real-world measurements. Notably, the algorithm effectively handles complex geometric features, such as holes and grooves, and offers flexibility in generating point cloud data in both local and global coordinate systems. This work not only reduces the need for physical prototyping, thereby contributing to sustainability, but also supports AI training by generating accurate synthetic data. Future work should aim to further optimize the simulation speed and explore noise modeling to enhance the realism of simulated measurements.
Ključne besede: 2D/3D laser line sensor, profilometry, simulation, point cloud, measurement generation, STL, Matlab
Objavljeno v DKUM: 10.01.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (8,99 MB)
Gradivo ima več datotek! Več...