Iskanje po katalogu digitalne knjižnice

Opis: Building detection is a crucial task in the field of remote sensing, which can facilitate urban construction planning, disaster survey, and emergency landing. However, for large-size remote sensing images, the great majority of existing works have ignored the image tilt problem. This problem can result in partitioning buildings into separately oblique parts when the large-size images are partitioned. This is not beneficial to preserve semantic completeness of the building objects. Motivated by the above fact, we first propose a framework for detecting objects in a large-size image, particularly for building detection. The framework mainly consists of two phases. In the first phase, we particularly propose a tilt correction (TC) algorithm, which contains three steps: texture mapping, tilt angle assessment, and image rotation. In the second phase, building detection is performed with object detectors, especially deep-neural-network-based methods. Last but not least, the detection results will be inversely mapped to the original large-size image. Furthermore, a challenging dataset named Aerial Image Building Detection is contributed for the public research. To evaluate the TC method, we also define an evaluation metric to compute the cost of building partition. The experimental results demonstrate the effects of the proposed method for building detection.
Ključne besede: building detection, cost of building partition, deep neural network, remote sensing, tilt correction
Objavljeno v DKUM: 26.09.2024; Ogledov: 0; Prenosov: 1
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Opis: During the COVID-19 pandemic, countries took several restrictions to contain the spread of coronavirus. In the second wave of the COVID-19 pandemic, primary schools in Slovenia were closed for a period long time (from October 19th 2020 until January 18th 2021 when they were partially reopened for 6–9 year olds until February 15th 2021 when they were reopened for all children) and organized sport activities for children and adolescents under the age of 15 was not allowed during this period. The aim of the study was to examine how these restrictions were reflected in the amount of different forms of physical activity (PA) of 6–12-year old children (N = 3,936). Data were collected using an online questionnaire (International Physical Activity Questionnaire Short Form) comparing different forms of PA before (BEFORE) and during (DURING) remote schooling. The results show that there has been a decline in children’s PA DURING, specifically, only 4.3% of children had their physical education ≥ 45 min (or 77.7% ≤ 30 min), as is the usual duration in Slovenia. There was also a remarkable decline in extracurricular sports activities (p < 0.001), which BEFORE had been participated by 72.2% of children, while DURING remote schooling, as many as 83.5% of children did not participate these activities. 69.7% of children participated in organized sports in clubs at least once a week, while DURING remote schooling, as many as 88.1% (p < 0.001) did not participate in such form of activities. Furthermore, the time spent exercising in moderate to vigorous PA also decreased (BEFORE 8.2% vs. DURING 24.9%; p < 0.001). We found that during lockdown there has been an alarming decrease in the frequency and duration of organized PA at school and at sports clubs. These findings are a good starting point for designing (developing) an effective strategy for promoting health-enhancing PA of children in the event of a future lockdown or similar situations. The strategy should focus on the appropriate implementation of PA curriculum and motivate young people to participate regularly in extracurricular organized and non-organized activities.
Ključne besede: COVID-19 pandemic, physical activity, children, remote schooling, physical education
Objavljeno v DKUM: 01.07.2024; Ogledov: 134; Prenosov: 9
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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: 30
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Opis: The handbook “Remote working management skills for HR professionals-HANDBOOK FOR TRAINERS” is related to online training for HR professionals. In the handbook, we present the curricular aspects of the training. The emphasis is on the following: list of skills for mangers; list of learning outcomes; list of learning contents; learning strategies and methods; learning materials; instructions for examination and grading; instructions for implementation. There are six learning modules in the list of learning contents: Communicational skills, Digital skills, Work-life balance, skills, Organizational skills, Leadership skills and HRM skills. For each learning module, we prepared the learning material: Case studies; Practical exercises; Questions and answeres; Multiple choice questions. The trainer also has available for each learning module: Power point presentations (5 files for each learning module); Lecture notes (10-15 pages for each learning module). Presentations and lecture notes are not included in this handbook. They are published separately.
Ključne besede: remote working, education, curriculum, human resources management, management, iorganisation
Objavljeno v DKUM: 27.07.2022; Ogledov: 1113; Prenosov: 101
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Opis: This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC) and the graphical user interface (GUI). Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI), running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote's user.
Ključne besede: LabVIEW, Matlab/Simulink, neural network control, remote educational tool
Objavljeno v DKUM: 19.07.2017; Ogledov: 1484; Prenosov: 92
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