Iskanje po katalogu digitalne knjižnice

Opis: Walking, as a mode of transport, is becoming widespread, in a world, where urban conglomerates are broadening and becoming denser. Modern lifestyle trends on a side, and eco-friendly policies on the other, push people into walking habits, increasing the need for a suitable, attractive, accessible, connected and safe walking infrastructure. To reach such a result, it is necessary to understand, what are the needs of the users of this infrastructure, taking into consideration the behavioral specificities and the safety needs of pedestrians. In this process pedestrian microsimulation models, surrogate safety techniques, and technologies able to measure specific traits of pedestrian dynamics play a central role. The firsts allow to reproduce repeatedly in a virtual environment a specific infrastructure and to study the response of pedestrians. Nevertheless, to be accurate and efficient, they need to go through long and tedious calibration and validation processes, that are often seen as an important limitation by technicians. Surrogate safety techniques are methods, that are based on the concept, that it is possible to predict the safety level of a location, using near accidents. The main advantage of such techniques is that they are proactive. Till this moment, these techniques have been mainly applied to on-field measurements and are primarily centered on motorized road users. Less interest has been shown for vulnerable road users, especially for pedestrians, who have been less extensively studied. Finally, an element that could highly affect pedestrian safety is their reaction time. Nevertheless, its measurement has long been a big issue. Eye-tracking technology could be one of the solutions, allowing to analyze the directions and objects fixated by pedestrians. These listed issues are also the topics that are addressed by this research work. Focusing on the study of the action of pedestrians while crossing the road on an unsignalized crosswalk set on a roundabout entry leg, the dissertation thesis aims at studying the crossing time, reaction time and surrogate safety aspects typical of pedestrians at the recalled location. The main purpose of the research work is to develop a methodology to calibrate pedestrian Social Force Model at a selected location, using a specifically formulated neural network as a tool to fine-tune model's behavioral parameters. Eight parameters have been chosen to be fine-tuned, five of those are related to pedestrian behavior and three of them are related to car-following behavior. After the selection of input parameters, a feedforward network has been formulated. Its application in the framework of the whole calibration process has brought to considerably positive results, finding a combination of input parameters that improved the performance of the microsimulation model of 37 % in comparison to the default one. The outputs of the calibrated model have been used to calculate three measures of surrogate safety, and also in this case results demonstrated an improvement in the calculation of surrogate safety measures when using the calibrated outcomes in comparison to their calculation on the “default” model outputs. Finally, reaction time measurement and prediction have been addressed by the thesis, in order to be able to describe pedestrian crossing action in its completeness. Quantitative eye-tracking outputs have been the starting point for the calculation of pedestrian reaction time at different locations, and they allowed to create a database of behavioral, geometric, regulatory and flow characteristics, which was the foundation for the formulation of a new prediction model for pedestrian reaction time. The prediction model, which consists of a cascade-correlation neural network, gave a good response to the learning and generalization steps, turning a 74 % correlation between the measured reaction time values and the predicted ones, and being able to follow the variability of these values.
Ključne besede: pedestrian, microsimulation model, calibration, neural network, surrogate safety indicators, reaction time.
Objavljeno v DKUM: 03.10.2022; Ogledov: 284; Prenosov: 42
Celotno besedilo (5,93 MB)

Opis: The main purpose of this thesis is to compare two state-of-the-art machine learning models, LSTM (Long Short-Term Memory) and TCN (Temporal Convolutional Network), on an AVL List GmbH case use, where the goal is to predict vehicle brake torque. Dataset used for model testing consists of multiple features which are preprocessed using several preprocessing methods. For model implementation Python’s libraries Keras and TensorFlow are used. Results from this thesis show that TCN is able to outperform LSTM. TCN achieves lower RMSE on the test dataset and is significantly faster in training and evaluation.
Ključne besede: brake torque, machine learning, neural network, LSTM, TCN, RNN, CNN
Objavljeno v DKUM: 24.09.2020; Ogledov: 780; Prenosov: 0

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: 1093; Prenosov: 82
Celotno besedilo (792,88 KB)
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Opis: As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning.
Ključne besede: artificial neural network, dimensional dviation, particle swarm optimization, regression
Objavljeno v DKUM: 12.07.2017; Ogledov: 839; Prenosov: 143
Celotno besedilo (1,17 MB)
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Opis: Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbonand volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV.
Ključne besede: Slovenian coal, higher heating value, HHV, regression, artificial neural network
Objavljeno v DKUM: 03.04.2017; Ogledov: 28689; Prenosov: 356
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