Iskanje po katalogu digitalne knjižnice

Opis: This study examined the environmental attitude of drivers towards vehicle emission. The survey design that employed a five point Likert scale questionnaire and administered to 402 respondents (drivers) generated the data analysed. Data analysis involved descriptive and regression statistical tools. The results suggest that there was significant association between respondents’ sex, occupation, education and their environmental attitudes. A greater percentage (87. 3%) of the respondents was slightly more likely to agree that emissions from cars and trucks have serious impact on air quality. Majority (57.5%) of the respondents who were civil servants appeared to possess positive (favourable) attitudes towards the influence of emissions on the environment. Logistic regression suggest that respondents’ sex, occupation, education and vehicle’s purpose, income, age and social group significantly predicted their environmental attitudes. The study concludes that most of the sample possessed positive (favourable) environmental attitude towards vehicle emissions. This suggests that the environmental attitude of drivers towards emissions is not responsible for poor air quality. The policy implications of the findings include the need for the adoption of the polluter-pay-principle to reduce the volume of vehicles on our road, the necessity of promoting mass public transportation (such as the BRT and LAGBUS) as a way of reducing vehicle emission. Finally, sensitization program through social groups and schools is imperative.
Ključne besede: air quality, environmental attitude, drivers, vehicle emissions
Objavljeno v DKUM: 23.08.2024; Ogledov: 56; Prenosov: 8
Celotno besedilo (1,23 MB)
Gradivo ima več datotek! Več...

Opis: Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used �-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.
Ključne besede: green vehicle routing problem, alternative fuel vehicles, epsilon-constraint, multi-objective optimization, Pareto optimality
Objavljeno v DKUM: 22.08.2024; Ogledov: 44; Prenosov: 4
Celotno besedilo (764,60 KB)
Gradivo ima več datotek! Več...

Opis: To promote the penetration of electric vehicles (EVs), it is of great importance to plan and construct charging stations rationally. In this sense, the state of Slovenia's charging station coverage was analysed. Using discrete and network geographic information system (GIS) models, with ArcGIS software, the density of electric vehicle charging stations (EVCSs), geographic distribution, nearness along a street network, and clustering analyses were performed. A survey conducted among Slovenian users of EVs supported the GIS analysis. It was found out that the distribution of EVCSs has an east-northeast to west-southwest directional trend. Only 13% of EVCSs are accessible from the nearest motorway at a distance of 500 meters or less. An insight into intrinsic clustering structure revealed 11 clusters of EVCSs from which the most distinct is the cluster on the area of Ljubljana. The scientific contribution of the research is in the integration of GIS, spatial analysis and the results of a survey to study the coverage of EVCSs in a certain region. Spatial analyses are carefully selected, and, in complementarity, give a comprehensive picture of EVCSs coverage. The research is important for further spatial planning of EVCSs.
Ključne besede: electric vehicles, electric vehicle charging stations, spatial analysis, geographic information systems, GIS, logistics, Slovenia
Objavljeno v DKUM: 02.07.2024; Ogledov: 107; Prenosov: 7
Celotno besedilo (1,57 MB)
Gradivo ima več datotek! Več...

Opis: Electric vehicles are developing at an increased rate due to electrification trends and are trying to achieve the comfort level of existing vehicles with internal combustion engines. In this thesis, we considered a battery electric vehicle, with which measurements were carried out. We created a complete vehicle model in GT Suite software based on available measurements and vehicle data. The model was used to simulate measured test cases. We described the calibration procedure of standalone models and the assembly of a complete thermal model of the entire vehicle. At the end we compared three simulated test cases with vehicle measurements. We concluded that the developed vehicle simulation model behavior fits well to the measured vehicle.
Ključne besede: 1D CFD, VTMS, energy consumption, measurements, battery electric vehicle
Objavljeno v DKUM: 22.04.2024; Ogledov: 203; Prenosov: 0
Celotno besedilo (6,01 MB)

Opis: A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People's Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 x 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants' detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model's complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems.
Ključne besede: unmanned aerial vehicle, unexploded ordnance, thermal imaging, UXOTi_NPA dataset, convolutional neural networks, deep learning
Objavljeno v DKUM: 12.02.2024; Ogledov: 389; Prenosov: 18
Celotno besedilo (16,94 MB)
Gradivo ima več datotek! Več...