Emissions at airports and their impact at the habitat

One of the most important problems in air traffic is the production of air pollutants at and near airports. The estimation can be made today with help of calculation methods and measuring techniques. In most cases, calculations lead to a usable approach, but considering the intensive development of air traffic, measurement technologies are growing in importance. To reduce the high concentration of pollutants in the atmosphere, airports have to consider four sources: fleet operation in-house energy supply ground feeder traffic of motor vehicles catering and supply services for airports. This system measures emissions during flight events (take off, landing and runway operations) from airplanes and from other airport specific sources. This very precise method makes it possible to compare this data with the calculated emissions (LASPORT) of air pollutants in the big area of airports. This combined method is much more accurate, than the single mathematical modelling of emissions and their distribution. This method leads to a much improved accuracy of planning of airports and air traffic near and at airports compared with the current situation

A jövő dízelüzemű meghajtási rendszerei : A Szkoll Környezet és klimabarát gépjármûvek c. pályázat keretében a 2016/17 tanévben Szombathelyen megvalósított előadássorozat összefoglaló publikációja

A dízelmotor a mobilitás „motorja” lesz várhatóan még jó ideig különösen a nagyméretű személygépkocsik, a könnyű és nehéz tehergépkocsik, az autóbuszok, a mező/erdőgazdasági munkagépek területén. Így káros kipufogógáz emissziójának csökkentése fontos feladat marad a következő évtizedekben. Előnyei között említendő a kedvező tüzelőanyag fogyasztása és a megújuló biogázolaj alkalmazhatósága ez által a globális klímaváltozás szempontjából fontos CO2- kibocsátás csökkentési potenciál is. A szerzők színvonalas áttekintést adnak a gépkocsi dízelmotorok kipufogógáz utókezelése területén kifejlesztett legújabb műszaki megoldásokról

Where Networks Draw Lines: Computer Line Drawing with Deep Learning

In this project, we explore a potential new approach for producing computer-generated line drawings from three-dimensional models through the use of deep convolutional neural networks. We base our approach on predicting where people are likely to draw lines to represent a given object, using the dataset collected by Cole et. al [2008]. We evaluate the performance of two network structures, trained on this dataset to take as input, an array of properties representing a three-dimensional object and a viewpoint, and to predict the average of where people would draw lines to represent that object. In addition, we examine two different methods for extracting a clean line drawing from the resulting image

Where Networks Draw Lines: Computer Line Drawing with Deep Learning

In this project, we explore a potential new approach for producing computer-generated line drawings from three-dimensional models through the use of deep convolutional neural networks. We base our approach on predicting where people are likely to draw lines to represent a given object, using the dataset collected by Cole et. al [2008]. We evaluate the performance of two network structures, trained on this dataset to take as input, an array of properties representing a three-dimensional object and a viewpoint, and to predict the average of where people would draw lines to represent that object. In addition, we examine two different methods for extracting a clean line drawing from the resulting image

Where Networks Draw Lines: Computer Line Drawing with Deep Learning

In this project, we explore a potential new approach for producing computer-generated line drawings from three-dimensional models through the use of deep convolutional neural networks. We base our approach on predicting where people are likely to draw lines to represent a given object, using the dataset collected by Cole et. al [2008]. We evaluate the performance of two network structures, trained on this dataset to take as input, an array of properties representing a three-dimensional object and a viewpoint, and to predict the average of where people would draw lines to represent that object. In addition, we examine two different methods for extracting a clean line drawing from the resulting image

Emissions at airports and their impact at the habitat

One of the most important problems in air traffic is the production of air pollutants at and near airports. The estimation can be made today with help of calculation methods and measuring techniques. In most cases, calculations lead to a usable approach, but considering the intensive development of air traffic, measurement technologies are growing in importance. To reduce the high concentration of pollutants in the atmosphere, airports have to consider four sources: fleet operation in-house energy supply ground feeder traffic of motor vehicles catering and supply services for airports. This system measures emissions during flight events (take off, landing and runway operations) from airplanes and from other airport specific sources. This very precise method makes it possible to compare this data with the calculated emissions (LASPORT) of air pollutants in the big area of airports. This combined method is much more accurate, than the single mathematical modelling of emissions and their distribution. This method leads to a much improved accuracy of planning of airports and air traffic near and at airports compared with the current situation