The German Federal Government wants to establish Germany as a leading market for electric mobility. Potential environmental benefits and changes in the economic framework conditions of the energy sector are described in this paper. In order to quantify the electricity split which is actually used for charging electric vehicles, two economic models for the energy sector, a model for the market penetration of electric vehicles, a vehicle model and an LCA model are brought together. Based on an assumed dynamic increase of electric vehicles to 12 million in 2030, an additional electricity demand of about 18 TWh is calculated. If the vehicles are charged directly after their last daily trip, the peak load increases by 12% despite the small increase in electricity demand. First model calculations for the development of the European power generation system show that the direct impact on the construction of new power plants remains low even until 2030. An impact of electric mobility on CO2 certificate prices can only be seen from 2025 onwards and is limited to an increase in certificate prices by a maximum of 8 % in 2030. An optimisation is possible with intelligent charging strategies: The peak load without demand side management can be reduced by 5 GW and about 600 GWh of additional wind energy can used which would otherwise have been throttled due to feed-in management—about 3.5 % of the total electricity demand of electric vehicles. On the other hand, demand side management leads to more coal power plants instead of gas power plants being used to meet the additional electricity demand. If additional renewable sources are installed along with demand side management, the electricity for electric vehicles is almost carbon free. This is also reflected in the life cycle balance of electric vehicles which also includes vehicle and battery production: With today’s average electricity split in Germany, the greenhouse gas emissions of electric vehicles are about comparable to vehicles with conventional combustion engines. However, the electricity split in 2030 or the use of additional renewable energy sources lead to a significant advantage in the greenhouse gas balance
An approach for extracting the radial force load on an implanted stent from medical images is proposed. To exemplify the approach, a system is presented which computes a radial force estimation from computer tomography images acquired from patients who underwent transcatheter aortic valve implantation (TAVI). The deformed shape of the implanted valve prosthesis' Nitinol frame is extracted from the images. A set of displacement vectors is computed that parameterizes the observed deformation. An iterative relaxation algorithm is employed to adapt the information extracted from the images to a finite-element model of the stent, and the radial components of the interaction forces between the stent and the tissue are extracted. For the evaluation of the method, tests were run using the clinical data from 21 patients. Stent modeling and extraction of the radial forces were successful in 18 cases. Synthetic test cases were generated, in addition, for assessing the sensitivity to the measurement errors. In a sensitivity analysis, the geometric error of the stent reconstruction was below 0.3 mm, which is below the image resolution. The distribution of the radial forces was qualitatively and quantitatively reasonable. An uncertainty remains in the quantitative evaluation of the radial forces due to the uncertainty in defining a radial direction on the deformed stent. With our approach, the mechanical situation of TAVI stents after the implantation can be studied in vivo, which may help to understand the mechanisms that lead to the complications and improve stent design.
While for static cameras several background subtraction approaches have been developed in the past, for non-static pan/tilt cameras efficient and robust motion detection is still a challenging task. Known approaches use image-to-image registration methods to generate a panorama background model of the scene, which spans a joint pixel coordinate system for later background estimation and subtraction. However, for a real-time panorama-based background subtraction a highly efficient image-to-panorama registration is needed. For this purpose, in this paper a key-frame representation of the panorama image is proposed and a strategy for fast global homography estimation in large panorama images is presented
Online augmentation of an oblique aerial image sequence with structural information is an essential aspect in the process of 3D scene interpretation and analysis. One key aspect in this is the efficient dense image matching and depth estimation. Here, the Semi-Global Matching (SGM) approach has proven to be one of the most widely used algorithms for efficient depth estimation, providing a good trade-off between accuracy and computational complexity. However, SGM only models a first-order smoothness assumption, thus favoring fronto-parallel surfaces. In this work, we present a hierarchical algorithm that allows for efficient depth and normal map estimation together with confidence measures for each estimate. Our algorithm relies on a plane-sweep multi-image matching followed by an extended SGM optimization that allows to incorporate local surface orientations, thus achieving more consistent and accurate estimates in areas made up of slanted surfaces, inherent to oblique aerial imagery. We evaluate numerous configurations of our algorithm on two different datasets using an absolute and relative accuracy measure. In our evaluation, we show that the results of our approach are comparable to the ones achieved by refined Structure-from-Motion (SfM) pipelines, such as COLMAP, which are designed for offline processing. In contrast, however, our approach only considers a confined image bundle of an input sequence, thus allowing to perform an online and incremental computation at 1Hz−2Hz.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.