Analyse von Lkw-Überholmanövern auf Autobahnen für die Entwicklung kooperativer Fahrerassistenzsysteme

Forschung im Ingenieurwesen - Lkw-Überholmanöver auf Autobahnen können Konfliktsituationen provozieren. Das lange Blockieren der Überholspur und plötzliche Ausscheren kann...     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

The Nexus of Corporate Entrepreneurship and Radical Innovation

This paper explores the linkage between the entrepreneurial orientation of established firms and the development of radical innovation. Through five case studies in firms involved in radical innovation, three propositions are developed, suggesting that proactiveness, risk‐taking and autonomy stimulate the development of radical innovation, whereas competitive aggressiveness does not necessarily do so, as radical innovations are directed towards the creation of entirely new arenas of business, where existing competitors are not present.