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We analyze the influence of dipole-dipole interactions in an electromagnetically induced transparency set up for a density at the onset of cooperative effects. To this end, we include mean-field models for the influence of local-field corrections and radiation trapping into our calculation. We show both analytically and numerically that the polarization contribution to the local field strongly modulates the phase of a weak pulse. We give an intuitive explanation for this local-field-induced phase modulation and demonstrate that it distinctively differs from the nonlinear self-phase-modulation that a strong pulse experiences in a Kerr medium.
Usability engineering
(2008)
Expeditious building of ring-porous earlywood vessel chronologies without loosing signal information
(2009)
Studying global change through investigation of the plastic responses of xylem anatomy in tree rings
(2010)
Variability in xylem anatomy is of interest to plant scientists because of the role water transport plays in plant performance and survival. Insights into plant adjustments to changing environmental conditions have mainly been obtained through structural and functional comparative studies between taxa or within taxa on contrasting sites or along environmental gradients. Yet, a gap exists regarding the study of hydraulic adjustments in response to environmental changes over the lifetimes of plants. In trees, dated tree-ring series are often exploited to reconstruct dynamics in ecological conditions, and recent work in which wood-anatomical variables have been used in dendrochronology has produced promising results. Environmental signals identified in water-conducting cells carry novel information reflecting changes in regional conditions and are mostly related to short, sub-annual intervals. Although the idea of investigating environmental signals through wood anatomical time series goes back to the 1960s, it is only recently that low-cost computerized image-analysis systems have enabled increased scientific output in this field. We believe that the study of tree-ring anatomy is emerging as a promising approach in tree biology and climate change research, particularly if complemented by physiological and ecological studies. This contribution presents the rationale, the potential, and the methodological challenges of this innovative approach.
The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.