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his report summarizes the results of a workshop on Groupware related task design which took place at the International Conference on Supporting Group Work Group'99, Arizona, from 14 th to 17 th November 1999.
The workshop was addressed to people from different
viewpoints, backgrounds, and domains:
- Researchers dealing with questions of task analysis
and task modeling for Groupware application from an
academic point of view. They may contribute modelbased design
approaches or theoretically oriented
work
- Practitioners with experience in the design and
everyday use of groupware systems. They might refer
to the practical side of the topic: "real" tasks, "real"
problems, "real" users, etc.
Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. In practice, the focus is set on the most beneficial maintenance measures and/or capacity adaptations of existing water distribution systems (WDS). Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of WDS, i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, metrics based on graph theory have been proposed. In this study, a promising approach is applied to assess the resilience of the WDS for a district in a major German City. The conducted analysis provides insight into the process of actively influencing the
resilience of WDS
Grain boundary and surface segragation of Ba-Ti-O-Phases in rutile. O´Bryan, H. M.; Hagemann, H. J.
(1987)
One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.