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Institution

Technische Universität Darmstadt

EducationDarmstadt, Germany
About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.


Papers
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TL;DR: This paper surveys the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems, and includes recent surveys that have compared commercial project scheduling systems.
Abstract: There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.

423 citations

Journal ArticleDOI
24 Jan 2013-Langmuir
TL;DR: It is shown that a variation of the contact angle from 90° on graphite to 127° on graphene would imply that both of the first two carbon layers of graphite contribute approximately the same interaction energy with water, which is incompatible with the short-range nature of the interaction between water and this substrate.
Abstract: Although experimental and theoretical studies have addressed the question of the wetting properties of graphene, the actual value of the contact angle of water on an isolated graphene monolayer remains unknown. While recent experimental literature indicates that the contact angle of water on graphite is in the range 90–95°, it has been suggested that the contact angle on graphene may either be as high as 127° or moderately enhanced in comparison with graphite. With the support of classical molecular dynamics simulations using empirical force-fields, we develop an argumentation to show that the value of 127° is an unrealistic estimate and that a value of the order of 95–100° should be expected. Our study establishes a connection between the variation of the work of adhesion of water on graphene-based surfaces and the interaction potential between individual water molecules and these surfaces. We show that a variation of the contact angle from 90° on graphite to 127° on graphene would imply that both of the...

422 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: A benchmark suite for visual perception based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level and high-level vision tasks, providing reference baselines and highlighting challenges for future research is presented.
Abstract: We present a benchmark suite for visual perception. The benchmark is based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level and high-level vision tasks, including optical flow, semantic instance segmentation, object detection and tracking, object-level 3D scene layout, and visual odometry. Ground-truth data for all tasks is available for every frame. The data was collected while driving, riding, and walking a total of 184 kilometers in diverse ambient conditions in a realistic virtual world. To create the benchmark, we have developed a new approach to collecting ground-truth data from simulated worlds without access to their source code or content. We conduct statistical analyses that show that the composition of the scenes in the benchmark closely matches the composition of corresponding physical environments. The realism of the collected data is further validated via perceptual experiments. We analyze the performance of state-of-the-art methods for multiple tasks, providing reference baselines and highlighting challenges for future research.

420 citations

Journal ArticleDOI
TL;DR: The implementation of fundamental knowledge of high-speed machining into the manufacture of industrial products took a relatively long time as discussed by the authors, not only due to a cautious attitude of the industry, but also to the production facilities existing at the time when the first findings became available from research not meeting the requirements of high speed machining.

419 citations

Journal ArticleDOI
TL;DR: VMD is a newly developed technique for adaptive signal decomposition, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions and shows that the multiple features can be better extracted with the VMD, simultaneously.

418 citations


Authors

Showing all 17627 results

NameH-indexPapersCitations
Yang Gao1682047146301
Herbert A. Simon157745194597
Stephen Boyd138822151205
Jun Chen136185677368
Harold A. Mooney135450100404
Bernt Schiele13056870032
Sascha Mehlhase12685870601
Yuri S. Kivshar126184579415
Michael Wagner12435154251
Wolf Singer12458072591
Tasawar Hayat116236484041
Edouard Boos11675764488
Martin Knapp106106748518
T. Kuhl10176140812
Peter Braun-Munzinger10052734108
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023135
2022624
20212,462
20202,585
20192,609
20182,493