Testing new tribo-systems for sheet metal forming of advanced high strength steels and stainless steels

Testing of new tribo-systems in sheet metal forming has become an important issue due to new legislation, which forces industry to replace current, hazardous lubricants. The present paper summarizes the work done in a recent PhD project at the Technical University of Denmark on the development of a methodology for off-line testing of new tribo-systems for advanced high strength steels and stainless steels. The methodology is presented and applied to an industrial case, where different tribo-systems are tested. A universal sheet tribotester has been developed, which can run automatically repetitive Bending Under Tension tests. The overall results show that the methodology ensures satisfactory agreement between laboratory tests and production tests, although disagreement can occur, if tribological conditions are not the same in the two cases

Beyond single-photon localization at the edge of a Photonic Band Gap

We study spontaneous emission in an atomic ladder system, with both transitions coupled near-resonantly to the edge of a photonic band gap continuum. The problem is solved through a recently developed technique and leads to the formation of a ``two-photon+atom'' bound state with fractional population trapping in both upper states. In the long-time limit, the atom can be found excited in a superposition of the upper states and a ``direct'' two-photon process coexists with the stepwise one. The sensitivity of the effect to the particular form of the density of states is also explored.Comment: to appear in Physical Review

Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning