Attempting to Extract Power from Earth's Rotation:An Experimental Test

A recently proposed method for the extraction of electrical energy from a circuit that is static with respect to the surface of Earth is investigated experimentally. In this method an electrical circuit moves due to Earth's rotation in Earth's magnetic field, and thus is supposed to generate an electromotive force. Our experimental results demonstrate that the proposed method yields voltages more than 3 orders of magnitude smaller than the predicted signal. This discrepancy is explained, and constraints on alternative schemes are briefly considered

SHREC 2020: 3D Point Cloud Semantic Segmentation for Street Scenes

Scene understanding of large-scale 3D point clouds of an outer space is still a challenging task. Compared with simulated 3D point clouds, the raw data from LiDAR scanners consist of tremendous points returned from all possible reflective objects and they are usually non-uniformly distributed. Therefore, its cost-effective to develop a solution for learning from raw large-scale 3D point clouds. In this track, we provide large-scale 3D point clouds of street scenes for the semantic segmentation task. The data set consists of 80 samples with 60 for training and 20 for testing. Each sample with over 2 million points represents a street scene and includes a couple of objects. There are five meaningful classes: building, car, ground, pole and vegetation. We aim at localizing and segmenting semantic objects from these large-scale 3D point clouds. Four groups contributed their results with different methods. The results show that learning-based methods are the trend and one of them achieves the best performance on both Overall Accuracy and mean Intersection over Union. Next to the learning-based methods, the combination of hand-crafted detectors are also reliable and rank second among comparison algorithms

SHREC 2020: 3D Point Cloud Semantic Segmentation for Street Scenes

Scene understanding of large-scale 3D point clouds of an outer space is still a challenging task. Compared with simulated 3D point clouds, the raw data from LiDAR scanners consist of tremendous points returned from all possible reflective objects and they are usually non-uniformly distributed. Therefore, its cost-effective to develop a solution for learning from raw large-scale 3D point clouds. In this track, we provide large-scale 3D point clouds of street scenes for the semantic segmentation task. The data set consists of 80 samples with 60 for training and 20 for testing. Each sample with over 2 million points represents a street scene and includes a couple of objects. There are five meaningful classes: building, car, ground, pole and vegetation. We aim at localizing and segmenting semantic objects from these large-scale 3D point clouds. Four groups contributed their results with different methods. The results show that learning-based methods are the trend and one of them achieves the best performance on both Overall Accuracy and mean Intersection over Union. Next to the learning-based methods, the combination of hand-crafted detectors are also reliable and rank second among comparison algorithms

Creep and drainage in the fast destabilization of emulsions

The destabilization of emulsions is important for many applications but remains incompletely understood. We perform squeeze flow measurements on oil-in-water emulsions, finding that the spontaneous destabilization of emulsions is generally very slow under normal conditions, with a characteristic time scale given by the drainage of the continuous phase and the coalescence of the dispersed phase. We show that if the emulsion is compressed between two plates, the destabilization can be sped up significantly; on the one hand, the drainage is faster due to the application of the squeezing force. On the other hand, creep processes lead to rearrangements that also contribute to the destabilization

Creep and drainage in the fast destabilization of emulsions

The destabilization of emulsions is important for many applications but remains incompletely understood. We perform squeeze flow measurements on oil-in-water emulsions, finding that the spontaneous destabilization of emulsions is generally very slow under normal conditions, with a characteristic time scale given by the drainage of the continuous phase and the coalescence of the dispersed phase. We show that if the emulsion is compressed between two plates, the destabilization can be sped up significantly; on the one hand, the drainage is faster due to the application of the squeezing force. On the other hand, creep processes lead to rearrangements that also contribute to the destabilization