617 research outputs found
Geometric Multi-Model Fitting by Deep Reinforcement Learning
This paper deals with the geometric multi-model fitting from noisy,
unstructured point set data (e.g., laser scanned point clouds). We formulate
multi-model fitting problem as a sequential decision making process. We then
use a deep reinforcement learning algorithm to learn the optimal decisions
towards the best fitting result. In this paper, we have compared our method
against the state-of-the-art on simulated data. The results demonstrated that
our approach significantly reduced the number of fitting iterations
InvVis: Large-Scale Data Embedding for Invertible Visualization
We present InvVis, a new approach for invertible visualization, which is
reconstructing or further modifying a visualization from an image. InvVis
allows the embedding of a significant amount of data, such as chart data, chart
information, source code, etc., into visualization images. The encoded image is
perceptually indistinguishable from the original one. We propose a new method
to efficiently express chart data in the form of images, enabling
large-capacity data embedding. We also outline a model based on the invertible
neural network to achieve high-quality data concealing and revealing. We
explore and implement a variety of application scenarios of InvVis.
Additionally, we conduct a series of evaluation experiments to assess our
method from multiple perspectives, including data embedding quality, data
restoration accuracy, data encoding capacity, etc. The result of our
experiments demonstrates the great potential of InvVis in invertible
visualization.Comment: IEEE VIS 202
Exploration of the characteristics and trends of electric vehicle crashes: a case study in Norway
With the rapid growth of electric vehicles (EVs) in the past decade, many new traffic safety challenges are also emerging. With the crash data of Norway from 2011 to 2018, this study gives an overview of the status quo of EV crashes. In the survey period, the proportion of EV crashes in total traffic crashes had risen from zero to 3.11% in Norway. However, in terms of severity, EV crashes do not show statistically significant differences from the Internal Combustion Engine Vehicle (ICEV) crashes. Compared to ICEV crashes, the occurrence of EV crashes features on weekday peak hours, urban areas, roadway junctions, low-speed roadways, and good visibility scenarios, which can be attributed to the fact that EVs are mainly used for urban local commuting travels in Norway. Besides, EVs are confirmed to be much more likely to collide with cyclists and pedestrians, probably due to their low-noise engines. Then, the separate logistic regression models are built to identify important factors influencing the severity of ICEV and EV crashes, respectively. Many factors show very different effects on ICEV and EV crashes, which implies the necessity of reevaluating many current traffic safety strategies in the face of the EV era. Although the Norway data is analyzed here, the findings are expected to provide new insights to other countries also in the process of the complete automotive electrification
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