This paper presents a method to address system prognosis. It also details a successful application to complex vacuum pumping systems. The proposed approach relies on an automateddata-driven learning process as oppose...
详细信息
ISBN:
(纸本)9783642130212
This paper presents a method to address system prognosis. It also details a successful application to complex vacuum pumping systems. The proposed approach relies on an automateddata-driven learning process as opposed to hand-built models that are based on human expertise. More precisely, using historical vibratory data, we first model the behavior of a system by extracting a given type of episode rules, namely First Local Maximum episode rules (FLM-rules). A subset of the extracted FLM-rules is then selected in order to further predict pumping system failures in a datastream context. The results that we got for production data are very encouraging as we predict failures with a good time scale precision. We are now deploying our solution for a customer of the semi-conductor market.
Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Distances can be more easily estimated ...
详细信息
ISBN:
(数字)9781728141497
ISBN:
(纸本)9781728141503
Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Distances can be more easily estimated when the camera perspective is transformed to a bird's eye view (BEV). For flat surfaces, Inverse Perspective Mapping (IPM) can accurately transform images to a BEV. Three-dimensional objects such as vehicles and vulnerable road users are distorted by this transformation making it difficult to estimate their position relative to the sensor. This paper describes a methodology to obtain a corrected 360° BEV image given images from multiple vehicle-mounted cameras. The corrected BEV image is segmented into semantic classes and includes a prediction of occluded areas. The neural network approach does not rely on manually labeled data, but is trained on a synthetic dataset in such a way that it generalizes well to real-world data. By using semantically segmented images as input, we reduce the reality gap between simulated and real-world data and are able to show that our method can be successfully applied in the real world. Extensive experiments conducted on the synthetic data demonstrate the superiority of our approach compared to IPM. Source code and datasets are available at https://***/ika-rwth-aachen/Cam2BEV.
暂无评论