From a Point Cloud to a Simulation Model-Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling.

The 3D modelling of indoor environments and the generation of process simulations play an important role in factory and assembly planning. In brownfield planning cases, existing data are often outdated and incomplete especially for older plants, which were mostly planned in 2D. Thus, current environment models cannot be generated directly on the basis of existing data and a holistic approach on how to build such a factory model in a highly automated fashion is mostly non-existent. Major steps in generating an environment model of a production plant include data collection, data pre-processing and object identification as well as pose estimation. In this work, we elaborate on a methodical modelling approach, which starts with the digitalization of large-scale indoor environments and ends with the generation of a static environment or simulation model. The object identification step is realized using a Bayesian neural network capable of point cloud segmentation. We elaborate on the impact of the uncertainty information estimated by a Bayesian segmentation framework on the accuracy of the generated environment model. The steps of data collection and point cloud segmentation as well as the resulting model accuracy are evaluated on a real-world data set collected at the assembly line of a large-scale automotive production plant. The Bayesian segmentation network clearly surpasses the performance of the frequentist baseline and allows us to considerably increase the accuracy of the model placement in a simulation scene.

Design of a multiplex nested PCR for genotyping of the NSP4 from group A rotavirus.

A novel PCR method was developed to discriminate amongst genotypes A-C of the rotavirus non-structural protein 4 (NSP4). Genotype-specific primers were designed that correctly identified the NSP4 genotype when evaluated as a multiplex PCR with cell culture adapted rotavirus strains. Rotavirus strains B223 SGIG6P6[1], NCDV SGIG6P6[1] and SA11 SGIG3P5B[2] were used as control for NSP4 genotype A; A34 SGIG5P14[23], Gottfried SGIIG4P2B[6] and Wa SGIIG1P1A[8] for NSP4 genotype B; RRV SGIG3P5B[3] for NSP4 genotype C. Subsequently, the same set of specific primers was used to genotype a set of 77 Swedish clinical samples. The results showed that all human clinical samples analyzed belong to the NSP4 genotype B and the VP6 subgroup II.