Abstract

Background"/jats:title""jats:p"The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation and aid reproducibility of analyses. Many contemporary workflow tools are specialized and not designed for highly complex workflows, such as with nested loops, dynamic scheduling and parametriza-tion, which is common in e.g. machine learning."/jats:p""/jats:sec""jats:sec""jats:title"Findings"/jats:title""jats:p"SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on Flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development, and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX and PDF on-demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline."/jats:p""/jats:sec""jats:sec""jats:title"Conclusions"/jats:title""jats:p"SciPipe provides a solution for agile development of complex and dynamic pipelines, espe-cially in machine leaning, through a flexible programming API suitable for scientists used to programming or scripting."/jats:p""/jats:sec Document type: Article

Abstract

Recently, the developments in the field of ADA systems shifted from comfort oriented systems to safety oriented systems. Consequently, the complexity of testing those systems increased. This leads to a demand for reproducible, efficient and safe testing methods for ADA systems in general, and pre-crash systems in particular. A method for pre-crash systems evaluation is presented, involving TNO's Vehicle Hardware-inthe-Loop facility (VeHIL) and its Crash Lab. This method, called VeHIL/Pre-crash, aims to support the development and verification of pre-crash systems, increasing the level of reproducibility, effectiveness and safety of the testing process. To this end, a test vehicle, positioned on a roller bench, is placed in a simulated traffic environment. In order to provide the test vehicle's environment sensor input, one of the simulated traffic participants is represented by a rail guided target vehicle, which is stopped by crumple tubes. Although the realization of this setup is not straightforward due to practical aspects such as high decelerations of the target and the challenge to establish a realistic sensory input in a confined environment, it is considered a promising development allowing for nondestructive testing of pre-crash systems in a hardwarein-the-loop setup. © 2008 IEEE.