Application of the multi-level time-harmonic fast multipole BEM to 3-D visco-elastodynamics

Engineering Analysis with Boundary elements (accepted, to appear)International audienceThis article extends previous work by the authors on the single- and multi-domain time-harmonic elastodynamic multi-level fast multipole BEM formulations to the case of weakly dissipative viscoelastic media. The underlying boundary integral equation and fast multipole formulations are formally identical to that of elastodynamics, except that the wavenumbers are complex-valued due to attenuation. Attention is focused on evaluating the multipole decomposition of the viscoelastodynamic fundamental solution. A damping-dependent modification of the selection rule for the multipole truncation parameter, required by the presence of complex wavenumbers, is proposed. It is empirically adjusted so as to maintain a constant accuracy over the damping range of interest in the approximation of the fundamental solution, and validated on numerical tests focusing on the evaluation of the latter. The proposed modification is then assessed on 3D single-region and multi-region visco-elastodynamic examples for which exact solutions are known. Finally, the multi-region formulation is applied to the problem of a wave propagating in a semi-infinite medium with a lossy semi-spherical inclusion (seismic wave in alluvial basin). These examples involve problem sizes of up to about $3\,10^{5}$ boundary unknowns

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

MAGIC and H.E.S.S. detect VHE gamma rays from the blazar OT081 for the first time: a deep multiwavelength study

https://pos.sissa.it/395/815/pdfPublished versio

Model-Based-Systems-Engineering (MBSE) as a Gamechanger in the Development Process of Railway Vehicles

For a majority of underperforming railway vehicle development projects, the failure can be traced back to an insufficient handling of the requirements. This exemplifies the risk caused by a missing or inconsistent Systems Engineering (SE) approach. SE and subsequently Model-Based-Systems-Engineering (MBSE) aims to provide the missing link between requirements, functions and architecture and system design. As the awareness of SE is still small in the railway community, so it is decided to implement a MBSE approach as a use-case for a subsystem to demonstrate the benefits

On the modelling of the friction characteristics of railway vehicle brakes

The rules and standards that define the requirements and methods for the layout of friction brake systems for railway vehicles still demand extensive experimental surveys since the general confidence in the theoretical predictability of the brake pad friction behaviour is limited. In fact, a review of numerous measurements from dynamometer test rigs exposes a large variation of the friction characteristics. Nevertheless, these measurements could be exploited to develop an elaborate friction modelling approach that includes deterministic and stochastic influences. The comparison of vehicle measurements from field tests with simulation results reveals that a significant improvement of the theoretical predictability of braking distances is within reach. Consequently, this applies as well for a more virtual layout and acceptance procedure for railway vehicle brake systems in the future

A Method for learning a Fault Detection Model from Component Communication Data in Robotic Systems

Golombek R, Wrede S, Hanheide M, Heckmann M. A Method for learning a Fault Detection Model from Component Communication Data in Robotic Systems. In: Seventh IARP Workshop on Technical Challenges for Dependable Robots in Human Environments. 2010: 9-14

On-line Data-Driven Fault Detection for Robotic Systems

Golombek R, Wrede S, Hanheide M, Martin H. On-line Data-Driven Fault Detection for Robotic Systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; 2011: 3011-3016

Learning a Probabilistic Error Detection Model for Robotic Systems

Golombek R, Wrede S, Hanheide M, Heckmann M. Learning a Probabilistic Error Detection Model for Robotic Systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. 2010: 2745-2750.In order to address the problem of failure detection in the robotics domain, we present in this contribution an error detection model, based on the system’s internal data exchange and the inherent dynamics of inter-component communication. The model is strongly data driven and provides an anomaly detector for robotics systems both applicable in-situ at runtime as well as a-posteriori in post-mortem analysis. Current architectures or methods for failure detection in autonomous robots are either implementations of watch dog concepts or are based on excessive amounts of domain-specific error detection code. The approach presented in this contribution provides an avenue for the detection of more subtle anomalies originating from external sources such as the environment itself or system failures such as resource starvation. Additionally, developers are alleviated from explicitly modeling and foreseeing every exceptional situation, instead training the presented probabilistic model with the known normal modes within the specification of the robot system. As we developed and evaluated the self-awareness model on a mobile robot platform featuring an event-driven software architecture, the presented method can easily be applied in other current robotics software architectures