14,559 research outputs found
Computation of Components' Interfaces in Highly Complex Assemblies
International audienceThe preparation of CAD models from complex assemblies for simulation purposes is a very time-consuming and tedious process, since many tasks \rev{such as meshing and idealization} are still completed manually. Herein, the detection and extraction of geometric interfaces between components of the assembly is of central importance not only for the simulation objectives but also for all necessary shape transformations such as idealizations or detail removals. It is a repetitive task in particular when complex assemblies have to be dealt with. This paper proposes a method to rapidly and fully automatically generate a precise geometric description of interfaces in generic B-Rep CAD models. The approach combines an efficient GPU ray-casting technique commonly used in computer graphics with a graph-based curve extraction algorithm. Not only is it able to detect a large number of interfaces efficiently, but it also provides an accurate Nurbs geometry of the interfaces, that can be stored in a plain STEP file ~\cite{step:1994} for further downstream treatment. We demonstrate our approach on examples from aeronautics and automotive industry
Analysis and control of complex collaborative design systems
This paper presents a novel method for modelling the complexity of collaborative design systems based on its analysis and proposes a solution to reducing complexity and improving performance of such systems. The interaction and interfacing properties among many components of a complex design system are analysed from different viewpoints and then a complexity model for collaborative design is established accordingly. In order to simplify complexity and improve performance of collaborative design, a general solution of decomposing a whole system into sub-systems and using unified interface mechanism between them has been proposed. This proposed solution has been tested with a case study. It has been shown that the proposed solution is meaningful and practical
Evaluating Component Assembly Specialization for 3D FFT
The Fast Fourier Transform (FFT) is a widely-used building block for many high-performance scienti c applications. Ef-
cient computing of FFT is paramount for the performance of these applications. This has led to many e orts to implement
machine and computation speci c optimizations. However, no existing FFT library is capable of easily integrating and au-
tomating the selection of new and/or unique optimizations.
To ease FFT specialization, this paper evaluates the use of component-based software engineering, a programming paradigm
which consists in building applications by assembling small software units. Component models are known to have many software
engineering bene ts but usually have insucient performance for high-performance scienti c applications.
This paper uses the L2C model, a general purpose high-performance component model, and studies its performance and
adaptation capabilities on 3D FFTs. Experiments show that L2C, and components in general, enables easy handling of 3D FFT
specializations while obtaining performance comparable to that of well-known libraries. However, a higher-level component
model is needed to automatically generate an adequate L2C assembly
Deep Space Network information system architecture study
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
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