36,597 research outputs found
Towards a Tool-based Development Methodology for Pervasive Computing Applications
Despite much progress, developing a pervasive computing application remains a
challenge because of a lack of conceptual frameworks and supporting tools. This
challenge involves coping with heterogeneous devices, overcoming the
intricacies of distributed systems technologies, working out an architecture
for the application, encoding it in a program, writing specific code to test
the application, and finally deploying it. This paper presents a design
language and a tool suite covering the development life-cycle of a pervasive
computing application. The design language allows to define a taxonomy of
area-specific building-blocks, abstracting over their heterogeneity. This
language also includes a layer to define the architecture of an application,
following an architectural pattern commonly used in the pervasive computing
domain. Our underlying methodology assigns roles to the stakeholders, providing
separation of concerns. Our tool suite includes a compiler that takes design
artifacts written in our language as input and generates a programming
framework that supports the subsequent development stages, namely
implementation, testing, and deployment. Our methodology has been applied on a
wide spectrum of areas. Based on these experiments, we assess our approach
through three criteria: expressiveness, usability, and productivity
An improved approach for automatic process plan generation of complex borings
The authors are grateful for funding provided to this project by the French Ministry of Industry, Dassault Aviation, Dassault Systemes, and F. Vernadat for his review and recommendations.The research concerns automated generation of process plans using knowledge formalization and capitalization. Tools allowing designers to deal with issues and specifications of the machining domain are taken into account. The main objective of the current work is to prevent designers from designing solutions that would be expensive and difficult to machine. Among all available solutions to achieve this goal, two are distinguished: the generative approach and the analogy approach. The generative approach is more adapted to generate the machining plans of parts composed of numerous boring operations in interaction. However, generative systems have two major problems: proposed solutions are often too numerous and are only geometrically but not technologically relevant. In order to overcome these drawbacks, two new concepts of feature and three control algorithms are developed. The paper presents the two new features: the Machining Enabled Geometrical Feature (MEGF) and the Machinable Features (MbF). This development is the result of the separation of the geometrical and the technological data contained in one machining feature. The second objective of the paper is to improve the current Process Ascending Generation (PAG) system with control algorithms in order to limit the combinatorial explosion and disable the generation of unusable or not machinable solutions
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based
algorithms are becoming increasingly recognized by the machine learning
community. As a result, interest in probabilistic programming frameworks has
much increased over the past few years. This paper explores a specific
probabilistic programming paradigm, namely message passing in Forney-style
factor graphs (FFGs), in the context of automated design of efficient Bayesian
signal processing algorithms. To this end, we developed "ForneyLab"
(https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message
passing-based inference in FFGs. We show by example how ForneyLab enables
automatic derivation of Bayesian signal processing algorithms, including
algorithms for parameter estimation and model comparison. Crucially, due to the
modular makeup of the FFG framework, both the model specification and inference
methods are readily extensible in ForneyLab. In order to test this framework,
we compared variational message passing as implemented by ForneyLab with
automatic differentiation variational inference (ADVI) and Monte Carlo methods
as implemented by state-of-the-art tools "Edward" and "Stan". In terms of
performance, extensibility and stability issues, ForneyLab appears to enjoy an
edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate
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