1,109 research outputs found

    On the Extensibility of Formal Methods Tools

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    Modern software systems often have long lifespans over which they must continually evolve to meet new, and sometimes unforeseen, requirements. One way to effectively deal with this is by developing the system as a series of extensions. As requirements change, the system evolves through the addition of new extensions and, potentially, the removal of existing extensions. In order for this kind of development process to thrive, it is necessary that the system have a high level of extensibility. Extensibility is the capability of a system to support the gradual addition of new, unplanned functionalities. This dissertation investigates extensibility of software systems and focuses on a particular class of software: formal methods tools. The approach is broad in scope. Extensibility of systems is addressed in terms of design, analysis and improvement, which are carried out in terms of source code and software architecture. For additional perspective, extensibility is also considered in the context of formal modelling. The work carried out in this dissertation led to the development of various extensions to the Overture tool supporting the Vienna Development Method, including a new proof obligation generator and integration with theorem provers. Additionally, the extensibility of Overture itself was also improved and it now better supports the development and integration of various kinds of extensions. Finally, extensibility techniques have been applied to formal modelling, leading to an extensible architectural style for formal models

    Post-processing and visualisation of large-scale DEM simulation data with the open-source VELaSSCo platform

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    Regardless of its origin, in the near future the challenge will not be how to generate data, but rather how to manage big and highly distributed data to make it more easily handled and more accessible by users on their personal devices. VELaSSCo (Visualization for Extremely Large-Scale Scientific Computing) is a platform developed to provide new visual analysis methods for large-scale simulations serving the petabyte era. The platform adopts Big Data tools/architectures to enable in-situ processing for analytics of engineering and scientific data and hardware-accelerated interactive visualization. In large-scale simulations, the domain is partitioned across several thousand nodes, and the data (mesh and results) are stored on those nodes in a distributed manner. The VELaSSCo platform accesses this distributed information, processes the raw data, and returns the results to the users for local visualization by their specific visualization clients and tools. The global goal of VELaSSCo is to provide Big Data tools for the engineering and scientific community, in order to better manipulate simulations with billions of distributed records. The ability to easily handle large amounts of data will also enable larger, higher resolution simulations, which will allow the scientific and engineering communities to garner new knowledge from simulations previously considered too large to handle. This paper shows, by means of selected Discrete Element Method (DEM) simulation use cases, that the VELaSSCo platform facilitates distributed post-processing and visualization of large engineering datasets

    An Overview of the FAUST Developer Ecosystem

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    International audienceThe FAUST language has been designed to provide developers an alternative to C/C++ code, to easily develop and deploy DSP algorithms, effects, instruments etc. The ecosystem is composed of the language and its compiler, as well as different components that help test, benchmark and optimize, and run the resulting code on a large variety of platforms. In this paper we present various architectures files, optimization and testing tools, that have been developed over the years as part of the FAUST ecosystem, in order to expand the use of the compiler on various targets, and help developers optimize their DSP code. Some of them were publicly announced and can help when deploying DSPs, some are more experimental to be tested by more adventurous developers

    Semi-Autonomous Behaviour Tree-Based Framework for Sorting Electric Vehicle Batteries Components

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    The process of recycling electric vehicle (EV) batteries currently represents a significant challenge to the waste management automation industry. One example of it is the necessity of removing and sorting dismantled components from EV battery pack. This paper proposes a novel framework to semi-automate the process of removing and sorting different objects from an EV battery pack using a mobile manipulator. The work exploits the Behaviour Trees model for cognitive task execution and monitoring, which links different robot capabilities such as navigation, object tracking and motion planning in a modular fashion. The framework was tested in simulation, in both static and dynamic environments, and it was evaluated based on task time and the number of objects that the robot successfully placed in the respective containers. Results suggested that the robot’s success rate in accomplishing the task of sorting the battery components was 95% and 82% in static and dynamic environments, respectively
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