944 research outputs found
An approach to open virtual commissioning for component-based automation
Increasing market demands for highly customised products with shorter time-to-market and
at lower prices are forcing manufacturing systems to be built and operated in a more efficient
ways. In order to overcome some of the limitations in traditional methods of automation
system engineering, this thesis focuses on the creation of a new approach to Virtual
Commissioning (VC).
In current VC approaches, virtual models are driven by pre-programmed PLC control
software. These approaches are still time-consuming and heavily control expertise-reliant as
the required programming and debugging activities are mainly performed by control
engineers. Another current limitation is that virtual models validated during VC are difficult
to reuse due to a lack of tool-independent data models. Therefore, in order to maximise the
potential of VC, there is a need for new VC approaches and tools to address these limitations.
The main contributions of this research are: (1) to develop a new approach and the related
engineering tool functionality for directly deploying PLC control software based on
component-based VC models and reusable components; and (2) to build tool-independent
common data models for describing component-based virtual automation systems in order to
enable data reusability. [Continues.
'HighChest': An augmented freezer designed for smart food management and promotion of eco-efficient behaviour
This paper introduces HighChest, an innovative smart freezer designed to promote energy efficient behavior and the responsible use of food. Introducing a novel humanâmachine interface (HMI) design developed through assessment phases and a user involvement stage, HighChest is state of the art, featuring smart services that exploit embedded sensors and Internet of things functionalities, which enhance the local capabilities of the appliance. The industrial design thinking approach followed for the advanced HMI is intended to maximize the social impact of the food management service, enhancing both the user experience of the product and the userâs willingness to adopt eco- and energy-friendly behaviors. The sensor equipment realizes automatic recognition of food by learning from the users, as well as automatic localization inside the deposit space. Moreover, it provides monitoring of the applianceâs usage, avoiding temperature and humidity issues related to improper use. Experimental tests were conducted to evaluate the localization system, and the results showed 100% accuracy for weights greater or equal to 0.5 kg. Drifts due to the lid opening and prolonged usage time were also measured, to implement automatic reset corrections
SNAVA—A real-time multi-FPGA multi-model spiking neural network simulation architecture
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities.Peer ReviewedPostprint (author's final draft
Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
Human Machine Interaction
In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction
Intelligent Machining Systems
Machining is one of the most widespread manufacturing processes and plays a critical role
in industries. As a matter of fact, machine tools are often called mother machines as they
are used to produce other machines and production plants. The continuous development
of innovative materials and the increasing competitiveness are two of the challenges that
nowadays manufacturing industries have to cope with. The increasing attention to environmental
issues and the rising costs of raw materials drive the development of machining
systems able to continuously monitor the ongoing process, identify eventual arising problems
and adopt appropriate countermeasures to resolve or prevent these issues, leading
to an overall optimization of the process. This work presents the development of intelligent
machining systems based on in-process monitoring which can be implemented on
production machines in order to enhance their performances. Therefore, some cases of
monitoring systems developed in different fields, and for different applications, are presented
in order to demonstrate the functions which can be enabled by the adoption of
these systems. Design and realization of an advanced experimental machining testbed is
presented in order to give an example of a machine tool retrofit aimed to enable advanced
monitoring and control solutions. Finally, the implementation of a data-driven simulation
of the machining process is presented. The modelling and simulation phases are presented
and discussed. So, the model is applied to data collected during an experimental campaign
in order to tune it. The opportunities enabled by integrating monitoring systems
with simulation are presented with preliminary studies on the development of two virtual
sensors for the material conformance and cutting parameter estimation during machining
processes
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