5,694 research outputs found
Adapter module for self-learning production systems
Dissertação para obtenção do Grau de Mestre em
Engenharia Electrotécnica, Sistemas e ComputadoresThe dissertation presents the work done under the scope of the NP7 Self-Learning
project regarding the design and development of the Adapter component as a foundation
for the Self-Learning Production Systems (SLPS). This component is responsible to confer additional proprieties to production systems such as lifecycle learning, optimization of process parameters and, above all, adaptation to different production contexts. Therefore, the SLPS will be an evolvable system capable to self-adapt and learn in response to
dynamic contextual changes in manufacturing production process in which it operates.
The key assumption is that a deeper use of data mining and machine learning techniques
to process the huge amount of data generated during the production activities will allow
adaptation and enhancement of control and other manufacturing production activities
such as energy use optimization and maintenance. In this scenario, the SLPS Adapter acts as a doer and is responsible for dynamically adapting the manufacturing production system parameters according to changing manufacturing production contexts and, most important, according to the history of the manufacturing production process acquired during SLPS run time.To do this, a Learning Module has been also developed and embedded into the SLPS Adapter. The SLPS Learning Module represents the processing unit of the SLPS Adapter and is responsible to deliver Self-learning capabilities relying on data mining and operator’s feedback to up-date the execution of adaptation and context extraction at run time.
The designed and implemented SLPS Adapter architecture is assessed and validated
into several application scenario provided by three industrial partners to assure industrial relevant self-learning production systems. Experimental results derived by the application of the SLPS prototype into real industrial environment are also presented
Automotive gestures recognition based on capacitive sensing
Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresDriven by technological advancements, vehicles have steadily increased in
sophistication, specially in the way drivers and passengers interact with their
vehicles. For example, the BMW 7 series driver-controlled systems, contains
over 700 functions. Whereas, it makes easier to navigate streets, talk on phone
and more, this may lead to visual distraction, since when paying attention to
a task not driving related, the brain focus on that activity. That distraction is,
according to studies, the third cause of accidents, only surpassed by speeding
and drunk driving.
Driver distraction is stressed as the main concern by regulators, in particular,
National Highway Transportation Safety Agency (NHTSA), which is developing
recommended limits for the amount of time a driver needs to spend
glancing away from the road to operate in-car features. Diverting attention
from driving can be fatal; therefore, automakers have been challenged to design
safer and comfortable human-machine interfaces (HMIs) without missing
the latest technological achievements.
This dissertation aims to mitigate driver distraction by developing a gestural
recognition system that allows the user a more comfortable and intuitive
experience while driving. The developed system outlines the algorithms to recognize
gestures using the capacitive technology.Impulsionados pelos avanços tecnológicos, os automóveis tem de forma
continua aumentado em complexidade, sobretudo na forma como os conductores
e passageiros interagem com os seus veículos. Por exemplo, os sistemas
controlados pelo condutor do BMW série 7 continham mais de 700 funções.
Embora, isto facilite a navegação entre locais, falar ao telemóvel entre outros,
isso pode levar a uma distração visual, já que ao prestar atenção a uma tarefa
não relacionados com a condução, o cérebro se concentra nessa atividade. Essa
distração é, de acordo com os estudos, a terceira causa de acidentes, apenas
ultrapassada pelo excesso de velocidade e condução embriagada.
A distração do condutor é realçada como a principal preocupação dos reguladores,
em particular, a National Highway Transportation Safety Agency
(NHTSA), que está desenvolvendo os limites recomendados para a quantidade
de tempo que um condutor precisa de desviar o olhar da estrada para controlar
os sistemas do carro. Desviar a atenção da conducção, pode ser fatal; portanto,
os fabricante de automóveis têm sido desafiados a projetar interfaces homemmáquina
(HMIs) mais seguras e confortáveis, sem perder as últimas conquistas
tecnológicas.
Esta dissertação tem como objetivo minimizar a distração do condutor, desenvolvendo
um sistema de reconhecimento gestual que permite ao utilizador
uma experiência mais confortável e intuitiva ao conduzir. O sistema desenvolvido
descreve os algoritmos de reconhecimento de gestos usando a tecnologia
capacitiva.It is worth noting that this work has been financially supported by the Portugal Incentive System for Research and Technological Development in scope of the projects in co-promotion number 036265/2013 (HMIExcel 2013-2015), number 002814/2015 (iFACTORY 2015-2018) and number 002797/2015 (INNOVCAR 2015-2018)
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
An Adaptive User Interface Framework for eHealth Services based on UIML
New sensory technologies and smaller, more capable mobile devices open opportunities for pervasive computing in the healthcare sector. Patients as well as medical professionals are, from a information and communication technology (ICT) point of view, better equipped than ever before. Despite this, many hospitals and other healthcare service providers have yet to exploit the potential unleashed by these technologies. In this paper, we present a framework for adaptive user interfaces for home care and smart hospital services. The framework uses the current context to provide healthcare professionals or patients with simpler, more efficient user interfaces. In a home care environment, user interface adaption is needed to tailor user interfaces to patients needs and impairments. In a smart hospital, user interface adaption considers medical professionals’ preferences and priorities. In addition, by using context to make input suggestions simplifies the input and limits the scope for errors. Our frameworks uses a modelbased approach and includes the current context in the interface generation process
Process model for the successful implementation and demonstration of SME-based industry 4.0 showcases in global production networks
Small and medium-sized enterprises (SMEs), many of which operate as suppliers in global production networks (GPN), often times lack behind large enterprises in terms of Industry 4.0 implementation. For this reason, scientific contributions recommend SMEs to approach Industry 4.0 through pilot projects in which individual Industry 4.0 use cases are developed and implemented. Hence, to allow for a targeted development and implementation of Industry 4.0 use cases for SMEs in GPN, this paper proposes a five-step process model that seeks to make use of Industry 4.0 potentials in terms of increased product qualities and logistics performances within such networks. In contrast to existing process models, this paper follows a holistic approach that initially focuses on the identification of potential problems that impede increased product qualities and logistics performances. Building upon these problems, potential Industry 4.0 solutions are derived and transferred into use cases using a structured idea generation and selection process. After the successful implementation of the use case, the procedure is completed by the conversion of the use case into a showcase that might serve as a lighthouse project illustrating the potentials of Industry 4.0 for other production network partners. For testing its practicability, the procedure is exemplarily applied to the GPN of an automotive supplier
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