5,654 research outputs found
Learning Opposites with Evolving Rules
The idea of opposition-based learning was introduced 10 years ago. Since then
a noteworthy group of researchers has used some notions of oppositeness to
improve existing optimization and learning algorithms. Among others,
evolutionary algorithms, reinforcement agents, and neural networks have been
reportedly extended into their opposition-based version to become faster and/or
more accurate. However, most works still use a simple notion of opposites,
namely linear (or type- I) opposition, that for each assigns its
opposite as . This, of course, is a very naive estimate of
the actual or true (non-linear) opposite , which has been
called type-II opposite in literature. In absence of any knowledge about a
function that we need to approximate, there seems to be no
alternative to the naivety of type-I opposition if one intents to utilize
oppositional concepts. But the question is if we can receive some level of
accuracy increase and time savings by using the naive opposite estimate
according to all reports in literature, what would we be able to
gain, in terms of even higher accuracies and more reduction in computational
complexity, if we would generate and employ true opposites? This work
introduces an approach to approximate type-II opposites using evolving fuzzy
rules when we first perform opposition mining. We show with multiple examples
that learning true opposites is possible when we mine the opposites from the
training data to subsequently approximate .Comment: Accepted for publication in The 2015 IEEE International Conference on
Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke
Power transformers thermal modeling using an Enhanced Set-Membership Multivariable Gaussian Evolving Fuzzy System
Knowledge of temperature distribution in power transformers is essential for the management of electrical distribution systems. Monitoring the hot-spot temperature of a power transformer can extend its lifetime. In this work, we present two new models based on Set-Membership filtering: the Set-Membership evolving Multivariable Gaussian and the Enhanced Set-Membership evolving Multivariable Gaussian. Both approaches are acting by adjusting the learning rate in the evolving fuzzy modeling system. To evaluate its performance were applied synthetic data sets, as benchmarks, and data for thermal modeling of real power transformers, under two load conditions: with and without an overload condition. The obtained results are compared with the performance of the original evolving Multivariable Gaussian and with other classical models suggested in the literature. Both proposed models obtained lower errors and presenting a competitive number of rules,
suggesting that the models are flexible and efficient approaches in these scenarios.O conhecimento da distribuição de temperatura em transformadores de potência é essencial para o gerenciamento de sistemas de distribuição elétrica. O monitoramento da temperatura do ponto quente de um transformador de energia pode estender sua vida útil. Neste trabalho, apresentamos dois novos modelos baseados na filtragem Set-Membership: o Set-Membership evolutivo Gaussiano Multivariado e o Enhanced Set-Membership evolutivo Gaussiano Multivariado. Ambas as abordagens agem ajustando a taxa de aprendizagem no sistema de modelagem fuzzy evolutivo. Para avaliar seu desempenho foram aplicados
conjuntos de dados sintéticos, como benchmarks, e dados para modelagem térmica de transformadores de potência reais, sob duas condições de carga: com e sem sobrecarga. Os resultados obtidos são comparados com o desempenho do modelo evolutivo Gaussiano Multivariado original e com outros modelos clássicos sugeridos na literatura. Ambos os modelos propostos obtiveram erros menores e apresentam número competitivo de regras, sugerindo que os modelos são abordagens flexíveis e eficientes nestes cenários.PROQUALI (UFJF
A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing
The idea of social participatory sensing provides a substrate to benefit from
friendship relations in recruiting a critical mass of participants willing to
attend in a sensing campaign. However, the selection of suitable participants
who are trustable and provide high quality contributions is challenging. In
this paper, we propose a recruitment framework for social participatory
sensing. Our framework leverages multi-hop friendship relations to identify and
select suitable and trustworthy participants among friends or friends of
friends, and finds the most trustable paths to them. The framework also
includes a suggestion component which provides a cluster of suggested friends
along with the path to them, which can be further used for recruitment or
friendship establishment. Simulation results demonstrate the efficacy of our
proposed recruitment framework in terms of selecting a large number of
well-suited participants and providing contributions with high overall trust,
in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Scenario of the organic food market in Europe
Scenario analysis is a qualitative tool for strategic policy analysis that enables researchers and policymakers
to support decision making, and a systemic analysis of the main determinants of a business or sector.
In this study, a scenario analysis is developed regarding the future development of the market of organic
food products in Europe. The scenario follows a participatory approach, exploiting potential interactions
among the relevant driving forces, as selected by experts. Network analysis is used to identify the roles of
driving forces in the different scenarios, and the results are discussed in comparison with the main findings
from existing scenarios on the future development of the organic sector
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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