88 research outputs found
Mecanismos para controlo e gestão de redes 5G: redes de operador
In 5G networks, time-series data will be omnipresent for the monitoring of network
metrics. With the increase in the number of Internet of Things (IoT) devices
in the next years, it is expected that the number of real-time time-series
data streams increases at a fast pace. To be able to monitor those streams,
test and correlate different algorithms and metrics simultaneously and in a
seamless way, time-series forecasting is becoming essential for the pro-active
successful management of the network.
The objective of this dissertation is to design, implement and test a prediction
system in a communication network, that allows integrating various networks,
such as a vehicular network and a 4G operator network, to improve the network
reliability and Quality-of-Service (QoS). To do that, the dissertation has
three main goals: (1) the analysis of different network datasets and implementation
of different approaches to forecast network metrics, to test different
techniques; (2) the design and implementation of a real-time distributed
time-series forecasting architecture, to enable the network operator to make
predictions about the network metrics; and lastly, (3) to use the forecasting
models made previously and apply them to improve the network performance
using resource management policies.
The tests done with two different datasets, addressing the use cases of congestion
management and resource splitting in a network with a limited number
of resources, show that the network performance can be improved with proactive
management made by a real-time system able to predict the network
metrics and act on the network accordingly.
It is also done a study about what network metrics can cause reduced accessibility
in 4G networks, for the network operator to act more efficiently and
pro-actively to avoid such eventsEm redes 5G, séries temporais serão omnipresentes para a monitorização
de métricas de rede. Com o aumento do número de dispositivos da Internet
das Coisas (IoT) nos próximos anos, é esperado que o número de fluxos de
séries temporais em tempo real cresça a um ritmo elevado. Para monitorizar
esses fluxos, testar e correlacionar diferentes algoritmos e métricas simultaneamente
e de maneira integrada, a previsão de séries temporais está a
tornar-se essencial para a gestão preventiva bem sucedida da rede.
O objetivo desta dissertação é desenhar, implementar e testar um sistema
de previsão numa rede de comunicações, que permite integrar várias redes
diferentes, como por exemplo uma rede veicular e uma rede 4G de operador,
para melhorar a fiabilidade e a qualidade de serviço (QoS). Para isso,
a dissertação tem três objetivos principais: (1) a análise de diferentes datasets
de rede e subsequente implementação de diferentes abordagens para
previsão de métricas de rede, para testar diferentes técnicas; (2) o desenho
e implementação de uma arquitetura distribuída de previsão de séries temporais
em tempo real, para permitir ao operador de rede efetuar previsões
sobre as métricas de rede; e finalmente, (3) o uso de modelos de previsão
criados anteriormente e sua aplicação para melhorar o desempenho da rede
utilizando políticas de gestão de recursos.
Os testes efetuados com dois datasets diferentes, endereçando os casos de
uso de gestão de congestionamento e divisão de recursos numa rede com
recursos limitados, mostram que o desempenho da rede pode ser melhorado
com gestão preventiva da rede efetuada por um sistema em tempo real capaz
de prever métricas de rede e atuar em conformidade na rede.
Também é efetuado um estudo sobre que métricas de rede podem causar
reduzida acessibilidade em redes 4G, para o operador de rede atuar mais
eficazmente e proativamente para evitar tais acontecimentos.Mestrado em Engenharia de Computadores e Telemátic
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Mathematical Modeling and Simulation in Mechanics and Dynamic Systems
The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity
Bibliography of Lewis Research Center technical publications announced in 1993
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses
Renewable Energies for Sustainable Development
In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed
- …