13 resultados para Multi-input fuzzy inference system

em Universidade do Minho


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Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

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Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

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Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

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Dissertação de mestrado em Bioinformática

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This paper deals with the problem of estimation maintenance costs for the case of the pitch controls system of wind farms turbines. Previous investigations have estimated these costs as (traditional) “crisp” values, simply ignoring the uncertainty nature of data and information available. This paper purposes an extended version of the estimation model by making use of the Fuzzy Set Theory. The results alert decision-makers to consequent uncertainty of the estimations along with their overall level, thus improving the information given to the mainte-nance support system.

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A new integrated mathematical model for the simulation of an offshore wind system having a rectifier input voltage malfunction at one phase is presented in this paper. The mathematical model considers an offshore variable-speed wind turbine on a floating platform, equipped with a permanent magnet synchronous generator using full-power three-level converter to inject energy into the electric network, through a high voltage direct current transmission submarine cable. The model for the drive train is a discrete three mass, incorporating the dynamic of the moving surface. A case study is presented to access conclusion about the malfunction.

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Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores

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Tese de Doutoramento em Engenharia Civil.

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Dissertação de mestrado em Bioquímica Aplicada (área de especialização em Biotecnologia)