Mestrado em Informática

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Agora exibindo 1 - 5 de 273

FrameWeb-LD : uma abordagem baseada em ontologias para a Integração de Sistemas de Informação Web e a Web Semântica
(Universidade Federal do Espírito Santo, 2017-11-20) Celino, Danillo Ricardo; Vítor Estêvão Silva Souza; https://orcid.org/0000-0003-1869-5704; http://lattes.cnpq.br/2762374760685577; https://orcid.org/0000-0002-6570-2164; http://lattes.cnpq.br/6786947145681297; Almeida, João Paulo Andrade; https://orcid.org/; http://lattes.cnpq.br/; Siqueira, Frank Augusto; https://orcid.org/; http://lattes.cnpq.br/
With the enormous amount of data available on the Web, Linked Data technologies have been proposed to try and achieve the vision of the Semantic Web, allowing the efficient access, discovery and combination of the available data. Such data should be published in a structured way and bound to known vocabularies, so they can be understood by software agents. Moreover, the abstract conceptual models behind this data, i.e., their ontologies, can also have a great influence in the adoption of a Linked Data set and its vocabularies. In 2007, a Web Engineering method for the design and development of Web applications based on frameworks, named FrameWeb, was proposed, along with an extension of the method, called S-FrameWeb, that proposed the subsequent integration of the applica tion’s data with the Semantic Web. Given the advances of the literature in this area of research, such as well-founded ontologies and the evolution of Linked Data technologies, we propose an evolution of S-FrameWeb called FrameWeb-LD, an approach for the integration of Web-based Information Systems with the Semantic Web. Our proposal uses well-founded languages and methods for the construction of ontologies and aids developers in publishing the application’s data and services in the Web of Data, by offering a system atic process that brings to architectural design models how the data from the system is integrated with Semantic Web vocabularies and a tool that generates automatically most of the source code related to Linked Data publishing
Lane marking detection and classification using spatial-temporal feature pooling
(Universidade Federal do Espírito Santo, 2023-07-31) Torres, Lucas Tabelini; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; https://orcid.org/0000-0001-5371-6692; http://lattes.cnpq.br/0954275990134963; Moreira, Gladston Juliano Prates; https://orcid.org/0000-0001-7747-5926; http://lattes.cnpq.br/9902619084565293; Varejão, Flavio Miguel; https://orcid.org/0000-0002-5444-1974; http://lattes.cnpq.br/6501574961643171
The lane detection problem has been extensively researched in the past decades, especially since the advent of deep learning. Despite the numerous works proposing solutions to the localization task (i.e., localizing the lane boundaries in an input image), the classification task has not seen the same focus. Nonetheless, knowing the type of lane boundary, particularly that of the ego lane, can be very useful for many applications. For instance, a vehicle might not be allowed by law to overtake depending on the type of the ego lane. Beyond that, very few works take advantage of the temporal information available in the videos captured by the vehicles: most methods employ a single-frame approach. In this work, building upon the recent deep learning-based model LaneATT, we propose an approach to exploit the temporal information and integrate the classification task into the model. This is accomplished by extracting features from multiple frames using a deep neural network (instead of only one as in LaneATT). Our results show that the proposed modifications can improve the detection performance on the most recent benchmark (VIL-100) by 2.34%, establishing a new state-of-the-art. Finally, an extensive evaluation shows that it enables a high classification performance (89.37%) that serves as a future benchmark for the field.
Predicting temperature in blast furnaces using machine learning regression methods
(Universidade Federal do Espírito Santo, 2023-03-07) Navarro, Letícia Carvalheiro; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; http://lattes.cnpq.br/8545926240379536; Souza, Alberto Ferreira de; https://orcid.org/0000000315618447; http://lattes.cnpq.br/7573837292080522; Cavalcanti, George Darmiton da Cunha; https://orcid.org/0000-0001-7714-2283; http://lattes.cnpq.br/8577312109146354
In the iron and steel industry, the stable operation of blast furnaces with efficient hot metal temperature monitoring and control is a very important task in the process to generate high-quality hot metal. In general, the operation of blast furnaces mostly relies on experience based decisions of human operators, which use the most recent measures of hot metal temperature and other operational variables to execute control decisions. However, due to the large number of variables and complex interaction among them, the operation of such equipment is not an easy task. This work proposes a prediction system as the first step of a larger and more complex control system for improving the efficiency of iron production considering the scenario in Brazil. It compares several machine learning models (K-Nearest Neighbors, Linear Regression, Extreme Boosting Machine, Light Gradient Boosting Machine, Random Forest, Support Vector Machine, XGBoost, and Multilayer Perceptron) in the task of hot metal temperature prediction. A good temperature prediction system will allow to better plan the control actions ahead in order to stabilize the furnace temperature during hot metal production. The proposed method was evaluated using real-world data from an steel-producing company. Results shown that the system can predict the hot metal temperature with mean absolute error of 9.56 when compared to the baselines with mean average error of 12.61.
Ontologically correct taxonomies by construction: a graph grammar-based approach
(Universidade Federal do Espírito Santo, 2022-03-25) Batista, Jeferson de Oliveira; Almeida, João Paulo Andrade; https://orcid.org/0000-0002-9819-3781; http://lattes.cnpq.br/4332944687727598; https://orcid.org/0000000250264819; http://lattes.cnpq.br/7310031541080438; Souza, Vitor Estevão Silva; https://orcid.org/0000000318695704; http://lattes.cnpq.br/2762374760685577; Sales, Tiago Prince; https://orcid.org/0000-0002-5385-5761; http://lattes.cnpq.br/8436504586462308
Taxonomies play a central role in conceptual domain modeling, having a direct impact in areas such as knowledge representation, ontology engineering, and software engineering, as well as knowledge organization in information sciences. Despite this, there is little guidance on how to build high-quality taxonomies, with notable exceptions being the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of rigidity and sortality of types to establish wellfounded rules on the formation of taxonomic structures. The rigidity meta-property defines whether a type applies essentially or contingently to its instances, while the sortality defines whether a type provides a uniform principle of identity for its instances. In this dissertation, we show how to leverage the formal rules underlying these techniques in order to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. In addition to considering the ontological micro-theory of endurant types underlying OntoClean and OntoUML, we also employ the MLT (Multi-Level Theory) micro-theory of high-order types, which allows us to address multi-level taxonomies based on the powertype pattern, in which an entity can be both a type and an instance at the same time. To validate our proposal, we formalize the model building operations as a graph grammar that incorporates both microtheories. A graph grammar is a formal way to specify an initial graph and a set of graph transformation rules. Each graph represents a model, in our case, a taxonomy. A transformation rule consists of preconditions that must be true for a model in order to the rule be applicable, and a set of creation and deletion operations for vertices and edges. The set of models reachable applying the grammar rules is called the grammar language. We apply automatic verification techniques over the grammar language to show that the graph grammar is sound, i.e., that all taxonomies produced by the grammar rules are correct, at least up to a certain size. We also show that the rules can generate all correct taxonomies up to a certain size (a completeness result).
Uso de Redes Adversárias Geradoras Condicionais para Construção de Modelos de Velocidades Sísmicas
(Universidade Federal do Espírito Santo, 2022-10-27) Saraiva, Marcus Vinicius de Oliveira; Rauber, Thomas Walter; https://orcid.org/0000000263806584; http://lattes.cnpq.br/0462549482032704; https://orcid.org/; http://lattes.cnpq.br/; Silva, Avelino Forechi; https://orcid.org/; http://lattes.cnpq.br/; Machado, Marcos de Carvalho; https://orcid.org/; http://lattes.cnpq.br/; Rey, Antonio Cosme Del; https://orcid.org/; http://lattes.cnpq.br/; Krohling, Renato Antonio; http://lattes.cnpq.br/5300435085221378
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