Analysis and Treatment of a male portrait in oil and a study of the size layer with reconstructions from Vibert’s recipe (1892)

This thesis project concentrated on both the study and treatment of an early 20th century male portrait in oil from Colecção Caixa Geral de Depósitos, Lisbon, Portugal. The portrait of Januário Correia de Almeida, exhibits a tear (approximately 4.0 cm by 2.3 cm) associated with paint loss on the right upper side, where it is possible to observe an unusually thick size layer (approximately 50 microns) and an open weave mesh canvas. Size layers made from animal glue remain subject to severe dimensional changes due to changes in relative humidity (RH), thereby affecting the stability of the painting. In this case, the response to moisture of the size layer is minimal and the painting is largely uncracked with very little active flaking. This suggests that the size layer has undergone pre-treatment to render it unresponsive to moisture or water. Reconstructions based on late nineteenth century recipes using historically appropriate materials are used to explore various options for modifying the characteristics of gelatine, some of which may relate to the Portrait’s size layer. The thesis is separated into two parts: Part 1: Describes the history, condition, materials and techniques of the painting. It also details the treatment of Januário Correia de Almeida as well as the choices made and problems encountered during the treatment. Part 2: Discusses the history of commercial gelatine production, the choice of the appropriate animal source to extract the collagen to produce reconstructions of the portrait’s size layer as well as the characterization of selected reconstructions. The execution of a shallow textured infill led to one publication and one presentation: Abstract accepted for presentation and publication, International Meeting on Retouching of Cultural Heritage (RECH3), Francisco Brites, Leslie Carlyle and Raquel Marques, ‘’Hand building a Low Profile Textured Fill for a Large Loss’’.

Semantic relations extraction from unstructured information for domain ontologies enrichment

Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.

An agent based architecture to support monitoring in plug and produce manufacturing systems using knowledge extraction

In recent years a set of production paradigms were proposed in order to capacitate manufacturers to meet the new market requirements, such as the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production standardized consumables. These new paradigms advocate solutions capable of facing these requirements, empowering manufacturing systems with a high capacity to adapt along with elevated flexibility and robustness in order to deal with disturbances, like unexpected orders or malfunctions. Evolvable Production Systems propose a solution based on the usage of modularity and self-organization with a fine granularity level, supporting pluggability and in this way allowing companies to add and/or remove components during execution without any extra re-programming effort. However, current monitoring software was not designed to fully support these characteristics, being commonly based on centralized SCADA systems, incapable of re-adapting during execution to the unexpected plugging/unplugging of devices nor changes in the entire system’s topology. Considering these aspects, the work developed for this thesis encompasses a fully distributed agent-based architecture, capable of performing knowledge extraction at different levels of abstraction without sacrificing the capacity to add and/or remove monitoring entities, responsible for data extraction and analysis, during runtime.

Consulting project for the marketing oil department of Galp Energia evaluation of marketing campaigns

Field lab: Consulting lab

Noble energy oil & Gas

Equity research report

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Economics consulting project for the Iberian Marketing Oil department of Galp Energia: Strategy and Marketing guidelines for Galp Energia’sconvenience business in the Iberian market

Field lab: Consulting lab

A technical investigation of an oil painting on copper support, including a study on consolidants for treatment

Paper submitted to e-conservation Journal: Maria Leonor Oliveira, Leslie Carlyle, Sara Fragoso, Isabel Pombo Cardoso and João Coroado, “Investigations into paint delamination and consolidation of an oil painting on copper support”.

Consulting project for the Iberian marketing Oil department of Galp Energia: Strategy and marketing guidelines for Galp Energia's convenience business in the Iberian market

Field lab: Consulting lab