DSpace Collection:http://hdl.handle.net/10174/1572024-03-28T13:53:08Z2024-03-28T13:53:08ZPM-1758: from handwritten records to its linksOlival, FernandaVieira, RenataCameron, HelenaSantos, IvoSantos, JoaquimSequeira, Oféliahttp://hdl.handle.net/10174/290592023-01-04T17:23:22Z2020-12-04T00:00:00ZTitle: PM-1758: from handwritten records to its links
Authors: Olival, Fernanda; Vieira, Renata; Cameron, Helena; Santos, Ivo; Santos, Joaquim; Sequeira, Ofélia
Abstract: Teve-se em vista apresentar a equipa interdisciplinar, os seus seus objetivos, o corpus documental e os seus problemas.2020-12-04T00:00:00ZSimulation of a Billet Heating FurnaceCavaleiro Costa, SérgioMalico, IsabelSantos, DanielBarão, MiguelGonçalves, TeresaRato, LuísCanhoto, PauloLima, Rui PedroOliveira, SofiaFontes, PauloCravo, Susanahttp://hdl.handle.net/10174/267582020-02-04T10:49:18Z2019-08-31T23:00:00ZTitle: Simulation of a Billet Heating Furnace
Authors: Cavaleiro Costa, Sérgio; Malico, Isabel; Santos, Daniel; Barão, Miguel; Gonçalves, Teresa; Rato, Luís; Canhoto, Paulo; Lima, Rui Pedro; Oliveira, Sofia; Fontes, Paulo; Cravo, Susana
Abstract: This work presents the method developed in the scope of the “Audit Furnace” project to support the manufacturing industry in understanding the energy efficiencies of its furnaces and to identify strategies for the continuous improvement of its processes. A digital representation to support the development, calibration, and training of a physical-based reduced-order model for industrial furnaces is sought by integrating experimental data obtained in energy audits performed at several industrial units with detailed numerical results from computational fluid dynamics simulations of the furnaces. Composite models with two blocks, a physics-based reduced-order block, and a machine learning model block, are proposed in order to simultaneously achieve performance and flexibility in its adaptation to different furnaces, while keeping the computational load in acceptable levels. In this paper, preliminary results of the application of the method to a billet heating furnace are presented, namely the results of the computational fluid dynamics simulations of the furnace and their comparison with the measurements performed in an energy audit. This is the first, essential step of the proposed method. The numerical results generated will allow calibrating and training the reduced-order model and will feed the machine learning model training process.2019-08-31T23:00:00ZClustering of Gaussian Random Vector Fields in Multiple Trajectory ModellingBarão, MiguelMarques, Jorge Salvadorhttp://hdl.handle.net/10174/249692019-02-26T16:56:51Z2018-06-03T23:00:00ZTitle: Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling
Authors: Barão, Miguel; Marques, Jorge Salvador
Abstract: This paper concerns the estimation of multiple dynamical models from a set of observed trajectories. It proposes vector valued gaussian random fields, representing dynamical models and their vector fields, combined with a modified k- means clustering algorithm to assign observed trajectories to models. The assignment is done according to a likelihood function obtained from applying the random field associated to a cluster, to the data. The algorithm is shown to have several advantages when compared with others: 1) it does not depend on a grid, region of interest, grid resolution or interpolation method; 2) the estimated vector fields has an associated uncertainty which is given by the algorithm and taken into account. The paper presents results obtained on synthetic trajectories that illustrate the performance of the proposed algorithm.2018-06-03T23:00:00ZGaussian random field-based log odds occupancy mappingLi, HongjunBarão, MiguelRato, Luíshttp://hdl.handle.net/10174/249622019-02-26T15:42:55Z2018-05-23T23:00:00ZTitle: Gaussian random field-based log odds occupancy mapping
Authors: Li, Hongjun; Barão, Miguel; Rato, Luís
Abstract: This paper focuses on mapping problem with known robot pose in static environments and proposes a Gaussian random field-based log odds occupancy mapping (GRF-LOOM). In this method, occupancy probability is regarded as an unknown parameter and the dependence between parameters are considered. Given measurements and the dependence, the parameters of not only observed space but also unobserved space can be predicted. The occupancy probabilities in log odds form are regarded as a GRF. This mapping task can be solved by the well-known prediction equation in Gaussian processes, which involves an inverse problem. Instead of the prediction equation, a new recursive algorithm is also proposed to avoid the inverse problem. Finally, the proposed method is evaluated in simulations.2018-05-23T23:00:00Z