2017
Authors
Pinto, JR; Tavares, JMRS;
Publication
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE
Abstract
This article presents the design and evaluation of an algorithm for urinary bladder segmentation in medical images, from contrastless computed tomography studies of patients suffering from bladder wall tumours. These situations require versatile methods of segmentation, able to adapt to the structural changes the tumours provoke in the bladder wall, reflected as irregularities on the images obtained, creating adversities to the segmentation process. This semi-automatic method uses fuzzy c-means clustering, a Gaussian-curve-based intensity transformation, and active contour models, requiring only the physician's input of a single seed point for each anatomical view, in order to segment the bladder volume in all frames that include it. The performance of the method was evaluated on eight patients of The Cancer Genome Atlas-Urothelial Bladder Carcinoma collection, achieving approximately 79% of successful segmentations for small tumour patients (below 2.0 cm of diameter) and approximately 72% between 2.0 and 2.9 cm. Successful segmentations for small tumour patients presented an average of 3.7 mm Hausdorff distance and 91.0% degree of overlap. The promising performance attained, especially for small tumour patients, revealed a high potential of this method to serve as basis for an effective early-stage bladder wall tumour computer-aided diagnosis system.
2017
Authors
Faia, R; Pinto, T; Sousa, T; Vale, Z; Corchado, JM;
Publication
CEUR Workshop Proceedings
Abstract
This paper proposes a case-based reasoning methodology to automatically choose the most appropriate optimization algorithms and respective parameterizations to solve the problem of optimal resource scheduling in smart energy grids. The optimal resource scheduling is, however, a heavy computation problem, which deals with a large number of variables. Moreover, depending on the time horizon of this optimization, fast response times are usually required, which makes it impossible to apply traditional exact optimization methods. For this reason, the application of metaheuristic methods is the natural solution, providing near-optimal solutions in a much faster execution time. Choosing which optimization approaches to apply in each time is the focus of this work, considering the requirements for each problem and the information of previous executions. A case-based reasoning methodology is proposed, considering previous cases of execution of different optimization approaches for different problems. A fuzzy logic approach is used to adapt the solutions considering the balance between execution time and quality of results Copyright © 2017 for this paper by its authors.
2017
Authors
Romano, RA; Pait, F; dos Santos, PL;
Publication
2017 AMERICAN CONTROL CONFERENCE (ACC)
Abstract
While most physical systems or phenomena occur in continuous-time, identification methods based on discrete-time models are more widespread among practitioners and academic community, possibly due to the discrete-time nature of the data records. There has been a growing interest in estimating continuous-time (CT) models in the last decade. This work develops algorithms to estimate the parameters of multivariable state-space CT models from input-output samples using a method based on the recently developed MOLI-ZOFT approach. The performance of the algorithm is evaluated using real data from an industrial winding process.
2017
Authors
Dias Pereira L.; Neto L.; Bernardo H.; Gameiro da Silva M.;
Publication
Energy Research and Social Science
Abstract
A major rehabilitation programme of secondary school buildings has been carried out in the last few years in Portugal. With the introduction of HVAC systems in buildings that were previously naturally ventilated, an increase on energy consumption has been verified. During the first occupancy periods of new and refurbished buildings, energy and indoor climate quality audits are important strategies to improve the buildings’ energy use. In this context, this paper aims at showing the relations between the energy consumption and indoor environment quality (IEQ) parameters, obtained from the energy and IEQ audit in six representative modernised secondary schools – part of a larger R&D project untitled 3Es – geographically and climatically distributed in Portugal mainland. The monitoring period during the mid-season 2013 varied between schools, between two and three weeks. Air exchange rates, more specifically infiltration rates, were quantified aiming at determining the current airtightness condition of the refurbished schools. A subjective IEQ assessment was also performed, focusing on occupants' feedback, providing insight on the potential linkages between energy use and occupants’ comfort. A reflection on the energy consumption indicators and the indoor conditions obtained in the classrooms was proposed, and some suggestions were anticipated.
2017
Authors
Varela, MLR; Manupati, VK; Manoj, K; Putnik, GD; Araújo, A; Madureira, AM;
Publication
INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA 2016)
Abstract
Social network analysis (SNA) is a widely studied research topics, which has been increasingly being applied for solving different kind of problems, including industrial manufacturing ones. This paper focuses on the application of SNA on an industrial plant layout problem. The study aims at analyzing the importance of using SNA techniques to analyze important relations between entities in a manufacturing environment, such as jobs and resources in the context of industrial plant layout analysis. The study carried out enabled to obtain relevant results for the identification of relations among these entities for supporting to establish an appropriate plant layout for producing the jobs.
2017
Authors
Faria, P; Pinto, A; Vale, Z; Khorram, M; Neto, FBD; Pinto, T;
Publication
2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
Abstract
Electricity consumption has increased all around the world in the last decades. This has caused a rise in the use of fossil fuels and in the harming of the environment. In the past years the use of renewable energies and reduction of consumption has growth in order to deal with that problem. The change in the production paradigm led to an increasing search of ways to shorten consumption and adapt to the production. One of the solutions for this problem is to use Demand Response systems. Lighting systems have a major role in electricity consumption, so they are very suitable to be applied in a Demand Response system, optimizing their use. This optimization can be made in different ways being one of them by using a heuristic algorithm. This paper focuses on the use of Fish School Search algorithm to optimize a lighting system, in order to understand its capability of dealing with a problem of this nature and compare it with other algorithms to evaluate its performance.
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