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About

Paulo Moura Oliveira received the Electrical Engineering degree in 1991, from the UTAD University, Portugal, MSc in Industrial Control Systems in 1994 and PhD in Control Engineering in 1998, both from Salford University, Manchester, UK. He is a Tenured Associated Professor at the Engineering Department of UTAD University and a researcher at the INESC TEC institute. Currently, he is the director of the PhD Course in Electrical and Computers Engineering in UTAD. His research interests are focused on the fields of control engineering, intelligent control, PID control, control engineering education, evolutionary and natural inspired metaheuristics for single and multiple objective optimization problem solving. He is author in more than 100 peer-reviewed scientific publications.

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Details

Details

Publications

2023

Model-Free VRFT-Based Tuning Method for PID Controllers

Authors
Vrancic, D; Oliveira, PM; Bistak, P; Huba, M;

Publication
MATHEMATICS

Abstract
The main objective of this work was to develop a tuning method for PID controllers suitable for use in an industrial environment. Therefore, a computationally simple tuning method is presented based on a simple experiment on the process without requiring any input from the user. Essentially, the method matches the closed-loop response to the response obtained in the steady-state change experiment. The proposed method requires no prior knowledge of the process and, in its basic form, only the measurement of the change in the steady state of the process in the manually or automatically performed experiment is needed, which is not limited to step-like process input signals. The user does not need to provide any prior information about the process or any information about the closed-loop behavior. Although the control loop dynamics is not defined by the user, it is still known in advance because it is implicitly defined by the process open-loop response. Therefore, no exaggerated control signal swings are expected when the reference signal changes, which is an advantage in many industrial plants. The presented method was designed to be computationally undemanding and can be easily implemented on less powerful hardware, such as lower-end PLC controllers. The work has shown that the proposed model-free method is relatively insensitive to process output noise. Another advantage of the proposed tuning method is that it automatically handles the tuning of highly delayed processes, since the method discards the initial process response. The simplicity and efficiency of the tuning method is demonstrated on several process models and on a laboratory thermal system. The method was also compared to a tuning method based on a similar closed-loop criterion. In addition, all necessary Matlab/Octave files for the calculation of the controller parameters are provided online. © 2023 by the authors.

2023

Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP

Authors
Pereira, SD; Pires, EJS; Oliveira, PBD;

Publication
ALGORITHMS

Abstract
A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.

2023

Pocket Labs as a STEM Learning Tool and for Engineering Motivation

Authors
Cardoso, A; Oliveira, PM; Sá, J;

Publication
Lecture Notes in Networks and Systems

Abstract

2023

Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions

Authors
Pinheiro, I; Moreira, G; da Silva, DQ; Magalhaes, S; Valente, A; Oliveira, PM; Cunha, M; Santos, F;

Publication
AGRONOMY-BASEL

Abstract
The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%.

2022

Forecasting Student s Dropout: A UTAD University Study

Authors
Da Silva, DEM; Pires, EJS; Reis, A; Oliveira, PBD; Barroso, J;

Publication
FUTURE INTERNET

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

Supervised
thesis

2022

Classificação de doenças pulmonares obstrutivas crónicas

Author
Inês de Almeida

Institution
UTAD

2022

Técnicas de aprendizagem máquina aplicadas à covid-19

Author
Milene Sofia Alves Fraga

Institution
UTAD

2021

Revisão de técnicas de pesquisa inspiradas em enxames

Author
Daniel da Silva Duarte

Institution
UTAD

2019

Chat Bot -o diagmóstico de bolso

Author
Duarte Rui Afonso Gomes Tavares do Amaral

Institution
UTAD

2018

SUCESSO DA GESTÃO DE PROJETOS DE DESENVOLVIMENTO DE SISTEMAS DE INFORMAÇÃO - O CASO DOS PROJETOS EM INSTITUIÇÕES PÚBLICAS

Author
Antero Manuel de Carvalho Gonçalves

Institution
UTAD