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About

José Boaventura-Cunha is an Engineer in Electronics and Telecommunications from the University of Aveiro (1985) and has a PhD in Electrotechnical and Computer Engineering from UTAD-University of Trás-os-Montes and Alto Douro, Portugal (2002). Currently holds the position of Associate Professor with habilitation at the the School of Sciences and Technology of UTAD.
Since 2012 he is a member of the CRIIS-Center for Robotics in Industry and Intelligent Systems at INESC TEC - Institute of Systems and Computer Engineering, Technology and Science and is Coordinator of the pole of INESC TEC at UTAD.
His research interests are related to the areas of Instrumentation, modeling and control applied to industrial and agro-forestry processes.

Interest
Topics
Details

Details

008
Publications

2022

Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data

Authors
Aguiar, AS; dos Santos, FN; Sobreira, H; Boaventura Cunha, J; Sousa, AJ;

Publication
FRONTIERS IN ROBOTICS AND AI

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

2022

State of the Art of Wind and Power Prediction for Wind Farms

Authors
Puga, R; Baptista, J; Boaventura, J; Ferreira, J; Madureira, A;

Publication
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by “Biological Inspired Optimization Techniques” applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

State of the Art on Advanced Control of Electric Energy Transformation to Hydrogen

Authors
Puga, R; Boaventura, J; Ferreira, J; Madureira, A;

Publication
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
The need for sustainable power production has led to the development of more innovative approaches to production and storage. In light of this hydrogen production through wind power has emerged as sufficient in ensuring that the objectives of the Paris Agreement are made. This paper discusses the state-of-art models and controls used in ensuring that greater efficiency is achieved in the processes of energy to hydrogen transformation. The paper concludes with a comparison of the models and determination of one which suffices in ensuring that hydrogen/energy transformation is more efficient.

2022

Dynamic Modelling of a Thermal Solar Heating System

Authors
Boaventura-Cunha, J; Ferreira, J;

Publication
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
Nowadays the world faces the challenge to rapidly diminish the use of fossil fuels in order to reduce pollutants and the emission of greenhouse gases and to mitigate the global warming. Renewable energies, such as solar radiation, among others, are playing a relevant role in this context. Namely, the use of thermal energy storage systems in buildings and industry is increasing enabling to reduce operational costs and carbon dioxide emissions. Heat storage systems based in solar thermal panels for heating water in buildings are industrially mature but some improvements can be made to improve their efficiencies. In this work are presented the methods and the results achieved to model the dynamic behavior of the hot water temperature as function of the weather, operating conditions and technical parameters of the thermal solar system. This type of dynamic models will enable to optimize the efficiency of this type of systems regarding the use of auxiliary energy sources to heat the water whenever the temperature in the storage tank falls below a defined threshold level. As future work it is intended to use adaptive control algorithms to reduce the use of backup power sources (electricity, oil, gas) by using the information of the system status as well predictions for hot water consumption profiles and solar radiation.

2022

Dynamic Modelling of a Thermal Solar Heating System

Authors
Boaventura-Cunha, J; Ferreira, J;

Publication
Innovations in Bio-Inspired Computing and Applications - Lecture Notes in Networks and Systems

Abstract

Supervised
thesis

2021

Development of a multimodal management platform for patients in physical rehabilitation

Author
Tiago Luís Salgueiro dos Santos

Institution
UP-FEUP

2020

Localization and Mapping based on Semantic and Multi-Layer Maps Concepts

Author
André Silva Pinto de Aguiar

Institution
UP-FEUP

2018

Driving innovation through social data: a methodology for building buyer personas

Author
Natália Cavalcanti Carneiro Leão

Institution
UP-FEUP

2016

Modelos e Sistemas de controlo preditivo aplicados à industria de base florestal

Author
Tatiana de Fátima Martins Pinho

Institution
UTAD

2016

Modulo de Gestão de Actualizações

Author
José Francisco Ângelo Oliveira

Institution
UP-FEUP