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About

Tatiana M. Pinho graduated in Energy Engineering from the University of Trás-os-Montes e Alto Douro (UTAD), Portugal, in 2011 and received the MSc degree in Energy Engineering from UTAD, in 2013. Presently she is a Ph.D. student of Electrical and Computer Engineering in UTAD and INESC-TEC Technology and Science, supported by the FCT (Fundação para a Ciência e a Tecnologia). Her research interests include modeling and adaptive control, in particular Model Predictive Control, applied to agri-forestry systems. 

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Details

Details

Publications

2018

PID Posicast Control for Uncertain Oscillatory Systems: A Practical Experiment

Authors
Oliveira, J; Oliveira, PM; Pinho, TM; Cunha, JB;

Publication
IFAC-PapersOnLine

Abstract

2018

A Sliding Mode-Based Predictive Strategy for Irrigation Canal Pools

Authors
Oliveira, J; Pinho, TM; Coelho, J; Boaventura-Cunha, J; Moura Oliveira, P;

Publication

Abstract
This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.

2018

Digital Technologies for Forest Supply Chain Optimization: Existing Solutions and Future Trends

Authors
Scholz, J; De Meyer, A; Marques, AS; Pinho, TM; Boaventura Cunha, J; Van Orshoven, J; Rosset, C; Kunzi, J; Kaarle, J; Nummila, K;

Publication
Environmental Management

Abstract

2017

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

Authors
Pinho, TM; Coelho, JP; Oliveira, JB; Cunha, JB;

Publication
Journal of Sensors

Abstract
Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

2017

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

Authors
Pinho, TM; Coelho, JP; Veiga, G; Moreira, AP; Cunha, JB;

Publication
Complexity

Abstract
Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.