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About

About

I am a researcher at CPES (Power and Energy Systems), which is a R&D Centre of the INESC-TEC.

My research focus are Artificial Intelligence, modelling and statistical analysis, renewable production, distributed energy. 

Interest
Topics
Details

Details

  • Name

    Conceição Nunes Rocha
  • Role

    Assistant Researcher
  • Since

    31st January 2014
009
Publications

2023

Report on the 6th International Workshop on Narrative Extraction from Texts (Text2Story 2023) at ECIR 2023

Authors
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Litvak, M; Cordeiro, JP; Rocha, C; Sousa, H; Mansouri, B;

Publication
SIGIR Forum

Abstract

2022

Data-Driven Anomaly Detection and Event Log Profiling of SCADA Alarms

Authors
Andrade, JR; Rocha, C; Silva, R; Viana, JP; Bessa, RJ; Gouveia, C; Almeida, B; Santos, RJ; Louro, M; Santos, PM; Ribeiro, AF;

Publication
IEEE ACCESS

Abstract
Network human operators' decision-making during grid outages requires significant attention and the ability to perceive real-time feedback from multiple information sources to minimize the number of control actions required to restore service, while maintaining the system and people safety. Data-driven event and alarm management have the potential to reduce human operator cognitive burden. However, the high complexity of events, the data semantics, and the large variety of equipment and technologies are key barriers for the application of Artificial Intelligence (AI) to raw SCADA data. In this context, this paper proposes a methodology to convert a large volume of alarm events into data mining terminology, creating the conditions for the application of modern AI techniques to alarm data. Moreover, this work also proposes two novel data-driven applications based on SCADA data: (i) identification of anomalous behaviors regarding the performance of the protection relays of primary substations, during circuit breaker tripping alarms in High Voltage (HV) and Medium Voltage (MV) lines; (ii) unsupervised learning to cluster similar events in HV line panels, classify new event logs based on the obtained clusters and membership grade with a control parameter that helps to identify rare events. Important aspects associated with data handling and pre-processing are also covered. The results for real data from a Distribution System Operator (DSO) showed: (i) that the proposed method can detect unexpected relay pickup events, e.g., one substation with nearly 41% of the circuit breaker alarms had an 'atypical' event in their context (revealed an overlooked problem on the electrification of a protection relay); (ii) capability to automatically detect and group issues into specific clusters, e.g., SF6 low-pressure alarms and blocks with abnormal profiles caused by event time-delay problems.

2022

On-line atracurium dose prediction: a nonparametric approach

Authors
Rocha, C; Mendonça, T; Silva, ME;

Publication
IEEE Conference on Control Technology and Applications, CCTA 2022, Trieste, Italy, August 23-25, 2022

Abstract

2022

Report on the 5th International Workshop on Narrative Extraction from Texts (Text2Story 2022) at ECIR 2022

Authors
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Litvak, M; Cordeiro, JP; Rocha, C; Sousa, H; Mansouri, B;

Publication
SIGIR Forum

Abstract

2022

On-line atracurium dose prediction: a nonparametric approach.

Authors
Rocha C.; Mendonca T.; Silva M.E.;

Publication
2022 IEEE Conference on Control Technology and Applications, CCTA 2022

Abstract
This paper aims at contributing to personalize anesthetic drug administration during surgery. This study devel-ops an online robust model to predict the maintenance dose of atracurium necessary for the resulting effect, i.e. neuromuscular blockade, to attain a target profile. The model is based on the patient's neuromuscular blockade (NMB) response to the initial bolus only, overcoming the need for information on the patient's weight, age, height and Lean Body Mass usually associated to pharmacokinetic and pharmacodynamic models. To achieve this, a statistical analysis of the response of the patient to the initial bolus is carried out and a set of variables is established as predictors of the maintenance dose. The prediction is accomplished using Classification and Regression Trees, CART, which is a supervised learning method. Simulated data from a stochastic model for the NMB induced by atracurium is used as training set. All the 5000 doses predicted by the model lead to NMB level between 5% and 10%, which supports the proposed predictive model since it is clinically required that the steady state NMB level lies between this two values. The methodology is applied both to simulated and to clinical data sets and is found appropriate for online dose prediction.

Supervised
thesis

2020

Mapeamento automático da topologia de redes inteligentes de baixa tensão

Author
João Afonso da Silva Picão

Institution
UP-FEUP

2019

Tweens: Uma visão objetiva acerca das intenções dos influenciadores digitais para os pré-adolescentes

Author
Marta Cabral Ferreira dos Santos

Institution
UP-FEP

2016

Bomberman as an Artificial Intelligence Platform

Author
Manuel António da Cruz Lopes

Institution
UP-FCUP