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About

About

Ricardo Sousa has a PhD in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto since 2011 and is currently a assistant researcher and assistant to the coordination at the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at INESC TEC. He participated in European projects (e.g., MAESTRA), national (e.g., ADIRA4.0) and scientific projects with companies (e.g., NDTech-Amorim) related to Signal Processing, Data Mining and Machine Learning. Currently, he coordinates teams in a PRODUTECH mobilizing program (related to Production and Quality Management) and in a P2020/FCT/MIT Portugal project (Technology for power transformers). Has specific interest in the areas of Maintenance and Predictive Quality, Process Mining and Forecasting with application in the field of Industry/Production. He lectured at the Faculty of Engineering of the University of Porto, in programming and information systems subjects. Co-supervised/supervised more than 17 master's dissertations in the areas of Signal Processing and Data mining/Machine Learning.

Interest
Topics
Details

Details

  • Name

    Ricardo Teixeira Sousa
  • Role

    Advisor to the Centre Coordinator
  • Since

    16th September 2005
011
Publications

2024

Estimating the Likelihood of Financial Behaviours Using Nearest Neighbors A case study on market sensitivities

Authors
Mendes Neves, T; Seca, D; Sousa, R; Ribeiro, C; Mendes Moreira, J;

Publication
COMPUTATIONAL ECONOMICS

Abstract
As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts' efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.

2024

Optimal gas subset selection for dissolved gas analysis in power transformers

Authors
Pinto, J; Esteves, V; Tavares, S; Sousa, R;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval's triangle, Roger's ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.

2023

Unsupervised Online Event Ranking for IT Operations

Authors
Mendes, TC; Barata, AA; Pereira, M; Moreira, JM; Camacho, R; Sousa, RT;

Publication
Intelligent Data Engineering and Automated Learning - IDEAL 2023 - 24th International Conference, Évora, Portugal, November 22-24, 2023, Proceedings

Abstract
Keeping high service levels of a fast-growing number of servers is crucial and challenging for IT operations teams. Online monitoring systems trigger many occurrences that experts find hard to keep up with. In addition, most of the triggered warnings do not correspond to real, critical problems, making it difficult for technicians to know which to focus on and address in a timely manner. Outlier and concept drift detection techniques can be applied to multiple streams of readings related to server monitoring metrics, but they also generate many False Positives. Ranking algorithms can already prioritize relevant results in information retrieval and recommender systems. However, these approaches are supervised, making them inapplicable in event detection on data streams. We propose a framework that combines event aggregations and uses a customized clustering algorithm to score and rank alarms in the context of IT operations. To the best of our knowledge, this is the first unsupervised, online, high-dimensional approach to rank IT ops events and contributes to advancing knowledge about associated key concepts and challenges of this problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

2023

Time-Series Pattern Verification in CNC Machining Data

Authors
Silva, JM; Nogueira, AR; Pinto, J; Alves, AC; Sousa, R;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I

Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series' target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % +/- 1.2 and an F1-score of 95.4 % +/- 1.3.

2023

Predicting US Energy Consumption Utilizing Artificial Neural Network

Authors
Pasandidehpoor, M; Mendes Moreira, J; Rahman Mohammadpour, S; Sousa, RT;

Publication
Handbook of Smart Energy Systems

Abstract

Supervised
thesis

2024

Online multi stream prediction for CNC machining

Author
Mohammad Pasandidehpoor

Institution
UP-FEUP

2023

Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning

Author
João Gabriel Luís Patrício

Institution
UP-FEUP

2023

Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques

Author
Gabriel Copolecchia Carvalhal

Institution
UP-FEUP

2022

Combination of multi-paradigm models for Power Transformer fault prediction

Author
Francisco José Guedes de Melo Aguiar

Institution
UP-FEUP

2022

Online Process Extractor and Updater

Author
Sónia Rafaela Costa da Rocha

Institution
UP-FEUP