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Publications

2022

Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
IEEE ACCESS

Abstract
Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

2022

On the influence of overlap in automatic root cause analysis in manufacturing

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

2022

VisAC: An interactive tool for visual analysis of consanguinity in the ancestry of individuals

Authors
Borges, J;

Publication
INFORMATION VISUALIZATION

Abstract
Genealogy studies are growing in popularity, and researchers are increasingly using visualization methods to summarize and communicate their findings. A family tree is a visual representation of family members and their relationships that is commonly used to support the research of a family's history and publish the results. In some cases, an ancestor may occur in more than one place in the lineage of an individual, which is one of the reasons for the occurrence of consanguineous marriages, that is, marriages between blood relative spouses. Current methods for family tree visualization were not designed to analyze and assess the level of consanguinity in the ancestry of individuals. This paper proposes VisAC, an interactive tool to support the visual analysis of consanguinity in individuals' ancestry. The inbreeding coefficient is used as a measure of consanguinity. The coefficient corresponds to an estimate of the probability that two alleles (a variant of a given gene) in the DNA were inherited from the same individual. A visualization design and an interactive tool were developed with genealogists' support. In addition, the feedback collected through a questionnaire about two demo videos and tests with three target users strongly supports the effectiveness of the family tree visual representation and the adequacy of the interactive tool for the exploratory analysis task. Real-world examples are given to illustrate the usefulness of the visualization design, and an example of exploratory analysis is presented to illustrate the use of the interactive tool. In summary, this work formulates the task of visual analysis of consanguinity in ancestors' trees and proposes VisAC, a new visualization tool to support the task.

2021

Forecasting of Urban Public Transport Demand Based on Weather Conditions

Authors
Correia, R; Fontes, T; Borges, JL;

Publication
Advances in Intelligent Systems and Computing

Abstract
Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

2021

Knowledge-Assisted Visualization of Multi-Level Origin-Destination Flows Using Ontologies

Authors
Sobral, T; Galvao, T; Borges, J;

Publication
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Abstract

Supervised
thesis

2021

Immersive Technologies in Complex Manufacturing Systems as an Informational Problem: A Human-Centered Approach

Author
Filipa Rente Ramalho

Institution
UP-FEUP

2021

Implementação de uma Ferramenta de Business Intelligence de Suporte à Gestão Comercial num Operador de Transportes

Author
João Ferreira Monteiro

Institution
UP-FEUP

2021

Overlap in Automatic Root Cause Analysis in Manufacturing

Author
Eduardo Luís de Meireles e Oliveira

Institution
UP-FEUP

2021

Advanced Analytics na Saúde: Algoritmos de machine learning para melhoria da previsão da duração de cirurgias

Author
Andreia Nunes Moreira

Institution
UP-FEUP

2021

Otimização da exploração de redes de distribuição com integração de centrais elétricas virtuais

Author
Joana Moura Pereira Duro

Institution
UTAD