Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Interest
Topics
Details

Details

001
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.

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

2021

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.

2020

An Ontology-based approach to Knowledge-assisted Integration and Visualization of Urban Mobility Data

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

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT. © 2020

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

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

Author
Andreia Nunes Moreira

Institution
UP-FEUP

2021

Overlap in Automatic Root Cause Analysis in Manufacturing

Author
Eduardo Luís de Meireles e Oliveira

Institution
UP-FEUP

2021

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

Author
Joana Moura Pereira Duro

Institution
UTAD