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Publications

Publications by CEGI

2022

Automatic root cause analysis in manufacturing: an overview & conceptualization

Authors
e Oliveira E.; Miguéis V.L.; Borges J.L.;

Publication
Journal of Intelligent Manufacturing

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

2022

Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition

Authors
Gharahbagh, AA; Hajihashemi, V; Ferreira, MC; Machado, JJM; Tavares, JMRS;

Publication
Applied Sciences (Switzerland)

Abstract
In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algorithms is also rapidly expanding in various fields. Due to the high volume of information in a video, its processing is still expensive for most hardware systems, mainly in terms of its required runtime and memory. Hence, the optimal selection of keyframes to minimize redundant information in video processing systems has become noteworthy in facilitating this problem. Eliminating some frames can simultaneously reduce the required computational load, hardware cost, memory and processing time of intelligent video-based systems. Based on the aforementioned reasons, this research proposes a method for selecting keyframes and adaptive cropping input video for human action recognition (HAR) systems. The proposed method combines edge detection, simple difference, adaptive thresholding and 1D and 2D average filter algorithms in a hierarchical method. Some HAR methods are trained with videos processed by the proposed method to assess its efficiency. The results demonstrate that the application of the proposed method increases the accuracy of the HAR system by up to 3% compared to random image selection and cropping methods. Additionally, for most cases, the proposed method reduces the training time of the used machine learning algorithm. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

2022

Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion

Authors
Hajihashemi, V; Gharahbagh, AA; Cruz, PM; Ferreira, MC; Machado, JJM; Tavares, JMRS;

Publication
Sensors

Abstract
The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.

2022

A comprehensive framework and literature review of supplier selection under different purchasing strategies

Authors
Saputro, TE; Figueira, G; Almada-Lobo, B;

Publication
Computers and Industrial Engineering

Abstract
Supplier selection has received substantial consideration in the literature since it is considered one of the key levers contributing to a firm's success. Selecting the right suppliers for different product items requires an appropriate problem framing and a suitable approach. Despite the vast literature on this topic, there is not a comprehensive framework underlying the supplier selection process that addresses those concerns. This paper formalizes a framework that provides guidance on how supplier selection should be formulated and approached for different types of items segmented in Kraljic's portfolio matrix and production policies. The framework derives from a thorough literature review, which explores the main dimensions in supplier selection, including sourcing strategy, decision scope and environment, selection criteria, and solution approaches. 326 papers, published from 2000 to 2021, were reviewed for said purpose. The results indicate that supplier selection regarding items with a high purchasing importance should lead to holistic selection criteria. In addition, items comprising a high complexity of supply and production activities should require integrated selection and different sources of uncertainty associated with decision scope and environment, respectively, to solve it, as well as hybrid approaches. There are still many research opportunities in the supplier selection area, particularly in the integrated selection problems and hybrid solution methods, as well as in the risk mitigation, sustainability goals, and new technology adoption.

2022

Energy Citizenship in Positive Energy Districts— Towards a Transdisciplinary Approach to Impact Assessment

Authors
van Wees, M; Revilla, BP; Fitzgerald, H; Ahlers, D; Romero, N; Alpagut, B; Kort, J; Tjahja, C; Kaiser, G; Blessing, V; Patricio, L; Smit, S;

Publication
Buildings

Abstract
It is commonly assumed by the projects demonstrating concepts for positive energy districts in cities across Europe that citizens want and need to be involved in the development of these concepts as an essential condition for positive energy districts to be deployed successfully and to achieve the expected societal goals. Six different research and innovation projects are investigating the different forms of energy citizenship in positive energy districts and their impacts. They aim to apply a transdisciplinary approach to collaborative research and to impact assessment. The interim results are described, and preliminary conclusions on impact are drawn. The projects each used different approaches to engaging citizens, while differentiating between different groups. Progress is monitored but only fragmentary evidence on the impact has been gathered. Transdisciplinary approaches are being developed but are still immature.

2022

Merging make-to-stock/make-to-order decisions into sales and operations planning: A multi-objective approach

Authors
Pereira, DF; Oliveira, JF; Carravilla, MA;

Publication
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE

Abstract
With the advent of mass customization and product proliferation, the appearance of hybrid Make-toStock(MTS)/Make-to-Order(MTO) policies arise as a strategy to cope with high product variety maintaining satisfactory lead times. In companies operating under this reality, Sales and Operations Planning (S&OP) practices must be adapted accordingly during the coordinated planning of procurement, production, logistics, and sales activities. This paper proposes a novel S&OP decision-making framework for a flow shop/batch company that produces standard products under an MTS strategy and customized products under an MTO strategy. First, a multi-objective mixed-integer programming model is formulated to characterize the problem. Then, a matrix containing the different strategies a firm in this context may adopt is proposed. This rationale provides a business-oriented approach towards the analysis of different plans and helps to frame the different Pareto-optimal solutions given the priority on MTS or MTO segments and the management positioning regarding cost minimization or service level orientation. The research is based on a real case faced by an electric cable manufacturer. The computational experiments demonstrate the applicability of the proposed methodology. Our approach brings a practical, supply chain-oriented, and mid-term perspective on the study of operations planning policies in MTS/MTO contexts.

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