a b s t r a c tThis work introduces a new information-theoretic methodology for choosing variables and their time lags in a prediction setting, particularly when neural networks are used in non-linear modeling. The first contribution of this work is the Cross Entropy Function (XEF) proposed to select input variables and their lags in order to compose the input vector of black-box prediction models. The proposed XEF method is more appropriate than the usually applied Cross Correlation Function (XCF) when the relationship among the input and output signals comes from a non-linear dynamic system. The second contribution is a method that minimizes the Joint Conditional Entropy (JCE) between the input and output variables by means of a Genetic Algorithm (GA). The aim is to take into account the dependence among the input variables when selecting the most appropriate set of inputs for a prediction problem. In short, theses methods can be used to assist the selection of input training data that have the necessary information to predict the target data. The proposed methods are applied to a petroleum engineering problem; predicting oil production. Experimental results obtained with a real-world dataset are presented demonstrating the feasibility and effectiveness of the method.
Purpose – The purpose of this paper is to identify critical success factors (CSFs) that are regarded as the most important in Six Sigma programs in Brazil and to compare these rankings with those in international literature. Design/methodology/approach – A sample of industrial companies was selected to complete a survey. In total, 104 questionnaires were obtained. The results were compared with a literature review consisting of 26 papers from 13 countries. In total, 70 CSFs were found in the papers, but 19 CSFs were analyzed and reduced to ten. A multivariate factor analysis further reduced this number to two underlying constructs. Findings – The authors identified a CSFs common denominator/ranking based on the sample of international articles. The authors found that there are four CSFs that are more prevalent in Brazil and in the international papers studied and that there are no differences between the importance of CSFs in terms of hierarchical levels. Three gaps, five levers and two CSFs constructs were identified. Social implications – This study may initiate cooperation between the studied companies and academia, thus possibly increasing these organization’ knowledge regarding Six Sigma. Originality/value – The originality of this study is that the survey was conducted with companies in Brazil, a country where little information exists on Six Sigma programs. The authors also contributed a literature review on CSFs, a comparison based on most of the consulted papers and the use of a robust methodological strategy that was made possible by the sample size.
An important open problem in robotic planning is the autonomous generation of 3D inspection paths -that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously -and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together ensures a good balance between the two -both when 100% coverage is feasible, and when large parts of the object are hidden.
Purpose – The purpose of this paper is to present a survey on Six Sigma and key observations on which variables/management practices are the most important for a successful Six Sigma implementation, as well as the strengths and weaknesses of the practices observed in companies in Brazil. Design/methodology/approach – Five research questions were proposed, and an exploratory field study was carried out. A sample of 103 questionnaires was obtained. In total, 26 independent variables were analyzed, with a factor analysis reducing this number to 14, for which three main constructs were observed. Findings – Large manufacturing industries are implementing the Six Sigma program in Brazil. Three main constructs were observed to be critical for the success of Six Sigma. The strengths and weaknesses of the 14 independent variables studied within each construct were found. Practical implications – This paper has practical implications in that companies can use the conclusions of this study to improve their implementation of the Six Sigma program. Social implications – This research may initiate cooperation between the companies within the study and academia, which may lead to a better understanding of Six Sigma within these organizations. Originality/value – The originality of this study is that the survey was conducted with companies in Brazil, a country that suffers from a lack of information on Six Sigma programs. A robust methodological strategy was used that determined the three most important constructs for successful implementation of Six Sigma.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.