The Experts below are selected from a list of 43203 Experts worldwide ranked by ideXlab platform
Joh F Macgrego - One of the best experts on this subject based on the ideXlab platform.
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experiences with Industrial applications of projection methods for multivariate statistical process control
Computers & Chemical Engineering, 1996Co-Authors: Theodora Kourti, Jennife Lee, Joh F MacgregoAbstract:With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Recent approaches to multivariate statistical process control, which utilize not only the product quality data (as traditional approaches have done) but also the available process data, are based on multivariate projection methods (Principal Component Analysis, PCA, and Partial Least Squares, PLS). These methods have been rapidly accepted and utilized by industry. This paper gives a brief overview of these methods and illustrates their use for process monitoring and fault diagnosis with applications to a wide range of Industrial batch and continuous processes. Emphasis is placed on the practical issues that arise when dealing with process data. Several of these issues are discussed and solutions are suggested for a successful outcome of the application of these methods in an Industrial Setting.
Philippe Vaillergues - One of the best experts on this subject based on the ideXlab platform.
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the squale model a practice based Industrial quality model
International Conference on Software Maintenance, 2009Co-Authors: Karine Mordalmanet, Francoise Balmas, Simon Denier, Stephane Ducasse, Harald Wertz, Jannik Laval, Fabrice Bellingard, Philippe VaillerguesAbstract:ISO 9126 promotes a three-level model of quality (factors, criteria, and metrics) which allows one to assess quality at the top level of factors and criteria. However, it is difficult to use this model as a tool to increase software quality. In the Squale model, we add practices as an intermediate level between metrics and criteria. Practices abstract away from raw information (metrics, tool reports, audits) and provide technical guidelines to be respected. Moreover, practice marks are adjusted using formulae to suit company development habits or exigences: for example bad marks are stressed to point to places which need more attention. The Squale model has been developed and validated over the last couple of years in an Industrial Setting with Air France-KLM and PSA Peugeot-Citroen.
Robert J. Mcqueen - One of the best experts on this subject based on the ideXlab platform.
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An approach for developing domain specific CASE tools and its application to manufacturing process control
Journal of Systems and Software, 1997Co-Authors: Douglas Troy, Robert J. McqueenAbstract:An investigation into the development and evaluation of a domain specific computer aided software engineering (CASE) tool, known as a methodology companion, is described. A development methodology for designing domain specific CASE tools supporting model-based analysis and automatic code generation is described. These ideas are used to produce a prototype methodology companion designed to assist the task of developing control software for programmable logic controllers (PLCs) in batch process manufacturing. The CASE tool is domain specific to the industry and organization described, and supports specification, analysis, simulation, report generation, and code generation of the manufacturing control steps. The tool is then evaluated in an Industrial Setting to determine its overall potential and limitations and the impact it will have on the domain.
Theodora Kourti - One of the best experts on this subject based on the ideXlab platform.
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experiences with Industrial applications of projection methods for multivariate statistical process control
Computers & Chemical Engineering, 1996Co-Authors: Theodora Kourti, Jennife Lee, Joh F MacgregoAbstract:With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Recent approaches to multivariate statistical process control, which utilize not only the product quality data (as traditional approaches have done) but also the available process data, are based on multivariate projection methods (Principal Component Analysis, PCA, and Partial Least Squares, PLS). These methods have been rapidly accepted and utilized by industry. This paper gives a brief overview of these methods and illustrates their use for process monitoring and fault diagnosis with applications to a wide range of Industrial batch and continuous processes. Emphasis is placed on the practical issues that arise when dealing with process data. Several of these issues are discussed and solutions are suggested for a successful outcome of the application of these methods in an Industrial Setting.
Peter J F Lucas - One of the best experts on this subject based on the ideXlab platform.
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using bayesian networks in an Industrial Setting making printing systems adaptive
European Conference on Artificial Intelligence, 2010Co-Authors: Arjen Hommersom, Peter J F LucasAbstract:Control engineering is a field of major Industrial importance as it offers principles for engineering controllable physical devices, such as cell phones, television sets, and printing systems. Control engineering techniques assume that a physical system's dynamic behaviour can be completely described by means of a set of equations. However, as modern systems are often of high complexity, drafting such equations has become more and more difficult. Moreover, to dynamically adapt the system's behaviour to a changing environment, observations obtained from sensors at runtime need to be taken into account. However, such observations give an incomplete picture of the system's behaviour; when combined with the incompletely understood complexity of the device, control engineering solutions increasingly fall short. Probabilistic reasoning would allow one to deal with these sources of incompleteness, yet in the area of control engineering such AI solutions are rare. When using a Bayesian network in this context the required model can be learnt, and tuned, from data, uncertainty can be handled, and the model can be subsequently used for stochastic control of the system's behaviour. In this paper we discuss Industrial research in which Bayesian networks were successfully used to control complex printing systems.
