The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Quirico Semeraro - One of the best experts on this subject based on the ideXlab platform.
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Using recurrent neural networks to detect changes in autocorrelated Processes for Quality monitoring
Computers & Industrial Engineering, 2007Co-Authors: Massimo Pacella, Quirico SemeraroAbstract:With the growing of automation in Manufacturing, Process Quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical Process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of Process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for Quality control in the case of serially correlated data. In particular, the Elman's recurrent neural network is proposed for Manufacturing Process Quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the Process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for Quality monitoring in the case of autocorrelated Process data.
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Understanding ART-based neural algorithms as statistical tools for Manufacturing Process Quality control
Engineering Applications of Artificial Intelligence, 2005Co-Authors: Massimo Pacella, Quirico SemeraroAbstract:Neural networks have recently received a great deal of attention in the field of Manufacturing Process Quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for Quality monitoring is discussed from a statistical perspective. The neural network is based on Fuzzy ART, which is exploited for recognising any unnatural change in the state of a Manufacturing Process. Initially, the neural algorithm is analysed by means of geometrical arguments. Then, in order to evaluate control performances in terms of errors of Types I and II, the effects of three tuneable parameters are examined through a statistical model. Upper bound limits for the error rates are analytically computed, and then numerically illustrated for different combinations of the tuneable parameters. Finally, a criterion for the neural network designing is proposed and validated in a specific test case through simulation. The results demonstrate the effectiveness of the proposed neural-based procedure for Manufacturing Quality monitoring.
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Adaptive resonance theory-based neural algorithms for Manufacturing Process Quality control
International Journal of Production Research, 2004Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:The demand for Quality products in industry is continuously increasing. To produce products with consistent Quality, Manufacturing systems need to be closely monitored for any unnatural deviation in the state of the Process. Neural networks are potential tools that can be used to improve the analysis of Manufacturing Processes. Indeed, neural networks have been applied successfully for detecting groups of predictable unnatural patterns in the Quality measurements of Manufacturing Processes. The feasibility of using Adaptive Resonance Theory (ART) to implement an automatic on-line Quality control method is investigated. The aim is to analyse the performance of the ART neural network as a means for recognizing any structural change in the state of the Process when predictable unnatural patterns are not available for training. To reach such a goal, a simplified ART neural algorithm is discussed then studied by means of extensive Monte Carlo simulation. Comparisons between the performances of the proposed neu...
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Manufacturing Process Quality control by means of a Fuzzy ART neural network algorithm
Artificial Neural Nets and Genetic Algorithms, 2003Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:Neural networks are potential tools that can be used to improve Process Quality control. In fact, various neural algorithms have been applied successfully for detecting groups of well-defined unnatural patterns in the output measurements of Manufacturing Processes. This paper discusses the use of a neural network as a means for recognising changes in the state of the monitored Process, rather than for identifying a restricted set of unnatural patterns on the output data. In particular, a control algorithm, which is based on the Fuzzy ART neural network, is first presented, and then studied in a specific reference case by means of Monte Carlo simulation. Comparisons between the performances of the proposed neural approach, and those of the CUSUM control chart, are also presented in the paper. The results indicate that the proposed neural network is a practical alternative to the existing control schemes.
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Detecting Changes in Autoregressive Processes with a Recurrent Neural Network for Manufacturing Quality Monitoring
AMST’05 Advanced Manufacturing Systems and Technology, 1Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:The traditional use of control charts assumes the independence of data. It is widely recognized that many Processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to Quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for Manufacturing Process Quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for Quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts.
Massimo Pacella - One of the best experts on this subject based on the ideXlab platform.
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Using recurrent neural networks to detect changes in autocorrelated Processes for Quality monitoring
Computers & Industrial Engineering, 2007Co-Authors: Massimo Pacella, Quirico SemeraroAbstract:With the growing of automation in Manufacturing, Process Quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical Process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of Process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for Quality control in the case of serially correlated data. In particular, the Elman's recurrent neural network is proposed for Manufacturing Process Quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the Process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for Quality monitoring in the case of autocorrelated Process data.
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Understanding ART-based neural algorithms as statistical tools for Manufacturing Process Quality control
Engineering Applications of Artificial Intelligence, 2005Co-Authors: Massimo Pacella, Quirico SemeraroAbstract:Neural networks have recently received a great deal of attention in the field of Manufacturing Process Quality control, where statistical techniques have traditionally been used. In this paper, a neural-based procedure for Quality monitoring is discussed from a statistical perspective. The neural network is based on Fuzzy ART, which is exploited for recognising any unnatural change in the state of a Manufacturing Process. Initially, the neural algorithm is analysed by means of geometrical arguments. Then, in order to evaluate control performances in terms of errors of Types I and II, the effects of three tuneable parameters are examined through a statistical model. Upper bound limits for the error rates are analytically computed, and then numerically illustrated for different combinations of the tuneable parameters. Finally, a criterion for the neural network designing is proposed and validated in a specific test case through simulation. The results demonstrate the effectiveness of the proposed neural-based procedure for Manufacturing Quality monitoring.
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Adaptive resonance theory-based neural algorithms for Manufacturing Process Quality control
International Journal of Production Research, 2004Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:The demand for Quality products in industry is continuously increasing. To produce products with consistent Quality, Manufacturing systems need to be closely monitored for any unnatural deviation in the state of the Process. Neural networks are potential tools that can be used to improve the analysis of Manufacturing Processes. Indeed, neural networks have been applied successfully for detecting groups of predictable unnatural patterns in the Quality measurements of Manufacturing Processes. The feasibility of using Adaptive Resonance Theory (ART) to implement an automatic on-line Quality control method is investigated. The aim is to analyse the performance of the ART neural network as a means for recognizing any structural change in the state of the Process when predictable unnatural patterns are not available for training. To reach such a goal, a simplified ART neural algorithm is discussed then studied by means of extensive Monte Carlo simulation. Comparisons between the performances of the proposed neu...
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Manufacturing Process Quality control by means of a Fuzzy ART neural network algorithm
Artificial Neural Nets and Genetic Algorithms, 2003Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:Neural networks are potential tools that can be used to improve Process Quality control. In fact, various neural algorithms have been applied successfully for detecting groups of well-defined unnatural patterns in the output measurements of Manufacturing Processes. This paper discusses the use of a neural network as a means for recognising changes in the state of the monitored Process, rather than for identifying a restricted set of unnatural patterns on the output data. In particular, a control algorithm, which is based on the Fuzzy ART neural network, is first presented, and then studied in a specific reference case by means of Monte Carlo simulation. Comparisons between the performances of the proposed neural approach, and those of the CUSUM control chart, are also presented in the paper. The results indicate that the proposed neural network is a practical alternative to the existing control schemes.
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Detecting Changes in Autoregressive Processes with a Recurrent Neural Network for Manufacturing Quality Monitoring
AMST’05 Advanced Manufacturing Systems and Technology, 1Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:The traditional use of control charts assumes the independence of data. It is widely recognized that many Processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to Quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for Manufacturing Process Quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for Quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts.
David Tollervey - One of the best experts on this subject based on the ideXlab platform.
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RNA lost in translation
Nature, 2006Co-Authors: David TollerveyAbstract:In any Manufacturing Process, Quality control is crucial, and gene expression is no exception. A new pathway monitors mRNAs — the intermediaries of gene expression — and destroys faulty molecules.
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Molecular biology: RNA lost in translation.
Nature, 2006Co-Authors: David TollerveyAbstract:In any Manufacturing Process, Quality control is crucial, and gene expression is no exception. A new pathway monitors mRNAs — the intermediaries of gene expression — and destroys faulty molecules.
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In any Manufacturing Process, Quality control is crucial, and gene expression is no exception. A new pathway monitors mRNAs — the intermediaries of gene expression — and destroys faulty molecules.
2006Co-Authors: David TollerveyAbstract:For most human genes to be usefully expressedthey must first be copied into messenger RNAsby the Process of transcription. These then program large, RNA–protein complexes calledribosomes to synthesize a specific protein by‘translation.’ The fidelity of mRNA translationinto protein is vital for the overall accuracy ofgene expression, and cells have evolved ways todetect any aberrant mRNAs that have struc-tural defects. Doma and Parker (page 561 ofthis issue)
A. Anglani - One of the best experts on this subject based on the ideXlab platform.
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Adaptive resonance theory-based neural algorithms for Manufacturing Process Quality control
International Journal of Production Research, 2004Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:The demand for Quality products in industry is continuously increasing. To produce products with consistent Quality, Manufacturing systems need to be closely monitored for any unnatural deviation in the state of the Process. Neural networks are potential tools that can be used to improve the analysis of Manufacturing Processes. Indeed, neural networks have been applied successfully for detecting groups of predictable unnatural patterns in the Quality measurements of Manufacturing Processes. The feasibility of using Adaptive Resonance Theory (ART) to implement an automatic on-line Quality control method is investigated. The aim is to analyse the performance of the ART neural network as a means for recognizing any structural change in the state of the Process when predictable unnatural patterns are not available for training. To reach such a goal, a simplified ART neural algorithm is discussed then studied by means of extensive Monte Carlo simulation. Comparisons between the performances of the proposed neu...
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Manufacturing Process Quality control by means of a Fuzzy ART neural network algorithm
Artificial Neural Nets and Genetic Algorithms, 2003Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:Neural networks are potential tools that can be used to improve Process Quality control. In fact, various neural algorithms have been applied successfully for detecting groups of well-defined unnatural patterns in the output measurements of Manufacturing Processes. This paper discusses the use of a neural network as a means for recognising changes in the state of the monitored Process, rather than for identifying a restricted set of unnatural patterns on the output data. In particular, a control algorithm, which is based on the Fuzzy ART neural network, is first presented, and then studied in a specific reference case by means of Monte Carlo simulation. Comparisons between the performances of the proposed neural approach, and those of the CUSUM control chart, are also presented in the paper. The results indicate that the proposed neural network is a practical alternative to the existing control schemes.
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Detecting Changes in Autoregressive Processes with a Recurrent Neural Network for Manufacturing Quality Monitoring
AMST’05 Advanced Manufacturing Systems and Technology, 1Co-Authors: Massimo Pacella, Quirico Semeraro, A. AnglaniAbstract:The traditional use of control charts assumes the independence of data. It is widely recognized that many Processes are autocorrelated thus violating the main assumption of independence. As a result, there is a need for a broader approach to Quality monitoring when data are time-dependent or autocorrelated. The aim of this work is to present a new procedure for Manufacturing Process Quality control in the case of serially correlated data. In particular, a recurrent neural network is introduced for Quality control problem. Performance comparisons between the neural-based algorithm and control charts are also presented in the paper in order to validate the proposed approach. The simulation results indicate that the neural-based procedure is quite effective as it achieves improved performance over control charts.
Dai Hai-fei - One of the best experts on this subject based on the ideXlab platform.
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Modeling method of multivariate statistical control chart for small-batch Manufacturing Process Quality
Journal of Computer Applications, 2008Co-Authors: Dai Hai-feiAbstract:Based on the study of multivariate statistical Process Quality control chart and small-batch Manufacturing Process, a kind of synthetical modeling method was put forward to deal with multivariate statistical Process for small-batch Manufacturing. Practical application and emulational instance show that this modeling method can make the best use of the obtained data, build control model dynamically, consequently resolve the problem of lacking modeling data in small-batch Manufacturing Process.