FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1578164523> ?p ?o ?g. }

• W1578164523 endingPage "383" @default.
• W1578164523 startingPage "382" @default.
• W1578164523 abstract "The authors would like to thank the discussants for their interesting contributions, their insightful comments and suggestions. We believe that the discussions provide evidence that the field of statistical process control (SPC) for high dimensional and / or nonstationary processes deserves great attention and is key to keeping modern processes in-control. Indeed, nowadays process data are gathered at high sampling rates, providing multivariate (and often repeated) information about each and every sample or batch. Those data acquisition characteristics have their repercussion on two main aspects of classical univariate SPC. First, modern process sensors such as (near infrared) spectrophotometers, high frequency vibration analyzers or digital cameras result in data streams where often the number of variables is higher than the number of observations leading to ill-conditioned settings. This issue is mainly tackled in our paper and the discussion by Ferrer by considering latent variables multivariate SPC (MSPC). Second, the fact that sampling intervals are often much smaller than the natural process dynamics results in data that display a high degree of autocorrelation. The issue of autocorrelation and nonstationarity also comes in from a totally different perspective, namely that of monitoring living organisms whose behavior is time dependent. Incorporating such autocorrelation and time dependency in the SPC setting is discussed from a time series perspective in our paper, and is further elaborated in the discussion by De Vries. The issue raised by Ferrer concerning in-control processes indeed is an important one — in-control should mean in any case that the process behaves as expected given its inherent natural variability. In a sense, this is not restricted to a fixed distribution (e.g., a normal distribution) nor autocorrelation structure. As stated both in our paper as in the discussion, ‘the type of data and the way they are collected are subject to change’, with high sampling rates and high dimensional data being key characteristics. Seeing the peculiarities of most multivariate data (often generated by spectral devices) with correlations amongst variables > 0.99, latent variables-based MSPC seem to be the natural choice to avoid an ill-conditioned covariance matrix. Publications in this field have spurred during the last 15 years, and we feel that in the coming years this research field will further develop. As Ferrer suggested, the field of Functional Data Analysis (FDA) is an interesting one when considering spectral data. Saeys et al. 1 already showed the potential of FDA in the field of chemometrics (i.e., the field of using spectral data for classification / prediction purposes). They showed that the use of latent variables model such as PCA or partial least squares is prone to important risks because they do not require an understanding of the physical problem. Using a simple example of spectral data (transmissions as a function of the wavelength λ), Saeys et al. 1 showed that altering the dataset by random permutation of the covariates (i.e., the data vector xi = xi(λ) is altered to xi = xi(κ) with κ being a random permutation of λ, so destroying the spectral information) has no influence on the predictive power of a PLS model. As far as the authors are aware of, the use of FDA as an alternative to the latent variables MSPC is un(der)developed and requires attention as Ferrer suggested. In conclusion, it is generally agreed that the way we acquire data using modern process sensors poses important challenges for controlling processes, and we believe that this field, where engineering and statistical principles go hand in hand, has a prosperous future ahead." @default.
• W1578164523 created "2016-06-24" @default.
• W1578164523 creator A5012602156 @default.
• W1578164523 creator A5046496940 @default.
• W1578164523 creator A5063233784 @default.
• W1578164523 creator A5082483873 @default.
• W1578164523 creator A5090206599 @default.
• W1578164523 date "2011-07-01" @default.
• W1578164523 modified "2023-10-17" @default.
• W1578164523 title "Nonstationarity in statistical process control - issues, cases, ideas: rejoinder" @default.
• W1578164523 cites W1972647763 @default.
• W1578164523 doi "https://doi.org/10.1002/asmb.914" @default.
• W1578164523 hasPublicationYear "2011" @default.
• W1578164523 type Work @default.
• W1578164523 sameAs 1578164523 @default.
• W1578164523 citedByCount "0" @default.
• W1578164523 crossrefType "journal-article" @default.
• W1578164523 hasAuthorship W1578164523A5012602156 @default.
• W1578164523 hasAuthorship W1578164523A5046496940 @default.
• W1578164523 hasAuthorship W1578164523A5063233784 @default.
• W1578164523 hasAuthorship W1578164523A5082483873 @default.
• W1578164523 hasAuthorship W1578164523A5090206599 @default.
• W1578164523 hasConcept C105795698 @default.
• W1578164523 hasConcept C111919701 @default.
• W1578164523 hasConcept C113644684 @default.
• W1578164523 hasConcept C119857082 @default.
• W1578164523 hasConcept C124101348 @default.
• W1578164523 hasConcept C12713177 @default.
• W1578164523 hasConcept C140779682 @default.
• W1578164523 hasConcept C149782125 @default.
• W1578164523 hasConcept C154945302 @default.
• W1578164523 hasConcept C161584116 @default.
• W1578164523 hasConcept C199163554 @default.
• W1578164523 hasConcept C2522767166 @default.
• W1578164523 hasConcept C33923547 @default.
• W1578164523 hasConcept C41008148 @default.
• W1578164523 hasConcept C5297727 @default.
• W1578164523 hasConcept C76155785 @default.
• W1578164523 hasConcept C94915269 @default.
• W1578164523 hasConcept C98045186 @default.
• W1578164523 hasConceptScore W1578164523C105795698 @default.
• W1578164523 hasConceptScore W1578164523C111919701 @default.
• W1578164523 hasConceptScore W1578164523C113644684 @default.
• W1578164523 hasConceptScore W1578164523C119857082 @default.
• W1578164523 hasConceptScore W1578164523C124101348 @default.
• W1578164523 hasConceptScore W1578164523C12713177 @default.
• W1578164523 hasConceptScore W1578164523C140779682 @default.
• W1578164523 hasConceptScore W1578164523C149782125 @default.
• W1578164523 hasConceptScore W1578164523C154945302 @default.
• W1578164523 hasConceptScore W1578164523C161584116 @default.
• W1578164523 hasConceptScore W1578164523C199163554 @default.
• W1578164523 hasConceptScore W1578164523C2522767166 @default.
• W1578164523 hasConceptScore W1578164523C33923547 @default.
• W1578164523 hasConceptScore W1578164523C41008148 @default.
• W1578164523 hasConceptScore W1578164523C5297727 @default.
• W1578164523 hasConceptScore W1578164523C76155785 @default.
• W1578164523 hasConceptScore W1578164523C94915269 @default.
• W1578164523 hasConceptScore W1578164523C98045186 @default.
• W1578164523 hasIssue "4" @default.
• W1578164523 hasLocation W15781645231 @default.
• W1578164523 hasOpenAccess W1578164523 @default.
• W1578164523 hasPrimaryLocation W15781645231 @default.
• W1578164523 hasRelatedWork W1976669489 @default.
• W1578164523 hasRelatedWork W2007830322 @default.
• W1578164523 hasRelatedWork W2024770159 @default.
• W1578164523 hasRelatedWork W2033490192 @default.
• W1578164523 hasRelatedWork W2052288160 @default.
• W1578164523 hasRelatedWork W2622157825 @default.
• W1578164523 hasRelatedWork W3124016299 @default.
• W1578164523 hasRelatedWork W3124118004 @default.
• W1578164523 hasRelatedWork W4255876030 @default.
• W1578164523 hasRelatedWork W3125276240 @default.
• W1578164523 hasVolume "27" @default.
• W1578164523 isParatext "false" @default.
• W1578164523 isRetracted "false" @default.
• W1578164523 magId "1578164523" @default.
• W1578164523 workType "article" @default.