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Real-time adjustment of injection molding process settings by utilizing Design of Experiment, time series profiles and PLS-DA

• Authors : Rønsch, G.; Dybdahl, M.; Kulahci, M.

Subjects: Design of experiments; Machine learning; PLS-DA

• Source: Rønsch , G , Dybdahl , M & Kulahci , M 2022 , ' Real-time adjustment of injection molding process settings by utilizing Design of Experiment, time series profiles and

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IR-spectroscopy and PLS modelling for advanced bio-process monitoring: simultaneous and rapid sensing of various components

• Authors : Pontius, Katrin; Bevilacqua, Marta; Junicke, Helena

• Source: Pontius , K , Bevilacqua , M , Junicke , H & Gernaey , K V 2018 , ' IR-spectroscopy and PLS modelling for advanced bio-process monitoring: simultaneous and rapid sensing

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Multiblock PLS: Block dependent prediction modeling for Python

• Authors : Baum, Andreas; Vermue, Laurent

• Source: Baum , A & Vermue , L 2019 , ' Multiblock PLS: Block dependent prediction modeling for Python ' , The Journal of Open Source Software , vol. 4 , no. 34 , 1190 .

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Sparse kernel orthonormalized PLS for feature extraction in large datasets

• Authors : Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai

• Source: Arenas-García , J , Petersen , K B & Hansen , L K 2006 , Sparse kernel orthonormalized PLS for feature extraction in large datasets . in NIPS 2006 . NIPS 2006 ,

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Determination of zero dimensional, apparent devolatilization kinetics for biomass particles at suspension firing conditions

• Authors : Espekvist, Anna; Li, Tian; Glarborg, Peter

Subjects: Biomass devolatilization; Partial least squares regression (PLS); Suspension firing

• Source: Espekvist , A , Li , T , Glarborg , P , Løvås , T & Jensen , P A 2021 , ' Determination of zero dimensional, apparent devolatilization kinetics for biomass particles at suspension firing conditions

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Bladder tissue characterization using probe-based Raman spectroscopy: Evaluation of tissue heterogeneity and influence on the model prediction

• Authors : Cordero, Eliana; Rüger, Jan; Marti, Dominik

Subjects: Raman spectroscopy; Bladder cancer; PLS-LDA

• Source: Cordero , E , Rüger , J , Marti , D , Mondol , A S , Hasselager , T , Mogensen , K , Hermann , G G , Popp , J & Schie , I W 2020 , ' Bladder tissue characterization using probe-based Raman

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Analysis of the production of salmon fillet - Prediction of production yield

• Authors : Johansson, Gine Ørnholt; Guðjónsdóttir, María; Nielsen, Michael Engelbrecht

Subjects: Production analysis; Prediction; Atlantic salmon

• Source: Johansson , G Ø , Guðjónsdóttir , M , Nielsen , M E , Skytte , J L & Frosch , S 2017 , ' Analysis of the production of salmon fillet - Prediction of production yield ' , Journal of Food

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Applying multivariate analysis as decision tool for evaluating sediment-specific remediation strategies

• Authors : Pedersen, Kristine B.; Lejon, Tore; Jensen, Pernille Erland

Subjects: Electrodialytic remediation; Harbour sediments; Heavy metals

• Source: Pedersen , K B , Lejon , T , Jensen , P E & Ottosen , L M 2016 , ' Applying multivariate analysis as decision tool for evaluating sediment-specific remediation strategies ' , Chemosphere , vol. 151 ,

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Multiset Canonical Correlations Analysis and Multispectral, Truly Multitemporal Remote Sensing Data

• Authors : Nielsen, Allan Aasbjerg

Subjects: Geographical information systems (GIS); multivariate empirical orthogonal functions (EOF); multisource data fusion

• Source: Nielsen , A A 2002 , ' Multiset Canonical Correlations Analysis and Multispectral, Truly Multitemporal Remote Sensing Data ' , I E E E Transactions on Image Processing , vol. 11 , no. 3 , pp. 293-305

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Modernizing non-classical protein crystallization through industry 4.0: Advanced monitoring and modelling utilizing process analytical technology

• Authors : Jul-Jørgensen, I.; Oliver, R.; Gernaey, K. V.

Subjects: Crystallization; Process analytical technology; Raman spectroscopy

• Source: Jul-Jørgensen , I , Oliver , R , Gernaey , K V & Hundahl , C A 2024 , ' Modernizing non-classical protein crystallization through industry 4.0: Advanced monitoring and modelling utilizing process

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