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Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling

Sensors (Basel, Switzerland), 2022-05, Vol.22 (10), p.3734

2022

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Foreword to the Special Issue on “Data Science in Process Industries”

Applied stochastic models in business and industry, 2022-09, Vol.38 (5), p.748-749

2022

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0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems

Sensors (Basel, Switzerland), 2022-05, Vol.22 (10), p.3657

2022

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Establishing the optimal blocks' order in SO‐PLS: Stepwise SO‐PLS and alternative formulations

Journal of chemometrics, 2018-08, Vol.32 (8), p.n/a

2018

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Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

Processes, 2017-09, Vol.5 (4), p.35

2017

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Sialic acids expression in newborn rat lungs: implications for pulmonary developmental biology

Acta histochemica, 2020-12, Vol.122 (8), p.151626-151626, Article 151626

2020

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Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives

European journal of pharmaceutics and biopharmaceutics, 2020-02, Vol.147, p.19-37

2020

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Assessing the value of information of data‐centric activities in the chemical processing industry 4.0

AIChE journal, 2018-11, Vol.64 (11), p.3868-3881

2018

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Persistence Landscapes—Implementing a Dataset Verification Method in Resource-Scarce Embedded Systems

Computers (Basel), 2023-06, Vol.12 (6), p.110

2023

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Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale

Computers & chemical engineering, 2019-05, Vol.124 (C), p.1-13

2019

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Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Chemometrics and intelligent laboratory systems, 2013-06, Vol.125, p.101-108

2013

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Advantage of Using Decorrelated Residuals in Dynamic Principal Component Analysis for Monitoring Large-Scale Systems

Industrial & engineering chemistry research, 2013-09, Vol.52 (38), p.13685-13698

2013

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Building Optimal Multiresolution Soft Sensors for Continuous Processes

Industrial & engineering chemistry research, 2018-08, Vol.57 (30), p.9750-9765

2018

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SS-DAC: A systematic framework for selecting the best modeling approach and pre-processing for spectroscopic data

Computers & chemical engineering, 2019-09, Vol.128, p.437-449

2019

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Discussion of "Industrial statistics and manifold data"

Quality engineering, 2020-04, Vol.32 (2), p.168-172

Reis, Marco S.

2020

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Multiresolution Soft Sensors: A New Class of Model Structures for Handling Multiresolution Data

Industrial & engineering chemistry research, 2017-04, Vol.56 (13), p.3640-3654

2017

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Network-induced supervised learning: Network-induced classification (NI-C) and network-induced regression (NI-R)

AIChE journal, 2013-05, Vol.59 (5), p.1570-1587

Reis, Marco S.

2013

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Markovian and Non-Markovian sensitivity enhancing transformations for process monitoring

Chemical engineering science, 2017-05, Vol.163, p.223-233

2017

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A physics-informed Run-to-Run control framework for semiconductor manufacturing

Expert systems with applications, 2020-10, Vol.155, p.113424, Article 113424

2020

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Advanced predictive methods for wine age prediction: Part I – A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods

Talanta (Oxford), 2017-08, Vol.171, p.341-350

2017

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Sensor Fusion with Irregular Sampling and Varying Measurement Delays

Industrial & engineering chemistry research, 2020-02, Vol.59 (6), p.2328-2340

2020

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Optimal fusion of industrial data streams with different granularities

Computers & chemical engineering, 2019-11, Vol.130, p.106564, Article 106564

2019

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Optimal selection of time resolution for batch data analysis. Part I: Predictive modeling

AIChE journal, 2018-11, Vol.64 (11), p.3923-3933

2018

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Multi-target optimization of solid phase microextraction to analyse key flavour compounds in wort and beer

Food chemistry, 2020-07, Vol.317, p.126466-126466, Article 126466

2020

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Forecasting the research octane number in a Continuous Catalyst Regeneration (CCR) reformer

Quality and reliability engineering international, 2022-04, Vol.38 (3), p.1463-1481

2022

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Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling

Foreword to the Special Issue on “Data Science in Process Industries”

0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems

Establishing the optimal blocks' order in SO‐PLS: Stepwise SO‐PLS and alternative formulations

Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis

Sialic acids expression in newborn rat lungs: implications for pulmonary developmental biology

Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives

Assessing the value of information of data‐centric activities in the chemical processing industry 4.0

Persistence Landscapes—Implementing a Dataset Verification Method in Resource-Scarce Embedded Systems

Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Advantage of Using Decorrelated Residuals in Dynamic Principal Component Analysis for Monitoring Large-Scale Systems

Building Optimal Multiresolution Soft Sensors for Continuous Processes

SS-DAC: A systematic framework for selecting the best modeling approach and pre-processing for spectroscopic data

Discussion of "Industrial statistics and manifold data"

Multiresolution Soft Sensors: A New Class of Model Structures for Handling Multiresolution Data

Network-induced supervised learning: Network-induced classification (NI-C) and network-induced regression (NI-R)

Markovian and Non-Markovian sensitivity enhancing transformations for process monitoring

A physics-informed Run-to-Run control framework for semiconductor manufacturing

Advanced predictive methods for wine age prediction: Part I – A comparison study of single-block regression approaches based on variable selection, penalized regression, latent variables and tree-based ensemble methods

Sensor Fusion with Irregular Sampling and Varying Measurement Delays

Optimal fusion of industrial data streams with different granularities

Optimal selection of time resolution for batch data analysis. Part I: Predictive modeling

Multi-target optimization of solid phase microextraction to analyse key flavour compounds in wort and beer

Forecasting the research octane number in a Continuous Catalyst Regeneration (CCR) reformer

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