Kim Phuc Tran

Associate Professor, Dr. habil. (Maître de Conférences HDR, equivalent of a UK Reader)

France

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About

Dr. Kim Phuc Tran is an Associate Professor at ENSAIT, University of Lille, France. He specializes in statistical process control and anomaly detection in smart manufacturing, focusing on monitoring compositional data and reliability modeling of materials. His recent publications include developments in control chart techniques and security strategies for AI systems within Industry 4.0.

Articles (18)

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2025

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2023

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2023

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2022

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2022

Collaborators (10)

Arne Johannssen

GERMANY

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Philippe Castagliola

FRANCE

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AHMAD RASHED LABANIEH

Assistant professor

Ecole Nationale Supérieure des Arts et Industries Textiles

FRANCE

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WEI LIN TEOH

Heriot-Watt University

UNITED KINGDOM

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Shujun Li

Professor of Cyber Security

University of Kent

UNITED KINGDOM

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Sébastien Thomassey

Full Professor

Ecole Nationale Supérieure des Arts et Industries Textiles

FRANCE

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Damien SOULAT

Professeur

FRANCE

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Nataliya Chukhrova

Universität Hamburg

GERMANY

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Giovanni Celano

Professore Associato

Università degli Studi di Napoli Federico II

ITALY

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Jing Wei Teoh

Heriot-Watt University

UNITED KINGDOM

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Kim Phuc Tran

Research Interests

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About

Articles (18)

Security strategies for AI systems in Industry 4.0

Reliability modeling of tensile properties in recycled carbon fibers: A comparative fit of statistical distributions pre- and post-textile processing

A new omnibus SPRT chart for monitoring process mean and variability based on the average number of observations to signal

SVDD control charts based on MEWMA technique for monitoring compositional data

Analyzing out-of-control signals of T2 control chart for compositional data using artificial neural networks

An integrated optimal-GICP design for the SPRT control chart with estimated process parameters based on the average number of observations to signal

Anomaly detection in smart manufacturing: An Adaptive Adversarial Transformer-based model

The Shewhart-type RZ control chart for monitoring the ratio of autocorrelated variables

Monitoring autocorrelated compositional data vectors using an enhanced residuals Hotelling T2 control chart

Optimal design and evaluation of adaptive EWMA monitoring schemes for Inverse Maxwell distribution

Analyzing abnormal pattern of hotelling T2 control chart for compositional data using artificial neural networks

Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction

AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description

Cost-effective optimization strategies and sampling plan for Weibull quantiles under type-II censoring

Incorporating principal component analysis into Hotelling T2 control chart for compositional data monitoring

The effects of measurement errors on estimating and assessing the multivariate process capability with imprecise characteristic

Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning

Collaborators (10)

Arne Johannssen

Philippe Castagliola

AHMAD RASHED LABANIEH

WEI LIN TEOH

Shujun Li

Sébastien Thomassey

Damien SOULAT

Nataliya Chukhrova

Giovanni Celano

Jing Wei Teoh

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