Kim Phuc TRANUniversité de Lille · ENSAIT & GEMTEX
Kim Phuc TRAN
Ph.D. and Dr. habil. in Automation and Industrial Informatics
My research focuses on Anomaly Detection, Decision Support System, Explainable Artificial Intelligence for Industry 5.0
About
167
Publications
46,603
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2,610
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Introduction
Additional affiliations
April 2019 - May 2019
March 2019 - April 2019
November 2018 - December 2018
University of Liège
Position
- Researcher
Description
- Machine Learning, Deep Learning and Data Mining in Manufacturing, Supply Chain and Logistics
Education
September 2016 - February 2022
April 2014 - December 2016
October 2009 - October 2011
Da Nang University of Technology
Field of study
- Automated production
Publications
Publications (167)
In the literature, median control charts have been introduced under
the assumption of no measurement error. However, measurement
errors always exist in practice and may considerably affect the ability
of control charts to detect out-of-control situations. In this paper, we
investigate the performance of Shewhart median chart by using a linear
covar...
Literature about Statistical Process Control reports some applications of monitoring quality characteristics
defined as ratio variables by means of control charts. It is well known that the Shewhart type control charts are rather slow in the detection of small or moderate process shifts. The statistical sensitivity of a Shewhart control chart can b...
Recent studies show that Shewhart-type control charts monitoring the ratio of two normal random variables are useful to perform continuous surveillance in several manufacturing environments; anyway, they have a poor statistical sensitivity in the detection of small or moderate process shifts. The statistical sensitivity of a Shewhart control chart...
In many fields, there is the need to monitor quality characteristics defined as the ratio of two random variables. The design and implementation of control charts directly monitoring the ratio stability is required for the continuous surveillance of these quality characteristics. In this paper, we propose two one-sided exponentially weighted moving...
Recent literature about quality control has investigated the continuous surveillance of the ratio of two
normal random variables under the assumption of no measurement error. However, in practice, measure-
ment errors always exist in quality control applications and may considerably affect the performance
of control charts. In this paper, the perfo...
The integration of Human-Centered Edge Artificial Intelligence (HCE-AI) in smart factories within the framework of Industry 5.0. Industry 5.0 emphasizes collaboration between humans and advanced technologies to create sustainable, people-centered production environments. The vast amount of data generated by factory sensors presents opportunities fo...
The convergence of physics-informed and machine learning has led to the emergence of Physics-Informed Machine Learning (PIML), a powerful paradigm to enhance the reliability and safety of complex industrial systems. Traditional methods in reliability engineering often rely on physics-based models, which, despite their robustness, face limitations w...
The evolution of manufacturing processes, fueled by the transition towards Industry 5.0, has ushered in an era marked by the integration of sophisticated technologies such as the Industrial Internet of Things (IIoT), artificial intelligence (AI), and machine learning. This transformation has spawned smarter, more efficient production environments t...
The pervasive integration of Artificial Intelligence (AI) in various facets of human life, driven by increasingly sophisticated algorithms, underscores the importance of its safety and reliability. AI’s role in Industry 4.0, connecting machines and processes to solve complex issues, is paving the way for the 5.0 Industrial Revolution (5IR). This re...
Predictive maintenance (PdM) is a crucial technology for the industry’s future. It involves making maintenance decisions based on the prediction of the system’s performance in the future. It helps to reduce maintenance costs and ensures operational efficiency and flexibility of the future industrial systems (FIS) under different dynamic and uncerta...
This research explores the integration of human-centered edge artificial intelligence (AI) and wearable technology to enhance workplace health and safety within Industry 5.0. It highlights the importance of real-time monitoring and analysis facilitated by wearable devices equipped with sensors to measure physiological and environmental parameters,...
This chapter offers an overview of the book about Artificial Intelligence (AI) for Safety and Reliability Engineering within Industry 5.0, setting the stage for detailed discussions in subsequent chapters. AI can deliver novel and more accurate insights compared to traditional methods in the field of Safety and Reliability Engineering. Additionally...
Due to the heterogeneity of recycled paper materials and the production conditions, pollutants in papermaking wastewater fluctuate sharply over time. Quality control of the papermaking wastewater treatment process (PWTP) is challenging and costly. As regulations are also growing about the environmental effects of the PWTP on greenhouse gas (GHG) em...
Purpose
We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.
Design/m...
Anomaly detection plays a crucial role across various domains, including healthcare, where identifying deviations from normal patterns can lead to early intervention and improved outcomes. In healthcare, such as in ECG analysis, detecting anomalous signals is essential for timely diagnosis and treatment, as it can help identify potentially life-thr...
The Internet of Things (IoT) combines sensors and other small devices interconnected locally and via the Internet. Specifically, IoT devices collect data from the environment through sensors, analyze it, and respond to the actual through controllers. The integration of these devices can be seen in various areas like home appliances, healthcare, con...
In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling T2
control chart using principal component analysis to monitor CoDa explicitly. The proposed method overcomes the limitations of previous approaches by utilizing isometric log-ratio transformation...
Monitoring a process over time is so important in manufacturing processes to reduce the waste of money and time. Some charts such as Shewhart, CUSUM, and EWMA are common to monitor a process with a single intended attribute which is used in different kinds of processes with various ranges of shifts. In some cases, the process quality is characteriz...
This book provides readers with a comprehensive overview of the latest developments in the field of smart manufacturing, exploring theoretical research, technological advancements, and practical applications of AI approaches. With Industry 4.0 paving the way for intelligent systems and innovative technologies to enhance productivity and quality, th...
Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combin...
Industry 4.0 was first presented in 2011 and has revolutionized manufacturing in enormous applications by integrating artificial intelligence (AI), the Internet of Things (IoT), cloud computing, and other leading technologies. As technology continues to grow and expand, the concept of a new Industry 5.0 paradigm could be investigated. Industry 5.0...
Artificial Intelligence (AI) and especially Machine Learning (ML) are the driving energy behind industrial and technological transformation. With the transition from industry 4.0 to 5.0, smart manufacturing proves the efficiency in industry, where systems become increasingly complex, producing massive data, necessitating more demand for transparenc...
The innovation of wearable Internet of Things devices has fuelled the transition from Industry 4.0 to Industry 5.0. Increasing resource efficiency, safety, and economic efficiency are some of the main goals of Industry 5.0. Herein, wearable Internet of Things devices is parallel to humans to optimize human tasks and meet a new Industry’s requiremen...
This chapter provides an overview of Artificial Intelligence-based methods, applications, and challenges for Smart Manufacturing in Industry 5.0. We elaborate on the essential issues related to the applications and the potential of Artificial Intelligence algorithms in Smart Manufacturing. We will introduce crucial topics that will be discussed in...
Smart manufacturing is widely accepted as the new emerging transformation of the manufacturing industry today. In addition, quality control, an important aspect that contributes to the successful process of smart manufacturing attracts attention from the community. However, there are certain challenges in implementing quality control methods in Ind...
Equipment monitoring and process fault prediction are increasingly concerned in the modern industry due to the growing complexity of the production process and the high risk derived from severe consequences on the paper mills in case of production failure. Whereas the paper manufacturing process is continuous that is difficult to be warned early of...
With the complexity of the production process, the mass quantification of production jobs, and the diversification of production scenarios, research on scheduling problems are bound to develop in a direction closer to the actual production problems. Considering the combination of workshop scheduling problems and process planning problems, the study...
As the fourth industrial revolution has passed an early stage of development, many companies are developing intelligent systems and cutting-edge innovations of Industry 4.0 to improve productivity and quality. Meanwhile, the next phase of industrialization has been started to introduce, known as Industry 5.0. One of the most prominent features of I...
This article introduces a conditional reliability sampling plan for the Weibull distribution. The plan is applicable when the interested quality characteristic is a lower quantile and the life data are observed according to a progressive type-II censoring scheme. It is called “conditional”, since its operating characteristic function is derived by...
Compositional data (CoDa) has been monitored in statistical process monitoring, where multivariate control charts (CCs) such as Hotelling T2c, MEWMA-CoDa, and MCUSUM-CoDa are commonly used to determine if a process is in-control. However, these charts can encounter problems when there is an out-of-control (OOC) process due to various factors such a...
Predictive maintenance (PdM) plays an important role in industrial manufacturing. One of the most fundamental ideas underlying many PdM solutions is to estimate Remaining Useful Life (RUL) of machines. Recently, advanced deep learning models like convolutional neural network (CNN) and long short-term memory (LSTM) have been widely used for RUL pred...
In digital healthcare applications, anomaly detection is an important task to be taken into account. For instance, in ECG (Electrocardiogram) analysis, the aim is often to detect abnormal ECG signals that are considered outliers. For such tasks, it has been shown that deep learning models such as Autoencoders (AEs) and Variational Autoencoders (VAE...
In the field of statistical process control, the cumulative sum (CUSUM) control chart is used as a powerful tool to detect process shifts. One of the main features of the CUSUM control chart is that it takes into account the past information at each sampling time of the process. Recently, the rapid development of optimization algorithms and softwar...
In recent decades, wearable devices have constantly improved in response to market trends and the requirements in Industry 4.0/5.0 and are now on a steady increase. Although initial wearable sensor versions have shown their potential in supporting humans in work and lives, the limitation is that a device only works well in an individual. Therefore,...
Since the beginning of Industry 4.0, humanity has witnessed significant changes in all aspects of life, most notably the manufacturing sector. By integrating many advanced technologies such as Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cyber Physical Systems (CPS), Big Data, and Cloud computing, Industry 4.0 has substantial...
Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a mechanism is required to combin...
Accurate demand forecasting has always been essential for retailers in order to be able to survive in the highly competitive, volatile modern market. However, anticipating product demand is an extremely difficult task in the context of short product life cycles in which consumer demand is influenced by many heterogeneous variables. During the COVID...
In many industrial manufacturing processes, the quality of products can depend on the relative amount between two quality characteristics X and Y. Often, this calls for the on-line monitoring of the ratio Z=X/Y as a quality characteristic itself by means of a control chart. A large number of control charts monitoring the ratio have been investigate...
Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detecti...
In recent years, the monitoring of compositional data using control charts has been investigated in the Statistical Process Control field. In this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) control chart with variable sampling intervals to monitor compositional data based on isometric log-ratio trans...
Customer requirements and specifications are becoming increasingly complex, resulting in more complicate production processes to meet these requirements. With this complexity come anomalies and deviations into processes. On the other hand, we are seeing a new generation of technology can handle complexity of process and discover unusual executions...
In industrial environments, process capability indices are daily employed as numerical metrics to summarize the performance of a process according to a predefined set of specification limits. Neglecting gauge measurement errors is a common phenomenon in process capability evaluations by researchers in laboratory investigations and by practitioners...
The application of Federated Learning (FL) is steadily increasing, especially in privacy-aware applications, such as healthcare. However, its applications have been limited by security concerns due to various adversarial attacks, such as poisoning attacks (model and data poisoning). Such attacks attempt to poison the local models and data to manipu...
This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control...
We are now witnessing the rapid development and powerful application of advanced technologies, leading to the fourth industrial revolution, or Industry 4.0. The wide use of cyber-physical systems (CPS) and the internet of things (IoT) lead to the era of Big Data in industrial manufacturing. Artificial intelligence (AI) algorithms emerge as powerful...
Being in a fast-changing and highly competitive
environment, fashion companies must constantly adapt to the
changing tastes and needs of their consumers. In fact, fashion
market trends are among the most essential factors influencing
consumers’ tastes. Therefore, this paper proposes fashion trends
detection system based on an experts’ approach. Ins...
We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing. The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which is connecting all the industrial assets, including machines and control systems, with the information systems and the business proce...
Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detecti...
With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart manufacturing systems are increasingly becoming challenging, causing severe damage to production operations and vital industrial assets, even sensitive information. Hence, detecting irregularities for time-series data in industrial control systems that should...
Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detecti...
With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart manufacturing systems are increasingly becoming challenging, causing severe damage to production operations and vital industrial assets, even sensitive information. Hence, detecting irregularities for time-series data in industrial control systems that should...
In recent years, the monitoring of compositional data using control charts has been investigated in the Statistical Process Control field. In this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) control chart with variable sampling intervals to monitor compositional data based on isometric log-ratio trans...
Nowadays, industrial systems, such as production systems, industrial control systems, wireless communication networks, robotic systems, and healthcare systems, are becoming complex and more and more connected to the Internet with the Internet of things (IoT) technology. The recent development of information and communication technologies such as th...
We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which is connecting all industrial assets, including machines and control systems, with the information systems and the business proc...
In recent years, the development of digital technologies brings a lot of changes in the way of operating, leading, and working processes in companies. Accordingly, advanced technologies such as Artificial Intelligent, Big Data, Internet of things, etc., are widely applied to aggregate, transform, and analyze data, thereby inferring meaningful infor...
The last decades have witnessed the rapid growth of advanced technologies and their application which has a significant influence on industrial manufacturing, leading to smart manufacturing (SM). The recent development of information and communication technologies has engendered the concept of the smart factory that adds intelligence into the manuf...
It can be said that a well-functioning supply chain management (SCM) is a key to ensuring the success of any business in a competitive global economy. SCM consists of the management of the entire production flow, ranging from supplying raw materials all the way to delivering the final products to the consumer. It aims to minimize the total expenses...
The main purpose of a healthcare support system is to provide %timely and accurate information to clinicians, patients, and others to inform decisions about healthcare. Healthcare support systems can potentially lower costs, improve efficiency, and reduce patient inconvenience. For example, Healthcare support systems can help by alerting clinicians...
Thanks to the applications of advanced technologies like cloud computing, the Internet of Things, Big data analytics, and Artificial Intelligence, we are now transferring to smart manufacturing in which the process of manufacturing becomes more intelligent. The system of machines in production lines requires higher accuracy in operation. Any interr...
In this chapter, we provide an introduction to Machine Learning %and Probabilistic Graphical Models for Decision Support Systems. We elaborate on the peculiarities of Decision Support Systems with Machine Learning and Probabilistic Graphical Models, and especially their applications in the Fourth Industrial Revolution. We present the main research...
The textile industry contributes significantly to the world economy. In the Industry 4.0 era, the textile manufacturing process is expected to be more flexible. Whereas the intricate relationship between the large-scale variables from a variety of textile processes can lead to the extreme difficulty of decision-making issues. In order to overcome t...
In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). However, the Industrial IoT (IIoT)-based manufacturing systems are now one of the top industries targeted by a variety of attacks. In this research, we propose detecting Cyberat...
Deep learning plays a vital role in classifying different arrhythmias using electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning...
This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anom...
Human Activity Recognition (HAR) has been a challenging problem yet it needs to be solved. It will mainly be used for eldercare and healthcare as an assistive technology when ensemble with other technologies like Internet of Things(IoT). HAR can be achieved with the help of sensors, smartphones or images. Deep neural network techniques like artific...
As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process...
Demand-Driven Manufacturing (DDM) is the solution that most companies are heading to in our days. Although this strategy consists of producing goods based on what consumers demand, companies should also rely on accurate forecasting systems to prepare their production chain for such an operation by supplying enough raw material, increasing productio...