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Quoc-Thông Nguyen

Quoc-Thông Nguyen

PhD

About

31
Publications
5,895
Reads
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197
Citations
Citations since 2016
25 Research Items
189 Citations
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20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
Additional affiliations
October 2011 - May 2015
IMT Lille Douai
Position
  • PhD Student
April 2011 - July 2011
École Polytechnique
Position
  • Intern
Education
October 2011 - May 2015
Université de Lille
Field of study

Publications

Publications (31)
Article
Full-text available
Microscopic analysis of paper printing shows regularly spaced dots whose random shape depends on the printing technology, the configuration of the printer as well as the paper properties. The modelling and identification of paper and ink interactions are required for qualifying the printing quality, for controlling the printing process and for appl...
Chapter
This study investigates the applicability of the Auto Machine Learning-based approach (AutoML) for analyzing microscopic printed document images to attribute that document to its source printer. In this perspective, AutoML, a new rising star of machine learning in practice, has shone brightly as it can satisfy the demand of Machine Learning practit...
Article
Due to the advent of digital technologies, it has become much easier to falsify printed documents for malicious purposes. Therefore, it is essential to research and develop efficient algorithms to distinguish authentic printed documents from fakes. A source printer identification technique is one of the methods to trace the source of the documents,...
Conference Paper
Due to the advent of digital technologies, it has become much easier to falsify printed documents for malicious purposes. Therefore, it is essential to research and develop efficient algorithms to distinguish authentic printed documents from fakes. A source printer identification technique is one of the methods to trace the source of the documents,...
Chapter
Full-text available
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...
Chapter
With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. Its high turnover is the reason making this sector is lucrative with fraud. This gave rise to fraudulent activity on the accounts of...
Article
Full-text available
Online monitoring of the multivariate coefficient of variation (MCV) can be of interest in many real situations in which the dispersion of a multi-variate process is meant to remain constant with regards to its position. To this aim, several control charts have been recently proposed in the literature. In this paper, the new one-sided adaptive char...
Article
Full-text available
Identifying a forged printed document with scanned evidence can be a challenge. Microscopic printing is showing random shape which depends on the printing source as well as printing material. This paper presents a statistical analysis of the printing patterns under a microscopic scale, analyses the effect of printing direction, printing substrate (...
Conference Paper
Among the anomaly detection methods, control charts have been considered important techniques. In practice, however, even under the normal behaviour of the data, the standard deviation of the sequence is not stable. In such cases, the coefficient of variation (CV) is a more appropriate measure for assessing system stability. In this paper, we consi...
Article
Full-text available
Measurement error always exists in quality control applications and may considerably affect the ability of control charts to detect an out-of-control situation. In this paper, we study the performance of the EWMA median chart using a Markov Chain approach with a linear covariate error model and a corrected formula for the distribution of the sample...
Article
Full-text available
We study the effect of nutritional diet characteristics on the lactating Holstein-Friesian dairy cows in Brittany, France from 36 individuals. An analysis of the relations between fat/protein content and milk yield was implemented for our dataset. The fat and protein production increase at a slower rate as milk yield increases. The importance of ch...
Preprint
Full-text available
In practice, there are processes where the in-control mean and standard deviation of a quality characteristic is not stable. In such cases, the coefficient of variation (CV) is a more appropriate measure for assessing process stability. In this paper, we consider the statistical design of Run Rules based control charts for monitoring the CV of mult...
Article
Full-text available
In this paper, we propose a variable sampling interval Shewhart control chart to monitor the coefficient of variation (CV) squared, denoted by VSI SH-CV2. The new model overcomes the ARL-biased (average run length) property of the control chart monitoring the CV in a previous study by designing two one-sided charts rather than one two-sided chart....
Conference Paper
Full-text available
In this study, we propose a new approach to determine intrusions of network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the...
Article
In many industrial manufacturing processes, the ratio of the variance to the mean of a quantity of interest is an important characteristic to ensure the quality of the processes. This ratio is called the coefficient of variation (CV). A lot of control charts have been designed for monitoring the CV of univariate quantity in the literature. However,...
Chapter
In this study, we propose a new approach to determine intrusions of network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the...
Article
Full-text available
In this study, we propose a new approach to determine the intrusions of the network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score is computed from testing samples in order to determ...
Conference Paper
Full-text available
Production monitoring in real-time is a very important problem in smart manufacturing. It helps enterprises to timely detect abnormalities in the production process and then guarantee the product quality and reduce waste. In this paper, we develop a novel method to monitor the real-time production based on the Convolution Neural Network and the Sup...
Article
Shewhart's type control charts for monitoring the Multivariate Coefficient of Variation (MCV) have recently been proposed in order to monitor the relative variability compared with the mean. These approaches are known to be rather slow in the detection of small or moderate process shifts. In this paper, in order to improve the detection efficiency,...
Conference Paper
In this paper, we present a method to monitor the coefficient of variation (CV) squared using two one-sided synthetic control charts. The numerical results show that our design outperforms the two-sided synthetic control chart monitoring the CV. The steady-state, which is have practical meaning in many situations, is also considered. We use a Marko...
Conference Paper
Full-text available
One-class support vector machines (OCSVM) have been recently applied in intrusion detection. Typically, OCSVM is kernelized by radial basis functions (RBF, or Gaussian kernel) whereas selecting Gaussian kernel hyperparameter is based upon availability of attacks, which is rarely applicable in practice. This paper investigates the application of nes...
Conference Paper
Full-text available
Optical character recognition, halftoning or document authentication requires the modelling of the printing degradations. This paper is focused on both the modelling of printings at the micrometer scale and on the model estimation. We propose to model a print as a set of independent Bernoulli variables parametrized by a kernel which takes into acco...
Thesis
We develop the probabilistic models of the print at the microscopic scale. We study the shape randomness of the dots that originates the prints, and the new models could improve many applications such as the authentication. An analysis was conducted on various papers, printers. The study shows a large variety of shape that depends on the printing t...
Conference Paper
Full-text available
Microscopic analyses of paper printing show some regularly spaced dots whose the shape depends on the technology and the tuning of the printer as well as on the paper properties. The modeling and the identification of paper and ink interactions are required for qualifying the printing quality, for controlling the printing process and also for authe...
Conference Paper
Full-text available
In this paper we are concerned by authentication of printer technologies from microscopic analysis of paper print. At this scale, a print is made of regularly spaced dots whose shape varies from a print to another and also inside the same document. Thus, dot at the microscopic scale can be considered as an intrinsic signature of printer technologie...
Technical Report
Full-text available
Diffusion magnetic resonance imaging provides a measure of the average distance travelled by water molecules in a medium and can give useful information on cellular structure and structural change when the medium is biological tissue. In this paper, two approximate models for the apparent diffusion coefficient at low b-values and long diffusion tim...

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Projects

Projects (5)
Project
We cordially invite you to submit your papers or extended abstracts for the International Conference on Data Science in Business, Finance and Industry (DSBFI 2023) January 8-10, 2023 Da Nang, Vietnam Submission deadline: October 15, 2022. Conference website: https://www.issatconferences.org/dsbfi2023.html Contact: dsbfi@issatconferences.org Conference Chair Hoang Pham, Rutgers University, USA Dao Nguyen Thi Anh, Dong A University, Vietnam Program Chairs Phuc Kim Tran, ENSAIT& GEMTEX, University of Lille, France Xufeng Zhao, Nanjing University of Aeronautics and Astronautics, China Topics of Interest Advanced Statistical Methods in Data Science Algorithms, Models and Theory of Deep Learning Machine Learning and Statistical Methods for Data Mining Predictive Modeling and Analytics in Business, Finance and Industry Data Mining Applications in Healthcare, Finance and Industry Recommender Systems in Data Science Quantitative Modeling in Big Data Data Warehouse for Business Intelligence Artificial Intelligence (AI) and Autonomous Machines Big Data Mining and Analytics Statistical Techniques and Tools for Data Science Healthcare Systems and Management Information and Data Processing in Business Spatial Data Analysis Search and Knowledge Discovery Data Intelligence, Security and Privacy Cyber Resilience and Security Security, Trust and Risk in Big Data Mobile Systems and Development for Handheld Devices Business and Operation Analytics Service Innovation and Management Supply Chain Management Systems Modeling and Simulation Technology and Knowledge Management Applications of data science in business, finance, social sciences, physical sciences, life sciences, web, marketing, precision medicine, education, health informatics, and industry
Project
The Industrial Internet of Things (IIoT) is opening new opportunities for the industry. Smart manufacturing is really all about data. It’s about collecting and using accurate data to make good decisions quickly to allow for slick, efficient, and flexible manufacturing which can adapt to sudden changes in demand or circumstances. Also, to develop a roadmap that starts from Industry 4.0 to reach Industry 5.0 and beyond which drives sustainability, studies about Industry 5.0 will focus on combining human creativity and craftsmanship with the speed, productivity, and consistency of AI systems In this project, we will try to use Machine Learning and Data Mining algorithms to develop new approaches for monitoring and predicting of manufacturing processes to reduce production costs, and improve productivity and product quality. The application of Deep Multi-Agent Reinforcement Learning for Supply Chain Planning (SCP), Warehouse Management, Predictive Analytics for Supplier Selection and Supplier Relationship Management (SRM), and Predictive Analytics for Demand Forecasting...will be studied. When applied to the multi-agent domains, traditional RL approaches suffers from several problems (e.g. non stationarity environments). It seems very important to develop new methods for scaling Reinforcement Learning to those environments and for creating artificial intelligence which is able to interact with both each other and humans. In this project, we will also develop novel methods for deep multi-agent reinforcement learning in the context of manufacturing applications, Supply Chain, and Logistics. New knowledge and insights from Artificial intelligence and Big Data are revolutionizing supply chain management as a result.
Project
Performance authentication against forgery of printed documents and counterfeiting of products. A trusted mark by printing a reliable, non-copyable, non-modifiable Graphic Code (CG) within the document, packaging or label of the original medium.