Huu-Du Nguyen

Huu-Du Nguyen
Dong A University

PhD

About

39
Publications
6,304
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432
Citations
Citations since 2016
39 Research Items
430 Citations
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150

Publications

Publications (39)
Article
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Chapter
Full-text available
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...
Preprint
Full-text available
In many industrial manufacturing processes, the quality of products depends on the relation between two main ingredients or characteristics. Often, this calls for monitoring the ratio of two normal random variables with statistical process control (SPC) techniques. A large number of studies related to designing control charts monitoring this ratio...
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
Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time seri...
Article
Investigating the effect of measurement errors on the control chart monitoring the ratio of two normal random variables is an important task to facilitate the use of this kind of control chart in practice. Moreover, a deep insight into the problem can help practitioners to find a way to reduce unexpected impacts of measurement errors on the chart p...
Article
Full-text available
In beekeeping, monitoring beehives plays an important role in making sure bee colonies stay healthy and in reducing negative effects that could happen in the colonies. A large number of studies have been carried out to improve the performance of monitoring beehives from the traditional manual methods. Most importantly, the application of artificial...
Article
In the literature, many control charts monitoring the median is designed under a perfect condition that there is no measurement error. This may make the practitioners confusing to apply these control charts because the measurement error is the true problem in practice. In this paper, we consider the effect of measurement error on the performance of...
Article
We investigate, in this paper, the effect of the measurement error (ME) on the performance of Run Rules control charts monitoring the coefficient of variation (CV) squared. The previous Run Rules CV chart in the literature is improved slightly by monitoring the CV squared using two one-sided Run Rules charts instead of monitoring the CV itself usin...
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
This paper proposes a methodology to handle the causality to make inference on common cause failure (CCF) in a missing data situation. The data are collected in the form of contingency tables but the only available tokens of information are the numbers of CCFs of different orders and the numbers of failures due to a given cause, i.e. the margins of...
Article
Monitoring the ratio between two random normal variables plays an important role in many industrial manufacturing processes. In this paper, we suggest designing two one‐sided Shewhart control charts monitoring this ratio. The numerical results show that the one‐sided charts have more advantages compared with the two‐sided Shewhart chart proposed pr...
Article
Full-text available
In the industrial practice, control charts are frequently implemented assuming that the quality characteristic of interest can be accurately measured without errors. In general, this assumption is not realistic: measurement error always exists in quality control applications and may considerably affect the performance of control charts in detecting...
Chapter
Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptro...
Preprint
Full-text available
We investigate in this paper the effect of the measurement error on the performance of Run Rules control charts monitoring the coefficient of variation (CV) squared. The previous Run Rules CV chart in the literature is improved slightly by monitoring the CV squared using two one-sided Run Rules charts instead of monitoring the CV itself using a two...
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...
Conference Paper
Anomaly detection is the identification of observations that deviates from the data set's normal behavioral patterns. It is an important problem that has been researched within diverse research areas and application domains such as intrusion detection, fraud detection, fault detection, and event detection in sensor networks. Among the anomaly detec...
Article
In many industrial manufacturing processes, the ratio between two normal random variables plays a key role in ensuring quality of products. Thus, monitoring this ratio is an important task that is well worth considering. In this paper, we combine a variable sampling interval (VSI) strategy with a cumulative sum (CUSUM) scheme to create a new type o...
Article
It is well documented that the distribution of the ratio between two random normal variables is asymmetric. As a consequence, the two-sided Shewhart control chart monitoring this ratio (denoted by Shewhart-RZ chart) has an ARL−biased property. In order to overcome this drawback, we propose in this paper designing two separated one-sided control cha...
Article
Full-text available
In this paper, we investigate the effect of the measurement error on the performance of the cumulative sum control charts monitoring the coefficient of variation. The measurement errors are supposed to follow a linear covariate error model. The obtained results show that the precision error ratio and the accuracy error have negative impact on the c...
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
Anomaly detection has been becoming an important problem in several domains. In this paper, we propose a new method to detect anomalies in time series based on Long Short Term Memory (LSTM) networks. After being trained on normal data, the networks are used to predict interested steps in time series. The difference between the predicted values and...
Article
In many industrial manufacturing processes, the ratio between two normal random variables plays a key role in ensuring the quality of products. Thus, monitoring this ratio is an important task that is well worth considering. In this paper, we combine a variable sampling (VSI) strategy with a cumulative sum (CUSUM) scheme to create a new type of con...
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...
Thesis
The effective operation of an entire industrial system is sometimes strongly dependent on the reliability of its components. A failure of one of these components can lead to the failure of the system with consequences that can be catastrophic, especially in the nuclear industry or in the aeronautics industry. To reduce this risk of catastrophic fai...
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...
Conference Paper
Full-text available
Thanks to the rapid development and applications of advanced technologies, we are experiencing the fourth industrial revolution, or Industry 4.0, which is a revolution towards smart manufacturing. The wide use of cyber physical systems and Internet of Things leads to the era of Big Data in industrial manufacturing. Artificial Intelligence algorithm...
Conference Paper
Recent years have witnessed the rapid development of human activity recognition (HAR) based on werable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor and Multilayer Perceptron...
Preprint
Full-text available
Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptro...
Article
Anomaly detection has been becoming an important problem in several domains. In this paper, we propose a new method to detect anomalies in time series based on Long Short Term Memory (LSTM) networks. After being trained on normal data, the networks are used to predict interested steps in time series. The difference between the predicted values and...
Article
We investigate in this paper a new type of control chart called VSI EWMA‐RZ by integrating the variable sampling interval feature (VSI) with the exponentially weighted moving average (EWMA) scheme to monitor the ratio of two normal random variables. Because the distribution of the ratio is skewed, we suggest designing two separated one‐sided charts...
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...
Article
We investigate in this paper a new type of control chart called VSI EWMA-RZ by integrating the variable sampling interval feature (VSI) with the exponentially weighted moving average (EWMA) scheme to monitor the ratio of two normal random variables. Because the distribution of the ratio is skewed, we suggest designing two separated one-sided charts...

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Cited By

Projects

Projects (5)
Project
In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to Industry 4.0. Industry 4.0 leverages the automation of processes with less human intervention, the flexibility that allows for early system failure detection, and system automation. In order to achieve that with a lower cost and higher productivity, a wide range of sensors has to connect with a wide range of devices in a factory. Maintenance and especially predictive maintenance (PdM) policies. The decision support system is widely used in maintenance and anomaly detection (AD) concepts. Although there are several studies in the field of PdM and AD, there are still matters that remain unresolved. Up to now, the structure of the multi-component complex systems and the interaction between their components, as two crucial matters, have not been considered in the development of data-driven PdM policies. Moreover, the existing data-driven PdM methods routinely used the ML which are traditionally been criticism due to their black box. Explainable Machine Learning (XML) is a newly developed alternative to traditional machine learning (ML). This project proposal leads to knowledge transfer among partners to reduce these gaps. This project develops DSS frameworks for PdM based on the run to failure and suspension data by taking the structural function of the system and dependencies of its components into account and implements the XML approaches to solve the problems. Furthermore, the copula concept uses to model the dependency between the components of the system.
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.