Duc Tran Kim

Duc Tran Kim
Dong A University · International Research Institute for Artificial Intelligence and Data Science

MSc in Business Administration and Data Science

About

12
Publications
1,699
Reads
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31
Citations
Citations since 2016
12 Research Items
31 Citations
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201620172018201920202021202205101520

Publications

Publications (12)
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...
Article
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...
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...
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...
Article
Full-text available
Monitoring the ratio of two normal random variables plays an important role in several manufacturing environments. In addition, the traditional control charts that have been developed for infinite production horizon can not function effectively to detect anomalies in short production runs. In this paper, we tackle this problem by proposing two one-...
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...
Chapter
Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things and Artificial Intelligence (AI), Smart Manufacturing (SM) has bec...
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...
Preprint
Full-text available
Monitoring the ratio of two normal random variables plays an important role in several manufacturing environments. For short production runs, however, the control charts assumed infinite processes cannot function effectively to detect anomalies. In this paper, we tackle this problem by proposing two one-sided Shewhart-type charts to monitor the rat...
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...
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...

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Projects

Project (1)
Project
In this project, we are going to propose new control charts. The performance of each control chart has been evaluated and the optimal parameters will be computed. An empirical validation of the results will be developed for real industrial processes.