Kamran Paynabar

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KAMRAN PAYNABAR1, JIONGHUA (JUDY) JIN2,∗ and MASSIMO PACELLA3 1H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA 2Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109-2117, USA E-mail: jhjin@umich.edu 3Dipartimento di Ingegneria(More)
Research Identifying thePeriodofaStep Change inHigh-YieldProcesses Rassoul Noorossana1,∗,†, Abbas Saghaei2, Kamran Paynabar3 and Sara Abdi4 1Industrial Engineering Department, Iran University of Science and Technology, Tehran 16844, Iran 2Industrial Engineering Department, Islamic Azad University—Science and Research Campus, Tehran, Iran 3Industrial and(More)
This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents(More)
With the increase of market fluctuation, assembly systems moved from a mass production scheme to a mass customization scheme. Mixed model assembly systems (MMASs) have been recognized as enablers of mass customization manufacturing. However, effective implementation of MMASs requires, among other things, a highly proactive and knowledgeable workforce.(More)
In recent years risk-adjusted control charts have been increasingly studied for monitoring surgical outcomes by accounting for patients’ health conditions prior to surgery. However, most existing research focuses on phase II monitoring, and very little work has been done on phase I control of surgical outcomes. In this paper, a general phase I risk-adjusted(More)
A novel algorithm is developed for feature selection and parameter tuning in quality monitoring of manufacturing processes using cross-validation. Due to the recent development in sensing technology, many on-line signals are collected for manufacturing process monitoring and feature extraction is then performed to extract critical features related to(More)
Image and video sensors are increasingly being deployed in complex systems due to the rich process information that these sensors can capture. As a result, image data play an important role in process monitoring and control in different application domains such as manufacturing processes, food industries, medical decision-making, and structural health(More)
In this expository paper we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and(More)