ISyE Statistics Seminar: Qiang Huang

Event Details
  • Date/Time:
    • Friday March 9, 2007 - Saturday March 10, 2007
      10:00 am - 10:59 am
  • Location: ISyE MAIN, Executive Classroom--Rm 228
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Yajun Mei
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Summary Sentence: ISyE Statistics Seminar: Qiang Huang

Full Summary: ISyE Statistics Seminar: Modeling and Analysis of Fundamental Phenomena in Complex Systems for Quality and Productivity Improvement

ISyE Statistics Seminar: Modeling and Analysis of Fundamental Phenomena in Complex Systems for Quality and Productivity Improvement

Professor Qiang Huang

Department of Industrial and Management Systems Engineering, University of South Florida

Understanding fundamental phenomena is an important step to improve quality and productivity in complex systems. In traditional manufacturing, it has been observed that different error sources may result in identical error patterns on product features. This so-called error equivalence phenomenon could have dual effects on dimensional control: significantly increasing the complexity of root cause identification, and providing an opportunity to use one error source to counteract or compensate the others. In a complex system like human physiology, interaction is a fundamental mechanism of processing and coding information. Understanding complex interactions through observed physiologic signals is vital to diagnosis and treatment of a wide range of medical problems. In nanomanufacturing, relating macro/micro scale functional process variables with nanoscale nanostructure defects is a key step to implement process control for improving process yield and repeatability.

A common methodological framework of modeling and analyzing complex process/system phenomena is to integrate subject matter knowledge with mathematical modeling tools. The challenge lies in the interdisciplinary nature of the problems of interest. In this talk, the speaker will first introduce error equivalence modeling through kinematic analysis. The impact of error equivalence concept on dimensional variation control will be demonstrated through a new sequential root cause identification procedure and a novel error-compensating-error strategy. To understand physiologic interactions, a novel nonlinear dynamics model was developed to consider not only complex spatial patterns among multiple nonstationary signals, but also temporal patterns within individual signals. Validated by controlled physiologic experiments, the proposed model formulation provides better insight into interaction mechanisms, such as the interaction order, strength, and structure. In nanomanufacturing application, the speaker will discuss the research challenges and ideas for quality control of nanostructure growth processes. The ongoing activities at the speaker

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ISyE Statistics Seminar, Qiang Huang
  • Created By: Ruth Gregory
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  • Created On: Oct 12, 2009 - 5:22pm
  • Last Updated: Oct 7, 2016 - 9:48pm