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Microelectronics

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A Solution to the Recall Problem using Rough Set Theory Professor Djamel Bouchaffra (Advisor) Tarek Dakhlallah (Ph.D. Student) Computer Science & Engineering 131 ... – PowerPoint PPT presentation

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Title: Microelectronics


1
A Solution to the Recall Problem using Rough Set
Theory
Professor Djamel Bouchaffra
(Advisor) Tarek
Dakhlallah (Ph.D. Student)
Computer Science Engineering 131 Dodge Hall
Phone
248-370-2242, email bouchaff_at_oakland.edu
Mass production recall solutions
Current process
Current system inputs
  • Recalls are necessary for a number of reasons,
    today's heavy industries products are incredibly
    complex with many interacting electronic systems
  • The development cycles are extremely compressed
  • With such a short gestation period, certain
    interactions or problems can go undetected in the
    development process of a new vehicle

Manufacturer reports a problem
Product recall issued!
Customer reports a problem
Current process could be costly!
  • Rough Set Theory has shown its fruitfulness in a
    variety of data mining areas (information
    retrieval, decision support, machine learning,
    and knowledge based systems)
  • Concept is proven in may fields like medical data
    analysis
  • and aircraft pilot performance evaluation
  • An important result from the theory is that it
    simplifies
  • the search for dominating attributes leading to
    specific properties, or just rules pending in the
    data
  • Rough set theory proposes a formal framework for
    the automated transformation of data into
    knowledge
  • This method shows that the principles for
    learning by examples can be formulated in the
    basis of this theory
  • An important result from the theory is that it
    simplifies the search for dominating attributes
    leading to specific properties, or just rules
    pending in the data

Proposed concept
Proposed concept
Rough Set Theory box
Proposed process
Vehicle is viewed as The system Information
source ltU,Agt
universe of vehicle components U
I can predict a possible future problem before
mass production
Current system inputs
Any collection X of vehicle components can be
estimated via the equivalence relation through
lower and upper approximations
Lower approximation Filters out noise of set X
and keeps only objects that matter the most
A is a finite set of attributes
Rough Set Theory Filter
Product recall issued!. But on early stage
Manufacturers product issues (recalls) history
Higher approximation
Objects that surely belong to the rough set
X. These vehicle components are of interest
Objects that approximately belong To rough set
Proposed process could be saving !
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