2016 INFORMS Annual Meeting Program

172 / 561

INFORMS Nashville – 2016
172
2 - Degradation Prediction Of Printed Images
Ziyi Wang, Rutgers University, Piscataway, NJ, 08854,
United States, , Elsayed A. Elsayed
Today, a great number of images are produced by digital color printers, especially
inkjet printers. Many factors lead to the degradation of such images and accurate
prediction modeling of the degradation is of interest. Previous research that
addresses image degradation usually measures the density loss or color change of
the prints. In this presentation, the area coverage of the Neugebauer primaries for
the basic four-colors (CMYK) ink-set is estimated from the spectral information of
the print. A degradation model is developed to predict the area coverage loss over
time. A numerical example is used to illustrate the proposed approach.
3 - Modeling Spatio-temporal Degradation Data
Xiao Liu, IBM T.J. Watson Research Center,
This talk presents a modeling approach for an important type of degradation data,
i.e., the degradation data collected over time and from a spatial domain. The
connection between the proposed model and traditional pure time-dependent
univariate stochastic degradation models is discussed, and an application example
is provided.
MB68
Mockingbird 4 - Omni
Joint Session QSR/DM: Data analytics for system
improvement I
Sponsored: Quality, Statistics and Reliability/Data Mining
Sponsored Session
Chair: Kaibo Liu, University of Wisconsin-Madison, WI,
Co-chair: Haitao Liao, University of Arkansas, Fayetteville, AR,
1 - Kernel Fisher Discriminant Analysis For Uncertain Data Objects
Behnam Tavakkol, Rutgers University, Piscataway, NJ,
, Myong K Jeong, Susan Albin
Uncertain data problems have features represented by multiple observations or
their fitted PDFs. We propose measures of scatter for uncertain data objects which
include covariance matrix along with within and between scatter matrices. We
also propose Fisher linear discriminant and kernel Fisher discriminant for
classifying uncertain data objects.
2 - An Efficient Statistical Quality Control Scheme For High-
dimensional Process
Sangahn Kim1, Rutgers University, Piscatawy, Piscataway, NJ,
, Myong K Jeong, Elsayed A.
Elsayed
As the number of quality characteristics to be monitored increases in those
complex processes, the simultaneous monitoring becomes less sensitive to the
out-of-control signals especially when only a few variables are responsible for
abnormal situation. We introduce a new process control chart for monitoring
high-dimensional processes based on the ridge penalizing likelihood. The accurate
probability distributions under null and alternative hypotheses are obtained. In
addition, we find out several theoretical properties of the proposed method, and
finally demonstrate the proposed chart performs well in monitoring high
dimensional processes.
3 - A Nonparametric Adaptive Sampling Strategy For Online
Monitoring Of Big Data Streams
Xiaochen Xian, UW-Madison, Madison, WI,
Andi Wang, Kaibo Liu
Modern and rapid advancement in sensor technology generates huge amount of
data, posing unique challenges for Statistical Process Control. We propose a
Nonparametric Adaptive Sampling (NAS) strategy to online monitor non-normal
big data streams in the context of limited resources, such that only partial
observations are available. In particular, this proposed method integrates a rank-
based CUSUM scheme that corrects with the anti-rank statistics due to partial
observations, which can effectively detect a wide range of possible mean shifts in
all directions when each data stream follows arbitrary distribution. Two
theoretical properties of the NAS algorithm are investigated.
MB69
Old Hickory- Omni
Military Operations Research II
Sponsored: Military Applications
Sponsored Session
Chair: Natalie M Scala, Towson University, 8000 York Road, Towson,
MD, 21252, United States,
1 - A Value Model For Cybersecurity Metrics
Natalie M Scala, Assistant Professor, Towson University, 8000 York
Road, Towson, MD, 21252, United States,
Paul L Goethals
This research applies decision analysis perspectives to cybersecurity and creates a
value model for performance metrics and best practices that is supported by
industry data and interviews with subject matter experts. The utility-theory based
value model will include attributes and values, score metrics on their contribution
to value, and provide a rank ordered list of important metrics and best practices
for implementation. We illustrate the value model but contribute an overall
framework that can be customized for any organization. Results will enable
organizations to assess the performance of cyber systems.
2 - Efficient Benchmarking Tool Regarding Optimal Detection Of
Critical Components In A Network
Gokhan Karakose, University of Missouri,
Ronald McGarvey
Many mathematical and heuristic approaches have been provided to assess critical
components of the network based on the network connectivity metric. Since
examined objectives through this metric (e.g. minimum connectivity) have
important values in many areas (e.g. immunization), proposing an effective
solution framework to determine optimal values of such objectives is crucial. In
this regard, we provide efficient mathematical models along with new valid
inequality constraints to further decrease computational complexity compare to
the most recent best models. With this improvement, we broaden the application
scope of the exact solution method for the determination of critical component.
3 - OMEGA: Evaluating Effectiveness Of Proposed Systems Using
Bayesian Networks
Freeman Marvin, Innovative Decisions, 5848 Hunton Wood Drive,
Broad Run, VA, 20137, United States,
Amanda Hepler
OMEGA is a new approach for designing affordable systems architectures that
meet user needs. OMEGA uses a Bayesian network of probability distributions
that describes functional needs, system capabilities and customer satisfaction.
Measures of Effectiveness (MOE) are combined to estimate the probability that a
proposed system will meet mission needs. Additionally, OMEGA can “back cast”
the system requirements necessary to achieve alternative levels of mission
effectiveness. This innovative approach was developed by a collaborative team of
requirements engineers and decision analysts. OMEGA is a flexible, low cost
approach for conducting architecture trades and developing requirements for any
kind of system.
4 - Designing An Objective Metric For Evaluating Army
Unit Readiness
Paul Goethals, United States Military Academy, West Point, NY,
United States, , Natalie M Scala
Perhaps one of the most difficult assessments to make with some level of accuracy
is military readiness - it is a frequent topic of interest in defense news both in
times of combat and peace. This research proposes a readiness index tailored to
objectively evaluate units based upon their current status and future mission,
using quality engineering tools as a foundation for measurement. A simulated
comparison of the current and proposed readiness indices is provided to illustrate
their differences in assessing Army units.
MB68