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Risk-Averse Stochastic Integer Programs For Mixed-Model Assembly Line Sequencing Problems, Ge Guo, Sarah M. Ryan 2017 Iowa State University

Risk-Averse Stochastic Integer Programs For Mixed-Model Assembly Line Sequencing Problems, Ge Guo, Sarah M. Ryan

Sarah M. Ryan

A variety of optimization formulations have been proposed for mixed-model assembly sequencing problems with stochastic demand and task times. In the real world, however, mixed-model assembly lines are faced with more challenging uncertainties including timely part delivery, material quality, upstream sub-assembly completion and availability of other resources. In addition, sub-assembly lines must meet deadlines imposed by downstream stations. The inevitable disruptions require resequencing. We present a risk-averse stochastic mixed-integer model for mixed-model assembly line resequencing problems to increase on-time performance.


Calibration, Error Analysis, And Ongoing Measurement Process Monitoring For Mass Spectrometry, Stephen B. Vardeman, Joanne Wendelberger, Lily Wang 2017 Iowa State University

Calibration, Error Analysis, And Ongoing Measurement Process Monitoring For Mass Spectrometry, Stephen B. Vardeman, Joanne Wendelberger, Lily Wang

Stephen B. Vardeman

We consider problems of quantifying and monitoring accuracy and precision of measurement in mass spectrometry, particularly in contexts where there is unavoidable day-to-day/period-to-period changes in instrument sensitivity. First, we consider the issue of estimating instrument sensitivity based on data from a typical calibration study. Simple method-of-moments methods, likelihood-based methods, and Bayes methods based on the one-way random effects model are illustrated. Then, we consider subsequently assessing the precision of an estimate of a mole fraction of a gas of interest in an unknown. Finally, we turn to the problem of ongoing measurement process monitoring and illustrate appropriate setup of ...


Modeling And Inference For Measured Crystal Orientations And A Tractable Class Of Symmetric Distributions For Rotations In Three Dimensions, Melissa Ann Bingham, Daniel J. Nordman, Stephen B. Vardeman 2017 University of Wisconsin - La Crosse

Modeling And Inference For Measured Crystal Orientations And A Tractable Class Of Symmetric Distributions For Rotations In Three Dimensions, Melissa Ann Bingham, Daniel J. Nordman, Stephen B. Vardeman

Stephen B. Vardeman

Electron backscatter diffraction (EBSD) is a technique used in materials science to study the microtexture of metals, producing data that measure the orientations of crystals in a specimen. We examine the precision of such data based on a useful class of distributions on orientations in three dimensions (as represented by 3×3 orthogonal matrices with positive determinants). Although such modeling has received attention in the statistical literature, the approach taken typically has been based on general “special manifold” considerations, and the resulting methodology may not be easily accessible to nonspecialists. We take a more direct modeling approach, beginning from a ...


Likelihood-Based Statistical Estimation From Quantized Data View Document, Stephen B. Vardeman, Chiang-Sheng Lee 2017 Iowa State University

Likelihood-Based Statistical Estimation From Quantized Data View Document, Stephen B. Vardeman, Chiang-Sheng Lee

Stephen B. Vardeman

Most standard statistical methods treat numerical data as if they were real (infinite-number-of-decimal-places) observations. The issue of quantization or digital resolution can render such methods inappropriate and misleading. This article discusses some of the difficulties of interpretation and corresponding difficulties of inference arising in even very simple measurement contexts, once the presence of quantization is admitted. It then argues (using the simple case of confidence interval estimation based on a quantized random sample from a normal distribution as a vehicle) for the use of statistical methods based on "rounded data likelihood functions" as an effective way of handling the matter.


Development Programs For One-Shot Systems Using Multiple-State Design Reliability Models, Suntichai Shevasuthisilp, Stephen B. Vardeman 2017 Chiang Mai University

Development Programs For One-Shot Systems Using Multiple-State Design Reliability Models, Suntichai Shevasuthisilp, Stephen B. Vardeman

Stephen B. Vardeman

Design reliability at the beginning of a product development program is typically low, and development costs can account for a large proportion of total product cost. We consider how to conduct development programs (series of tests and redesigns) for one-shot systems (which are destroyed at first use or during testing). In rough terms, our aim is to both achieve high final design reliability and spend as little of a fixed budget as possible on development. We employ multiple-state reliability models. Dynamic programming is used to identify a best test-and-redesign strategy and is shown to be presently computationally feasible for at ...


Uniformly Hyper-Efficient Bayes Inference In A Class Of Nonregular Problems, Danial J. Nordman, Stephen B. Vardeman, Melissa Ann Bingham 2017 Iowa State University

Uniformly Hyper-Efficient Bayes Inference In A Class Of Nonregular Problems, Danial J. Nordman, Stephen B. Vardeman, Melissa Ann Bingham

Stephen B. Vardeman

We present a tractable class of nonregular continuous statistical models where 1) likelihoods have multiple singularities and ordinary maximum likelihood is intrinsically unavailable, but 2) Bayes procedures achieve convergence rates better than n−1 across the whole parameter space. In fact, for every p>1, there is a member of the class for which the posterior distribution is consistent at rate n−puniformly in the parameter.


Likelihood And Bayesian Methods For Accurate Identification Of Measurement Biases In Pseudo Steady-State Processes, Sriram Devanathan, Stephen B. Vardeman, Derrick K. Rollins Sr. 2017 Iowa State University

Likelihood And Bayesian Methods For Accurate Identification Of Measurement Biases In Pseudo Steady-State Processes, Sriram Devanathan, Stephen B. Vardeman, Derrick K. Rollins Sr.

Stephen B. Vardeman

Two new approaches are presented for improved identification of measurement biases in linear pseudo steady-state processes. Both are designed to detect a change in the mean of a measured variable leading to an inference regarding the presence of a biased measurement. The first method is based on a likelihood ratio test for the presence of a mean shift. The second is based on a Bayesian decision rule (relying on prior distributions for unknown parameters) for the detection of a mean shift. The performance of these two methods is compared with that of a method given by Devanathan et al. (2000 ...


Sheppard's Correction For Variances And The "Quantization Noise Model", Stephen B. Vardeman 2017 Iowa State University

Sheppard's Correction For Variances And The "Quantization Noise Model", Stephen B. Vardeman

Stephen B. Vardeman

In this paper, we examine the relevance of Sheppard's correction for variances and (both the original and a valid weak form of) the so-called "quantization noise model" to understanding the effects of integer rounding on continuous random variables. We further consider whether there is any real relationship between the two. We observe that the strong form of the model is not really relevant to describing rounding effects. We demonstrate using simple cases the substantial limitations of the Sheppard correction, and use simple versions of a weak form of the model to establish that there is no real connection between ...


Majority Voting By Independent Classifiers Can Increase Error Rates, Stephen B. Vardeman, Max Morris 2017 Iowa State University

Majority Voting By Independent Classifiers Can Increase Error Rates, Stephen B. Vardeman, Max Morris

Stephen B. Vardeman

The technique of “majority voting” of classifiers is used in machine learning with the aim of constructing a new combined classification rule that has better characteristics than any of a given set of rules. The “Condorcet Jury Theorem” is often cited, incorrectly, as support for a claim that this practice leads to an improved classifier (i.e., one with smaller error probabilities) when the given classifiers are sufficiently good and are uncorrelated. We specifically address the case of two-category classification, and argue that a correct claim can be made for independent (not just uncorrelated) classification errors (not the classifiers themselves ...


Bayes Inference For A Tractable New Class Of Non-Symmetric Distributions For 3-Dimensional Rotations, Melissa Ann Bingham, Danial J. Nordman, Stephen B. Vardeman 2017 University of Wisconsin - La Crosse

Bayes Inference For A Tractable New Class Of Non-Symmetric Distributions For 3-Dimensional Rotations, Melissa Ann Bingham, Danial J. Nordman, Stephen B. Vardeman

Stephen B. Vardeman

Both existing models for non-symmetric distributions on 3-dimensional rotations and their associated one-sample inference methods have serious limitations in terms of both interpretability and ease of use. Based on the intuitively appealing Uniform Axis- Random Spin (UARS) construction of Bingham, Nordman, and Vardeman (2009) for symmetric families of distributions, we propose new highly interpretable and tractable classes of non-symmetric distributions that are derived from mixing UARS distributions. These have an appealing Preferred Axis-Random Spin (PARS) construction and (unlike existing models) directly interpretable parameters. Non-informative one-sample Bayes inference in these models is a direct generalization of UARS methods introduced in Bingham ...


Elementary Statistical Methods And Measurement Error, Stephen B. Vardeman, Joanne Wendelberger, Tom Burr, Michael S. Hamada, Leslie M. Moore, Marcus Jobe, Max Morris, Huaiqing Wu 2017 Iowa State University

Elementary Statistical Methods And Measurement Error, Stephen B. Vardeman, Joanne Wendelberger, Tom Burr, Michael S. Hamada, Leslie M. Moore, Marcus Jobe, Max Morris, Huaiqing Wu

Stephen B. Vardeman

How the sources of physical variation interact with a data collection plan determines what can be learned from the resulting dataset, and in particular, how measurement error is reflected in the dataset. The implications of this fact are rarely given much attention in most statistics courses. Even the most elementary statistical methods have their practical effectiveness limited by measurement variation; and understanding how measurement variation interacts with data collection and the methods is helpful in quantifying the nature of measurement error. We illustrate how simple one- and two-sample statistical methods can be effectively used in introducing important concepts of metrology ...


Modern Measurement, Probability, And Statistics: Some Generalities And Multivariate Illustrations, Stephen B. Vardeman 2017 Iowa State University

Modern Measurement, Probability, And Statistics: Some Generalities And Multivariate Illustrations, Stephen B. Vardeman

Stephen B. Vardeman

In broad terms, effective probability modeling of modern measurement requires the development of (usually parametric) distributions for increasingly complex multivariate outcomes driven by the physical realities of particular measurement technologies. “Differences” between measures of distribution center and truth function as “bias.” Model features that allow hierarchical compounding of variation function to describe “variance components” like “repeatability,” “reproducibility,” “batch-to-batch variation,” etc. Mixture features in models allow for description (and subsequent downweighting) of outliers. For a variety of reasons (including high-dimensionality of parameter spaces relative to typical sample sizes, the ability to directly include “Type B” considerations in assessing uncertainty, and the ...


A Pseudo-Likelihood Analysis For Incomplete Warranty Data With A Time Usage Rate Variable And Production Counts, Yu Qiu, Danial J. Nordman, Stephen B. Vardeman 2017 Iowa State University

A Pseudo-Likelihood Analysis For Incomplete Warranty Data With A Time Usage Rate Variable And Production Counts, Yu Qiu, Danial J. Nordman, Stephen B. Vardeman

Stephen B. Vardeman

The most direct purpose of collecting warranty data is tracking associated costs. However, they are also useful for quantifying a relationship between use rate and product time-to-first-failure and for estimating the distribution of product time-to-first-failure (which is modeled in this article as depending on use rate and a unit potential life length under continuous use). Employing warranty data for such reliability analysis purposes is typically complicated by the fact that some parts of some warranty data records are missing. A pseudo-likelihood methodology is introduced to deal with some kinds of incomplete warranty data (such as that available in a motivating ...


One-Sample Bayes Inference For Symmetric Distributions Of 3-D Rotations, Yu Qiu, Danial J. Nordman, Stephen B. Vardeman 2017 Iowa State University

One-Sample Bayes Inference For Symmetric Distributions Of 3-D Rotations, Yu Qiu, Danial J. Nordman, Stephen B. Vardeman

Stephen B. Vardeman

A variety of existing symmetric parametric models for 3-D rotations found in both statistical and materials science literatures are considered from the point of view of the “uniform-axis-random-spin” (UARS) construction. One-sample Bayes methods for non-informative priors are provided for all of these models and attractive frequentist properties for corresponding Bayes inference on the model parameters are confirmed. Taken together with earlier work, the broad efficacy of non-informative Bayes inference for symmetric distributions on 3-D rotations is conclusively demonstrated.


Life Cycle Assessment Of The Production Of Hydrogen And Transportation Fuels From Corn Stover Via Fast Pyrolysis, Yanan Zhang, Guiping Hu, Robert C. Brown 2017 Iowa State University

Life Cycle Assessment Of The Production Of Hydrogen And Transportation Fuels From Corn Stover Via Fast Pyrolysis, Yanan Zhang, Guiping Hu, Robert C. Brown

Robert C. Brown

This life cycle assessment evaluates and quantifies the environmental impacts of the production of hydrogen and transportation fuels from the fast pyrolysis and upgrading of corn stover. Input data for this analysis come from Aspen Plus modeling, a GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model database and a US Life Cycle Inventory Database. SimaPro 7.3 software is employed to estimate the environmental impacts. The results indicate that the net fossil energy input is 0.25 MJ and 0.23 MJ per km traveled for a light-duty vehicle fueled by gasoline and diesel fuel, respectively. Bio-oil ...


An Agricultural Harvest Knowledge Survey To Distinguish Types Of Expertise, Chase Meusel, Chase Grimm, Stephen B. Gilbert, Greg R. Luecke 2017 Iowa State University

An Agricultural Harvest Knowledge Survey To Distinguish Types Of Expertise, Chase Meusel, Chase Grimm, Stephen B. Gilbert, Greg R. Luecke

Stephen B. Gilbert

Gaining insight into the unique characteristics of participants during user research is a valuable tool for both recruitment and understanding differences within the target population. This work describes an agricultural harvest knowledge survey that was created for user research studies that observed experienced combine operators driving a combine simulator in virtual crop fields. Two variations of the survey were designed, utilized, and evaluated in two separate studies. Both studies found a difference between low and high knowledge operators' performance on the knowledge survey in addition to performance differences. Based on the success of this survey as a population segmentation tool ...


Capturing Cognitive Fingerprints From Keystroke Dynamics, J. Morris Chang, Chi-Chen Fang, Kuan-Hsing Ho, Norene Kelly, Pei-Yuan Wu, Yixiao Ding, Chris Chu, Stephen B. Gilbert, Amed E. Kamal, Sun-Yuan Kung 2017 Iowa State University

Capturing Cognitive Fingerprints From Keystroke Dynamics, J. Morris Chang, Chi-Chen Fang, Kuan-Hsing Ho, Norene Kelly, Pei-Yuan Wu, Yixiao Ding, Chris Chu, Stephen B. Gilbert, Amed E. Kamal, Sun-Yuan Kung

Morris Chang

Conventional authentication systems identify a user only at the entry point. Keystroke dynamics can continuously authenticate users by their typing rhythms without extra devices. This article presents a new feature called cognitive typing rhythm (CTR) to continuously verify the identities of computer users. Two machine techniques, SVM and KRR, have been developed for the system. The best results from experiments conducted with 1,977 users show a false-rejection rate of 0.7 percent and a false-acceptance rate of 5.5 percent. CTR therefore constitutes a cognitive fingerprint for continuous. Its effectiveness has been verified through a large-scale dataset. This article ...


Staticstutor: Free Body Diagram Tutor For Problem Framing, Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, LeAnn Faidley, Mostafa Amin-Naseri 2017 Iowa State University

Staticstutor: Free Body Diagram Tutor For Problem Framing, Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-Naseri

Stephen B. Gilbert

While intelligent tutoring systems have been designed to teach freebody diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the ...


Convocons: A Tool For Building Affinity Among Distributed Team Members, Michael Oren, Stephen B. Gilbert 2017 Iowa State University

Convocons: A Tool For Building Affinity Among Distributed Team Members, Michael Oren, Stephen B. Gilbert

Stephen B. Gilbert

In this paper we present the result of a user interface designed to increase social affinity between two remote collaborators working on design tasks. The results suggest that the tool is successful in creating an overall affinity that is 14.6% higher than the control group without adding a significant difference in task completion time. Affinity is measured with a framework with demonstrated inter-rater reliability using codes assigned to specific conversational patterns and video recorded interactions. This research approach provides a platform for future work codifying affinity and trust among larger numbers of remote collaborators.


Assessing Multiple Participant View Positioning In Virtual Reality-Based Training, Jonathan W. Kelly, Eliot Winer, Stephen B. Gilbert, Michael Curtis, Eduardo Rubio, Ken Kopecky, Joseph Scott Holub, Julio de la Cruz 2017 Iowa State University

Assessing Multiple Participant View Positioning In Virtual Reality-Based Training, Jonathan W. Kelly, Eliot Winer, Stephen B. Gilbert, Michael Curtis, Eduardo Rubio, Ken Kopecky, Joseph Scott Holub, Julio De La Cruz

Stephen B. Gilbert

As cost, time, and other challenging resource requirements are placed on U.S. Joint forces training, the role of simulations will play an even greater role than it does today. To effectively aid a Warfighter in gaining critical skills and to assess the proficiency of those skills, computer-based training must advance beyond traditional desktop simulations and monoscopic projection technology. Virtual Reality (VR) based training has been proven in fields such as medical and engineering to increase a trainee’s level of immersion, and increase training performance in several metrics including accuracy and efficiency, while simultaneously decreasing cost. Warfighter training offers ...


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