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Knowledge Management Determinants Of Continuance Behavior: Evaluating The Air Force Knowledge Now Knowledge Management System, Eric Tucker 2017 University of Central Florida

Knowledge Management Determinants Of Continuance Behavior: Evaluating The Air Force Knowledge Now Knowledge Management System, Eric Tucker

Eric M. Tucker

Knowledge management (KM) encompasses the set of capabilities, processes, tools, and techniques for the most effective use of knowledge by an organization. The goal of KM is to improve the organization's ability to create, transfer, retain, and apply knowledge. Knowledge management is a goal that many organizations seek to achieve. Organizations apply their strategies, plans, and implementation to achieve KM. Organizations use technology to implement their KM strategy. For some, this approach has worked well; however, for others, the results have fallen short. KM shortcomings revolve around employees' infrequent use of the technology. This research seeks to understand what ...


Virtual Operator Models For Off-Highway Machine Virtual Prototyping, Yu Du, Michael C. Dorneich, Brian L. Steward 2017 Iowa State University

Virtual Operator Models For Off-Highway Machine Virtual Prototyping, Yu Du, Michael C. Dorneich, Brian L. Steward

Michael C. Dorneich

Increasing demands on the world’s resources require the design of off-highway machines that provide greater functionality and productivity along with greater efficiency. Model-based or virtual design provides a means for achieving these design improvements with reduced time and costs. However, virtual design is often limited by the fidelity with which human operators are modeled. A greater understanding of how highly skilled operators obtain high machine performance and productivity can inform machine development and advance agricultural and construction machine automation technology. This research investigated how machine operator expertise, strategies, and decision-making can be integrated into operator models that simulate authentic ...


Systematic Design For Trait Introgression Projects, John N. Cameron, Ye Han, Lizhi Wang, William D. Beavis 2017 Iowa State University

Systematic Design For Trait Introgression Projects, John N. Cameron, Ye Han, Lizhi Wang, William D. Beavis

Lizhi Wang

We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Research. If the designs of TI projects are based on clear and measurable objectives, they can be translated into mathematical models with decision variables and constraints that can be translated into Pareto optimality plots associated with any arbitrary selection strategy. The Pareto plots can be used to make rational decisions concerning the trade-offs between maximizing the probability of success while minimizing costs and time. The systematic rigor associated with a cost, time and probability of success (CTP) framework is well suited to designing ...


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.


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 ...


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 ...


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 ...


An Active Learning Environment In An Integrated Industrial Engineering Curriculum, Frank Peters, John K. Jackman, Sarah M. Ryan, Sigurdur Olafsson 2017 Iowa State University

An Active Learning Environment In An Integrated Industrial Engineering Curriculum, Frank Peters, John K. Jackman, Sarah M. Ryan, Sigurdur Olafsson

Sigurdur Olafsson

We are developing a new learning environment that supports a suite of interrelated modules based on real-world scenarios. The primary goals of the project are to integrate industrial engineering courses, improve students’ information technology skills, and enhance students’ problem solving skills. In particular, metacognitive abilities will be strengthened as students apply domain knowledge, data, methods and software tools while monitoring their own solution processes. This paper presents the design of two modules that have been developed.


Utilizing Six Sigma Methodology For Training Undergraduate Student For Conducting Global Field Research, Kritika Chopra, Shweta Chopra, Chad Laux 2017 Iowa State University

Utilizing Six Sigma Methodology For Training Undergraduate Student For Conducting Global Field Research, Kritika Chopra, Shweta Chopra, Chad Laux

Shweta Chopra

With the increase in demand for the global research, scholars in engineering and technology discipline do not hesitate in taking up global opportunity for conducting research. Training the next generation for such international research opportunity is key and involving undergraduate students’ beyond study abroad is important.


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 ...


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 ...


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 ...


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