College of Engineering (CoE)
http://hdl.handle.net/1853/5985
For more than 110 years, Georgia Tech has been producing engineers. Today, we are recognized as one of the nation's top ranked engineering colleges.
20170723T11:07:13Z

Characterization of cementitious materials using Xray synchrotron radiation: What we know, what we don’t know, and what we want to know
http://hdl.handle.net/1853/58434
Characterization of cementitious materials using Xray synchrotron radiation: What we know, what we don’t know, and what we want to know
Monteiro, Paulo
For the last two decades, our research group has conducted research in various international synchrotron facilities, including ALS (Lawrence Berkeley National Lab), APS (Argonne National Lab), BESSY (Germany), ELLETRA (Italy), LNSL (Brazil), and SLRI (Thailand). In this lecture, I will summarize the highlights and pitfalls of using this powerful characterization technique. The presentation will also describe new results on
a) understanding catalytic reactions using insitu XPS,
b) linking the chemical and mechanical properties of calcium (alumino)silicate hydrate using HPXRD, and
c) characterizing the early hydration with highresolution spectromicroscopy.
The lecture will end with personal recommendations for future research.
Presented on June 27, 2017 at 5:30 p.m. in the Bill Moore Student Success Center, Clary Theater.; This Della Roy Lecture was presented at The American Ceramic Society's 8th Advances in CementBased Materials (Cements 2017) annual meeting in Atlanta, Georgia.; Paulo Monteiro is the Roy W. Carlson Distinguished Professor in Civil and Environment Engineering at the University of California, Berkeley.; Runtime: 59:19 minutes
20170627T00:00:00Z
Monteiro, Paulo
For the last two decades, our research group has conducted research in various international synchrotron facilities, including ALS (Lawrence Berkeley National Lab), APS (Argonne National Lab), BESSY (Germany), ELLETRA (Italy), LNSL (Brazil), and SLRI (Thailand). In this lecture, I will summarize the highlights and pitfalls of using this powerful characterization technique. The presentation will also describe new results on
a) understanding catalytic reactions using insitu XPS,
b) linking the chemical and mechanical properties of calcium (alumino)silicate hydrate using HPXRD, and
c) characterizing the early hydration with highresolution spectromicroscopy.
The lecture will end with personal recommendations for future research.

An Investigation of Methods for Imputing Attitudes from One Sample to Another
http://hdl.handle.net/1853/58418
An Investigation of Methods for Imputing Attitudes from One Sample to Another
Malokin, Aliaksandr; Mokhtarian, Patricia L.; Circella, Giovanni
Often in practice, researchers have a (“target”) dataset that is desirable in many ways, but is missing some key variables, or “knowledge”, that would greatly enrich the value of the data for investigating questions of interest. If this knowledge could be extracted from a different but related (“source”) dataset and transferred between them by way of variables common to both datasets, it could improve the ability to perform analyses and increase the value of the dataset itself at a relatively minimal cost. In the current study, the target dataset comprises responses to the 2009 National Household Travel Survey (N ≈ 100,000), and the key missing variables are transportationrelated attitudes, which could greatly improve the ability to predict travel behaviors. Our source dataset is obtained from the 201112 Multitasking Survey of Northern California Commuters (MSNCC, N ≈ 2000).
To evaluate approaches to informing one dataset with knowledge from another and to evaluate the performance of the knowledge transferred into the target dataset, we developed transfer learning and external validation frameworks, respectively. To implement the transfer learning framework, the set of common variables was first augmented by obtaining a large number of built and social environment characteristics linked to the residential locations of observations in each dataset. Then, applying machinelearning methods to the categorical and continuous attitudinal variables of the MSNCC, the LASSO (least absolute shrinkage and selection operator) regression learner showed the lowest generalization error over the 10 crossvalidation folds in the context of the source dataset. The protransit, proactive transportation, and prodensity attitudinal factor scores showed the greatest improvement over a naïve learner of assigning the average; correlations of the predicted and observed scores on these factors were 0.564, 0.538, and 0.571, respectively.
The external validation framework was implemented by estimating vehicle ownership linear regression models, and comparing their goodness of fit with and without attitudes. The results showed that in the source dataset the observed attitudes account for an 8.0% model lift (i.e., improvement in goodness of fit), while in the target dataset the predicted attitudes account for a 1.2–5.4% model lift, depending on the extensiveness and nature of the variables used to impute them. Although these initial results are modest, we believe they show substantial promise, and the process has identified a number of opportunities for improvement and further research.
20170601T00:00:00Z
Malokin, Aliaksandr
Mokhtarian, Patricia L.
Circella, Giovanni
Often in practice, researchers have a (“target”) dataset that is desirable in many ways, but is missing some key variables, or “knowledge”, that would greatly enrich the value of the data for investigating questions of interest. If this knowledge could be extracted from a different but related (“source”) dataset and transferred between them by way of variables common to both datasets, it could improve the ability to perform analyses and increase the value of the dataset itself at a relatively minimal cost. In the current study, the target dataset comprises responses to the 2009 National Household Travel Survey (N ≈ 100,000), and the key missing variables are transportationrelated attitudes, which could greatly improve the ability to predict travel behaviors. Our source dataset is obtained from the 201112 Multitasking Survey of Northern California Commuters (MSNCC, N ≈ 2000).
To evaluate approaches to informing one dataset with knowledge from another and to evaluate the performance of the knowledge transferred into the target dataset, we developed transfer learning and external validation frameworks, respectively. To implement the transfer learning framework, the set of common variables was first augmented by obtaining a large number of built and social environment characteristics linked to the residential locations of observations in each dataset. Then, applying machinelearning methods to the categorical and continuous attitudinal variables of the MSNCC, the LASSO (least absolute shrinkage and selection operator) regression learner showed the lowest generalization error over the 10 crossvalidation folds in the context of the source dataset. The protransit, proactive transportation, and prodensity attitudinal factor scores showed the greatest improvement over a naïve learner of assigning the average; correlations of the predicted and observed scores on these factors were 0.564, 0.538, and 0.571, respectively.
The external validation framework was implemented by estimating vehicle ownership linear regression models, and comparing their goodness of fit with and without attitudes. The results showed that in the source dataset the observed attitudes account for an 8.0% model lift (i.e., improvement in goodness of fit), while in the target dataset the predicted attitudes account for a 1.2–5.4% model lift, depending on the extensiveness and nature of the variables used to impute them. Although these initial results are modest, we believe they show substantial promise, and the process has identified a number of opportunities for improvement and further research.

Epitaxial growth of GaNbased LEDs on simple sacrificial substrates
http://hdl.handle.net/1853/58392
Epitaxial growth of GaNbased LEDs on simple sacrificial substrates
Ferguson, Ian T.; Summers, Christopher
Issued as final report
20091221T00:00:00Z
Ferguson, Ian T.
Summers, Christopher

State Estimation using Gaussian Process Regression for Colored Noise Systems
http://hdl.handle.net/1853/58390
State Estimation using Gaussian Process Regression for Colored Noise Systems
Lee, Kyuman; Johnson, Eric N.
The goal of this study is to use Gaussian process (GP) regression models to estimate the state of colored noise systems. The derivation of a Kalman filter assumes that the process noise and measurement noise are uncorrelated and both white. In relaxing those assumptions, the Kalman filter equations were modified to deal with the nonwhiteness of each noise source. The standard Kalman filter ran on an augmented system that had white noises and other approaches were also introduced depending on the forms of the noises. Those existing methods can only work when the characteristics of the colored noise are perfectly known. However, it is usually difficult to model a noise without additional knowledge of the noise statistics. When the parameters of colored noise models are totally unknown and the functions of each underlying model (nonlinear dynamic and measurement functions) are uncertain or partially known, filtering using GPColor models can perform regardless of whatever forms of colored noise. The GPs can learn the residual outputs between the GP models and the approximate parametric models (or between actual sensor readings and predicted measurement readings), as a member of a distribution over functions, typically with a mean and covariance function. Lastly, a series of simulations, including Monte Carlo results, will be run to compare the GP based filtering techniques with the existing methods to handle the sequentially correlated noise.
© 2017 IEEE
20170601T00:00:00Z
Lee, Kyuman
Johnson, Eric N.
The goal of this study is to use Gaussian process (GP) regression models to estimate the state of colored noise systems. The derivation of a Kalman filter assumes that the process noise and measurement noise are uncorrelated and both white. In relaxing those assumptions, the Kalman filter equations were modified to deal with the nonwhiteness of each noise source. The standard Kalman filter ran on an augmented system that had white noises and other approaches were also introduced depending on the forms of the noises. Those existing methods can only work when the characteristics of the colored noise are perfectly known. However, it is usually difficult to model a noise without additional knowledge of the noise statistics. When the parameters of colored noise models are totally unknown and the functions of each underlying model (nonlinear dynamic and measurement functions) are uncertain or partially known, filtering using GPColor models can perform regardless of whatever forms of colored noise. The GPs can learn the residual outputs between the GP models and the approximate parametric models (or between actual sensor readings and predicted measurement readings), as a member of a distribution over functions, typically with a mean and covariance function. Lastly, a series of simulations, including Monte Carlo results, will be run to compare the GP based filtering techniques with the existing methods to handle the sequentially correlated noise.