Matching Items (70)
Description
The Department of Defense (DoD) acquisition system is a complex system riddled with cost and schedule overruns. These cost and schedule overruns are very serious issues as the acquisition system is responsible for aiding U.S. warfighters. Hence, if the acquisition process is failing that could be a potential threat to our nation's security. Furthermore, the DoD acquisition system is responsible for proper allocation of billions of taxpayer's dollars and employs many civilians and military personnel. Much research has been done in the past on the acquisition system with little impact or success. One reason for this lack of success in improving the system is the lack of accurate models to test theories. This research is a continuation of the effort on the Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation modeling research on DoD acquisition system. We propose to extend ERAM using agent-based simulation principles due to the many interactions among the subsystems of the acquisition system. We initially identify ten sub models needed to simulate the acquisition system. This research focuses on three sub models related to the budget of acquisition programs. In this thesis, we present the data collection, data analysis, initial implementation, and initial validation needed to facilitate these sub models and lay the groundwork for a full agent-based simulation of the DoD acquisition system.
ContributorsBucknell, Sophia Robin (Author) / Wu, Teresa (Thesis director) / Li, Jing (Committee member) / Colombi, John (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The thesis, titled Identifying Emerging Technologies and Techniques to Assess Indoor Environmental Quality and its Impact on Occupant Health, consists of an in-depth literature review outlining the various impacts of building factors on inhabitant health. Approximately 120 studies analyzing how environmental factors influence occupant health were reviewed and 25 were used to build this literature review. The thesis provides insight into the definitions of well-being, health, and the built environment and analyzes the relationship between the three. This complex relationship has been at the forefront of academic research in recent years, especially given the impact of the COVID-19 pandemic. Essentially, an individual’s health and well-being is encompassed by their physical, mental, and social state of being. Due to the increasing amount of time spent in indoor environments the built environment influences these measures of health and well-being through various environmental factors (Indoor Air Quality, humidity, temperature, lighting, acoustics, ergonomics) defining the overall Indoor Environmental Quality. This thesis reviewed the mentioned intervention and experimental studies conducted to determine how fluctuations in environmental factors influence reported health results of occupants in the short and long term. Questionnaires, interviews, medical tests, physical measurements, and sensors were used to track occupant health measures. Sensors are also used to record environmental factor levels and are now beginning to be incorporated into the building production process to promote occupant health in healthy and smart buildings. The goal is ultimately to develop these smart and healthy buildings using study results and advancing technologies and techniques as outlined in the thesis.
ContributorsWhite, Elizabeth Mary (Author) / Wu, Teresa (Thesis director) / Wen, Jin (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Our thesis is a cross collaboration between international relations and industrial engineering. We used a combination of database logic, programming, and Microsoft Visual Studio to organize and analyze Middle Eastern politics. Not only does the final product show raw data entry, but it also can answer complex questions about Middle Eastern relations- queries so complex that Google can’t answer them. We organized and analyzed geopolitical data to make it more accessible and easy, hopefully you enjoy!
ContributorsGranillo-Walker, Erin (Co-author) / Gomez, Livingstone (Co-author) / Wu, Teresa (Thesis director) / Thomson, Henry (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Social Transformation (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Public Affairs (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Our thesis is a cross collaboration between international relations and industrial engineering. We used a combination of database logic, programming, and Microsoft Visual Studio to organize and analyze Middle Eastern politics. Not only does the final product show raw data entry, but it also can answer complex questions about Middle Eastern relations- queries so complex that Google can’t answer them. We organized and analyzed geopolitical data to make it more accessible and easy, hopefully you enjoy!
ContributorsGomez, Livingstone Labaco (Co-author) / Granillo-Walker, Erin (Co-author) / Wu, Teresa (Thesis director) / Thomson, Henry (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents a decision support framework that provides a holistic view on customer preference by following a two-phase procedure. Phase-1 uses cluster analysis to create product profiles based on which customer profiles are derived. Phase-2 then delves deep into each of the customer profiles and investigates causality behind their preference using Bayesian networks. This thesis illustrates the working of the framework using the case of Intel Corporation, world’s largest semiconductor manufacturing company.
ContributorsRam, Sudarshan Venkat (Author) / Kempf, Karl G. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Ju, Feng (Committee member) / Arizona State University (Publisher)
Created2017
Description
Due to environmental and geopolitical reasons, many countries are embracing electric vehicles (EVs) as an alternative to gasoline powered automobiles. Other alternative-fuel vehicles (AFVs) powered by compressed gas, hydrogen or biodiesel have also been tested for replacing gasoline powered vehicles. However, since the associated refueling infrastructure of AFVs is sparse and is gradually being built, the distance between recharging points (RPs) becomes a crucial prohibitive attribute in attracting drivers to use such vehicles. Optimally locating RPs will both increase demand and help in developing the refueling infrastructure.
The major emphasis in this dissertation is the development of theories and associated algorithms for a new set of location problems defined on continuous network space related to siting multiple RPs for range limited vehicles.
This dissertation covers three optimization problems: locating multiple RPs on a line network, locating multiple RPs on a comb tree network, and locating multiple RPs on a general tree network. For each of the three problems, finding the minimum number of RPs needed to refuel all Origin-Destination (O-D) flows is considered as the first objective. For this minimum number, the location objective is to locate this number of RPs to minimize weighted sum of the travelling distance for all O-D flows. Different exact algorithms are proposed to solve each of the three algorithms.
In the first part of this dissertation, the simplest case of locating RPs on a line network is addressed. Scenarios include single one-way O-D pair, multiple one-way O-D pairs, round trips, etc. A mixed integer program with linear constraints and quartic objective function is formulated. A finite dominating set (FDS) is identified, and based on the existence of FDS, the problem is formulated as a shortest path problem. In the second part, the problem is extended to comb tree networks. Finally, the problem is extended to general tree networks. The extension to a probabilistic version of the location problem is also addressed.
The major emphasis in this dissertation is the development of theories and associated algorithms for a new set of location problems defined on continuous network space related to siting multiple RPs for range limited vehicles.
This dissertation covers three optimization problems: locating multiple RPs on a line network, locating multiple RPs on a comb tree network, and locating multiple RPs on a general tree network. For each of the three problems, finding the minimum number of RPs needed to refuel all Origin-Destination (O-D) flows is considered as the first objective. For this minimum number, the location objective is to locate this number of RPs to minimize weighted sum of the travelling distance for all O-D flows. Different exact algorithms are proposed to solve each of the three algorithms.
In the first part of this dissertation, the simplest case of locating RPs on a line network is addressed. Scenarios include single one-way O-D pair, multiple one-way O-D pairs, round trips, etc. A mixed integer program with linear constraints and quartic objective function is formulated. A finite dominating set (FDS) is identified, and based on the existence of FDS, the problem is formulated as a shortest path problem. In the second part, the problem is extended to comb tree networks. Finally, the problem is extended to general tree networks. The extension to a probabilistic version of the location problem is also addressed.
ContributorsSong, Yazhu (Author) / Mirchandani, Pitu B. (Thesis advisor) / Wu, Teresa (Committee member) / Sefair, Jorge A (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2020
Description
Nonalcoholic Steatohepatitis (NASH) is a severe form of Nonalcoholic fatty liverdisease, that is caused due to excessive calorie intake, sedentary lifestyle and in the
absence of severe alcohol consumption. It is widely prevalent in the United States
and in many other developed countries, affecting up to 25 percent of the population.
Due to being asymptotic, it usually goes unnoticed and may lead to liver failure if
not treated at the right time.
Currently, liver biopsy is the gold standard to diagnose NASH, but being an
invasive procedure, it comes with it's own complications along with the inconvenience
of sampling repeated measurements over a period of time. Hence, noninvasive
procedures to assess NASH are urgently required. Magnetic Resonance Elastography
(MRE) based Shear Stiffness and Loss Modulus along with Magnetic Resonance
Imaging based proton density fat fraction have been successfully combined to predict
NASH stages However, their role in the prediction of disease progression still remains
to be investigated.
This thesis thus looks into combining features from serial MRE observations to
develop statistical models to predict NASH progression. It utilizes data from an experiment
conducted on male mice to develop progressive and regressive NASH and
trains ordinal models, ordered probit regression and ordinal forest on labels generated
from a logistic regression model. The models are assessed on histological data collected
at the end point of the experiment. The models developed provide a framework
to utilize a non-invasive tool to predict NASH disease progression.
ContributorsDeshpande, Eeshan (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this thesis, the applications of deep learning in the analysis, detection and classification of medical imaging datasets were studied, with a focus on datasets having a limited sample size. A combined machine learning-deep learning model was designed to classify one small dataset, prostate cancer provided by Mayo Clinic, Arizona. Deep learning model was implemented to extract imaging features followed by machine learning classifier for prostate cancer diagnosis. The results were compared against models trained on texture-based features, namely gray level co-occurrence matrix (GLCM) and Gabor. Some of the challenges of performing diagnosis on medical imaging datasets with limited sample sizes, have been identified. Lastly, a set of future works have been proposed. Keywords: Deep learning, radiology, transfer learning, convolutional neural network.
ContributorsSarkar, Suryadipto (Author) / Wu, Teresa (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Silva, Alvin (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the increased demand for genetically modified T-cells in treating hematological malignancies, the need for an optimized measurement policy within the current good manufacturing practices for better quality control has grown greatly. There are several steps involved in manufacturing gene therapy. These steps are for the autologous-type gene therapy, in chronological order, are harvesting T-cells from the patient, activation of the cells (thawing the cryogenically frozen cells after transport to manufacturing center), viral vector transduction, Chimeric Antigen Receptor (CAR) attachment during T-cell expansion, then infusion into patient. The need for improved measurement heuristics within the transduction and expansion portions of the manufacturing process has reached an all-time high because of the costly nature of manufacturing the product, the high cycle time (approximately 14-28 days from activation to infusion), and the risk for external contamination during manufacturing that negatively impacts patients post infusion (such as illness and death).
The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.
The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.
ContributorsStarkey, Michaela (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2020
Description
Additive manufacturing consists of successive fabrication of materials layer upon layer to manufacture three-dimensional items. Several key problems such as poor quality of finished products and excessive operational costs are yet to be addressed before it becomes widely applicable in the industry. Retroactive/offline actions such as post-manufacturing inspections for defect detection in finished products are not only extremely expensive and ineffective but are also incapable of issuing corrective action signals during the building span. In-situ monitoring and optimal control methods, on the other hand, can provide viable alternatives to aid with the online detection of anomalies and control the process. Nevertheless, the complexity of process assumptions, unique structure of collected data, and high-frequency data acquisition rate severely deteriorates the performance of traditional and parametric control and process monitoring approaches. Out of diverse categories of additive manufacturing, Large-Scale Additive Manufacturing (LSAM) by material extrusion and Laser Powder Bed Fusion (LPBF) suffer the most due to their more advanced technologies and are therefore the subjects of study in this work. In LSAM, the geometry of large parts can impact the heat dissipation and lead to large thermal gradients between distance locations on the surface. The surface's temperature profile is captured by an infrared thermal camera and translated to a non-linear regression model to formulate the surface cooling dynamics. The surface temperature prediction methodology is then combined into an optimization model with probabilistic constraints for real-time layer time and material flow control. On-axis optical high-speed cameras can capture streams of melt pool images of laser-powder interaction in real-time during the process. Model-agnostic deep learning methods offer a great deal of flexibility when facing such unstructured big data and thus are appealing alternatives to their physical-related and regression-based modeling counterparts. A configuration of Convolutional Long-Short Term Memory (ConvLSTM) auto-encoder is proposed to learn a deep spatio-temporal representation from sequences of melt pool images collected from experimental builds. The unfolded bottleneck tensors are then further mined to construct a high accuracy and low false alarm rate anomaly detection and monitoring procedure.
ContributorsFathizadan, Sepehr (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Lu, Yan (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022