Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Li, Jing
Description
Under different environmental conditions, the relationship between the design and operational variables of a system and the system’s performance is likely to vary and is difficult to be described by a single model. The environmental variables (e.g., temperature, humidity) are not controllable while the variables of the system (e.g. heating, cooling) are mostly controllable. This phenomenon has been widely seen in the areas of building energy management, mobile communication networks, and wind energy. To account for the complicated interaction between a system and the multivariate environment under which it operates, a Sparse Partitioned-Regression (SPR) model is proposed, which automatically searches for a partition of the environmental variables and fits a sparse regression within each subdivision of the partition. SPR is an innovative approach that integrates recursive partitioning and high-dimensional regression model fitting within a single framework. Moreover, theoretical studies of SPR are explicitly conducted to derive the oracle inequalities for the SPR estimators which could provide a bound for the difference between the risk of SPR estimators and Bayes’ risk. These theoretical studies show that the performance of SPR estimator is almost (up to numerical constants) as good as of an ideal estimator that can be theoretically achieved but is not available in practice. Finally, a Tree-Based Structure-Regularized Regression (TBSR) approach is proposed by considering the fact that the model performance can be improved by a joint estimation on different subdivisions in certain scenarios. It leverages the idea that models for different subdivisions may share some similarities and can borrow strength from each other. The proposed approaches are applied to two real datasets in the domain of building energy. (1) SPR is used in an application of adopting building design and operational variables, outdoor environmental variables, and their interactions to predict energy consumption based on the Department of Energy’s EnergyPlus data sets. SPR produces a high level of prediction accuracy and provides insights into the design, operation, and management of energy-efficient buildings. (2) TBSR is used in an application of predicting future temperature condition which could help to decide whether to activate or not the Heating, Ventilation, and Air Conditioning (HVAC) systems in an energy-efficient manner.
ContributorsNing, Shuluo (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Rafi, Tanveer A (Committee member) / Arizona State University (Publisher)
Created2018
Description
In healthcare facilities, health information systems (HISs) are used to serve different purposes. The radiology department adopts multiple HISs in managing their operations and patient care. In general, the HISs that touch radiology fall into two categories: tracking HISs and archive HISs. Electronic Health Records (EHR) is a typical tracking HIS, which tracks the care each patient receives at multiple encounters and facilities. Archive HISs are typically specialized databases to store large-size data collected as part of the patient care. A typical example of an archive HIS is the Picture Archive and Communication System (PACS), which provides economical storage and convenient access to diagnostic images from multiple modalities. How to integrate such HISs and best utilize their data remains a challenging problem due to the disparity of HISs as well as high-dimensionality and heterogeneity of the data. My PhD dissertation research includes three inter-connected and integrated topics and focuses on designing integrated HISs and further developing statistical models and machine learning algorithms for process and patient care improvement.
Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients.
Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks.
Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.
Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients.
Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks.
Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.
ContributorsWang, Kun (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Zwart, Christine M. (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the development of computer and sensing technology, rich datasets have become available in many fields such as health care, manufacturing, transportation, just to name a few. Also, data come from multiple heterogeneous sources or modalities. This is a common phenomenon in health care systems. While multi-modality data fusion is a promising research area, there are several special challenges in health care applications. (1) The integration of biological and statistical model is a big challenge; (2) It is commonplace that data from various modalities is not available for every patient due to cost, accessibility, and other reasons. This results in a special missing data structure in which different modalities may be missed in “blocks”. Therefore, how to train a predictive model using such a dataset poses a significant challenge to statistical learning. (3) It is well known that different modality data may contain different aspects of information about the response. The current studies cannot afford to solve this problem. My dissertation includes new statistical learning model development to address each of the aforementioned challenges as well as application case studies using real health care datasets, included in three chapters (Chapter 2, 3, and 4), respectively. Collectively, it is expected that my dissertation could provide a new sets of statistical learning models, algorithms, and theory contributed to multi-modality heterogeneous data fusion driven by the unique challenges in this area. Also, application of these new methods to important medical problems using real-world datasets is expected to provide solutions to these problems, and therefore contributing to the application domains.
ContributorsLiu, Xiaonan (Ph.D.) (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Fatyga, Mirek (Committee member) / Arizona State University (Publisher)
Created2019
Description
Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.
ContributorsYeh, Huai-Ming (Author) / Yan, Hao (Thesis advisor) / Pan, Rong (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2019