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: Yang, Yezhou
- Creators: Kashiwagi, Dean
Description
The construction industry in India suffers from major time and cost overruns. Data from government and industry reports suggest that projects suffer from 20 to 25 percent time and cost overruns. Waste of resources has been identified as a major source of inefficiency. Despite a substantial increase in the past few years, demand for professionals and contractors still exceeds supply by a large margin. The traditional methods adopted in the Indian construction industry may not suffice the needs of this dynamic environment, as they have produced large inefficiencies. Innovative ways of procurement and project management can satisfy the needs aspired to as well as bring added value. The problems faced by the Indian construction industry are very similar to those faced by other developing countries. The objective of this paper is to discuss and analyze the economic concerns, inefficiencies and investigate a model that both explains the Indian construction industry structure and provides a framework to improve efficiencies. The Best Value (BV) model is examined as an approach to be adopted in lieu of the traditional approach. This could result in efficient construction projects by minimizing cost overruns and delays, which until now have been a rarity.
ContributorsNihas, Syed (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Kashiwagi, Jacob (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over the past couple of decades, quality has been an area of increased focus. Multiple models and approaches have been proposed to measure the quality in the construction industry. This paper focuses on determining the quality of one of the types of roofing systems used in the construction industry, i.e. Sprayed Polyurethane Foam Roofs (SPF roofs). Thirty seven urethane coated SPF roofs that were installed in 2005 / 2006 were visually inspected to measure the percentage of blisters and repairs three times over a period of 4 year, 6 year and 7 year marks. A repairing criteria was established after a 6 year mark based on the data that were reported to contractors as vulnerable roofs. Furthermore, the relation between four possible contributing time of installation factors i.e. contractor, demographics, season, and difficulty (number of penetrations and size of the roof in square feet) that could affect the quality of the roof was determined. Demographics and difficulty did not affect the quality of the roofs whereas the contractor and the season when the roof was installed did affect the quality of the roofs.
ContributorsGajjar, Dhaval (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT Facility managers have an important job in today's competitive business world by caring for the backbone of the corporation's capital. Maintaining assets and the support efforts cause facility managers to fight an uphill battle to prove the worth of their organizations. This thesis will discuss the important and flexible use of measurement and leadership reports and the benefits of justifying the work required to maintain or upgrade a facility. The task is streamlined by invoking accountability to subject experts. The facility manager must trust in the ability of his or her work force to get the job done. However, with accountability comes increased risk. Even though accountability may not alleviate total control or cease reactionary actions, facility managers can develop key leadership based reports to reassign accountability and measure subject matter experts while simultaneously reducing reactionary actions leading to increased cost. Identifying and reassigning risk that are not controlled to subject matter experts is imperative for effective facility management leadership and allows facility managers to create an accurate and solid facility management plan, supports the organization's succession plan, and allows the organization to focus on key competencies.
ContributorsTellefsen, Thor (Author) / Sullivan, Kenneth (Thesis advisor) / Kashiwagi, Dean (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2011
Description
The price based marketplace has dominated the construction industry. The majority of owners use price based practices of management (expectation and decision making, control, direction, and inspection.) The price based/management and control paradigm has not worked. Clients have now been moving toward the best value environment (hire contractors who know what they are doing, who preplan, and manage and minimize risk and deviation.) Owners are trying to move from client direction and control to hiring an expert and allowing them to do the quality control/risk management. The movement of environments changes the paradigm for the contractors from a reactive to a proactive, from a bureaucratic
on-accountable to an accountable position, from a relationship based
on-measuring to a measuring entity, and to a contractor who manages and minimizes the risk that they do not control. Years of price based practices have caused poor quality and low performance in the construction industry. This research identifies what is a best value contractor or vendor, what factors make up a best value vendor, and the methodology to transform a vendor to a best value vendor. It will use deductive logic, a case study to confirm the logic and the proposed methodology.
on-accountable to an accountable position, from a relationship based
on-measuring to a measuring entity, and to a contractor who manages and minimizes the risk that they do not control. Years of price based practices have caused poor quality and low performance in the construction industry. This research identifies what is a best value contractor or vendor, what factors make up a best value vendor, and the methodology to transform a vendor to a best value vendor. It will use deductive logic, a case study to confirm the logic and the proposed methodology.
ContributorsPauli, Michele (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2011
Description
Current information on successful leadership and management practices is contradictory and inconsistent, which makes difficult to understand what successful business practices are and what are not. The purpose of this study is to identify a simple process that quickly and logically identifies consistent and inconsistent leadership and management criteria. The hypothesis proposed is that Information Measurement Theory (IMT) along with the Kashiwagi Solution Model (KSM) is a methodology than can differentiate between accurate and inaccurate principles the initial part of the study about authors in these areas show how information is conflictive, and also served to establish an initial baseline of recommended practices aligned with IMT. The one author that excels in comparison to the rest suits the "Initial Baseline Matrix from Deming" which composes the first model. The second model is denominated the "Full Extended KSM-Matrix" composed of all the LS characteristics found among all authors and IMT. Both models were tested-out for accuracy. The second part of the study was directed to evaluate the perception of individuals on these principles. Two different groups were evaluated, one group of people that had prior training and knowledge of IMT; another group of people without any knowledge of IMT. The results of the survey showed more confusion in the group of people without knowledge to IMT and improved consistency and less variation in the group of people with knowledge in IMT. The third part of the study, the analysis of case studies of success and failure, identified principles as contributors, and categorized them into LS/type "A" characteristics and RS/type "C" characteristics, by applying the KSM. The results validated the initial proposal and led to the conclusion that practices that fall into the LS side of the KSM will lead to success, while practices that fall into the RS of the KSM will lead to failure. The comparison and testing of both models indicated a dominant support of the IMT concepts as contributors to success; while the KSM model has a higher accuracy of prediction.
ContributorsReynolds, Harry (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2011
Description
As global competition continues to grow more disruptive, organizational change is an ever-present reality that affects companies in all industries at both the operational and strategic level. Organizational change capabilities have become a necessary aspect of existence for organizations in all industries worldwide. Research suggests that more than half of all organizational change efforts fail to achieve their original intended results, with some studies quoting failure rates as high as 70 percent. Exasperating this problem is the fact that no single change methodology has been universally accepted. This thesis examines two aspect of organizational change: the implementation of tactical and strategic initiatives, primarily focusing on successful tactical implementation techniques. This research proposed that tactical issues typically dominate the focus of change agents and recipients alike, often to the detriment of strategic level initiatives that are vital to the overall value and success of the organizational change effort. The Delphi method was employed to develop a tool to facilitate the initial implementation of organizational change such that tactical barriers were minimized and available resources for strategic initiatives were maximized. Feedback from two expert groups of change agents and change facilitators was solicited to develop the tool and evaluate its impact. Preliminary pilot testing of the tool confirmed the proposal and successfully served to minimize tactical barriers to organizational change.
ContributorsLines, Brian (Author) / Sullivan, Kenneth T. (Thesis advisor) / Badger, William (Committee member) / Kashiwagi, Dean (Committee member) / Arizona State University (Publisher)
Created2011
Description
In a collaborative environment where multiple robots and human beings are expected
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
ContributorsKumar, Ashish, M.S (Author) / Amor, Hani Ben (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017