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.
Displaying 1 - 10 of 94
Description
Creative design lies at the intersection of novelty and technical feasibility. These objectives can be achieved through cycles of divergence (idea generation) and convergence (idea evaluation) in conceptual design. The focus of this thesis is on the latter aspect. The evaluation may involve any aspect of technical feasibility and may be desired at component, sub-system or full system level. Two issues that are considered in this work are: 1. Information about design ideas is incomplete, informal and sketchy 2. Designers often work at multiple levels; different aspects or subsystems may be at different levels of abstraction Thus, high fidelity analysis and simulation tools are not appropriate for this purpose. This thesis looks at the requirements for a simulation tool and how it could facilitate concept evaluation. The specific tasks reported in this thesis are: 1. The typical types of information available after an ideation session 2. The typical types of technical evaluations done in early stages 3. How to conduct low fidelity design evaluation given a well-defined feasibility question A computational tool for supporting idea evaluation was designed and implemented. It was assumed that the results of the ideation session are represented as a morphological chart and each entry is expressed as some combination of a sketch, text and references to physical effects and machine components. Approximately 110 physical effects were identified and represented in terms of algebraic equations, physical variables and a textual description. A common ontology of physical variables was created so that physical effects could be networked together when variables are shared. This allows users to synthesize complex behaviors from simple ones, without assuming any solution sequence. A library of 16 machine elements was also created and users were given instructions about incorporating them. To support quick analysis, differential equations are transformed to algebraic equations by replacing differential terms with steady state differences), only steady state behavior is considered and interval arithmetic was used for modeling. The tool implementation is done by MATLAB; and a number of case studies are also done to show how the tool works. textual description. A common ontology of physical variables was created so that physical effects could be networked together when variables are shared. This allows users to synthesize complex behaviors from simple ones, without assuming any solution sequence. A library of 15 machine elements was also created and users were given instructions about incorporating them. To support quick analysis, differential equations are transformed to algebraic equations by replacing differential terms with steady state differences), only steady state behavior is considered and interval arithmetic was used for modeling. The tool implementation is done by MATLAB; and a number of case studies are also done to show how the tool works.
ContributorsKhorshidi, Maryam (Author) / Shah, Jami J. (Thesis advisor) / Wu, Teresa (Committee member) / Gel, Esma (Committee member) / Arizona State University (Publisher)
Created2014
Description
Vehicles powered by electricity and alternative-fuels are becoming a more popular form of transportation since they have less of an environmental impact than standard gasoline vehicles. Unfortunately, their success is currently inhibited by the sparseness of locations where the vehicles can refuel as well as the fact that many of the vehicles have a range that is less than those powered by gasoline. These factors together create a "range anxiety" in drivers, which causes the drivers to worry about the utility of alternative-fuel and electric vehicles and makes them less likely to purchase these vehicles. For the new vehicle technologies to thrive it is critical that range anxiety is minimized and performance is increased as much as possible through proper routing and scheduling. In the case of long distance trips taken by individual vehicles, the routes must be chosen such that the vehicles take the shortest routes while not running out of fuel on the trip. When many vehicles are to be routed during the day, if the refueling stations have limited capacity then care must be taken to avoid having too many vehicles arrive at the stations at any time. If the vehicles that will need to be routed in the future are unknown then this problem is stochastic. For fleets of vehicles serving scheduled operations, switching to alternative-fuels requires ensuring the schedules do not cause the vehicles to run out of fuel. This is especially problematic since the locations where the vehicles may refuel are limited due to the technology being new. This dissertation covers three related optimization problems: routing a single electric or alternative-fuel vehicle on a long distance trip, routing many electric vehicles in a network where the stations have limited capacity and the arrivals into the system are stochastic, and scheduling fleets of electric or alternative-fuel vehicles with limited locations to refuel. Different algorithms are proposed to solve each of the three problems, of which some are exact and some are heuristic. The algorithms are tested on both random data and data relating to the State of Arizona.
ContributorsAdler, Jonathan D (Author) / Mirchandani, Pitu B. (Thesis advisor) / Askin, Ronald (Committee member) / Gel, Esma (Committee member) / Xue, Guoliang (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Accelerated life testing (ALT) is the process of subjecting a product to stress conditions (temperatures, voltage, pressure etc.) in excess of its normal operating levels to accelerate failures. Product failure typically results from multiple stresses acting on it simultaneously. Multi-stress factor ALTs are challenging as they increase the number of experiments due to the stress factor-level combinations resulting from the increased number of factors. Chapter 2 provides an approach for designing ALT plans with multiple stresses utilizing Latin hypercube designs that reduces the simulation cost without loss of statistical efficiency. A comparison to full grid and large-sample approximation methods illustrates the approach computational cost gain and flexibility in determining optimal stress settings with less assumptions and more intuitive unit allocations.
Implicit in the design criteria of current ALT designs is the assumption that the form of the acceleration model is correct. This is unrealistic assumption in many real-world problems. Chapter 3 provides an approach for ALT optimum design for model discrimination. We utilize the Hellinger distance measure between predictive distributions. The optimal ALT plan at three stress levels was determined and its performance was compared to good compromise plan, best traditional plan and well-known 4:2:1 compromise test plans. In the case of linear versus quadratic ALT models, the proposed method increased the test plan's ability to distinguish among competing models and provided better guidance as to which model is appropriate for the experiment.
Chapter 4 extends the approach of Chapter 3 to ALT sequential model discrimination. An initial experiment is conducted to provide maximum possible information with respect to model discrimination. The follow-on experiment is planned by leveraging the most current information to allow for Bayesian model comparison through posterior model probability ratios. Results showed that performance of plan is adversely impacted by the amount of censoring in the data, in the case of linear vs. quadratic model form at three levels of constant stress, sequential testing can improve model recovery rate by approximately 8% when data is complete, but no apparent advantage in adopting sequential testing was found in the case of right-censored data when censoring is in excess of a certain amount.
Implicit in the design criteria of current ALT designs is the assumption that the form of the acceleration model is correct. This is unrealistic assumption in many real-world problems. Chapter 3 provides an approach for ALT optimum design for model discrimination. We utilize the Hellinger distance measure between predictive distributions. The optimal ALT plan at three stress levels was determined and its performance was compared to good compromise plan, best traditional plan and well-known 4:2:1 compromise test plans. In the case of linear versus quadratic ALT models, the proposed method increased the test plan's ability to distinguish among competing models and provided better guidance as to which model is appropriate for the experiment.
Chapter 4 extends the approach of Chapter 3 to ALT sequential model discrimination. An initial experiment is conducted to provide maximum possible information with respect to model discrimination. The follow-on experiment is planned by leveraging the most current information to allow for Bayesian model comparison through posterior model probability ratios. Results showed that performance of plan is adversely impacted by the amount of censoring in the data, in the case of linear vs. quadratic model form at three levels of constant stress, sequential testing can improve model recovery rate by approximately 8% when data is complete, but no apparent advantage in adopting sequential testing was found in the case of right-censored data when censoring is in excess of a certain amount.
ContributorsNasir, Ehab (Author) / Pan, Rong (Thesis advisor) / Runger, George C. (Committee member) / Gel, Esma (Committee member) / Kao, Ming-Hung (Committee member) / Montgomery, Douglas C. (Committee member) / Arizona State University (Publisher)
Created2014
Description
With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices.
This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
ContributorsDsouza, Clinton (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2015
Description
Android is currently the most widely used mobile operating system. The permission model in Android governs the resource access privileges of applications. The permission model however is amenable to various attacks, including re-delegation attacks, background snooping attacks and disclosure of private information. This thesis is aimed at understanding, analyzing and performing forensics on application behavior. This research sheds light on several security aspects, including the use of inter-process communications (IPC) to perform permission re-delegation attacks.
Android permission system is more of app-driven rather than user controlled, which means it is the applications that specify their permission requirement and the only thing which the user can do is choose not to install a particular application based on the requirements. Given the all or nothing choice, users succumb to pressures and needs to accept permissions requested. This thesis proposes a couple of ways for providing the users finer grained control of application privileges. The same methods can be used to evade the Permission Re-delegation attack.
This thesis also proposes and implements a novel methodology in Android that can be used to control the access privileges of an Android application, taking into consideration the context of the running application. This application-context based permission usage is further used to analyze a set of sample applications. We found the evidence of applications spoofing or divulging user sensitive information such as location information, contact information, phone id and numbers, in the background. Such activities can be used to track users for a variety of privacy-intrusive purposes. We have developed implementations that minimize several forms of privacy leaks that are routinely done by stock applications.
Android permission system is more of app-driven rather than user controlled, which means it is the applications that specify their permission requirement and the only thing which the user can do is choose not to install a particular application based on the requirements. Given the all or nothing choice, users succumb to pressures and needs to accept permissions requested. This thesis proposes a couple of ways for providing the users finer grained control of application privileges. The same methods can be used to evade the Permission Re-delegation attack.
This thesis also proposes and implements a novel methodology in Android that can be used to control the access privileges of an Android application, taking into consideration the context of the running application. This application-context based permission usage is further used to analyze a set of sample applications. We found the evidence of applications spoofing or divulging user sensitive information such as location information, contact information, phone id and numbers, in the background. Such activities can be used to track users for a variety of privacy-intrusive purposes. We have developed implementations that minimize several forms of privacy leaks that are routinely done by stock applications.
ContributorsGollapudi, Narasimha Aditya (Author) / Dasgupta, Partha (Thesis advisor) / Xue, Guoliang (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2014
Description
The increasing number of continually connected mobile persons has created an environment conducive to real time user data gathering for many uses both public and private in nature. Publicly, one can envision no longer requiring a census to determine the demographic composition of the country and its sub regions. The information provided is vastly more up to date than that of a census and allows civil authorities to be more agile and preemptive with planning. Privately, advertisers take advantage of a persons stated opinions, demographics, and contextual (where and when) information in order to formulate and present pertinent offers.
Regardless of its use this information can be sensitive in nature and should therefore be under the control of the user. Currently, a user has little say in the manner that their information is processed once it has been released. An ad-hoc approach is currently in use, where the location based service providers each maintain their own policy over personal information usage.
In order to allow more user control over their personal information while still providing for targeted advertising, a systematic approach to the release of the information is needed. It is for that reason we propose a User-Centric Context Aware Spatiotemporal Anonymization framework. At its core the framework will unify the current spatiotemporal anonymization with that of traditional anonymization so that user specified anonymization requirement is met or exceeded while allowing for more demographic information to be released.
Regardless of its use this information can be sensitive in nature and should therefore be under the control of the user. Currently, a user has little say in the manner that their information is processed once it has been released. An ad-hoc approach is currently in use, where the location based service providers each maintain their own policy over personal information usage.
In order to allow more user control over their personal information while still providing for targeted advertising, a systematic approach to the release of the information is needed. It is for that reason we propose a User-Centric Context Aware Spatiotemporal Anonymization framework. At its core the framework will unify the current spatiotemporal anonymization with that of traditional anonymization so that user specified anonymization requirement is met or exceeded while allowing for more demographic information to be released.
ContributorsSanchez, Michael Andrew (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2014
Description
The rate at which new malicious software (Malware) is created is consistently increasing each year. These new malwares are designed to bypass the current anti-virus countermeasures employed to protect computer systems. Security Analysts must understand the nature and intent of the malware sample in order to protect computer systems from these attacks. The large number of new malware samples received daily by computer security companies require Security Analysts to quickly determine the type, threat, and countermeasure for newly identied samples. Our approach provides for a visualization tool to assist the Security Analyst in these tasks that allows the Analyst to visually identify relationships between malware samples.
This approach consists of three steps. First, the received samples are processed by a sandbox environment to perform a dynamic behavior analysis. Second, the reports of the dynamic behavior analysis are parsed to extract identifying features which are matched against other known and analyzed samples. Lastly, those matches that are determined to express a relationship are visualized as an edge connected pair of nodes in an undirected graph.
This approach consists of three steps. First, the received samples are processed by a sandbox environment to perform a dynamic behavior analysis. Second, the reports of the dynamic behavior analysis are parsed to extract identifying features which are matched against other known and analyzed samples. Lastly, those matches that are determined to express a relationship are visualized as an edge connected pair of nodes in an undirected graph.
ContributorsHolmes, James Edward (Author) / Ahn, Gail-Joon (Thesis advisor) / Dasgupta, Partha (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2014
Description
For more than twenty years, clinical researchers have been publishing data regarding incidence and risk of adverse events (AEs) incurred during hospitalizations. Hospitals have standard operating policies and procedures (SOPP) to protect patients from AE. The AE specifics (rates, SOPP failures, timing and risk factors) during heart failure (HF) hospitalizations are unknown. There were 1,722 patients discharged with a primary diagnosis of HF from an academic hospital between January 2005 and December 2007. Three hundred eighty-one patients experienced 566 AEs, classified into four categories: medication (43.9%), infection (18.9%), patient care (26.3%), or procedural (10.9%). Three distinct analyses were performed: 1) patient's perspective of SOPP reliability including cumulative distribution and hazard functions of time to AEs; 2) Cox proportional hazards model to determine independent patient-specific risk factors for AEs; and 3) hospital administration's perspective of SOPP reliability through three years of the study including cumulative distribution and hazard functions of time between AEs and moving range statistical process control (SPC) charts for days between failures of each type. This is the first study, to our knowledge, to consider reliability of SOPP from both the patient's and hospital administration's perspective. AE rates in hospitalized patients are similar to other recently published reports and did not improve during the study period. Operations research methodologies will be necessary to improve reliability of care delivered to hospitalized patients.
ContributorsHuddleston, Jeanne (Author) / Fowler, John (Thesis advisor) / Montgomery, Douglas C. (Thesis advisor) / Gel, Esma (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Every year, more than 11 million maritime containers and 11 million commercial trucks arrive to the United States, carrying all types of imported goods. As it would be costly to inspect every container, only a fraction of them are inspected before being allowed to proceed into the United States. This dissertation proposes a decision support system that aims to allocate the scarce inspection resources at a land POE (L-POE), to minimize the different costs associated with the inspection process, including those associated with delaying the entry of legitimate imports. Given the ubiquity of sensors in all aspects of the supply chain, it is necessary to have automated decision systems that incorporate the information provided by these sensors and other possible channels into the inspection planning process. The inspection planning system proposed in this dissertation decomposes the inspection effort allocation process into two phases: Primary and detailed inspection planning. The former helps decide what to inspect, and the latter how to conduct the inspections. A multi-objective optimization (MOO) model is developed for primary inspection planning. This model tries to balance the costs of conducting inspections, direct and expected, and the waiting time of the trucks. The resulting model is exploited in two different ways: One is to construct a complete or a partial efficient frontier for the MOO model with diversity of Pareto-optimal solutions maximized; the other is to evaluate a given inspection plan and provide possible suggestions for improvement. The methodologies are described in detail and case studies provided. The case studies show that this MOO based primary planning model can effectively pick out the non-conforming trucks to inspect, while balancing the costs and waiting time.
ContributorsXue, Liangjie (Author) / Villalobos, Jesus René (Thesis advisor) / Gel, Esma (Committee member) / Runger, George C. (Committee member) / Maltz, Arnold (Committee member) / Arizona State University (Publisher)
Created2012
Description
The reliability assessment of future distribution networks is an important issue in power engineering for both utilities and customers. This is due to the increasing demand for more reliable service with less interruption frequency and duration. This research consists of two main parts related to the evaluation of the future distribution system reliability. An innovative algorithm named the encoded Markov cut set (EMCS) is proposed to evaluate the reliability of the networked power distribution system. The proposed algorithm is based on the identification of circuit minimal tie sets using the concept of Petri nets. Prime number encoding and unique prime factorization are then utilized to add more flexibility in communicating between the systems states, and to classify the states as tie sets, cut sets, or minimal cut sets. Different reduction and truncation techniques are proposed to reduce the size of the state space. The Markov model is used to compute the availability, mean time to failure, and failure frequency of the network. A well-known Test Bed is used to illustrate the analysis (the Roy Billinton test system (RBTS)), and different load and system reliability indices are calculated. The method shown is algorithmic and appears suitable for off-line comparison of alternative secondary distribution system designs on the basis of their reliability. The second part assesses the impact of the conventional and renewable distributed generation (DG) on the reliability of the future distribution system. This takes into account the variability of the power output of the renewable DG, such as wind and solar DGs, and the chronological nature of the load demand. The stochastic nature of the renewable resources and its influence on the reliability of the system are modeled and studied by computing the adequacy transition rate. Then, an integrated Markov model that incorporates the DG adequacy transition rate, DG mechanical failure, and starting and switching probability is proposed and utilized to give accurate results for the DG reliability impact. The main focus in this research is the conventional, solar, and wind DG units. However, the technique used appears to be applicable to any renewable energy source.
ContributorsAlmuhaini, Mohammad (Author) / Heydt, Gerald (Thesis advisor) / Ayyanar, Raja (Committee member) / Gel, Esma (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012