ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 115
Filtering by
- Creators: Pan, Rong
- Creators: Seo, Jae-Sun
Description
Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized linear model (GLM) to simplify the computational process. A sensitivity study is also given to show the effects brought by parameters to the designs. Second, an extended version of I-optimal design for ALT is discussed and then a dual-objective design criterion is defined and showed with several examples. Also in order to evaluate different candidate designs, several graphical tools are developed. Finally, when there are more than one models available, different model checking designs are discussed.
ContributorsYang, Tao (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Borror, Connie (Committee member) / Rigdon, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
To uncover the neural correlates to go-directed behavior, single unit action potentials are considered fundamental computing units and have been examined by different analytical methodologies under a broad set of hypotheses. Using a behaving rat performing a directional choice learning task, we aim to study changes in rat's cortical neural patterns while he improved his task performance accuracy from chance to 80% or higher. Specifically, simultaneous multi-channel single unit neural recordings from the rat's agranular medial (AGm) and Agranular lateral (AGl) cortices were analyzed using joint peristimulus time histogram (JPSTHs), which effectively unveils firing coincidences in neural action potentials. My results based on data from six rats revealed that coincidences of pair-wise neural action potentials are higher when rats were performing the task than they were not at the learning stage, and this trend abated after the rats learned the task. Another finding is that the coincidences at the learning stage are stronger than that when the rats learned the task especially when they were performing the task. Therefore, this coincidence measure is the highest when the rats were performing the task at the learning stage. This may suggest that neural coincidences play a role in the coordination and communication among populations of neurons engaged in a purposeful act. Additionally, attention and working memory may have contributed to the modulation of neural coincidences during the designed task.
ContributorsCheng, Bing (Author) / Si, Jennie (Thesis advisor) / Chae, Junseok (Committee member) / Seo, Jae-Sun (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
Photovoltaic (PV) systems are affected by converter losses, partial shading and other mismatches in the panels. This dissertation introduces a sub-panel maximum power point tracking (MPPT) architecture together with an integrated CMOS current sensor circuit on a chip to reduce the mismatch effects, losses and increase the efficiency of the PV system. The sub-panel MPPT increases the efficiency of the PV during the shading and replaces the bypass diodes in the panels with an integrated MPPT and DC-DC regulator. For the integrated MPPT and regulator, the research developed an integrated standard CMOS low power and high common mode range Current-to-Digital Converter (IDC) circuit and its application for DC-DC regulator and MPPT. The proposed charge based CMOS switched-capacitor circuit directly digitizes the output current of the DC-DC regulator without an analog-to-digital converter (ADC) and the need for high-voltage process technology. Compared to the resistor based current-sensing methods that requires current-to-voltage circuit, gain block and ADC, the proposed CMOS IDC is a low-power efficient integrated circuit that achieves high resolution, lower complexity, and lower power consumption. The IDC circuit is fabricated on a 0.7 um CMOS process, occupies 2mm x 2mm and consumes less than 27mW. The IDC circuit has been tested and used for boost DC-DC regulator and MPPT for photo-voltaic system. The DC-DC converter has an efficiency of 95%. The sub-module level power optimization improves the output power of a shaded panel by up to 20%, compared to panel MPPT with bypass diodes.
ContributorsMarti-Arbona, Edgar (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kitchen, Jennifer (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2014
Description
In the three phases of the engineering design process (conceptual design, embodiment design and detailed design), traditional reliability information is scarce. However, there are different sources of information that provide reliability inputs while designing a new product. This research considered these sources to be further analyzed: reliability information from similar existing products denominated as parents, elicited experts' opinions, initial testing and the customer voice for creating design requirements. These sources were integrated with three novels approaches to produce reliability insights in the engineering design process, all under the Design for Reliability (DFR) philosophy. Firstly, an enhanced parenting process to assess reliability was presented. Using reliability information from parents it was possible to create a failure structure (parent matrix) to be compared against the new product. Then, expert opinions were elicited to provide the effects of the new design changes (parent factor). Combining those two elements resulted in a reliability assessment in early design process. Extending this approach into the conceptual design phase, a methodology was created to obtain a graphical reliability insight of a new product's concept. The approach can be summarized by three sequential steps: functional analysis, cognitive maps and Bayesian networks. These tools integrated the available information, created a graphical representation of the concept and provided quantitative reliability assessments. Lastly, to optimize resources when product testing is viable (e.g., detailed design) a type of accelerated life testing was recommended: the accelerated degradation tests. The potential for robust design engineering for this type of test was exploited. Then, robust design was achieved by setting the design factors at some levels such that the impact of stress factor variation on the degradation rate can be minimized. Finally, to validate the proposed approaches and methods, different case studies were presented.
ContributorsMejia Sanchez, Luis (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Jesus R (Committee member) / See, Tung-King (Committee member) / Arizona State University (Publisher)
Created2014
Description
This work describes the development of automated flows to generate pad rings, mixed signal power grids, and mega cells in a multi-project test chip. There were three major design flows that were created to create the test chip. The first was the pad ring which was used as the staring block for creating the test chip. This flow put all of the signals for the chip in the order that was wanted along the outside of the die along with creation of the power ring that is used to supply the chip with a robust power source.
The second flow that was created was used to put together a flash block that is based off of a XILIX XCFXXP. This flow was somewhat similar to how the pad ring flow worked except that optimizations and a clock tree was added into the flow. There was a couple of design redoes due to timing and orientation constraints.
Finally, the last flow that was created was the top level flow which is where all of the components are combined together to create a finished test chip ready for fabrication. The main components that were used were the finished flash block, HERMES, test structures, and a clock instance along with the pad ring flow for the creation of the pad ring and power ring.
Also discussed is some work that was done on a previous multi-project test chip. The work that was done was the creation of power gaters that were used like switches to turn the power on and off for some flash modules. To control the power gaters the functionality change of some pad drivers was done so that they output a higher voltage than what is seen in the core of the chip.
The second flow that was created was used to put together a flash block that is based off of a XILIX XCFXXP. This flow was somewhat similar to how the pad ring flow worked except that optimizations and a clock tree was added into the flow. There was a couple of design redoes due to timing and orientation constraints.
Finally, the last flow that was created was the top level flow which is where all of the components are combined together to create a finished test chip ready for fabrication. The main components that were used were the finished flash block, HERMES, test structures, and a clock instance along with the pad ring flow for the creation of the pad ring and power ring.
Also discussed is some work that was done on a previous multi-project test chip. The work that was done was the creation of power gaters that were used like switches to turn the power on and off for some flash modules. To control the power gaters the functionality change of some pad drivers was done so that they output a higher voltage than what is seen in the core of the chip.
ContributorsLieb, Christopher (Author) / Clark, Lawrence (Thesis advisor) / Holbert, Keith E. (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2015
Description
This thesis presents a meta-analysis of lead-free solder reliability. The qualitative analyses of the failure modes of lead- free solder under different stress tests including drop test, bend test, thermal test and vibration test are discussed. The main cause of failure of lead- free solder is fatigue crack, and the speed of propagation of the initial crack could differ from different test conditions and different solder materials. A quantitative analysis about the fatigue behavior of SAC lead-free solder under thermal preconditioning process is conducted. This thesis presents a method of making prediction of failure life of solder alloy by building a Weibull regression model. The failure life of solder on circuit board is assumed Weibull distributed. Different materials and test conditions could affect the distribution by changing the shape and scale parameters of Weibull distribution. The method is to model the regression of parameters with different test conditions as predictors based on Bayesian inference concepts. In the process of building regression models, prior distributions are generated according to the previous studies, and Markov Chain Monte Carlo (MCMC) is used under WinBUGS environment.
ContributorsXu, Xinyue (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2014
Description
The main objective of this research is to develop an approach to PV module lifetime prediction. In doing so, the aim is to move from empirical generalizations to a formal predictive science based on data-driven case studies of the crystalline silicon PV systems. The evaluation of PV systems aged 5 to 30 years old that results in systematic predictive capability that is absent today. The warranty period provided by the manufacturers typically range from 20 to 25 years for crystalline silicon modules. The end of lifetime (for example, the time-to-degrade by 20% from rated power) of PV modules is usually calculated using a simple linear extrapolation based on the annual field degradation rate (say, 0.8% drop in power output per year). It has been 26 years since systematic studies on solar PV module lifetime prediction were undertaken as part of the 11-year flat-plate solar array (FSA) project of the Jet Propulsion Laboratory (JPL) funded by DOE. Since then, PV modules have gone through significant changes in construction materials and design; making most of the field data obsolete, though the effect field stressors on the old designs/materials is valuable to be understood. Efforts have been made to adapt some of the techniques developed to the current technologies, but they are too often limited in scope and too reliant on empirical generalizations of previous results. Some systematic approaches have been proposed based on accelerated testing, but no or little experimental studies have followed. Consequently, the industry does not exactly know today how to test modules for a 20 - 30 years lifetime.
This research study focuses on the behavior of crystalline silicon PV module technology in the dry and hot climatic condition of Tempe/Phoenix, Arizona. A three-phase approach was developed: (1) A quantitative failure modes, effects, and criticality analysis (FMECA) was developed for prioritizing failure modes or mechanisms in a given environment; (2) A time-series approach was used to model environmental stress variables involved and prioritize their effect on the power output drop; and (3) A procedure for developing a prediction model was proposed for the climatic specific condition based on accelerated degradation testing
This research study focuses on the behavior of crystalline silicon PV module technology in the dry and hot climatic condition of Tempe/Phoenix, Arizona. A three-phase approach was developed: (1) A quantitative failure modes, effects, and criticality analysis (FMECA) was developed for prioritizing failure modes or mechanisms in a given environment; (2) A time-series approach was used to model environmental stress variables involved and prioritize their effect on the power output drop; and (3) A procedure for developing a prediction model was proposed for the climatic specific condition based on accelerated degradation testing
ContributorsKuitche, Joseph Mathurin (Author) / Pan, Rong (Thesis advisor) / Tamizhmani, Govindasamy (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2014