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- Creators: Arizona State University
- Creators: Davulcu, Hasan
Description
Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic for application in metallic and composite material systems. The primary means of interrogating a structure in this work is through the use of Lamb wave propagation which works well for the thin structures used in aerospace applications. Piezoelectric transducers (PZTs) were selected for this application since they can be used as both sensors and actuators of guided waves. Placement of these transducers is an important issue in wave based approaches as Lamb waves are sensitive to changes in material properties, geometry, and boundary conditions which may obscure the presence of damage if they are not taken into account during sensor placement. The placement scheme proposed in this dissertation arranges piezoelectric transducers in a pitch-catch mode so the entire structure can be covered using a minimum number of sensors. The stress distribution of the structure is also considered so PZTs are placed in regions where they do not fail before the host structure. In order to process the data from these transducers, advanced signal processing techniques are employed to detect the presence of damage in complex structures. To provide a better estimate of the damage for accurate life estimation, machine learning techniques are used to classify the type of damage in the structure. A data structure analysis approach is used to reduce the amount of data collected and increase computational efficiency. In the case of low velocity impact damage, fiber Bragg grating (FBG) sensors were used with a nonlinear regression tool to reconstruct the loading at the impact site.
ContributorsCoelho, Clyde (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Wu, Tong (Committee member) / Das, Santanu (Committee member) / Rajadas, John (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH) is so far the most practical approximate KNN search algorithm for high dimensional data. Algorithms such as Multi-Probe LSH and LSH-Forest improve upon the basic LSH algorithm by varying hash bucket size dynamically at query time, so these two algorithms can answer different KNN queries adaptively. However, these two algorithms need a data access post-processing step after candidates' collection in order to get the final answer to the KNN query. In this thesis, Multi-Probe LSH with data access post-processing (Multi-Probe LSH with DAPP) algorithm and LSH-Forest with data access post-processing (LSH-Forest with DAPP) algorithm are improved by replacing the costly data access post-processing (DAPP) step with a much faster histogram-based post-processing (HBPP). Two HBPP algorithms: LSH-Forest with HBPP and Multi- Probe LSH with HBPP are presented in this thesis, both of them achieve the three goals for KNN search in large scale high dimensional data set: high search quality, high time efficiency, high space efficiency. None of the previous KNN algorithms can achieve all three goals. More specifically, it is shown that HBPP algorithms can always achieve high search quality (as good as LSH-Forest with DAPP and Multi-Probe LSH with DAPP) with much less time cost (one to several orders of magnitude speedup) and same memory usage. It is also shown that with almost same time cost and memory usage, HBPP algorithms can always achieve better search quality than LSH-Forest with random pick (LSH-Forest with RP) and Multi-Probe LSH with random pick (Multi-Probe LSH with RP). Moreover, to achieve a very high search quality, Multi-Probe with HBPP is always a better choice than LSH-Forest with HBPP, regardless of the distribution, size and dimension number of the data set.
ContributorsYu, Renwei (Author) / Candan, Kasim S (Thesis advisor) / Sapino, Maria L (Committee member) / Chen, Yi (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2011
Description
A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment challenging, including the massive amounts of data available, large numbers of users, and a highly dynamic environment, provide unique and untapped opportunities for solving the provenance problem for social media. Current approaches for tracking provenance data do not scale for online social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities. The guiding vision is the use of social media information itself to realize a useful amount of provenance data for information in social media. This departs from traditional approaches for data provenance which rely on a central store of provenance information. The contemporary online social media environment is an enormous and constantly updated "central store" that can be mined for provenance information that is not readily made available to the average social media user. This research introduces an approach and builds a foundation aimed at realizing a provenance data capability for social media users that is not accessible today.
ContributorsBarbier, Geoffrey P (Author) / Liu, Huan (Thesis advisor) / Bell, Herbert (Committee member) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation studies routing in small-world networks such as grids plus long-range edges and real networks. Kleinberg showed that geography-based greedy routing in a grid-based network takes an expected number of steps polylogarithmic in the network size, thus justifying empirical efficiency observed beginning with Milgram. A counterpart for the grid-based model is provided; it creates all edges deterministically and shows an asymptotically matching upper bound on the route length. The main goal is to improve greedy routing through a decentralized machine learning process. Two considered methods are based on weighted majority and an algorithm of de Farias and Megiddo, both learning from feedback using ensembles of experts. Tests are run on both artificial and real networks, with decentralized spectral graph embedding supplying geometric information for real networks where it is not intrinsically available. An important measure analyzed in this work is overpayment, the difference between the cost of the method and that of the shortest path. Adaptive routing overtakes greedy after about a hundred or fewer searches per node, consistently across different network sizes and types. Learning stabilizes, typically at overpayment of a third to a half of that by greedy. The problem is made more difficult by eliminating the knowledge of neighbors' locations or by introducing uncooperative nodes. Even under these conditions, the learned routes are usually better than the greedy routes. The second part of the dissertation is related to the community structure of unannotated networks. A modularity-based algorithm of Newman is extended to work with overlapping communities (including considerably overlapping communities), where each node locally makes decisions to which potential communities it belongs. To measure quality of a cover of overlapping communities, a notion of a node contribution to modularity is introduced, and subsequently the notion of modularity is extended from partitions to covers. The final part considers a problem of network anonymization, mostly by the means of edge deletion. The point of interest is utility preservation. It is shown that a concentration on the preservation of routing abilities might damage the preservation of community structure, and vice versa.
ContributorsBakun, Oleg (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andrea (Thesis advisor) / Syrotiuk, Violet R. (Committee member) / Czygrinow, Andrzej (Committee member) / Arizona State University (Publisher)
Created2011
Description
Transmission expansion planning (TEP) is a complex decision making process that requires comprehensive analysis to determine the time, location, and number of electric power transmission facilities that are needed in the future power grid. This dissertation investigates the topic of solving TEP problems for large power systems. The dissertation can be divided into two parts. The first part of this dissertation focuses on developing a more accurate network model for TEP study. First, a mixed-integer linear programming (MILP) based TEP model is proposed for solving multi-stage TEP problems. Compared with previous work, the proposed approach reduces the number of variables and constraints needed and improves the computational efficiency significantly. Second, the AC power flow model is applied to TEP models. Relaxations and reformulations are proposed to make the AC model based TEP problem solvable. Third, a convexified AC network model is proposed for TEP studies with reactive power and off-nominal bus voltage magnitudes included in the model. A MILP-based loss model and its relaxations are also investigated. The second part of this dissertation investigates the uncertainty modeling issues in the TEP problem. A two-stage stochastic TEP model is proposed and decomposition algorithms based on the L-shaped method and progressive hedging (PH) are developed to solve the stochastic model. Results indicate that the stochastic TEP model can give a more accurate estimation of the annual operating cost as compared to the deterministic TEP model which focuses only on the peak load.
ContributorsZhang, Hui (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald T (Thesis advisor) / Mittelmann, Hans D (Committee member) / Hedman, Kory W (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
This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.
ContributorsLi, Xun (Author) / Anselin, Luc (Thesis advisor) / Koschinsky, Julia (Committee member) / Maciejewski, Ross (Committee member) / Rey, Sergio (Committee member) / Griffin, William (Committee member) / Arizona State University (Publisher)
Created2012