Matching Items (62)
Filtering by
- Creators: Arizona State University
- Member of: Theses and Dissertations
- Resource Type: Text
- Status: Published
Description
The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.
ContributorsHaghnevis, Moeed (Author) / Askin, Ronald G. (Thesis advisor) / Armbruster, Dieter (Thesis advisor) / Mirchandani, Pitu (Committee member) / Wu, Tong (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
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
Buildings consume a large portion of the world's energy, but with the integration of phase change materials (PCMs) in building elements this energy cost can be greatly reduced. The addition of PCMs into building elements, however, becomes a challenge to model and analyze how the material actually affects the energy flow and temperatures in the system. This research work presents a comprehensive computer program used to model and analyze PCM embedded wall systems. The use of the finite element method (FEM) provides the tool to analyze the energy flow of these systems. Finite element analysis (FEA) can model the transient analysis of a typical climate cycle along with nonlinear problems, which the addition of PCM causes. The use of phase change materials is also a costly material expense. The initial expense of using PCMs can be compensated by the reduction in energy costs it can provide. Optimization is the tool used to determine the optimal point between adding PCM into a wall and the amount of energy savings that layer will provide. The integration of these two tools into a computer program allows for models to be efficiently created, analyzed and optimized. The program was then used to understand the benefits between two different wall models, a wall with a single layer of PCM or a wall with two different PCM layers. The effect of the PCMs on the inside wall temperature along with the energy flow across the wall are computed. The numerical results show that a multi-layer PCM wall was more energy efficient and cost effective than the single PCM layer wall. A structural analysis was then performed on the optimized designs using ABAQUS v. 6.10 to ensure the structural integrity of the wall was not affected by adding PCM layer(s).
ContributorsStockwell, Amie (Author) / Rajan, Subramaniam D. (Thesis advisor) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2013
Description
As renewable energy becomes more prevalent in transmission and distribution systems, it is vital to understand the uncertainty and variability that accompany these resources. Microgrids have the potential to mitigate the effects of resource uncertainty. With the ability to exist in either an islanded mode or maintain connections with the main-grid, a microgrid can increase reliability, defer T&D; infrastructure and effectively utilize demand response. This study presents a co-optimization framework for a microgrid with solar photovoltaic generation, emergency generation, and transmission switching. Today unit commitment models ensure reliability with deterministic criteria, which are either insufficient to ensure reliability or can degrade economic efficiency for a microgrid that uses a large penetration of variable renewable resources. A stochastic mixed integer linear program for day-ahead unit commitment is proposed to account for uncertainty inherent in PV generation. The model incorporates the ability to trade energy and ancillary services with the main-grid, including the designation of firm and non-firm imports, which captures the ability to allow for reserve sharing between the two systems. In order to manage the computational complexities, a Benders' decomposition approach is utilized. The commitment schedule was validated with solar scenario analysis, i.e., Monte-Carlo simulations are conducted to test the proposed dispatch solution. For this test case, there were few deviations to power imports, 0.007% of solar was curtailed, no load shedding occurred in the main-grid, and 1.70% load shedding occurred in the microgrid.
ContributorsHytowitz, Robin Broder (Author) / Hedman, Kory W (Thesis advisor) / Heydt, Gerald T (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
As networks are playing an increasingly prominent role in different aspects of our lives, there is a growing awareness that improving their performance is of significant importance. In order to enhance performance of networks, it is essential that scarce networking resources be allocated smartly to match the continuously changing network environment. This dissertation focuses on two different kinds of networks - communication and social, and studies resource allocation problems in these networks. The study on communication networks is further divided into different networking technologies - wired and wireless, optical and mobile, airborne and terrestrial. Since nodes in an airborne network (AN) are heterogeneous and mobile, the design of a reliable and robust AN is highly complex. The dissertation studies connectivity and fault-tolerance issues in ANs and proposes algorithms to compute the critical transmission range in fault free, faulty and delay tolerant scenarios. Just as in the case of ANs, power optimization and fault tolerance are important issues in wireless sensor networks (WSN). In a WSN, a tree structure is often used to deliver sensor data to a sink node. In a tree, failure of a node may disconnect the tree. The dissertation investigates the problem of enhancing the fault tolerance capability of data gathering trees in WSN. The advent of OFDM technology provides an opportunity for efficient resource utilization in optical networks and also introduces a set of novel problems, such as routing and spectrum allocation (RSA) problem. This dissertation proves that RSA problem is NP-complete even when the network topology is a chain, and proposes approximation algorithms. In the domain of social networks, the focus of this dissertation is study of influence propagation in presence of active adversaries. In a social network multiple vendors may attempt to influence the nodes in a competitive fashion. This dissertation investigates the scenario where the first vendor has already chosen a set of nodes and the second vendor, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes so that after the influence propagation, the second vendor's market share is larger than the first.
ContributorsShirazipourazad, Shahrzad (Author) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Richa, Andrea (Committee member) / Saripalli, Srikanth (Committee member) / Arizona State University (Publisher)
Created2014
Description
Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful, are not the most suitable vehicle for understanding the dynamics of dosage changes to patient response. A secondary analysis of intensive longitudinal data from a naltrexone intervention for fibromyalgia examined in this dissertation shows the promise of system identification and control. This includes datacentric identification methods such as Model-on-Demand, which are attractive techniques for estimating nonlinear dynamical systems from noisy data. These methods rely on generating a local function approximation using a database of regressors at the current operating point, with this process repeated at every new operating condition. This dissertation examines generating input signals for data-centric system identification by developing a novel framework of geometric distribution of regressors and time-indexed output points, in the finite dimensional space, to generate sufficient support for the estimator. The input signals are generated while imposing “patient-friendly” constraints on the design as a means to operationalize single-subject clinical trials. These optimization-based problem formulations are examined for linear time-invariant systems and block-structured Hammerstein systems, and the results are contrasted with alternative designs based on Weyl's criterion. Numerical solution to the resulting nonconvex optimization problems is proposed through semidefinite programming approaches for polynomial optimization and nonlinear programming methods. It is shown that useful bounds on the objective function can be calculated through relaxation procedures, and that the data-centric formulations are amenable to sparse polynomial optimization. In addition, input design formulations are formulated for achieving a desired output and specified input spectrum. Numerical examples illustrate the benefits of the input signal design formulations including an example of a hypothetical clinical trial using the drug gabapentin. In the final part of the dissertation, the mixed logical dynamical framework for hybrid model predictive control is extended to incorporate a switching time strategy, where decisions are made at some integer multiple of the sample time, and manipulation of only one input at a given sample time among multiple inputs. These are considerations important for clinical use of the algorithm.
ContributorsDeśapāṇḍe, Sunīla (Author) / Rivera, Daniel E. (Thesis advisor) / Peet, Matthew M. (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Skyline queries are a well-established technique used in multi criteria decision applications. There is a recent interest among the research community to efficiently compute skylines but the problem of presenting the skyline that takes into account the preferences of the user is still open. Each user has varying interests towards each attribute and hence "one size fits all" methodology might not satisfy all the users. True user satisfaction can be obtained only when the skyline is tailored specifically for each user based on his preferences.
This research investigates the problem of preference aware skyline processing which consists of inferring the preferences of users and computing a skyline specific to that user, taking into account his preferences. This research proposes a model that transforms the data from a given space to a user preferential space where each attribute represents the preference of the user. This study proposes two techniques "Preferential Skyline Processing" and "Latent Skyline Processing" to efficiently compute preference aware skylines in the user preferential space. Finally, through extensive experiments and performance analysis the correctness of the recommendations and the algorithm's ability to outperform the naïve ones is confirmed.
This research investigates the problem of preference aware skyline processing which consists of inferring the preferences of users and computing a skyline specific to that user, taking into account his preferences. This research proposes a model that transforms the data from a given space to a user preferential space where each attribute represents the preference of the user. This study proposes two techniques "Preferential Skyline Processing" and "Latent Skyline Processing" to efficiently compute preference aware skylines in the user preferential space. Finally, through extensive experiments and performance analysis the correctness of the recommendations and the algorithm's ability to outperform the naïve ones is confirmed.
ContributorsRathinavelu, Sriram (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2014
Description
Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
ContributorsGupta, Ujjwala (Author) / Ogras, Umit Y. (Thesis advisor) / Ozev, Sule (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014