Matching Items (39)
Description
The Internet is transforming its look, in a short span of time we have come very far from black and white web forms with plain buttons to responsive, colorful and appealing user interface elements. With the sudden rise in demand of web applications, developers are making full use of the power of HTML5, JavaScript and CSS3 to cater to their users on various platforms. There was never a need of classifying the ways in which these languages can be interconnected to each other as the size of the front end code base was relatively small and did not involve critical business logic. This thesis focuses on listing and defining all dependencies between HTML5, JavaScript and CSS3 that will help developers better understand the interconnections within these languages. We also explore the present techniques available to a developer to make his code free of dependency related defects. We build a prototype tool, HJCDepend, based on our model, which aims at helping developers discover and remove defects early in the development cycle.
ContributorsVasugupta (Author) / Gary, Kevin (Thesis advisor) / Lindquist, Timothy (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Thermodynamic development and balance of plant study is completed for a 30 MW solar thermochemical water splitting process that generates hydrogen gas and electric power. The generalized thermodynamic model includes 23 components and 45 states. Quasi-steady state simulations are completed for design point system sizing, annual performance analysis and sensitivity analysis. Detailed consideration is given to water splitting reaction kinetics with governing equations generalized for use with any redox-active metal oxide material. Specific results for Ceria illustrate particle reduction in two solar receivers for target oxygen partial pressure of 10 Pa and particle temperature of 1773 K at a design point DNI of 900 W/m2. Sizes of the recuperator, steam generator and hydrogen separator are calculated at the design point DNI to achieve 100,000 kg of hydrogen production per day from the plant. The total system efficiency of 39.52% is comprised of 50.7% hydrogen fraction and 19.62% electrical fraction. Total plant capital costs and operating costs are estimated to equate a hydrogen production cost of $4.40 per kg for a 25-year plant life. Sensitivity analysis explores the effect of environmental parameters and design parameters on system performance and cost. Improving recuperator effectiveness from 0.7 to 0.8 is a high-value design modification resulting in a 12.1% decrease in hydrogen cost for a modest 2.0% increase in plant $2.85M. At the same time, system efficiency is relatively inelastic to recuperator effectiveness because 81% of excess heat is recovered from the system for electricity production 39 MWh/day and revenue is $0.04 per kWh. Increasing water inlet pressure up to 20 bar reduces the size and cost of super heaters but further pressure rises increasing pump at a rate that outweighs super heater cost savings.
ContributorsBudama, Vishnu Kumar (Author) / Johnson, Nathan (Thesis advisor) / Stechel, Ellen (Committee member) / Rykaczewski, Konrad (Committee member) / Phelan, Patrick (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the optimization of a home energy management system (HEMS) with the presence of PEVs. For a household microgrid with photovoltaic (PV) panels and PEVs, a HEMS using model predictive control (MPC) is designed to achieve the optimal PEV charging. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed. Furthermore, the hardware development of a microgrid prototype is also described in this thesis.
ContributorsZhao, Yue (Author) / Chen, Yan (Thesis advisor) / Johnson, Nathan (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service outages, including cascading failures in transmission-level components that can result in blackouts. The most critical dependency identified was the increase in peak electricity demand with higher air temperatures. Historical and future air temperatures were characterized within and across Los Angeles County, California (LAC) and Maricopa County (Phoenix), Arizona. LAC was identified as more vulnerable to heat waves than Phoenix due to a wider distribution of historical temperatures. Two approaches were developed to estimate peak demand based on air temperatures, a top-down statistical model and bottom-up spatial building energy model. Both approaches yielded similar results, in that peak demand should increase sub-linearly at temperatures above 40°C (104 °F) due to saturation in the coincidence of air conditioning (AC) duty cycles. Spatial projections for peak demand were developed for LAC to 2060 considering potential changes in population, building type, building efficiency, AC penetration, appliance efficiency, and air temperatures due climate change. These projections were spatially allocated to delivery system components (generation, transmission lines, and substations) to consider their vulnerability in terms of thermal de-rated capacity and weather adjusted load factor (load divided by capacity). Peak hour electricity demand was projected to increase in residential and commercial sectors by 0.2–6.5 GW (2–51%) by 2060. All grid components, except those near Santa Monica Beach, were projected to experience 2–20% capacity loss due to air temperatures exceeding 40 °C (104 °F). Based on scenario projections, and substation load factors for Southern California Edison (SCE), SCE will require 848—6,724 MW (4-32%) of additional substation capacity or peak shaving in its LAC service territories by 2060 to meet additional demand associated with population growth projections.
ContributorsBurillo, Daniel (Author) / Chester, Mikhail V (Thesis advisor) / Ruddell, Benjamin (Committee member) / Johnson, Nathan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Academia is not what it used to be. In today’s fast-paced world, requirements
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
are constantly changing, and adapting to these changes in an academic curriculum
can be challenging. Given a specific aspect of a domain, there can be various levels of
proficiency that can be achieved by the students. Considering the wide array of needs,
diverse groups need customized course curriculum. The need for having an archetype
to design a course focusing on the outcomes paved the way for Outcome-based
Education (OBE). OBE focuses on the outcomes as opposed to the traditional way of
following a process [23]. According to D. Clark, the major reason for the creation of
Bloom’s taxonomy was not only to stimulate and inspire a higher quality of thinking
in academia – incorporating not just the basic fact-learning and application, but also
to evaluate and analyze on the facts and its applications [7]. Instructional Module
Development System (IMODS) is the culmination of both these models – Bloom’s
Taxonomy and OBE. It is an open-source web-based software that has been
developed on the principles of OBE and Bloom’s Taxonomy. It guides an instructor,
step-by-step, through an outcomes-based process as they define the learning
objectives, the content to be covered and develop an instruction and assessment plan.
The tool also provides the user with a repository of techniques based on the choices
made by them regarding the level of learning while defining the objectives. This helps
in maintaining alignment among all the components of the course design. The tool
also generates documentation to support the course design and provide feedback
when the course is lacking in certain aspects.
It is not just enough to come up with a model that theoretically facilitates
effective result-oriented course design. There should be facts, experiments and proof
that any model succeeds in achieving what it aims to achieve. And thus, there are two
research objectives of this thesis: (i) design a feature for course design feedback and
evaluate its effectiveness; (ii) evaluate the usefulness of a tool like IMODS on various
aspects – (a) the effectiveness of the tool in educating instructors on OBE; (b) the
effectiveness of the tool in providing appropriate and efficient pedagogy and
assessment techniques; (c) the effectiveness of the tool in building the learning
objectives; (d) effectiveness of the tool in document generation; (e) Usability of the
tool; (f) the effectiveness of OBE on course design and expected student outcomes.
The thesis presents a detailed algorithm for course design feedback, its pseudocode, a
description and proof of the correctness of the feature, methods used for evaluation
of the tool, experiments for evaluation and analysis of the obtained results.
ContributorsRaj, Vaishnavi (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2018
Description
The concept of the microgrid is widely studied and explored in both academic and industrial societies. The microgrid is a power system with distributed generations and loads, which is intentionally planned and can be disconnected from the main utility grid. Nowadays, various distributed power generations (wind resource, photovoltaic resource, etc.) are emerging to be significant power sources of the microgrid.
This thesis focuses on the system structure of Photovoltaics (PV)-dominated microgrid, precisely modeling and stability analysis of the specific system. The grid-connected mode microgrid is considered, and system control objectives are: PV panel is working at the maximum power point (MPP), the DC link voltage is regulated at a desired value, and the grid side current is also controlled in phase with grid voltage. To simulate the real circuits of the whole system with high fidelity instead of doing real experiments, PLECS software is applied to construct the detailed model in chapter 2. Meanwhile, a Simulink mathematical model of the microgrid system is developed in chapter 3 for faster simulation and energy management analysis. Simulation results of both the PLECS model and Simulink model are matched with the expectations. Next chapter talks about state space models of different power stages for stability analysis utilization. Finally, the large signal stability analysis of a grid-connected inverter, which is based on cascaded control of both DC link voltage and grid side current is discussed. The large signal stability analysis presented in this thesis is mainly focused on the impact of the inductor and capacitor capacity and the controller parameters on the DC link stability region. A dynamic model with the cascaded control logic is proposed. One Lyapunov large-signal stability analysis tool is applied to derive the domain of attraction, which is the asymptotic stability region. Results show that both the DC side capacitor and the inductor of grid side filter can significantly influence the stability region of the DC link voltage. PLECS simulation models developed for the microgrid system are applied to verify the stability regions estimated from the Lyapunov large signal analysis method.
This thesis focuses on the system structure of Photovoltaics (PV)-dominated microgrid, precisely modeling and stability analysis of the specific system. The grid-connected mode microgrid is considered, and system control objectives are: PV panel is working at the maximum power point (MPP), the DC link voltage is regulated at a desired value, and the grid side current is also controlled in phase with grid voltage. To simulate the real circuits of the whole system with high fidelity instead of doing real experiments, PLECS software is applied to construct the detailed model in chapter 2. Meanwhile, a Simulink mathematical model of the microgrid system is developed in chapter 3 for faster simulation and energy management analysis. Simulation results of both the PLECS model and Simulink model are matched with the expectations. Next chapter talks about state space models of different power stages for stability analysis utilization. Finally, the large signal stability analysis of a grid-connected inverter, which is based on cascaded control of both DC link voltage and grid side current is discussed. The large signal stability analysis presented in this thesis is mainly focused on the impact of the inductor and capacitor capacity and the controller parameters on the DC link stability region. A dynamic model with the cascaded control logic is proposed. One Lyapunov large-signal stability analysis tool is applied to derive the domain of attraction, which is the asymptotic stability region. Results show that both the DC side capacitor and the inductor of grid side filter can significantly influence the stability region of the DC link voltage. PLECS simulation models developed for the microgrid system are applied to verify the stability regions estimated from the Lyapunov large signal analysis method.
ContributorsXu, Hongru (Author) / Chen, Yan (Thesis advisor) / Johnson, Nathan (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Semantic Web contains large amounts of related information in the form of knowledge graphs such as DBpedia. These knowledge graphs are typically enormous and are not easily accessible for users as they need specialized knowledge in query languages (such as SPARQL) as well as deep familiarity of the ontologies used by these knowledge graphs. So, to make these knowledge graphs more accessible (even for non- experts) several question answering (QA) systems have been developed over the last decade. Due to the complexity of the task, several approaches have been undertaken that include techniques from natural language processing (NLP), information retrieval (IR), machine learning (ML) and the Semantic Web (SW). At a higher level, most question answering systems approach the question answering task as a conversion from the natural language question to its corresponding SPARQL query. These systems then utilize the query to retrieve the desired entities or literals. One approach to solve this problem, that is used by most systems today, is to apply deep syntactic and semantic analysis on the input question to derive the SPARQL query. This has resulted in the evolution of natural language processing pipelines that have common characteristics such as answer type detection, segmentation, phrase matching, part-of-speech-tagging, named entity recognition, named entity disambiguation, syntactic or dependency parsing, semantic role labeling, etc.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
ContributorsAthreya, Ram G (Author) / Bansal, Srividya (Thesis advisor) / Usbeck, Ricardo (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
Description
"Seventy five percent of the world's poor live in rural areas of developing countries, where most people's livelihoods rely directly on agriculture." (USAid, 2014) Reduced levels of crop production and the accompanying problems of malnourishment exist all over the world. In rural Peru, for example, 11 percent of the population is malnourished. (Global Healthfacts.org, 2012) Since the success in agriculture relies importantly on the fertility of the soil, it is imperative that any efforts at reversing this trend be primarily directed at improving the existing soils. This, in turn, will increase crop yields, and if done properly, will also conserve natural resources and maximize profits for farmers. In order to improve the lives of those at the bottom of the pyramid through agriculture, certain tools and knowledge must be provided in order to empower such persons to help themselves. An ancient method of soil improvement, known as Terra Preta do Indio (Indian dark earth), was discovered by Anthropologists in the 1800's. These dark, carbon-rich, soils are notable for their high fertility, high amounts of plant available nutrients, and their high moisture retention rates. The key to their long-lasting fertility and durability is the presence of high levels of biochar, a highly stable organic carbon \u2014 produced when organic matter (crop residues, food waste, manure, etc.) is burned at low temperatures in the absence of oxygen. Research has shown that when charcoal (biochar) and fertilizers are combined, it can yield as much as 880 percent more than when fertilizers are used by themselves. (Steiner, University of Bayreuth, 2004)
ContributorsStefanik, Kathleen Ann (Author) / Henderson, Mark (Thesis director) / Johnson, Nathan (Committee member) / Barrett, The Honors College (Contributor) / Human Systems Engineering (Contributor)
Created2014-12
Description
High concentrations of carbon monoxide and particulate matter can cause respiratory disease, illness, and death in high doses. Air pollution is a concern in many urban areas of emerging markets that rely on outdated technologies for transportation and electricity generation; rural air quality is also a concern when noting the high prevalence of products of incomplete combustion resulting from open fires for cooking and heating. Monitoring air quality is an essential step to identifying these and other factors that affect air quality, and thereafter informing engineering and policy decisions to improve the quality of air. This study seeks to measure changes in air quality across spatial and temporal domains, with a specific focus on microclimates within an urban area. A prototype, low-cost air quality monitoring device has been developed to measure the concentrations of particulate matter, ozone, and carbon monoxide multiple times per minute. The device communicates data wirelessly via cell towers, and can run off-grid using a solar PV-battery system. The device can be replicated and deployed across urban regions for high-fidelity emissions monitoring to explore the effect of anthropogenic and environmental factors on intra-hour air quality. Hardware and software used in the device is described, and the wireless data communication protocols and capabilities are discussed.
ContributorsReilly, Kyle (Co-author) / Birner, Michael (Co-author) / Johnson, Nathan (Thesis director) / Gary, Kevin (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05