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- Creators: Arizona State University
- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Numerical simulation of environmental flow over urban landscape for applications to renewable energy
Description
Development of renewable energy solutions has become a major interest among environmental organizations and governments around the world due to an increase in energy consumption and global warming. One fast growing renewable energy solution is the application of wind energy in cities. To qualitative and quantitative predict wind turbine performance in urban areas, CFD simulation is performed on real-life urban geometry and wind velocity profiles are evaluated. Two geometries in Arizona is selected in this thesis to demonstrate the influence of building heights; one of the simulation models, ASU campus, is relatively low rise and without significant tall buildings; the other model, the downtown phoenix model, are high-rise and with greater building height difference. The content of this thesis focuses on using RANS computational fluid dynamics approach to simulate wind acceleration phenomenon in two complex geometries, ASU campus and Phoenix downtown model. Additionally, acceleration ratio and locations are predicted, the results are then used to calculate the best location for small wind turbine installments.
ContributorsYing, Xiaoyan (Author) / Huang, Huei-Ping (Thesis advisor) / Peet, Yulia (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2015
Description
Browsing Twitter users, or browsers, often find it increasingly cumbersome to attach meaning to tweets that are displayed on their timeline as they follow more and more users or pages. The tweets being browsed are created by Twitter users called originators, and are of some significance to the browser who has chosen to subscribe to the tweets from the originator by following the originator. Although, hashtags are used to tag tweets in an effort to attach context to the tweets, many tweets do not have a hashtag. Such tweets are called orphan tweets and they adversely affect the experience of a browser.
A hashtag is a type of label or meta-data tag used in social networks and micro-blogging services which makes it easier for users to find messages with a specific theme or content. The context of a tweet can be defined as a set of one or more hashtags. Users often do not use hashtags to tag their tweets. This leads to the problem of missing context for tweets. To address the problem of missing hashtags, a statistical method was proposed which predicts most likely hashtags based on the social circle of an originator.
In this thesis, we propose to improve on the existing context recovery system by selectively limiting the candidate set of hashtags to be derived from the intimate circle of the originator rather than from every user in the social network of the originator. This helps in reducing the computation, increasing speed of prediction, scaling the system to originators with large social networks while still preserving most of the accuracy of the predictions. We also propose to not only derive the candidate hashtags from the social network of the originator but also derive the candidate hashtags based on the content of the tweet. We further propose to learn personalized statistical models according to the adoption patterns of different originators. This helps in not only identifying the personalized candidate set of hashtags based on the social circle and content of the tweets but also in customizing the hashtag adoption pattern to the originator of the tweet.
A hashtag is a type of label or meta-data tag used in social networks and micro-blogging services which makes it easier for users to find messages with a specific theme or content. The context of a tweet can be defined as a set of one or more hashtags. Users often do not use hashtags to tag their tweets. This leads to the problem of missing context for tweets. To address the problem of missing hashtags, a statistical method was proposed which predicts most likely hashtags based on the social circle of an originator.
In this thesis, we propose to improve on the existing context recovery system by selectively limiting the candidate set of hashtags to be derived from the intimate circle of the originator rather than from every user in the social network of the originator. This helps in reducing the computation, increasing speed of prediction, scaling the system to originators with large social networks while still preserving most of the accuracy of the predictions. We also propose to not only derive the candidate hashtags from the social network of the originator but also derive the candidate hashtags based on the content of the tweet. We further propose to learn personalized statistical models according to the adoption patterns of different originators. This helps in not only identifying the personalized candidate set of hashtags based on the social circle and content of the tweets but also in customizing the hashtag adoption pattern to the originator of the tweet.
ContributorsMallapura Umamaheshwar, Tejas (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Radioactive cesium (137Cs), released from nuclear power plants and nuclear accidental releases, is a problem due to difficulties regarding its removal. Efforts have been focused on removing cesium and the remediation of the contaminated environment. Traditional treatment techniques include Prussian blue and nano zero-valent ion (nZVI) and nano-Fe/Cu particles to remove Cs from water; however, they are not efficient at removing Cs when present at low concentrations of about 10 parts-per-billion (ppb), typical of concentrations found in the radioactive contaminated sites.
The objective of this study was to develop an innovative and simple method to remove Cs+ present at low concentrations by engineering a proteoliposome transporter composed of an uptake protein reconstituted into a liposome vesicle. To achieve this, the uptake protein, Kup, from E. coli, was isolated through protein extraction and purification procedures. The new and simple extraction methodology developed in this study was highly efficient and resulted in purified Kup at ~1 mg/mL. A new method was also developed to insert purified Kup protein into the bilayers of liposome vesicles. Finally, removal of CsCl (10 and 100 ppb) was demonstrated by spiking the constructed proteoliposome in lab-fortified water, followed by incubation and ultracentrifugation, and measuring Cs+ with inductively coupled plasma mass spectrometry (ICP-MS).
The ICP-MS results from testing water contaminated with 100 ppb CsCl, revealed that adding 0.1 – 8 mL of Kup proteoliposome resulted in 0.29 – 12.7% Cs removal. Addition of 0.1 – 2 mL of proteoliposome to water contaminated with 10 ppb CsCl resulted in 0.65 – 3.43% Cs removal. These removal efficiencies were greater than the control, liposome with no protein.
A linear relationship was observed between the amount of proteoliposome added to the contaminated water and removal percentage. Consequently, by adding more volumes of proteoliposome, removal can be simply improved. This suggests that with ~ 60-70 mL of proteoliposome, removal of about 90% can be achieved. The novel technique developed herein is a contribution to emerging technologies in the water and wastewater treatment industry.
The objective of this study was to develop an innovative and simple method to remove Cs+ present at low concentrations by engineering a proteoliposome transporter composed of an uptake protein reconstituted into a liposome vesicle. To achieve this, the uptake protein, Kup, from E. coli, was isolated through protein extraction and purification procedures. The new and simple extraction methodology developed in this study was highly efficient and resulted in purified Kup at ~1 mg/mL. A new method was also developed to insert purified Kup protein into the bilayers of liposome vesicles. Finally, removal of CsCl (10 and 100 ppb) was demonstrated by spiking the constructed proteoliposome in lab-fortified water, followed by incubation and ultracentrifugation, and measuring Cs+ with inductively coupled plasma mass spectrometry (ICP-MS).
The ICP-MS results from testing water contaminated with 100 ppb CsCl, revealed that adding 0.1 – 8 mL of Kup proteoliposome resulted in 0.29 – 12.7% Cs removal. Addition of 0.1 – 2 mL of proteoliposome to water contaminated with 10 ppb CsCl resulted in 0.65 – 3.43% Cs removal. These removal efficiencies were greater than the control, liposome with no protein.
A linear relationship was observed between the amount of proteoliposome added to the contaminated water and removal percentage. Consequently, by adding more volumes of proteoliposome, removal can be simply improved. This suggests that with ~ 60-70 mL of proteoliposome, removal of about 90% can be achieved. The novel technique developed herein is a contribution to emerging technologies in the water and wastewater treatment industry.
ContributorsHakim Elahi, Sepideh (Author) / Conroy-Ben, Otakuye (Thesis advisor) / Abbaszadegan, Morteza (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2018
Description
Virtual digital assistants are automated software systems which assist humans by understanding natural languages such as English, either in voice or textual form. In recent times, a lot of digital applications have shifted towards providing a user experience using natural language interface. The change is brought up by the degree of ease with which the virtual digital assistants such as Google Assistant and Amazon Alexa can be integrated into your application. These assistants make use of a Natural Language Understanding (NLU) system which acts as an interface to translate unstructured natural language data into a structured form. Such an NLU system uses an intent finding algorithm which gives a high-level idea or meaning of a user query, termed as intent classification. The intent classification step identifies the action(s) that a user wants the assistant to perform. The intent classification step is followed by an entity recognition step in which the entities in the utterance are identified on which the intended action is performed. This step can be viewed as a sequence labeling task which maps an input word sequence into a corresponding sequence of slot labels. This step is also termed as slot filling.
In this thesis, we improve the intent classification and slot filling in the virtual voice agents by automatic data augmentation. Spoken Language Understanding systems face the issue of data sparsity. The reason behind this is that it is hard for a human-created training sample to represent all the patterns in the language. Due to the lack of relevant data, deep learning methods are unable to generalize the Spoken Language Understanding model. This thesis expounds a way to overcome the issue of data sparsity in deep learning approaches on Spoken Language Understanding tasks. Here we have described the limitations in the current intent classifiers and how the proposed algorithm uses existing knowledge bases to overcome those limitations. The method helps in creating a more robust intent classifier and slot filling system.
In this thesis, we improve the intent classification and slot filling in the virtual voice agents by automatic data augmentation. Spoken Language Understanding systems face the issue of data sparsity. The reason behind this is that it is hard for a human-created training sample to represent all the patterns in the language. Due to the lack of relevant data, deep learning methods are unable to generalize the Spoken Language Understanding model. This thesis expounds a way to overcome the issue of data sparsity in deep learning approaches on Spoken Language Understanding tasks. Here we have described the limitations in the current intent classifiers and how the proposed algorithm uses existing knowledge bases to overcome those limitations. The method helps in creating a more robust intent classifier and slot filling system.
ContributorsGarg, Prashant (Author) / Baral, Chitta (Thesis advisor) / Kumar, Hemanth (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.
A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population
ii
density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
ContributorsSong, Yang (Author) / Gurney, Kevin (Thesis advisor) / Kuby, Michael (Committee member) / Golub, Aaron (Committee member) / Chester, Mikhail (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2018
Description
Iodide (I-) in surface and groundwaters is a potential precursor for the formation of iodinated disinfection by-products (I-DBPs) during drinking water treatment. The aim of this thesis is to provide a perspective on the sources and occurrence of I- in United States (US) source waters based on ~9200 surface water (SW) and groundwater (GW) sampling locations. The median I- concentrations observed was 16 μg/l and 14 μg/l, respectively in SW and GW. However, these samples were rarely collected at water treatment plant (WTP) intakes, where such iodide occurrence data is needed to understand impacts on DBPs. Most samples were collected in association with geochemical studies. We conclude that I- occurrence appears to be influenced by geological features, including halite rock/river basin formations, saline aquifers and organic rich shale/oil formations. Halide ratios (Cl-/I-, Br-/I- and Cl-/Br-) were analyzed to determine the I- origin in source waters. SW and GW had median Cl-/I- ratios of ~3600 μg/μg and median Br-/I- ratios of ~15 μg/μg. For states with I- concentration >50 μg/l (e.g., Montana and North Dakota), a single source (i.e., organic rich formations) can be identified. However, for states like California and Texas that have wide-ranging I- concentration of below detection limit to >250 μg/l, I- occurrence can be attributed to a mixture of marine and organic signatures. The lack of information of organic iodine, inorganic I- and IO3- in source waters limits our ability to predict I-DBPs formed during drinking water treatment, and new occurrence studies are needed to fill these data gaps. This is first of its kind study to understand the I- occurrence through historical data, however we also identify the shortcomings of existing databases used to carry out this study.
ContributorsSharma, Naushita (Author) / Westerhoff, Paul (Thesis advisor) / Lackner, Klaus (Committee member) / Herckes, Pierre (Committee member) / Arizona State University (Publisher)
Created2018
Description
Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.
ContributorsDe La Rosa, Matthew Lee (Author) / Chen, Yinong (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2018
Description
The formation and stability of a slowly evolving zonal jet in 2-D flow with beta effect is analyzed using the model developed by Manfroi and Young in which the final governing equation was derived by means of a perturbation analysis of a barotropic vorticity equation with sinusoidal meridional mean flow. However in the original study the term β0, that represents the effect of large-scale Rossby waves, was dropped and was proceeded on a path of finding solutions for a simplified 1-D flow. The idea of this study is to understand the effects of the dropped term on the overall dynamics of the zonal jet evolution. For this purpose the system that is entirely deterministic with no additional forcing is solved by means of a standard finite difference scheme. The Numerical solutions are found for varying β0 and μ values where μ represents the bottom drag. In addition to this the criteria for the formation of zonal jets developed originally for the 1-D system is verified for the 2-D system as well. The study reveals the similarity in some of the results of the 1-D and the 2-D system like the merging of jets in the absence of bottom drag, formation of steady jets in presence of a non-zero bottom drag and the adherence to the boundary criteria for the formation of zonal jets. But when it comes to the formation of steady jets, a finite β0 value is required above which the solution is similar to the 1-D system. Also the jets formed under the presence of non-zero bottom drag seem wavy in nature which is different from the steady horizontal jets produced in the 1-D system.
ContributorsRaghunathan, Girish Nigamanth (Author) / Huang, Huei-Ping (Thesis advisor) / Herrmann, Marcus (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2017
Description
Reading comprehension is a critical aspect of life in America, but many English language learners struggle with this skill. Enhanced Moved by Reading to Accelerate Comprehension in English (EMBRACE) is a tablet-based interactive learning environment is designed to improve reading comprehension. During use of EMBRACE, all interactions with the system are logged, including correct and incorrect behaviors and help requests. These interactions could potentially be used to predict the child’s reading comprehension, providing an online measure of understanding. In addition, time-related features have been used for predicting learning by educational data mining models in mathematics and science, and may be relevant in this context. This project investigated the predictive value of data mining models based on user actions for reading comprehension, with and without timing information. Contradictory results of the investigation were obtained. The KNN and SVM models indicated that elapsed time is an important feature, but the linear regression models indicated that elapsed time is not an important feature. Finally, a new statistical test was performed on the KNN algorithm which indicated that the feature selection process may have caused overfitting, where features were chosen due coincidental alignment with the participants’ performance. These results provide important insights which will aid in the development of a reading comprehension predictor that improves the EMBRACE system’s ability to better serve ELLs.
ContributorsDexheimer, Matthew Scott (Author) / Walker, Erin (Thesis advisor) / Glenberg, Arthur (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2017
Description
Virtual Reality (hereafter VR) and Mixed Reality (hereafter MR) have opened a new line of applications and possibilities. Amidst a vast network of potential applications, little research has been done to provide real time collaboration capability between users of VR and MR. The idea of this thesis study is to develop and test a real time collaboration system between VR and MR. The system works similar to a Google document where two or more users can see what others are doing i.e. writing, modifying, viewing, etc. Similarly, the system developed during this study will enable users in VR and MR to collaborate in real time.
The study of developing a real-time cross-platform collaboration system between VR and MR takes into consideration a scenario in which multiple device users are connected to a multiplayer network where they are guided to perform various tasks concurrently.
Usability testing was conducted to evaluate participant perceptions of the system. Users were required to assemble a chair in alternating turns; thereafter users were required to fill a survey and give an audio interview. Results collected from the participants showed positive feedback towards using VR and MR for collaboration. However, there are several limitations with the current generation of devices that hinder mass adoption. Devices with better performance factors will lead to wider adoption.
The study of developing a real-time cross-platform collaboration system between VR and MR takes into consideration a scenario in which multiple device users are connected to a multiplayer network where they are guided to perform various tasks concurrently.
Usability testing was conducted to evaluate participant perceptions of the system. Users were required to assemble a chair in alternating turns; thereafter users were required to fill a survey and give an audio interview. Results collected from the participants showed positive feedback towards using VR and MR for collaboration. However, there are several limitations with the current generation of devices that hinder mass adoption. Devices with better performance factors will lead to wider adoption.
ContributorsSeth, Nayan Sateesh (Author) / Nelson, Brian (Thesis advisor) / Walker, Erin (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2017