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- Creators: Davulcu, Hasan
- Creators: Huang, Huei-Ping
- Member of: ASU Electronic Theses and Dissertations
Description
Emerging trends in cyber system security breaches in critical cloud infrastructures show that attackers have abundant resources (human and computing power), expertise and support of large organizations and possible foreign governments. In order to greatly improve the protection of critical cloud infrastructures, incorporation of human behavior is needed to predict potential security breaches in critical cloud infrastructures. To achieve such prediction, it is envisioned to develop a probabilistic modeling approach with the capability of accurately capturing system-wide causal relationship among the observed operational behaviors in the critical cloud infrastructure and accurately capturing probabilistic human (users’) behaviors on subsystems as the subsystems are directly interacting with humans. In our conceptual approach, the system-wide causal relationship can be captured by the Bayesian network, and the probabilistic human behavior in the subsystems can be captured by the Markov Decision Processes. The interactions between the dynamically changing state graphs of Markov Decision Processes and the dynamic causal relationships in Bayesian network are key components in such probabilistic modelling applications. In this thesis, two techniques are presented for supporting the above vision to prediction of potential security breaches in critical cloud infrastructures. The first technique is for evaluation of the conformance of the Bayesian network with the multiple MDPs. The second technique is to evaluate the dynamically changing Bayesian network structure for conformance with the rules of the Bayesian network using a graph checker algorithm. A case study and its simulation are presented to show how the two techniques support the specific parts in our conceptual approach to predicting system-wide security breaches in critical cloud infrastructures.
ContributorsNagaraja, Vinjith (Author) / Yau, Stephen S. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2015
Description
With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the results are not effective towards the search query of the user. The search engine user has to go through certain webpages before getting at the webpage he/she wanted. This problem of Information Overload can be solved using Automatic Text Summarization. Summarization is a process of obtaining at abridged version of documents so that user can have a quick view to understand what exactly the document is about. Email threads from W3C are used in this system. Apart from common IR features like Term Frequency, Inverse Document Frequency, Term Rank, a variation of page rank based on graph model, which can cluster the words with respective to word ambiguity, is implemented. Term Rank also considers the possibility of co-occurrence of words with the corpus and evaluates the rank of the word accordingly. Sentences of email threads are ranked as per features and summaries are generated. System implemented the concept of pyramid evaluation in content selection. The system can be considered as a framework for Unsupervised Learning in text summarization.
ContributorsNadella, Sravan (Author) / Davulcu, Hasan (Thesis advisor) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
Muslim radicalism is recognized as one of the greatest security threats for the United States and the rest of the world. Use of force to eliminate specific radical entities is ineffective in containing radicalism as a whole. There is a need to understand the origin, ideologies and behavior of Radical and Counter-Radical organizations and how they shape up over a period of time. Recognizing and supporting counter-radical organizations is one of the most important steps towards impeding radical organizations. A lot of research has already been done to categorize and recognize organizations, to understand their behavior, their interactions with other organizations, their target demographics and the area of influence. We have a huge amount of information which is a result of the research done over these topics. This thesis provides a powerful and interactive way to navigate through all this information, using a Visualization Dashboard. The dashboard makes it easier for Social Scientists, Policy Analysts, Military and other personnel to visualize an organization's propensity towards violence and radicalism. It also tracks the peaking religious, political and socio-economic markers, their target demographics and locations. A powerful search interface with parametric search helps in narrowing down to specific scenarios and view the corresponding information related to the organizations. This tool helps to identify moderate Counter-Radical organizations and also has the potential of predicting the orientation of various organizations based on the current information.
ContributorsNair, Shreejay (Author) / Davulcu, Hasan (Thesis advisor) / Dasgpta, Partha (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
The study of deflagration to detonation transition (DDT) in explosives is of prime importance with regards to insensitive munitions (IM). Critical damage owing to thermal or shock stimuli could translate to significant loss of life and material. The present study models detonation and deflagration of a commonly used granular explosive: cyclotetramethylene-tetranitramine, HMX. A robust literature review is followed by computational modeling of gas gun and DDT tube test data using the Sandia National Lab three-dimensional multi-material Eulerian hydrocode CTH. This dissertation proposes new computational practices and models that aid in predicting shock stimulus IM response. CTH was first used to model experimental data sets of DDT tubes from both Naval Surface Weapons Center and Los Alamos National Laboratory which were initiated by pyrogenic material and a piston, respectively. Analytical verification was performed, where possible, for detonation via empirical based equations at the Chapman Jouguet state with errors below 2.1%, and deflagration via pressure dependent burn rate equations. CTH simulations include inert, history variable reactive burn and Arrhenius models. The results are in excellent agreement with published HMX detonation velocities. Novel additions include accurate simulation of the pyrogenic material BKNO3 and the inclusion of porosity in energetic materials. The treatment of compaction is especially important in modeling precursory hotspots, caused by hydrodynamic collapse of void regions or grain interactions, prior to DDT of granular explosives. The CTH compaction model of HMX was verified within 11% error via a five pronged validation approach using gas gun data and employed use of a newly generated set of P-α parameters for granular HMX in a Mie-Gruneisen Equation of State. Next, the additions of compaction were extended to a volumetric surface burning model of HMX and compare well to a set of empirical burn rates. Lastly, the compendium of detonation and deflagration models was applied to the aforementioned DDT tubes and demonstrate working functionalities of all models, albeit at the expense of significant computational resources. A robust hydrocode methodology is proposed to make use of the deflagration, compaction and detonation models as a means to predict IM response to shock stimulus of granular explosive materials.
ContributorsMahon, Kelly Susan (Author) / Lee, Taewoo (Thesis advisor) / Herrmann, Marcus (Committee member) / Chen, Kangping (Committee member) / Jiao, Yang (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2015
Description
This study uses the Weather Research and Forecasting (WRF) model to simulate and predict the changes in local climate attributed to the urbanization for five desert cities. The simulations are performed in the fashion of climate downscaling, constrained by the surface boundary conditions generated from high resolution land-use maps. For each city, the land-use maps of 1985 and 2010 from Landsat satellite observation, and a projected land-use map for 2030, are used to represent the past, present, and future. An additional set of simulations for Las Vegas, the largest of the five cities, uses the NLCD 1992 and 2006 land-use maps and an idealized historical land-use map with no urban coverage for 1900.
The study finds that urbanization in Las Vegas produces a classic urban heat island (UHI) at night but a minor cooling during the day. A further analysis of the surface energy balance shows that the decrease in surface Albedo and increase effective emissivity play an important role in shaping the local climate change over urban areas. The emerging urban structures slow down the diurnal wind circulation over the city due to an increased effective surface roughness. This leads to a secondary modification of temperature due to the interaction between the mechanical and thermodynamic effects of urbanization.
The simulations for the five desert cities for 1985 and 2010 further confirm a common pattern of the climatic effect of urbanization with significant nighttime warming and moderate daytime cooling. This effect is confined to the urban area and is not sensitive to the size of the city or the detail of land cover in the surrounding areas. The pattern of nighttime warming and daytime cooling remains robust in the simulations for the future climate of the five cities using the projected 2030 land-use maps. Inter-city differences among the five urban areas are discussed.
The study finds that urbanization in Las Vegas produces a classic urban heat island (UHI) at night but a minor cooling during the day. A further analysis of the surface energy balance shows that the decrease in surface Albedo and increase effective emissivity play an important role in shaping the local climate change over urban areas. The emerging urban structures slow down the diurnal wind circulation over the city due to an increased effective surface roughness. This leads to a secondary modification of temperature due to the interaction between the mechanical and thermodynamic effects of urbanization.
The simulations for the five desert cities for 1985 and 2010 further confirm a common pattern of the climatic effect of urbanization with significant nighttime warming and moderate daytime cooling. This effect is confined to the urban area and is not sensitive to the size of the city or the detail of land cover in the surrounding areas. The pattern of nighttime warming and daytime cooling remains robust in the simulations for the future climate of the five cities using the projected 2030 land-use maps. Inter-city differences among the five urban areas are discussed.
ContributorsKamal, Samy (Author) / Huang, Huei-Ping (Thesis advisor) / Anderson, James (Thesis advisor) / Herrmann, Marcus (Committee member) / Calhoun, Ronald (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Created2015
Description
For the increasing concerns of influence on environment by fossil-electricity generation, application of renewable energy becomes one of the most focused issues in society. Based on the limitation on urban environment, wind turbines, which can be mounted on rooftop or between buildings, are regarded as a feasible way for wind energy generation. This study presents wind flow simulations in a large-scale environment with certain dimension buildings. Different inlet velocity boundary conditions are tested firstly, and the non-uniform inlet boundary condition shows better agreement with realistic situation. Turbulence intensity is set to be 10% for comparison consistency. The k-epsilon turbulence model is regarded as a better simulation for this certain condition. After that, three different structures, which include single building, pristine double building and modified circular gap double building systems, are tested in this environment condition. The result shows 18.8% velocity increasing on the top of single building system. Pristine double building systems are tested with 4 different gap distances, and building with 10 meters gap achieved the best velocity condition, which 32.8% velocity increasing and 11.8% improvement comparing to single building system, respectively. But the location of maximum velocity moves to the gap and the maximum velocity on the rooftop of double building system is approximately 5.1% lower than single building system. Based on previous study, modified circular gap double building system is created with 10 meters gap. Comparing result with single building system, modified circular gap system achieves higher improvement for wind flow, whose improvement of velocity increasing in the gap and on the rooftop of building are 47.1% and 3.0%, respectively. As a result, the modified circular gap double building can be regarded as a high efficiency system of environmental wind flow over buildings for renewable energy system.
ContributorsLi, Guoyi (Author) / Huang, Huei-Ping (Thesis advisor) / Lee, Taewoo (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2015
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
Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights and window blinds along with illumination sensors that are distributed in the building, while temperature control can be automated by having computer controlled air-conditioning systems. However, programming actuators in a large-scale environment for buildings and homes can be time consuming and expensive. This dissertation presents an approach that algorithmically sets up the control system that can automate any building without requiring custom programming. This is achieved by imbibing the system self calibrating and self learning abilities.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
ContributorsWang, Yuan (Author) / Dasgupta, Partha (Thesis advisor) / Davulcu, Hasan (Committee member) / Huang, Dijiang (Committee member) / Reddy, T. Agami (Committee member) / Arizona State University (Publisher)
Created2015
Description
Micro-blogging platforms like Twitter have become some of the most popular sites for people to share and express their views and opinions about public events like debates, sports events or other news articles. These social updates by people complement the written news articles or transcripts of events in giving the popular public opinion about these events. So it would be useful to annotate the transcript with tweets. The technical challenge is to align the tweets with the correct segment of the transcript. ET-LDA by Hu et al [9] addresses this issue by modeling the whole process with an LDA-based graphical model. The system segments the transcript into coherent and meaningful parts and also determines if a tweet is a general tweet about the event or it refers to a particular segment of the transcript. One characteristic of the Hu et al’s model is that it expects all the data to be available upfront and uses batch inference procedure. But in many cases we find that data is not available beforehand, and it is often streaming. In such cases it is infeasible to repeatedly run the batch inference algorithm. My thesis presents an online inference algorithm for the ET-LDA model, with a continuous stream of tweet data and compare their runtime and performance to existing algorithms.
ContributorsAcharya, Anirudh (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2015
Description
One of the most remarkable outcomes resulting from the evolution of the web into Web 2.0, has been the propelling of blogging into a widely adopted and globally accepted phenomenon. While the unprecedented growth of the Blogosphere has added diversity and enriched the media, it has also added complexity. To cope with the relentless expansion, many enthusiastic bloggers have embarked on voluntarily writing, tagging, labeling, and cataloguing their posts in hopes of reaching the widest possible audience. Unbeknown to them, this reaching-for-others process triggers the generation of a new kind of collective wisdom, a result of shared collaboration, and the exchange of ideas, purpose, and objectives, through the formation of associations, links, and relations. Mastering an understanding of the Blogosphere can greatly help facilitate the needs of the ever growing number of these users, as well as producers, service providers, and advertisers into facilitation of the categorization and navigation of this vast environment. This work explores a novel method to leverage the collective wisdom from the infused label space for blog search and discovery. The work demonstrates that the wisdom space can provide a most unique and desirable framework to which to discover the highly sought after background information that could aid in the building of classifiers. This work incorporates this insight into the construction of a better clustering of blogs which boosts the performance of classifiers for identifying more relevant labels for blogs, and offers a mechanism that can be incorporated into replacing spurious labels and mislabels in a multi-labeled space.
ContributorsGalan, Magdiel F (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015