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Title: Performance Predication Model for Advance Traffic Control System (ATCS) using field data.
Creator: Mirza, Masood, Radwan, Essam, Abou-Senna, Hatem, Abdel-Aty, Mohamed, Zheng, Qipeng, University of Central Florida
Abstract / Description: Reductions in capital expenditure revenues have created greater demands from users for quality service from existing facilities at lower costs forcing agencies to evaluate the performance of projects in more comprehensive and "greener" ways. The use of Adaptive Traffic Controls Systems (ATCS) is a step in the right direction by enabling practitioners and engineers to develop and implement traffic optimization strategies to achieve greater capacity out of the existing systems by optimizing...
Show moreReductions in capital expenditure revenues have created greater demands from users for quality service from existing facilities at lower costs forcing agencies to evaluate the performance of projects in more comprehensive and "greener" ways. The use of Adaptive Traffic Controls Systems (ATCS) is a step in the right direction by enabling practitioners and engineers to develop and implement traffic optimization strategies to achieve greater capacity out of the existing systems by optimizing traffic signal based on real time traffic demands and flow pattern. However, the industry is lagging in developing modeling tools for the ATCS which can predict the changes in MOEs due to the changes in traffic flow (i.e. volume and/or travel direction) making it difficult for the practitioners to measure the magnitude of the impacts and to develop an appropriate mitigation strategy. The impetus of this research was to explore the potential of utilizing available data from the ATCS for developing prediction models for the critical MOEs and for the entire intersection. Firstly, extensive data collections efforts were initiated to collect data from the intersections in Marion County, Florida. The data collected included volume, geometry, signal operations, and performance for an extended period. Secondly, the field data was scrubbed using macros to develop a clean data set for model development. Thirdly, the prediction models for the MOEs (wait time and queue) for the critical movements were developed using General Linear Regression Modeling techniques and were based on Poisson distribution with log linear function. Finally, the models were validated using the data collected from the intersections within Orange County, Florida. Also, as a part of this research, an Intersection Performance Index (IPI) model, a LOS prediction model for the entire intersection, was developed. This model was based on the MOEs (wait time and queue) for the critical movements.In addition, IPI Thresholds and corresponding intersection capacity designations were developed to establish level of service at the intersection. The IPI values and thresholds were developed on the same principles as Intersection Capacity Utilization (ICU) procedures, tested, and validated against corresponding ICU values and corresponding ICU LOS.
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Date Issued: 2018
Identifier: CFE0007055, ucf:51975
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007055

Title: PREDICTING RISKS OF INVASION OF CAULERPA SPECIES IN FLORIDA.
Creator: Glardon, Christian, Walters, Linda, University of Central Florida
Abstract / Description: Invasions of exotic species are one of the primary causes of biodiversity loss on our planet (National Research Council 1995). In the marine environment, all habitat types including estuaries, coral reefs, mud flats, and rocky intertidal shorelines have been impacted (e.g. Bertness et al. 2001). Recently, the topic of invasive species has caught the public's attention. In particular, there is worldwide concern about the aquarium strain of the green alga Caulerpa taxifolia (Vahl) C. Agardh...
Show moreInvasions of exotic species are one of the primary causes of biodiversity loss on our planet (National Research Council 1995). In the marine environment, all habitat types including estuaries, coral reefs, mud flats, and rocky intertidal shorelines have been impacted (e.g. Bertness et al. 2001). Recently, the topic of invasive species has caught the public's attention. In particular, there is worldwide concern about the aquarium strain of the green alga Caulerpa taxifolia (Vahl) C. Agardh that was introduced to the Mediterranean Sea in 1984 from the Monaco Oceanographic Museum. Since that time, it has flourished in thousands of hectares of near-shore waters. More recently, C. taxifolia has invaded southern Californian and Australian waters. Since the waters of Florida are similar to the waters of the Mediterranean Sea and other invasive sites my study will focus on determining potential invasion locations in Florida. I will look at the present distribution of C. taxifolia - native strain in Florida as well as the distribution of the whole genus around the state. During this study, I address three questions: 1) What is the current distribution of Caulerpa spp. in Florida? 2) Can I predict the location of potential Caulerpa spp. invasions using a set of environmental parameters and correlate them to the occurrence of the algae with the support of Geographic Information System (GIS) maps? 3) Using the results of part two, is there an ecological preferred environment for one or all Caulerpa spp. in Florida? To answer these questions, I surveyed 24 areas in each of 6 zones chosen in a stratified manner along the Floridian coastline to evaluate the association of potential indicators Caulerpa. Latitude, presence or absence of seagrass beds, human population density, and proximity to marinas were chosen as the 4 parameters expected to correlate to Caulerpa occurrences. A logistic regression model assessing the association of Caulerpa occurrence with measured variables has been developed to predict current and future probabilities of Caulerpa spp. presence throughout the state. Fourteen different species of Caulerpa spp. were found in 26 of the 132 sites visited. There was a positive correlation between Caulerpa spp. and seagrass beds presence and proximity to marinas. There was a negative correlation with latitude and human population density. C. taxifolia – aquarium strain wasn't found. Percent correct for our model was of 61.5% for presence and 98.1% for absence. This prediction model will allow us to focus on particular areas for future surveys.
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Date Issued: 2006
Identifier: CFE0001041, ucf:46796
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001041

Title: Thermomechanical Fatigue Life Prediction of Notched 304 Stainless Steel.
Creator: Karl, Justin, Gordon, Ali, Bai, Yuanli, Raghavan, Seetha, Nicholson, David, University of Central Florida
Abstract / Description: The behavior of materials as they are subjected to combined thermal and mechanical fatigue loads is an area of research that carries great significance in a number of engineering applications. Power generation, petrochemical, and aerospace industries operate machinery with expensive components that undergo repeated applications of force while simultaneously being exposed to variable temperature working fluids. A case of considerable importance is found in steam turbines, which subject blades...
Show moreThe behavior of materials as they are subjected to combined thermal and mechanical fatigue loads is an area of research that carries great significance in a number of engineering applications. Power generation, petrochemical, and aerospace industries operate machinery with expensive components that undergo repeated applications of force while simultaneously being exposed to variable temperature working fluids. A case of considerable importance is found in steam turbines, which subject blades to cyclic loads from rotation as well as the passing of heated gases. The complex strain and temperature histories from this type of operation, combined with the geometric profile of the blades, make accurate prediction of service life for such components challenging. Development of a deterministic life prediction model backed by physical data would allow design and operation of turbines with higher efficiency and greater regard for reliability. The majority of thermomechanical fatigue (TMF) life prediction modeling research attempts to correlate basic material property data with simplistic strain and thermal histories. With the exception of very limited cases, these types of efforts have been insufficient and imprecise in their capabilities. Early researchers did not account for the multiple damage mechanisms that operate and interact within a material during TMF loads, and did not adequately address the extent of the relationship between smooth and notched parts. More recent research that adequately recognizes the multivariate nature of TMF develops models that handle life reduction through summation of constitutive damage terms. It is feasible that a modification to the damage-based approach can sufficiently include cases that involve complex geometry. The focus of this research is to construct an experimentally-backed extension of the damage-based approach that improves handling of geometric discontinuities. Smooth and notched specimens of Type 304 stainless steel were subjected to several types of idealized fatigue conditions to assemble a clear picture of the types of damage occurring in a steam turbine and similarly-loaded mechanical systems. These results were compared with a number of idealized TMF experiments, and supplemented by numerical simulation and microscopic observation. A non-uniform damage-summation constitutive model was developed primarily based on physical observations. An additional simplistic model was developed based on phenomenological effect. Findings from this study will be applicable to life prediction efforts in other similar material and load cases.
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Date Issued: 2013
Identifier: CFE0004870, ucf:49666
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004870

Title: Modeling User Transportation Patterns Using Mobile Devices.
Creator: Davami, Erfan, Sukthankar, Gita, Gonzalez, Avelino, Foroosh, Hassan, Sukthankar, Rahul, University of Central Florida
Abstract / Description: Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts...
Show moreParticipatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data.Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data.This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance.Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality.
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Date Issued: 2015
Identifier: CFE0005597, ucf:50258
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005597

Title: A PREDICTIVE MODEL FOR BENCHMARKING ACADEMIC PROGRAMS (PBAP)USING U.S. NEWS RANKING DATA FOR ENGINEERING COLLEGES OFFERING GRADUATE PROGRAMS.
Creator: Chuck, Lisa, Tubbs, LeVester, University of Central Florida
Abstract / Description: Improving national ranking is an increasingly important issue for university administrators. While research has been conducted on performance measures in higher education, research designs have lacked a predictive quality. Studies on the U.S. News college rankings have provided insight into the methodology; however, none of them have provided a model to predict what change in variable values would likely cause an institution to improve its standing in the rankings. The purpose of this study...
Show moreImproving national ranking is an increasingly important issue for university administrators. While research has been conducted on performance measures in higher education, research designs have lacked a predictive quality. Studies on the U.S. News college rankings have provided insight into the methodology; however, none of them have provided a model to predict what change in variable values would likely cause an institution to improve its standing in the rankings. The purpose of this study was to develop a predictive model for benchmarking academic programs (pBAP) for engineering colleges. The 2005 U.S. News ranking data for graduate engineering programs were used to create a four-tier predictive model (pBAP). The pBAP model correctly classified 81.9% of the cases in their respective tier. To test the predictive accuracy of the pBAP model, the 2005 U.S .News data were entered into the pBAP variate developed using the 2004 U.S. News data. The model predicted that 88.9% of the institutions would remain in the same ranking tier in the 2005 U.S. News rankings (compared with 87.7% in the actual data), and 11.1% of the institutions would demonstrate tier movement (compared with an actual 12.3% movement in the actual data). The likelihood of improving an institution's standing in the rankings was greater when increasing the values of 3 of the 11 variables in the U.S. News model: peer assessment score, recruiter assessment score, and research expenditures.
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Date Issued: 2005
Identifier: CFE0000431, ucf:46377
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000431

Title: A PREDICTIVE MODEL FOR BENCHMARKING ACADEMIC PROGRAMS (PBAP) USING U.S. NEWS RANKING DATA FOR ENGINEERING COLLEGES OFFERING GRADUATE PROGRAMS.
Creator: Chuck, Lisa, Tubbs, LeVester, University of Central Florida
Abstract / Description: Improving national ranking is an increasingly important issue for university administrators. While research has been conducted on performance measures in higher education, research designs have lacked a predictive quality. Studies on the U.S. News college rankings have provided insight into the methodology; however, none of them have provided a model to predict what change in variable values would likely cause an institution to improve its standing in the rankings. The purpose of this study...
Show moreImproving national ranking is an increasingly important issue for university administrators. While research has been conducted on performance measures in higher education, research designs have lacked a predictive quality. Studies on the U.S. News college rankings have provided insight into the methodology; however, none of them have provided a model to predict what change in variable values would likely cause an institution to improve its standing in the rankings. The purpose of this study was to develop a predictive model for benchmarking academic programs (pBAP) for engineering colleges. The 2005 U.S. News ranking data for graduate engineering programs were used to create a four-tier predictive model (pBAP). The pBAP model correctly classified 81.9% of the cases in their respective tier. To test the predictive accuracy of the pBAP model, the 2005 U.S .News data were entered into the pBAP variate developed using the 2004 U.S. News data. The model predicted that 88.9% of the institutions would remain in the same ranking tier in the 2005 U.S. News rankings (compared with 87.7% in the actual data), and 11.1% of the institutions would demonstrate tier movement (compared with an actual 12.3% movement in the actual data). The likelihood of improving an institution's standing in the rankings was greater when increasing the values of 3 of the 11 variables in the U.S. News model: peer assessment score, recruiter assessment score, and research expenditures.
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Date Issued: 2005
Identifier: CFE0000576, ucf:46422
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000576

Title: SPATIO-TEMPORAL ANALYSES FOR PREDICTION OF TRAFFIC FLOW, SPEED AND OCCUPANCY ON I-4.
Creator: Chilakamarri Venkata, Srinivasa Ravi Chandra, Al-Deek, Haitham, University of Central Florida
Abstract / Description: Traffic data prediction is a critical aspect of Advanced Traffic Management System (ATMS). The utility of the traffic data is in providing information on the evolution of traffic process that can be passed on to the various users (commuters, Regional Traffic Management Centers (RTMCs), Department of Transportation (DoT),etc) for user-specific objectives. This information can be extracted from the data collected by various traffic sensors. Loop detectors collect traffic data in the form of...
Show moreTraffic data prediction is a critical aspect of Advanced Traffic Management System (ATMS). The utility of the traffic data is in providing information on the evolution of traffic process that can be passed on to the various users (commuters, Regional Traffic Management Centers (RTMCs), Department of Transportation (DoT),…etc) for user-specific objectives. This information can be extracted from the data collected by various traffic sensors. Loop detectors collect traffic data in the form of flow, occupancy, and speed throughout the nation. Freeway traffic data from I-4 loop detectors has been collected and stored in a data warehouse called the Central Florida Data Warehouse (CFDWTM) by the University of Central Florida for the periods between 1993 – 1994 and 2000 - 2003. This data is raw, in the form of time stamped 30-second aggregated data collected from about 69 stations over a 36 mile stretch on I-4 from Lake Mary in the east to Disney-World in the west. This data has to be processed to extract information that can be disseminated to various users. Usually, most statistical procedures assume that each individual data point in the sample is independent of other data points. This is not true to traffic data as they are correlated across space and time. Therefore, the concept of time sequence and the layout of data collection devices in space, introduces autocorrelations in a single variable and cross correlations across multiple variables. Significant autocorrelations prove that past values of a variable can be used to predict future values of the same variable. Furthermore, significant cross-correlations between variables prove that past values of one variable can be used to predict future values of another variable. The traditional techniques in traffic prediction use univariate time series models that account for autocorrelations but not cross-correlations. These models have neglected the cross correlations between variables that are present in freeway traffic data, due to the way the data are collected. There is a need for statistical techniques that incorporate the effect of these multivariate cross-correlations to predict future values of traffic data. The emphasis in this dissertation is on the multivariate prediction of traffic variables. Unlike traditional statistical techniques that have relied on univariate models, this dissertation explored the cross-correlation between multivariate traffic variables and variables collected across adjoining spatial locations (such as loop detector stations). The analysis in this dissertation proved that there were significant cross correlations among different traffic variables collected across very close locations at different time scales. The nature of cross-correlations showed that there was feedback among the variables, and therefore past values can be used to predict future values. Multivariate time series analysis is appropriate for modeling the effect of different variables on each other. In the past, upstream data has been accounted for in time series analysis. However, these did not account for feedback effects. Vector Auto Regressive (VAR) models are more appropriate for such data. Although VAR models have been applied to forecast economic time series models, they have not been used to model freeway data. Vector Auto Regressive models were estimated for speeds and volumes at a sample of two locations, using 5-minute data. Different specifications were fit – estimation of speeds from surrounding speeds; estimation of volumes from surrounding volumes; estimation of speeds from volumes and occupancies from the same location; estimation of speeds from volumes from surrounding locations (and vice versa). These specifications were compared to univariate models for the respective variables at three levels of data aggregation (5-minutes, 10 minutes, and 15 minutes) in this dissertation. For data aggregation levels of <15 minutes, the VAR models outperform the univariate models. At data aggregation level of 15 minutes, VAR models did not outperform univariate models. Since VAR models were used for all traffic variables reported by the loop detectors, this made the application of VAR a true multivariate procedure for dynamic prediction of the multivariate traffic variables – flow, speed and occupancy. Also, VAR models are generally deemed more complex than univariate models due to the estimation of multiple covariance matrices. However, a VAR model for k variables must be compared to k univariate models and VAR models compare well with AutoRegressive Integrated Moving Average (ARIMA) models. The added complexity helps model the effect of upstream and downstream variables on the future values of the response variable. This could be useful for ATMS situations, where the effect of traffic redistribution and redirection is not known beforehand with prediction models. The VAR models were tested against more traditional models and their performances were compared against each other under different traffic conditions. These models significantly enhance the understanding of the freeway traffic processes and phenomena as well as identifying potential knowledge relating to traffic prediction. Further refinements in the models can result in better improvements for forecasts under multiple conditions.
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Date Issued: 2009
Identifier: CFE0002593, ucf:48276
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002593

Title: Provider Recommendation of HPV Vaccination: Bridging the Intention-Behavior Gap.
Creator: Landis, Erica, Neuberger, Lindsay, Sandoval, Jennifer, Miller, Ann, University of Central Florida
Abstract / Description: The present study, guided by preproduction formative research principles, employed in-depth interviews and a brief survey with pediatric healthcare providers (N=15) to investigate the consistency between behavioral intention to strongly recommend the HPV vaccine, and implementation of the actual behavior. Specifically, the Integrative Model of Behavioral Prediction (IMBP) was used as a framework to examine the impact of skills and environmental constraints on that behavioral intention...
Show moreThe present study, guided by preproduction formative research principles, employed in-depth interviews and a brief survey with pediatric healthcare providers (N=15) to investigate the consistency between behavioral intention to strongly recommend the HPV vaccine, and implementation of the actual behavior. Specifically, the Integrative Model of Behavioral Prediction (IMBP) was used as a framework to examine the impact of skills and environmental constraints on that behavioral intention-behavioral performance relationship. Results suggest providers intend to strongly recommend the HPV vaccine at a high level, but actually recommend the vaccine with a slightly lesser frequency. A thematic analysis of interview transcripts yielded a list of skills (e.g., tact, cultural competence) and environmental constraints (e.g., a lack of policy or school entry requirement, limited time designated for each patient) that contribute to that consistency gap. Additionally, healthcare providers indicated several preferences on training design (e.g., Continuing Medical Education course, delivered by medical and communication professionals) that could be used to inform future message construction. Suggestions for overcoming the environmental constraints reported by providers are presented, and implications for incorporating the emergent skills and preferences into training as a novel strategy for improving provider communication about the HPV vaccine outlined.
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Date Issued: 2016
Identifier: CFE0006132, ucf:51162
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006132

Title: Characterization of a Spiking Neuron Model via a Linear Approach.
Creator: Jabalameli, Amirhossein, Behal, Aman, Hickman, James, Haralambous, Michael, University of Central Florida
Abstract / Description: In the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not...
Show moreIn the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not obtained and the predicted model is unsuccessful in duplicating the spikelocations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptivethreshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equippedwith an adaptive threshold, a constrained iterative linear least squares method is implemented tofit the model to the reference data. Through manipulation of the system dynamics, the thresholdvoltage can be obtained as a realizable model that is linear in the unknown parameters. This linearlyparametrized realizable model is then utilized inside a prediction error based framework to identifythe threshold parameters with the purpose of predicting single neuron precise firing times. Thisestimation scheme is evaluated using both synthetic data obtained from an exact model as well asthe experimental data obtained from in vitro rat somatosensory cortical neurons. Results show theability of this approach to fit the MAT model to different types of reference data.
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Date Issued: 2015
Identifier: CFE0005958, ucf:50803
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005958

Title: Streamflow prediction in ungauged basins located within data-scarce regions.
Creator: Alipour, Mohammadhossein, Kibler, Kelly, Wang, Dingbao, Mayo, Talea, Emrich, Christopher, University of Central Florida
Abstract / Description: Preservation and or restoration of riverine ecosystem requires quantification of alterations inflicted by water resources development projects. Long records of streamflow data are the first piece of information required in order to enable this analysis. Ungauged catchments located within data-scarce regions lack long records of streamflow data. In this dissertation, a multi-objective framework named Streamflow Prediction under Extreme Data-scarcity (SPED) is proposed for streamflow prediction...
Show morePreservation and or restoration of riverine ecosystem requires quantification of alterations inflicted by water resources development projects. Long records of streamflow data are the first piece of information required in order to enable this analysis. Ungauged catchments located within data-scarce regions lack long records of streamflow data. In this dissertation, a multi-objective framework named Streamflow Prediction under Extreme Data-scarcity (SPED) is proposed for streamflow prediction in ungauged catchments located within large-scale regions of minimal hydrometeorologic observation. Multi-objective nature of SPED allows for balancing runoff efficiency with selection of parameter values that resemble catchment physical characteristics. Uncertain and low-resolution information are incorporated in SPED as soft data along with sparse observations. SPED application in two catchments in southwestern China indicates high runoff efficiency for predictions and good estimation of soil moisture capacity in the catchments. SPED is then slightly modified and tested more comprehensively by application to six catchments with diverse hydroclimatic conditions. SPED performance proves satisfactory where traditional flow prediction approaches fail. SPED also proves comparable or even better than data-intensive approaches. Utility of SPED versus a simpler catchment similarity model for the study of flow regime alteration is pursued next by streamflow prediction in 32 rivers in southwestern China. The results indicate that diversion adversely alters the flow regime of the rivers while direction and pattern of change remain the same regardless of the flow prediction method of choice. However, the results based on SPED consistently indicate more substantial alterations to the flow regime of the rivers after diversion. Finally, the value added by a limited number of streamflow observations to improvement of predictions in an ungauged catchment located within a data-scarce region is studied. The large number of test scenarios indicate that there may be very few near-universal schemes to improve flow predictions in such catchments.
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Date Issued: 2019
Identifier: CFE0007426, ucf:52713
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007426

Title: EXPLOITING OPPONENT MODELING FOR LEARNING IN MULTI-AGENT ADVERSARIAL GAMES.
Creator: Laviers, Kennard, Sukthankar, Gita, University of Central Florida
Abstract / Description: An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent's actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of...
Show moreAn issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent's actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players' physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics.
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Date Issued: 2011
Identifier: CFE0003914, ucf:48720
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003914

Title: Theoretical Studies of Nanostructure Formation and Transport on Surfaces.
Creator: Aminpour, Maral, Rahman, Talat, Stolbov, Sergey, Roldan Cuenya, Beatriz, Blair, Richard, University of Central Florida
Abstract / Description: This dissertation undertakes theoretical and computational research to characterize and understand in detail atomic configurations and electronic structural properties of surfaces and interfaces at the nano-scale, with particular emphasis on identifying the factors that control atomic-scale diffusion and transport properties. The overarching goal is to outline, with examples, a predictive modeling procedure of stable structures of novel materials that, on the one hand, facilitates a better...
Show moreThis dissertation undertakes theoretical and computational research to characterize and understand in detail atomic configurations and electronic structural properties of surfaces and interfaces at the nano-scale, with particular emphasis on identifying the factors that control atomic-scale diffusion and transport properties. The overarching goal is to outline, with examples, a predictive modeling procedure of stable structures of novel materials that, on the one hand, facilitates a better understanding of experimental results, and on the other hand, provide guidelines for future experimental work. The results of this dissertation are useful in future miniaturization of electronic devices, predicting and engineering functional novel nanostructures. A variety of theoretical and computational tools with different degrees of accuracy is used to study problems in different time and length scales. Interactions between the atoms are derived using both ab-initio methods based on Density Functional Theory (DFT), as well as semi-empirical approaches such as those embodied in the Embedded Atom Method (EAM), depending on the scale of the problem at hand. The energetics for a variety of surface phenomena (adsorption, desorption, diffusion, and reactions) are calculated using either DFT or EAM, as feasible. For simulating dynamic processes such as diffusion of ad-atoms on surfaces with dislocations the Molecular Dynamics (MD) method is applied. To calculate vibrational mode frequencies, the infinitesimal displacement method is employed. The combination of non-equilibrium Green's function (NEGF) and DFT is used to calculate electronic transport properties of molecular devices as well as interfaces and junctions.
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Date Issued: 2013
Identifier: CFE0005298, ucf:50504
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005298

Title: Structural Identification through Monitoring, Modeling and Predictive Analysis under Uncertainty.
Creator: Gokce, Hasan, Catbas, Fikret, Chopra, Manoj, Mackie, Kevin, Yun, Hae-Bum, DeMara, Ronald, University of Central Florida
Abstract / Description: Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior...
Show moreBridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure's response and performance in a probabilistic manner.The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric St-Id approach is employed without the use of an analytical model. The new methodology, which is successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels.
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Date Issued: 2012
Identifier: CFE0004232, ucf:48997
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004232

Title: MODELING SCENES AND HUMAN ACTIVITIES IN VIDEOS.
Creator: Basharat, Arslan, Shah, Mubarak, University of Central Florida
Abstract / Description: In this dissertation, we address the problem of understanding human activities in videos by developing a two-pronged approach: coarse level modeling of scene activities and fine level modeling of individual activities. At the coarse level, where the resolution of the video is low, we rely on person tracks. At the fine level, richer features are available to identify different parts of the human body, therefore we rely on the body joint tracks. There are three main goals of this dissertation: ...
Show moreIn this dissertation, we address the problem of understanding human activities in videos by developing a two-pronged approach: coarse level modeling of scene activities and fine level modeling of individual activities. At the coarse level, where the resolution of the video is low, we rely on person tracks. At the fine level, richer features are available to identify different parts of the human body, therefore we rely on the body joint tracks. There are three main goals of this dissertation: (1) identify unusual activities at the coarse level, (2) recognize different activities at the fine level, and (3) predict the behavior for synthesizing and tracking activities at the fine level. The first goal is addressed by modeling activities at the coarse level through two novel and complementing approaches. The first approach learns the behavior of individuals by capturing the patterns of motion and size of objects in a compact model. Probability density function (pdf) at each pixel is modeled as a multivariate Gaussian Mixture Model (GMM), which is learnt using unsupervised expectation maximization (EM). In contrast, the second approach learns the interaction of object pairs concurrently present in the scene. This can be useful in detecting more complex activities than those modeled by the first approach. We use a 14-dimensional Kernel Density Estimation (KDE) that captures motion and size of concurrently tracked objects. The proposed models have been successfully used to automatically detect activities like unusual person drop-off and pickup, jaywalking, etc. The second and third goals of modeling human activities at the fine level are addressed by employing concepts from theory of chaos and non-linear dynamical systems. We show that the proposed model is useful for recognition and prediction of the underlying dynamics of human activities. We treat the trajectories of human body joints as the observed time series generated from an underlying dynamical system. The observed data is used to reconstruct a phase (or state) space of appropriate dimension by employing the delay-embedding technique. This transformation is performed without assuming an exact model of the underlying dynamics and provides a characteristic representation that will prove to be vital for recognition and prediction tasks. For recognition, properties of phase space are captured in terms of dynamical and metric invariants, which include the Lyapunov exponent, correlation integral, and correlation dimension. A composite feature vector containing these invariants represents the action and will be used for classification. For prediction, kernel regression is used in the phase space to compute predictions with a specified initial condition. This approach has the advantage of modeling dynamics without making any assumptions about the exact form (polynomial, radial basis, etc.) of the mapping function. We demonstrate the utility of these predictions for human activity synthesis and tracking.
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Date Issued: 2009
Identifier: CFE0002897, ucf:48042
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002897

Title: DATA MINING METHODS FOR MALWARE DETECTION.
Creator: Siddiqui, Muazzam, Wang, Morgan, University of Central Florida
Abstract / Description: This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to...
Show moreThis research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares.
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Date Issued: 2008
Identifier: CFE0002303, ucf:47870
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002303

Title: Predictive Modeling of Functional Materials for Catalytic and Sensor Applications.
Creator: Rawal, Takat, Rahman, Talat, Chang, Zenghu, Leuenberger, Michael, Zou, Shengli, University of Central Florida
Abstract / Description: The research conducted in my dissertation focuses on theoretical and computational studies of the electronic and geometrical structures, and the catalytic and optical properties of functional materials in the form of nano-structures, extended surfaces, two-dimensional systems and hybrid structures. The fundamental aspect of my research is to predict nanomaterial properties through ab-initio calculations using methods such as quantum mechanical density functional theory (DFT) and kinetic Monte...
Show moreThe research conducted in my dissertation focuses on theoretical and computational studies of the electronic and geometrical structures, and the catalytic and optical properties of functional materials in the form of nano-structures, extended surfaces, two-dimensional systems and hybrid structures. The fundamental aspect of my research is to predict nanomaterial properties through ab-initio calculations using methods such as quantum mechanical density functional theory (DFT) and kinetic Monte Carlo simulation, which help rationalize experimental observations, and ultimately lead to the rational design of materials for the electronic and energy-related applications. Focusing on the popular single-layer MoS2, I first show how its hybrid structure with 29-atom transition metal nanoparticles (M29 where M=Cu, Ag, and Au) can lead to composite catalysts suitable for oxidation reactions. Interestingly, the effect is found to be most pronounced for Au29 when MoS2 is defect-laden (S vacancy row). Second, I show that defect-laden MoS2 can be functionalized either by deposited Au nanoparticles or when supported on Cu(111) to serve as a cost-effective catalyst for methanol synthesis via CO hydrogenation reactions. The charge transfer and electronic structural changes in these sub systems lead to the presence of 'frontier' states near the Fermi level, making the systems catalytically active. Next, in the emerging area of single metal atom catalysis, I provide rationale for the viability of single Pd sites stabilized on ZnO(101 ?0) as the active sites for methanol partial oxidation, an important reaction for the production of H2. We trace its excellent activity to the modified electronic structure of the single Pd site as well as neighboring Zn cationic sites. With the DFT-calculated activation energy barriers for a large set of reactions, we perform ab-initio kMC simulations to determine the selectivity of the products (CO2 and H2). These findings offer an opportunity for maximizing the efficiency of precious metal atoms, and optimizing their activity and selectivity (for desired products). In related work on extended surfaces while trying to explain the Scanning Tunneling Microscopy images observed by our experimental collaborators, I discovered a new mechanism involved in the process of Ag vacancy formation on Ag(110), in the presence of O atoms which leads to the reconstruction and eventually oxidation of the Ag surface. In a similar vein, I was able to propose a mechanism for the orange photoluminescence (PL), observed by our experimental collaborators, of a coupled system of benzylpiperazine (BZP) molecule and iodine on a copper surface. Our results show that the adsorbed BZP and iodine play complimentary roles in producing the PL in the visible range. Upon photo-excitation of the BZP-I/CuI(111) system, excited electrons are transferred into the conduction band (CB) of CuI, and holes are trapped by the adatoms. The relaxation of holes into BZP HOMO is facilitated by its realignment. Relaxed holes subsequently recombine with excited electrons in the CB of the CuI film, thus producing a luminescence peak at ~2.1 eV. These results can be useful for forensic applications in detecting illicit substances.
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Date Issued: 2017
Identifier: CFE0006783, ucf:51813
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006783

Title: CMOS RF CITUITS VARIABILITY AND RELIABILITY RESILIENT DESIGN, MODELING, AND SIMULATION.
Creator: Liu, Yidong, Yuan, Jiann-Shiun, University of Central Florida
Abstract / Description: The work presents a novel voltage biasing design that helps the CMOS RF circuits resilient to variability and reliability. The biasing scheme provides resilience through the threshold voltage (VT) adjustment, and at the mean time it does not degrade the PA performance. Analytical equations are established for sensitivity of the resilient biasing under various scenarios. Power Amplifier (PA) and Low Noise Amplifier (LNA) are investigated case by case through modeling and experiment. PTM 65nm...
Show moreThe work presents a novel voltage biasing design that helps the CMOS RF circuits resilient to variability and reliability. The biasing scheme provides resilience through the threshold voltage (VT) adjustment, and at the mean time it does not degrade the PA performance. Analytical equations are established for sensitivity of the resilient biasing under various scenarios. Power Amplifier (PA) and Low Noise Amplifier (LNA) are investigated case by case through modeling and experiment. PTM 65nm technology is adopted in modeling the transistors within these RF blocks. A traditional class-AB PA with resilient design is compared the same PA without such design in PTM 65nm technology. Analytical equations are established for sensitivity of the resilient biasing under various scenarios. A traditional class-AB PA with resilient design is compared the same PA without such design in PTM 65nm technology. The results show that the biasing design helps improve the robustness of the PA in terms of linear gain, P1dB, Psat, and power added efficiency (PAE). Except for post-fabrication calibration capability, the design reduces the majority performance sensitivity of PA by 50% when subjected to threshold voltage (VT) shift and 25% to electron mobility (¼n) degradation. The impact of degradation mismatches is also investigated. It is observed that the accelerated aging of MOS transistor in the biasing circuit will further reduce the sensitivity of PA. In the study of LNA, a 24 GHz narrow band cascade LNA with adaptive biasing scheme under various aging rate is compared to LNA without such biasing scheme. The modeling and simulation results show that the adaptive substrate biasing reduces the sensitivity of noise figure and minimum noise figure subject to process variation and device aging such as threshold voltage shift and electron mobility degradation. Simulation of different aging rate also shows that the sensitivity of LNA is further reduced with the accelerated aging of the biasing circuit. Thus, for majority RF transceiver circuits, the adaptive body biasing scheme provides overall performance resilience to the device reliability induced degradation. Also the tuning ability designed in RF PA and LNA provides the circuit post-process calibration capability.
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Date Issued: 2011
Identifier: CFE0003595, ucf:48861
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003595

Predictive Modeling (2)	+ -
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