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Title: Security of Autonomous Systems under Physical Attacks: With application to Self-Driving Cars.
Creator: Dutta, Raj, Jin, Yier, Sundaram, Kalpathy, DeMara, Ronald, Zhang, Shaojie, Zhang, Teng, University of Central Florida
Abstract / Description: The drive to achieve trustworthy autonomous cyber-physical systems (CPS), which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for fulfilling these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system,...
Show moreThe drive to achieve trustworthy autonomous cyber-physical systems (CPS), which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for fulfilling these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system, uncertainties can arise from the operating environment, adversarial attacks, and from within the system. As a result of these concerns, human-beings lack trust in these systems and hesitate to use them for day-to-day use.In this dissertation, we develop algorithms to enhance trust by mitigating physical attacks targeting the integrity and security of sensing units of autonomous CPS. The sensors of these systems are responsible for gathering data of the physical processes. Lack of measures for securing their information can enable malicious attackers to cause life-threatening situations. This serves as a motivation for developing attack resilient solutions.Among various security solutions, attention has been recently paid toward developing system-level countermeasures for CPS whose sensor measurements are corrupted by an attacker. Our methods are along this direction as we develop an active and multiple passive algorithm to detect the attack and minimize its effect on the internal state estimates of the system. In the active approach, we leverage a challenge authentication technique for detection of two types of attacks: The Denial of Service (DoS) and the delay injection on active sensors of the systems. Furthermore, we develop a recursive least square estimator for recovery of system from attacks. The majority of the dissertation focuses on designing passive approaches for sensor attacks. In the first method, we focus on a linear stochastic system with multiple sensors, where measurements are fused in a central unit to estimate the state of the CPS. By leveraging Bayesian interpretation of the Kalman filter and combining it with the Chi-Squared detector, we recursively estimate states within an error bound and detect the DoS and False Data Injection attacks. We also analyze the asymptotic performance of the estimator and provide conditions for resilience of the state estimate.Next, we propose a novel distributed estimator based on l1 norm optimization, which could recursively estimate states within an error bound without restricting the number of agents of the distributed system that can be compromised. We also extend this estimator to a vehicle platoon scenario which is subjected to sparse attacks. Furthermore, we analyze the resiliency and asymptotic properties of both the estimators. Finally, at the end of the dissertation, we make an initial effort to formally verify the control system of the autonomous CPS using the statistical model checking method. It is done to ensure that a real-time and resource constrained system such as a self-driving car, with controllers and security solutions, adheres to strict timing constrains.
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Date Issued: 2018
Identifier: CFE0007174, ucf:52253
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007174

Title: Analysis, Design and Efficiency Optimization of Power Converters for Renewable Energy Applications.
Creator: Chen, Xi, Batarseh, Issa, Zhou, Qun, Mikhael, Wasfy, Sun, Wei, Kutkut, Nasser, University of Central Florida
Abstract / Description: DC-DC power converters are widely used in renewable energy-based power generation systems due to the constant demand of high-power density and high-power conversion efficiency. DC-DC converters can be classified into non-isolated and isolated topologies. For non-isolated topologies, they are typically derived from buck, boost, buck-boost or forth order (such as Cuk, Sepic and Zeta) converters and they usually have relatively higher conversion efficiency than isolated topologies. However, with...
Show moreDC-DC power converters are widely used in renewable energy-based power generation systems due to the constant demand of high-power density and high-power conversion efficiency. DC-DC converters can be classified into non-isolated and isolated topologies. For non-isolated topologies, they are typically derived from buck, boost, buck-boost or forth order (such as Cuk, Sepic and Zeta) converters and they usually have relatively higher conversion efficiency than isolated topologies. However, with the applications where the isolation is required, either these topologies should be modified, or alternative topologies are needed. Among various isolated DC-DC converters, the LLC resonant converter is an attractive selection due to its soft switching, isolation, wide gain range, high reliability, high power density and high conversion efficiency.In low power applications, such as battery chargers and solar microinverters, increasing the switching frequency can reduce the size of passive components and reduce the current ripple and root-mean-square (RMS) current, resulting in higher power density and lower conduction loss. However, switching losses, gate driving loss and electromagnetic interference (EMI) may increase as a consequence of higher switching frequency. Therefore, switching frequency modulation, components optimization and soft switching techniques have been proposed to overcome these issues and achieve a tradeoff to reach the maximum conversion efficiency.This dissertation can be divided into two categories: the first part is focusing on the well-known non-isolated bidirectional cascaded-buck-boost converter, and the second part is concentrating on the isolated dual-input single resonant tank LLC converter. Several optimization approaches have been presented to improve the efficiency, power density and reliability of the power converters. In the first part, an adaptive switching frequency modulation technique has been proposed based on the precise loss model in this dissertation to increase the efficiency of the cascaded-buck-boost converter. In adaptive switching frequency modulation technique, the optimal switching frequency for the cascaded-buck-boost converter is adaptively selected to achieve the minimum total power loss. In addition, due to the major power losses coming from the inductor, a new low profile nanocrystalline inductor filled with copper foil has been designed to significantly reduce the core loss and winding loss. To further improve the efficiency of the cascaded-buck-boost converter, the adaptive switching frequency modulation technique has been applied on the converter with designed nanocrystalline inductor, in which the peak efficiency of the converter can break the 99% bottleneck.In the second part, a novel dual-input DC-DC converter is developed according to the LLC resonant topology. This design concept minimizes the circuit components by allowing single resonant tank to interface with multiple input sources. Based on different applications, the circuit configuration for the dual-input LLC converter will be a little different. In order to improve the efficiency of the dual-input LLC converter, the semi-active rectifiers have been used on the transformer secondary side to replace the low-side bridge diodes. In this case, higher magnetizing inductance can be selected while maintaining the same voltage gain. Besides, a burst-mode control strategy has been proposed to improve the light load and very light load efficiency of the dual- input LLC converter. This control strategy is able to be readily implemented on any power converter since it can be achieved directly through firmware and no circuit modification is needed in implementation of this strategy.
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Date Issued: 2019
Identifier: CFE0007612, ucf:52531
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007612

Title: Evaluation of an On-Line Device to Monitor Scale Formation in a Brackish Water Reverse Osmosis Membrane Process.
Creator: Roque, Jennifer, Duranceau, Steven, Randall, Andrew, Zhang, Husen, University of Central Florida
Abstract / Description: A modified two-element membrane pressure vessel assembly has been used to monitor process operational changes in a full-scale reverse osmosis (RO) water treatment plant (WTP). This study evaluated the effectiveness of the assembly as an on-line monitoring device intended to detect scale formation conditions when connected to an operating RO process train. This study was implemented to support the requirements of a larger University of Central Florida (UCF) research project ongoing at the city...
Show moreA modified two-element membrane pressure vessel assembly has been used to monitor process operational changes in a full-scale reverse osmosis (RO) water treatment plant (WTP). This study evaluated the effectiveness of the assembly as an on-line monitoring device intended to detect scale formation conditions when connected to an operating RO process train. This study was implemented to support the requirements of a larger University of Central Florida (UCF) research project ongoing at the city of Sarasota's Public Works and Utilities (City) water treatment facilities located in Sarasota, Florida. During the time-frame of this study, the City was in the process of eliminating their sulfuric acid feed from the pretreatment system of their existing 4.5 million gallon per day (MGD) RO membrane process. The City was motivated to eliminate its dependence on sulfuric acid to reduce operating costs as well as reduce operation health and safety risks associated with the use of the acid as a pretreatment chemical. Because the City was concerned with secondary process impacts associated with acid elimination, additional measures were desired in order to protect the full-scale process.This thesis reports on the design, fabrication and installation of a third-stage two membrane element pressure vessel (")canary(") sentinel monitoring device (Canary), its effectiveness as an on-line scaling monitor during full-scale acid elimination, and presents the results of the study. The Canary sentinel device was controlled using the normalized specific flux of the two membrane elements fed by a portion of the second stage concentrate of one of the City's full-scale RO process skids. Although the Canary demonstrated the ability to detect changes in an RO process operation, scaling did not occur under the conditions evaluated in this study. An autopsy of one of the Canary elements revealed that no scaling had occurred during the acid elimination process. Therefore, the Canary was found to be useful in its function as a sentinel, even though no scaling was detected by the device after acid elimination at the City's full-scale plant had been accomplished.
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Date Issued: 2012
Identifier: CFE0004433, ucf:49353
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004433

Title: THREE ESSAYS ON DIFFERENTIAL GAMES AND RESOURCE ECONOMICS.
Creator: Ling, Chen, Caputo, Michael, University of Central Florida
Abstract / Description: This dissertation consists of three chapters on the topic of differential games and resource economics. The first chapter extends the envelope theorem to the class of discounted infinite horizon differential games that posses locally differentiable Nash equilibria. The theorems cover both the open-loop and feedback information structures, and are applied to a simple analytically solvable linear-quadratic game. The results show that the conventional interpretation of the costate variable as...
Show moreThis dissertation consists of three chapters on the topic of differential games and resource economics. The first chapter extends the envelope theorem to the class of discounted infinite horizon differential games that posses locally differentiable Nash equilibria. The theorems cover both the open-loop and feedback information structures, and are applied to a simple analytically solvable linear-quadratic game. The results show that the conventional interpretation of the costate variable as the shadow value of the state variable along the equilibrium path is only valid for feedback Nash equilibria, but not for open-loop Nash equilibria. The specific linear-quadratic structure provides some extra insights on the theorem. For example, the costate variable is shown to uniformly overestimate the shadow value of the state variable in the open-loop case, but the growth rate of the costate variable are the same as the shadow value under open-loop and feedback information structures. Chapter two investigates the qualitative properties of symmetric open-loop Nash equilibria for a ubiquitous class of discounted infinite horizon differential games. The results show that the specific functional forms and the value of parameters used in the game are crucial in determining the local asymptotic stability of steady state, the steady state comparative statics, and the local comparative dynamics. Several sufficient conditions are provided to identify a local saddle point type of steady state. An important steady state policy implication from the model is that functional forms and parameter values are not only quantitatively important to differentiate policy tools, but they are also qualitatively important. Chapter three shifts the interests to the lottery mechanism for rationing public resources. It characterizes the optimal pricing strategies of lotteries for a welfare-maximization agency. The optimal prices are shown to be positive for a wide range of individual private value distributions, suggesting that the sub-optimal pricing may result in a significant efficiency loss and that the earlier studies under zero-pricing may need to be re-examined. In addition, I identify the revenue and welfare equivalency propositions across lottery institutions. Finally, the numerical simulations strongly support the findings.
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Date Issued: 2010
Identifier: CFE0003195, ucf:48752
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003195

Title: Solving Constraint Satisfaction Problems with Matrix Product States.
Creator: Pelton, Sabine, Mucciolo, Eduardo, Ishigami, Masa, Leuenberger, Michael, University of Central Florida
Abstract / Description: In the past decade, Matrix Product State (MPS) algorithms have emerged as an efficient method of modeling some many-body quantum spin systems. Since spin system Hamiltonians can be considered constraint satisfaction problems (CSPs), it follows that MPS should provide a versatile framework for studying a variety of general CSPs. In this thesis, we apply MPS to two types of CSP. First, use MPS to simulate adiabatic quantum computation (AQC), where the target Hamiltonians are instances of a...
Show moreIn the past decade, Matrix Product State (MPS) algorithms have emerged as an efficient method of modeling some many-body quantum spin systems. Since spin system Hamiltonians can be considered constraint satisfaction problems (CSPs), it follows that MPS should provide a versatile framework for studying a variety of general CSPs. In this thesis, we apply MPS to two types of CSP. First, use MPS to simulate adiabatic quantum computation (AQC), where the target Hamiltonians are instances of a fully connected, random Ising spin glass. Results of the simulations help shed light on why AQC fails for some optimization problems. We then present the novel application of a modified MPS algorithm to classical Boolean satisfiability problems, specifically k-SAT and max k-SAT. By construction, the algorithm also counts solutions to a given Boolean formula (\#-SAT). For easy satisfiable instances, the method is more expensive than other existing algorithms; however, for hard and unsatisfiable instances, the method succeeds in finding satisfying assignments where other algorithms fail to converge.
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Date Issued: 2017
Identifier: CFE0006902, ucf:51713
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006902

Title: Energy Efficient and Secure Wireless Sensor Networks Design.
Creator: Attiah, Afraa, Zou, Changchun, Chatterjee, Mainak, Wang, Jun, Yuksel, Murat, Wang, Chung-Ching, University of Central Florida
Abstract / Description: ABSTRACTWireless Sensor Networks (WSNs) are emerging technologies that have the ability to sense,process, communicate, and transmit information to a destination, and they are expected to have significantimpact on the efficiency of many applications in various fields. The resource constraintsuch as limited battery power, is the greatest challenge in WSNs design as it affects the lifetimeand performance of the network. An energy efficient, secure, and trustworthy system is vital whena WSN...
Show moreABSTRACTWireless Sensor Networks (WSNs) are emerging technologies that have the ability to sense,process, communicate, and transmit information to a destination, and they are expected to have significantimpact on the efficiency of many applications in various fields. The resource constraintsuch as limited battery power, is the greatest challenge in WSNs design as it affects the lifetimeand performance of the network. An energy efficient, secure, and trustworthy system is vital whena WSN involves highly sensitive information. Thus, it is critical to design mechanisms that are energyefficient and secure while at the same time maintaining the desired level of quality of service.Inspired by these challenges, this dissertation is dedicated to exploiting optimization and gametheoretic approaches/solutions to handle several important issues in WSN communication, includingenergy efficiency, latency, congestion, dynamic traffic load, and security. We present severalnovel mechanisms to improve the security and energy efficiency of WSNs. Two new schemes areproposed for the network layer stack to achieve the following: (a) to enhance energy efficiencythrough optimized sleep intervals, that also considers the underlying dynamic traffic load and (b)to develop the routing protocol in order to handle wasted energy, congestion, and clustering. Wealso propose efficient routing and energy-efficient clustering algorithms based on optimization andgame theory. Furthermore, we propose a dynamic game theoretic framework (i.e., hyper defense)to analyze the interactions between attacker and defender as a non-cooperative security game thatconsiders the resource limitation. All the proposed schemes are validated by extensive experimentalanalyses, obtained by running simulations depicting various situations in WSNs in orderto represent real-world scenarios as realistically as possible. The results show that the proposedschemes achieve high performance in different terms, such as network lifetime, compared with thestate-of-the-art schemes.
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Date Issued: 2018
Identifier: CFE0006971, ucf:51672
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006971

Title: Integrating the macroscopic and microscopic traffic safety analysis using hierarchical models.
Creator: Cai, Qing, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Lee, JaeYoung, Yan, Xin, University of Central Florida
Abstract / Description: Crash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so...
Show moreCrash frequency analysis is a crucial tool to investigate traffic safety problems. With the objective of revealing hazardous factors which would affect crash occurrence, crash frequency analysis has been undertaken at the macroscopic and microscopic levels. At the macroscopic level, crashes from a spatial aggregation (such as traffic analysis zone or county) are considered to quantify the impacts of socioeconomic and demographic characteristics, transportation demand and network attributes so as to provide countermeasures from a planning perspective. On the other hand, the microscopic crashes on a segment or intersection are analyzed to identify the influence of geometric design, lighting and traffic flow characteristics with the objective of offering engineering solutions (such as installing sidewalk and bike lane, adding lighting). Although numerous traffic safety studies have been conducted, still there are critical limitations at both levels. In this dissertation, several methodologies have been proposed to alleviate several limitations in the macro- and micro-level safety research. Then, an innovative method has been suggested to analyze crashes at the two levels, simultaneously. At the macro-level, the viability of dual-state models (i.e., zero-inflated and hurdle models) were explored for traffic analysis zone based pedestrian and bicycle crash analysis. Additionally, spatial spillover effects were explored in the models by employing exogenous variables from neighboring zones. Both conventional single-state model (i.e., negative binomial) and dual-state models such as zero-inflated negative binomial and hurdle negative binomial models with and without spatial effects were developed. The model comparison results for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency. Then, the modifiable areal unit problem for macro-level crash analysis was discussed. Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), traffic analysis zones (TAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) were developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposed a method to compare the modeling performance of the three types of geographic units at different spatial configuration through a grid based framework. Specifically, the study region was partitioned to grids of various sizes and the model prediction accuracy of the various macro models was considered within these grids of various sizes. These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperformed the ones that do not consider it. Finally, based on the modeling results, it is recommended to adopt TADs for transportation safety planning.After determining the optimal traffic safety analysis zonal system, further analysis was conducted for non-motorist crashes (pedestrian and bicycle crashes). This study contributed to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we converted the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulated a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model was estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model was also estimated and compared with the joint model. The results indicated that the joint model provides better data fit and could identify more significant variables. Subsequently, a novel joint screening method was suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes were identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. At the microscopic level, crash modeling analysis was conducted for road facilities. This study, first, explored the potential macro-level effects which are always excluded or omitted in the previous studies. A Bayesian hierarchical model was proposed to analyze crashes on segments and intersection incorporating the macro-level data, which included both explanatory variables and total crashes of all segments and intersections. Besides, a joint modeling structure was adopted to consider the potentially spatial autocorrelation between segments and their connected intersections. The proposed model was compared with three other models: a model considering micro-level factors only, one hierarchical model considering macro-level effects with random terms only, and one hierarchical model considering macro-level effects with explanatory variables. The results indicated that models considering macro-level effects outperformed the model having micro-level factors only, which supports the idea to consider macro-level effects for micro-level crash analysis. Besides, the micro-level models were even further enhanced by the proposed model. Finally, significant spatial correlation could be found between segments and their adjacent intersections, supporting the employment of the joint modeling structure to analyze crashes at various types of road facilities. In addition to the separated analysis at either the macro- or micro-level, an integrated approach has been proposed to examine traffic safety problems at the two levels, simultaneously. If conducted in the same study area, the macro- and micro-level crash analyses should investigate the same crashes but aggregating the crashes at different levels. Hence, the crash counts at the two levels should be correlated and integrating macro- and micro-level crash frequency analyses in one modeling structure might have the ability to better explain crash occurrence by realizing the effects of both macro- and micro-level factors. This study proposed a Bayesian integrated spatial crash frequency model, which linked the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considered the spatial autocorrelation of different types of road facilities (i.e., segments and intersections) at the micro-level with a joint modeling structure. Two independent non-integrated models for macro- and micro-levels were also estimated separately and compared with the integrated model. The results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model provides more valuable insights about the crash occurrence at the two levels by revealing both macro- and micro-level factors. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels. It is expected that the proposed integrated model and hotspot identification method can help practitioners implement more reasonable transportation safety plans and more effective engineering treatments to proactively enhance safety.
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Date Issued: 2017
Identifier: CFE0006724, ucf:51891
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006724

Title: A Hybrid Constitutive Model For Creep, Fatigue, And Creep-Fatigue Damage.
Creator: Stewart, Calvin, Gordon, Ali, Nicholson, David, Moslehy, Faissal, University of Central Florida
Abstract / Description: In the combustion zone of industrial- and aero- gas turbines, thermomechanical fatigue (TMF) is the dominant damage mechanism. Thermomechanical fatigue is a coupling of independent creep, fatigue, and oxidation damage mechanisms that interact and accelerate microstructural degradation. A mixture of intergranular cracking due to creep, transgranular cracking due to fatigue, and surface embrittlement due to oxidation is often observed in gas turbine components removed from service. The current...
Show moreIn the combustion zone of industrial- and aero- gas turbines, thermomechanical fatigue (TMF) is the dominant damage mechanism. Thermomechanical fatigue is a coupling of independent creep, fatigue, and oxidation damage mechanisms that interact and accelerate microstructural degradation. A mixture of intergranular cracking due to creep, transgranular cracking due to fatigue, and surface embrittlement due to oxidation is often observed in gas turbine components removed from service. The current maintenance scheme for gas turbines is to remove components from service when any criteria (elongation, stress-rupture, crack length, etc.) exceed the designed maximum allowable. Experimental, theoretical, and numerical analyses are performed to determine the state of the component as it relates to each criterion (a time consuming process). While calculating these metrics individually has been successful in the past, a better approach would be to develop a unified mechanical modeling that incorporates the constitutive response, microstructural degradation, and rupture of the subject material via a damage variable used to predict the cumulative (")damage state(") within a component. This would allow for a priori predictions of microstructural degradation, crack propagation/arrest, and component-level lifing. In this study, a unified mechanical model for creep-fatigue (deformation, cracking, and rupture) is proposed. It is hypothesized that damage quantification techniques can be used to develop accurate creep, fatigue, and plastic/ductile cumulative- nonlinear- damage laws within the continuum damage mechanics principle. These damage laws when coupled with appropriate constitutive equations and a degrading stiffness tensor can be used to predict the mechanical state of a component. A series of monotonic, creep, fatigue, and tensile-hold creep-fatigue tests are obtained from literature for 304 stainless steel at 600(&)deg;C (1112(&)deg;F) in an air. Cumulative- nonlinear- creep, fatigue, and a coupled creep-fatigue damage laws are developed. The individual damage variables are incorporated as an internal state variable within a novel unified viscoplasticity constitutive model (zero yield surface) and degrading stiffness tensor. These equations are implemented as a custom material model within a custom FORTRAN one-dimensional finite element code. The radial return mapping technique is used with the updated stress vector solved by Newton-Raphson iteration. A consistent tangent stiffness matrix is derived based on the inelastic strain increment. All available experimental data is compared to finite element results to determine the ability of the unified mechanical model to predict deformation, damage evolution, crack growth, and rupture under a creep-fatigue environment.
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Date Issued: 2013
Identifier: CFE0005061, ucf:49985
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005061

Title: OPTIMAL DETOUR PLANNING AROUND BLOCKED CONSTRUCTION ZONES.
Creator: Jardaneh , Mutasem, Khalafallah, Ahmed, University of Central Florida
Abstract / Description: Construction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their...
Show moreConstruction zones are traffic way areas where construction, maintenance or utility work is identified by warning signs, signals and indicators, including those on transport devices that mark the beginning and end of construction zones. Construction zones are among the most dangerous work areas, with workers facing workplace safety challenges that often lead to catastrophic injuries or fatalities. In addition, daily commuters are also impacted by construction zone detours that affect their safety and daily commute time. These problems represent major challenges to construction planners as they are required to plan vehicle routes around construction zones in such a way that maximizes the safety of construction workers and reduces the impact on daily commuters. This research aims at developing a framework for optimizing the planning of construction detours. The main objectives of the research are to first identify all the decision variables that affect the planning of construction detours and secondly, implement a model based on shortest path formulation to identify the optimal alternatives for construction detours. The ultimate goal of this research is to offer construction planners with the essential guidelines to improve construction safety and reduce construction zone hazards as well as a robust tool for selecting and optimizing construction zone detours.
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Date Issued: 2011
Identifier: CFE0003586, ucf:48900
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003586

Title: Microscopic Assessment of Transportation Emissions on Limited Access Highways.
Creator: Abou-Senna, Hatem, Radwan, Ahmed, Abdel-Aty, Mohamed, Al-Deek, Haitham, Cooper, Charles, Johnson, Mark, University of Central Florida
Abstract / Description: On-road vehicles are a major source of transportation carbon dioxide (CO2) greenhouse gas emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, e.g., carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). The need to accurately quantify transportation-related emissions from vehicles is essential. Transportation agencies and researchers in the past have...
Show moreOn-road vehicles are a major source of transportation carbon dioxide (CO2) greenhouse gas emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, e.g., carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). The need to accurately quantify transportation-related emissions from vehicles is essential. Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The new United States Environmental Protection Agency (USEPA) mobile source emissions model, MOVES2010a (MOVES) can estimate vehicle emissions on a second-by-second basis creating the opportunity to develop new software (")VIMIS 1.0(") (VISSIM/MOVES Integration Software) to facilitate the integration process. This research presents a microscopic examination of five key transportation parameters (traffic volume, speed, truck percentage, road grade and temperature) on a 10-mile stretch of Interstate 4 (I-4) test bed prototype; an urban limited access highway corridor in Orlando, Florida. The analysis was conducted utilizing VIMIS 1.0 and using an advanced custom design technique; D-Optimality and I-Optimality criteria, to identify active factors and to ensure precision in estimating the regression coefficients as well as the response variable.The analysis of the experiment identified the optimal settings of the key factors and resulted in the development of Micro-TEM (Microscopic Transportation Emissions Meta-Model). The main purpose of Micro-TEM is to serve as a substitute model for predicting transportation emissions on limited access highways to an acceptable degree of accuracy in lieu of running simulations using a traffic model and integrating the results in an emissions model. Furthermore, significant emission rate reductions were observed from the experiment on the modeled corridor especially for speeds between 55 and 60 mph while maintaining up to 80% and 90% of the freeway's capacity. However, vehicle activity characterization in terms of speed was shown to have a significant impact on the emission estimation approach.Four different approaches were further examined to capture the environmental impacts of vehicular operations on the modeled test bed prototype. First, (at the most basic level), emissions were estimated for the entire 10-mile section (")by hand(") using one average traffic volume and average speed. Then, three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link driving schedules (LDS), and second-by-second operating mode distributions (OPMODE). This research analyzed how the various approaches affect predicted emissions of CO, NOx, PM and CO2. The results demonstrated that obtaining accurate and comprehensive operating mode distributions on a second-by-second basis improves emission estimates. Specifically, emission rates were found to be highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, frequent braking/coasting and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach.Additionally, model applications and mitigation scenarios were examined on the modeled corridor to evaluate the environmental impacts in terms of vehicular emissions and at the same time validate the developed model (")Micro-TEM("). Mitigation scenarios included the future implementation of managed lanes (ML) along with the general use lanes (GUL) on the I-4 corridor, the currently implemented variable speed limits (VSL) scenario as well as a hypothetical restricted truck lane (RTL) scenario. Results of the mitigation scenarios showed an overall speed improvement on the corridor which resulted in overall reduction in emissions and emission rates when compared to the existing condition (EX) scenario and specifically on link by link basis for the RTL scenario.The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.
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Date Issued: 2012
Identifier: CFE0004777, ucf:49788
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004777

Title: Advanced Control Techniques for Efficiency and Power Density Improvement of a Three-Phase Microinverter.
Creator: Tayebi, Seyed Milad, Batarseh, Issa, Mikhael, Wasfy, Sundaram, Kalpathy, Sun, Wei, Kutkut, Nasser, University of Central Florida
Abstract / Description: Inverters are widely used in photovoltaic (PV) based power generation systems. Most of these systems have been based on medium to high power string inverters. Microinverters are gaining popularity over their string inverter counterparts in PV based power generation systems due to maximized energy harvesting, high system reliability, modularity, and simple installation. They can be deployed on commercial buildings, residential rooftops, electric poles, etc and have a huge potential market....
Show moreInverters are widely used in photovoltaic (PV) based power generation systems. Most of these systems have been based on medium to high power string inverters. Microinverters are gaining popularity over their string inverter counterparts in PV based power generation systems due to maximized energy harvesting, high system reliability, modularity, and simple installation. They can be deployed on commercial buildings, residential rooftops, electric poles, etc and have a huge potential market. Emerging trend in power electronics is to increase power density and efficiency while reducing cost. A powerful tool to achieve these objectives is the development of an advanced control system for power electronics. In low power applications such as solar microinverters, increasing the switching frequency can reduce the size of passive components resulting in higher power density. However, switching losses and electromagnetic interference (EMI) may increase as a consequence of higher switching frequency. Soft switching techniques have been proposed to overcome these issues. This dissertation presents several innovative control techniques which are used to increase efficiency and power density while reducing cost. Dynamic dead time optimization and dual zone modulation techniques have been proposed in this dissertation to significantly improve the microinverter efficiency. In dynamic dead time optimization technique, pulse width modulation (PWM) dead times are dynamically adjusted as a function of load current to minimize MOSFET body diode conduction time which reduces power dissipation. This control method also improves total harmonic distortion (THD) of the inverter output current. To further improve the microinverter efficiency, a dual-zone modulation has been proposed which introduces one more soft-switching transition and lower inductor peak current compared to the other boundary conduction mode (BCM) modulation methods.In addition, an advanced DC link voltage control has been proposed to increase the microinverter power density. This concept minimizes the storage capacitance by allowing greater voltage ripple on the DC link. Therefore, the microinverter reliability can be significantly increased by replacing electrolytic capacitors with film capacitors. These control techniques can be readily implemented on any inverter, motor controller, or switching power amplifier. Since there is no circuit modification involved in implementation of these control techniques and can be easily added to existing controller firmware, it will be very attractive to any potential licensees.
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Date Issued: 2017
Identifier: CFE0007136, ucf:52328
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007136

Title: Networking and security solutions for VANET initial deployment stage.
Creator: Aslam, Baber, Zou, Changchun, Turgut, Damla, Bassiouni, Mostafa, Wang, Chung-Ching, University of Central Florida
Abstract / Description: Vehicular ad hoc network (VANET) is a special case of mobile networks, where vehicles equipped with computing/communicating devices (called (")smart vehicles(")) are the mobile wireless nodes. However, the movement pattern of these mobile wireless nodes is no more random, as in case of mobile networks, rather it is restricted to roads and streets. Vehicular networks have hybrid architecture; it is a combination of both infrastructure and infrastructure-less architectures. The direct vehicle...
Show moreVehicular ad hoc network (VANET) is a special case of mobile networks, where vehicles equipped with computing/communicating devices (called (")smart vehicles(")) are the mobile wireless nodes. However, the movement pattern of these mobile wireless nodes is no more random, as in case of mobile networks, rather it is restricted to roads and streets. Vehicular networks have hybrid architecture; it is a combination of both infrastructure and infrastructure-less architectures. The direct vehicle to vehicle (V2V) communication is infrastructure-less or ad hoc in nature. Here the vehicles traveling within communication range of each other form an ad hoc network. On the other hand, the vehicle to infrastructure (V2I) communication has infrastructure architecture where vehicles connect to access points deployed along roads. These access points are known as road side units (RSUs) and vehicles communicate with other vehicles/wired nodes through these RSUs. To provide various services to vehicles, RSUs are generally connected to each other and to the Internet. The direct RSU to RSU communication is also referred as I2I communication. The success of VANET depends on the existence of pervasive roadside infrastructure and sufficient number of smart vehicles. Most VANET applications and services are based on either one or both of these requirements. A fully matured VANET will have pervasive roadside network and enough vehicle density to enable VANET applications. However, the initial deployment stage of VANET will be characterized by the lack of pervasive roadside infrastructure and low market penetration of smart vehicles. It will be economically infeasible to initially install a pervasive and fully networked roadside infrastructure, which could result in the failure of applications and services that depend on V2I or I2I communications. Further, low market penetration means there are insufficient number of smart vehicles to enable V2V communication, which could result in failure of services and applications that depend on V2V communications. Non-availability of pervasive connectivity to certification authorities and dynamic locations of each vehicle will make it difficult and expensive to implement security solutions that are based on some central certificate management authority. Non-availability of pervasive connectivity will also affect the backend connectivity of vehicles to the Internet or the rest of the world. Due to economic considerations, the installation of roadside infrastructure will take a long time and will be incremental thus resulting in a heterogeneous infrastructure with non-consistent capabilities. Similarly, smart vehicles will also have varying degree of capabilities. This will result in failure of applications and services that have very strict requirements on V2I or V2V communications. We have proposed several solutions to overcome the challenges described above that will be faced during the initial deployment stage of VANET. Specifically, we have proposed: 1) a VANET architecture that can provide services with limited number of heterogeneous roadside units and smart vehicles with varying capabilities, 2) a backend connectivity solution that provides connectivity between the Internet and smart vehicles without requiring pervasive roadside infrastructure or large number of smart vehicles, 3) a security architecture that does not depend on pervasive roadside infrastructure or a fully connected V2V network and fulfills all the security requirements, and 4) optimization solutions for placement of a limited number of RSUs within a given area to provide best possible service to smart vehicles. The optimal placement solutions cover both urban areas and highways environments.
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Date Issued: 2012
Identifier: CFE0004186, ucf:48993
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004186

Title: ANALYSIS OF THE FLORIDAÃ‚Â'S SHOWCASE GREEN ENVIROHOME WATER/WASTEWATER SYSTEMS AND DEVELOPMENT OF A COST-BENEFIT GREEN ROOF OPTIMIZATION MODEL.
Creator: Rivera, Brian, Chang, Ni-Bin, University of Central Florida
Abstract / Description: The Florida Showcase Green Envirohome (FSGE) incorporates many green technologies. FSGE is built to meet or exceed 12 green building guidelines and obtain 8 green building certificates. The two-story 3292 ft2 home is a Ã‚Â"Near Zero-Loss HomeÃ‚Â™Ã‚Â", Ã‚Â"Near Zero-Energy HomeÃ‚Â™Ã‚Â", "Near Zero-Runoff HomeÃ‚Â™", and Ã‚Â"Near Zero...
Show moreThe Florida Showcase Green Envirohome (FSGE) incorporates many green technologies. FSGE is built to meet or exceed 12 green building guidelines and obtain 8 green building certificates. The two-story 3292 ft2 home is a Ã‚Â"Near Zero-Loss HomeÃ‚Â™Ã‚Â", Ã‚Â"Near Zero-Energy HomeÃ‚Â™Ã‚Â", "Near Zero-Runoff HomeÃ‚Â™", and Ã‚Â"Near Zero-Maintenance HomeÃ‚Â™Ã‚Â". It is spawned from the consumer-driven necessity to build a home resistant to hurricanes, tornadoes, floods, fire, mold, termites, impacts, and even earthquakes given up to 500% increase in insurance premiums in natural disaster zones, the dwindling flexibility and coverage of insurance policies, and rising energy, water and maintenance costs (FSGE 2008). The FSGE captures its stormwater runoff from the green roof, metal roof and wood decking area and routes it to the sustainable water cistern. Graywater from the home (after being disinfected using ozone) is also routed to the sustainable water cistern. This water stored in the sustainable water cistern is used for irrigation of the green roof, ground level landscape, and for toilet flushing water. This study was done in two phases. During phase one, only stormwater runoff from the green roof, metal roof and wood decking area is routed to the sustainable water cistern. Then, during phase two, the water from the graywater system is added to the sustainable water cistern. The sustainable water cistern quality is analyzed during both phases to determine if the water is acceptable for irrigation and also if it is suitable for use as toilet flushing water. The water quality of the sustainable cistern is acceptable for irrigation. The intent of the home is to not pollute the environment, so as much nutrients as possible should be removed from the wastewater before it is discharged into the groundwater. Thus, the FSGE design is to evaluate a new on-site sewage treatment and disposal (OSTD) system which consists of a sorption media labeled as Bold and GoldTM filtration media. The Bold and GoldTM filtration media is a mixture of tire crumb and other materials. This new OSTD system has sampling ports through the system to monitor the wastewater quality as it passes through. Also, the effluent wastewater quality is compared to that of a conventional system on the campus of the University of Central Florida. The cost-benefit optimization model focused on designing a residential home which incorporated a green roof, cistern and graywater systems. This model had two forms, the base model and the grey linear model. The base model used current average cost of construction of materials and installation. The grey model used an interval for the cost of construction materials and green roof energy savings. Both models included a probabilistic term to describe the rainfall amount. The cost and energy operation of a typical Florida home was used as a case study for these models. Also, some of the parameters of the model were varied to determine their effect on the results. The modeling showed that the FSGE 4500 gallon cistern design was cost effective in providing irrigation water. Also, the green roof area could have been smaller to be cost effective, because the green roof cost is relatively much higher than the cost of a regular roof.
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Date Issued: 2010
Identifier: CFE0003297, ucf:48499
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003297

Title: Development of Regional Optimization and Market Penetration Models For the Electric Vehicles in the United States.
Creator: Noori, Mehdi, Tatari, Omer, Oloufa, Amr, Nam, Boo Hyun, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Since the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation...
Show moreSince the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation sector is to support alternative fuel technologies, including electric vehicles (EVs). As vehicles become electrified, the transportation fleet will rely on the electric grid as well as traditional transportation fuels for energy. The life cycle cost and environmental impacts of EVs are still very uncertain, but are nonetheless extremely important for making policy decisions. Moreover, the use of EVs will help to diversify the fuel mix and thereby reduce dependence on petroleum. In this respect, the United States has set a goal of a 20% share of EVs on U.S. roadways by 2030. However, there is also a considerable amount of uncertainty in the market share of EVs that must be taken into account. This dissertation aims to address these inherent uncertainties by presenting two new models: the Electric Vehicles Regional Optimizer (EVRO), and Electric Vehicle Regional Market Penetration (EVReMP). Using these two models, decision makers can predict the optimal combination of drivetrains and the market penetration of the EVs in different regions of the United States for the year 2030.First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different EV types (gasoline plug-in hybrid EVs, gasoline extended-range EVs, and all-electric EVs) are evaluated with their inherent uncertainties duly considered. Then, the environmental damage costs and water footprints of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties related to the life cycle costs, environmental damage costs, and water footprints of the studied vehicle types are modeled for different U.S. electricity grid regions. Next, an optimization model is used in conjunction with this Exploratory Modeling and Analysis method to find the ideal combination of different vehicle types in each U.S. region for the year 2030. Finally, an agent-based model is developed to identify the optimal market shares of the studied vehicles in each of 22 electric regions in the United States. The findings of this research will help policy makers and transportation planners to prepare our nation's transportation system for the future influx of EVs.The findings of this research indicate that the decision maker's point of view plays a vital role in selecting the optimal fleet array. While internal combustion engine vehicles have the lowest life cycle cost, the highest environmental damage cost, and a relatively low water footprint, they will not be a good choice in the future. On the other hand, although all-electric vehicles have a relatively low life cycle cost and the lowest environmental damage cost of the evaluated vehicle options, they also have the highest water footprint, so relying solely on all-electric vehicles is not an ideal choice either. Rather, the best fleet mix in 2030 will be an electrified fleet that relies on both electricity and gasoline. From the agent-based model results, a deviation is evident between the ideal fleet mix and that resulting from consumer behavior, in which EV shares increase dramatically by the year 2030 but only dominate 30 percent of the market. Therefore, government subsidies and the word-of-mouth effect will play a vital role in the future adoption of EVs.
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Date Issued: 2015
Identifier: CFE0005852, ucf:50927
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005852

Title: Assessment, Optimization, and Enhancement of Ultrafiltration (UF) Membrane Processes in Potable Water Treatment.
Creator: Boyd, Christopher, Duranceau, Steven, Cooper, Charles, Randall, Andrew, University of Central Florida
Abstract / Description: This dissertation reports on research related to ultrafiltration (UF) membranes in drinking water applications. A pilot-scale investigation identified seasonal surface water quality impacts on UF performance and resulted in the development of a dynamic chemically enhanced backwash protocol for fouling management. Subsequent analysis of UF process data revealed limitations with the use of specific flux, transmembrane pressure (TMP), and other normalization techniques for assessing UF process...
Show moreThis dissertation reports on research related to ultrafiltration (UF) membranes in drinking water applications. A pilot-scale investigation identified seasonal surface water quality impacts on UF performance and resulted in the development of a dynamic chemically enhanced backwash protocol for fouling management. Subsequent analysis of UF process data revealed limitations with the use of specific flux, transmembrane pressure (TMP), and other normalization techniques for assessing UF process fouling. A new TMP balance approach is presented that identifies the pressure contribution of membrane fouling and structural changes, enables direct process performance comparisons at different operating fluxes, and distinguishes between physically and chemically unresolved fouling. In addition to the TMP balance, a five component optimization approach is presented for the systematic improvement of UF processes on the basis of TMP variations. Terms are defined for assessing process event performance, a new process utilization term is presented to benchmark UF productivity, and new measures for evaluating maintenance procedures are discussed. Using these tools, a correlation between process utilization and operating pressures was established and a sustainable process utilization of 93.5% was achieved. UF process capabilities may be further enhanced by pre-coating media onto the membrane surface. Silicon dioxide (SiO2) and powdered activated carbon (PAC) are evaluated as pre-coating materials, and the applicability of the TMP balance for assessing pre-coated membrane performance is demonstrated. The first use of SiO2 as a support layer for PAC in a membrane pre-coating application is presented at the laboratory-scale. SiO2-PAC pre-coatings successfully reduced physically unresolved fouling and enhanced UF membrane organics removal capabilities.
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Date Issued: 2013
Identifier: CFE0005088, ucf:50758
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005088

Title: Spatial and Temporal Modeling for Human Activity Recognition from Multimodal Sequential Data.
Creator: Ye, Jun, Hua, Kien, Foroosh, Hassan, Zou, Changchun, Karwowski, Waldemar, University of Central Florida
Abstract / Description: Human Activity Recognition (HAR) has been an intense research area for more than a decade. Different sensors, ranging from 2D and 3D cameras to accelerometers, gyroscopes, and magnetometers, have been employed to generate multimodal signals to detect various human activities. With the advancement of sensing technology and the popularity of mobile devices, depth cameras and wearable devices, such as Microsoft Kinect and smart wristbands, open a unprecedented opportunity to solve the...
Show moreHuman Activity Recognition (HAR) has been an intense research area for more than a decade. Different sensors, ranging from 2D and 3D cameras to accelerometers, gyroscopes, and magnetometers, have been employed to generate multimodal signals to detect various human activities. With the advancement of sensing technology and the popularity of mobile devices, depth cameras and wearable devices, such as Microsoft Kinect and smart wristbands, open a unprecedented opportunity to solve the challenging HAR problem by learning expressive representations from the multimodal signals recording huge amounts of daily activities which comprise a rich set of categories.Although competitive performance has been reported, existing methods focus on the statistical or spatial representation of the human activity sequence;while the internal temporal dynamics of the human activity sequence arenot sufficiently exploited. As a result, they often face the challenge of recognizing visually similar activities composed of dynamic patterns in different temporal order. In addition, many model-driven methods based on sophisticated features and carefully-designed classifiers are computationally demanding and unable to scale to a large dataset. In this dissertation, we propose to address these challenges from three different perspectives; namely, 3D spatial relationship modeling, dynamic temporal quantization, and temporal order encoding.We propose a novel octree-based algorithm for computing the 3D spatial relationships between objects from a 3D point cloud captured by a Kinect sensor. A set of 26 3D spatial directions are defined to describe the spatial relationship of an object with respect to a reference object. These 3D directions are implemented as a set of spatial operators, such as "AboveSouthEast" and "BelowNorthWest," of an event query language to query human activities in an indoor environment; for example, "A person walks in the hallway from north to south." The performance is quantitatively evaluated in a public RGBD object dataset and qualitatively investigated in a live video computing platform.In order to address the challenge of temporal modeling in human action recognition, we introduce the dynamic temporal quantization, a clustering-like algorithm to quantize human action sequences of varied lengths into fixed-size quantized vectors. A two-step optimization algorithm is proposed to jointly optimize the quantization of the original sequence. In the aggregation step, frames falling into the sample segment are aggregated by max-polling and produce the quantized representation of the segment. During the assignment step, frame-segment assignment is updated according to dynamic time warping, while the temporal order of the entire sequence is preserved. The proposed technique is evaluated on three public 3D human action datasets and achieves state-of-the-art performance.Finally, we propose a novel temporal order encoding approach that models the temporal dynamics of the sequential data for human activity recognition. The algorithm encodes the temporal order of the latent patterns extracted by the subspace projection and generates a highly compact First-Take-All (FTA) feature vector representing the entire sequential data. An optimization algorithm is further introduced to learn the optimized projections in order to increase the discriminative power of the FTA feature. The compactness of the FTA feature makes it extremely efficient for human activity recognition with nearest neighbor search based on Hamming distance. Experimental results on two public human activity datasets demonstrate the advantages of the FTA feature over state-of-the-art methods in both accuracy and efficiency.
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Date Issued: 2016
Identifier: CFE0006516, ucf:51367
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006516

Title: Automated Hybrid Singularity Superposition and Anchored Grid Pattern BEM Algorithm for the Solution of the Inverse Geometric Problem.
Creator: Ni, Marcus, Kassab, Alain, Divo, Eduardo, Chopra, Manoj, University of Central Florida
Abstract / Description: A method for solving the inverse geometrical problem is presented by reconstructing the unknown subsurface cavity geometry using boundary element methods, a genetic algorithm, and Nelder-Mead non-linear simplex optimization. The heat conduction problem is solved utilizing the boundary element method, which calculates the difference between the measured temperature at the exposed surface and the computed temperature under the current update of the unknown subsurface flaws and cavities. In a...
Show moreA method for solving the inverse geometrical problem is presented by reconstructing the unknown subsurface cavity geometry using boundary element methods, a genetic algorithm, and Nelder-Mead non-linear simplex optimization. The heat conduction problem is solved utilizing the boundary element method, which calculates the difference between the measured temperature at the exposed surface and the computed temperature under the current update of the unknown subsurface flaws and cavities. In a first step, clusters of singularities are utilized to solve the inverse problem and to identify the location of the centroid(s) of the subsurface cavity(ies)/flaw(s). In a second step, the reconstruction of the estimated cavity(ies)/flaw(s) geometry(ies) is accomplished by utilizing an anchored grid pattern upon which cubic spline knots are restricted to move in the search for unknown geometry. Solution of the inverse problem is achieved using a genetic algorithm accelerated with the Nelder-Mead non-linear simplex. To optimize the cubic spline interpolated geometry, the flux (Neumann) boundary conditions are minimized using a least squares functional. The automated algorithm successfully reconstructs single and multiple subsurface cavities within two dimensional mediums. The solver is also shown to accurately predict cavity geometries with random noise in the boundary condition measurements. Subsurface cavities can be difficult to detect based on their location. By applying different boundary conditions to the same geometry, more information is supplied at the boundary, and the subsurface cavity is easily detected despite its low heat signature effect at the boundaries. Extensions to three-dimensional applications are outlined.
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Date Issued: 2013
Identifier: CFE0004900, ucf:49644
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004900

Title: On Distributed Estimation for Resource Constrained Wireless Sensor Networks.
Creator: Sani, Alireza, Vosoughi, Azadeh, Rahnavard, Nazanin, Wei, Lei, Atia, George, Chatterjee, Mainak, University of Central Florida
Abstract / Description: We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically...
Show moreWe study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression,dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly:1- Consider an inhomogeneous WSN where the sensors' observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidth-constrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multi-bit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE.2- Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multi-bit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closed-form. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian Cramer-Rao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound.As a dual problem we also answer the question: what is the minimum required bandwidth of thenetwork to satisfy a predetermined target MSE?3- Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as 'enhancement mode' of multiplicative noise. Through deriving different lower bounds, such as BCRLB,Weiss-Weinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. Wealso compare the performance of well-known estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes.
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Date Issued: 2017
Identifier: CFE0006913, ucf:51698
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006913

Title: Design and Implementation of PV-Firming and Optimization Algorithms For Three-Port Microinverters.
Creator: Alharbi, Mahmood, Batarseh, Issa, Haralambous, Michael, Mikhael, Wasfy, Yuan, Jiann-Shiun, Kutkut, Nasser, University of Central Florida
Abstract / Description: With the demand increase for electricity, the ever-increasing awareness of environmental issues, coupled with rolling blackouts, the role of renewable energy generation is increasing along with the thirst for electricity and awareness of environmental issues. This dissertation proposes the design and implementation of PV-firming and optimization algorithms for three-port microinverters.Novel strategies are proposed in Chapters 3 and 4 for harvesting stable solar power in spite of intermittent...
Show moreWith the demand increase for electricity, the ever-increasing awareness of environmental issues, coupled with rolling blackouts, the role of renewable energy generation is increasing along with the thirst for electricity and awareness of environmental issues. This dissertation proposes the design and implementation of PV-firming and optimization algorithms for three-port microinverters.Novel strategies are proposed in Chapters 3 and 4 for harvesting stable solar power in spite of intermittent solar irradiance. PV firming is implemented using a panel-level three-port grid-tied PV microinverter system instead of the traditional high-power energy storage and management system at the utility scale. The microinverter system consists of a flyback converter and an H-bridge inverter/rectifier, with a battery connected to the DC-link. The key to these strategies lies in using static and dynamic algorithms to generate a smooth PV reference power. The outcomes are applied to various control methods to charge/discharge the battery so that a stable power generation profile is obtained. In addition, frequency-based optimization for the inverter stage is presented.One of the design parameters of grid-tied single-phase H-bridge sinusoidal pulse-width modulation (SPWM) microinverters is switching frequency. The selection of the switching frequency is a tradeoff between improving the power quality by reducing the total harmonic distortion (THD), and improving the efficiency by reducing the switching loss. In Chapter 5, two algorithms are proposed for optimizing both the power quality and the efficiency of the microinverter. They do this by using a frequency tracking technique that requires no hardware modification. The first algorithm tracks the optimal switching frequency for maximum efficiency at a given THD value. The second maximizes the power quality of the H-bridge micro-inverter by tracking the switching frequency that corresponds to the minimum THD.Real-time PV intermittency and usable capacity data were evaluated and then further analyzed in MATLAB/SIMULINK to validate the PV firming control. The proposed PV firming and optimization algorithms were experimentally verified, and the results evaluated. Finally, Chapter 6 provides a summary of key conclusions and future work to optimize the presented topology and algorithms.
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Date Issued: 2018
Identifier: CFE0007305, ucf:52166
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007305

Title: A Comparative Evaluation of FDSA,GA, and SA Non-Linear Programming Algorithms and Development of System-Optimal Dynamic Congestion Pricing Methodology on I-95 Express.
Creator: Graham, Don, Radwan, Ahmed, Abdel-Aty, Mohamed, Al-Deek, Haitham, Uddin, Nizam, University of Central Florida
Abstract / Description: As urban population across the globe increases, the demand for adequatetransportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities.High (-)Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today's highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases,...
Show moreAs urban population across the globe increases, the demand for adequatetransportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities.High (-)Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today's highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for in- depth study of demand patterns and development of an appropriate pricing scheme which reduces congestion.This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution.We perform a comparative evaluation of FDSA, GA and SA non-linear programmingalgorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 2014.
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Date Issued: 2013
Identifier: CFE0005000, ucf:50019
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005000

Optimization (17)	+ -
optimization (17)	+ -
Efficiency (3)	+ -
Optimism (3)	+ -
cooperative control (3)	+ -

optimal control (3)	+ -
optimism (3)	+ -
Auxetic (2)	+ -
CFD (2)	+ -
CubeSat (2)	+ -
Hope (2)	+ -
Modeling (2)	+ -
Optimal control (2)	+ -
Optimal trajectory design (2)	+ -
Particle Swarm Optimization (2)	+ -
Reliability (2)	+ -
Robust (2)	+ -
Simulation (2)	+ -
Stochastic Optimization (2)	+ -
algorithm (2)	+ -
combinatorial optimization (2)	+ -
energy storage (2)	+ -
multi-objective optimization (2)	+ -
multiobjective optimization (2)	+ -
portfolio optimization (2)	+ -
security (2)	+ -
trajectory planning (2)	+ -
virtual motion camouflage (2)	+ -
0-1 multi-dimensional knapsack problems (1)	+ -
304 stainless steel (1)	+ -

ETD Collection (95)	+ -
Honors Collection (7)	+ -

University of Central Florida (101)	+ -
Xu, Yunjun (11)	+ -
Lin, Kuo-Chi (8)	+ -
Batarseh, Issa (6)	+ -
Kassab, Alain (6)	+ -

Qu, Zhihua (6)	+ -
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