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Title: Analysis of Behaviors in Crowd Videos.
Creator: Mehran, Ramin, Shah, Mubarak, Sukthankar, Gita, Behal, Aman, Tappen, Marshall, Moore, Brian, University of Central Florida
Abstract / Description: In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of...
Show moreIn this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity recognition. We present new representations of human group motion for static cameras, and propose algorithms for their application to variety of problems.We first propose a method to model and learn the scene activity of a crowd using Social Force Model for the first time in the computer vision community. We present a method to densely estimate the interaction forces between people in a crowd, observed by a static camera. Latent Dirichlet Allocation (LDA) is used to learn the model of the normal activities over extended periods of time. Randomly selected spatio-temporal volumes of interaction forces are used to learn the model of normal behavior of the scene. The model encodes the latent topics of social interaction forces in the scene for normal behaviors. We classify a short video sequence of $n$ frames as normal or abnormal by using the learnt model. Once a sequence of frames is classified as an abnormal, the regions of anomalies in the abnormal frames are localized using the magnitude of interaction forces.The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a global estimation of the interaction forces within the crowd. It, therefore, is incapable of identifying different groups of objects based on motion or behavior in the scene. Although the algorithm is capable of learning the normal behavior and detects the abnormality, but it is incapable of capturing the dynamics of different behaviors.To overcome these limitations, we then propose a method based on the Lagrangian framework for fluid dynamics, by introducing a streakline representation of flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position.This approach is advantageous over the previous representations in two aspects: first, its rich representation captures the dynamics of the crowd and changes in space and time in the scene where the optical flow representation is not enough, and second, this model is capable of discovering groups of similar behavior within a crowd scene by performing motion segmentation. We propose a method to distinguish different group behaviors such as divergent/convergent motion and lanes using this framework. Finally, we introduce flow potentials as a discriminative feature to recognize crowd behaviors in a scene. Results of extensive experiments are presented for multiple real life crowd sequences involving pedestrian and vehicular traffic.The proposed method exploits optical flow as the low level feature and performs integration and clustering to obtain coherent group motion patterns. However, we observe that in crowd video sequences, as well as a variety of other vision applications, the co-occurrence and inter-relation of motion patterns are the main characteristics of group behaviors. In other words, the group behavior of objects is a mixture of individual actions or behaviors in specific geometrical layout and temporal order.We, therefore, propose a new representation for group behaviors of humans using the inter-relation of motion patterns in a scene. The representation is based on bag of visual phrases of spatio-temporal visual words. We present a method to match the high-order spatial layout of visual words that preserve the geometry of the visual words under similarity transformations. To perform the experiments we collected a dataset of group choreography performances from the YouTube website. The dataset currently contains four categories of group dances.
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Date Issued: 2011
Identifier: CFE0004482, ucf:49317
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004482

Title: Stability and Control in Complex Networks of Dynamical Systems.
Creator: Manaffam, Saeed, Vosoughi, Azadeh, Behal, Aman, Atia, George, Rahnavard, Nazanin, Javidi, Tara, Das, Tuhin, University of Central Florida
Abstract / Description: Stability analysis of networked dynamical systems has been of interest in many disciplines such as biology and physics and chemistry with applications such as LASER cooling and plasma stability. These large networks are often modeled to have a completely random (Erd\"os-R\'enyi) or semi-random (Small-World) topologies. The former model is often used due to mathematical tractability while the latter has been shown to be a better model for most real life networks.The recent emergence of cyber...
Show moreStability analysis of networked dynamical systems has been of interest in many disciplines such as biology and physics and chemistry with applications such as LASER cooling and plasma stability. These large networks are often modeled to have a completely random (Erd\"os-R\'enyi) or semi-random (Small-World) topologies. The former model is often used due to mathematical tractability while the latter has been shown to be a better model for most real life networks.The recent emergence of cyber physical systems, and in particular the smart grid, has given rise to a number of engineering questions regarding the control and optimization of such networks. Some of the these questions are: \emph{How can the stability of a random network be characterized in probabilistic terms? Can the effects of network topology and system dynamics be separated? What does it take to control a large random network? Can decentralized (pinning) control be effective? If not, how large does the control network needs to be? How can decentralized or distributed controllers be designed? How the size of control network would scale with the size of networked system?}Motivated by these questions, we began by studying the probability of stability of synchronization in random networks of oscillators. We developed a stability condition separating the effects of topology and node dynamics and evaluated bounds on the probability of stability for both Erd\"os-R\'enyi (ER) and Small-World (SW) network topology models. We then turned our attention to the more realistic scenario where the dynamics of the nodes and couplings are mismatched. Utilizing the concept of $\varepsilon$-synchronization, we have studied the probability of synchronization and showed that the synchronization error, $\varepsilon$, can be arbitrarily reduced using linear controllers.We have also considered the decentralized approach of pinning control to ensure stability in such complex networks. In the pinning method, decentralized controllers are used to control a fraction of the nodes in the network. This is different from traditional decentralized approaches where all the nodes have their own controllers. While the problem of selecting the minimum number of pinning nodes is known to be NP-hard and grows exponentially with the number of nodes in the network we have devised a suboptimal algorithm to select the pinning nodes which converges linearly with network size. We have also analyzed the effectiveness of the pinning approach for the synchronization of oscillators in the networks with fast switching, where the network links disconnect and reconnect quickly relative to the node dynamics.To address the scaling problem in the design of distributed control networks, we have employed a random control network to stabilize a random plant network. Our results show that for an ER plant network, the control network needs to grow linearly with the size of the plant network.
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Date Issued: 2015
Identifier: CFE0005834, ucf:50902
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005834

Title: investigation of dual-stage high efficiency (&)density micro inverter for solar application.
Creator: Chen, Lin, Batarseh, Issa, Mikhael, Wasfy, Wu, Xinzhang, Behal, Aman, Kutkut, Nasser, University of Central Florida
Abstract / Description: Module integrated converters (MIC), also called micro inverter, in single phase have witnessed recent market success due to unique features (1) improved energy harvest, (2) improved system efficiency, (3) lower installation costs, (4) plug-N-play operation, (5) and enhanced flexibility and modularity. The MIC sector has grown from a niche market to mainstream, especially in the United States. Due to the fact that two-stage architecture is commonly used for single phase MIC application. A DC...
Show moreModule integrated converters (MIC), also called micro inverter, in single phase have witnessed recent market success due to unique features (1) improved energy harvest, (2) improved system efficiency, (3) lower installation costs, (4) plug-N-play operation, (5) and enhanced flexibility and modularity. The MIC sector has grown from a niche market to mainstream, especially in the United States. Due to the fact that two-stage architecture is commonly used for single phase MIC application. A DC-DC stage with maximum power point tracking to boost the output voltage of the Photovoltaic (PV) panel is employed in the first stage, DC-AC stage is used for use to connect the grid or the residential application. As well known, the cost of MIC is key issue compared to convention PV system, such as the architecture: string inverter or central inverter. A high efficiency and density DC-DC converter is proposed and dedicated for MIC application. Assuming further expansion of the MIC market, this dissertation presents the micro-inverter concept incorporated in large size PV installations such as MW-class solar farms where a three phase AC connection is employed. A high efficiency three phase MIC with two-stage ZVS operation for grid tied photovoltaic system is proposed which will reduce cost per watt, improve reliability, and increase scalability of MW-class solar farms through the development of new solar farm system architectures. This dissertation presents modeling and triple-loop control for a high efficiency three-phase four-wire inverter for use in grid-connected two-stage micro inverter applications. An average signal model based on a synchronous rotation frame for a three-phase four-wire inverter has been developed. The inner current loop consists of a variable frequency bidirectional current mode (VFBCM) controller which regulates output filter inductor current thereby achieving ZVS, improved system response, and reduced grid current THD. Active damping of the LCL output filter using filter inductor current feedback is discussed along with small signal modeling of the proposed control method. Since the DC-link capacitor plays a critical role in two-stage micro inverter applications, a DC-link controller is implemented outside of the two current control loops to keep the bus voltage constant. In the end, simulation and experimental results from a 400 watt prototype are presented to verify the validity of the theoretical analysis.
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Date Issued: 2014
Identifier: CFE0005148, ucf:50699
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005148

Title: Towards Improving Human-Robot Interaction For Social Robots.
Creator: Khan, Saad, Boloni, Ladislau, Behal, Aman, Sukthankar, Gita, Garibay, Ivan, Fiore, Stephen, University of Central Florida
Abstract / Description: Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture...
Show moreAutonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. We validated the results of the Market Patrol scenario by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. 3. We modeled the movement of a socially aware mobile robot in a dense crowds, using the concept of a micro-conflict to represent the challenge of giving or not giving way to pedestrians. 4. We developed an approach for the robot behavior in micro-conflicts based on the psychological observation that human opponents will use a consistent strategy. For this, the mobile robot classifies the opponent strategy reflected by the personality and social status of the person and chooses an appropriate counter-strategy that takes into account the urgency of the robots' mission. 5. We developed an alternative approach for the resolution of micro-conflicts based on the imitation of the behavior of the human agent. This approach aims to make the behavior of an autonomous robot closely resemble that of a remotely operated one.
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Date Issued: 2015
Identifier: CFE0005965, ucf:50819
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005965

Title: Microgrid Control and Protection: Stability and Security.
Creator: Keshavarztalebi, Morteza, Behal, Aman, Haralambous, Michael, Sun, Wei, Jain, Amit Kumar, Kutkut, Nasser, University of Central Florida
Abstract / Description: When the microgrid disconnects from the main grid in response to, say, upstream disturbance orvoltage fluctuation and goes to islanding mode, both voltage and frequency at all locations in themicrogrid have to be regulated to nominal values in a short amount of time before the operation ofprotective relays. Motivated by this, we studied the application of intelligent pinning of distributed cooperative secondary control of distributed generators in islanded microgrid operation in a power...
Show moreWhen the microgrid disconnects from the main grid in response to, say, upstream disturbance orvoltage fluctuation and goes to islanding mode, both voltage and frequency at all locations in themicrogrid have to be regulated to nominal values in a short amount of time before the operation ofprotective relays. Motivated by this, we studied the application of intelligent pinning of distributed cooperative secondary control of distributed generators in islanded microgrid operation in a power system. In the first part, the problem of single and multi-pinning of distributed cooperative secondary control of DGs in a microgrid is formulated. It is shown that the intelligent selection of a pinning set based on the number of its connections and distance of leader DG/DGs from the rest of the network, i.e., degree of connectivity, strengthens microgrid voltage and frequency regulation performance both in transient and steady state. The proposed control strategy and algorithm are validated by simulation in MATLAB/SIMULINK using different microgrid topologies. It is shown that it is much easier to stabilize the microgrid voltage and frequency in islanding mode operationby specifically placing the pinning node on the DGs with high degrees of connectivity than byrandomly placing pinning nodes into the network. In all of these research study cases, DGs areonly required to communicate with their neighboring units which facilitates the distributed controlstrategy.Historically, the models for primary control are developed for power grids with centralized powergeneration, in which the transmission lines are assumed to be primarily inductive. However, fordistributed power generation, this assumption does not hold since the network has significant resistive impedance as well. Hence, it is of utmost importance to generalize the droop equations, i.e., primary control, to arrive at a proper model for microgrid systems. Motivated by this, we proposed the secondary adaptive voltage and frequency control of distributed generators for low and medium voltage microgrid in autonomous mode to overcome the drawback of existing classical droop based control techniques. Our proposed secondary control strategy is adaptive with line parameters and can be applied to all types of microgrids to address the simultaneous impacts of active and reactive power on the microgrids voltage and frequency. Also, since the parameters in the network model are unknown or uncertain, the second part of our research studies adaptive distributed estimation/compensation. It is shown that this is an effective method to robustly regulate the microgrid variables to their desired values.The security of power systems against malicious cyberphysical data attacks is the third topic of this dissertation. The adversary always attempts to manipulate the information structure of the power system and inject malicious data to deviate state variables while evading the existing detection techniques based on residual test. The solutions proposed in the literature are capable of immunizing the power system against false data injection but they might be too costly and physically not practical in the expansive distribution network. To this end, we define an algebraic condition for trustworthy power system to evade malicious data injection. The proposed protection scheme secures the power system by deterministically reconfiguring the information structure and corresponding residual test. More importantly, it does not require any physical effort in either microgrid or network level. The identification scheme of finding meters being attacked is proposed as well. Eventually, a well-known IEEE 30-bus system is adopted to demonstrate the effectiveness of the proposed schemes.
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Date Issued: 2016
Identifier: CFE0006338, ucf:51569
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006338

Title: Data-driven Predictive Analytics For Distributed Smart Grid Control: Optimization of Energy Storage, Voltage and Demand Response.
Creator: Valizadehhaghi, Hamed, Qu, Zhihua, Behal, Aman, Atia, George, Turgut, Damla, Pensky, Marianna, University of Central Florida
Abstract / Description: The smart grid is expected to support an interconnected network of self-contained microgrids. Nonetheless, the distributed integration of renewable generation and demand response adds complexity to the control and optimization of smart grid. Forecasts are essential due to the existence of stochastic variations and uncertainty. Forecasting data are spatio-temporal which means that the data correspond to regular intervals, say every hour, and the analysis has to take account of spatial...
Show moreThe smart grid is expected to support an interconnected network of self-contained microgrids. Nonetheless, the distributed integration of renewable generation and demand response adds complexity to the control and optimization of smart grid. Forecasts are essential due to the existence of stochastic variations and uncertainty. Forecasting data are spatio-temporal which means that the data correspond to regular intervals, say every hour, and the analysis has to take account of spatial dependence among the distributed generators or locations. Hence, smart grid operations must take account of, and in fact benefit from the temporal dependence as well as the spatial dependence. This is particularly important considering the buffering effect of energy storage devices such as batteries, heating/cooling systems and electric vehicles. The data infrastructure of smart grid is the key to address these challenges, however, how to utilize stochastic modeling and forecasting tools for optimal and reliable planning, operation and control of smart grid remains an open issue.Utilities are seeking to become more proactive in decision-making, adjusting their strategies based on realistic predictive views into the future, thus allowing them to side-step problems and capitalize on the smart grid technologies, such as energy storage, that are now being deployed atscale. Predictive analytics, capable of managing intermittent loads, renewables, rapidly changing weather patterns and other grid conditions, represent the ultimate goal for smart grid capabilities.Within this framework, this dissertation develops high-performance analytics, such as predictive analytics, and ways of employing analytics to improve distributed and cooperative optimization software which proves to be the most significant value-add in the smart grid age, as new network management technologies prove reliable and fundamental. Proposed optimization and control approaches for active and reactive power control are robust to variations and offer a certain level of optimality by combining real-time control with hours-ahead network operation schemes. The main objective is managing spatial and temporal availability of the energy resources in different look-ahead time horizons. Stochastic distributed optimization is realized by integrating a distributed sub-gradient method with conditional ensemble predictions of the energy storage capacity and distributed generation. Hence, the obtained solutions can reflect on the system requirements for the upcoming times along with the instantaneous cooperation between distributed resources. As an important issue for smart grid, the conditional ensembles are studied for capturing wind, photovoltaic, and vehicle-to-grid availability variations. The following objectives are pursued:- Spatio-temporal adaptive modeling of data including electricity demand, electric vehicles and renewable energy (wind and solar power)- Predictive data analytics and forecasting- Distributed control- Integration of energy storage systemsFull distributional characterization and spatio-temporal modeling of data ensembles are utilized in order to retain the conditional and temporal interdependence between projection data and available capacity. Then, by imposing measures of the most likely ensembles, the distributed control method is carried out for cooperative optimization of the renewable generation and energy storage within the smart grid.
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Date Issued: 2016
Identifier: CFE0006408, ucf:51481
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006408

Title: Modified System Design and Implementation of an Intelligent Assistive Robotic Manipulator.
Creator: Paperno, Nicholas, Behal, Aman, Haralambous, Michael, Sukthankar, Gita, Boloni, Ladislau, Smither, Janan, University of Central Florida
Abstract / Description: This thesis presents three improvements to the current UCF MANUS systems. The first improvement modifies the existing fine motion controller into PI controller that has been optimized to prevent the object from leaving the view of the cameras used for visual servoing. This is achieved by adding a weight matrix to the proportional part of the controller that is constrained by an artificial ROI. When the feature points being used are approaching the boundaries of the ROI, the optimized...
Show moreThis thesis presents three improvements to the current UCF MANUS systems. The first improvement modifies the existing fine motion controller into PI controller that has been optimized to prevent the object from leaving the view of the cameras used for visual servoing. This is achieved by adding a weight matrix to the proportional part of the controller that is constrained by an artificial ROI. When the feature points being used are approaching the boundaries of the ROI, the optimized controller weights are calculated using quadratic programming and added to the nominal proportional gain portion of the controller. The second improvement was a compensatory gross motion method designed to ensure that the desired object can be identified. If the object cannot be identified after the initial gross motion, the end-effector will then be moved to one of three different locations around the object until the object is identified or all possible positions are checked. This framework combines the Kanade-Lucase-Tomasi local tracking method with the ferns global detector/tracker to create a method that utilizes the strengths of both systems to overcome their inherent weaknesses. The last improvement is a particle-filter based tracking algorithm that robustifies the visual servoing function of fine motion. This method performs better than the current global detector/tracker that was being implemented by allowing the tracker to successfully track the object in complex environments with non-ideal conditions.
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Date Issued: 2015
Identifier: CFE0005681, ucf:50180
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005681

Title: Bio-inspired, Varying Manifold Based Method with Enhanced Initial Guess Strategies for Single Vehicle's Optimal Trajectory Planning.
Creator: Li, Ni, Xu, Yunjun, Lin, Kuo-Chi, Bai, Yuanli, Behal, Aman, University of Central Florida
Abstract / Description: Trajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species,...
Show moreTrajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species, such as hoverflies, ants, and bats, have developed many efficient hunting strategies. It is hypothesized that these types of predators only move along paths in a carefully selected manifold based on the prey's motion in some of their hunting activities. Inspired by these studies, the predator-prey relationships are organized into a unified form and incorporated into the trajectory optimization formulation, which can reduce the computational cost in solving nonlinear constrained optimal trajectory planning problems. Specifically, three motion strategies are studied in this dissertation: motion camouflage, constant absolute target direction, and local pursuit. Necessary conditions based on the speed and obstacle avoidance constraints are derived. Strategies to tune initial guesses are proposed based on these necessary conditions to enhance the convergence rate and reduce the computational cost of the motion camouflage inspired strategy. The following simulations have been conducted to show the advantages of the proposed methods: a supersonic aircraft minimum-time-to-climb problem, a ground robot obstacle avoidance problem, and a micro air vehicle minimum time trajectory problem. The results show that the proposed methods can find the optimal solution with higher success rate and faster convergent speed as compared with some other popular methods. Among these three motion strategies, the method based on the local pursuit strategy has a relatively higher success rate when compared to the other two.In addition, the optimal trajectory planning method is embedded into a receding horizon framework with unknown parameters updated in each planning horizon using an Extended Kalman Filter.
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Date Issued: 2013
Identifier: CFE0005023, ucf:49986
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005023

Title: The Effects of Assumption on Subspace Identification Methods Using Simulation and Experimental Data.
Creator: Kim, Yoonhwak, Yun, Hae-Bum, Catbas, Fikret, Mackie, Kevin, Nam, Boo Hyun, Behal, Aman, University of Central Florida
Abstract / Description: In the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of...
Show moreIn the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of sensors is limited by cost and difficulty of data archive. Furthermore, some informative data has technical difficulty when measuring input force and, even if obtaining the desired data were possible, it could include a lot of noise in the measuring data. As a result, researchers have developed many analytical tools with limited informative data. Subspace identification method is used one of tools in these achievements.Subspace identification method includes three different approaches: Deterministic Subspace Identification (DSI), Stochastic Subspace Identification (SSI), and Deterministic-Stochastic Subspace Identification (DSSI). The subspace identification method is widely used for fast computational speed and its accuracy. Based on the given information, such as output only, input/output, and input/output with noises, DSI, SSI, and DSSI are differently applied under specific assumptions, which could affect the analytical results. The objective of this study is to observe the effect of assumptions on subspace identification with various data conditions. Firstly, an analytical simulation study is performed using a six-degree-of-freedom mass-damper-spring system which is created using MATLAB. Various conditions of excitation insert to the simulation test model, and its excitation and response are analyzed using the subspace identification method. For stochastic problems, artificial noise is contained to the excitation and followed the same steps. Through this simulation test, the effects of assumption on subspace identification are quantified.Once the effects of the assumptions are studied using the simulation model, the subspace identification method is applied to dynamic response data collected from large-scale 12-story buildings with different foundation types that are tested at Tongji University, Shanghai, China. Noise effects are verified using three different excitation types. Furthermore, using the DSSI, which has the most accurate result, the effect of different foundations on the superstructure are analyzed.
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Date Issued: 2013
Identifier: CFE0004703, ucf:49822
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004703

Title: Transient and Distributed Algorithms to Improve Islanding Detection Capability of Inverter Based Distributed Generation.
Creator: Alhosani, Mohamed, Qu, Zhihua, Mikhael, Wasfy, Haralambous, Michael, Behal, Aman, Xu, Chengying, University of Central Florida
Abstract / Description: Recently, a lot of research work has been dedicated toward enhancing performance, reliability and integrity of distributed energy resources that are integrated into distribution networks. The problem of islanding detection and islanding prevention (i.e. anti-islanding) has stimulated a lot of research due to its role in severely compromising the safety of working personnel and resulting in equipment damages. Various Islanding Detection Methods (IDMs) have been developed within the last ten...
Show moreRecently, a lot of research work has been dedicated toward enhancing performance, reliability and integrity of distributed energy resources that are integrated into distribution networks. The problem of islanding detection and islanding prevention (i.e. anti-islanding) has stimulated a lot of research due to its role in severely compromising the safety of working personnel and resulting in equipment damages. Various Islanding Detection Methods (IDMs) have been developed within the last ten years in anticipation of the tremendous increase in the penetration of Distributed Generation (DG) in distribution system. This work proposes new IDMs that rely on transient and distributed behaviors to improve integrity and performance of DGs while maintaining multi-DG islanding detection capability.In this thesis, the following questions have been addressed: How to utilize the transient behavior arising from an islanding condition to improve detectability and robust performance of IDMs in a distributive manner? How to reduce the negative stability impact of the well-known Sandia Frequency Shift (SFS) IDM while maintaining its islanding detection capability? How to incorporate the perturbations provided by each of DGs in such a way that the negative interference of different IDMs is minimized without the need of any type of communication among the different DGs?It is shown that the proposed techniques are local, scalable and robust against different loading conditions and topology changes. Also, the proposed techniques can successfully distinguish an islanding condition from other disturbances that may occur in power system networks. This work improves the efficiency, reliability and safety of integrated DGs, which presents a necessary advance toward making electric power grids a smart grid.
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Date Issued: 2013
Identifier: CFE0005295, ucf:50567
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005295

Title: Lyapunov-Based Robust and Adaptive Control Design for nonlinear Uncertain Systems.
Creator: Zhang, Kun, Behal, Aman, Haralambous, Michael, Xu, Yunjun, Boloni, Ladislau, Marzocca, Piergiovanni, University of Central Florida
Abstract / Description: The control of systems with uncertain nonlinear dynamics is an important field of control scienceattracting decades of focus. In this dissertation, four different control strategies are presentedusing sliding mode control, adaptive control, dynamic compensation, and neural network for a nonlinear aeroelastic system with bounded uncertainties and external disturbance. In Chapter 2, partial state feedback adaptive control designs are proposed for two different aeroelastic systems operating in...
Show moreThe control of systems with uncertain nonlinear dynamics is an important field of control scienceattracting decades of focus. In this dissertation, four different control strategies are presentedusing sliding mode control, adaptive control, dynamic compensation, and neural network for a nonlinear aeroelastic system with bounded uncertainties and external disturbance. In Chapter 2, partial state feedback adaptive control designs are proposed for two different aeroelastic systems operating in unsteady flow. In Chapter 3, a continuous robust control design is proposed for a class of single input and single output system with uncertainties. An aeroelastic system with a trailingedge flap as its control input will be considered as the plant for demonstration of effectiveness of the controller. The controller is proved to be robust by both athematical proof and simulation results. In Chapter 3, a robust output feedback control strategy is discussed for the vibration suppression of an aeroelastic system operating in an unsteady incompressible flowfield. The aeroelastic system is actuated using a combination of leading-edge (LE) and trailing-edge (TE) flaps in the presence of different kinds of gust disturbances. In Chapter 5, a neural-network based model-free controller is designed for an aeroelastic system operating at supersonic speed. The controller is shown to be able to effectively asymptotically stabilize the system via both a Lyapunov-based stability proof and numerical simulation results.
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Date Issued: 2015
Identifier: CFE0005748, ucf:50110
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005748

Title: Speech Detection using Gammatone Features and One-Class Support Vector Machine.
Creator: Cooper, Douglas, Mikhael, Wasfy, Wahid, Parveen, Behal, Aman, Richie, Samuel, University of Central Florida
Abstract / Description: A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD's rely on time-domain features and simple thresholds for...
Show moreA network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD's rely on time-domain features and simple thresholds for efficient speech detection however this doesn't say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and non- speech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5dB.
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Date Issued: 2013
Identifier: CFE0005091, ucf:50731
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005091

Title: Nonlinear dynamic modeling, simulation and characterization of the mesoscale neuron-electrode interface.
Creator: Thakore, Vaibhav, Hickman, James, Mucciolo, Eduardo, Rahman, Talat, Johnson, Michael, Behal, Aman, Molnar, Peter, University of Central Florida
Abstract / Description: Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron...
Show moreExtracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signal-to-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.
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Date Issued: 2012
Identifier: CFE0004797, ucf:49718
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004797

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