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Title: Cell-state-space-based fuzzy logic controller automatic design and optimization for high-order systems.
Creator: Song, Feijun., Florida Atlantic University, Smith, Samuel M., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Recent advances in computer engineering make the computational approaches to controller design for high order systems practical. In this dissertation, a series of computational methods based on cell state space for the design and optimization of Takagi-Sugeno (TS) type Fuzzy Logic Controllers (FLCs) are presented. The approaches proposed in this research can be classified into two categories: feed forward design and feedback design. An Optimal Control Table (OCT) based on cell state space is...
Show moreRecent advances in computer engineering make the computational approaches to controller design for high order systems practical. In this dissertation, a series of computational methods based on cell state space for the design and optimization of Takagi-Sugeno (TS) type Fuzzy Logic Controllers (FLCs) are presented. The approaches proposed in this research can be classified into two categories: feed forward design and feedback design. An Optimal Control Table (OCT) based on cell state space is used in all the feed forward design approaches. An FLC can be trained by Least Mean Square (LMS) algorithm with an OCT serving as the training set. For high order systems, due to physical memory limit, the cell resolution is generally low. A specially modified k-d tree representation of cell space is proposed to save the memory while keeping the cell resolution as high as possible. The control command for a point that is not a cell center is approximated by interpolating an OCT. All these commands can be used as training data to train an FLC. An iterative feedback design approach named Incremental Best Estimate Directed Search (IBEDS) is proposed to further optimize a training set. It is a kind of globally directed random search method. The general philosophy is that since the best possible performance of an FLC largely depends on the quality of the training set, if the training set is optimized, an FLC trained by the set would also be optimized. Based on IBEDS, two other feedback FLC design algorithms are also proposed. In one algorithm, subtractive clustering method is used to extract the structure of an FLC from an OCT. The coefficients of the FLC obtained are then optimized with IBEDS. The other algorithm applies IBEDS to three system models and finds the training set that has the worst performance for all the models. This training set is further optimized to improve robustness of a trained FLC.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/12608
Subject Headings: Fuzzy logic, Automatic control, Fuzzy systems
Format: Document (PDF)

Title: Implementation of a fuzzy logic controller for laser tracking system.
Creator: Wu, Xiaomin., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: With the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic...
Show moreWith the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic Controllers (FLCs) is followed, which is then realized in a PC-based environment in the design. An essential issue in this thesis study is the auto tuning of the Fuzzy Logic Controller. An efficient tuning method, mu-law functions, which can adjust both the shape and scaling gain of fuzzy controller's decision table is adopted. Also a search process called Downhill Simplex Search is chosen. Combining these two methods, a Simplex-mu-law auto-tuning algorithm that fits our application is applied to tune the FLC for the laser tracking system. Another issue covered in this research is to modify the Fuzzy Logic Controller structure by changing the distribution of the membership functions. Based on the analysis of the real time error histogram of the system, a novel method is proposed in the thesis for the modification of the membership functions To assess the effectiveness of the methods proposed in this thesis, a prototype laser tracking system is constructed at the FAU Robotics Center. The control strategy proposed in this thesis is tested extensively by simulations and experimentations on the prototype system.
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Date Issued: 1999
PURL: http://purl.flvc.org/fcla/dt/15717
Subject Headings: Fuzzy logic, Automatic control, Fuzzy systems
Format: Document (PDF)

Title: Fuzzy auto-detection of bottom mines.
Creator: Bauer, Eric John., Florida Atlantic University, Schock, Steven G.
Abstract/Description: An automatic mine detection method has been designed for the purpose of locating mine-like objects on the seabed in real time using a high frequency, high resolution side scan sonar. The processing flow includes a calculation of the average scattering function of the local environment, shadow detection, and a fuzzy logic clustering/fuzzy logic detection procedure for identifying mine-like shadows. An Autonomous Underwater Vehicle (AUV) equipped with a fuzzy detection system gives the Navy the...
Show moreAn automatic mine detection method has been designed for the purpose of locating mine-like objects on the seabed in real time using a high frequency, high resolution side scan sonar. The processing flow includes a calculation of the average scattering function of the local environment, shadow detection, and a fuzzy logic clustering/fuzzy logic detection procedure for identifying mine-like shadows. An Autonomous Underwater Vehicle (AUV) equipped with a fuzzy detection system gives the Navy the capability of rapidly locating bottom mines in littoral underwater environments during over-the-horizon operations.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/12705
Subject Headings: Submarine mines, Sonar, Fuzzy systems
Format: Document (PDF)

Title: Design and tuning of fuzzy control surface with Bezier functions.
Creator: Wongsoontorn, Songwut., Florida Atlantic University, Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design and Tuning a fuzzy logic controller (FLCs) are usually done in two stages. In the first stage, the structure of a FLC is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC are selected to optimize the performance of the system. The task of tuning FLCs can be performed by a number of methods such as adjusting control gains, changing membership functions, modifying control rules and varying control surfaces. A method for the design...
Show moreDesign and Tuning a fuzzy logic controller (FLCs) are usually done in two stages. In the first stage, the structure of a FLC is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC are selected to optimize the performance of the system. The task of tuning FLCs can be performed by a number of methods such as adjusting control gains, changing membership functions, modifying control rules and varying control surfaces. A method for the design and tuning of FLCs through modifying their control surfaces is presented in this dissertation. The method can be summarized as follows. First, fuzzy control surfaces are modeled with Bezier functions. Shapes of the control surface are then adjusted through varying Bezier parameters. A Genetic Algorithm (GA) is used to search for the optimal set of parameters based on the control performance criteria. Then, tuned control surfaces are sampled to create rule-based FLCs. To further improve the system performance, continuity constraints of the curves are imposed. Under the continuity constraints with the same number of tunable parameters, one can obtain more flexible curves that have the potential to improve the overall system performance. An important issue is to develop a new method to self-tune a fuzzy PD controller. The method is based on two building blocks: (I) Bezier functions used to model the control surfaces of the fuzzy PD controller; and, shapes of control surfaces are then adjusted by varying Bezier parameters. (II) The next step involves using a gradient-based optimization algorithm with which the input scaling factors and Bezier parameters are on-line tuned until the controller drives the output of the process as close as possible to the reference position. To protect vendors and consumers from being victimized, various trust models have been used in e-commerce practices. However, a strict verification and authentication process may pose unnecessary heavy cost to the vendor. As an application of the control strategy proposed, this dissertation presents a solution to the reduction of costs of a vendor. With two fuzzy variables (price, credit-history), a trust-surface can be tuned to achieve an optimal solution in terms of profit margin of the vendor. With this new approach, more realistic trust decisions can be reached.
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Date Issued: 2005
PURL: http://purl.flvc.org/fcla/dt/12172
Subject Headings: Fuzzy systems, Nonlinear control theory, Process control--Data processing, Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: Fuzzy logic techniques for software reliability engineering.
Creator: Xu, Zhiwei., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises...
Show moreModern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises caused by computer failures also increases. High reliability is an important attribute for almost any software system. Consequently, software developers are seeking ways to forecast and improve quality before release. Since many quality factors cannot be measured until after the software becomes operational, software quality models are developed to predict quality factors based on measurements collected earlier in the life cycle. Due to incomplete information in the early life cycle of software development, software quality models with fuzzy characteristics usually perform better because fuzzy concepts deal with phenomenon that is vague in nature. This study focuses on the usage of fuzzy logic in software reliability engineering. Discussing will include the fuzzy expert systems and the application of fuzzy expert systems in early risk assessment; introducing the interval prediction using fuzzy regression modeling; demonstrating fuzzy rule extraction for fuzzy classification and its usage in software quality models; demonstrating the fuzzy identification, including extraction of both rules and membership functions from fuzzy data and applying the technique to software project cost estimations. The following methodologies were considered: nonparametric discriminant analysis, Z-test and paired t-test, neural networks, fuzzy linear regression, fuzzy nonlinear regression, fuzzy classification with maximum matched method, fuzzy identification with fuzzy clustering, and fuzzy projection. Commercial software systems and the COCOMO database are used throughout this dissertation to demonstrate the usefulness of concepts and to validate new ideas.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/11948
Subject Headings: Software engineering, Fuzzy logic, Computer software--Quality control, Fuzzy systems
Format: Document (PDF)

Title: Design and implementation of a control system for a laser-tracking measurement system.
Creator: Bai, Ying., Florida Atlantic University, Roth, Zvi S., Zhuang, Hanqi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: To assess and evaluate the performance of robots and machine tools dynamically, it is desirable to have a precision measuring device that performs dynamic measurement of end-effector positions of such robots and machine tools. Among possible measurement techniques, Laser Tracking Systems (LTSs) exlnbit the capability of high accuracy, large workspace, high sampling rate, and automatic target-tracking,. and thus are well-suited for robot calibration both kinematically and dynamically. In this...
Show moreTo assess and evaluate the performance of robots and machine tools dynamically, it is desirable to have a precision measuring device that performs dynamic measurement of end-effector positions of such robots and machine tools. Among possible measurement techniques, Laser Tracking Systems (LTSs) exlnbit the capability of high accuracy, large workspace, high sampling rate, and automatic target-tracking,. and thus are well-suited for robot calibration both kinematically and dynamically. In this dissertation, the design and implementation of a control system for a homemade laser tracking measurement systems is addressed and calibration of a robot using the laser tracking system is demonstrated Design and development of a control system for a LTS is a challenging task. It involves a deep understanding of laser interferometry,. controls, mechanics and optics,. both in theoretical perspective and in implementation aspect. One of the most important requirements for a successful design and implementation of a control system for the LTS is proper installation and alignment of the laser and optical system,. or laser transducer system. The precision of measurement using the LTS depends highly on the accuracy of the laser transducer system, as well as the accuracy of the installation and alignment of the optical system. Hence, in reference to the experimental alignment method presented in this dissertation, major error sources affecting the system measurement accuracy are identified and analyzed. A manual compensation method is developed to eliminate the effects of these error sources effectively in the measurement system. Considerations on proper design and installation of laser and optical components are indicated in this dissertation. As a part of the conventional control system design, a dynamic system model of the LTS is required. In this study, a detailed derivation and analysis of the dynamic model of the motor gimbal system using Lagrange-Euler equations of motion is developed for both ideal and complete gimbal systems. Based on this system model,. a conventional controller is designed. Fuzzy Logic Controllers (FLC) are designed in order to suppress noise or disturbances that exist in the motor driver subsystem. By using the relevant control strategies. noise and disturbances present in the electrical control channels are shown to reduce significantly. To improve the system performance further, a spectrum analysis of the error sources and disturbances existing in the system is conducted. Major noise sources are effectively suppressed by using a two-stage fuzzy logic control strategy. A comparison study on the performances of different control strategies is given in this dissertation, in reference to the following: An ideal system model, a system with a long time delay, a system with various noise sources and a system model with uncertainties. Both simulation and experimental results are furnished to illustrate the advantages of the FLC in respect of its transient response, steady-state response, and tracking performance. Furthermore, noise reduction in the laser tracking system is demonstrated. Another important issue concerning a successful application of the LTS in the calibration of a robot is the estimation of system accuracy. Hence, a detailed analysis of system accuracy of the LTS is presented in this worL This analysis is also verified by experimental methods by means of tracking a Coordinate Measuring Machine available in the FAU Robotics Center. Using the developed LTS, a PUMA robot in the FAU Robotics Center is calibrated. The results obtained are confirmative with the data available in the literature. In summary, the proposed methodology towards the design and implementation of a control system for LTSs has been shown to be successful by performing experimental tracking and calibration studies at the FAU Robotics Center.
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/12622
Subject Headings: Robots--Calibration, Robots--Control systems, Fuzzy logic
Format: Document (PDF)

Title: LEARNING AND OPTIMIZATION FOR REAL-TIME MICROGRID ENERGY MANAGEMENT SYSTEMS.
Creator: Das, Avijit, Ni, Zhen, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Microgrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid...
Show moreMicrogrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid applications with missing forecast information, large state space, and multiple probabilistic events. This dissertation focuses on these challenges and proposes efficient online learning and optimization-based approaches. For addressing the missing forecast challenges on online microgrid operations, a new fitted rolling horizon control (fitted-RHC) approach is proposed in Chapter 2. The proposed fitted-RHC approach is designed with a regression algorithm that utilizes the empirical knowledge obtain from the day-ahead forecast to make microgrid real-time decisions whenever the intra-day forecast data is unavailable. Simulation results show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case study and perform efficiently with the uncertain environment in the stochastic case study.
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Date Issued: 2021
PURL: http://purl.flvc.org/fau/fd/FA00013671
Subject Headings: Microgrids (Smart power grids), Renewable energy, Fuzzy systems
Format: Document (PDF)

Title: FACILITATING PEER-TO-PEER ENERGY TRADING THROUGH COOPERATIVE GAMES AND FUZZY INFERENCE SYSTEMS.
Creator: Lopez, Hector, Zilouchian, Ali, Abtahi, Amir, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: This dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation,...
Show moreThis dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation, driving the adoption of P2P energy markets. It also builds on demand-side payment mechanisms like NRG-X-Change by adapting it to provide fair payment distribution to prosumer coalitions. The interoperable energy storage systems with P2P trading also presented battery chemistry detection using neural network models. A fuzzy inference system is also designed to facilitate prosumers' choice in participating in P2P markets, providing flexibility for energy trading preferences. The simulation results demonstrated the effectiveness of the proposed design schemes.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014425
Subject Headings: Energy, Fuzzy systems, Cooperative game theory, Electrical engineering
Format: Document (PDF)

Title: A high-level fuzzy logic guidance system for an unmanned surface vehicle (USV) tasked to perform an autonomous launch and recovery (ALR) of an unmanned underwater vehicle (UUV).
Creator: Pearson, David, An, Pak-Cheung, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: There have been much technological advances and research in Unmanned Surface Vehicles (USV) as a support and delivery platform for Autonomous/Unmanned Underwater Vehicles (AUV/UUV). Advantages include extending underwater search and survey operations time and reach, improving underwater positioning and mission awareness, in addition to minimizing the costs and risks associated with similar manned vessel operations. The objective of this thesis is to present the design and development a high...
Show moreThere have been much technological advances and research in Unmanned Surface Vehicles (USV) as a support and delivery platform for Autonomous/Unmanned Underwater Vehicles (AUV/UUV). Advantages include extending underwater search and survey operations time and reach, improving underwater positioning and mission awareness, in addition to minimizing the costs and risks associated with similar manned vessel operations. The objective of this thesis is to present the design and development a high-level fuzzy logic guidance controller for a WAM-V 14 USV in order to autonomously launch and recover a REMUS 100 AUV. The approach to meeting this objective is to develop ability for the USV to intercept and rendezvous with an AUV that is in transit in order to maximize the probability of a final mobile docking maneuver. Specifically, a fuzzy logic Rendezvous Docking controller has been developed that generates Waypoint-Heading goals for the USV to minimize the cross-track errors between the USV and AUV. A subsequent fuzzy logic Waypoint-Heading controller has been developed to provide the desired heading and speed commands to the low-level controller given the Waypoint-Heading goals. High-level mission control has been extensively simulated using Matlab and partially characterized in real-time during testing. Detailed simulation, experimental results and findings will be reported in this paper.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004315, http://purl.flvc.org/fau/fd/FA00004315
Subject Headings: Adaptive signal processing, Fuzzy sets, Fuzzy systems, Nonlinear control theory, Oceanographic submersibles -- Automatic control, Submersibles -- Control systems, Underwater acoustic telemetry
Format: Document (PDF)

Title: Intelligent systems using GMDH algorithms.
Creator: Gupta, Mukul., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based...
Show moreDesign of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2976442
Subject Headings: GMDH algorithms, Genetic algorithms, Pattern recognition systems, Expert systems (Computer science), Neural networks (Computer science), Fuzzy logic, Intelligent control systems
Format: Document (PDF)

Title: Application of artificial neural networks to deduce robust forecast performance in technoeconomic contexts.
Creator: Dabbas, Mohammad A., Neelakanta, Perambur S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows...
Show moreThe focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004097, http://purl.flvc.org/fau/fd/FA00004097
Subject Headings: Artificial intelligence, Fuzzy systems, Long waves (Economics), Multisensor data fusion, Neural networks (Computer science) -- Mathematical models
Format: Document (PDF)

Title: An evaluation of machine learning algorithms for tweet sentiment analysis.
Creator: Prusa, Joseph D., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we...
Show moreSentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004460, http://purl.flvc.org/fau/fd/FA00004460
Subject Headings: Social media., Natural language processing (Computer science), Machine learning., Algorithms., Fuzzy expert systems., Artificial intelligence.
Format: Document (PDF)

Title: A robust AUV docking guidance and navigation approach to handling unknown current disturbances.
Creator: Teo, Hoe Eng., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: The main contribution in this thesis is the design of a robust AUV docking guidance and navigation approach that can guide and home an AUV towards an acoustic source located on an oriented bottom-mounted underwater docking station, under presence of unknown current disturbances and in the absence of any form of onboard velocity sensor. A Complementary Filter and various forms of Kalman Filters were separately formulated to estimate the current and vehicle positions with strategic vehicle...
Show moreThe main contribution in this thesis is the design of a robust AUV docking guidance and navigation approach that can guide and home an AUV towards an acoustic source located on an oriented bottom-mounted underwater docking station, under presence of unknown current disturbances and in the absence of any form of onboard velocity sensor. A Complementary Filter and various forms of Kalman Filters were separately formulated to estimate the current and vehicle positions with strategic vehicle manoeuvres. A current compensator uses the estimated current to maintain the desired vehicle course while under current disturbance. Tagaki-Sugeno-Kang Fuzzy Inference System was designed to realize fuzzy docking guidance manoeuvres. Finally, Monte Carlo runs were performed on a designed AUV docking simulator to evaluate the docking robustness against various docking conditions. Simulation results demonstrated robustness in the designed docking guidance and navigation approach.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2683137
Subject Headings: Oceanographic submersibles, Computer simulation, Underwater navigation, Fuzzy systems, Automatic control, Mathematical models
Format: Document (PDF)

Title: A novel NN paradigm for the prediction of hematocrit value during blood transfusion.
Creator: Thakkar, Jay., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: During the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data...
Show moreDuring the Leukocytapheresis (LCAP) process used to treat patients suffering from acute Ulcerative Colitis, medical practitioners have to continuously monitor the Hematocrit (Ht) level in the blood to ensure it is within the acceptable range. The work done, as a part of this thesis, attempts to create an early warning system that can be used to predict if and when the Ht values will deviate from the acceptable range. To do this we have developed an algorithm based on the Group Method of Data Handling (GMDH) and compared it to other Neural Network algorithms, in particular the Multi Layer Perceptron (MLP). The standard GMDH algorithm captures the fluctuation very well but there is a time lag that produces larger errors when compared to MLP. To address this drawback we modified the GMDH algorithm to reduce the prediction error and produce more accurate results.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3174078
Subject Headings: Neural networks (Computer science), Scientific applications, GMDH algorithms, Pattern recognition systems, Genetic algorithms, Fuzzy logic
Format: Document (PDF)

Title: A fuzzy logic material selection methodology for renewable ocean energy applications.
Creator: Welling, Donald Anthony., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: The purpose of this thesis is to develop a renewable ocean energy material selection methodology for use in FAU's Ocean Energy Projects. A detailed and comprehensive literature review has been performed concerning all relevant material publications and forms the basis of the developed method. A database of candidate alloys has been organized and is used to perform case study material selections to validate the developed fuzzy logic approach. The ultimate goal of this thesis is to aid in the...
Show moreThe purpose of this thesis is to develop a renewable ocean energy material selection methodology for use in FAU's Ocean Energy Projects. A detailed and comprehensive literature review has been performed concerning all relevant material publications and forms the basis of the developed method. A database of candidate alloys has been organized and is used to perform case study material selections to validate the developed fuzzy logic approach. The ultimate goal of this thesis is to aid in the selection of materials that will ensure the successful performance of renewable ocean energy projects so that clean and renewable energy becomes a reality for all.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/227980
Subject Headings: Oceanic submersibles, Control systems, Acoustical engineering, Fuzzy algorithms, Renewable energy sources
Format: Document (PDF)

Title: Smart Adaptive Beaconing Schemes for VANET.
Creator: Alhameed, Mohammed, Mahgoub, Imad, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Vehicular Ad hoc Networks (VANET) is a wireless ad-hoc network that includes two types of communications, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). In VANET there are two types of messages. The first type is the event-driven messages that are only triggered in case of emergency. The second type is the periodical messages named beacons that are exchanged frequently between vehicles. A beacon message contains basic information about the sending vehicle such as id, location...
Show moreVehicular Ad hoc Networks (VANET) is a wireless ad-hoc network that includes two types of communications, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). In VANET there are two types of messages. The first type is the event-driven messages that are only triggered in case of emergency. The second type is the periodical messages named beacons that are exchanged frequently between vehicles. A beacon message contains basic information about the sending vehicle such as id, location and velocity. Beacons are frequently exchanged to increase the cooperative awareness between vehicles. Increasing beacon frequency helps increasing neighborhood awareness and improving information accuracy. However, this causes more congestion in the network, specially when the number of vehicles increases. On the other hand, reducing beacon frequency alleviates network congestion, but results in out-dated information. In this dissertation, we address the aforementioned challenges and propose a number of smart beaconing protocols and evaluate their performance in di↵erent environments and network densities. The four adaptive beaconing protocols are designed to increase the cooperative awareness and information freshness, while alleviating the network congestion. All the proposed protocols take into account the most important aspects, which are critical to beaconing rate adaptation. These aspects include channel status, traffic conditions and link quality. The proposed protocols employ fuzzy logic-based techniques to determine the congestion rank, which is used to adjust beacon frequency. The first protocol considers signal to interference-noise ratio (SINR), number of neighboring nodes and mobility to determine the congestion rank and adjust the beacon rate accordingly. This protocol works well in sparse conditions and highway environments. The second protocol works well in sparse conditions and urban environments. It uses channel busy time (CBT), mobility and packet delivery ratio (PDR) to determine the congestion rank and adjust the beacon rate. The third protocol utilizes CBT, SINR, PDR, number of neighbors and mobility as inputs for the fuzzy logic system to determine the congestion rank and adjust the beacon rate. This protocol works well in dense conditions in both highway and urban environments. Through extensive simulation experiments, we established that certain input parameters are more e↵ective in beacon rate adaptation for certain environments and conditions. Based on this, we propose a high awareness and channel efficient scheme that adapts to di↵erent environments and conditions. First, the protocol estimates the network density using adaptive threshold function. Then, it looks at the spatial distribution of nodes using the quadrat method to determine whether the environment is highway or urban. Based on the density conditions and nodes distribution, the protocol utilizes the appropriate fuzzy input parameters to adapt the beaconing rate. In addition, the protocol optimizes the performance by adapting the transmission power based on network density and nodes distribution. Finally, an investigation of the impact of adaptive beaconing on broadcasting is conducted. The simulation results confirm that our adaptive beaconing scheme can improve performance of the broadcast protocols in terms of reachability and bandwidth consumption when compared to a fixed rate scheme.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013112
Subject Headings: Vehicular ad hoc networks (Computer networks), Beacons, Fuzzy logic, Adaptive computing systems
Format: Document (PDF)

Title: Techniques for combining binary classifiers: A comparative study in network intrusion detection systems.
Creator: Lin, Hua., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: We discuss a set of indirect combining techniques for addressing multi-category classification problems that have been used in many domains, but not for intrusion detection systems. In contrast to the indirect combining techniques, direct techniques generally extend associated binary classifiers to handle multi-category classification problems. An indirect combining technique decomposes the original multi-category problem into, based on some criteria, multiple binary-category problems. We...
Show moreWe discuss a set of indirect combining techniques for addressing multi-category classification problems that have been used in many domains, but not for intrusion detection systems. In contrast to the indirect combining techniques, direct techniques generally extend associated binary classifiers to handle multi-category classification problems. An indirect combining technique decomposes the original multi-category problem into, based on some criteria, multiple binary-category problems. We investigated two different approaches for building the binary classifiers. The results of the binary classifiers are then merged using a combining technique---three different combining techniques were studied. We implement some of the indirect combining techniques proposed in recent literature, and apply them to a case study of the DARPA KDD-1999 network intrusion detection project. The results demonstrate the usefulness of using indirect combining techniques for the multi-category classification problem of intrusion detection systems.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13111
Subject Headings: Computer networks--Security measures, Computer security, Computers--Access control, Electronic countermeasures, Fuzzy systems
Format: Document (PDF)

Title: A fuzzy logic based flight control system for the FAU "Ocean Voyager" autonomous underwater vehicle.
Creator: Anderson, Donald Taylor., Florida Atlantic University, Smith, Samuel M.
Abstract/Description: The development of a Flight Control System for a non-linear six degree of freedom model of an Autonomous Underwater Vehicle is described. Heading, pitch and depth are regulated by three independent Fuzzy Logic Controllers (FLCs). Numerical methods are used to tune rule bases to control tables that are based on the minimum time characteristics of the model. Setpoint errors are eliminated using fuzzily constrained integrators. A scheme to vary control policy with forward speed is also detailed....
Show moreThe development of a Flight Control System for a non-linear six degree of freedom model of an Autonomous Underwater Vehicle is described. Heading, pitch and depth are regulated by three independent Fuzzy Logic Controllers (FLCs). Numerical methods are used to tune rule bases to control tables that are based on the minimum time characteristics of the model. Setpoint errors are eliminated using fuzzily constrained integrators. A scheme to vary control policy with forward speed is also detailed. System stability is evaluated using cell-to-cell mapping. A variable structure fuzzy heading controller is designed for an unstable non-linear model of an Unmanned Underwater Vehicle. Scheduling of scaling parameters accommodates changes in forward speed as predicted by thruster RPM and angular distance turned. This FLC combines bang-bang and linear type control to respond more rapidly and robustly than a gain scheduled linear PID controller.
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Date Issued: 1993
PURL: http://purl.flvc.org/fcla/dt/14899
Subject Headings: Fuzzy sets, Submersibles--Control systems, Oceanographic submersibles--Automatic control
Format: Document (PDF)

Title: Automated Launch and Recovery of an Autonomous Underwater Vehicle from an Unmanned Surface Vessel.
Creator: Sarda, Edoardo I, Dhanak, Manhar R., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: Research on collaboration among unmanned platforms is essential to improve the applications for autonomous missions, by expanding the working environment of the robotic systems, and reducing the risks and the costs associated with conducting manned operations. This research is devoted to enable the collaboration between an Unmanned Surface Vehicle (USV) and an Autonomous Underwater Vehicle (AUV), by allowing the first one to launch and recover the second one. The objective of this...
Show moreResearch on collaboration among unmanned platforms is essential to improve the applications for autonomous missions, by expanding the working environment of the robotic systems, and reducing the risks and the costs associated with conducting manned operations. This research is devoted to enable the collaboration between an Unmanned Surface Vehicle (USV) and an Autonomous Underwater Vehicle (AUV), by allowing the first one to launch and recover the second one. The objective of this dissertation is to identify possible methods to launch and recover a REMUS 100 AUV from a WAM-V 16 USV, thus developing this capability by designing and implementing a launch and recovery system (LARS). To meet this objective, a series of preliminary experiments was first performed to identify two distinct methods to launch and recover the AUV: mobile and semi-stationary. Both methods have been simulated using the Orcaflex software. Subsequently, the necessary control systems to create the mandatory USV autonomy for the purpose of launch and recovery were developed. Specifically, a series of low-level controllers were designed and implemented to enable two autonomous maneuvers on the USV: station-keeping and speed & heading control. In addition, a level of intelligence to autonomously identify the optimal operating conditions within the vehicles' working environment, was derived and integrated on the USV. Lastly, a LARS was designed and implemented on the vehicles to perform the operation following the proposed methodology. The LARS and all subsystems developed for this research were extensively tested through sea-trials. The methodology for launch and recovery, the design of the LARS and the experimental findings are reported in this document.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004631, http://purl.flvc.org/fau/fd/FA00004631
Subject Headings: Underwater acoustic telemetry., Fuzzy systems., Nonlinear control theory., Adaptive signal processing., Oceanographic submersibles--Automatic control., Submersibles--Control systems.
Format: Document (PDF)

Title: Spectral refinement to speech enhancement.
Creator: Charoenruengkit, Werayuth., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The goal of a speech enhancement algorithm is to remove noise and recover the original signal with as little distortion and residual noise as possible. Most successful real-time algorithms thereof have done in the frequency domain where the frequency amplitude of clean speech is estimated per short-time frame of the noisy signal. The state of-the-art short-time spectral amplitude estimator algorithms estimate the clean spectral amplitude in terms of the power spectral density (PSD) function...
Show moreThe goal of a speech enhancement algorithm is to remove noise and recover the original signal with as little distortion and residual noise as possible. Most successful real-time algorithms thereof have done in the frequency domain where the frequency amplitude of clean speech is estimated per short-time frame of the noisy signal. The state of-the-art short-time spectral amplitude estimator algorithms estimate the clean spectral amplitude in terms of the power spectral density (PSD) function of the noisy signal. The PSD has to be computed from a large ensemble of signal realizations. However, in practice, it may only be estimated from a finite-length sample of a single realization of the signal. Estimation errors introduced by these limitations deviate the solution from the optimal. Various spectral estimation techniques, many with added spectral smoothing, have been investigated for decades to reduce the estimation errors. These algorithms do not address significantly issue on quality of speech as perceived by a human. This dissertation presents analysis and techniques that offer spectral refinements toward speech enhancement. We present an analytical framework of the effect of spectral estimate variance on the performance of speech enhancement. We use the variance quality factor (VQF) as a quantitative measure of estimated spectra. We show that reducing the spectral estimator VQF reduces significantly the VQF of the enhanced speech. The Autoregressive Multitaper (ARMT) spectral estimate is proposed as a low VQF spectral estimator for use in speech enhancement algorithms. An innovative method of incorporating a speech production model using multiband excitation is also presented as a technique to emphasize the harmonic components of the glottal speech input., The preconditioning of the noisy estimates by exploiting other avenues of information, such as pitch estimation and the speech production model, effectively increases the localized narrow-band signal-to noise ratio (SNR) of the noisy signal, which is subsequently denoised by the amplitude gain. Combined with voicing structure enhancement, the ARMT spectral estimate delivers enhanced speech with sound clarity desirable to human listeners. The resulting improvements in enhanced speech are observed to be significant with both Objective and Subjective measurement.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/186327
Subject Headings: Adaptive signal processing, Digital techniques, Spectral theory (Mathematics), Noise control, Fuzzy algorithms, Speech processing systems, Digital techniques
Format: Document (PDF)

Fuzzy systems (11)	+ -
Fuzzy logic (8)	+ -
Automatic control (3)	+ -
Adaptive signal processing (2)	+ -
Fuzzy algorithms (2)	+ -

Fuzzy sets (2)	+ -
GMDH algorithms (2)	+ -
Genetic algorithms (2)	+ -
Intelligent control systems (2)	+ -
Neural networks (Computer science) (2)	+ -
Nonlinear control theory (2)	+ -
Pattern recognition systems (2)	+ -
Acoustical engineering (1)	+ -
Adaptive computing systems (1)	+ -
Adaptive signal processing. (1)	+ -
Algorithms. (1)	+ -
Artificial intelligence (1)	+ -
Artificial intelligence. (1)	+ -
Beacons (1)	+ -
Computer networks--Security measures (1)	+ -
Computer security (1)	+ -
Computer simulation (1)	+ -
Computer software--Quality control (1)	+ -
Computers--Access control (1)	+ -
Control systems (1)	+ -
Cooperative game theory (1)	+ -
Digital techniques (1)	+ -
Electrical engineering (1)	+ -
Electronic countermeasures (1)	+ -
Energy (1)	+ -

College of Engineering and Computer Science (18)	+ -
Florida Atlantic University (15)	+ -
Department of Computer and Electrical Engineering and Computer Science (13)	+ -
Department of Ocean and Mechanical Engineering (5)	+ -
Khoshgoftaar, Taghi M. (3)	+ -

Zhuang, Hanqi (3)	+ -
Smith, Samuel M. (2)	+ -
Abtahi, Amir (1)	+ -
Alhameed, Mohammed (1)	+ -
An, Pak-Cheung (1)	+ -
Anderson, Donald Taylor. (1)	+ -
Bai, Ying. (1)	+ -
Bauer, Eric John. (1)	+ -
Charoenruengkit, Werayuth. (1)	+ -
Dabbas, Mohammad A. (1)	+ -
Das, Avijit (1)	+ -
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