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Docking the Ocean Explorer Autonomous Underwater Vehicle using a low-cost acoustic positioning system and a fuzzy logic guidance algorithm
Title
Docking the Ocean Explorer Autonomous Underwater Vehicle using a low-cost acoustic positioning system and a fuzzy logic guidance algorithm.
Creator
Kronen, David Mitchell., Florida Atlantic University, Smith, Samuel M., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
Having the ability to dock an Autonomous Underwater Vehicle (AUV) can significantly enhance the operation of such vehicles. In order to dock an AUV, the vehicle's position must be known precisely and a guidance algorithm must be used to drive the AUV to its dock. This thesis will examine and implement a low cost acoustic positioning system to meet the positioning requirements. At-sea tests will be used as a method of verifying the systems specifications and proper incorporation into the AUV....
Show moreHaving the ability to dock an Autonomous Underwater Vehicle (AUV) can significantly enhance the operation of such vehicles. In order to dock an AUV, the vehicle's position must be known precisely and a guidance algorithm must be used to drive the AUV to its dock. This thesis will examine and implement a low cost acoustic positioning system to meet the positioning requirements. At-sea tests will be used as a method of verifying the systems specifications and proper incorporation into the AUV. Analyses will be run on the results using several methods of interpreting the data. The second portion of this thesis will develop and test a fuzzy logic docking algorithm which will guide the AUV from a location within the range of the sonar system to the docking station. A six degree of freedom simulation incorporating the Ocean Explorer's hydrodynamic coefficients will be used for the simulation.
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Date Issued
1997
PURL
http://purl.flvc.org/fcla/dt/15502
Subject Headings
Oceanographic submersibles, Acoustical engineering, Underwater acoustics, Fuzzy algorithms
Format
Document (PDF)
novel NN paradigm for the prediction of hematocrit value during blood transfusion
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)
Intelligent systems using GMDH algorithms
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)
An evaluation of machine learning algorithms for tweet sentiment analysis
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)
fuzzy logic material selection methodology for renewable ocean energy applications
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)
Fuzzy identification of processes on finite training sets with known features
Title
Fuzzy identification of processes on finite training sets with known features.
Creator
Diaz-Robainas, Regino R., Florida Atlantic University, Huang, Ming Z., Zilouchian, Ali, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
A methodology is presented to construct an approximate fuzzy-mapping algorithm that maps multiple inputs to single outputs given a finite training set of argument vectors functionally linked to corresponding scalar outputs. Its scope is limited to problems where the features are known in advance, or equivalently, where the expected functional representation is known to depend exclusively on the known selected variables. Programming and simulations to implement the methodology make use of...
Show moreA methodology is presented to construct an approximate fuzzy-mapping algorithm that maps multiple inputs to single outputs given a finite training set of argument vectors functionally linked to corresponding scalar outputs. Its scope is limited to problems where the features are known in advance, or equivalently, where the expected functional representation is known to depend exclusively on the known selected variables. Programming and simulations to implement the methodology make use of Matlab Fuzzy and Neural toolboxes and a PC application of Prolog, and applications range from approximate representations of the direct kinematics of parallel manipulators to fuzzy controllers.
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Date Issued
1996
PURL
http://purl.flvc.org/fcla/dt/12487
Subject Headings
Fuzzy algorithms, Set theory, Logic, Symbolic and mathematical, Finite groups, Representations of groups
Format
Document (PDF)
Spectral refinement to speech enhancement
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)