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Pages
- Title
- Exploration of life and decay in technological civilization.
- Creator
-
Wieser, Mauro, Kovach, Keith, Adams, JoAnne, Burrell, Jason, University of Central Florida
- Abstract / Description
-
Reflecting upon humanity's obligatory use of technology and its place in our collective evolution has become my endeavor. These reflections happen in a studio and through a process that influences the fine art objects produced. In turn the objects both celebrate and warn us of the dynamic and immanent enhanced human. I balance the use of modern machining processes with dark humor to comment and raise questions about the coexistence of man and machine in an increasingly absurd environment.
- Date Issued
- 2019
- Identifier
- CFE0007555, ucf:52608
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007555
- Title
- A STRUCTURAL AND FUNCTIONAL ANALYSIS OF HUMAN BRAIN MRI WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER.
- Creator
-
Watane, Arjun A, Bagci, Ulas, University of Central Florida
- Abstract / Description
-
Attention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is...
Show moreAttention Deficit Hyperactivity Disorder (ADHD) affects 5-10% of children worldwide. Its effects are mainly behavioral, manifesting in symptoms such as inattention, hyperactivity, and impulsivity. If not monitored and treated, ADHD may adversely affect a child's health, education, and social life. Furthermore, the neurological disorder is currently diagnosed through interviews and opinions of teachers, parents, and physicians. Because this is a subjective method of identifying ADHD, it is easily prone to error and misdiagnosis. Therefore, there is a clear need to develop an objective diagnostic method for ADHD. The focus of this study is to explore the use of machine language classifiers on information from the brain MRI and fMRI of both ADHD and non-ADHD subjects. The imaging data are preprocessed to remove any intra-subject and inter-subject variation. For both MRI and fMRI, similar preprocessing stages are performed, including normalization, skull stripping, realignment, smoothing, and co-registration. The next step is to extract features from the data. For MRI, anatomical features such as cortical thickness, surface area, volume, and intensity are obtained. For fMRI, region of interest (ROI) correlation coefficients between 116 cortical structures are determined. A large number of image features are collected, yet many of them may include redundant and useless information. Therefore, the features used for training and testing the classifiers are selected in two separate ways, feature ranking and stability selection, and their results are compared. Once the best features from MRI and fMRI are determined, the following classifiers are trained and tested through leave-one-out cross validation, experimenting with varying feature numbers, for each imaging modality and feature selection method: support vector machine, support vector regression, random forest, and elastic net. Thus, there are four experiments (MRI-rank, MRI-stability, fMRI-rank, fMRI-stability) with four classifiers in each for a total of 16 classifiers trained per each feature count attempted. The results of each classifier are the decisions of each subject, ADHD or non-ADHD. Finally, a classifier decision ensemble is created through the combination of the outputs of the best classifiers in a majority voting method that includes results of both the MRI and fMRI classifiers and keeps both feature selection results independent. The results suggest that ADHD is more easily identified through fMRI because the classification accuracies are a lot higher using fMRI data rather than MRI data. Furthermore, significant activity correlation differences exist between the brain's frontal lobe and cerebellum and also the left and right hemispheres among ADHD and non-ADHD subjects. When including MRI decisions with fMRI in the classifier ensemble, performance is boosted to a high ADHD detection accuracy of 96.2%, suggesting that MRI information assists in validating fMRI classification decisions. This study is an important step towards the development of an automatic and objective method for ADHD diagnosis. While more work is needed to externally validate and improve the classification accuracy, new applications of current methods with promising results are introduced here.
Show less - Date Issued
- 2017
- Identifier
- CFH2000203, ucf:45978
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000203
- Title
- MODERATORS OF TRUST AND RELIANCE ACROSS MULTIPLE DECISION AIDS.
- Creator
-
Ross, Jennifer, Szalma, James, University of Central Florida
- Abstract / Description
-
The present work examines whether user's trust of and reliance on automation, were affected by the manipulations of user's perception of the responding agent. These manipulations included agent reliability, agent type, and failure salience. Previous work has shown that automation is not uniformly beneficial; problems can occur because operators fail to rely upon automation appropriately, by either misuse (overreliance) or disuse (underreliance). This is because operators often face...
Show moreThe present work examines whether user's trust of and reliance on automation, were affected by the manipulations of user's perception of the responding agent. These manipulations included agent reliability, agent type, and failure salience. Previous work has shown that automation is not uniformly beneficial; problems can occur because operators fail to rely upon automation appropriately, by either misuse (overreliance) or disuse (underreliance). This is because operators often face difficulties in understanding how to combine their judgment with that of an automated aid. This difficulty is especially prevalent in complex tasks in which users rely heavily on automation to reduce their workload and improve task performance. However, when users rely on automation heavily they often fail to monitor the system effectively (i.e., they lose situation awareness – a form of misuse). However, if an operator realizes a system is imperfect and fails, they may subsequently lose trust in the system leading to underreliance. In the present studies, it was hypothesized that in a dual-aid environment poor reliability in one aid would impact trust and reliance levels in a companion better aid, but that this relationship is dependent upon the perceived aid type and the noticeability of the errors made. Simulations of a computer-based search-and-rescue scenario, employing uninhabited/unmanned ground vehicles (UGVs) searching a commercial office building for critical signals, were used to investigate these hypotheses. Results demonstrated that participants were able to adjust their reliance and trust on automated teammates depending on the teammate's actual reliability levels. However, as hypothesized there was a biasing effect among mixed-reliability aids for trust and reliance. That is, when operators worked with two agents of mixed-reliability, their perception of how reliable and to what degree they relied on the aid was effected by the reliability of a current aid. Additionally, the magnitude and direction of how trust and reliance were biased was contingent upon agent type (i.e., 'what' the agents were: two humans, two similar robotic agents, or two dissimilar robot agents). Finally, the type of agent an operator believed they were operating with significantly impacted their temporal reliance (i.e., reliance following an automation failure). Such that, operators were less likely to agree with a recommendation from a human teammate, after that teammate had made an obvious error, than with a robotic agent that had made the same obvious error. These results demonstrate that people are able to distinguish when an agent is performing well but that there are genuine differences in how operators respond to agents of mixed or same abilities and to errors by fellow human observers or robotic teammates. The overall goal of this research was to develop a better understanding how the aforementioned factors affect users' trust in automation so that system interfaces can be designed to facilitate users' calibration of their trust in automated aids, thus leading to improved coordination of human-automation performance. These findings have significant implications to many real-world systems in which human operators monitor the recommendations of multiple other human and/or machine systems.
Show less - Date Issued
- 2008
- Identifier
- CFE0002077, ucf:47579
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002077
- Title
- ADAPTIVE TECHNOMYTHOGRAPHY: THE APOTHEOSIS OF MACHINE AND DEVELOPMENT OF LEGEND IN A SYSTEM OF DYNAMIC TECHNOLOGY.
- Creator
-
wolf, roger, Robinson, Brady, University of Central Florida
- Abstract / Description
-
Human beings will effectively deify any suitably complex system that cannot be explained through basic haptic interaction. Our culture loves technology. These days it seems we need it to feel whole. In an effort to explore the development of mythology and modular aesthetic in a technological age I have designed and constructed a number of interactive robotic 'organisms' to engage in arbitrary movement in geometric enclosures. Through observation and dialog I seek to assess the extent...
Show moreHuman beings will effectively deify any suitably complex system that cannot be explained through basic haptic interaction. Our culture loves technology. These days it seems we need it to feel whole. In an effort to explore the development of mythology and modular aesthetic in a technological age I have designed and constructed a number of interactive robotic 'organisms' to engage in arbitrary movement in geometric enclosures. Through observation and dialog I seek to assess the extent to which people assign human characteristics to the random and oft times aberrant mechanical behavior. To supplement this endeavor, a fictional astrological system that proposes logical (albeit mythological) explanations for the peculiarities in these relationships has been created.
Show less - Date Issued
- 2007
- Identifier
- CFE0001677, ucf:47197
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001677
- Title
- Robust, Scalable, and Provable Approaches to High Dimensional Unsupervised Learning.
- Creator
-
Rahmani, Mostafa, Atia, George, Vosoughi, Azadeh, Mikhael, Wasfy, Nashed, M, Pensky, Marianna, University of Central Florida
- Abstract / Description
-
This doctoral thesis focuses on three popular unsupervised learning problems: subspace clustering, robust PCA, and column sampling. For the subspace clustering problem, a new transformative idea is presented. The proposed approach, termed Innovation Pursuit, is a new geometrical solution to the subspace clustering problem whereby subspaces are identified based on their relative novelties. A detailed mathematical analysis is provided establishing sufficient conditions for the proposed method...
Show moreThis doctoral thesis focuses on three popular unsupervised learning problems: subspace clustering, robust PCA, and column sampling. For the subspace clustering problem, a new transformative idea is presented. The proposed approach, termed Innovation Pursuit, is a new geometrical solution to the subspace clustering problem whereby subspaces are identified based on their relative novelties. A detailed mathematical analysis is provided establishing sufficient conditions for the proposed method to correctly cluster the data points. The numerical simulations with both real and synthetic data demonstrate that Innovation Pursuit notably outperforms the state-of-the-art subspace clustering algorithms. For the robust PCA problem, we focus on both the outlier detection and the matrix decomposition problems. For the outlier detection problem, we present a new algorithm, termed Coherence Pursuit, in addition to two scalable randomized frameworks for the implementation of outlier detection algorithms. The Coherence Pursuit method is the first provable and non-iterative robust PCA method which is provably robust to both unstructured and structured outliers. Coherence Pursuit is remarkably simple and it notably outperforms the existing methods in dealing with structured outliers. In the proposed randomized designs, we leverage the low dimensional structure of the low rank component to apply the robust PCA algorithm to a random sketch of the data as opposed to the full scale data. Importantly, it is analytically shown that the presented randomized designs can make the computation or sample complexity of the low rank matrix recovery algorithm independent of the size of the data. At the end, we focus on the column sampling problem. A new sampling tool, dubbed Spatial Random Sampling, is presented which performs the random sampling in the spatial domain. The most compelling feature of Spatial Random Sampling is that it is the first unsupervised column sampling method which preserves the spatial distribution of the data.
Show less - Date Issued
- 2018
- Identifier
- CFE0007083, ucf:52010
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007083
- Title
- Relating First-person and Third-person Vision.
- Creator
-
Ardeshir Behrostaghi, Shervin, Borji, Ali, Shah, Mubarak, Hu, Haiyan, Atia, George, University of Central Florida
- Abstract / Description
-
Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.),...
Show moreThanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones and glasses, we have access to a plethora of videos captured from the first person (egocentric) perspective. Capturing the world from the perspective of one's self, egocentric videos bear characteristics distinct from the more traditional third-person (exocentric) videos. In many computer vision tasks (e.g. identification, action recognition, face recognition, pose estimation, etc.), the human actors are the main focus. Hence, detecting, localizing, and recognizing the human actor is often incorporated as a vital component. In an egocentric video however, the person behind the camera is often the person of interest. This would change the nature of the task at hand, given that the camera holder is usually not visible in the content of his/her egocentric video. In other words, our knowledge about the visual appearance, pose, etc. on the egocentric camera holder is very limited, suggesting reliance on other cues in first person videos. First and third person videos have been separately studied in the past in the computer vision community. However, the relationship between first and third person vision has yet to be fully explored. Relating these two views systematically could potentially benefit many computer vision tasks and applications. This thesis studies this relationship in several aspects. We explore supervised and unsupervised approaches for relating these two views seeking different objectives such as identification, temporal alignment, and action classification. We believe that this exploration could lead to a better understanding the relationship of these two drastically different sources of information.
Show less - Date Issued
- 2018
- Identifier
- CFE0007151, ucf:52322
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007151
- Title
- Methods for online feature selection for classification problems.
- Creator
-
Razmjoo, Alaleh, Zheng, Qipeng, Rabelo, Luis, Boginski, Vladimir, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
-
Online learning is a growing branch of machine learning which allows all traditional data miningtechniques to be applied on an online stream of data in real-time. In this dissertation, we presentthree efficient algorithms for feature ranking in online classification problems. Each of the methodsare tailored to work well with different types of classification tasks and have different advantages.The reason for this variety of algorithms is that like other machine learning solutions, there is...
Show moreOnline learning is a growing branch of machine learning which allows all traditional data miningtechniques to be applied on an online stream of data in real-time. In this dissertation, we presentthree efficient algorithms for feature ranking in online classification problems. Each of the methodsare tailored to work well with different types of classification tasks and have different advantages.The reason for this variety of algorithms is that like other machine learning solutions, there is usuallyno algorithm which works well for all types of tasks. The first method, is an online sensitivitybased feature ranking (SFR) which is updated incrementally, and is designed for classificationtasks with continuous features. We take advantage of the concept of global sensitivity and rankfeatures based on their impact on the outcome of the classification model. In the feature selectionpart, we use a two-stage filtering method in order to first eliminate highly correlated and redundantfeatures and then eliminate irrelevant features in the second stage. One important advantage of ouralgorithm is its generality, which means the method works for correlated feature spaces withoutpreprocessing. It can be implemented along with any single-pass online classification method withseparating hyperplane such as SVMs. In the second method, with help of probability theory wepropose an algorithm which measures the importance of the features by observing the changes inlabel prediction in case of feature substitution. A non-parametric version of the proposed methodis presented to eliminate the distribution type assumptions. These methods are application to alldata types including mixed feature spaces. At last, we present a class-based feature importanceranking method which evaluates the importance of each feature for each class, these sub-rankingsare further exploited to train an ensemble of classifiers. The proposed methods will be thoroughlytested using benchmark datasets and the results will be discussed in the last chapter.
Show less - Date Issued
- 2018
- Identifier
- CFE0007584, ucf:52567
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007584
- Title
- D-FENS: DNS Filtering (&) Extraction Network System for Malicious Domain Names.
- Creator
-
Spaulding, Jeffrey, Mohaisen, Aziz, Leavens, Gary, Bassiouni, Mostafa, Fu, Xinwen, Posey, Clay, University of Central Florida
- Abstract / Description
-
While the DNS (Domain Name System) has become a cornerstone for the operation of the Internet, it has also fostered creative cases of maliciousness, including phishing, typosquatting, and botnet communication among others. To address this problem, this dissertation focuses on identifying and mitigating such malicious domain names through prior knowledge and machine learning. In the first part of this dissertation, we explore a method of registering domain names with deliberate typographical...
Show moreWhile the DNS (Domain Name System) has become a cornerstone for the operation of the Internet, it has also fostered creative cases of maliciousness, including phishing, typosquatting, and botnet communication among others. To address this problem, this dissertation focuses on identifying and mitigating such malicious domain names through prior knowledge and machine learning. In the first part of this dissertation, we explore a method of registering domain names with deliberate typographical mistakes (i.e., typosquatting) to masquerade as popular and well-established domain names. To understand the effectiveness of typosquatting, we conducted a user study which helped shed light on which techniques were more (")successful(") than others in deceiving users. While certain techniques fared better than others, they failed to take the context of the user into account. Therefore, in the second part of this dissertation we look at the possibility of an advanced attack which takes context into account when generating domain names. The main idea is determining the possibility for an adversary to improve their (")success(") rate of deceiving users with specifically-targeted malicious domain names. While these malicious domains typically target users, other types of domain names are generated by botnets for command (&) control (C2) communication. Therefore, in the third part of this dissertation we investigate domain generation algorithms (DGA) used by botnets and propose a method to identify DGA-based domain names. By analyzing DNS traffic for certain patterns of NXDomain (non-existent domain) query responses, we can accurately predict DGA-based domain names before they are registered. Given all of these approaches to malicious domain names, we ultimately propose a system called D-FENS (DNS Filtering (&) Extraction Network System). D-FENS uses machine learning and prior knowledge to accurately predict unreported malicious domain names in real-time, thereby preventing Internet devices from unknowingly connecting to a potentially malicious domain name.
Show less - Date Issued
- 2018
- Identifier
- CFE0007587, ucf:52540
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007587
- Title
- Multi-Sensor Optimization of the Simultaneous Turning and Boring Operation.
- Creator
-
Deane, Erick, Xu, Chengying, Gou, Jihua, Gordon, Ali, University of Central Florida
- Abstract / Description
-
To remain competitive in today's demanding economy, there is an increasing demand for improved productivity and scrap reduction in manufacturing. Traditional manufacturing metal removal processes such as turning and boring are still one of the most used techniques for fabricating metal products. Although the essential metal removal process is the same, new advances in technology have led to improvements in the monitoring of the process allowing for reduction of power consumption, tool wear,...
Show moreTo remain competitive in today's demanding economy, there is an increasing demand for improved productivity and scrap reduction in manufacturing. Traditional manufacturing metal removal processes such as turning and boring are still one of the most used techniques for fabricating metal products. Although the essential metal removal process is the same, new advances in technology have led to improvements in the monitoring of the process allowing for reduction of power consumption, tool wear, and total cost of production. Replacing used CNC lathes from the 1980's in a manufacturing facility may prove costly, thus finding a method to modernize the lathes is vital.This research focuses on Phase I and II of a three phase research project where the final goal is to optimize the simultaneous turning and boring operation of a CNC Lathe. From the optimization results it will be possible to build an adaptive controller that will produce parts rapidly while minimizing tool wear and machinist interaction with the lathe. Phase I of the project was geared towards selecting the sensors that were to be used to monitor the operation and designing a program with an architecture that would allow for simultaneous data collection from the selected sensors at high sampling rates. Signals monitored during the operation included force, temperature, vibration, sound, acoustic emissions, power, and metalworking fluid flow rates. Phase II of this research is focused on using the Response Surface Method to build empirical models for various responses and to optimize the simultaneous cutting process. The simultaneous turning and boring process was defined by the four factors of spindle speed, feed rate, outer diameter depth of cut, and inner diameter depth of cut. A total of four sets of experiments were performed. The first set of experiments screened the experimental region toiiidetermine if the cutting parameters were feasible. The next three set s of designs of experiments used Central Composite Designs to build empirical models of each desired response in terms of the four factors and to optimize the process. Each design of experiments was compared with one another to validate that the results achieved were accurate within the experimental region.By using the Response Surface Method optimal machining parameter settings were achieved. The algorithm used to search for optimal process parameter settings was the desirability function. By applying the results from this research to the manufacturing facility, they will achieve reduction in power consumption, reduction in production time, and decrease in the total cost of each part.
Show less - Date Issued
- 2011
- Identifier
- CFE0004098, ucf:49087
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004098
- Title
- THE IMPLICATIONS OF VIRTUAL ENVIRONMENTS IN DIGITAL FORENSIC INVESTIGATIONS.
- Creator
-
Patterson, Farrah, Lang, Sheau-Dong, Guha, Ratan, Zou, Changchun, University of Central Florida
- Abstract / Description
-
This research paper discusses the role of virtual environments in digital forensic investigations. With virtual environments becoming more prevalent as an analysis tool in digital forensic investigations, it's becoming more important for digital forensic investigators to understand the limitation and strengths of virtual machines. The study aims to expose limitations within commercial closed source virtual machines and open source virtual machines. The study provides a brief overview of...
Show moreThis research paper discusses the role of virtual environments in digital forensic investigations. With virtual environments becoming more prevalent as an analysis tool in digital forensic investigations, it's becoming more important for digital forensic investigators to understand the limitation and strengths of virtual machines. The study aims to expose limitations within commercial closed source virtual machines and open source virtual machines. The study provides a brief overview of history digital forensic investigations and virtual environments, and concludes with an experiment with four common open and closed source virtual machines; the effects of the virtual machines on the host machine as well as the performance of the virtual machine itself. My findings discovered that while the open source tools provided more control and freedom to the operator, the closed source tools were more stable and consistent in their operation. The significance of these findings can be further researched by applying them in the context of exemplifying reliability of forensic techniques when presented as analysis tool used in litigation.
Show less - Date Issued
- 2011
- Identifier
- CFE0004152, ucf:49050
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004152
- Title
- INTEGRATED SERVOMECHANISM AND PROCESS CONTROL FOR MACHINING PROCESSES.
- Creator
-
Tang, Yan, Xu, Chengying, University of Central Florida
- Abstract / Description
-
In this research, the integration of the servomechanism control and process control for machining processes has been studied. As enabling strategies for next generation quality control, process monitoring and open architecture machine tools will be implemented on production floor. This trend brings a new method to implement control algorithm in machining processes. Instead of using separate modules for servomechanism control and process control individually, the integrated controller is...
Show moreIn this research, the integration of the servomechanism control and process control for machining processes has been studied. As enabling strategies for next generation quality control, process monitoring and open architecture machine tools will be implemented on production floor. This trend brings a new method to implement control algorithm in machining processes. Instead of using separate modules for servomechanism control and process control individually, the integrated controller is proposed in this research to simultaneously achieve goals in servomechanism level and the process level. This research is motivated by the benefits brought by the integration of servomechanism control and process control. Firstly, the integration simplifies the control system design. Secondly, the integration promotes the adoption of process control on production floor. Thirdly, the integration facilitates portability between machine tools. Finally, the integration provides convenience for both the servomechanism and process simulation in virtual machine tool environment. The servomechanism control proposed in this research is based on error space approach. This approach is suitable for motion control for complex contour. When implement the integration of servomechanism control and process control, two kinds of processes may be encountered. One is the process whose model parameters can be aggregated with the servomechanism states and the tool path does not need real time offset. The other is the process which does not have direct relationship with the servomechanism states and tool path may need to be modified real time during machining. The integration strategies applied in error space are proposed for each case. Different integration strategies would propagate the process control goal into the motion control scheme such that the integrated control can simultaneously achieve goals of both the servomechanism and the process levels. Integrated force-contour-position control in turning is used as one example in which the process parameters can be aggregated with the servomechanism states. In this case, the process level aims to minimize cutting force variation while the servomechanism level is to achieve zero contour error. Both force variation and contour error can be represented by the servomechanism states. Then, the integrated control design is formulated as a linear quadratic regulator (LQR) problem in error space. Force variation and contour error are treated as part of performance index to be minimized in the LQR problem. On the other hand, the controller designed by LQR in error space can guarantee the asymptotic tracking stability of the servomechanism for complex contour. Therefore, the integrated controller can implement the process control and the servomechanism control simultaneously. Cutter deflection compensation for helical end milling processes is used as one example in which the process cannot be directly associated with the servomechanism states. Cutter deflection compensation requires real-time tool path offset to reduce the surface error due to cutter deflection. Therefore, real time interpolation is required to provide reference trajectory for the servomechanism controller. With the real time information about surface error, the servomechanism controller can not only implement motion control for contour requirement, but also compensation for the dimensional error caused by cutter deflection. In other words, the real time interpolator along with the servomechanism controller can achieve the goals of both the servomechanism and process level. In this study, the cutter deflection in helical end milling processes is analyzed first to illustrate the indirect relationship between cutter deflection and surface accuracy. Cutter deflection is examined for three kinds of surfaces including straight surface, circular surface, and curved surface. The simulation-based deflection analysis will be used to emulate measurement from sensors and update the real-time interpolator to offset tool path. The controller designed through pole placement in error space can guarantee the robust tracking performance of the updated reference trajectory combining both contour and tool path offset required for deflection compensation. A variety of cutting conditions are simulated to demonstrate the compensation results. In summary, the process control is integrated with the servomechanism control through either direct servomechanism controller design without tool path modification or servomechanism control with real time interpolation responding to process variation. Therefore, the process control can be implemented as a module within machine tools. Such integration will enhance the penetration of process control on production floor to increase machining productivity and product quality.
Show less - Date Issued
- 2009
- Identifier
- CFE0002758, ucf:48116
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002758
- Title
- A MACHINE LEARNING APPROACH TO ASSESS THE SEPARATION OF SEISMOCARDIOGRAPHIC SIGNALS BY RESPIRATION.
- Creator
-
Solar, Brian, Mansy, Hansen, University of Central Florida
- Abstract / Description
-
The clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically...
Show moreThe clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically significant information, such as systolic time intervals, can thus manifest themselves differently in an SCG signal during inspiration and expiration. Grouping SCG signals into their respective respiratory cycle can mitigate this issue. Prior research has focused on developing machine learning classification methods to classify SCG events as according to their respiration cycle. However, recent research at the Biomedical Acoustics Research Laboratory (BARL) at UCF suggests grouping SCG signals into high and low lung volume may be more effective. This research aimed at com- paring the efficiency of grouping SCG signals according to their respiration and lung volume phase and also developing a method to automatically identify the respiration and lung volume phase of SCG events.
Show less - Date Issued
- 2018
- Identifier
- CFH2000310, ucf:45877
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000310
- Title
- EVOLUTIONARY OPTIMIZATION OF SUPPORT VECTOR MACHINES.
- Creator
-
Gruber, Fred, Rabelo, Luis, University of Central Florida
- Abstract / Description
-
Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is...
Show moreSupport vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce.
Show less - Date Issued
- 2004
- Identifier
- CFE0000244, ucf:46251
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000244
- Title
- USING STUDENT MOOD AND TASK PERFORMANCE TO TRAIN CLASSIFIER ALGORITHMS TO SELECT EFFECTIVE COACHING STRATEGIES WITHIN INTELLIGENT TUTORING SYSTEMS (ITS).
- Creator
-
Sottilare, Robert, Proctor, Michael, University of Central Florida
- Abstract / Description
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The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to...
Show moreThe ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student's affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student's interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student's mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle's (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank's (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables.
Show less - Date Issued
- 2009
- Identifier
- CFE0002528, ucf:47644
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002528
- Title
- SUPER HIGH-SPEED MINIATURIZED PERMANENT MAGNET SYNCHRONOUS MOTOR.
- Creator
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Zheng, Liping, Sundaram, Kalpathy, University of Central Florida
- Abstract / Description
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This dissertation is concerned with the design of permanent magnet synchronous motors (PMSM) to operate at super-high speed with high efficiency. The designed and fabricated PMSM was successfully tested to run upto 210,000 rpm The designed PMSM has 2000 W shaft output power at 200,000 rpm and at the cryogenic temperature of 77 K. The test results showed the motor to have an efficiency reaching above 92%. This achieved efficiency indicated a significant improvement compared to commercial...
Show moreThis dissertation is concerned with the design of permanent magnet synchronous motors (PMSM) to operate at super-high speed with high efficiency. The designed and fabricated PMSM was successfully tested to run upto 210,000 rpm The designed PMSM has 2000 W shaft output power at 200,000 rpm and at the cryogenic temperature of 77 K. The test results showed the motor to have an efficiency reaching above 92%. This achieved efficiency indicated a significant improvement compared to commercial motors with similar ratings. This dissertation first discusses the basic concept of electrical machines. After that, the modeling of PMSM for dynamic simulation is provided. Particular design strategies have to be adopted for super-high speed applications since motor losses assume a key role in the motor drive performance limit. The considerations of the PMSM structure for cryogenic applications are also discussed. It is shown that slotless structure with multi-strand Litz-wire is favorable for super-high speeds and cryogenic applications. The design, simulation, and test of a single-sided axial flux pancake PMSM is presented. The advantages and disadvantages of this kind of structure are discussed, and further improvements are suggested and some have been verified by experiments. The methodologies of designing super high-speed motors are provided in details. Based on these methodologies, a super high-speed radial-flux PMSM was designed and fabricated. The designed PMSM meets our expectation and the tested results agree with the design specifications. 2-D and 3-D modeling of the complicated PMSM structure for the electromagnetic numerical simulations of motor performance and parameters such as phase inductors, core losses, rotor eddy current loss, torque, and induced electromotive force (back-EMF) are also presented in detail in this dissertation. Some mechanical issues such as thermal analysis, bearing pre-load, rotor stress analysis, and rotor dynamics analysis are also discussed. Different control schemes are presented and suitable control schemes for super high- speed PMSM are also discussed in detail.
Show less - Date Issued
- 2005
- Identifier
- CFE0000762, ucf:46562
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000762
- Title
- INDIVIDUAL PREFERENCES IN THE USE OF AUTOMATION.
- Creator
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Thropp, Jennifer, Hancock, Peter, University of Central Florida
- Abstract / Description
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As system automation increases and evolves, the intervention of the supervising operator becomes ever less frequent but ever more crucial. The adaptive automation approach is one in which control of tasks dynamically shifts between humans and machines, being an alternative to traditional static allocation in which task control is assigned during system design and subsequently remains unchanged during operations. It is proposed that adaptive allocation should adjust to the individual operators...
Show moreAs system automation increases and evolves, the intervention of the supervising operator becomes ever less frequent but ever more crucial. The adaptive automation approach is one in which control of tasks dynamically shifts between humans and machines, being an alternative to traditional static allocation in which task control is assigned during system design and subsequently remains unchanged during operations. It is proposed that adaptive allocation should adjust to the individual operators' characteristics in order to improve performance, avoid errors, and enhance safety. The roles of three individual difference variables relevant to adaptive automation are described: attentional control, desirability of control, and trait anxiety. It was hypothesized that these traits contribute to the level of performance for target detection tasks for different levels of difficulty as well as preferences for different levels of automation. The operators' level of attentional control was inversely proportional to automation level preferences, although few objective performance changes were observed. The effects of sensory modality were also assessed, and auditory signal detection was superior to visual signal detection. As a result, the following implications have been proposed: operators generally preferred either low or high automation while neglecting the intermediary level; preferences and needs for automation may not be congruent; and there may be a conservative response bias associated with high attentional control, notably in the auditory modality.
Show less - Date Issued
- 2006
- Identifier
- CFE0001096, ucf:46771
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001096
- Title
- MULTIZOOM ACTIVITY RECOGNITION USING MACHINE LEARNING.
- Creator
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Smith, Raymond, Shah, Mubarak, University of Central Florida
- Abstract / Description
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In this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The...
Show moreIn this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The approach taken here is to build a large set of activity features, though TemporalBoost itself is able to work with any feature set other boosting algorithms use. We also show how multiple levels of zoom can cooperate to solve problems related to activity recognition.
Show less - Date Issued
- 2005
- Identifier
- CFE0000865, ucf:46658
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000865
- Title
- AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS.
- Creator
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Kaylani, Assem, Georgiopoulos, Michael, University of Central Florida
- Abstract / Description
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This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing...
Show moreThis dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have focused on the evolutionary optimization of neural networks, but no research has been performed on the evolutionary optimization of ART neural networks, prior to 2006, when Daraiseh has used evolutionary techniques for the optimization of ART structures. This dissertation extends in many ways and expands in different directions the evolution of ART architectures, such as: (a) uses a multi-objective optimization of ART structures, thus providing to the user multiple solutions (ART networks) with varying degrees of merit, instead of a single solution (b) uses GA parameters that are adaptively determined throughout the ART evolution, (c) identifies a proper size of the validation set used to calculate the fitness function needed for ART's evolution, thus speeding up the evolutionary process, (d) produces experimental results that demonstrate the evolved ART's effectiveness (good accuracy and small size) and efficiency (speed) compared with other competitive ART structures, as well as other classifiers (CART (Classification and Regression Trees) and SVM (Support Vector Machines)). The overall methodology to evolve ART using a multi-objective approach, the chromosome representation of an ART neural network, the genetic operators used in ART's evolution, and the automatic adaptation of some of the GA parameters in ART's evolution could also be applied in the evolution of other exemplar based neural network classifiers such as the probabilistic neural network and the radial basis function neural network.
Show less - Date Issued
- 2008
- Identifier
- CFE0002212, ucf:47907
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002212
- Title
- Navigation of an Autonomous Differential Drive Robot for Field Scouting in Semi-structured Environments.
- Creator
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Freese, Douglas, Xu, Yunjun, Lin, Kuo-Chi, Kauffman, Jeffrey L., Behal, Aman, University of Central Florida
- Abstract / Description
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In recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary...
Show moreIn recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary encoding, time of flight as well as vision. To compensate for anticipated disturbances, variances and constraints contingent to the outdoor semi-structured environment, a differential style drive vehicle will be implemented as an easily controllable system to conduct tasks such as imaging and sampling.In order to verify the motion control of a robot, custom-designed for strawberry fields, the task is separated into multiple phases to manage the over-bed and cross-bed operation needs. In particular, during the cross-bed segment an elevated strawberry bed will provide distance references utilized in a logic filter and tuned PID algorithm for safe and efficient travel. Due to the significant sources of uncertainty such as wheel slip and the vehicle model, nonlinear robust controllers are designed for the cross-bed motion, purely relying on vision feedback. A simple image filter algorithm was developed for strawberry row detection, in which pixels corresponding to the bed center will be tracked while the vehicle is in controlled motion. This incorporated derivation and formulation of a bounded uncertainty parameter that will be employed in the nonlinear control. Simulation of the entire system was subsequently completed to ensure the control capability before successful validation in multiple commercial farms. It is anticipated that with the developed algorithms the authentication of fully autonomous robotic systems functioning in agricultural crops will provide heightened efficiency of needed costly services; scouting, disease detection, collection, and distribution.
Show less - Date Issued
- 2018
- Identifier
- CFE0007401, ucf:52743
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007401
- Title
- Characterization, Classification, and Genesis of Seismocardiographic Signals.
- Creator
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Taebi, Amirtaha, Mansy, Hansen, Kassab, Alain, Huang, Helen, Vosoughi, Azadeh, University of Central Florida
- Abstract / Description
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Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction.In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency...
Show moreSeismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction.In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms.Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features.SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG.
Show less - Date Issued
- 2018
- Identifier
- CFE0007106, ucf:51944
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007106
