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(1 - 8 of 8)
- Title
- Weighting Policies for Robust Unsupervised Ensemble Learning.
- Creator
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Unlu, Ramazan, Xanthopoulos, Petros, Zheng, Qipeng, Rabelo, Luis, Yun, Hae-Bum, University of Central Florida
- Abstract / Description
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The unsupervised ensemble learning, or consensus clustering, consists of finding the optimal com- bination strategy of individual partitions that is robust in comparison to the selection of an algorithmic clustering pool. Despite its strong properties, this approach assigns the same weight to the contribution of each clustering to the final solution. We propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other modern approaches...
Show moreThe unsupervised ensemble learning, or consensus clustering, consists of finding the optimal com- bination strategy of individual partitions that is robust in comparison to the selection of an algorithmic clustering pool. Despite its strong properties, this approach assigns the same weight to the contribution of each clustering to the final solution. We propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other modern approaches. Results on publicly available datasets show that weights can significantly improve the accuracy performance while retaining the robust properties. Since the issue of determining an appropriate number of clusters, which is a primary input for many clustering methods is one of the significant challenges, we have used the same methodology to predict correct or the most suitable number of clusters as well. Among various methods, using internal validity indexes in conjunction with a suitable algorithm is one of the most popular way to determine the appropriate number of cluster. Thus, we use weighted consensus clustering along with four different indexes which are Silhouette (SH), Calinski-Harabasz (CH), Davies-Bouldin (DB), and Consensus (CI) indexes. Our experiment indicates that weighted consensus clustering together with chosen indexes is a useful method to determine right or the most appropriate number of clusters in comparison to individual clustering methods (e.g., k-means) and consensus clustering. Lastly, to decrease the variance of proposed weighted consensus clustering, we borrow the idea of Markowitz portfolio theory and implement its core idea to clustering domain. We aim to optimize the combination of individual clustering methods to minimize the variance of clustering accuracy. This is a new weighting policy to produce partition with a lower variance which might be crucial for a decision maker. Our study shows that using the idea of Markowitz portfolio theory will create a partition with a less variation in comparison to traditional consensus clustering and proposed weighted consensus clustering.
Show less - Date Issued
- 2017
- Identifier
- CFE0006813, ucf:51786
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006813
- Title
- SIMULATION STUDIES OF SELF-ASSEMBLY AND PHASE DIAGRAMOF AMPHIPHILIC MOLECULES.
- Creator
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Bourov, Geuorgui, Bhattacharya, Aniket, University of Central Florida
- Abstract / Description
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The aim of this dissertation is to investigate self-assembled structures and the phase diagram of amphiphilic molecules of diverse geometric shapes using a number of different computer simulation methods. The semi-realistic coarse-grained model, used extensively for simulation of polymers and surfactant molecules, is adopted in an off-lattice approach to study how the geometric structure of amphiphiles affects the aggregation properties. The results of simulations show that the model system...
Show moreThe aim of this dissertation is to investigate self-assembled structures and the phase diagram of amphiphilic molecules of diverse geometric shapes using a number of different computer simulation methods. The semi-realistic coarse-grained model, used extensively for simulation of polymers and surfactant molecules, is adopted in an off-lattice approach to study how the geometric structure of amphiphiles affects the aggregation properties. The results of simulations show that the model system behavior is consistent with theoretical predictions, experiments and lattice simulation models. We demonstrate that by modifying the geometry of the molecules, self-assembled aggregates are altered in a way close to theoretical predictions. In several two and three dimensional off-lattice Brownian Dynamics simulations, the influence of the shape of the amphiphilic molecules on the size and form of the aggregates is studied systematically. Model phospholipid molecules, with two hydrophobic chains connected to one hydrophilic head group, are simulated and the formation of stable bilayers is observed. In addition, (practically very important) mixtures of amphiphiles with diverse structures are studied under different mixing ratios and molecular structures. We find that in several systems, with Poisson distributed chain lengths, the effect on the aggregation distribution is negligible compared to that of the pure amphiphilic system with the mean length of the Poisson distribution. The phase diagrams of different amphiphilic molecular structures are investigated in separate simulations by employing the Gibbs Ensemble Monte Carlo method with an implemented configurational-bias technique. The computer simulations of the above mentioned amphiphilic systems are done in an area where physics, biology and chemistry are closely connected and advances in applications require the use of new theoretical, experimental and simulation methods for a better understanding of their self-assembling properties. Obtained simulation results demonstrate the connection between the structure of amphiphilic molecules and the properties of their thermodynamically stable aggregates and thus build a foundation for many applications of the remarkable phenomena of amphiphilic self-assembly in the area of nanotechnology.
Show less - Date Issued
- 2005
- Identifier
- CFE0000695, ucf:46491
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000695
- Title
- DERIVING THE DENSITY OF STATES FOR GRANULAR CONTACT FORCES.
- Creator
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Metzger, Philip, Bhattacharya, Aniket, University of Central Florida
- Abstract / Description
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The density of single grain states in static granular packings is derived from first principles for an idealized yet fundamental case. This produces the distribution of contact forces P_f(f) in the packing. Because there has been some controversy in the published literature over the exact form of the distribution, this dissertation begins by reviewing the existing empirical observations to resolve those controversies. A method is then developed to analyze Edwards' granular contact force...
Show moreThe density of single grain states in static granular packings is derived from first principles for an idealized yet fundamental case. This produces the distribution of contact forces P_f(f) in the packing. Because there has been some controversy in the published literature over the exact form of the distribution, this dissertation begins by reviewing the existing empirical observations to resolve those controversies. A method is then developed to analyze Edwards' granular contact force probability functional from first principles. The derivation assumes Edwards' flat measure -- a density of states (DOS) that is uniform within the metastable regions of phase space. A further assumption, supported by physical arguments and empirical evidence, is that contact force correlations arising through the closure of loops of grains may be neglected. Then, maximizing a state-counting entropy results in a transport equation that can be solved numerically. For the present it has been solved using the "Mean Structure Approximation," projecting the DOS across all angular coordinates to more clearly identify its predominant features in the remaining stress coordinates. These features are: (1) the Grain Factor related to grain stability and strong correlation between the contact forces on the same grain, and (2) the Structure Factor related to Newton's third law and strong correlation between neighboring grains. Numerical simulations were then performed for idealized granular packings to permit a direct comparison with the theory, and the data including P_f(f) were found to be in excellent agreement. Where the simulations and theory disagree, it is primarily due to the coordination number Z because the theory assumes Z to be a constant whereas in disordered packings it is not. The form of the empirical DOS is discovered to have an elegant, underlying pattern related to Z. This pattern consists entirely of the functional forms correctly predicted by the theory, but with only slight parameter changes as a function of Z. This produces significant physical insight and suggests how the theory may be generalized in the future.
Show less - Date Issued
- 2005
- Identifier
- CFE0000381, ucf:46325
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000381
- Title
- An Unsupervised Consensus Control Chart Pattern Recognition Framework.
- Creator
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Haghtalab, Siavash, Xanthopoulos, Petros, Pazour, Jennifer, Rabelo, Luis, University of Central Florida
- Abstract / Description
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Early identification and detection of abnormal time series patterns is vital for a number of manufacturing.Slide shifts and alterations of time series patterns might be indicative of some anomalyin the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less...
Show moreEarly identification and detection of abnormal time series patterns is vital for a number of manufacturing.Slide shifts and alterations of time series patterns might be indicative of some anomalyin the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantageof unsupervised methodology for less manual data labeling their use is limited due to thefact that their performance is not robust enough for practical purposes. In this study we propose the use of a consensus clustering framework. Computational results show robust behavior compared to individual clustering algorithms.
Show less - Date Issued
- 2014
- Identifier
- CFE0005178, ucf:50670
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005178
- Title
- Rebirth of the Renaissance Man: Creating Actor Agency through Ensemble Theatre.
- Creator
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Grassett, Kody, Ingram, Kate, Thomas, Aaron, Reed, David, University of Central Florida
- Abstract / Description
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Contemporary models of educational and commercial theatres espouse the belief that theatre is the true collaborative art form: one in which artists of different talents, training programs, and experiences can come together to briefly create something more significant than themselves. However, as the theatre has moved into the twenty-first century, the ensemble nature that is so unique to theatrical performance is frequently abandoned for a streamlined top-down structure of theatre making, one...
Show moreContemporary models of educational and commercial theatres espouse the belief that theatre is the true collaborative art form: one in which artists of different talents, training programs, and experiences can come together to briefly create something more significant than themselves. However, as the theatre has moved into the twenty-first century, the ensemble nature that is so unique to theatrical performance is frequently abandoned for a streamlined top-down structure of theatre making, one in which monetary, scheduling, and efficiency concerns inhibit the true creation of an ensemble. For multi-faceted theatre artists who have interest and talents in more than one field of the theatre, the current reigning structure of theatrical creation can seem restrictive, even reductive to their creative potentials. In this thesis, I explore a revived form of theatrical creation centered around the concept of the total ensemble artist, or the modern-day equivalent to the Renaissance man, an artist and student of many different passions. By developing a model of theatrical creation that allows and encourages an actor's agency in the creative process, I hope to show that the ensemble approach to theatre making, in which actors must work together to create and support a production in intimate and challenging ways, is beneficial and necessary to both theatre artists and the audiences that come to view theatrical productions. Rather than being limited to the confines of the categorized and structured model of commercial theatre, these artists will be able to work together to create individualized, meaningful stories on stage that allow the theatre to remain influential, relevant, and representational of our collective experiences.
Show less - Date Issued
- 2017
- Identifier
- CFE0006943, ucf:51667
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006943
- Title
- Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
- Creator
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Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
- Abstract / Description
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This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
Show less - Date Issued
- 2018
- Identifier
- CFE0007358, ucf:52103
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007358
- Title
- Context-Centric Affect Recognition From Paralinguistic Features of Speech.
- Creator
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Marpaung, Andreas, Gonzalez, Avelino, DeMara, Ronald, Sukthankar, Gita, Wu, Annie, Lisetti, Christine, University of Central Florida
- Abstract / Description
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As the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic...
Show moreAs the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic features of speech can improve its performance by integrating relevant contextual knowledge. This dissertation describes our research to integrate contextual knowledge into the paralinguistic affect recognition process from acoustic features of speech. We conceived, built, and tested a two-phased system called the Context-Based Paralinguistic Affect Recognition System (CxBPARS). The first phase of this system is context-free and uses the AdaBoost classifier that applies data on the acoustic pitch, jitter, shimmer, Harmonics-to-Noise Ratio (HNR), and the Noise-to-Harmonics Ratio (NHR) to make an initial judgment about the emotion most likely exhibited by the human elicitor. The second phase then adds context modeling to improve upon the context-free classifications from phase I. CxBPARS was inspired by a human subject study performed as part of this work where test subjects were asked to classify an elicitor's emotion strictly from paralinguistic sounds, and then subsequently provided with contextual information to improve their selections. CxBPARS was rigorously tested and found to, at the worst case, improve the success rate from the state-of-the-art's 42% to 53%.
Show less - Date Issued
- 2019
- Identifier
- CFE0007836, ucf:52831
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007836
- Title
- State (Hydrodynamics) Identification in the Lower St. Johns River using the Ensemble Kalman filter.
- Creator
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Tamura, Hitoshi, Hagen, Scott, Wang, Dingbao, Bacopoulos, Peter, University of Central Florida
- Abstract / Description
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This thesis presents a method, Ensemble Kalman Filter (EnKF), applied to a high-resolution, shallow water equations model (DG ADCIRC-2DDI) of the Lower St. Johns River with observation data at four gauging stations. EnKF, a sequential data assimilation method for non-linear problems, is developed for tidal flow simulation for estimation of state variables, i.e., water levels and depth-integrated currents for overland unstructured finite element meshes. The shallow water equations model is...
Show moreThis thesis presents a method, Ensemble Kalman Filter (EnKF), applied to a high-resolution, shallow water equations model (DG ADCIRC-2DDI) of the Lower St. Johns River with observation data at four gauging stations. EnKF, a sequential data assimilation method for non-linear problems, is developed for tidal flow simulation for estimation of state variables, i.e., water levels and depth-integrated currents for overland unstructured finite element meshes. The shallow water equations model is combined with observation data, which provides the basis of the EnKF applications. In this thesis, EnKF is incorporated into DG ADCIRC-2DDI code to estimate the state variables.Upon its development, DG ADCIRC-2DDI with EnKF is first validated by implementing to a low-resolution, shallow water equations model of a quarter annular harbor with synthetic observation data at six gauging stations. Second, DG ADCIRC-2DDI with EnKF is implemented to a high-resolution, shallow water equations model of the Lower St. Johns River with real observation data at four gauging stations. Third, four different experiments are performed by applying DG ADCIRC-2DDI with EnKF to the Lower St. Johns River.
Show less - Date Issued
- 2012
- Identifier
- CFE0004331, ucf:49455
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004331
