You searched for +publisher:"Virginia Tech" +contributor:("Yu, Guoqiang").
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Virginia Tech
1.
Mishra, Sourav.
Collimator width Optimization in X-ray Luminescent Computed Tomography.
Degree: MS, Computer Engineering, 2013, Virginia Tech
URL: http://hdl.handle.net/10919/51118 
► X-ray Luminescent Computed Tomography (XLCT) is a new imaging modality which is under extensive trials at present. The modality works by selective excitation of X-ray…
(more)
▼ X-ray Luminescent Computed Tomography (XLCT) is a new imaging modality which is under extensive trials at present. The modality works by selective excitation of X-ray sensitive nanophosphors and detecting the optical signal thus generated. This system can be used towards recreating high quality tomographic slices even with low X-ray dose. There have been many studies which have reported successful validation of the underlying philosophy. However, there is still lack of information about optimal settings or combination of imaging parameters, which could yield best outputs. Research groups participating in this area have reported results on basis of dose, signal to noise ratio or resolution only. In this thesis, the candidate has evaluated XLCT taking into consideration noise and resolution in terms of composite indices. Simulations have been performed for various beam widths and noise & resolution metrics deduced. This information has been used in evaluating quality of images on basis of CT Figure of Merit & a modified Wang-Bovik Image Quality index. Simulations indicate the presence of an optimal setting which can be set prior to extensive scans. The conducted study, although focusing on a particular implementation, hopes to establish a paradigm in finding best settings for any XLCT system. Scanning with an optimal setting preconfigured can help in vastly reducing the cost and risks involved with this imaging modality.
Advisors/Committee Members: Wang, Ge (committeechair), Xuan, Jianhua (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: X-ray Luminescence; Computed Tomography; Monte Carlo Methods
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APA (6th Edition):
Mishra, S. (2013). Collimator width Optimization in X-ray Luminescent Computed Tomography. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51118
Chicago Manual of Style (16th Edition):
Mishra, Sourav. “Collimator width Optimization in X-ray Luminescent Computed Tomography.” 2013. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/51118.
MLA Handbook (7th Edition):
Mishra, Sourav. “Collimator width Optimization in X-ray Luminescent Computed Tomography.” 2013. Web. 11 Apr 2021.
Vancouver:
Mishra S. Collimator width Optimization in X-ray Luminescent Computed Tomography. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/51118.
Council of Science Editors:
Mishra S. Collimator width Optimization in X-ray Luminescent Computed Tomography. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/51118
Virginia Tech
2.
Aggarwal, Deepti.
Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge.
Degree: MS, Electrical Engineering, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/56601
► Plants have developed specific responses to external stimuli such as drought, cold, high salinity in soil, and precipitation in addition to internal developmental stimuli. These…
(more)
▼ Plants have developed specific responses to external stimuli such as drought, cold, high salinity in soil, and precipitation in addition to internal developmental stimuli. These stimuli trigger signal transduction pathways in plants, leading to cellular adaptation. A signal transduction pathway is a network of entities that interact with one another in response to given stimulus. Such participating entities control and affect gene expression in response to stimulus . For computational purposes, a signal transduction pathway is represented as a network where nodes are biological molecules. The interaction of two nodes is a directed edge.
A plethora of research has been conducted to understand signal transduction pathways. However, there are a limited number of approaches to explore and integrate signal transduction pathways. Therefore, we need a platform to integrate together and to expand the information of each signal transduction pathway. One of the major computational challenges in inferring signal transduction pathways is that the addition of new nodes and edges can affect the information flow between existing ones in an unknown manner. Here, I develop the Beacon inference engine to address these computational challenges. This software engine employs a network inference approach to predict new edges. First, it uses mutual information and context likelihood relatedness to predict edges from gene expression time-series data. Subsequently, it incorporates prior knowledge to limit false-positive predictions. Finally, a naive Bayes classifier is used to predict new edges. The Beacon inference engine predicts new edges
with a recall rate 77.6% and precision 81.4%. 24% of the total predicted edges are new i.e., they are not present in the prior knowledge.
Advisors/Committee Members: Parikh, Devi (committeechair), Heath, Lenwood S. (committeechair), Yu, Guoqiang (committee member), Grene, Ruth (committee member).
Subjects/Keywords: Signal Transduction Pathways; Gene Expression; Inference Engine
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APA (6th Edition):
Aggarwal, D. (2015). Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/56601
Chicago Manual of Style (16th Edition):
Aggarwal, Deepti. “Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge.” 2015. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/56601.
MLA Handbook (7th Edition):
Aggarwal, Deepti. “Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge.” 2015. Web. 11 Apr 2021.
Vancouver:
Aggarwal D. Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/56601.
Council of Science Editors:
Aggarwal D. Inferring Signal Transduction Pathways from Gene Expression Data using Prior Knowledge. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/56601
Virginia Tech
3.
Chen, Yang.
Robust Prediction of Large Spatio-Temporal Datasets.
Degree: MS, Computer Science and Applications, 2013, Virginia Tech
URL: http://hdl.handle.net/10919/23098
► This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors.…
(more)
▼ This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors. It is crucial to many applications in geographical information systems, medical imaging, urban planning, economy study, and climate forecasting. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a statistical model with linear order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student\'s t-distribution, instead of a traditional Gaussian distribution. To handle the analytical intractable inference of Student\'s t model, we propose an approximate inference algorithm in the framework of Expectation Propagation (EP). Extensive experimental evaluations, based on both simulation and real-life data sets, demonstrated the robustness and the efficiency of our Student-t prediction model compared with the STRE model.
Advisors/Committee Members: Chen, Ing-Ray (committeechair), Clancy, Thomas Charles (committeechair), Yu, Guoqiang (committee member).
Subjects/Keywords: Robust Prediction; Expectation Propagation; Student's t Model; Bayesian Hierarchical Model; Spatio-Temporal Process
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APA (6th Edition):
Chen, Y. (2013). Robust Prediction of Large Spatio-Temporal Datasets. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23098
Chicago Manual of Style (16th Edition):
Chen, Yang. “Robust Prediction of Large Spatio-Temporal Datasets.” 2013. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/23098.
MLA Handbook (7th Edition):
Chen, Yang. “Robust Prediction of Large Spatio-Temporal Datasets.” 2013. Web. 11 Apr 2021.
Vancouver:
Chen Y. Robust Prediction of Large Spatio-Temporal Datasets. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/23098.
Council of Science Editors:
Chen Y. Robust Prediction of Large Spatio-Temporal Datasets. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23098
Virginia Tech
4.
Blake, Patrick Michael.
Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.
Degree: MS, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/87392
► Many data sets have too many features for conventional pattern recognition techniques to work properly. This thesis investigates techniques that alleviate these difficulties. One such…
(more)
▼ Many data sets have too many features for conventional pattern recognition techniques to work properly. This thesis investigates techniques that alleviate these difficulties. One such technique, biclustering, clusters data in both dimensions and is inherently resistant to the challenges posed by having too many features. However, the algorithms that implement biclustering have limitations in that the user must know at least the structure of the data and how many biclusters to expect. This is where the VIsual Statistical Data Analyzer, or VISDA, can help. It is a visualization tool that successively and progressively explores the structure of the data, identifying clusters along the way. This thesis proposes coupling VISDA with biclustering to overcome some of the challenges of data sets with too many features. Further, to increase the performance, usability, and maintainability as well as reduce costs, VISDA was translated from Matlab to a Python version called VISDApy. Both VISDApy and the overall process were demonstrated with real and synthetic data sets. The results of this work have the potential to improve analysts’ understanding of the relationships within complex data sets and their ability to make informed decisions from such data.
Advisors/Committee Members: Wang, Yue J. (committeechair), Xuan, Jianhua (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: high-dimensional data; biclustering; VISDA; VISDApy
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APA (6th Edition):
Blake, P. M. (2019). Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87392
Chicago Manual of Style (16th Edition):
Blake, Patrick Michael. “Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.” 2019. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/87392.
MLA Handbook (7th Edition):
Blake, Patrick Michael. “Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.” 2019. Web. 11 Apr 2021.
Vancouver:
Blake PM. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/87392.
Council of Science Editors:
Blake PM. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87392
Virginia Tech
5.
Hou, Xuchu.
Accurate Identification of Significant Aberrations in Cancer Genome: Implementation and Applications.
Degree: MS, Computer Engineering, 2013, Virginia Tech
URL: http://hdl.handle.net/10919/19235
► Somatic Copy Number Alterations (CNAs) are common events in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical…
(more)
▼ Somatic Copy Number Alterations (CNAs) are common events in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical task in searching for cancer-associated genes. Advanced genome profiling technologies, such as SNP array technology, facilitate copy number study at a genome-wide scale with high resolution. However, due to normal tissue contamination, the observed intensity signals are actually the mixture of copy number signals contributed from both tumor and normal cells. This genetic confounding factor would significantly affect the subsequent copy number analyses. In order to accurately identify significant aberrations in contaminated cancer genome, we develop a Java AISAIC package (Accurate Identification of Significant Aberrations in Cancer) that incorporates recent novel algorithms in the literature, BACOM (Bayesian Analysis of Copy number Mixtures) and SAIC (Significant Aberrations in Cancer). Specifically, BACOM is used to estimate the normal tissue contamination fraction and recover the "true" copy number profiles. And SAIC is used to detect SCAs using large recovered tumor samples. Considering the popularity of modern multi-core computers and clusters, we adopt concurrent computing using Java Fork/Join API to speed up the analysis. We evaluate the performance of the AISAIC package in both empirical family-wise type I error rate and detection power on a large number of simulation data, and get promising results. Finally, we use AISAIC to analyze real cancer data from TCGA portal and detect many SCAs that not only cover majority of reported cancer-associated genes, but also some novel genome regions that may worth further study.
Advisors/Committee Members: Wang, Yue J. (committeechair), Ozcan, Ibrahim Alpay (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: Copy Number Alterations; Normal Tissue Contamination; Significant Copy number Aberrations; Concurrent Computing
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APA (6th Edition):
Hou, X. (2013). Accurate Identification of Significant Aberrations in Cancer Genome: Implementation and Applications. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/19235
Chicago Manual of Style (16th Edition):
Hou, Xuchu. “Accurate Identification of Significant Aberrations in Cancer Genome: Implementation and Applications.” 2013. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/19235.
MLA Handbook (7th Edition):
Hou, Xuchu. “Accurate Identification of Significant Aberrations in Cancer Genome: Implementation and Applications.” 2013. Web. 11 Apr 2021.
Vancouver:
Hou X. Accurate Identification of Significant Aberrations in Cancer Genome: Implementation and Applications. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/19235.
Council of Science Editors:
Hou X. Accurate Identification of Significant Aberrations in Cancer Genome: Implementation and Applications. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/19235
Virginia Tech
6.
Gaebel, Ethan Daniel.
Looks Good To Me (LGTM): Authentication for Augmented Reality.
Degree: MS, Computer Science and Applications, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/71638
► Augmented reality is poised to become the next dominant computing paradigm over the course of the next decade. With the three-dimensional graphics and interactive interfaces…
(more)
▼ Augmented reality is poised to become the next dominant computing paradigm over the course of the next decade. With the three-dimensional graphics and interactive interfaces that augmented reality promises it will rival the very best science fiction novels. Users will want to have shared experiences in these rich augmented reality scenarios, but surely users will want to restrict who can see their content. It is currently unclear how users of such devices will authenticate one another. Traditional authentication protocols reliant on centralized authorities fall short when different systems with different authorities try to communicate and extra infrastructure means extra resource expenditure. Augmented reality content sharing will usually occur in face-to-face scenarios where it will be advantageous for both performance and usability reasons to keep communications and authentication localized. Looks Good To Me (LGTM) is an authentication protocol for augmented reality headsets that leverages the unique hardware and context provided with augmented reality headsets to solve an old problem in a more usable and more secure way. LGTM works over point to point wireless communications so users can authenticate one another in any circumstance and is designed with usability at its core, requiring users to perform only two actions: one to initiate and one to confirm. LGTM allows users to intuitively authenticate one another, using seemingly only each other's faces. Under the hood LGTM uses a combination of facial recognition and wireless localization to ensure secure and extremely simple authentication.
Advisors/Committee Members: Lou, Wenjing (committeechair), Chen, Ing Ray (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: augmented reality; authentication; looks good to me; lgtm; mixed reality; hologram; holograms; wireless localization; localization
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APA (6th Edition):
Gaebel, E. D. (2016). Looks Good To Me (LGTM): Authentication for Augmented Reality. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71638
Chicago Manual of Style (16th Edition):
Gaebel, Ethan Daniel. “Looks Good To Me (LGTM): Authentication for Augmented Reality.” 2016. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/71638.
MLA Handbook (7th Edition):
Gaebel, Ethan Daniel. “Looks Good To Me (LGTM): Authentication for Augmented Reality.” 2016. Web. 11 Apr 2021.
Vancouver:
Gaebel ED. Looks Good To Me (LGTM): Authentication for Augmented Reality. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/71638.
Council of Science Editors:
Gaebel ED. Looks Good To Me (LGTM): Authentication for Augmented Reality. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71638
Virginia Tech
7.
Raman, Pujita.
Speaker Identification and Verification Using Line Spectral Frequencies.
Degree: MS, Electrical Engineering, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/52964
► State-of-the-art speaker identification and verification (SIV) systems provide near perfect performance under clean conditions. However, their performance deteriorates in the presence of background noise. Many…
(more)
▼ State-of-the-art speaker identification and verification (SIV) systems provide near perfect performance under clean conditions. However, their performance deteriorates in the presence of background noise. Many feature compensation, model compensation and signal enhancement techniques have been proposed to improve the noise-robustness of SIV systems. Most of these techniques require extensive training, are computationally expensive or make assumptions about the noise characteristics. There has not been much focus on analyzing the relative importance, or speaker-discriminative power of different speech zones, particularly under noisy conditions.
In this work, an automatic, text-independent speaker identification (SI) system and speaker verification (SV) system is proposed using Line Spectral Frequency (LSF) features. The performance of the proposed SI and SV systems are evaluated under various types of background noise. A score-level fusion based technique is implemented to extract complementary information from static and dynamic LSF features. The proposed score-level fusion based SI and SV systems are found to be more robust under noisy conditions.
In addition, we investigate the speaker-discriminative power of different speech zones such as vowels, non-vowels and transitions. Rapidly varying regions of speech such as consonant-vowel transitions are found to be most speaker-discriminative in high SNR conditions. Steady, high-energy vowel regions are robust against noise and are hence most speaker-discriminative in low SNR conditions. We show that selectively utilizing features from a combination of transition and steady vowel zones further improves the performance of the score-level fusion based SI and SV systems under noisy conditions.
Advisors/Committee Members: Beex, Aloysius A. (committeechair), Baumann, William T. (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: Speech; Speaker; Noise; Identification; Verification; Recognition; Feature; Line Spectral Frequency; Gaussian Mixture Model; Transition; Vowel
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APA (6th Edition):
Raman, P. (2015). Speaker Identification and Verification Using Line Spectral Frequencies. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52964
Chicago Manual of Style (16th Edition):
Raman, Pujita. “Speaker Identification and Verification Using Line Spectral Frequencies.” 2015. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/52964.
MLA Handbook (7th Edition):
Raman, Pujita. “Speaker Identification and Verification Using Line Spectral Frequencies.” 2015. Web. 11 Apr 2021.
Vancouver:
Raman P. Speaker Identification and Verification Using Line Spectral Frequencies. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/52964.
Council of Science Editors:
Raman P. Speaker Identification and Verification Using Line Spectral Frequencies. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/52964
Virginia Tech
8.
Chang, Yi Tan.
A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis.
Degree: MS, Computer Engineering, 2018, Virginia Tech
URL: http://hdl.handle.net/10919/83813
► This thesis consists of two projects in which various machine learning approaches and statistical analysis for the integration of biomedical data analysis were explored, developed…
(more)
▼ This thesis consists of two projects in which various machine learning approaches and statistical analysis for the integration of biomedical data analysis were explored, developed and tested. Integration of different biomedical data sources allows us to get a better understating of human body from a bigger picture. If we can get a more complete view of the data, we not only get a more complete view of the molecule basis of phenotype, but also possibly can identify abnormality in diseases which were not found when using only one type of biomedical data. The objective of the first project is to find biological pathways which are related to Duechenne Muscular Dystrophy(DMD) and Lamin A/C(LMNA) using the integration of multi-omics data. We proposed a novel method which allows us to integrate proteins, mRNAs and miRNAs to find disease related pathways. The goal of the second project is to develop a personalized recommendation system which recommend cancer treatments to patients. Compared to the traditional way of using only users' rating to impute missing values, we proposed a method to incorporate users' profile to help enhance the accuracy of the prediction.
Advisors/Committee Members: Yu, Guoqiang (committeechair), Mili, Lamine M. (committee member), Wang, Yue J. (committee member).
Subjects/Keywords: Data integration; machine learning; pathway enrichment; pathway prioritization; matrix completion; treatment recommendation.
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APA (6th Edition):
Chang, Y. T. (2018). A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83813
Chicago Manual of Style (16th Edition):
Chang, Yi Tan. “A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis.” 2018. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/83813.
MLA Handbook (7th Edition):
Chang, Yi Tan. “A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis.” 2018. Web. 11 Apr 2021.
Vancouver:
Chang YT. A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/83813.
Council of Science Editors:
Chang YT. A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83813
Virginia Tech
9.
Adhikari, Rajendra.
Algorithms and Simulation Framework for Residential Demand Response.
Degree: PhD, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/87585
► The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than…
(more)
▼ The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than the load, the match can be restored either by increasing the generation or by decreasing the load. Often, during system stress conditions, it is cheaper to decrease certain loads than to increase generation, and this method of achieving power balance is called demand response (DR). Residential sector consumes 37% of the total U.S. electricity consumption and is largely unexplored for demand response purpose, so the focus of the dissertation is on providing solutions to enable residential houses to provide demand response services. This dissertation presents two broad solutions. The first is a set of efficient algorithms that intelligently controls the customers’ heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid while keeping their comfort in mind. The second solution is a simulation software that can help evaluate and experiment with different residential demand response algorithms. The first algorithm is for reducing the collective power consumption of an aggregation of residential HVAC, whereas the second algorithm is for making the collective power follow a signal sent by the grid operators. It is shown that the algorithms presented can intelligently control the HVAC devices such that DR services can be provided to the grid while ensuring that the temperatures of the houses remain within comfortable range. The algorithms can enable demand response service providers to tap into the residential demand response market and earn revenue, while the simulation software can be valuable for future research in this area. The simulation software is simple to use and is designed with extensibility in mind, so adding new features is easy. The software is shown to work well for studying residential building control for demand response purpose and can be a useful tool for future research in residential DR.
Advisors/Committee Members: Rahman, Saifur (committeechair), Ramakrishnan, Naren (committee member), Yu, Guoqiang (committee member), Pipattanasomporn, Manisa (committee member), Centeno, Virgilio A. (committee member).
Subjects/Keywords: Demand Response; DR Simulation Framework; Aggregated HVAC Control; Regulation; Load Reduction; Residential Building Control
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APA (6th Edition):
Adhikari, R. (2019). Algorithms and Simulation Framework for Residential Demand Response. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87585
Chicago Manual of Style (16th Edition):
Adhikari, Rajendra. “Algorithms and Simulation Framework for Residential Demand Response.” 2019. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/87585.
MLA Handbook (7th Edition):
Adhikari, Rajendra. “Algorithms and Simulation Framework for Residential Demand Response.” 2019. Web. 11 Apr 2021.
Vancouver:
Adhikari R. Algorithms and Simulation Framework for Residential Demand Response. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/87585.
Council of Science Editors:
Adhikari R. Algorithms and Simulation Framework for Residential Demand Response. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87585
Virginia Tech
10.
Flowers, Bryse Austin.
Adversarial RFML: Evading Deep Learning Enabled Signal Classification.
Degree: MS, Computer Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/91987
► Deep learning is beginning to permeate many commercial products and is being included in prototypes for next generation wireless communications devices. This technology can provide…
(more)
▼ Deep learning is beginning to permeate many commercial products and is being included in prototypes for next generation wireless communications devices. This technology can provide huge breakthroughs in autonomy; however, it is not sufficient to study the effectiveness of deep learning in an idealized laboratory environment, the real world is often harsh and/or adversarial. Therefore, it is important to know how, and when, these deep learning enabled devices will fail in the presence of bad actors before they are deployed in high risk environments, such as battlefields or connected autonomous vehicle communications. This thesis studies a small subset of the security vulnerabilities of deep learning enabled wireless communications devices by attempting to evade deep learning enabled signal classification by an eavesdropper while maintaining effective wireless communications with a cooperative receiver. The primary goal of this thesis is to define the threats to, and identify the current vulnerabilities of, deep learning enabled signal classification systems, because a system can only be secured once its vulnerabilities are known.
Advisors/Committee Members: Buehrer, R. Michael (committeechair), Headley, William C. (committeechair), Gerdes, Ryan M. (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: Adversarial Signal Processing; Cognitive Radio Security; Machine Learning; Modulation Identification; Radio Frequency Machine Learning
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APA (6th Edition):
Flowers, B. A. (2019). Adversarial RFML: Evading Deep Learning Enabled Signal Classification. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91987
Chicago Manual of Style (16th Edition):
Flowers, Bryse Austin. “Adversarial RFML: Evading Deep Learning Enabled Signal Classification.” 2019. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/91987.
MLA Handbook (7th Edition):
Flowers, Bryse Austin. “Adversarial RFML: Evading Deep Learning Enabled Signal Classification.” 2019. Web. 11 Apr 2021.
Vancouver:
Flowers BA. Adversarial RFML: Evading Deep Learning Enabled Signal Classification. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/91987.
Council of Science Editors:
Flowers BA. Adversarial RFML: Evading Deep Learning Enabled Signal Classification. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91987
Virginia Tech
11.
Mukherjee, Anway.
Power-Performance-Predictability: Managing the Three Cornerstones of Resource Constrained Real-Time System Design.
Degree: PhD, Computer Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/95472
► Today's real-world problems demand real-time solutions. These solutions need to be practically feasible, and scale well with increasing end user demands. They also need to…
(more)
▼ Today's real-world problems demand real-time solutions. These solutions need to be practically feasible, and scale well with increasing end user demands. They also need to maintain a balance between system performance and predictability, while achieving minimum energy consumption. A recent example of technological design problem involves ways to improve the battery lifetime of mobile embedded devices, for example, smartphones, while still achieving the required performance objectives. For instance, smartphones that run Android OS has the capability to run multiple applications concurrently using a newly introduced split-screen mode of execution, where applications can run side-by-side at the same time on screen while using the same shared resources (e.g., CPU, memory bandwidth, peripheral devices etc.). While this can improve the overall performance of the system, it can also lead to increased energy consumption, thereby directly affecting the battery life.
Another technological design problem involves ways to protect confidential proprietary information from being siphoned out of devices by external attackers. Let us consider a surveillance unmanned aerial vehicle (UAV) as an example. The UAV must perform sensitive tasks, such as obtaining coordinates of interest for surveillance, within a given time duration, also known as task deadline. However, an attacker may learn how the UAV communicates with ground control, and take control of the UAV, along with the sensitive information it carries. Therefore, it is crucial to protect such sensitive information from access by an unauthorized party, while maintaining the system's task deadlines.
In this dissertation, we explore these two real-world design problems in depth, observe the challenges associated with them, while presenting several solutions to tackle the issues. We extensively assess the pros and cons of our proposed approaches in comparison to the state-of- the-art techniques in custom-built real-world hardware, and simulated environments to test our solutions' scalability.
Advisors/Committee Members: Chantem, Thidapat (committeechair), Gerdes, Ryan M. (committeechair), Yu, Guoqiang (committee member), Clancy, Thomas Charles (committee member), Tilevich, Eli (committee member).
Subjects/Keywords: Real-Time Systems; Trusted Execution; Voltage-Frequency Scaling; Android
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APA (6th Edition):
Mukherjee, A. (2019). Power-Performance-Predictability: Managing the Three Cornerstones of Resource Constrained Real-Time System Design. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/95472
Chicago Manual of Style (16th Edition):
Mukherjee, Anway. “Power-Performance-Predictability: Managing the Three Cornerstones of Resource Constrained Real-Time System Design.” 2019. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/95472.
MLA Handbook (7th Edition):
Mukherjee, Anway. “Power-Performance-Predictability: Managing the Three Cornerstones of Resource Constrained Real-Time System Design.” 2019. Web. 11 Apr 2021.
Vancouver:
Mukherjee A. Power-Performance-Predictability: Managing the Three Cornerstones of Resource Constrained Real-Time System Design. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/95472.
Council of Science Editors:
Mukherjee A. Power-Performance-Predictability: Managing the Three Cornerstones of Resource Constrained Real-Time System Design. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/95472
Virginia Tech
12.
Cai, Mengmeng.
A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints.
Degree: PhD, Electrical Engineering, 2020, Virginia Tech
URL: http://hdl.handle.net/10919/99094
► The electricity market plays a critical role in ensuring the economic and secure operation of the power system. The progress made by distributed energy resources…
(more)
▼ The electricity market plays a critical role in ensuring the economic and secure operation of the power system. The progress made by distributed energy resources (DERs) has reshaped the modern power industry bringing a larger proportion of price-responsive behaviors to the demand-side. It challenges the traditional wholesale-only electricity market and calls for an addition of retail markets to better utilize distributed and elastic assets. Therefore, this dissertation targets at offering a reliable and computational affordable retail market solution to bridge this knowledge gap.
Different from existing works, this study assumes that the retail market is managed by a profit-neutral retail market operator (RMO), who oversees and facilitates the system operation for maximizing the system efficiency rather than making profits. Market participants are categorized into two groups: inelastic participants and elastic participants, based on their sensitivity to the market price. The motivation behind this design is that instead of treating elastic participants as normal customers, it is more reasonable to treat them as grid service providers who offer operational flexibilities that benefit the system efficiency. Correspondingly, a double-signal pricing scheme is proposed, such that the flexibility, provided by elastic participants, and the electricity commodity, generated/consumed by inelastic participants, are separately valued by two distinct prices, namely retail grid service price and retail energy price. A grid service subsidy is also introduced in the pricing system to provide supplementary incentives to elastic customers. These two price signals in addition to the subsidy are determined by the RMO via solving a bi-level optimization problem given the interdependency between the prices and reaction of elastic participants.
Experimental results indicate that the proposed retail market model and pricing scheme are beneficial for both types of market participants, practical for the network-constrained real-world implementation, and supportive for the technology improvement of elastic assets.
Advisors/Committee Members: Rahman, Saifur (committeechair), Yu, Guoqiang (committee member), Pipattanasomporn, Manisa (committee member), Reddy, Chandan K. (committee member), Broadwater, Robert P. (committee member).
Subjects/Keywords: Retail Electricity Market; Load Forecasting; Battery Arbitrage; Bi-level Optimization; Deep Learning
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APA (6th Edition):
Cai, M. (2020). A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99094
Chicago Manual of Style (16th Edition):
Cai, Mengmeng. “A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/99094.
MLA Handbook (7th Edition):
Cai, Mengmeng. “A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints.” 2020. Web. 11 Apr 2021.
Vancouver:
Cai M. A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/99094.
Council of Science Editors:
Cai M. A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99094
Virginia Tech
13.
Youssef, Ibrahim Mohamed.
Multi-Platform Molecular Data Integration and Disease Outcome Analysis.
Degree: PhD, Electrical Engineering, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/73580
► One of the most common measures of clinical outcomes is the survival time. Accurately linking cancer molecular profiling with survival outcome advances clinical management of…
(more)
▼ One of the most common measures of clinical outcomes is the survival time. Accurately linking cancer molecular profiling with survival outcome advances clinical management of cancer. However, existing survival analysis relies intensively on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, opening the opportunity of designing biologically informed and integrative approaches for survival analysis. Many cancer tissues have been profiled for gene expression levels and genomic variants (such as copy number alterations, sequence mutations, DNA methylation, and histone modification). However, it is not clear how to integrate the gene expression and genetic variants to achieve a better prediction and understanding of the cancer survival.
To address this challenge, we propose two approaches for data integration in order to both biologically and statistically boost the features selection process for proper detection of the true predictive players of survival. The first approach is data-driven yet biologically informed. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. With mRNA expression levels in concern, predictive features are those mRNAs whose variation in expression levels are associated with the survival outcome, and mechanistic features are those mRNAs whose variation in expression levels are associated with genomic variants (copy number alterations (CNAs) in this study). Further, we propose simultaneously integrating information from both the predictive model and the mechanistic model through our new approach GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power than peer methods. Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant.
The second approach is a generic mathematical framework to biologically regularize the Cox's proportional hazards model that is widely used in survival analysis. We propose a penalty function that both links the mechanistic model to the clinical model and reflects the biological downstream regulatory effect of the genomic variants on the mRNA expression levels of the target genes. Fast and efficient optimization principles like the coordinate descent and majorization-minimization are adopted in the inference process of the coefficients of the Cox model predictors. Through this model, we develop the regulator-target gene relationship to a new one: regulator-target-outcome relationship of a disease. Assessed via a simulation study and analysis of two real cancer data sets, the proposed method showed better performance in terms of selecting the true…
Advisors/Committee Members: Yu, Guoqiang (committeechair), Wang, Yue J. (committee member), Ressom, Habtom W. (committee member), Lu, Chang Tien (committee member), Clancy, Thomas Charles (committee member).
Subjects/Keywords: survival analysis; Cox proportional hazards model; molecular data integration; intratumor vascular heterogeneity
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APA (6th Edition):
Youssef, I. M. (2016). Multi-Platform Molecular Data Integration and Disease Outcome Analysis. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73580
Chicago Manual of Style (16th Edition):
Youssef, Ibrahim Mohamed. “Multi-Platform Molecular Data Integration and Disease Outcome Analysis.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/73580.
MLA Handbook (7th Edition):
Youssef, Ibrahim Mohamed. “Multi-Platform Molecular Data Integration and Disease Outcome Analysis.” 2016. Web. 11 Apr 2021.
Vancouver:
Youssef IM. Multi-Platform Molecular Data Integration and Disease Outcome Analysis. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/73580.
Council of Science Editors:
Youssef IM. Multi-Platform Molecular Data Integration and Disease Outcome Analysis. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73580
Virginia Tech
14.
Fu, Yi.
Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers.
Degree: PhD, Electrical Engineering, 2020, Virginia Tech
URL: http://hdl.handle.net/10919/96634
► We witnessed the start of the human genome project decades ago and stepped into the era of omics since then. Omics are comprehensive approaches for…
(more)
▼ We witnessed the start of the human genome project decades ago and stepped into the era of omics since then. Omics are comprehensive approaches for analyzing genome-wide biomolecular profiles. The rapid development of high-throughput technologies enables us to produce an enormous amount of omics data such as genomics, transcriptomics, and proteomics data, which makes researchers swim in a sea of omics information that once never imagined. Yet, the era of omics brings new challenges to us: to process the huge volumes of data, to summarize the data, to reveal the interactions between entities, to link various types of omics data, and to discover mechanisms hidden behind omics data.
In processing omics data, one factor that weakens the strengths of follow up data analysis is sample impurity. We call impure tumor samples contaminated by normal cells as heterogeneous samples. The genomic signals measured from heterogeneous samples are a mixture of signals from both tumor cells and normal cells. To correct the mixed signals and get true signals from pure tumor cells, we propose a computational approach called BACOM 2.0 to estimate normal cell fraction and corrected genomics signals accordingly. By introducing a novel normalization method that identifies the neutral component in mixed signals of genomic copy number data, BACOM 2.0 could accurately detect genes' deletion types and abnormal chromosome numbers in tumor cells.
In cells, genes connect to other genes and form complex biological networks to perform their functions. Dysregulated genes can cause structural change in biological networks, also known as network rewiring. In a biological network with network rewiring events, a large quantity of network rewiring linking to a single hub gene suggests concentrated gene dysregulation. This hub gene has more impact on the network and hence is more likely to associate with the functional change of the network, which ultimately leads to abnormal phenotypes such as cancer diseases. Therefore, the hub genes linked with network rewiring are potential indicators of disease status or known as biomarkers. Differential dependency network (DDN) method was proposed to detect network rewiring events and biomarkers from omics data.
However, the DDN method still has a few drawbacks. Firstly, for two groups of data with unequal sample sizes, DDN consistently detects false targets of network rewiring. The permutation test, which uses the same method on randomly shuffled samples is supposed to distinguish the true targets from random effects, however, is also suffered from the same reason and could let pass those false targets. We propose a new formulation that corrects the mistakes brought by unequal group size and design a simulation study to test the new formulation's correctness. Secondly, the time used for computing in solving DDN problems is unbearably long when processing omics data with a large number of samples scale or a large number of genes. We propose several strategies to increase DDN's computation speed, including three…
Advisors/Committee Members: Wang, Yue J. (committeechair), Haghighat, Alireza (committee member), Zhang, Zhen (committee member), Clancy, Thomas Charles (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: molecular data integration; differential network analysis; biomarker
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APA ·
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APA (6th Edition):
Fu, Y. (2020). Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/96634
Chicago Manual of Style (16th Edition):
Fu, Yi. “Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/96634.
MLA Handbook (7th Edition):
Fu, Yi. “Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers.” 2020. Web. 11 Apr 2021.
Vancouver:
Fu Y. Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/96634.
Council of Science Editors:
Fu Y. Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/96634
Virginia Tech
15.
Wirsing, Karlton E.
Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders.
Degree: PhD, Electrical Engineering, 2020, Virginia Tech
URL: http://hdl.handle.net/10919/98781
► Earth’s weather affects all of us every day. The solar space environment has weather of its own that affects us as well. Storms of a…
(more)
▼ Earth’s weather affects all of us every day. The solar space environment has weather of its own that affects us as well. Storms of a size that far exceed anything on Earth can impact Earth and affect our infrastructure. One of the most powerful phenomena that occur, called solar corona mass ejections, results when the sun ejects a large amount of plasma. This can interact with the Earth’s magnetic field, which in turn induces perturbations that may have a significant impact on critical infrastructure, for instance, by disturbing communication systems, and inducing currents on pipelines and electric power lines. The currents can cause increased corrosion or blackouts, among other effects. In this dissertation, we analyze measured electrical signals provided to us by the Finnish Meteorological Institute, which were induced by geomagnetic storms on pipelines located in Finland and recorded in 2003. Specifically, we perform a statistical analysis of these current signals to decide whether they exhibit multifractal characteristics.
Advisors/Committee Members: Mili, Lamine M. (committeechair), Clauer, C. Robert (committee member), Yu, Guoqiang (committee member), Kekatos, Vassilis (committee member), Lu, Chang-Tien (committee member).
Subjects/Keywords: Wavelet; Fractal; Wavelet Leader; Multifractal; Geomagnetically Induced Currents
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APA (6th Edition):
Wirsing, K. E. (2020). Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/98781
Chicago Manual of Style (16th Edition):
Wirsing, Karlton E. “Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/98781.
MLA Handbook (7th Edition):
Wirsing, Karlton E. “Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders.” 2020. Web. 11 Apr 2021.
Vancouver:
Wirsing KE. Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/98781.
Council of Science Editors:
Wirsing KE. Multifractal Analysis of Geomagnetically Induced Currents using Wavelet Leaders. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/98781
Virginia Tech
16.
Wang, Yizhi.
Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data.
Degree: PhD, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/95988
► Astrocyte is an important type of glial cell in the brain. Unlike neurons, astrocyte cannot be electrically excited. However, the concentrations of many different molecules…
(more)
▼ Astrocyte is an important type of glial cell in the brain. Unlike neurons, astrocyte cannot be electrically excited. However, the concentrations of many different molecules inside and near astrocytes change over space and time and show complex patterns. Recording, analyzing, and deciphering these activity patterns enables the understanding of various roles astrocyte may play in the nervous system. Many of these important roles, such as sensory-motor integration and brain state modulation, were traditionally considered the territory of neurons, but recently found to be related to astrocytes. These activities can be monitored in the intracellular and extracellular spaces in either brain slices and living animals, thanks to the advancement of microscopes and genetically encoded fluorescent sensors. However, sophisticated analytical tools lag far behind the impressive capability of generating the data. The major reason is that existing tools are all based on the region-of-interest-based (ROI) approach. This approach assumes the field of view can be segmented to many regions, and all pixels in the region should be active together. In neuronal activity analysis, all pixels in an ROI (region of interest) correspond to a neuron and are assumed to share a common activity pattern (curve). This is not true for astrocyte activity data because astrocyte activities are spatially unfixed, size-varying, and propagative. In this dissertation, we developed a framework called AQuA to detect the activities directly. We designed an accurate and flexible detection pipeline that works with different types of astrocyte activity data sets. We designed a machine learning model to characterize the signal propagation for the pipeline. We also implemented a compressive and user-friendly software package. The advantage of AQuA is confirmed in both simulation studies and three different types of real data sets.
Advisors/Committee Members: Yu, Guoqiang (committeechair), Wang, Yue J. (committee member), Ressom, Habtom W. (committee member), Haghighat, Alireza (committee member), Chantem, Thidapat (committee member).
Subjects/Keywords: Astrocyte activity; Image analysis; Curve alignment; Graphical model
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APA ·
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APA (6th Edition):
Wang, Y. (2019). Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/95988
Chicago Manual of Style (16th Edition):
Wang, Yizhi. “Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data.” 2019. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/95988.
MLA Handbook (7th Edition):
Wang, Yizhi. “Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data.” 2019. Web. 11 Apr 2021.
Vancouver:
Wang Y. Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/95988.
Council of Science Editors:
Wang Y. Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/95988
Virginia Tech
17.
Xu, Yijun.
Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods.
Degree: PhD, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/97876
► It is a well-known fact that a power system contains many sources of uncertainties. These uncertainties coming from the loads, the renewables, the model and…
(more)
▼ It is a well-known fact that a power system contains many sources of uncertainties. These uncertainties coming from the loads, the renewables, the model and the measurement, etc, are influencing the steady state and dynamic response of the power system.
Facing this problem, traditional methods, such as the Monte Carlo method and the Perturbation method, are either too time consuming or suffering from the strong nonlinearity in the system.
To solve these, this Dissertation will mainly focus on developing the polynomial chaos based method to replace the traditional ones. Using it, the uncertainties from the
model and the measurement are propagated through the polynomial chaos bases at a set of collocation points. The approximated polynomial chaos coefficients contain the statistical information. The method can greatly accelerate the calculation efficiency while not losing the accuracy, even when the system is highly stressed.
In this dissertation, both the forward problem and the inverse problem of uncertainty quantification will be discussed. The forward problems will include the probabilistic power flow problem and statistical power system dynamic simulations. The generalized polynomial chaos method, the adaptive polynomial chaos-ANOVA method and the multi-element polynomial chaos method will be introduced and compared. The case studies show that the proposed methods have great performances in the statistical analysis of the large-scale power systems. The inverse problems will include the state and parameter estimation problem. A novel polynomial-chaos-based Kalman filter will be proposed. The comparison studies with other traditional Kalman filter demonstrate the good performances of the proposed Kalman filter. We further explored the area dynamic parameter estimation problem under the Bayesian inference framework. The polynomial-chaos-expansions are treated as the response surface of the full dynamic solver. Combing with hybrid Markov chain Monte Carlo method, the proposed method yields very high estimation accuracy while greatly reducing the computing time.
For both the forward problem and the inverse problems, the polynomial chaos based methods haven shown great advantages over the traditional methods. These computational techniques can improve the efficiency and accuracy in power system planning, guarantee the rationality and reliability in power system operations, and, finally, speed up the power system dynamic security assessment.
Advisors/Committee Members: Mili, Lamine M. (committeechair), Rahman, Saifur (committeechair), Sandu, Adrian (committee member), Kekatos, Vasileios (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: Uncertainty Quantification; Dynamic State Estimation; Generalized Polynomial Chaos; Multi-Element Polynomial Chaos; ANOVA; Polynomial-Chaos-Based Kalman Filter; Response Surface; Bayesian Inference; Markov Chain Monte Carlo.
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APA ·
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MLA ·
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APA (6th Edition):
Xu, Y. (2019). Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/97876
Chicago Manual of Style (16th Edition):
Xu, Yijun. “Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods.” 2019. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/97876.
MLA Handbook (7th Edition):
Xu, Yijun. “Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods.” 2019. Web. 11 Apr 2021.
Vancouver:
Xu Y. Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/97876.
Council of Science Editors:
Xu Y. Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/97876
Virginia Tech
18.
Chen, Lulu.
Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity.
Degree: PhD, Computer Engineering, 2020, Virginia Tech
URL: http://hdl.handle.net/10919/96553
► Tissue samples are essentially mixtures of tissue or cellular subtypes where the proportions of individual subtypes vary across different tissue samples. Data deconvolution aims to…
(more)
▼ Tissue samples are essentially mixtures of tissue or cellular subtypes where the proportions of individual subtypes vary across different tissue samples. Data deconvolution aims to dissect tissue heterogeneity into biologically important subtypes, their proportions, and their marker genes. The physical solution to mitigate tissue heterogeneity is to isolate pure tissue components prior to molecular profiling. However, these experimental methods are time-consuming, expensive and may alter the expression values during isolation. Existing literature primarily focuses on supervised deconvolution methods which require a priori information. This approach has an inherent problem as it relies on the quality and accuracy of the a priori information. In this dissertation, we propose and develop a fully unsupervised deconvolution method - deconvolution by Convex Analysis of Mixtures (debCAM) that can estimate the mixing proportions and 'averaged' expression profiles of individual subtypes present in heterogeneous tissue samples. Furthermore, we also propose and develop debCAM2.0 that can estimate 'individualized' expression profiles of participating subtypes in complex tissue samples.
Subtype-specific expressed markers, or marker genes (MGs), serves as critical a priori information for supervised deconvolution. MGs are exclusively and consistently expressed in a particular tissue or cell subtype while detecting such unique MGs involving many subtypes constitutes a challenging task. We propose and develop a statistically-principled method - One Versus Everyone Subtype Exclusively-expressed Genes (OVESEG-test) for robust detection of MGs from purified profiles of many subtypes.
Advisors/Committee Members: Wang, Yue J. (committeechair), Lou, Wenjing (committee member), Yu, Guoqiang (committee member), Baumann, William T. (committee member), Clancy, Thomas Charles (committee member).
Subjects/Keywords: bioinformatics; deconvolution; unsupervised learning; convex analysis; feature selection; tissue heterogeneity; biomarkers
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APA (6th Edition):
Chen, L. (2020). Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/96553
Chicago Manual of Style (16th Edition):
Chen, Lulu. “Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/96553.
MLA Handbook (7th Edition):
Chen, Lulu. “Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity.” 2020. Web. 11 Apr 2021.
Vancouver:
Chen L. Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/96553.
Council of Science Editors:
Chen L. Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/96553
Virginia Tech
19.
Zhang, Xiangyu.
A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events.
Degree: PhD, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/87517
► For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to…
(more)
▼ For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.
Advisors/Committee Members: Rahman, Saifur (committeechair), Broadwater, Robert P. (committee member), Yu, Guoqiang (committee member), Lu, Chang Tien (committee member), Pipattanasomporn, Manisa (committee member).
Subjects/Keywords: smart grid; demand response; HVAC coordination; building thermal model; reinforcement learning
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APA (6th Edition):
Zhang, X. (2019). A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87517
Chicago Manual of Style (16th Edition):
Zhang, Xiangyu. “A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events.” 2019. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/87517.
MLA Handbook (7th Edition):
Zhang, Xiangyu. “A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events.” 2019. Web. 11 Apr 2021.
Vancouver:
Zhang X. A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/87517.
Council of Science Editors:
Zhang X. A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87517
Virginia Tech
20.
Saha, Avijit.
Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications.
Degree: PhD, Electrical Engineering, 2017, Virginia Tech
URL: http://hdl.handle.net/10919/74423
► In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized…
(more)
▼ In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution.
First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform.
Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort.
Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives.
The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings.
Advisors/Committee Members: Rahman, Saifur (committeechair), De La Reelopez, Jaime (committee member), Yu, Guoqiang (committee member), Haghighat, Alireza (committee member), Pipattanasomporn, Manisa (committee member).
Subjects/Keywords: Building Energy Management System; Energy Efficiency; Demand Response; Internet of Things; Reinforcement Learning
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APA (6th Edition):
Saha, A. (2017). Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/74423
Chicago Manual of Style (16th Edition):
Saha, Avijit. “Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications.” 2017. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/74423.
MLA Handbook (7th Edition):
Saha, Avijit. “Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications.” 2017. Web. 11 Apr 2021.
Vancouver:
Saha A. Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/74423.
Council of Science Editors:
Saha A. Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/74423
Virginia Tech
21.
Drikas, Zachary Benjamin.
New Techniques for Time-Reversal-Based Ultra-wideband Microwave Pulse Compression in Reverberant Cavities.
Degree: PhD, Electrical Engineering, 2020, Virginia Tech
URL: http://hdl.handle.net/10919/100998
► Generation of high-peak power, microwave ultra-short pulses (USPs) is desirable for ultra-wideband communications and remote sensing. A variety of microwave USP generators exist today, or…
(more)
▼ Generation of high-peak power, microwave ultra-short pulses (USPs) is desirable for ultra-wideband communications and remote sensing. A variety of microwave USP generators exist today, or are described in the literature, and have benefits and limitations depending on application. A new class of pulse compressors for generating USPs using electromagnetic time reversal (TR) techniques have been developed in the last decade, and are the topic of this dissertation. This dissertation presents a compact TR-based microwave pulse-compression cavity that has unique features that make it optimal for high-power operations, with results from simulations as well as measurements showing improved performance over other similar cavities published in the literature with a record demonstrated peak output power of 39.2 kW. Additionally, new analysis on the operation and optimization of this cavity for increased performance is also presented. Finally, a new technique is presented that uses a cavity with only one feed that acts as both the input and output. This 1-port technique is demonstrated in both simulation and measurement. The proposed system achieves a two-times increase in compression gain over its 2-port counterpart. In conjunction with these measurements and simulations, a novel technique for predicting the performance of these cavities using Monte Carlo simulation is also presented.
Advisors/Committee Members: Raman, Sanjay (committeechair), Ellingson, Steven W. (committee member), Yu, Guoqiang (committee member), Clancy, Thomas Charles (committee member), Black, Jonathan T. (committee member).
Subjects/Keywords: Ultra-short pulse (USP); pulse compression; ultra-wideband (UWB); time-reversal; reconfigurable cavity; dispersive cavity
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APA (6th Edition):
Drikas, Z. B. (2020). New Techniques for Time-Reversal-Based Ultra-wideband Microwave Pulse Compression in Reverberant Cavities. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/100998
Chicago Manual of Style (16th Edition):
Drikas, Zachary Benjamin. “New Techniques for Time-Reversal-Based Ultra-wideband Microwave Pulse Compression in Reverberant Cavities.” 2020. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/100998.
MLA Handbook (7th Edition):
Drikas, Zachary Benjamin. “New Techniques for Time-Reversal-Based Ultra-wideband Microwave Pulse Compression in Reverberant Cavities.” 2020. Web. 11 Apr 2021.
Vancouver:
Drikas ZB. New Techniques for Time-Reversal-Based Ultra-wideband Microwave Pulse Compression in Reverberant Cavities. [Internet] [Doctoral dissertation]. Virginia Tech; 2020. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/100998.
Council of Science Editors:
Drikas ZB. New Techniques for Time-Reversal-Based Ultra-wideband Microwave Pulse Compression in Reverberant Cavities. [Doctoral Dissertation]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/100998
22.
Amanzadeh, Leila.
Architecting IoT-Enabled Smart Building Testbed.
Degree: MS, Electrical Engineering, 2018, Virginia Tech
URL: http://hdl.handle.net/10919/85579
► Smart building's benefits range from improving comfort of occupant, increased productivity, reduction in energy consumption and operating costs, lower CO2 emission, to improved life cycle…
(more)
▼ Smart building's benefits range from improving comfort of occupant, increased productivity, reduction in energy consumption and operating costs, lower CO2 emission, to improved life cycle of utilities, efficient operation of building systems, etc. [65]. Hence, modern building owners are turning towards smart buildings. However, the current smart buildings mostly are not capable of achieving the objectives they are designed for and they can improve a lot better [22]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Using this technology, e.g. a door can be a thing and can sense how many people have passed it's sensor to enter a space and let the lighting system know to prepare appropriate amount of light, or the HVAC (Heating Ventilation Air Conditioning) system to provide desirable temperature. IoT will provide a lot of useful information that before that accessibility to it was impossible, e.g., condition of water pipes in winter, which helps avoiding damages like frozen or broken pipes. However, despite all the benefits, IoT suffers from being vulnerable to cyber attacks. Examples have been provided later in Chapter 1.
In this project among building systems, HVAC system is chosen to be automated with a new control method called MPC (Model Predictive Control). This method is fast, very energy efficient and has a lower than 0.001 rate of error for regulating the space temperature to any temperature that the occupants desire according to the results of this project. Furthermore, a PID (Proportional–Integral–Derivative) controller has been designed for the HVAC system that in the exact same cases MPC shows a much better performance. To design controllers for HVAC system and set the temperature to the desired value a method to automate balancing the heat flow should be found, therefore a thermal model of building should be available that using this model, the amount of heat, flowing in and out of a space in the building disregarding the external weather would be known to estimate. To automate the HVAC system using the programming languages like MATLAB, there is a need to convert the thermal model of the building to a mathematical model. This mathematical model is unique for each building depending on how many floors it has, how wide it is, and what materials have been used to construct the building. This process is needs a lot of effort and time even for buildings with 2 floors and 2 rooms on each floor and at the end the engineer might have done it with error. In this project you will see a software that will do the conversion of thermal model of buildings in any size to their mathematical model automatically, which helps improving the HVAC controllers to set temperature to the value occupants…
Advisors/Committee Members: Gerdes, Ryan M. (committeechair), Yu, Guoqiang (committee member), Chantem, Thidapat (committee member).
Subjects/Keywords: MIMO MPC; HVAC; Testbed; MIMO PID; Applying KVL/KCL automatically
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APA (6th Edition):
Amanzadeh, L. (2018). Architecting IoT-Enabled Smart Building Testbed. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/85579
Chicago Manual of Style (16th Edition):
Amanzadeh, Leila. “Architecting IoT-Enabled Smart Building Testbed.” 2018. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/85579.
MLA Handbook (7th Edition):
Amanzadeh, Leila. “Architecting IoT-Enabled Smart Building Testbed.” 2018. Web. 11 Apr 2021.
Vancouver:
Amanzadeh L. Architecting IoT-Enabled Smart Building Testbed. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/85579.
Council of Science Editors:
Amanzadeh L. Architecting IoT-Enabled Smart Building Testbed. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/85579
23.
Lu, Yingzhou.
Multi-omics Data Integration for Identifying Disease Specific Biological Pathways.
Degree: MS, Computer Engineering, 2018, Virginia Tech
URL: http://hdl.handle.net/10919/83467
► Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies,…
(more)
▼ Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways.
Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes.
This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis.
This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term.
Advisors/Committee Members: Wang, Yue J. (committeechair), Chantem, Thidapat (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: Biological Pathways; Multi-omics Data Integration; Muscular Dystrophy; Statistical significance test; Gene set enrichment analysis
…math.arizona.edu/~jwatkins/ttest.pdf
[11] Yu, Guoqiang, et al. "Matched gene selection and…
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APA (6th Edition):
Lu, Y. (2018). Multi-omics Data Integration for Identifying Disease Specific Biological Pathways. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83467
Chicago Manual of Style (16th Edition):
Lu, Yingzhou. “Multi-omics Data Integration for Identifying Disease Specific Biological Pathways.” 2018. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/83467.
MLA Handbook (7th Edition):
Lu, Yingzhou. “Multi-omics Data Integration for Identifying Disease Specific Biological Pathways.” 2018. Web. 11 Apr 2021.
Vancouver:
Lu Y. Multi-omics Data Integration for Identifying Disease Specific Biological Pathways. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/83467.
Council of Science Editors:
Lu Y. Multi-omics Data Integration for Identifying Disease Specific Biological Pathways. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83467
24.
Lin, Jason.
Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets.
Degree: MS, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/87563
► The emergence of blockchain technology and increasing penetration of distributed energy resources (DERs) have created a new opportunity for peer-to-peer (P2P) energy trading. However, challenges…
(more)
▼ The emergence of blockchain technology and increasing penetration of distributed energy resources (DERs) have created a new opportunity for peer-to-peer (P2P) energy trading. However, challenges arise in such transactive markets to ensure individual rationality, incentive compatibility, budget balance, and economic efficiency during the trading process. This thesis creates an hour-ahead P2P energy trading network based on the Hyperledger Fabric blockchain and explores a comparative analysis of different auction mechanisms that form the basis of smart contracts. Considered auction mechanisms are discriminatory and uniform k-Double Auction with different k values. This thesis also investigates effects of four consumer and prosumer bidding strategies: random, preference factor, price-only game-theoretic approach, and supply-demand game-theoretic approach. A custom simulation framework that models the behavior of the transactive market is developed. Case studies of a 100-home microgrid at various photovoltaic (PV) penetration levels are presented using typical residential load and PV generation profiles in the metropolitan Washington, D.C. area. Results indicate that regardless of PV penetration levels and employed bidding strategies, discriminatory k-DA can outperform uniform k-DA. Despite so, discriminatory k-DA is more sensitive to market conditions than uniform k-DA. Additionally, results show that the price-only game-theoretic bidding strategy leads to near-ideal economic efficiencies regardless of auction mechanisms and PV penetration levels.
Advisors/Committee Members: Rahman, Saifur (committeechair), Pipattanasomporn, Manisa (committee member), Yu, Guoqiang (committee member).
Subjects/Keywords: auction mechanism; blockchain; game theory; smart contract; transactive energy
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APA (6th Edition):
Lin, J. (2019). Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87563
Chicago Manual of Style (16th Edition):
Lin, Jason. “Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets.” 2019. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/87563.
MLA Handbook (7th Edition):
Lin, Jason. “Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets.” 2019. Web. 11 Apr 2021.
Vancouver:
Lin J. Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/87563.
Council of Science Editors:
Lin J. Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87563
25.
Bijinemula, Sandeep Kumar.
An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks.
Degree: MS, Computer Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/87403
► Real-time systems require temporal correctness along with accuracy. This notion of temporal correctness is achieved by specifying deadlines to each of the tasks. In order…
(more)
▼ Real-time systems require temporal correctness along with accuracy. This notion of temporal correctness is achieved by specifying deadlines to each of the tasks. In order to ensure that all the deadlines are met, it is important to know the processor requirement, also known as demand, of a task over a given interval. For some tasks, the demand is not constant, instead it depends on several external factors. For such tasks, it becomes necessary to calculate the worst-case demand. Engine-triggered tasks are activated when the crankshaft in an engine is at certain points in its path of rotation. This makes their activation rate dependent on the angular speed and acceleration of the crankshaft. In addition, several properties of the engine triggered tasks like the execution time and deadlines are dependent on the speed profile of the crankshaft. Such tasks are referred to as adaptive-variable rate (AVR) tasks. Existing methods to calculate the worst-case demand of AVR tasks are either inaccurate or computationally intractable. We propose a method to efficiently calculate the worst-case demand of AVR tasks by transforming the problem into a variant of the knapsack problem. We then propose a framework to systematically narrow down the search space associated with finding the worst-case demand of AVR tasks. Experimental results show that our approach is at least 10 times faster, with an average runtime improvement of 146 times for randomly generated task sets when compared to the state-of-the-art technique.
Advisors/Committee Members: Chantem, Thidapat (committeechair), Yu, Guoqiang (committee member), Gerdes, Ryan M. (committee member).
Subjects/Keywords: Adaptive variable rate task; demand bound function; worst-case demand; knapsack problem
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APA (6th Edition):
Bijinemula, S. K. (2019). An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87403
Chicago Manual of Style (16th Edition):
Bijinemula, Sandeep Kumar. “An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks.” 2019. Masters Thesis, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/87403.
MLA Handbook (7th Edition):
Bijinemula, Sandeep Kumar. “An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks.” 2019. Web. 11 Apr 2021.
Vancouver:
Bijinemula SK. An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/87403.
Council of Science Editors:
Bijinemula SK. An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87403
Virginia Tech
26.
Zheng, Yao.
Privacy Preservation for Cloud-Based Data Sharing and Data Analytics.
Degree: PhD, Computer Science and Applications, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/73796
► Data privacy is a globally recognized human right for individuals to control the access to their personal information, and bar the negative consequences from the…
(more)
▼ Data privacy is a globally recognized human right for individuals to control the access to their personal information, and bar the negative consequences from the use of this information. As communication technologies progress, the means to protect data privacy must also evolve to address new challenges come into view. Our research goal in this dissertation is to develop privacy protection frameworks and techniques suitable for the emerging cloud-based data services, in particular privacy-preserving algorithms and protocols for the cloud-based data sharing and data analytics services.
Cloud computing has enabled users to store, process, and communicate their personal information through third-party services. It has also raised privacy issues regarding losing control over data, mass harvesting of information, and un-consented disclosure of personal content. Above all, the main concern is the lack of understanding about data privacy in cloud environments. Currently, the cloud service providers either advocate the principle of third-party doctrine and deny users' rights to protect their data stored in the cloud; or rely the notice-and-choice framework and present users with ambiguous, incomprehensible privacy statements without any meaningful privacy guarantee.
In this regard, our research has three main contributions. First, to capture users' privacy expectations in cloud environments, we conceptually divide personal data into two categories, i.e., visible data and invisible data. The visible data refer to information users intentionally create, upload to, and share through the cloud; the invisible data refer to users' information retained in the cloud that is aggregated, analyzed, and repurposed without their knowledge or understanding.
Second, to address users' privacy concerns raised by cloud computing, we propose two privacy protection frameworks, namely individual control and use limitation. The individual control framework emphasizes users' capability to govern the access to the visible data stored in the cloud. The use limitation framework emphasizes users' expectation to remain anonymous when the invisible data are aggregated and analyzed by cloud-based data services.
Finally, we investigate various techniques to accommodate the new privacy protection frameworks, in the context of four cloud-based data services: personal health record sharing, location-based proximity test, link recommendation for social networks, and face tagging in photo management applications. For the first case, we develop a key-based protection technique to enforce fine-grained access control to users' digital health records. For the second case, we develop a key-less protection technique to achieve location-specific user selection. For latter two cases, we develop distributed learning algorithms to prevent large scale data harvesting. We further combine these algorithms with query regulation techniques to achieve user anonymity.
The picture that is emerging from the above works is a bleak one. Regarding to personal data, the reality…
Advisors/Committee Members: Lou, Wenjing (committeechair), Yu, Guoqiang (committee member), Jajodia, Sushil (committee member), Ramakrishnan, Naren (committee member), Hou, Yiwei Thomas (committee member), Chen, Ing Ray (committee member).
Subjects/Keywords: information privacy; cryptography; machine learning
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APA (6th Edition):
Zheng, Y. (2016). Privacy Preservation for Cloud-Based Data Sharing and Data Analytics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73796
Chicago Manual of Style (16th Edition):
Zheng, Yao. “Privacy Preservation for Cloud-Based Data Sharing and Data Analytics.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/73796.
MLA Handbook (7th Edition):
Zheng, Yao. “Privacy Preservation for Cloud-Based Data Sharing and Data Analytics.” 2016. Web. 11 Apr 2021.
Vancouver:
Zheng Y. Privacy Preservation for Cloud-Based Data Sharing and Data Analytics. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/73796.
Council of Science Editors:
Zheng Y. Privacy Preservation for Cloud-Based Data Sharing and Data Analytics. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73796
27.
Tsai, Tsung-Heng.
Bayesian Alignment Model for Analysis of LC-MS-based Omic Data.
Degree: PhD, Electrical Engineering, 2014, Virginia Tech
URL: http://hdl.handle.net/10919/64151
► Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed…
(more)
▼ Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and provides estimates of the retention time variability along with uncertainty measures, enabling a natural framework to integrate information of various sources. From methodology development to practical application, we investigate the alignment problem through three research topics: 1) development of single-profile Bayesian alignment model, 2) development of multi-profile Bayesian alignment model, and 3) application to biomarker discovery research.
Chapter 2 introduces the profile-based Bayesian alignment using a single chromatogram, e.g., base peak chromatogram from each LC-MS run. The single-profile alignment model improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS).
Chapter 3 extends the model to integrate complementary information that better captures the variability in chromatographic separation. We use Gaussian process regression on the internal standards to derive a prior distribution for the mapping functions. In addition, a clustering approach is proposed to identify multiple representative chromatograms for each LC-MS run. With the Gaussian process prior, these chromatograms are simultaneously considered in the profile-based alignment, which greatly improves the model estimation and facilitates the subsequent peak matching process.
Chapter 4 demonstrates the applicability of the proposed Bayesian alignment model to biomarker discovery research. We integrate the proposed Bayesian alignment model into a rigorous preprocessing pipeline for LC-MS data analysis. Through the developed analysis pipeline, candidate biomarkers for hepatocellular carcinoma (HCC) are identified and confirmed on a complementary platform.
Advisors/Committee Members: Wang, Yue J. (committeechair), Yu, Guoqiang (committee member), Mun, Seong Ki (committee member), Silva, Luiz A. (committee member), Ressom, Habtom W. (committee member), Xuan, Jianhua (committee member).
Subjects/Keywords: alignment; Bayesian inference; biomarker discovery; liquid chromatography-mass spectrometry (LC-MS); Markov chain Monte Carlo (MCMC)
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APA (6th Edition):
Tsai, T. (2014). Bayesian Alignment Model for Analysis of LC-MS-based Omic Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64151
Chicago Manual of Style (16th Edition):
Tsai, Tsung-Heng. “Bayesian Alignment Model for Analysis of LC-MS-based Omic Data.” 2014. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/64151.
MLA Handbook (7th Edition):
Tsai, Tsung-Heng. “Bayesian Alignment Model for Analysis of LC-MS-based Omic Data.” 2014. Web. 11 Apr 2021.
Vancouver:
Tsai T. Bayesian Alignment Model for Analysis of LC-MS-based Omic Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/64151.
Council of Science Editors:
Tsai T. Bayesian Alignment Model for Analysis of LC-MS-based Omic Data. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/64151
28.
Ghosh, Shibani.
A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations.
Degree: PhD, Electrical Engineering, 2017, Virginia Tech
URL: http://hdl.handle.net/10919/74424
► Due to the rapid growth of grid-tied solar photovoltaic (PV) systems in the generation mix, the distribution grid will face complex operational challenges. High PV…
(more)
▼ Due to the rapid growth of grid-tied solar photovoltaic (PV) systems in the generation mix, the distribution grid will face complex operational challenges. High PV penetration can create overvoltages and voltage fluctuations in the network, which are major concerns for the grid operator. Traditional voltage control devices like switched capacitor banks or line voltage regulators can alleviate slow-moving fluctuations, but these devices need to operate more frequently than usual when PV generation fluctuates due to fast cloud movements. Such frequent operations will impact the life expectancy of these voltage control devices.
Advanced PV inverter functionalities enable solar PV systems to provide reliable grid support through controlled real injection and/or reactive power compensation. This dissertation proposes a voltage regulation technique to mitigate probable impacts of high PV penetrations on the distribution voltage profile using smart inverter functionalities. A droop-based reactive power compensation method with active power curtailment is proposed, which uses the local voltage regulation at the inverter end. This technique is further augmented with very short-term PV generation forecasts. A hybrid forecasting algorithm is proposed here which is based on measurement-dependent dynamic modeling of PV systems using the Kalman Filter theory. Physical modeling of the PV system is utilized by this forecasting algorithm. Because of the rise in distributed PV systems, modeling of geographic dispersion is also addressed under PV system modeling.
The proposed voltage regulation method is coordinated with existing voltage regulator operations to reduce required number of tap-change operations. Control settings of the voltage regulators are adjusted to achieve minimal number of tap-change operations within a predefined time window. Finally, integration of energy storage is studied to highlight the value of the proposed voltage regulation technique vis-à-vis increased solar energy use.
Advisors/Committee Members: Rahman, Saifur (committeechair), Centeno, Virgilio A. (committee member), Pipattanasomporn, Manisa (committee member), Yu, Guoqiang (committee member), Haghighat, Alireza (committee member).
Subjects/Keywords: Solar photovoltaic (PV) system; High PV penetration; Distribution voltage regulation; Solar generation forecasting; Active power curtailment
…Voltage Regulator
Virginia Tech- Advanced Research Institute
xii
1.
Introduction
1.1… …Virginia
Tech Advanced Research Institute (VT-ARI) building in Arlington, Virginia. The…
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APA (6th Edition):
Ghosh, S. (2017). A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/74424
Chicago Manual of Style (16th Edition):
Ghosh, Shibani. “A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations.” 2017. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/74424.
MLA Handbook (7th Edition):
Ghosh, Shibani. “A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations.” 2017. Web. 11 Apr 2021.
Vancouver:
Ghosh S. A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/74424.
Council of Science Editors:
Ghosh S. A Real-time Management of Distribution Voltage Fluctuations due to High Solar Photovoltaic (PV) Penetrations. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/74424
29.
Bitaraf, Hamideh.
Mitigating Impacts of High Wind Energy Penetration through Energy Storage and Demand Response.
Degree: PhD, Electrical Engineering, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/70864
► High renewable energy penetration is a goal for many countries to increase energy security and reduce carbon emissions from conventional power plants. Wind energy is…
(more)
▼ High renewable energy penetration is a goal for many countries to increase energy security and reduce carbon emissions from conventional power plants. Wind energy is one of leading sources among different renewable resources. However, high wind energy penetration in the system brings new challenges to the electric power system due to its variable and stochastic nature, and non-correlation between wind and load profiles. The term non-correlation is used in this research refers to the fact that wind or any other renewable generation, which is nature driven, does not follow the load like conventional power plants.
Wind spill is a challenge to utilities with high wind energy penetration levels. This occurs from situations mentioned above and the fact that wind generation sometimes exceeds the servable load minus must-run generation. In these cases there is no option but to curtail non-usable wind generation. This dissertation presents grid-scale energy storage and demand response options as an optimization problem to minimize spilled wind energy. Even after managing this spilled wind energy, there is still a challenge in a system with high wind energy penetration coming from wind power forecast error.
Wind power forecast error is handled by having more back-up energy and spilling the non-usable wind power. This research offers a way to use the grid-scale energy storage units to mitigate impacts of wind power forecast error by. A signal processing method is proposed to decompose the fluctuating wind power forecast error signal, based on the fact that each energy storage or conventional unit is more efficient to operate within specific cycling regimes. Finally, an algorithm is proposed schedule energy storage for mitigating both impacts.
Advisors/Committee Members: Rahman, Saifur (committeechair), Yu, Guoqiang (committee member), Mili, Lamine M. (committee member), Haghighat, Alireza (committee member), Pipattanasomporn, Manisa (committee member).
Subjects/Keywords: High wind energy penetration; Energy storage; Demand response
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APA ·
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CSE |
Export
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APA (6th Edition):
Bitaraf, H. (2016). Mitigating Impacts of High Wind Energy Penetration through Energy Storage and Demand Response. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/70864
Chicago Manual of Style (16th Edition):
Bitaraf, Hamideh. “Mitigating Impacts of High Wind Energy Penetration through Energy Storage and Demand Response.” 2016. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/70864.
MLA Handbook (7th Edition):
Bitaraf, Hamideh. “Mitigating Impacts of High Wind Energy Penetration through Energy Storage and Demand Response.” 2016. Web. 11 Apr 2021.
Vancouver:
Bitaraf H. Mitigating Impacts of High Wind Energy Penetration through Energy Storage and Demand Response. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/70864.
Council of Science Editors:
Bitaraf H. Mitigating Impacts of High Wind Energy Penetration through Energy Storage and Demand Response. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/70864
30.
Karra, Kiran.
Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning.
Degree: PhD, Electrical Engineering, 2018, Virginia Tech
URL: http://hdl.handle.net/10919/85110
► Many machine learning (ML) techniques rely on probability, random variables, and stochastic modeling. Although statistics pervades this field, there is a large disconnect between the…
(more)
▼ Many machine learning (ML) techniques rely on probability, random variables, and stochastic modeling. Although statistics pervades this field, there is a large disconnect between the copula modeling and the machine learning communities. Copulas are stochastic models that capture the full dependence structure between random variables and allow flexible modeling of multivariate joint distributions. Elidan was the first to recognize this disconnect, and introduced copula based models to the ML community that demonstrated magnitudes of order better performance than the non copula-based models Elidan [2013]. However, the limitation of these is that they are only applicable for continuous random variables and real world data is often naturally modeled jointly as continuous and discrete. This report details our work in bridging this gap of modeling and analyzing data that is jointly continuous and discrete using copulas.
Our first research contribution details modeling of jointly continuous and discrete random variables using the copula framework with Bayesian networks, termed Hybrid Copula Bayesian Networks (HCBN) [Karra and Mili, 2016], a continuation of Elidan’s work on Copula Bayesian Networks Elidan [2010]. In this work, we extend the theorems proved by Neslehov ˇ a [2007] from bivariate ´ to multivariate copulas with discrete and continuous marginal distributions. Using the multivariate copula with discrete and continuous marginal distributions as a theoretical basis, we construct an HCBN that can model all possible permutations of discrete and continuous random variables for parent and child nodes, unlike the popular conditional linear Gaussian network model. Finally, we demonstrate on numerous synthetic datasets and a real life dataset that our HCBN compares favorably, from a modeling and flexibility viewpoint, to other hybrid models including the conditional linear Gaussian and the mixture of truncated exponentials models.
Our second research contribution then deals with the analysis side, and discusses how one may use copulas for exploratory data analysis. To this end, we introduce a nonparametric copulabased index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals. Our index, termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies several desirable properties of measures of association, including Renyi’s properties, the data processing inequality (DPI), and ´ consequently self-equitability. Synthetic data simulations reveal that the statistical power of CIM compares favorably to other state-of-the-art measures of association that are proven to satisfy the DPI. Simulation results with real-world data reveal CIM’s unique ability to detect the monotonicity structure among stochastic signals to find interesting dependencies in large datasets. Additionally, simulations show that CIM shows favorable performance to estimators of mutual information when discovering Markov network structure.…
Advisors/Committee Members: Mili, Lamine M. (committeechair), Clancy, Thomas Charles (committee member), Ramakrishnan, Naren (committee member), Yu, Guoqiang (committee member), Raman, Sanjay (committee member).
Subjects/Keywords: copula; machine learning; big data; stochastic; probability
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APA ·
Chicago ·
MLA ·
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CSE |
Export
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APA (6th Edition):
Karra, K. (2018). Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/85110
Chicago Manual of Style (16th Edition):
Karra, Kiran. “Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning.” 2018. Doctoral Dissertation, Virginia Tech. Accessed April 11, 2021.
http://hdl.handle.net/10919/85110.
MLA Handbook (7th Edition):
Karra, Kiran. “Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning.” 2018. Web. 11 Apr 2021.
Vancouver:
Karra K. Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2021 Apr 11].
Available from: http://hdl.handle.net/10919/85110.
Council of Science Editors:
Karra K. Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/85110
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Open Access Theses and Dissertations
