Physical Sciences and Mathematics Commons^™

Towards Machine Learning-Based Demand Response Forecasting Using Smart Grid Data, Matthew S. Johnson

Theses, Dissertations and Culminating Projects

Demand response is a valuable tool for improving the reliability, stability, and financial efficiency of smart grids. With the intention of altering customer power consumption patterns, utility companies often implement strategies such as time-of-use (TOU) programs. Although effective in some situations, TOU programs struggle to perform in highly developed countries due to the complexity of human behavior. In this study, we analyze power consumption readings from smart meters from 5567 households in London, UK from November 2011 to February 2014 to measure the success of the TOU program. We additionally consider the variability of weather conditions and customer demographics when …

Deep-Learning Based Reconstruction Of The Shower Maximum Xmax Using The Water-Cherenkov Detectors Of The Pierre Auger Observatory, A. Aab, P. Abreu, M. Aglietta, J. M. Albury, I. Allekotte, A. Almela, B. Fick, D. F. Nitz, A. Puyleart, Et. Al.

Michigan Tech Publications

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov …

A Machine Learning Approach To Understanding Emerging Markets, Namita Balani

Graduate Theses and Dissertations

Logistic providers have learned to efficiently serve their existing customer bases with optimized routes and transportation resource allocation. The problem arises when there is potential for logistics growth in an emerging market with no previous data. The purpose of this work is to use industry data for previously known and well-documented markets to apply data analytic techniques such as machine learning to investigate the uncertainty in a new market. The thesis looks into machine learning techniques to predict miles per stop given historical data. It mainly focuses on Random Forest Regression Analysis, but concludes that additional techniques, such as Polynomial …

Intermittent Dynamics Of Dense Particulate Matter, Chao Cheng

Dissertations

Granular particle systems are scattered around the universe, and they can behave like solids when there exist strong force-bearing networks, so that the granular system can resist certain stress without deformation. When such a network is not present, particles yield to small stress and behave like a fluid. A wide range of systems exhibit intermittent dynamics as they are slowly loaded, with different dynamical regimes governing many industrial and natural phenomena. While a significant amount of research on exploring intermittent dynamics of granular systems has been carried out, not much is known about the connection between particle-scale response and the …

Statistical Analysis Of 2017-18 Premier League Match Statistics Using A Regression Analysis In R, Bergen Campbell

Undergraduate Theses and Capstone Projects

This thesis analyzes the correlation between a team’s statistics and the success of their performances, and develops a predictive model that can be used to forecast final season results for that team. Data from the 2017-2018 Premier League season is to be gathered and broken down within R to highlight what factors and variables are largely contributing to the success or downfall of a team. A multiple linear regression model and stepwise selection process is then used to include any factors that are significant in predicting in match results.

The predictions about the 17-18 season results based on the model …

Enhanced Detection Efficiencies And Reduced False Alarms In Searching For Gravitational Waves From Core Collapse Supernovae, Gaukhar Nurbek

Theses and Dissertations

A supernova is a star that flares up very suddenly and then slowly returns to its former luminosity or, explodes violently with energy $10^{52}$ erg. There are stars which are 10 times or more massive than the Sun, which usually end their lives going supernova. When there is no longer enough fuel for the fusion process in the core of the star and inward gravitational pull of the star’s great mass takes place, the star starts to explode. A series of nuclear reactions starts taking place after the star begins shrinking due to gravity. In the final phase of this …

On Tests Of Independence Among Multiplerandom Vectors Of Arbitrary Dimensions., Angshuman Roy Dr.

Doctoral Theses

Measures of dependence among several random vectors and associated tests of independence play a major role in different statistical applications. Blind source separation or independent component analysis (see, e.g., Hyv¨arinen et al., 2001; Shen et al., 2009), feature selection and feature extraction (see, e.g., Li et al., 2012), detection of serial correlation in time series (see, e.g., Ghoudi et al., 2001) and finding the causal relationships among the variables (see, e.g., Chakraborty and Zhang, 2019) are some examples of their wide-spread applications. Tests of independence has vast applications in other areas of sciences as well. For instance, to characterize the …

Dismastd: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition, Keyu Yang, Yunjun Gao, Yifeng Shen, Baihua Zheng, Lu Chen

Research Collection School Of Computing and Information Systems

Tensor decomposition is a fundamental multidimensional data analysis tool for many data-driven applications, such as social computing, computer vision, and bioinformatics, to name but a few. However, the rapidly increasing streaming data nowadays introduces new challenges to traditional static tensor decomposition. It requires an efficient distributed dynamic tensor decomposition without re-computing the whole tensor from scratch. In this paper, we propose DisMASTD, an efficient distributed multi-aspect streaming tensor decomposition. First, we prove the optimal tensor partitioning problem is NP-hard. Second, we present two heuristic tensor partitioning approaches to ensure the load balancing. Third, we develop a distributed multi-aspect streaming tensor …

Fourier Transform Of The Continuous Gravitational Wave Signal, Sree Ram Valluri, V. Dergachev, X. Zhang, Farrukh Chishtie

Physics and Astronomy Publications

The direct detection of continuous gravitational waves from pulsars is a much anticipated discovery in the emerging field of multimessenger gravitational wave (GW) astronomy. Because putative pulsar signals are exceedingly weak large amounts of data need to be integrated to achieve desired sensitivity. Contemporary searches use ingenious ad hoc methods to reduce computational complexity. In this paper we provide analytical expressions for the Fourier transform of realistic pulsar signals. This provides description of the manifold of pulsar signals in the Fourier domain, used by many search methods. We analyze the shape of the Fourier transform and provide explicit formulas for …

Multivariate Distributions Of Correlated Binary Variables Generated By Pair-Copulas, Huihui Lin, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Correlated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate binary distribution with specified marginals and bivariate correlations. In this paper, we study multivariate binary distributions that are based on D-vine pair-copula models as a superior alternative to these methods. We elucidate the construction of these binary distributions …

Analysis Of Subtelomeric Rextal Assemblies Using Quast, Tunazzina Islam, Desh Ranjan, Mohammad Zubair, Eleanor Young, Ming Xiao, Harold Riethman

Computer Science Faculty Publications

Genomic regions of high segmental duplication content and/or structural variation have led to gaps and misassemblies in the human reference sequence, and are refractory to assembly from whole-genome short-read datasets. Human subtelomere regions are highly enriched in both segmental duplication content and structural variations, and as a consequence are both impossible to assemble accurately and highly variable from individual to individual. Recently, we developed a pipeline for improved region-specific assembly called Regional Extension of Assemblies Using Linked-Reads (REXTAL). In this study, we evaluate REXTAL and genome-wide assembly (Supernova) approaches on 10X Genomics linked-reads data sets partitioned and barcoded using the …

A Review Of Energy-For-Water Data In Energy-Water Nexus Publications, Christopher M. Chini, Lauren E. Excell, Ashlynn S. Stillwell

Faculty Publications

Published literature on the energy-water nexus continues to increase, yet much of the supporting data, particularly regarding energy-for-water, remains obscure or inaccessible. We perform a systematic review of literature that describes the primary energy and electricity demands for drinking water and wastewater systems in urban environments. This review provides an analysis of the underlying data and other properties of over 170 published studies by systematically creating metadata on each study. Over 45% of the evaluated studies utilized primary data sources (data collected directly from utilities), potentially enabling large-scale data sharing and a more comprehensive understanding of global water-related energy demand. …

A Deep Machine Learning Approach For Predicting Freeway Work Zone Delay Using Big Data, Abdullah Shabarek

Dissertations

The introduction of deep learning and big data analytics may significantly elevate the performance of traffic speed prediction. Work zones become one of the most critical factors causing congestion impact, which reduces the mobility as well as traffic safety. A comprehensive literature review on existing work zone delay prediction models (i.e., parametric, simulation and non-parametric models) is conducted in this research. The research shows the limitations of each model. Moreover, most previous modeling approaches did not consider user delay for connected freeways when predicting traffic speed under work zone conditions. This research proposes Deep Artificial Neural Network (Deep ANN) and …

Dublin Smart City Data Integration, Analysis And Visualisation, Hammad Ul Ahad

Doctoral

Data is an important resource for any organisation, to understand the in-depth working and identifying the unseen trends with in the data. When this data is efficiently processed and analysed it helps the authorities to take appropriate decisions based on the derived insights and knowledge, through these decisions the service quality can be improved and enhance the customer experience. A massive growth in the data generation has been observed since two decades. The significant part of this generated data is generated from the dumb and smart sensors. If this raw data is processed in an efficient manner it could uplift …

Assessing Topical Homogeneity With Word Embedding And Distance Matrices, Jeffrey M. Stanton, Yisi Sang

School of Information Studies - Faculty Scholarship

Researchers from many fields have used statistical tools to make sense of large bodies of text. Many tools support quantitative analysis of documents within a corpus, but relatively few studies have examined statistical characteristics of whole corpora. Statistical summaries of whole corpora and comparisons between corpora have potential application in the analysis of topically organized applications such social media platforms. In this study, we created matrix representations of several corpora and examined several statistical tests to make comparisons between pairs of corpora with respect to the topical homogeneity of documents within each corpus. Results of three experiments suggested that a …

Prospects For Observing And Localizing Gravitational-Wave Transients With Advanced Ligo, Advanced Virgo And Kagra, B. P. Abbott, R. Abbott, T. D. Abbott, M. R. Abernathy, F. Acernese, K. Ackley, C. Adams, T. Adams, P. Addesso, R. X. Adhikari, V. B. Adya, C. Affeldt, M. Agathos, K. Agatsuma, N. Aggarwal, O. D. Aguiar, L. Aiello, A. Ain, P. Ajith, T. Akutsu, B. Allen, A. Allocca, P. A. Altin, A. Ananyeva, S. B. Anderson, W. G. Anderson, M. Ando, S. Appert, K. Arai, A. Araya, Teviet Creighton, Mario C. Diaz, S. Mukherjee, V. Quetschke, Malik Rakhmanov, K. E. Ramirez, Satzhan Sitmukhambetov, Robert Stone, D. Tuyenbayev, W. H. Wang, A. K. Zadrozny

Physics and Astronomy Faculty Publications and Presentations

We present our current best estimate of the plausible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next several years, with the intention of providing information to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals for the third (O3), fourth (O4) and fifth observing (O5) runs, including the planned upgrades of the Advanced LIGO and Advanced Virgo detectors. We study the capability of the network to determine the sky location of the source for gravitational-wave signals from the inspiral of binary systems of …

Analysis Of Surface Temperature Trends Of Global Lakes Using Satellite Remote Sensing And In Situ Observations, Christal Jean Soverall, Zahida Yasmin, Mahoutin Godnou, Wen Yong Huang, Ryan Chen, Abdou Bah, Hamidreza Norouzi, Reginald Blake

Publications and Research

Even though lakes make up a small percentage of the water bodies on the global land surface, lakes provide critically important ecosystem services. Unfortunately, however, several lake surface areas around the globe have been changing with many of them drastically decreasing due to climate variability and local mismanagement at the basin-scale level. Lake Surface Water Temperature (LSWT) is recognized as a critical indicator of climate change in lakes. The changes in water and the surrounding land temperatures may be an indicator of climate variability if there is consistency between changes in both temperatures. This project focuses on the application of …

Methods For Multi-Source Resolution In Pulsar Timing Array Based Gravitational Wave Detection, Yiqian Qian

Theses and Dissertations

With next-generation radio telescopes, namely the Five-hundred-meter Aperture Spherical Telescope(FAST) and Square Kilometer Array(SKA), scheduled to come online during this decade, hundreds of well-timed millisecond pulsars (MSPs) will be added to the Pulsar Timing Arrays (PTAs) being used currently for Gravitational Wave (GW) searches. This will greatly increase the distances to which GW sources in the very low frequency band can be detected. Among these sources will be super massive black hole binaries (SMBHBs) formed out of gigantic black holes weighing in at million to billion times the mass of our Sun. Although the large number of MSPs will improve …

Email Data Breach Analysis And Prevention Using Hook And Eye System, Shubhankar Jayant Jathar

Electronic Theses, Projects, and Dissertations

Due to the recent COVID-19 outbreak, there were a lot of data leaks from the health sector. This project is about the increase in data breach incidents that are taking place. In this project, There is an analysis of different types of breaches that are found online and are practiced to steal valuable information. Talking about different aspects that lead to data breaches and which are the main sector or main epicenter for data leaks. The analysis tells that most of the data breaches are done using emails and to overcome this limitation a system has been designed that will …

Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li

Journal of System Simulation

Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence …

Randomized And Evolutionary Approaches To Dataset Characterization, Feature Weighting, And Sampling In K-Nearest Neighbors, Suryoday Basak

Computer Science and Engineering Theses

K-Nearest Neighbors (KNN) has remained one of the most popular methods for supervised machine learning tasks. However, its performance often depends on the characteristics of the dataset and on appropriate feature scaling. In this thesis, characteristics of a dataset that make it suitable for being used within KNN are explored. As part of this, two new measures for dataset dispersion, called mean neighborhood target variance (MNTV), and mean neighborhood target entropy (MNTE) are developed to help determine the performance we expect while using KNN regressors and classifiers, respectively. It is empirically demonstrated that these measures of dispersion can be indicative …

Securing The Emerging Technologies Of Autonomous And Connected Vehicles, Shahab Tayeb, Matin Pirouz

Mineta Transportation Institute

The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in this area lacks consensus about security schemes and methods of disseminating messages. Furthermore, a current deficiency of information regarding resource optimization prevents further efficient development of this network. This paper takes an interdisciplinary approach to these issues by merging both cybersecurity and data science to optimize and secure the network. …

Analysis Of The Homeless Population Of Three Major Cities, Katherine Ferrara

Honors Senior Capstone Projects

No abstract provided.

Circada: Shiny Apps For Exploration Of Experimental And Synthetic Circadian Time Series With An Educational Emphasis, Lisa Cenek, Liubou Klindziuk, Cindy Lopez, Eleanor Mccartney, Blanca Martin Burgos, Selma Tir, Mary E. Harrington, Tanya L. Leise

Psychology: Faculty Publications

Circadian rhythms are daily oscillations in physiology and behavior that can be assessed by recording body temperature, locomotor activity, or bioluminescent reporters, among other measures. These different types of data can vary greatly in waveform, noise characteristics, typical sampling rate, and length of recording. We developed 2 Shiny apps for exploration of these data, enabling visualization and analysis of circadian parameters such as period and phase. Methods include the discrete wavelet transform, sine fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, giving a sense of how well each method works on each type of data. The apps also …

Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé

Theses and Dissertations

A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …

Utilizing Design Structure For Improving Design Selection And Analysis, Ahlam Ali Alzharani

Theses and Dissertations

Recent work has shown that the structure for design plays a role in the simplicity or complexity of data analysis. To increase the knowledge of research in these areas, this dissertation aims to utilize design structure for improving design selection and analysis. In this regard, minimal dependent sets and block diagonal structure are both important concepts that are relevant to the orthogonality of the columns of a design. We are interested in finding ways to improve the data analysis especially for active effect detection by utilizing minimal dependent sets and block diagonal structure for design.

We introduce a new classification …

Social Media Based Algorithmic Clinical Decision Support Learning From Behavioral Predispositions, Radhika V. Medury

Doctoral Dissertations

Behavioral disorders are disabilities characterized by an individual’s mood, thinking, and social interactions. The commonality of behavioral disorders amongst the United States population has increased in the last few years, with an estimated 50% of all Americans diagnosed with a behavioral disorder at some point in their lifetime. AttentionDeficit/Hyperactivity Disorder is one such behavioral disorder that is a severe public health concern because of its high prevalence, incurable nature, significant impact on domestic life, and peer relationships. Symptomatically, in theory, ADHD is characterized by inattention, hyperactivity, and impulsivity. Access to providers who can offer diagnosis and treat the disorder varies …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …

A Guide To Ligo–Virgo Detector Noise And Extraction Of Transient Gravitational-Wave Signals, Tiffany Summerscales, Ligo Scientific Collaboration And The Virgo Collaboration

Faculty Publications

The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with being from well-modeled mergers between compact stellarmass objects: black holes or neutron stars. The data around the time of each of these events have been made publicly available through the gravitationalwave open science center. The entirety of the gravitational-wave strain data from the first and second observing runs have also now been made publicly available. There is considerable interest among the broad scientific community in understanding the data …