Engineering Commons^™

Statistical Analysis And Degradation Pathway Modeling Of Photovoltaic Minimodules With Varied Packaging Strategies, Sameera Nalin Venkat, Xuanji Yu, Jiqi Liu, Jakob Wegmueller, Jayvic Cristian Jimenez, Erika I. Barcelos, Hein Htet Aung, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power …

Study Of Stochastic Market Clearing Problems In Power Systems With High Renewable Integration, Saumya Sakitha Sashrika Ariyarathne

Operations Research and Engineering Management Theses and Dissertations

Integrating large-scale renewable energy resources into the power grid poses several operational and economic problems due to their inherently stochastic nature. The lack of predictability of renewable outputs deteriorates the power grid’s reliability. The power system operators have recognized this need to account for uncertainty in making operational decisions and forming electricity pricing. In this regard, this dissertation studies three aspects that aid large-scale renewable integration into power systems. 1. We develop a nonparametric change point-based statistical model to generate scenarios that accurately capture the renewable generation stochastic processes; 2. We design new pricing mechanisms derived from alternative stochastic programming …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

The Wargaming Commodity Course Of Action Automated Analysis Method, William T. Deberry

Theses and Dissertations

This research presents the Wargaming Commodity Course of Action Automated Analysis Method (WCCAAM), a novel approach to assist wargame commanders in developing and analyzing courses of action (COAs) through semi-automation of the Military Decision Making Process (MDMP). MDMP is a seven-step iterative method that commanders and mission partners follow to build an operational course of action to achieve strategic objectives. MDMP requires time, resources, and coordination – all competing items the commander weighs to make the optimal decision. WCCAAM receives the MDMP's Mission Analysis phase as input, converts the wargame into a directed graph, processes a multi-commodity flow algorithm on …

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

Deal: Differentially Private Auction For Blockchain Based Microgrids Energy Trading, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Publications

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more …

Reliability Comparisons Of Mobile Network Operators: An Experimental Case Study From A Crowdsourced Dataset, Engi̇n Zeydan, Ahmet Yildirim

Turkish Journal of Electrical Engineering and Computer Sciences

It is of great interest for Mobile Network Operators (MNOs) to know how well their network infrastructure performance behaves in different geographical regions of their operating country compared to their horizontal competitors. However, traditional network monitoring and measurement methods of network infrastructure use limited numbers of measurement points that are insufficient for detailed analysis and expensive to scale using an internal workforce. On the other hand, the abundance of crowdsourced content can engender various unforeseen opportunities for MNOs to cope with this scaling problem. This paper investigates end-to-end reliability and packet loss (PL) performance comparisons of MNOs using a previously …

Statistical Methods To Unravel Cortical Mechanism Of Perception And Response To Auditory Stimuli, Ladan Moheimanian

Legacy Theses & Dissertations (2009 - 2024)

Behavioral responses to auditory stimuli have a critical role in our daily activities. The perception of these stimuli and the generation of appropriate behavioral responses requires the interaction of thousands of neurons in the auditory-motor pathways in the brain. Despite their importance, still many neuroscientific questions about these interactions are remained to be answered. This may result from the limitations of brain recordings as well as statistical methods to analyze brain recordings. In this dissertation, I investigated underlying mechanisms that govern these neural interactions in the auditory-motor pathways using novel statistical techniques applied to the brain recordings from the surface …

Online Clustering With Bayesian Nonparametrics, Matthew D. Scherreik

Browse all Theses and Dissertations

Clustering algorithms, such as Gaussian mixture models and K-means, often require the number of clusters to be specified a priori. Bayesian nonparametric (BNP) methods avoid this problem by specifying a prior distribution over the cluster assignments that allows the number of clusters to be inferred from the data. This can be especially useful for online clustering tasks, where data arrives in a continuous stream and the number of clusters may dynamically change over time. Classical BNP priors often overestimate the number of clusters, however, leading researchers to develop new priors with more control over this tendency. To date, BNP algorithms …

Fast Multi-Objective Cmode-Type Optimization Of Pm Machines Using Multicore Desktop Computers, Alireza Fatemi, Dan M. Ionel, Nabeel Demerdash, Thomas W. Nehl

Electrical and Computer Engineering Faculty Research and Publications

Large-scale design optimization of electric machines is oftentimes practiced to achieve a set of objectives, such as the minimization of cost and power loss, under a set of constraints, such as maximum permissible torque ripple. Accordingly, the design optimization of electric machines can be regarded as a constrained optimization problem (COP). Evolutionary algorithms (EAs) used in the design optimization of electric machines including differential evolution (DE), which has received considerable attention during recent years, are unconstrained optimization methods that need additional mechanisms to handle COPs. In this paper, a new optimization algorithm that features combined multi-objective optimization with differential evolution …

Constrained Biogeography-Based Optimization For Invariant Set Computation,, Arpit Shah, Daniel Simon, Hanz Richter

Hanz Richter

We discuss the application of biogeography-based optimization (BBO) to invariant set approximation. BBO is a recently developed evolutionary algorithm (EA) that is motivated by biogeography, which is the study and science of the geographical migration of biological species. Invariant sets are sets in the state space of a dynamic system such that if the state begins in the set, then it remains in the set for all time. Invariant sets have applications in many constrained control problems, and their computation amounts to a constrained optimization problem. We therefore frame the invariant set computation problem as a constrained optimization problem, and …

Carbon 1d/2d Nanoelectronics : Integration And Device Applications, Zhaoying Hu

Legacy Theses & Dissertations (2009 - 2024)

Graphene is a one-atom thick planar monolayer of sp2-bonded carbon atoms organized in a hexagonal crystal lattice. A single walled carbon nanotube (CNT) can be thought of as a graphene sheet rolled up into a seamless hollow cylinder with extremely high length-to-diameter ratio. Their ultra-thin body, large surface area, and exceptional electronic, optical and mechanical properties make these low-dimensional carbon materials ideal candidates for electronic applications. However, adopting low-dimensional carbon materials into semiconductor industry faces significant material and integration challenges. There is an urgent need for research at fundamental and applicative levels to find a roadmap for carbon nanomaterial to …

Three-Dimensional Modeling Of The Human Jaw/Teeth Using Optics And Statistics., Aly Saber Abdelrahim

Electronic Theses and Dissertations

Object modeling is a fundamental problem in engineering, involving talents from computer-aided design, computational geometry, computer vision and advanced manufacturing. The process of object modeling takes three stages: sensing, representation, and analysis. Various sensors may be used to capture information about objects; optical cameras and laser scanners are common with rigid objects, while X-ray, CT and MRI are common with biological organs. These sensors may provide a direct or an indirect inference about the object, requiring a geometric representation in the computer that is suitable for subsequent usage. Geometric representations that are compact, i.e., capture the main features of the …

Slides: Draft Power In Developing Country Agriculture--South Asia, Arjun Makhijani

2012 Energy Justice Conference and Technology Exposition (September 17-18)

Presenter: Dr. Arjun Makhijani, President, Institute for Energy and Environmental Research (IEER)

13 slides

Selection Criteria To Statistical Models, D. K. Chaturvedi

D. K. Chaturvedi Dr.

No abstract provided.

Probability Density Functions For Snir In Ds-Cdma, David W. Matolak

Faculty Publications

Analytical expressions for the probability density function of block-wise signal-to-noise-plus-interference ratio for both synchronous and asynchronous direct-sequence spread spectrum code-division multiple access systems are developed, for equal average energy signals on the Gaussian and Rayleigh flat fading channels. Using the standard Gaussian approximation for multi-user interference, accurate density approximations are obtained, which agree very well with computer simulation results.

Precise Measurement Of The Neutron Magnetic Form Factor Gnm In The Few-Gev² Region, Clas Collaboration, J. Lachniet, H. Bagdasaryan, S. Bültmann, N. Kalantarians, G. E. Dodge, T. A. Forest, G. Gavalian, C. E. Hyde-Wright, A. Klien, S. E. Kuhn, M. R. Niroula, R. A. Niyazov, L. M. Qin, L. B. Weinstein, J. Zhang

Physics Faculty Publications

The neutron elastic magnetic form factor was extracted from quasielastic electron scattering on deuterium over the range Q² = 1.0–4.8  GeV² with the CLAS detector at Jefferson Lab. High precision was achieved with a ratio technique and a simultaneous in situ calibration of the neutron detection efficiency. Neutrons were detected with electromagnetic calorimeters and time-of-flight scintillators at two beam energies. The dipole parametrization gives a good description of the data

Estimation Of The Statistical Variation Of Crosstalk In Wiring Harnesses, Meilin Wu, Daryl G. Beetner, Todd H. Hubing, Haixin Ke, Shishuang Sun

Electrical and Computer Engineering Faculty Research & Creative Works

Analyzing interference problems in vehicle wiring harnesses requires fast and accurate methods of approximating crosstalk. Worst-case approximations using lumped element models are fast and easy to use, but run the risk of overestimating problems. Statistical methods that account for the random variation of wire position help prevent overdesign, but are often difficult and time-consuming to apply and lack a clear link between problems and their cause. Here we investigate the use of simple lumped-element models to predict the statistical variation of crosstalk in wire harness bundles. Models are based on lumped-element approximations, where inductance and capacitance values are calculated for …

A Bayesian Perspective On Estimating Mean, Variance, And Standard-Deviation From Data, Travis E. Oliphant

Faculty Publications

This article shows how to compute confidence intervals for mean, standard-deviation, and variance using Bayesian methods. The method is implemented in SciPy as scipy.stats.bayes_mvs After reviewing some classical estimators for mean, variance, and standard-deviation and showing that un-biased estimates are not usually desirable, a Bayesian perspective is employed to determine what is known about mean, variance, and standard deviation given only that a data set in-fact has a common mean and variance. Maximum-entropy is used to argue that the likelihood function in this situation should be the same as if the data were independent and identically distributed Gaussian. A non-informative …

Statistical Correlation Of Gain And Buildup Time In Apds And Its Effects On Receiver Performance, Peng Sun, Majeed M. Hayat, Bahaa E.A. Saleh, Malvin Carl Teich

Electrical and Computer Engineering Faculty Research and Publications

This paper reports a novel recurrence theory that enables us to calculate the exact joint probability density function (pdf) of the random gain and the random avalanche buildup time in avalanche photodiodes (APDs) including the effect of dead space. Such calculations reveal a strong statistical correlation between the gain and the buildup time for all widths of the multiplication region. To facilitate the calculation of the photocurrent statistics in the presence of this correlation, the impulse-response function of the APD is approximately modeled by a function of time whose prespecified shape is appropriately parameterized by two random variables: the gain …

Computation Of Bit-Error Probabilities For Optical Receivers Using Thin Avalanche Photodiodes, Byonghyok Choi, Majeed M. Hayat

Electrical and Computer Engineering Faculty Research and Publications

The large-deviation-based asymptotic-analysis and importance-sampling methods for computing bit-error probabilities for avalanche-photodiode (APD) based optical receivers, developed by Letaief and Sadowsky [IEEE Trans. Inform. Theory, vol. 38, pp. 1162-1169, 1992], are extended to include the effect of dead space, which is significant in high-speed APDs with thin multiplication regions. It is shown that the receiver's bit-error probability is reduced as the magnitude of dead space increases relative to the APD's multiplication-region width. The calculated error probabilities and receiver sensitivities are also compared with those obtained from the Chernoff bound.

Asynchronous Ds-Ss Cdma Random Spreading Code Correlation Statistics In The Presence Of Timing Error, David W. Matolak

Faculty Publications

We quantify the effect of timing tracking errors upon 2nd order correlation statistics of random binary spreading codes and, in so doing, fill a gap in the literature. Using a Gaussian model for timing tracking error, new expressions for autocorrelation statistics are derived. For crosscorrelations, we show that a zero mean Gaussian timing error has no effect upon 2nd order crosscorrelation statistics.

Day 2: Thursday, August 5, 2004: Valmont Power Plant, Valmont Power Plant

Energy Field Tour 2004 (August 4-6)

5 pages (includes illustrations).

Channel Probing For An Indoor Wireless Communications Channel, Brandon Hunter

Theses and Dissertations

The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter …