Engineering Commons^™

A Feasibility Study Of A Low-Cost, Large-Scale Thermal Treatment Process For Human Feces, Ryan John Homeyer

Theses - ALL

To hygienically manage the global sanitation crisis, it is pertinent to develop new methods for treating human excreta. This thesis proposes a new design where human feces is stored in a steel shipping container that is subject to shortwave solar radiation that, according to theory, heats the enclosed excreta to temperatures that inactivate fecal pathogens. The feasibility of this design is analyzed by way of numerical and experimental modelling. The experimentally validated model is used to simulate the effectiveness of the design over the course of many days of irradiation. This study shows that a sufficient temperature distribution (i.e., T(x,y) …

Inferences From Interactions With Smart Devices: Security Leaks And Defenses, Diksha Shukla

Dissertations - ALL

We unlock our smart devices such as smartphone several times every day using a pin, password, or graphical pattern if the device is secured by one. The scope and usage of smart devices' are expanding day by day in our everyday life and hence the need to make them more secure. In the near future, we may need to authenticate ourselves on emerging smart devices such as electronic doors, exercise equipment, power tools, medical devices, and smart TV remote control. While recent research focuses on developing new behavior-based methods to authenticate these smart devices, pin and password still remain primary …

Predicting Wind Turbine Blade Erosion Using Machine Learning, Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, Viswanath Puttagunta

SMU Data Science Review

Using time-series data and turbine blade inspection assessments, we present a classification model in order to predict remaining turbine blade life in wind turbines. Capturing the kinetic energy of wind requires complex mechanical systems, which require sophisticated maintenance and planning strategies. There are many traditional approaches to monitoring the internal gearbox and generator, but the condition of turbine blades can be difficult to measure and access. Accurate and cost- effective estimates of turbine blade life cycles will drive optimal investments in repairs and improve overall performance. These measures will drive down costs as well as provide cheap and clean electricity …

How Will Climate Alter Efficiency Objectives? Simulated Impact Of Using Recent Versus Historic European Weather Data For The Cost-Optimal Design Of Nearly Zero Energy Buildings (Nzebs), Florida Solar Energy Center, Delia D'Agostino

FSEC Energy Research Center®

Achieving "nearly zero energy buildings" (NZEB) has been established as a vital objective over the next decade within the European Union (EU) [1, 2]. Previous work has shown that a series of very cost-effective thermal efficiency measures, equipment, appliance and renewable energy choices are available across climates to reach the NZEB objective. Resulting detailed energy and economic optimization findings have been obtained and published. One area that has just begun to be explored, however, is how selection of weather files and their application against coming climate change can influence outcomes from energy optimization procedures.

Presented at: CLIMA 2019, REHVA 13th …

Point Defects In Lithium Gallate And Gallium Oxide, Christopher A. Lenyk

Theses and Dissertations

Electron paramagnetic resonance (EPR), Fourier-Transform Infrared spectroscopy (FTIR), photoluminescence (PL), thermoluminescence (TL), and wavelength-dependent TL are used to identify and characterize point defects in lithium gallate and β-gallium oxide doped with Mg and Fe acceptor impurities single crystals. EPR investigations of LiGaO₂ identify fundamental intrinsic cation defects lithium (V⁻_Li) and gallium (V²⁻_Ga) vacancies. The defects’ principle g values are found through angular dependence studies and atomic-scale models for these new defects are proposed. Thermoluminescence measurements estimate the activation energy of lithium vacancies at Ea = 1.05 eV and gallium vacancies at Ea > 2 …

Study On The Reduction Of Carbon Emission，Results From The Vehicles In The Shanghai Port’S Container Collection And Distribution System, Chaofeng Chen

World Maritime University Dissertations

No abstract provided.

Research On Double-Stack Container Transport Organization In International Multimodal Transport, Xi Zhu

World Maritime University Dissertations

No abstract provided.

Optimization Of The Dedicated Corridor System Connecting Bohai Rim Gateways, Xinyi Shi

World Maritime University Dissertations

No abstract provided.

Numerical Algorithms For Solving Nonsmooth Optimization Problems And Applications To Image Reconstructions, Karina Rodriguez

REU Final Reports

In this project, we apply nonconvex optimization techniques to study the problems of image recovery and dictionary learning. The main focus is on reconstructing a digital image in which several pixels are lost and/or corrupted by Gaussian noise. We solve the problem using an optimization model involving a sparsity-inducing regularization represented as a difference of two convex functions. Then we apply different optimization techniques for minimizing differences of convex functions to tackle the research problem.

Ex Vivo Electrochemical Measurement Of Glutamate Release During Spinal Cord Injury, James K. Nolan, Tran N. H. Nguyen, Mara Fattah, Jessica C. Page, Riyi Shi, Hyowon Lee

Weldon School of Biomedical Engineering Faculty Publications

Excessive glutamate release following traumatic spinal cord injury (SCI) has been associated with exacerbating the extent of SCI. However, the mechanism behind sustained high levels of extracellular glutamate is unclear. Spinal cord segments mounted in a sucrose double gap recording chamber are an established model for traumatic spinal cord injury. We have developed a method to record, with micro-scale printed glutamate biosensors, glutamate release from ex vivo rat spinal cord segments following injury. This protocol would work equally well for similar glutamate biosensors.

Online Eeg Seizure Detection And Localization, Amirsalar Mansouri, Sanjay P. Singh, Khalid Sayood

Department of Electrical and Computer Engineering: Faculty Publications

Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists-- a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG …

Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan

SMU Data Science Review

In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …

Our Envirome, Spring/Summer 2019, Issue 40

Sustain Magazine

No abstract provided.

Plastic Pollution, Fall/Winter 2019, Issue 39.3

Sustain Magazine

No abstract provided.

Plastic Pollution, Fall/Winter 2019, Issue 39.2

Sustain Magazine

No abstract provided.

Plastic Pollution, Fall/Winter 2019, Issue 39

Sustain Magazine

No abstract provided.

Comparison Of Different Methods For Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation, Parastoo Dehkordi, Farad Khosrow-Khavar, Marco Di Rienzo, Omer T. Inan, Samuel E. Schmidt, Andrew P. Blaber, Kasper Sorensen, Johannes J. Struijk, Vahid Zakeri, Prospero Lombardi, Md. Mobaskir H. Shandhi, Mojtaba Borairi, John M. Zanetti, Kouhyar Tavakolian

Biomedical Sciences Faculty Publications

Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For …

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning, Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong, Jianjun Hu

Faculty Publications

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over …

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Effects Of Coefficient Of Thermal Expansion On Unbonded Concrete Overlay Design And Performance, Gauhar Sabih

Civil Engineering ETDs

With the deterioration of highway pavements across the country, more emphasis is being laid on the rehabilitation of existing pavements. Unbonded concrete overlay (UBCO) is a cost-effective technique used to rehabilitate damaged concrete pavements. The design and performance of UBCO rely on various properties of concrete, of which coefficient of thermal expansion (CTE) is an important one. Concrete CTE has a direct impact on the design and performance of rigid pavements and overlays. CTE regulates the magnitude of curling and related stresses that impact the performance of overlays with regards to cracking, faulting and pavement roughness. Previous research revealed that …

Operational Decision Making Under Uncertainty: Inferential, Sequential, And Adversarial Approaches, Andrew J. Keith

Theses and Dissertations

Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the …

Artificial Intelligence Based Wrist Fracture Classification, Dineep Thomas

LSU Master's Theses

The problem of predicting wrist fractures from X-rays using Artificial Intelligence (AI) methods is addressed. Wrist fractures are the most commonly misdiagnosed fractures because of the complex anatomical structure of the wrist bone which includes several different bones. This research provides a predictive solution to automate the process of wrist fracture classifications and outlines a visualization technique to identify the probable location of the fractured region on the X-rays. This thesis describes a deep learning based approach for wrist fracture classification. Deep convolutional neural network (CNN) based models have been used for wrist fracture classification by combining different optimization techniques. …

An Optical-Based Technique To Obtain Vibration Characteristics Of Rotating Tires, Aakash Mange, Theresa Atkinson, Jennifer Bastiaan, Javad Baqersad

Mechanical Engineering Publications

The dynamic characteristics of tires are critical in the overall vibrations of vehicles because the tire-road interface is the only medium of energy transfer between the vehicle and the road surface. Obtaining the natural frequencies and mode shapes of the tire helps in improving the comfort of the passengers. The vibrational characteristics of structures are usually obtained by performing conventional impact hammer modal testing, in which the structure is excited with an impact hammer and the response of the structure under excitation is captured using accelerometers. However, this approach only provides the response of the structure at a few discrete …

Docking Control Of An Autonomous Underwater Vehicle Using Reinforcement Learning, Enrico Anderlini, Gordon Parker, Giles Thomas

Michigan Tech Publications, Part 1

To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) will need to autonomously dock onto a charging station. Here, reinforcement learning strategies were applied for the first time to control the docking of an AUV onto a fixed platform in a simulation environment. Two reinforcement learning schemes were investigated: one with continuous state and action spaces, deep deterministic policy gradient (DDPG), and one with continuous state but discrete action spaces, deep Q network (DQN). For DQN, the discrete actions were selected as step changes in the control input signals. The performance of the reinforcement learning strategies was compared …

Use Of Logistic Regression To Identify Factors Influencing The Post-Incident State Of Occupational Injuries In Agribusiness Operations, Fatemeh Davoudi Kakhki, Steven Freeman, Gretchen Mosher

Faculty Publications

Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term “post-incident state” is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, …

Machine Learning To Predict The Likelihood Of A Personal Computer To Be Infected With Malware, Maryam Shahini, Ramin Farhanian, Marcus Ellis

SMU Data Science Review

In this paper, we present a new model to predict the prob- ability that a personal computer will become infected with malware. The dataset is selected from a Kaggle competition supported by Mi- crosoft. The data includes computer configuration, owner information, installed software, and configuration information. In our research, sev- eral classification models are utilized to assign a probability of a machine being infected with malware. The LightGBM classifier is the optimum machine learning model by performing faster with higher efficiency and lower memory usage in this research. The LightGBM algorithm obtained a cross-validation ROC-AUC score of 74%. Leading factors …

Aws Ec2 Instance Spot Price Forecasting Using Lstm Networks, Jeffrey Lancon, Yejur Kunwar, David Stroud, Monnie Mcgee, Robert Slater

SMU Data Science Review

Cloud computing is a network of remote computing resources hosted on the Internet that allow users to utilize cloud resources on demand. As such, it represents a paradigm shift in the way businesses and industries think about digital infrastructure. With the shift from IT resources being a capital expenditure to a managed service, companies must rethink how they approach utilizing and optimizing these resources in order to maximize productivity and minimize costs. With proper resource management, cloud resources can be instrumental in reducing computing expenses.

Cloud resources are perishable commodities; therefore, cloud service providers have developed strategies to maximize utilization …

Visualizing United States Energy Production Data, Bruce P. Kimbark, Melissa Luzardo, Charles South, James Taber

SMU Data Science Review

Power plants production, load, financials and environmental impact from power plants in the United States is publicly available either from the Energy Information Administration, the Environmental Protection Agency or Lazard among others. The general public is interested in US energy production and its potential environmental impact but the available information is complex and difficult to properly understand and not shared in ways that are accessible. Our objective was to gather this data and create different interactive visualizations that make it consumable. Each of the five visualization was designed to explain a specific part of energy that together can provide a …

Improve Image Classification Using Data Augmentation And Neural Networks, Shanqing Gu, Manisha Pednekar, Robert Slater

SMU Data Science Review

In this paper, we present how to improve image classification by using data augmentation and convolutional neural networks. Model overfitting and poor performance are common problems in applying neural network techniques. Approaches to bring intra-class differences down and retain sensitivity to the inter-class variations are important to maximize model accuracy and minimize the loss function. With CIFAR-10 public image dataset, the effects of model overfitting were monitored within different model architectures in combination of data augmentation and hyper-parameter tuning. The model performance was evaluated with train and test accuracy and loss, characteristics derived from the confusion matrices, and visualizations of …

Mechanical Engineering News, Georgia Southern University

Mechanical Engineering: News & Publications (2013-2023)

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