Engineering Commons^™

Managing Landfill Leachate In A World Without Ash Basins: Lessons Learned And Practical Considerations, Evan Andrews, Michael Lazar

World of Coal Ash Proceedings

No abstract provided.

Developing Enhanced Monitored Natural Attenuation Strategies Using Reactive Transport Models, Pj Nolan, Hugh Davies, Greg Hebeler, Rens Verburg

World of Coal Ash Proceedings

No abstract provided.

Experimental Test Method To Determine The Reactivity Of Fly Ash For Use In Concrete Mixture Proportioning, Antara Choudry, Deborah Glosser, O. Burkan Isgor, W. Jason Weiss

World of Coal Ash Proceedings

No abstract provided.

Overview Of Epri Research On Environmental Issues And Beneficial Use Of Coal Combustion Products, Bruce Hensel, Ken Ladwig

World of Coal Ash Proceedings

No abstract provided.

Is My Site A Candidate For A Groundwater Natural Attenuation Remedy? A Geochemist’S Perspective, Bob Glazier

World of Coal Ash Proceedings

No abstract provided.

Impoundment Closure Enhancement Measures To Support Groundwater Compliance, John Hesemann, Wayne Weber, Eric Dulle

World of Coal Ash Proceedings

No abstract provided.

Enhancing Coal Ash Impoundment Closure Using Value Engineering, Holt Wheeler, Trey Mangers, Paul Lear

World of Coal Ash Proceedings

No abstract provided.

Panel On Liquefaction Of Ponded Fly Ash: From Laboratory To Practice, Tarunjit S. Butalia

World of Coal Ash Proceedings

No abstract provided.

Comparison Between Two Group Signature Schemes, Hao Yang

Rose-Hulman Undergraduate Research Publications

Zerocoin is a cryptographic extension to Bitcoin. During its development, the developers decided to make use of group signature schemes to store and verify the coins. In order to compare the performance of Simple Authentication Scheme and the Dynamic Signature Scheme and figure out which one is the optimal choice for the Zerocoin scheme, I implemented them in Java and analyzed them theoretically. This paper will discuss the performance difference between two schemes, the Java implementation of them and the analysis.

Simple Surface Modification Of Poly(Dimethylsiloxane) Via Surface Segregating Smart Polymers For Biomicrofluidics, Aslıhan Gökaltun, Young Bok Abraham Kang, Martin L. Yarmush, O. Berk Usta, Ayse Asatekin

Faculty Publications - Biomedical, Mechanical, and Civil Engineering

Poly(dimethylsiloxane) (PDMS) is likely the most popular material for microfluidic devices in lab-on-achip and other biomedical applications. However, the hydrophobicity of PDMS leads to non-specific adsorption of proteins and other molecules such as therapeutic drugs, limiting its broader use. Here, we introduce a simple method for preparing PDMS materials to improve hydrophilicity and decrease nonspecific protein adsorption while retaining cellular biocompatibility, transparency, and good mechanical properties without the need for any post-cure surface treatment. This approach utilizes smart copolymers comprised of poly(ethylene glycol) (PEG) and PDMS segments (PDMS-PEG) that, when blended with PDMS during device manufacture, spontaneously segregate to surfaces …

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available …

Agent-Based Microgrid Architecture For Generation Following Protocols, Steven Goldsmith

Michigan Tech Patents

A system for predicting power and loads over a single, relatively short time horizon. More specifically, a system comprising a Storage Agent (S-agent) Cohort within a grid control society, wherein the system expands G and L intra-cohort protocols to allow the S-cohort to participate in power management of the grid by scheduling storage components in source or load roles as determined by the time-varying state of the power imbalance and by the risk-adjusting capacity margin relationship between the G and L cohorts.

Estimation Of Soil Moisture At Different Soil Levels Using Machine Learning Techniques And Unmanned Aerial Vehicle (Uav) Multispectral Imagery, Mahyar Aboutalebi, L. Niel Allen, Alfonso F. Torres-Rua, Mac Mckee, Calvin Coopmans

AggieAir Publications

Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications where they are trained and their parameters are tuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learning process …

Estimation Of Surface Thermal Emissivity In A Vineyard For Uav Microbolometer Thermal Cameras Using Nasa Hytes Hyperspectral Thermal, And Landsat And Aggieair Optical Data, Alfonso F. Torres-Rua, Mahyar Aboutalebi, Timothy Wright, Ayman Nassar, Pierre Guillevic, Lawrence Hipps, Feng Gao, Kevin Jim, Maria Mar Alsina, Calvin Coopmans, Mac Mckee, William Kustas

AggieAir Publications

Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of highresolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99– 0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health …

Validation Of Digital Surface Models (Dsms) Retrieved From Unmanned Aerial Vehicle (Uav) Point Clouds Using Geometrical Information From Shadows, Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac Mckee, William Kustas, Héctor Nieto, Calvin Coopmans

AggieAir Publications

Theoretically, the appearance of shadows in aerial imagery is not desirable for researchers because it leads to errors in object classification and bias in the calculation of indices. In contrast, shadows contain useful geometrical information about the objects blocking the light. Several studies have focused on estimation of building heights in urban areas using the length of shadows. This type of information can be used to predict the population of a region, water demand, etc., in urban areas. With the emergence of unmanned aerial vehicles (UAVs) and the availability of high- to super-high-resolution imagery, the important questions relating to shadows …

The Impact Of Shadows On Partitioning Of Radiometric Temperature To Canopy And Soil Temperature Based On The Contextual Two-Source Energy Balance Model (Tseb-2t), Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac Mckee, Hector Nieto, William Kustas, Calvin Coopmans

AggieAir Publications

Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m x 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where …

Fracture Toughness Improvement Of ����� Ceramics By Grain Size Control And Ductile Phase Reinforcement, Kesong Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

This study used grain size control and ductile phase reinforcement to improve fracture toughness of ����� ceramics. Alpha alumina particles of 100 nm, 0.5-1 micrometers, and 10 micrometers were coated with 1-5 nm nickel by electroless nickel plating (ENP). The coated powders were consolidated at 1200℃-1500℃ by spark plasma sintering (SPS). The sintered samples were annealed at 1100 oC for 1.5 hours and 10 hours to determine the effect of post sintering annealing on hardness and fracture toughness. Density of the samples were measured by the standard Archimedes method using a 5 mL pycnometer. Hardness values were determined by Vickers …

Information Technology News, Georgia Southern University

Information Technology: News & Publications (2012-2023)

• IT Candidate Needed for Brodie International Internship

Using Complex Orthogonal Decomposition To Extract Dispersion Relationships For Mass Chain, Nicholas A. Valente, Rickey A. Caldwell

Across the Bridge: The Merrimack Undergraduate Research Journal

Complex orthogonal decomposition (COD) was used to determine the extracted dispersion relationship of a traveling wave in a mass chain. When COD extracts a wavenumber it will produce M values for each wavenumber, γi, and N values for each frequency, ωi; where M is the number of masses and N is the number of time samples. In this work, least squares and a simple mean of the M-γi’s and N-ωi’s extracted values were used to determine each γi and ωi, respectively. An analytical dispersion relationship for the mass-chain is derived in addition to an approximate dispersion relationship. The approximate derivation …

Development Of A Foot Interface To Control Supernumerary Robotics Limbs, Emma Morris

Rose-Hulman Summer Undergraduate Research Fellowships

Supernumerary robotic limbs (SRLs) can be used to provide a person with extra arms to help with difficult tasks. For example, a task that normally requires three hands to complete could be accomplished by just one person with an SRL. One way to control an SRL and still leave both hands available is to use the foot. This paper describes two parts of developing this foot interface: characterizing the range of forces that the foot can apply, and prototyping systems for different control methods. First, a small sample of data was collected to learn how much force the foot can …

Fluid Transport In Porous Media For Engineering Applications, Eric M. Benner

Chemical and Biological Engineering ETDs

This doctoral dissertation presents three topics in modeling fluid transport through porous media used in engineering applications. The results provide insights into the design of fuel cell components, catalyst and drug delivery particles, and aluminum- based materials. Analytical and computational methods are utilized for the modeling of the systems of interest. Theoretical analysis of capillary-driven transport in porous media show that both geometric and evaporation effects significantly change the time dependent behavior of liquid imbibition and give a steady state flux into the medium. The evaporation–capillary number is significant in determining the time-dependent behavior of capillary flows in porous media. …

A Constrained Box Algorithm For Imbalanced Data In Remote Sensing Images, Wajira Abeysinghe

Master of Science in Computer Science Theses

Imbalanced data is a common problem in machine learning where the number of observations that belong to one class is significantly lower than other classes. Due to the skewed distribution among the classes, most classification algorithms fail to classify minority instances effectively. The class imbalance problem can be found in many domains such as credit card fraud detection and rare diseases diagnosis.

Imbalanced data is a prominent issue also in remote sensing images (RSI) which are used to obtain information of earth resources and the surrounding environment. RSI are collected by special cameras that capture information from a specific wavelength …

Measurement And Prediction Of Discharge Coefficients In Highly Compressible Pulsating Flows To Improve Egr Flow Estimation And Modeling Of Engine Flows, Indranil Brahma

Faculty Journal Articles

An assumption of constant discharge coefficient (Cd) is often made when modeling highly compressible pulsating engine flows through valves or other restrictions. Similarly, orifices and flow-nozzles used for real-time EGR flow estimation are often calibrated at a few steady-state points with one single constant Cd that minimizes the error over the selected points. This quasi-steady assumption is based on asymptotically constant Cd observed at high Reynolds number for steady (non-pulsating) flow. It has been shown in this work that this assumption is not accurate for pulsating flow, particularly at large amplitudes and low flow rates. …

Mechanical Engineering News, Georgia Southern University

Mechanical Engineering: News & Publications (2013-2023)

• Dr. Mingzhi Xu Awarded at American Foundry Society's CastExpo 2019

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians, …

Controlled Synthesis Of Pt-Sn/Al2o3 Catalysts And Their Application In The Hydrodeoxygenation Of Bio-Based Succinic Acid, Patrick Michael Howe

Theses - ALL

As environmental and economic forces push for movement away from traditional petroleum-sourced chemical and fuel production, it becomes essential for technologies in renewable carbon resources to be developed. In particular, the production of chemical commodities from renewable lignocellulosic biomass provides a unique path away from the use of petrol. Considering the high density of functional groups present in biomass feedstocks, new technologies must be developed to selectively target the removal of functional groups through the application of supported metal catalysts. The ability to target specific functional group removal would allow for biomass feedstocks to produce higher yields of desired commodities …

Dynamics Of Water, Carbon, And Nitrogen In Forest And Alpine Tundra Ecosystems In The Pacific Northwest And The Rocky Mountains Of The U.S. Under Future Climate Change, Zheng Dong

Dissertations - ALL

Projection of ecosystem functions and biogeochemical cycling of elements under future climate change requires a quantitative understanding of both ecosystem processes and site-specific climate change scenarios. Biogeochemical and ecological studies over the last decades have provided the intellectual basis for these projections, especially at the small watershed scale. Recent developments in biophysical sciences and computationally based meteorology coupled with advanced downscaling techniques have made it possible to project future climate change scenarios at the small watershed scale. Using a biogeochemical model, PnET-BGC, which has been extensively applied to the forest ecosystems in the northeastern United States, the interactive effects of …

Identification And Analysis Of Behavioral Phenotypes In Autism Spectrum Disorder Via Unsupervised Machine Learning, Elizabeth Stevens, Dennis R. Dixon, Marlena N. Novack, Doreen Granpeesheh, Tristram Smith, Erik Linstead

Engineering Faculty Articles and Research

Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes.

Materials and methods: The present study included a sample of children with ASD (N = 2400), …

Analyzing And Assuring Missions And Systems By Storm: Introducing And Analyzing Systems-Theoretic And Technical Operational Risk Management (Storm), Lori Denise Pickering

Theses - ALL

The complexity of today’s large, multi-component systems and missions presents a growing risk of failure because of emergent system-level properties. Furthermore, the interconnectivity of systems to other systems creates additional security problems. Yes- terday’s safety and security risk analysis methodologies are no longer effective. To manage this complexity, what is needed is a holistic, thorough, systematic, system-level, and for- mally verified approach to risk analysis to ensure stakeholder-required needs are met, asset losses are mitigated, and the system or mission operates with its intended function- ality. Furthermore, these system and mission risks need to be thoroughly documented to increase the …

Semantic Image Segmentation Via A Dense Parallel Network, Jiyang Wang

Theses - ALL

Image segmentation has been an important area of study in computer vision. Image segmentation is a challenging task, since it involves pixel-wise annotation, i.e. labeling each pixel according to the class to which it belongs. In image classification task, the goal is to predict to which class an entire image belongs. Thus, there is more focus on the abstract features extracted by Convolutional Neural Networks (CNNs), with less emphasis on the spatial information. In image segmentation task, on the other hand, the abstract information and spatial information are needed at the same time. One class of work in image segmentation …