Engineering Commons^™

In Operando Mechanism Analysis On Nanocrystalline Silicon Anode Material For Reversible And Ultrafast Sodium Storage, Lei Zhang, Xianluo Hu, Chaoji Chen, Haipeng Guo, Xiaoxiao Liu, Gengzhao Xu, Haijian Zhong, Shuang Cheng, Peng Wu, Jiashen Meng, Yunhui Huang, Shi Xue Dou, Hua-Kun Liu

Australian Institute for Innovative Materials - Papers

Presently, lithium-ion batteries (LIBs) are the most promising commercialized electrochemical energy storage systems. Unfortunately, the limited resource of Li results in increasing cost for its scalable application and a general consciousness of the need to find new type of energy storage technologies. Very recently, substantial effort has been invested to sodium-ion batteries (SIBs) due to their effectively unlimited nature of sodium resources. Furthermore, the potential of Li/Li+ is 0.3 V lower than that of Na/Na+, which makes it more effective to limit the electrolyte degradation on the outer surface of the electrode.[1] Nevertheless, one major obstacle ...

Studies On Stable Crack Growth, Mohammed Juma Humaid Al-Ghafri

Theses

The goal of this work is to experimentally investigate the stable crack growth (SCG) fracture behavior of AISI 4340 alloy steel. A series of mode I and mixed mode SCG fracture tests were carried out on 8 mm thick compact tension (CT) specimens subjected to quasistatic loading. The wire cutting technique was used to introduce a pre-notch/ pre-crack of 0.05 mm root radius to the specimen. Five different loading angles Ψ between the loading axis and the crack surface were employed; 90° (mode I), 75°, 65°, 60° and 50°. Five different ratios of original crack length to specimen width ...

Recursive Non-Local Means Filter For Video Denoising, Redha A. Ali, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window.

The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video ...

Fast On-Line Kernel Density Estimation For Active Object Localization, Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell

Computer Science Faculty Publications and Presentations

A major goal of computer vision is to enable computers to interpret visual situations—abstract concepts (e.g., “a person walking a dog,” “a crowd waiting for a bus,” “a picnic”) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations— a situation model—that represent the expected spatial structure of the ...

Combined Model Predictive Control And Scheduling With Dominant Time Constant Compensation, Logan Beal, Junho Park, Damon Petersen, Sean C. Warnick, John Hedengren

All Faculty Publications

Linear model predictive control is extended to both control and optimize a product grade schedule. The proposed methods are time-scaling of the linear dynamics based on throughput rates and grade-based objectives for product scheduling based on a mathematical program with complementarity constraints. The linear model is adjusted with a residence time approximation to time-scale the dynamics based on throughput. Although nonlinear models directly account for changing dynamics, the model form is restricted to linear differential equations to enable fast online cycle times for large-scale and real-time systems. This method of extending a linear time-invariant model for scheduling is designed for ...

Innovation Of Driving Gear Train System For Developer Unit Of Lexmark Home Printers, Jay A. Crist, Sarah Gore, Kun Xie, Michael Mixoon

University of Tennessee Honors Thesis Projects

No abstract provided.

Smooth Operator: Control Using The Smooth Robustness Of Temporal Logic, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it ...

An Evaluation Of The Efficiency Of Compartmented Alginate Fibres Encapsulating Rejuvenator As Asphalt Pavement Healing System, Amir Tabakovic, Luke Schuyffel, Aleksandar Karac, Erik Schlangen

Articles

This paper explores the potential methods for evaluating a healing system for asphalt pavements. The healing system under investigation involves compartmented calcium-alginate fibres encapsulating an asphalt binder healing agent (rejuvenator). This system presents a novel method of incorporating rejuvenators into asphalt pavement mixtures. The compartmented fibres are used to distribute the rejuvenator throughout the pavement mixture, thereby overcoming some of the problems associated with alternate asphalt pavement healing methods, i.e., spherical capsules and hollow fibres. The asphalt healing efficiency methods to be evaluated in this paper include: (i) standard test methods for asphalt pavements, such as the Indirect Tensile ...

Projected Nesterov’S Proximal-Gradient Algorithm For Sparse Signal Recovery, Renliang Gu, Aleksandar Dogandžić

Electrical and Computer Engineering Publications

Abstract: We develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov's momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the ℓ₁ -norm penalty on the signal's linear transform coefficients ...

Heat Transfer Analysis Of Localized Heat-Treatment For Grade 91 Steel, Jacob D. Walker

All Graduate Theses and Dissertations

Many of the projects utilizing Grade 91 steel are large in scale, therefore it is necessary to assemble on site. The assembly of the major pieces often requires welding in the assembly; welding drastically changes the superior mechanical properties of Grade 91 steel that it was specifically developed for. Therefore, because of the adverse effects of welding on the mechanical properties of Grade 91, it is necessary to do a localized post weld heat treatment.

In this study a localized post weld heat treatment is used to gather experimental data. The data is then used to derive unknown heat transfer ...

Development And Application Of Hydraulic And Hydrogeologic Models To Better Inform Management Decisions, Trinity L. Stout

All Graduate Theses and Dissertations

Water is one of the most important and limited resources in regions with little rainfall. As populations continue to grow, so does the need for water. Individuals in water management positions need to be well informed in order to avoid potential negative effects concerning the overall quality and amount of water available for both people and the environment. In order to provide better information for these individuals, computer models and mathematical relationships are commonly developed to estimate the outcome of different situations regarding surface water and groundwater. Along these lines, this study focused on two modeling studies that provide information ...

Machine Learning For High-Throughput Stress Phenotyping In Plants, Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar

Baskar Ganapathysubramanian

Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly ...

An End-To-End Convolutional Selective Autoencoder Approach To Soybean Cyst Nematode Eggs Detection, Adedotun Akintayo, Nigel Lee, Vikas Chawla, Mark P. Mullaney, Christopher C. Marett, Asheesh K. Singh, Arti Singh, Gregory L. Tylka, Baskar Ganapathysubramanian, Soumik Sarkar

Baskar Ganapathysubramanian

Soybean cyst nematodes (SCNs), Heterodera glycines, are unwanted micro-organisms that reduce yields of a major source of food–soybeans. In the United States alone, approximately $1 billion is lost per annum due to cyst nematode infections on soybean plants. Experts have conceived methods of mitigating the losses through phenotyping techniques via SCN eggs density estimation, and then applying the right control measures. Currently, they rely on labor intensive and time-consuming identification of SCN eggs in soil samples processed onto microscopic frames. However, phenotyping a vast array of fields requires automated high-throughput techniques. From an automation perspective, detection of rarely occurring ...

Pedagogical Resources For Industrial Control Systems Security: Design, Implementation, Conveyance, And Evaluation, Guillermo A. Francia Iii, Greg Randall, Jay Snellen

Journal of Cybersecurity Education, Research and Practice

Industrial Control Systems (ICS), which are pervasive in our nation’s critical infrastructures, are becoming increasingly at risk and vulnerable to internal and external threats. It is imperative that the future workforce be educated and trained on the security of such systems. However, it is equally important that careful and deliberate considerations must be exercised in designing and implementing the educational and training activities that pertain to ICS. To that end, we designed and implemented pedagogical materials and tools to facilitate the teaching and learning processes in the area of ICS security. In this paper, we describe those resources, the ...

On The Simulation And Mitigation Of Anisoplanatic Optical Turbulence For Long Range Imaging, Russell C. Hardie, Daniel A. Lemaster

Russell C. Hardie

We describe a numerical wave propagation method for simulating long range imaging of an extended scene under anisoplanatic conditions. Our approach computes an array of point spread functions (PSFs) for a 2D grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. To validate the simulation we compare simulated outputs with the theoretical anisoplanatic tilt correlation and differential tilt variance. This is in addition to comparing the long- and short-exposure PSFs, and isoplanatic angle. Our validation analysis shows an ...

Recursive Non-Local Means Filter For Video Denoising With Poisson-Gaussian Noise, Redha A. Almahdi, Russell C. Hardie

Russell C. Hardie

In this paper, we describe a new recursive Non-Local means (RNLM) algorithm for video denoising that has been developed by the current authors. Furthermore, we extend this work by incorporating a Poisson-Gaussian noise model. Our new RNLM method provides a computationally efficient means for video denoising, and yields improved performance compared with the single frame NLM and BM3D benchmarks methods. Non-Local means (NLM) based methods of denoising have been applied successfully in various image and video sequence denoising applications. However, direct extension of this method from 2D to 3D for video processing can be computationally demanding. The RNLM approach takes ...

Analysis Of Various Classification Techniques For Computer Aided Detection System Of Pulmonary Nodules In Ct, Barath Narayanan Narayanan, Russell C. Hardie, Temesguen Messay

Russell C. Hardie

Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii ...

Comparing Multiple Turbulence Restoration Algorithms Performance On Noisy Anisoplanatic Imagery, Michael Armand Rucci, Russell C. Hardie, Alexander J. Dapore

Russell C. Hardie

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a ...

Engineered, Spatially Varying Isothermal Holds: Enabling Combinatorial Studies Of Temperature Effects, As Applied To Metastable Titanium Alloy Β-21s, Brian Martin, Peyman Samimi, Peter C. Collins

Peter Collins

A novel method to systematically vary temperature and thus study the resulting microstructure of a material is presented. This new method has the potential to be used in a combinatorial fashion, allowing the rapid study of thermal holds on microstructures to be conducted. This is demonstrated on a beta titanium alloy, where the thermal history has a strong effect on microstructure. It is informed by simulation and executed using the resistive heating capabilities of a Gleeble 3800 thermomechanical simulator. Spatially varying isothermal holds of 4 h were affected, where the temperature range of the multiple isothermal holds varied by ~175 ...

Contextual Motivation In Physical Activity By Means Of Association Rule Mining, Sugam Sharma, Udoyara Sunday Tim, Marinelle Payton, Hari Cohly, Shashi Gadia, Johnny Wong, Sudharshanam Karakala

Sugam Sharma

The primary thrust of this work is to demonstrate the applicability of association rule mining in public health domain, focusing on physical activity and exercising. In this paper, the concept of association rule mining is shown assisting to promote the physical exercise as regular human activity. Specifically, similar to the prototypical example of association rule mining, market basket analysis, our proposed novel approach considers two events – exercise (sporadic) and sleep (regular) as the two items of the frequent set; and associating the former, exercise event, with latter, the daily occurring activity sleep at night, helps strengthening the frequency of the ...