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Articles 1 - 30 of 590
Full-Text Articles in Engineering
Predicting Iot Distributed Ledger Fraud Transactions With A Lightweight Gan Network, Charles Rawlins, Jagannathan Sarangapani
Predicting Iot Distributed Ledger Fraud Transactions With A Lightweight Gan Network, Charles Rawlins, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security …
Prescribed-Time Nash Equilibrium Seeking For Pursuit-Evasion Game, Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch
Prescribed-Time Nash Equilibrium Seeking For Pursuit-Evasion Game, Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Dear Editor, this letter is concerned with prescribed-time Nash equilibrium (PTNE) seeking problem in a pursuit-evasion game (PEG) involving agents with second-order dynamics. In order to achieve the prior given and user-defined convergence time for the PEG, a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global information. Then, it is theoretically proved that the prescribed-time convergence of the designed algorithm for achieving Nash equilibrium of PEG. Eventually, the effectiveness of the PTNE method was validated by numerical simulation results.
Advancing Simultaneous Extraction And Sequential Single-Particle Icp-Ms Analysis For Metallic Nanoparticle Mixtures In Plant Tissues, Lei Xu, Xingmao Ma, John Yang, Joel G. Burken, Paul Ki-Souk Nam, Honglan Shi, Hu Yang
Advancing Simultaneous Extraction And Sequential Single-Particle Icp-Ms Analysis For Metallic Nanoparticle Mixtures In Plant Tissues, Lei Xu, Xingmao Ma, John Yang, Joel G. Burken, Paul Ki-Souk Nam, Honglan Shi, Hu Yang
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Engineered Nanoparticles (ENPs) Have Been Increasingly Used in Agricultural Operations, leading to an Urgent Need for Robust Methods to Analyze Co-Occurring ENPs in Plant Tissues. in Response, This Study Advanced the Simultaneous Extraction of Coexisting Silver, Cerium Oxide, and Copper Oxide ENPs in Lettuce Shoots and Roots using Macerozyme R-10 and Analyzed Them by Single-Particle Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Additionally, the Standard Stock Suspensions of the ENPs Were Stabilized with Citrate, and the Long-Term Stability (Up to 5 Months) Was Examined for the First Time. the Method Performance Results Displayed Satisfactory Accuracies and Precisions and Achieved Low Particle Concentration …
A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla
A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla
Electrical and Computer Engineering Faculty Research & Creative Works
Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new …
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Computer Science Faculty Research & Creative Works
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …
Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Computer Science Faculty Research & Creative Works
The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …
Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Computer Science Faculty Research & Creative Works
The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …
Highly Efficient Dopamine Sensing With A Carbon Nanotube-Encapsulated Metal Chalcogenide Nanostructure, Harish Singh, Jiandong Wu, Kurt A.L. Lagemann, Manashi Nath
Highly Efficient Dopamine Sensing With A Carbon Nanotube-Encapsulated Metal Chalcogenide Nanostructure, Harish Singh, Jiandong Wu, Kurt A.L. Lagemann, Manashi Nath
Chemical and Biochemical Engineering Faculty Research & Creative Works
Carbon nanotube-encapsulated nickel selenide composite nanostructures were used as nonenzymatic electrochemical sensors for dopamine detection. These composite nanostructures were synthesized through a simple, one-step, and environmentally friendly chemical vapor deposition method, wherein the CNTs were formed in situ from pyrolysis of a carbon-rich metallo-organic precursor. The composition and morphology of these hybrid NiSe2-filled carbon nanostructures were confirmed by powder X-ray diffraction, Raman, X-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy images. Electrochemical tests demonstrated that the as-synthesized hybrid nanostructures exhibited outstanding electrocatalytic performance toward dopamine oxidation, with a high sensitivity of 19.62 μA μM-1 cm-2, low detection limit, broad linear …
Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch
Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch
Chemistry Faculty Research & Creative Works
Biomedical Datasets Distill Many Mechanisms Of Human Diseases, Linking Diseases To Genes And Phenotypes (Signs And Symptoms Of Disease), Genetic Mutations To Altered Protein Structures, And Altered Proteins To Changes In Molecular Functions And Biological Processes. It Is Desirable To Gain New Insights From These Data, Especially With Regard To The Uncovering Of Hierarchical Structures Relating Disease Variants. However, Analysis To This End Has Proven Difficult Due To The Complexity Of The Connections Between Multi-Categorical Symbolic Data. This Article Proposes Symbolic Tree Adaptive Resonance Theory (START), With Additional Supervised, Dual-Vigilance (DV-START), And Distributed Dual-Vigilance (DDV-START) Formulations, For The Clustering Of …
Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan
Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
A novel optimal trajectory tracking scheme is introduced for nonlinear continuous-time systems in strict feedback form with uncertain dynamics by using neural networks (NNs). The method employs an actor-critic-based NN back-stepping technique for minimizing a discounted value function along with an identifier to approximate unknown system dynamics that are expressed in augmented form. Novel online weight update laws for the actor and critic NNs are derived by using both the NN identifier and Hamilton-Jacobi-Bellman residual error. A new continual lifelong learning technique utilizing the Fisher Information Matrix via Hamilton-Jacobi-Bellman residual error is introduced to obtain the significance of weights in …
Pisa Printing Microneedles With Controllable Aqueous Dissolution Kinetics, Aaron Priester, Jimmy Yeng, Yuwei Zhang, Krista Hilmas, Risheng Wang, Anthony J. Convertine
Pisa Printing Microneedles With Controllable Aqueous Dissolution Kinetics, Aaron Priester, Jimmy Yeng, Yuwei Zhang, Krista Hilmas, Risheng Wang, Anthony J. Convertine
Chemistry Faculty Research & Creative Works
This study focused on the development of high-resolution polymeric structures using polymer-induced self-assembly (PISA) printing with commercially available digital light-processing (DLP) printers. Significantly, soluble solids could be 3D-printed using this methodology with controllable aqueous dissolution rates. This was achieved using a highly branched macrochain transfer agent (macro-CTA) containing multiple covalently attached CTA groups. In this work, the use of acrylamide as the self-assembling monomer in isopropyl alcohol was explored with the addition of N-(butoxymethyl)acrylamide to modulate the aqueous dissolution kinetics. PISA-printed microneedles were observed to have feature sizes as small as 27 μm, which was close to the resolution limit …
Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan
Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel …
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This Article Introduces a Novel Optimal Trajectory Tracking Control Scheme Designed for Uncertain Linear Discrete-Time (DT) Systems. in Contrast to Traditional Tracking Control Methods, Our Approach Removes the Requirement for the Reference Trajectory to Align with the Generator Dynamics of an Autonomous Dynamical System. Moreover, It Does Not Demand the Complete Desired Trajectory to Be Known in Advance, Whether through the Generator Model or Any Other Means. Instead, Our Approach Can Dynamically Incorporate Segments (Finite Horizons) of Reference Trajectories and Autonomously Learn an Optimal Control Policy to Track Them in Real Time. to Achieve This, We Address the Tracking Problem …
Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong
Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong
Computer Science Faculty Research & Creative Works
Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …
Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan
Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …
Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria
Deep Learning For Uav Detection And Classification Via Radio Frequency Signal Analysis, Prajoy Podder, Maciej Zawodniok, Sanjay Madria
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) are advertised as great tool that benefits society and humanity. However, UAVs also pose significant security threats ranging from privacy invasions, to interfering with commercial aircraft landing and takeoff, to accidently crashing into vehicles or people, to military or terrorist attacks. Consequently, there is a pressing need to detect and identify UAVs to mitigate such potential risks. While image-based methods are crucial for UAV detection, radio frequency (RF) emissions offer additional valuable insights. Analyzing RF signals, such as those used in UAV-ground station communications, can provide information about UAV types based on distinct frequency usage or …
Lessons Learned From Laboratory Study And Field Application Of Re-Crosslinkable Preformed Particle Gels Rppg For Conformance Control In Mature Oilfields With Conduits/Fractures/Fracture-Like Channels, Baojun Bai, Thomas P. Schuman, David Smith, Tao Song
Lessons Learned From Laboratory Study And Field Application Of Re-Crosslinkable Preformed Particle Gels Rppg For Conformance Control In Mature Oilfields With Conduits/Fractures/Fracture-Like Channels, Baojun Bai, Thomas P. Schuman, David Smith, Tao Song
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
This Paper Surveys the Role of Re-Crosslink able Preformed Particle Gels (RPPG) in Addressing Conformance Challenges within Mature Oilfields. Despite Widespread Preformed Particle Gel (PPG) Application in 15,000+ Wells, their Limitations in Sealing Fractures and Conduits Prevalent in Mature Reservoirs Have Driven the Development of RPPG Formulations. Synthesized in Various Sizes from Micrometer to Millimeter Levels, These Environmentally Friendly RPPGs Are Tailored for Diverse Reservoir Conditions. Findings Showcase the Successful Laboratory-Scale Creation and Upscaling of RPPG Products, Offering Adaptability to Temperatures from 20 to 175°C, Customizable Sizes, Swelling Ratios (5 to 40 Times), and Re-Crosslinking Times Spanning Minutes to Days. …
Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi
Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi
Mathematics and Statistics Faculty Research & Creative Works
Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …
Demo-Abstract: A Dtn System For Tracking Miners Using Gae-Lstm And Contact Graph Routing In An Underground Mine, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong
Demo-Abstract: A Dtn System For Tracking Miners Using Gae-Lstm And Contact Graph Routing In An Underground Mine, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong
Computer Science Faculty Research & Creative Works
Localization and prediction of movement of miners in underground mines have been a constant problem more so during a mine disaster. Due to the unavailability of GPS signals, the pillars are used as a method to locate these miners, and thus, location prediction is also carried out with reference to these pillars. In this work, we demon- strate a Delay-tolerant Network (DTN) system called Miner-Finder that leverages Machine Learning (ML) framework (GAE-LSTM) that works on edge devices (e.g., mobile phones, tablets) to predict the location of miners in an underground mine. The information such as speed, angle, time, nearest pillar …
Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan
Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
Recently Introduced Deep Reinforcement Learning (DRL) Techniques in Discrete-Time Have Resulted in Significant Advances in Online Games, Robotics, and So On. Inspired from Recent Developments, We Have Proposed an Approach Referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC-SANE) for Continuous Control Problems, Which Uses Quantile Loss to Train Critic and a Spiking Neural Network (NN) to Train an Ensemble of Actors. the NN Does an Internal Normalization using a Scaled Exponential Linear Unit (SELU) Activation Function and Ensures Robustness. the Empirical Study on Multijoint Dynamics with Contact (MuJoCo)-Based Environments Shows Improved Training and Test Results Than …
Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han
Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han
Mathematics and Statistics Faculty Research & Creative Works
This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …
Repeated Low-Level Blast Exposure Alters Urinary And Serum Metabolites, Austin Sigler, Jiandong Wu, Annalise Pfaff, Olajide Adetunji, Paul Ki-Souk Nam, Donald James, Casey Burton, Honglan Shi
Repeated Low-Level Blast Exposure Alters Urinary And Serum Metabolites, Austin Sigler, Jiandong Wu, Annalise Pfaff, Olajide Adetunji, Paul Ki-Souk Nam, Donald James, Casey Burton, Honglan Shi
Chemistry Faculty Research & Creative Works
Repeated exposure to low-level blast overpressures can produce biological changes and clinical sequelae that resemble mild traumatic brain injury (TBI). While recent efforts have revealed several protein biomarkers for axonal injury during repetitive blast exposure, this study aims to explore potential small molecule biomarkers of brain injury during repeated blast exposure. This study evaluated a panel of ten small molecule metabolites involved in neurotransmission, oxidative stress, and energy metabolism in the urine and serum of military personnel (n = 27) conducting breacher training with repeated exposure to low-level blasts. The metabolites were analyzed using HPLC—tandem mass spectrometry, and the Wilcoxon …
Dual Crosslinked Poly(Acrylamide-Co-N-Vinylpyrrolidone) Microspheres With Re-Crosslinking Ability For Fossil Energy Recovery, Jingyang Pu, Baojun Bai, Jiaming Geng, Na Zhang, Thomas P. Schuman
Dual Crosslinked Poly(Acrylamide-Co-N-Vinylpyrrolidone) Microspheres With Re-Crosslinking Ability For Fossil Energy Recovery, Jingyang Pu, Baojun Bai, Jiaming Geng, Na Zhang, Thomas P. Schuman
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Microspheres have been proposed to be applied in controlling wastewater production for mature oilfields and migrating leakage for gas and nuclear waste storage. However, it remains challenging for stacked microspheres to maintain strong blocking ability in micron-sized small pores or fractures. In this study, a novel microsphere was developed with comprehensive properties including high deformability and long re-crosslinking time upon tunable swelling ratio for the applications. A dual covalent and physical crosslinking strategy was used to develop novel microspheres reinforced by a hydrogen bond (H-bond, between pyrrole ring and amide group) and coordination bond (between chromium acetate (CrAc) and carboxyl …
Sharprazor: Automatic Removal Of Hair And Ruler Marks From Dermoscopy Images, Reda Kasmi, Jason Hagerty, Reagan Harris Young, Norsang Lama, Januka Nepal, Jessica Miinch, William V. Stoecker, R. Joe Stanley
Sharprazor: Automatic Removal Of Hair And Ruler Marks From Dermoscopy Images, Reda Kasmi, Jason Hagerty, Reagan Harris Young, Norsang Lama, Januka Nepal, Jessica Miinch, William V. Stoecker, R. Joe Stanley
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Background: The removal of hair and ruler marks is critical in handcrafted image analysis of dermoscopic skin lesions. No other dermoscopic artifacts cause more problems in segmentation and structure detection. Purpose: The aim of the work is to detect both white and black hair, artifacts and finally inpaint correctly the image. Method: We introduce a new algorithm: SharpRazor, to detect hair and ruler marks and remove them from the image. Our multiple-filter approach detects hairs of varying widths within varying backgrounds, while avoiding detection of vessels and bubbles. The proposed algorithm utilizes grayscale plane modification, hair enhancement, segmentation using tri-directional …
Are Natural Fractures In Sandstone Reservoir: Water Wet – Mixed Wet – Or Oil Wet?, Salah Almudhhi, Laila Abdullah, Waleed Al-Bazzaz, Saleh Alsayegh, Hussien Alajaj, Ralph E. Flori
Are Natural Fractures In Sandstone Reservoir: Water Wet – Mixed Wet – Or Oil Wet?, Salah Almudhhi, Laila Abdullah, Waleed Al-Bazzaz, Saleh Alsayegh, Hussien Alajaj, Ralph E. Flori
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
This study accurately measures the wettability contact angle of native Kuwaiti sandstone reservoir that hosts mixed pore size distributions in both the tight sandstone matrix as well as the natural fracture (NF) embedded in it. Also, this study, effectively, investigates the geometrical size and shape of natural available voids whether matrix voids or NF voids captured in the rock 2D image frame system. Correspondingly, this study is, successfully, measure tight matrix, NF Pore wall, and NF pore opening wettability performance and recovery efficiency contributions inside the sandstone reservoir. A model pore/ grain contact angle wettability is generated. Therefore, this study …
Seismic Azimuthal Anisotropy Beneath A Fast Moving Ancient Continent: Constraints From Shear Wave Splitting Analysis In Australia, Kailun Ba, Stephen S. Gao, Jianguo Song, Kelly H. Liu
Seismic Azimuthal Anisotropy Beneath A Fast Moving Ancient Continent: Constraints From Shear Wave Splitting Analysis In Australia, Kailun Ba, Stephen S. Gao, Jianguo Song, Kelly H. Liu
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Seismic Azimuthal Anisotropy Beneath Australia is Investigated using Splitting of the Teleseismic PKS, SKKS, and SKS Phases to Delineate Asthenospheric Flow and Lithospheric Deformation Beneath One of the Oldest and Fast-Moving Continents on Earth. in Total 511 Pairs of High-Quality Splitting Parameters Were Observed at 116 Seismic Stations. Unlike Other Stable Continental Areas in Africa, East Asia, and North America, Where Spatially Consistent Splitting Parameters Dominate, the Fast Orientations and Splitting Times Observed in Australia Show a Complex Pattern, with a Slightly Smaller Than Normal Average Splitting Time of 0.85 ± 0.33 S. on the North Australian Craton, the Fast …
Coreflooding Evaluation Of Fiber-Assisted Recrosslinkable Preformed Particle Gel Using An Open Fracture Model, Shuda Zhao, Ali Al Brahim, Junchen Liu, Baojun Bai, Thomas P. Schuman
Coreflooding Evaluation Of Fiber-Assisted Recrosslinkable Preformed Particle Gel Using An Open Fracture Model, Shuda Zhao, Ali Al Brahim, Junchen Liu, Baojun Bai, Thomas P. Schuman
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Recrosslinkable Preformed Particle Gels (RPPGs) Have Been Used to Treat the Problem of Void Space Conduits (VSC) and repair the "Short-Circuited" Waterflood in Alaska's West Sak Field. Field Results Showed a 23% Increase in Success Rates over Typical Preformed Particle Gel (PPG) Treatments. in This Paper, We Evaluated Whether Adding Fiber into RPPGs Can Increase the RPPG Plugging Efficiency and Thus Further Improve the Success Rate. We Designed Open Fracture Models to Represent vs.C and Investigated the Effect of Swelling Ratio (SR), Fracture Size, and Fiber Concentration on Gel Injection Pressure, Water Breakthrough Pressure, and Permeability Reduction. Results Show that …
Lysine Crosslinked Polyacrylamide─A Novel Green Polymer Gel For Preferential Flow Control, Tao Song, Baojun Bai, Yugandhara Eriyagama, Thomas P. Schuman
Lysine Crosslinked Polyacrylamide─A Novel Green Polymer Gel For Preferential Flow Control, Tao Song, Baojun Bai, Yugandhara Eriyagama, Thomas P. Schuman
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Acrylamide-based polymer gels have been applied to control the preferential flow in the subsurface for decades. However, some commonly used crosslinkers, such as Cr (III) and phenol-formaldehyde, are highly toxic and are being phased out because of stringent environmental regulations. This work uses l-lysine as the green crosslinker to produce acrylamide-based polymer gels. This article systematically studied the effect of lysine and polymer concentration, salinity, pH, and temperature on gelation behavior and thermal stability. Besides, the gelation mechanism and crosslinking density were elucidated in this work. A high-permeability sandstone core was used to test the plugging efficiency of this novel …
A Dtn-Based Spatio-Temporal Routing Using Location Prediction Model In Underground Mines, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong
A Dtn-Based Spatio-Temporal Routing Using Location Prediction Model In Underground Mines, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong
Computer Science Faculty Research & Creative Works
Situational awareness during any disaster depends on effective communication and location tracking. In the case of underground mines, where the communication methods are mostly central, the whole communication channel would be rendered unusable during a disaster. To this end, we propose the use of Delay Tolerant Networks (DTN) to allow the miners to function in a distributed manner and help in locating the injured miners and routing distress messages. Due to the unavailability of GPS signals, the pillar numbers are used to identify the locations of the miners. For spatio-temporal routing of messages, we formulate a new scheme using Contact …