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Articles 1 - 30 of 733

Full-Text Articles in Physical Sciences and Mathematics

A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla Apr 2024

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 …

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Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy Apr 2024

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 …

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Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch Mar 2024

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 …

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Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch Mar 2024

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, …

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Highly Efficient Dopamine Sensing With A Carbon Nanotube-Encapsulated Metal Chalcogenide Nanostructure, Harish Singh, Jiandong Wu, Kurt A.L. Lagemann, Manashi Nath Mar 2024

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 …

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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 Mar 2024

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 …

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Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan Mar 2024

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 …

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Pisa Printing Microneedles With Controllable Aqueous Dissolution Kinetics, Aaron Priester, Jimmy Yeng, Yuwei Zhang, Krista Hilmas, Risheng Wang, Anthony J. Convertine Feb 2024

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 …

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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 Jan 2024

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 …

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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 Jan 2024

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 …

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Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan Jan 2024

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 …

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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 Oct 2023

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 …

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Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan Sep 2023

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 …

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Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han Jul 2023

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. …

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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 May 2023

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 …

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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 May 2023

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 …

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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 Apr 2023

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 …

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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 Mar 2023

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 …

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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 Feb 2023

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 …

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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 Feb 2023

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 …

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Lysine Crosslinked Polyacrylamide─A Novel Green Polymer Gel For Preferential Flow Control, Tao Song, Baojun Bai, Yugandhara Eriyagama, Thomas P. Schuman Jan 2023

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 …

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A Dtn-Based Spatio-Temporal Routing Using Location Prediction Model In Underground Mines, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong Jan 2023

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 …

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Lifelong Learning-Based Multilayer Neural Network Control Of Nonlinear Continuous-Time Strict-Feedback Systems, Irfan Ahmad Ganie, S. (Sarangapani) Jagannathan Jan 2023

Lifelong Learning-Based Multilayer Neural Network Control Of Nonlinear Continuous-Time Strict-Feedback Systems, Irfan Ahmad Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Paper, We Investigate Lifelong Learning (LL)-Based Tracking Control for Partially Uncertain Strict Feedback Nonlinear Systems with State Constraints, employing a Singular Value Decomposition (SVD) of the Multilayer Neural Networks (MNNs) Activation Function based Weight Tuning Scheme. the Novel SVD-Based Approach Extends the MNN Weight Tuning to (Formula Presented.) Layers. a Unique Online LL Method, based on Tracking Error, is Integrated into the MNN Weight Update Laws to Counteract Catastrophic Forgetting. to Adeptly Address Constraints for Safety Assurances, Taking into Account the Effects Caused by Disturbances, We Utilize a Time-Varying Barrier Lyapunov Function (TBLF) that Ensures a Uniformly Ultimately …

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Optimal Tracking Of Nonlinear Discrete-Time Systems Using Zero-Sum Game Formulation And Hybrid Learning, Behzad Farzanegan, S. (Sarangapani) Jagannathan Jan 2023

Optimal Tracking Of Nonlinear Discrete-Time Systems Using Zero-Sum Game Formulation And Hybrid Learning, Behzad Farzanegan, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel hybrid learning-based optimal tracking method to address zero-sum game problems for partially uncertain nonlinear discrete-time systems. An augmented system and its associated discounted cost function are defined to address optimal tracking. Three multi-layer neural networks (NNs) are utilized to approximate the optimal control and the worst-case disturbance inputs, and the value function. The critic weights are tuned using the hybrid technique, whose weights are updated once at the sampling instants and in an iterative manner over finite times within the sampling instants. The proposed hybrid technique helps accelerate the convergence of the approximated value functional …

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Lifelong Deep Learning-Based Control Of Robot Manipulators, Irfan Ganie, Jagannathan Sarangapani Jan 2023

Lifelong Deep Learning-Based Control Of Robot Manipulators, Irfan Ganie, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition-based direct tracking error-driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi-task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight-tuning laws. Our results demonstrate that the resulting …

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Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan Jan 2023

Lifelong Learning Control Of Nonlinear Systems With Constraints Using Multilayer Neural Networks With Application To Mobile Robot Tracking, Irfan Ganie, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the …

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Towards Robust Consensus For Intelligent Decision-Making In Iot Blockchain Networks, Charles Rawlins, S. (Sarangapani) Jagannathan Jan 2023

Towards Robust Consensus For Intelligent Decision-Making In Iot Blockchain Networks, Charles Rawlins, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Distributed consensus is the core aspect of blockchain protocol security design. Recent protocols like IOTA have improved concurrency and scalability over Proof-of-work (PoW) with Bitcoin but have core design decisions that are inefficient for limited devices and do not take advantage of previous network experience to reduce calculations. This work proposes the first blockchain consensus protocol based on active machine-learning decisions, called Proof-of-history (PoH). PoH is setup as a distributed reinforcement-learning task for monitoring classification and training of blockchain transactions with an inner deep classifier. Early theoretical analysis and simulations show that PoH is robust to uncoordinated byzantine attacks through …

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A Novel Technique For The Quantitative Determination Of Wettability Of A Severely Heterogeneous Tight Carbonate Reservoir, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Abdulaziz Abbas, Ali Qubian, Hasan Al-Saedi Jan 2023

A Novel Technique For The Quantitative Determination Of Wettability Of A Severely Heterogeneous Tight Carbonate Reservoir, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Abdulaziz Abbas, Ali Qubian, Hasan Al-Saedi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The objective of this study is to accurately measure the wettability contact angle of a cretaceous carbonate reservoir in a vertical well set-up known for as an unconventional tight carbonate oil reservoir. Also, to investigate the relative heterogeneity of these samples using digitally captured images; these images accurately capture natural pore-system in this carbonate rock samples and their wettability performance attributed towards building a vertical depth wettability/heterogeneity model. To capture, measure and model natural tight matrix static contact angle wettability in order to understand their new physics that will advance unconventional tight oil reservoir characterization. Entire vertical well depth reservoir …

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Practical Imaging Applications Of Wettability Contact Angles On Kuwaiti Tight Carbonate Reservoir With Different Rock Types, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Sohaib Kholosy, Hasan Al-Saedi, Abdulaziz Abbas, Ali Qubian Jan 2023

Practical Imaging Applications Of Wettability Contact Angles On Kuwaiti Tight Carbonate Reservoir With Different Rock Types, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Sohaib Kholosy, Hasan Al-Saedi, Abdulaziz Abbas, Ali Qubian

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study focuses on a tight carbonate reservoir which is located in Northern Kuwait and is classified as an unconventional reservoir. A practical imaging technique of wettability contact angle (θ°) presents "big data" as well as relative-permeability (Krw and Kro) measurements. Also, modeling, through rock image technology, the vast well-documented grain/pore boundary morphology available inside fresh rock fragments have achieved good results. Conventional laboratory relative-permeability experiments are expensive and time-consuming. This study introduces a novel method to measure/calculate relative permeability through fast, less expensive, non-destructive, and environmentally friendly techniques of imaging technology. One tight carbonate reservoir is selected, imaged, processed, …

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Transport And Plugging Performance Evaluation Of A Novel Re-Crosslinkable Microgel Used For Conformance Control In Mature Oilfields With Super-Permeable Channels, Adel Alotibi, T. Song, Baojun Bai, Thomas P. Schuman Jan 2023

Transport And Plugging Performance Evaluation Of A Novel Re-Crosslinkable Microgel Used For Conformance Control In Mature Oilfields With Super-Permeable Channels, Adel Alotibi, T. Song, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Preformed particle gels (PPG) have been widely applied in oilfields to control excessive water production. However, PPG has limited success in treating opening features because the particles can be flushed readily during post-water flooding. We have developed a novel micro-sized Re-crosslinkable PPG (micro-RPPG) to solve the problem. The microgel can re-crosslink to form a bulk gel, avoiding being washed out easily. This paper evaluates the novel microgels' transport and plugging performance through super-permeable channels. Micro-RPPG was synthesized and evaluated for this study. Its storage moduli after fully swelling are approximately 82 Pa. The microgel characterization, self-healing process, transportation behavior, and …

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