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Articles 20941 - 20970 of 294560

Full-Text Articles in Physical Sciences and Mathematics

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar Jan 2025

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

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Feature Engineering And Anchor Optimization For Enhancing Faster R-Cnn Detection Of Low-Contrast Steel Surface Defects, Herdianti Darwis, Sitti Nurhalimah, Huzain Azis Jan 2025

Feature Engineering And Anchor Optimization For Enhancing Faster R-Cnn Detection Of Low-Contrast Steel Surface Defects, Herdianti Darwis, Sitti Nurhalimah, Huzain Azis

Knowledge Engineering and Data Science

Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of …

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Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana Jan 2025

Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana

Knowledge Engineering and Data Science

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN …

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Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra Jan 2025

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

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Comparative Performance Of Vgg16 And Efficientnetb0-Based Transfer Learning For Brain Tumor Classification, Huzain Azis, Rizqi Ananda Jalil, Abdul Rachman Manga' Jan 2025

Comparative Performance Of Vgg16 And Efficientnetb0-Based Transfer Learning For Brain Tumor Classification, Huzain Azis, Rizqi Ananda Jalil, Abdul Rachman Manga'

Knowledge Engineering and Data Science

The classification of brain tumors using Magnetic Resonance Imaging (MRI) images is essential for early diagnosis but remains challenging due to tumor diversity. This study evaluates the effectiveness of two distinct architectural approaches for feature extraction: VGG16, representing a classic sequential design, and EfficientNetB0, a modern architecture optimized for parameter efficiency through compound scaling. Using a dataset of 2,870 MRI images categorized into four classes, we implemented a static transfer learning strategy by freezing all pre-trained ImageNet weights to act as fixed feature extractors. Features were extracted from specific layers, the final pooling layer for VGG16 and the Global Average …

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Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra Jan 2025

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

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Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh Jan 2025

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

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A Bounded Custom Gpt Structures Operational Knowledge For Small-Industry Decision Support, Ikhwan Arief, Alizar Hasan, Nilda Tri Putri, Hafiz Rahmann Jan 2025

A Bounded Custom Gpt Structures Operational Knowledge For Small-Industry Decision Support, Ikhwan Arief, Alizar Hasan, Nilda Tri Putri, Hafiz Rahmann

Knowledge Engineering and Data Science

Small manufacturing and craft-based firms increasingly use Generative Artificial Intelligence (GenAI) through public chat interfaces, low-cost tools, and informal experimentation. However, these firms often make operational decisions with incomplete records, tacit owner knowledge, fragmented spreadsheets, and limited managerial capacity. Under such conditions, open-ended chatbots may generate fluent but unsafe recommendations by overlooking missing information, contradictory evidence, feasibility constraints, and implementation constraints. This study presents Asisten Cerdas Industri Kecil as a bounded Custom GPT artifact for operational diagnosis, priority selection, and short-horizon action planning in small industries. Using a Design Science Research approach, the study develops a documented artifact corpus comprising …

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Enhanced Biofuel Cells Based On A Hybrid Enzymatic/Bimetallic Composite For Complete Lactate Catalytic Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Shelley D. Minteer, Adalgisa R. De Andrade Jan 2025

Enhanced Biofuel Cells Based On A Hybrid Enzymatic/Bimetallic Composite For Complete Lactate Catalytic Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe complete lactate electrooxidation in an enzymatic biofuel cell that combines the catalytic action of the bimetallic composite Ru@Pt-CNT and the enzyme oxalate oxidase (OxOx). The Ru@Pt-CNT/OxOx hybrid electrode was 2.0-fold more catalytically active than the electrode containing the bimetallic composite only. During chronoamperometric experiments, the hybrid electrode achieved a 35% higher maximum current density (2.65 ± 0.15 mA cm-2) than the Ru@Pt-CNT electrode. Electrochemical impedance spectroscopy showed that the hybrid electrode had lower charge transfer resistance than the Ru@Pt-CNT electrode, confirming that OxOx had a high affinity for lactate during the bioelectrocatalytic reaction on the electrode …

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Cultivating Confidence, Leila Halawi, Mark Miller, Sam Holley Jan 2025

Cultivating Confidence, Leila Halawi, Mark Miller, Sam Holley

Publications

Artificial intelligence (AI) is pervasive in scholarly publications, internet sites, and public discourse. AI is a term with broad scope that refers to machines that can learn and perform tasks that typically require human intelligence. The specter of AI intruding into many aspects of aviation has raised alarms, concerns, and prodigious misunderstanding of potential and contemplated applications in systems and processes. The EASA AI Roadmap (EASA, 2023 ), a linear projection with three levels EASA, 2023 extending into 2050, places the human-AI teaming period (through 2035) at Level 2. This suggests a ten-year span to develop the interactive issues to …

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Embracing Ai In Higher Education: Redefining Teaching And Learning In The Digital Era, Najem Tala, Leila Halawi Jan 2025

Embracing Ai In Higher Education: Redefining Teaching And Learning In The Digital Era, Najem Tala, Leila Halawi

Publications

This research explores the transformative potential of Artificial Intelligence (AI) in education, focusing on its ability to address emerging challenges and revolutionize teaching practices. We critically assess the current educational landscape, evaluate AI's role as a catalyst for educational reform, and examine implementation challenges. Our analysis emphasizes the evolving impact of AI on personalized learning, student engagement, and administrative efficiency and contributes to the growing body of literature on educational technology. The discussion highlights both the opportunities and limitations of AI in education, pointing to critical areas that remain underexplored.

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Integrating Sustainability Into Aviation Training: Perspectives From Industry Professionals, Eva Maleviti, Sarah Talley Jan 2025

Integrating Sustainability Into Aviation Training: Perspectives From Industry Professionals, Eva Maleviti, Sarah Talley

Publications

This paper is part of a broader research project aimed at identifying the most appropriate training materials, formats, and delivery methods for aviation sustainability content for future aviation professionals, starting from the early stages of their training. The current paper presents the data analysis and results of a short online survey targeting aviation professionals. Specifically, it provides preliminary data on sustainability perspectives from global aviation professionals. Through qualitative analysis, the creation of a baseline from a random sample of aviation professionals offers insight into their perspectives and how they could be influenced toward a more sustainable pathway. Additionally, the paper …

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A Maintenance Repair And Overhaul Model For Operational Sustainability; Incorporating Processes And Risks., Linn Guinn, Mary M.H. Woody, Eva Maleviti Jan 2025

A Maintenance Repair And Overhaul Model For Operational Sustainability; Incorporating Processes And Risks., Linn Guinn, Mary M.H. Woody, Eva Maleviti

Publications

Maintenance, Repair, and Overhaul (MRO) companies are integral to the aviation industry, with the core responsibility of ensuring aircraft remain safe and airworthy. Their role is critical in maintaining the industry’s high standards of performance and safety. To remain competitive, MROs must provide a wide range of services—including inspections, periodic checks, engine overhauls, avionics repairs, fuselage and cabin modifications, and interior refurbishments— while consistently meeting regulatory and safety requirements. Beyond technical compliance, MROs must adopt a quality-driven, continuous improvement approach to manage risks effectively. This requires the integration of processes, programs, and systems aligned with international standards to strengthen resilience, …

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Quantifying The Performance Of The Sparta Toolkit For Use In Planetary Regolith Characterization Missions, Abigail S. Glover Jan 2025

Quantifying The Performance Of The Sparta Toolkit For Use In Planetary Regolith Characterization Missions, Abigail S. Glover

Honors Undergraduate Theses

This research investigates the use of the Soil Properties Assessment, Resistance, and Thermal Analysis (SPARTA) Cone Penetration Tester (CPT) to quantify bulk density in lunar regolith simulant LHS-1E. Twenty-five penetration tests were conducted across five density profiles to derive slope parameters (G), which represent the rate of increase in resistance with depth. Results showed a consistent, nonlinear relationship between G and bulk density, validating the use of G slope as a diagnostic parameter and supporting the primary hypothesis that SPARTA CPT measurements vary meaningfully with density. Compared to prior work by Lucas et al. (2024), G values from SPARTA were …

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Generation Z’S Perception Of Skin Cancer And How It Relates To Climate Change, Piercen Bogolea Jan 2025

Generation Z’S Perception Of Skin Cancer And How It Relates To Climate Change, Piercen Bogolea

Honors Undergraduate Theses

Skin cancer is the most common cancer worldwide, and climate change has been identified as a significant contributor. Preventative action early on can help reduce occurrence, but it is unknown if Generation Z is aware of this and is taking the necessary action now to protect themselves. This research aims to gauge the perception of Generation Z on the severity of skin cancer, the relationship between climate change and skin cancer, and the prevalence of preventative measures being taken. A survey was distributed to students aged 18 to 27 enrolled at the University of Central Florida through mixed sampling. The …

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Entangled Photoelectron Attosecond Spectroscopy, Jonathan Sar-Shalom Jan 2025

Entangled Photoelectron Attosecond Spectroscopy, Jonathan Sar-Shalom

Honors Undergraduate Theses

In this thesis we propose an interferometric scheme to retrieve the dynamics of an electron wave packet emitted from two distinct residual photo-ion channels in the ionization of an atom by an ultra-short UV pulse. EPAS (Entangled-Photoelectron Attosecond Spectroscopy) works by having a non-overlapping UV and few-cycle IR pulse, where the IR is tuned near the transition frequency between two electronic bound states of the photo-ion. Using a time-dependent simulation based on a two-channel atomic model, supported by a perturbative approach, we were able to compute the resulting interference in the angular distribution of the photoelectron and retrieve the energy-dependent …

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It’S Time To Stop Calling People "Stakeholders", Bethann Garramon Merkle, Evelyn Valdez-Ward, Priya Shukla, Skylar R. Bayer Jan 2025

It’S Time To Stop Calling People "Stakeholders", Bethann Garramon Merkle, Evelyn Valdez-Ward, Priya Shukla, Skylar R. Bayer

Human–Wildlife Interactions

Although the term “stakeholder” has been widely used in science communication, recent expansion in the discourse around the negative connotations of the term necessitate that scientists and practitioners cease using the term. We discuss why this shift is necessary, how it affects our own praxis, and suggest resources for working through more respectful and just language. Looking to the science communication literature can help recalibrate terminology in positive ways.

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Multi-Source Remote Sensing–Based Soil Moisture Prediction Using Machine Learning, Niraj Neupane Jan 2025

Multi-Source Remote Sensing–Based Soil Moisture Prediction Using Machine Learning, Niraj Neupane

Electronic Theses and Dissertations

Soil moisture (SM) plays a central role in climatic and environmental processes, influencing shear strength of soil, agricultural productivity, land–atmosphere interactions, and hydrologic functioning. However, accurately estimating SM across diverse climatic regions remains challenging due to spatial heterogeneity, limited in situ measurements, and inconsistencies in sensor resolution. Machine learning (ML) and remote sensing offer promising avenues for improving SM prediction, yet many existing approaches struggle with generalization across climatic gradients and often fail to capture temporal variability. This study integrates multi-source satellite and climate datasets, including SMAP L4_SM, MODIS land surface temperature, Daymet meteorological variables, and in situ observations from …

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Quantifying Avian And Vegetation Communities On Agricultural Conservation Easement Wetlands In Eastern South Dakota, Katherine Millman Jan 2025

Quantifying Avian And Vegetation Communities On Agricultural Conservation Easement Wetlands In Eastern South Dakota, Katherine Millman

Electronic Theses and Dissertations

The Prairie Pothole Region of eastern South Dakota contains numerous small depressional wetlands, colloquially known as potholes, that provide important ecosystem services, including water storage, water quality improvement, and wildlife habitat. The Agricultural Conservation Easement Program (ACEP) was established by the U.S. Department of Agriculture to protect and restore wetlands and grasslands on private land through easements such as Wetland Reserve Easements. Although ACEP requires management practices intended to maintain or improve habitat quality, few studies have evaluated their effectiveness at supporting wildlife communities and vegetation integrity. To address this gap, we evaluated wetlands at 30 ACEP sites and 20 …

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Enhancing Alfalfa Persistence In Rangelands: Evaluating Morphological Traits Of Medicago Sativa Subsp. Falcata Germplasm, Abdulallah Mohammed Alomair Jan 2025

Enhancing Alfalfa Persistence In Rangelands: Evaluating Morphological Traits Of Medicago Sativa Subsp. Falcata Germplasm, Abdulallah Mohammed Alomair

Electronic Theses and Dissertations

Rangelands cover nearly half of the Earth’s surface, representing about 54% (79 million km2), and occur in different types and climates worldwide. Alfalfa (Medicago sativa L.) is a major component in rangelands and covers about (320 thousand km2), representing about 0.4% of total global rangeland area. As a critical perennial legume crop, alfalfa can improve soil health by fixing approximately 336 kg/ha/year of nitrogen and sequestering up to 614 kg/ha/year of carbon, while enhancing livestock production due to its high content level of vitamins, magnesium, minerals, fiber, and protein. However, environmental stresses such as drought, freezing, and grazing limit alfalfa …

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Discovery Of Photosynthetic Oxic N2-Fixation In Cyanobacteria Using Wet Lab And Machine Learning Approaches, James A. Young Iii Jan 2025

Discovery Of Photosynthetic Oxic N2-Fixation In Cyanobacteria Using Wet Lab And Machine Learning Approaches, James A. Young Iii

Electronic Theses and Dissertations

No abstract provided.

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Computation-Efficient Deep Learning Models For Computer Vision And Multimodal Vision-Language Tasks Via Network Pruning, Abir Mohammad Hadi Jan 2025

Computation-Efficient Deep Learning Models For Computer Vision And Multimodal Vision-Language Tasks Via Network Pruning, Abir Mohammad Hadi

Electronic Theses and Dissertations

With the rapid evolution of deep neural networks over the past decade, the demand for efficient, generalizable, and task-adaptable models, especially in computer vision, has increased significantly. To address the computational and deployment challenges posed by overparameterized models, the research community has extensively explored model compression techniques such as pruning, quantization, and distillation. These approaches aim to enhance model efficiency without compromising performance, particularly when adapting to domain-specific tasks under limited resources. This dissertation investigates several underexplored yet critical aspects of task-aware deep learning model compression, spanning both convolutional and vision-language architectures. In the early part of this work, we …

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In The Footsteps Of Moose: Using Non-Invasive Techniques To Identify Cause-Specific Calf Mortalities And Evaluate Moose (Alces Alces) Movements Pertaining To Mineral Lick Locations On Ancestral Lands Of The Grand Portage Ojibwe, Anna B. Weesies Jan 2025

In The Footsteps Of Moose: Using Non-Invasive Techniques To Identify Cause-Specific Calf Mortalities And Evaluate Moose (Alces Alces) Movements Pertaining To Mineral Lick Locations On Ancestral Lands Of The Grand Portage Ojibwe, Anna B. Weesies

Electronic Theses and Dissertations

Following a sharp moose (Alces alces; mooz, Anishinaabemowin, Ojibwe language) population decline in northeastern Minnesota during the early 2000s, the Grand Portage Band of Lake Superior Chippewa initiated a long-term moose collaring effort to assess survival and mortality causes. Moose are a culturally important species to the Band, with annual subsistence hunts held on the Reservation and adjacent 1854 Ceded Territory. Continued research guides culturally appropriate management decisions aimed at bolstering calf and adult survival, helping to preserve this moose in Minnesota today and for subsequent generations. Chapter 1 identifies cause-specific calf mortality rates in this multi-predator system using minimally …

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Characterizing A Free-Roaming Cat Population To Predict Predation Of Threatened Species In An Island Ecosystem, Sarah Gheida Jan 2025

Characterizing A Free-Roaming Cat Population To Predict Predation Of Threatened Species In An Island Ecosystem, Sarah Gheida

Electronic Theses and Dissertations

No abstract provided.

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An Integrated Wetland Condition Index And Environmental Metric Framework For The Prairie Pothole Region, Mercedes L. Batalla Jan 2025

An Integrated Wetland Condition Index And Environmental Metric Framework For The Prairie Pothole Region, Mercedes L. Batalla

Electronic Theses and Dissertations

The USDA-NRCS Agricultural Conservation Easement Program (ACEP) assists landowners with protecting, restoring, and enhancing wetlands. Evaluating wetland conservation efforts is crucial for informing future management decisions. However, assessment frameworks vary across regions and among stakeholders. The Prairie Pothole Region is an ecologically unique landscape characterized by its abundance of wetlands that provide important ecosystem services. To evaluate wetlands enrolled in ACEP, I sampled forty-five semi-permanent wetlands in the Prairie Pothole Region from July – August 2023 and 2024. I selected and sampled twenty-six wetlands enrolled in ACEP. Nineteen additional wetlands not enrolled in ACEP were also sampled; these included nine …

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Simulating Positive Ion Distribution In The Protodune Single Phase Detector, Vishnu Pfeiffer Jan 2025

Simulating Positive Ion Distribution In The Protodune Single Phase Detector, Vishnu Pfeiffer

Electronic Theses and Dissertations

This work covers the validation and implementation of a model to solve for argon ion (Ar+) distribution in the ProtoDUNE-SP liquid argon time projection chamber (LArTPC). ProtoDUNE-SP undergoes a constant flux of cosmic ray muons due to surface operation. The performance of the ProtoDUNE-SP provided valuable insights into single-phase technology, membrane cryostat technology, and calibration of cryogenic instrumentation. Still, challenges in achieving precise measurements persist due to the space charge effect—the distortion of the electric field within the detector due to the accumulation of positive argon ions generated from constant cosmic muon flux. The overarching goal of this thesis is …

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Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan Jan 2025

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

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Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch Jan 2025

Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, …

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Aperiodically Intermittent Dynamic Event-Triggered Control For Predefined-Time Synchronization Of Stochastic Complex Networks, Lei Xue, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch Jan 2025

Aperiodically Intermittent Dynamic Event-Triggered Control For Predefined-Time Synchronization Of Stochastic Complex Networks, Lei Xue, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the problem of practical predefined-time synchronization in mean square (PTSMS) of stochastic complex networks (SCNs) is investigated through dynamic event-triggered control (E-TC). Different from the existing literature, this paper considers the dynamic E-TC in an a periodically intermittent control framework and employs the average control rate, which makes it easier to satisfy the conditions of the theorem. In comparison to existing finite-time and fixed-time synchronization, by introducing the time-varying function, it can be guaranteed that all states of SCNs achieve the practical PTSMS within a preset time without calculating the convergence time. Combined with stochastic analysis theory, …

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Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan Jan 2025

Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents an integral reinforcement learning-based optimal formation tracking scheme for multiple quadrotors unmanned aerial vehicles (QUAVs) experiencing nonlinear coupled dynamics and subject to constraints. We use multilayer neural networks (MNN) within an actor-critic framework where the MNN weights are tuned using singular value decomposition (SVD) of the activation function gradient to approximate optimal control policy via backstepping. Additionally, barrier Lyapunov functions (BLF) are introduced to ensure set invariance, thereby maintaining the quadrotors within a defined safety space due to constraints. A novel weight update law for each layer is derived using the HJB approximation error and control input …

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