Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 18721 - 18750 of 291657

Full-Text Articles in Physical Sciences and Mathematics

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang Jan 2025

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …

Go to article

Performance Evaluation Of Newsql Databases In A Distributed Architecture, Zhiyao Zhang, Alan @ Ali Madjelisi Megargel, Lingxiao Jiang Jan 2025

Performance Evaluation Of Newsql Databases In A Distributed Architecture, Zhiyao Zhang, Alan @ Ali Madjelisi Megargel, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In the last decade, application architectures have evolved drastically, moving from monolithic architectures to distributed architectures where deployment has shifted from dedicated on-premises servers to the cloud. Distributed architectures and cloud computing has enabled businesses to scale their application components across different geographical locations. While it is easy to scale the application layer, scaling its database layer that relies on traditional SQL databases is challenging and often is a common source of bottlenecks when it comes to application performance. This paper evaluates the performance characteristics between two NewSQL databases solutions, MySQL NDB Cluster vs. TIBCO ActiveSpaces IMDG. Serving as an …

Go to article

Llms-Based Augmentation For Domain Adaptation In Long-Tailed Food Datasets, Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun Jan 2025

Llms-Based Augmentation For Domain Adaptation In Long-Tailed Food Datasets, Qing Wang, Chong-Wah Ngo, Ee-Peng Lim, Qianru Sun

Research Collection School Of Computing and Information Systems

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment. In addition to this domain-shift problem, the real-world food datasets tend to be long-tailed distributed and some dishes of different categories exhibit subtle variations that are difficult to distinguish visually. In this paper, we present a framework empowered with large language models (LLMs) to address these challenges in food recognition. We first leverage LLMs to parse food images to generate food titles and ingredients. Then, we project the …

Go to article

Interactive Video Search With Multi-Modal Llm Video Captioning, Yu-Tong Cheng, Jiaxin Wu, Zhixin Ma, Jiangshan He, Xiao-Yong Wei, Chong-Wah Ngo Jan 2025

Interactive Video Search With Multi-Modal Llm Video Captioning, Yu-Tong Cheng, Jiaxin Wu, Zhixin Ma, Jiangshan He, Xiao-Yong Wei, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Cross-modal representation learning is essential for interactive text-to-video search tasks. However, the representation learning is limited by the size and quality of video-caption pairs. To improve the search accuracy, we propose to enlarge the size of available video-caption pairs by leveraging multi-model LLM on video captioning. Specifically, we use LLM to generate video captions for a large video collection (i.e., WebVid dataset) and use the generated video-caption pairs to pre-train a text-to-video search model. Additionally, we use LLM to generate fine-grained captions for test video collections to enable text-to-caption retrieval. Furthermore, we build a semantic overview of the retrieved rank …

Go to article

Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo Jan 2025

Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: Testing Adequacy: Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. Interpretability: The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which …

Go to article

Adapting Installation Instructions In Rapidly Evolving Software Ecosystems, Haoyu Gao, Christoph Treude, Mansooreh Zahedi Jan 2025

Adapting Installation Instructions In Rapidly Evolving Software Ecosystems, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

files play an important role in providing installation-related instructions to software users and are widely used in open source software systems on platforms such as GitHub. Software projects evolve rapidly alongside their dependencies in dynamic software ecosystems, requiring frequent updates to installation instructions. These instructions are crucial for users to start with a software project. Despite their significance, there is a lack of systematic understanding regarding the documentation efforts invested in README files and the triggers behind them. To fill the research gap, we conducted a qualitative study, investigating 400 GitHub repositories with 1,163 README commits that focused on updates …

Go to article

Don’T Complete It! Preventing Unhelpful Code Completion For Productive And Sustainable Neural Code Completion Systems, Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li, David Lo Jan 2025

Don’T Complete It! Preventing Unhelpful Code Completion For Productive And Sustainable Neural Code Completion Systems, Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li, David Lo

Research Collection School Of Computing and Information Systems

Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Github Copilot are not accepted by developers. Being reviewed but not accepted, their help to developer productivity is considerably limited and may conversely aggravate the workload of developers, as the code completions are automatically and actively generated in state-of-the-art code completion systems as developers type out once the service is enabled. Even worse, considering the high cost of the large code models, it is a huge waste of computing resources and …

Go to article

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna Jan 2025

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna

Research Collection Yong Pung How School Of Law

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensusbuilding between stakeholders (particularly government and industry but also citizens) andvoluntary or “quasi” regulation, …

Go to article

Kinetic Grain Growth In Firn Induced By Meltwater Infiltration On The Greenland Ice Sheet, Kirsten Leigh Gehl Jan 2025

Kinetic Grain Growth In Firn Induced By Meltwater Infiltration On The Greenland Ice Sheet, Kirsten Leigh Gehl

Graduate Student Theses, Dissertations, & Professional Papers

The microstructure of polar firn governs its porosity, permeability, and compaction rate, and is therefore important to studies of surface elevation change, heat and gas exchange, and meltwater infiltration on ice sheets. Previous work on high-elevation, dry-firn has identified two distinct atmospheric drivers of kinetic grain growth in deposited snow, but both mechanisms result in near-surface mm-scale layers. In this work, we show that meltwater infiltration through surface wetting fronts and deep preferential flow in the Greenland Ice Sheet (GrIS) percolation zone leads to the formation and preservation of centi- to decimeter scale layers of faceted firn, ranging from faceted …

Go to article

Integrating Spatial Statistics And Decision Analysis For Wildfire Risk Mapping: A Case Study Of The Kootenai National Forest, Elijah Kordieh Mensah Jan 2025

Integrating Spatial Statistics And Decision Analysis For Wildfire Risk Mapping: A Case Study Of The Kootenai National Forest, Elijah Kordieh Mensah

Graduate Student Theses, Dissertations, & Professional Papers

Wildfire risk in the western United States has intensified in recent decades due to intersecting forces of climate change, systematic fire suppression, and expanding human settlement in fire-prone regions. This thesis presents a comprehensive spatial assessment of wildfire risk in the Kootenai National Forest (KNF) and its surrounding landscape in northwestern Montana, integrating wildfire likelihood, suppression difficulty, evacuation vulnerability, and building exposure into a unified spatial statistical framework. The research addresses a critical gap in spatial wildfire risk modeling by focusing on the need to assess fire threats in relation to local operational constraints and community vulnerabilities.
Drawing on geospatial …

Go to article

Cfd Analysis Of Hydrodynamic Cavitation Through An Orifice: Influence Of Different Inlet Pressures And Number Of Orifice Holes, Lemthong Chanphavong, Vongsavanh Chanthaboune, Keophousone Phonhalath Jan 2025

Cfd Analysis Of Hydrodynamic Cavitation Through An Orifice: Influence Of Different Inlet Pressures And Number Of Orifice Holes, Lemthong Chanphavong, Vongsavanh Chanthaboune, Keophousone Phonhalath

ASEAN Journal on Science and Technology for Development

Hydrodynamic cavitation (HC) is considered an energy-efficient process with high potential for utilization in many chemical processes. This study presents a computational fluid dynamics (CFD) analysis of cavitating flow through an orifice with a constant flow area. The Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with turbulence and cavitation models, are employed to capture the complex flow behaviors. The effects of inlet pressures and number of orifice-holes on cavitation behavior are investigated. Result of the numerical simulation is validated with the existing experimental data from the literature. The CFD study revealed that cavitation initiates just behind the inlet edge of the orifice …

Go to article

Impact Of Color, Shape, And Typeface On Visual Attention: An Eye Tracking Study On Brand Logo, Suzayana Rosidah, Fransiskus Xaverius Ivan, Suatmi Murnani, Hafzatin Nurlatifa, Kristian Adi Nugraha, Sunu Wibirama Jan 2025

Impact Of Color, Shape, And Typeface On Visual Attention: An Eye Tracking Study On Brand Logo, Suzayana Rosidah, Fransiskus Xaverius Ivan, Suatmi Murnani, Hafzatin Nurlatifa, Kristian Adi Nugraha, Sunu Wibirama

ASEAN Journal on Science and Technology for Development

In numerous cases, companies undertake logo redesigns to enhance brand perception. However, little attention has been paid to the impact of each element of the redesigned logo on visual attention and brand perception. To address this research gap, we collected data from eye tracking and self-report questionnaires of 30 participants during exposure to the old and new logos of a prominent bookstore in Indonesia. The results of the questionnaires revealed a significant relationship between the responses concerning color, shape, typeface, and those pertaining to visual attention (p < 0.05). Most participants were able to grasp the value of creativity, flexibility, progress, change, and strength in the new logo shape. The results of eye tracking show that color was the most influential factor that attracted visual attention in old (F(1.5, 43.4) = 14.905, p < 0.05) and new logos (F(1.7, 50) = 34.757, p < 0.05). This study suggests that companies should selectively choose a color scheme of a logo that better attracts the attention of consumers. In addition, our finding is promising as a practical guide for similar research, as well as a case study on how logo redesign affects brand perception and visual attention.

Go to article

Distance Metric Learning Techniques For The Performance Improvement Of Ml-Knn And Ranking-Svm-Based Multi-Label Pattern Classification, Shajee Mohan B. S., Sneha S Mohan Jan 2025

Distance Metric Learning Techniques For The Performance Improvement Of Ml-Knn And Ranking-Svm-Based Multi-Label Pattern Classification, Shajee Mohan B. S., Sneha S Mohan

ASEAN Journal on Science and Technology for Development

A multi-label pattern classification system tries to predict the set of class labels of a test example by learning from the training examples with the relevant label sets. Classification that involve datasets having multiple labels found immense of applications in pattern analysis tasks involving image, music and video. A test sample can be labeled to indicate different objects, people, music categories or concepts. Classification problems involving data having multiple labels, have to consider training dataset associated with variety of labels. Multi-label extensions of popular algorithms, kNN and Support Vector Machine (SVM) called Multi-label kNN (ML-kNN) and Ranking-SVM are commonly used …

Go to article

Exploring Dynamics And Effective Strategies For Tidal Flood Risk Reduction In Indonesia's Coastal Cities, Satria Yudha Adhitama, Diana Puspitasari, Lucia Sandra Budiman, Azis Musthofa Jan 2025

Exploring Dynamics And Effective Strategies For Tidal Flood Risk Reduction In Indonesia's Coastal Cities, Satria Yudha Adhitama, Diana Puspitasari, Lucia Sandra Budiman, Azis Musthofa

ASEAN Journal on Science and Technology for Development

Indonesia’s coastal regions have distinct charateristics. However, nearly all of these coastal regions are vulnerable to tidal floods. The purpose of this study is to determine the features of coastal regions in Indonesia that have been damaged by tidal floods, as well as the actions implemented to mitigate disaster risk. Thus study takes a qualitiative method with explanatory analysis. The Island Spatial Planning is used to classify Indonesia’s archipelagic regions.An urban area vulnerable to tidal flood was selected from each archipelagic region. The characteristics of coastal regions affected by tidal flood were identified by the tidal flood characteristics, coastal physiography. …

Go to article

Wind Speed Forecasting: A Comparative Study Of Decomposition Techniques, Manisha Galphade, V.B. Nikam, Nilkamal More, Biplab Banerjee, Arvind W. Kiwelekar, Priyanka Sharma Jan 2025

Wind Speed Forecasting: A Comparative Study Of Decomposition Techniques, Manisha Galphade, V.B. Nikam, Nilkamal More, Biplab Banerjee, Arvind W. Kiwelekar, Priyanka Sharma

ASEAN Journal on Science and Technology for Development

Renewable energy is sourced from natural resources that are continually available. Wind energy is a main category of renewable energy, which largely depends on wind speed. Accurate wind speed forecasting is essential for incorporating renewable energy into the electrical grid. Moreover, it is crucial to ensure the safety of wind turbines by anticipating extreme weather conditions and implementing necessary precautions. Predicting wind speed presents several challenges because of dynamic and complex nature of atmospheric conditions. Traditional methods for wind speed forecasting, such as statistical models and basic physical approaches, often face limitations in accuracy, flexibility, and handling of complex data …

Go to article

Improving Ethanol Purity By Methanol Adsorption Using Mcm-41: A Study Of Kinetics And Thermodynamics For Industrial Applications, Ali A. Yahya, Nisreen S. Ali, Basma B. Hameed, Talib M. Albayati, Issam K. Salih, Riyadh S. Almukhtar, Narges Elmi Fard Jan 2025

Improving Ethanol Purity By Methanol Adsorption Using Mcm-41: A Study Of Kinetics And Thermodynamics For Industrial Applications, Ali A. Yahya, Nisreen S. Ali, Basma B. Hameed, Talib M. Albayati, Issam K. Salih, Riyadh S. Almukhtar, Narges Elmi Fard

ASEAN Journal on Science and Technology for Development

One of the by-products of the fermentation process that produces ethanol, which is mostly utilized in industry and food, is methanol. For methanol adsorption in batch operations, mixed amines modified (MCM-41) was utilized. A batch adsorption technique loaded with MCM-41 sorbent was used in the study to separate methanol from ethanol. This study used methanol at varying initial concentrations (40-80 mg/L). As a result, the effect of temperature, duration of adsorption, amount of adsorbent and initial concentration of pollutant in the field of ethanol alcohol purification using MCM-41 was investigated through the adsorption mechanism. In addition, first and pseudo-second order …

Go to article

Editorial: A Decade Of Apasti – Advancing Regional Prosperity Through Science, Technology And Innovation, Basilios Tsikouras Jan 2025

Editorial: A Decade Of Apasti – Advancing Regional Prosperity Through Science, Technology And Innovation, Basilios Tsikouras

ASEAN Journal on Science and Technology for Development

A decade after its establishment, APASTI has emerged as a key regional mechanism for promoting science, technology and innovation as drivers of sustainable development, resilience, and shared prosperity in Southeast Asia. This editorial highlights the contributions of APASTI to policy alignment, knowledge exchange, research visibility, and collaborative network-building across ASEAN, while also acknowledging ongoing constraints related to governance, capacity, funding disparities, and uneven regional integration. It introduces a special issue of 14 papers published in AJSTD that collectively evaluate the implementation and impact of APASTI across diverse sectors, including geohazards, energy, materials, health, infrastructure, transport, and information systems. By positioning …

Go to article

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan Jan 2025

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without …

Go to article

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, …

Go to article

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao Jan 2025

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise …

Go to article

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan Jan 2025

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Go to article

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan Jan 2025

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Go to article

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria Jan 2025

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …

Go to article

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 …

Go to article

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan Jan 2025

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

Go to article

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan Jan 2025

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Go to article

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore Jan 2025

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

Go to article

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan Jan 2025

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

Go to article

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 …

Go to article

Explainable And Safety Aware Deep Reinforcement Learning-Based Control Of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition, Behzad Farzanegan, S. Jagannathan Jan 2025

Explainable And Safety Aware Deep Reinforcement Learning-Based Control Of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents an explainable deep-reinforcement learning (DRL)-based safety-aware optimal adaptive tracking (SOAT) scheme for a class of nonlinear discrete-time (DT) affine systems subject to state inequality constraints. The DRL-based SOAT utilizes a multilayer neural network (MNN)-based actor-critic to estimate the cost function and optimal policy while the MNN update laws are tuned both using the singular value decomposition (SVD) of activation function gradient in order to mitigate the vanishing gradient issue and safety-aware Bellman error at each layer. An approximate safety-aware optimal policy is developed using Karush–Kuhn–Tucker (KKT) conditions by incorporating the higher-order control barrier function (HOCBF) into the …

Go to article

First Prev Next Last