Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Wright State University (1492)
- Singapore Management University (765)
- Washington University in St. Louis (683)
- Old Dominion University (299)
- University of Nebraska - Lincoln (236)
-
- Missouri University of Science and Technology (177)
- Portland State University (102)
- Technological University Dublin (94)
- Chapman University (79)
- University of New Haven (70)
- University of Nevada, Las Vegas (68)
- University of Kentucky (50)
- Loyola University Chicago (49)
- Air Force Institute of Technology (41)
- Florida International University (37)
- Western University (36)
- University of South Carolina (35)
- Edith Cowan University (34)
- Embry-Riddle Aeronautical University (30)
- San Jose State University (27)
- Michigan Technological University (25)
- Western Kentucky University (23)
- City University of New York (CUNY) (19)
- Purdue University (18)
- Wayne State University (15)
- Dartmouth College (14)
- Fordham University (13)
- MBZUAI (11)
- University of Dayton (11)
- Syracuse University (10)
- Keyword
-
- Machine learning (69)
- Deep learning (68)
- Artificial intelligence (46)
- Security (43)
- Algorithms (36)
-
- Optimization (35)
- Machine Learning (33)
- Cybersecurity (29)
- Robotics (27)
- Classification (25)
- Reinforcement learning (25)
- Neural networks (24)
- Simulation (23)
- Digital forensics (22)
- Computer science (21)
- Deep Learning (19)
- Undergraduate students (19)
- Blockchain (18)
- Clustering (18)
- Scheduling (18)
- Vehicle routing problem (18)
- College seniors (17)
- Computer vision (17)
- Convolutional neural networks (17)
- IoT (17)
- Science projects (17)
- Adaptive computing systems (16)
- Cloud computing (16)
- Feature extraction (16)
- Visualization (16)
- Publication Year
- Publication
-
- Computer Science & Engineering Syllabi (1312)
- Research Collection School Of Computing and Information Systems (759)
- All Computer Science and Engineering Research (683)
- Computer Science and Engineering Faculty Publications (180)
- Department of Computer Science and Engineering: Dissertations, Theses, and Student Research (139)
-
- Electrical and Computer Engineering Faculty Research & Creative Works (109)
- Faculty Publications (109)
- Electrical & Computer Engineering Faculty Publications (90)
- Computer Science Faculty Publications and Presentations (85)
- Computer Science Faculty Publications (82)
- Electrical & Computer Engineering and Computer Science Faculty Publications (68)
- Engineering Faculty Articles and Research (68)
- Computer Science: Faculty Publications and Other Works (49)
- Dissertations (47)
- CSE Conference and Workshop Papers (46)
- Publications (41)
- Electrical and Computer Engineering Publications (36)
- FIU Electronic Theses and Dissertations (35)
- Engineering Management & Systems Engineering Faculty Publications (30)
- Computer Science Faculty Research & Creative Works (26)
- Separations Campaign (TRP) (25)
- Engineering Technology Faculty Publications (24)
- VMASC Publications (23)
- Computational Modeling & Simulation Engineering Faculty Publications (22)
- Articles (21)
- Michigan Tech Publications (19)
- Fred and Harriet Cox Senior Design Competition Projects (18)
- Publications and Research (17)
- Australian Digital Forensics Conference (14)
- Biosystems and Agricultural Engineering Faculty Publications (14)
Articles 1 - 30 of 4790
Full-Text Articles in Engineering
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
Research Collection School Of Computing and Information Systems
The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …
Optimal Molecular Dynamics System Size For Increased Precision And Efficiency For Epoxy Materials, Khatereh Kashmari, Sagar Patil, Josh Kemppainen, Shankara Gowtham, Gregory Odegard
Optimal Molecular Dynamics System Size For Increased Precision And Efficiency For Epoxy Materials, Khatereh Kashmari, Sagar Patil, Josh Kemppainen, Shankara Gowtham, Gregory Odegard
Michigan Tech Publications, Part 2
Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted …
Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre
Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre
Whittier Scholars Program
The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.
A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla
A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla
Electrical and Computer Engineering Faculty Research & Creative Works
Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new …
Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler
MS in Computer Science Project Reports
In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Research Collection School Of Computing and Information Systems
The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Computer Science Faculty Research & Creative Works
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …
Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Computer Science Faculty Research & Creative Works
The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …
Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Computer Science Faculty Research & Creative Works
The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor
Faculty Publications
Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process …
Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch
Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch
Chemistry Faculty Research & Creative Works
Biomedical Datasets Distill Many Mechanisms Of Human Diseases, Linking Diseases To Genes And Phenotypes (Signs And Symptoms Of Disease), Genetic Mutations To Altered Protein Structures, And Altered Proteins To Changes In Molecular Functions And Biological Processes. It Is Desirable To Gain New Insights From These Data, Especially With Regard To The Uncovering Of Hierarchical Structures Relating Disease Variants. However, Analysis To This End Has Proven Difficult Due To The Complexity Of The Connections Between Multi-Categorical Symbolic Data. This Article Proposes Symbolic Tree Adaptive Resonance Theory (START), With Additional Supervised, Dual-Vigilance (DV-START), And Distributed Dual-Vigilance (DDV-START) Formulations, For The Clustering Of …
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
Research Collection School Of Computing and Information Systems
Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …
Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan
Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
A novel optimal trajectory tracking scheme is introduced for nonlinear continuous-time systems in strict feedback form with uncertain dynamics by using neural networks (NNs). The method employs an actor-critic-based NN back-stepping technique for minimizing a discounted value function along with an identifier to approximate unknown system dynamics that are expressed in augmented form. Novel online weight update laws for the actor and critic NNs are derived by using both the NN identifier and Hamilton-Jacobi-Bellman residual error. A new continual lifelong learning technique utilizing the Fisher Information Matrix via Hamilton-Jacobi-Bellman residual error is introduced to obtain the significance of weights in …
Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool
Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool
Henry M. Rowan College of Engineering Faculty Scholarship
Artificial intelligence and neuroscience have a long and intertwined history. Advancements in neuroscience research have significantly influenced the development of artificial intelligence systems that have the potential to retain knowledge akin to humans. Building upon foundational insights from neuroscience and existing research in adversarial and continual learning fields, we introduce a novel framework that comprises two key concepts: feature distillation and re-consolidation. The framework distills continual learning (CL) robust features and rehearses them while learning the next task, aiming to replicate the mammalian brain's process of consolidating memories through rehearsing the distilled version of the waking experiences. Furthermore, the proposed …
Ai-Based Investigation And Mitigation Of Rain Effect On Channel Performance With Aid Of A Novel 3d Slot Array Antenna Design For High Throughput Satellite System, Ali M. Al-Saegh, Fatma Taher, Taha A. Elwi, Mohammad Alibakhshikenari, Bal S. Virdee, Osama Abdullah, Salahuddin Khan, Patrizia Livreri, Abdulmajeed Al-Jumaily, Mohamed Fathy Abo Sree, Arkan Mousa Majeed, Lida Kouhalvandi, Zaid A. Abdul Hassain, Giovanni Pau
Ai-Based Investigation And Mitigation Of Rain Effect On Channel Performance With Aid Of A Novel 3d Slot Array Antenna Design For High Throughput Satellite System, Ali M. Al-Saegh, Fatma Taher, Taha A. Elwi, Mohammad Alibakhshikenari, Bal S. Virdee, Osama Abdullah, Salahuddin Khan, Patrizia Livreri, Abdulmajeed Al-Jumaily, Mohamed Fathy Abo Sree, Arkan Mousa Majeed, Lida Kouhalvandi, Zaid A. Abdul Hassain, Giovanni Pau
All Works
Rain attenuation poses a significant challenge for high-throughput communication systems. In response, this paper introduces an artificial intelligence (AI) model designed for predicting and mitigating rain-induced impairments in high-throughput satellite (HTS) to land channels. The model is based on three AI algorithms developed using 3D antenna design to characterize, analyze, and mitigate rain-induced attenuation, optimizing channel quality specifically in the United Arab Emirates (UAE). The study evaluates various parameters, including rain-specific attenuation, effective slant path through rain, rain-induced attenuation, signal carrier-to-noise ratio, and symbol error rate, for five conventional modulation schemes: Quadrature Phase-Shift Keying (QPSK), 8-Phase Shift Keying (8-PSK), 16-Quadrature …
Emoji Use In Social Media Posts: Relationships With Personality Traits And Word Usage, Shelia Kennison, Kameryn Fritz, Maria Andrea Hurtado Morales, Eric Chan-Tin
Emoji Use In Social Media Posts: Relationships With Personality Traits And Word Usage, Shelia Kennison, Kameryn Fritz, Maria Andrea Hurtado Morales, Eric Chan-Tin
Computer Science: Faculty Publications and Other Works
Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples (n = 76 and n = 245) in which we examined how emoji use on X (formerly Twitter) related to users’ personality traits and language use …
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Research Collection School Of Computing and Information Systems
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance …
Effect Of Resin Bleed Out On Compaction Behavior Of The Fiber Tow Gap Region During Automated Fiber Placement Manufacturing, Von Clyde Jamora, Virginia Rauch, Sergii G. Kravchenko, Oleksandr G. Kravchenko
Effect Of Resin Bleed Out On Compaction Behavior Of The Fiber Tow Gap Region During Automated Fiber Placement Manufacturing, Von Clyde Jamora, Virginia Rauch, Sergii G. Kravchenko, Oleksandr G. Kravchenko
Mechanical & Aerospace Engineering Faculty Publications
Automated fiber placement is a state-of-the-art manufacturing method which allows for precise control over layup design. However, AFP results in irregular morphology due to fiber tow deposition induced features such as tow gaps and overlaps. Factors such as the squeeze flow and resin bleed out, combined with large non-linear deformation, lead to morphological variability. To understand these complex interacting phenomena, a coupled multiphysics finite element framework was developed to simulate the compaction behavior around fiber tow gap regions, which consists of coupled chemo-rheological and flow-compaction analysis. The compaction analysis incorporated a visco-hyperelastic constitutive model with anisotropic tensorial prepreg viscosity, which …
A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari
A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari
Computer Science Faculty Publications
Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …
A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Computer Science Faculty Publications
The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …
Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Research outputs 2022 to 2026
Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …
The Feasibility Of Motion Tracking Camera System For Magnetic Suspension Wind Tunnel Tests, Hisham M. Shehata, David Cox, Mark Schoenenberger, Colin Britcher, Eli Shellabarger, Timothy Schott, Brendan Mcgovern
The Feasibility Of Motion Tracking Camera System For Magnetic Suspension Wind Tunnel Tests, Hisham M. Shehata, David Cox, Mark Schoenenberger, Colin Britcher, Eli Shellabarger, Timothy Schott, Brendan Mcgovern
Mechanical & Aerospace Engineering Faculty Publications
The Entry Systems Modeling (ESM) Program at NASA has actively participated in the re-development of the Magnetic Suspension Balance System (MSBS) at the six-inch subsonic wind tunnel at NASA Langley Research Center. This initiative aims to enhance the MSBS system's capabilities, enabling the testing of stingless entry vehicle models at supersonic speeds. To achieve this, control algorithms are required to ensure magnetic levitation control and stability for models during free-oscillation dynamic responses. Currently, the system relies on electromagnetic position sensors to provide real-time 3 degrees of freedom control of a rigid body. While this approach has proven successful for subsonic …
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Research Collection School Of Computing and Information Systems
The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …
Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz
Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz
Research Collection School Of Computing and Information Systems
Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge, particularly when dealing with large-scale problems. The deep reinforcement learning (DRL) based methods that learn useful patterns from massive data demonstrate notable advantages. However, their decision space will become prohibitively huge as the problem scales up, thus deteriorating the computation efficiency. To alleviate this issue, we propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework …
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Research Collection School Of Computing and Information Systems
This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac …
Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain
Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain
VMASC Publications
Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …
Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li
Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li
Electrical & Computer Engineering Faculty Publications
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in …
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …
Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li
Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li
Computer Science Faculty Publications
Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …