Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- China Simulation Federation (3382)
- Singapore Management University (1081)
- Old Dominion University (334)
- San Jose State University (267)
- MBZUAI (233)
-
- Selected Works (225)
- Western University (160)
- Technological University Dublin (150)
- SelectedWorks (109)
- Air Force Institute of Technology (102)
- City University of New York (CUNY) (94)
- California Polytechnic State University, San Luis Obispo (86)
- Lindenwood University (64)
- University of Arkansas, Fayetteville (62)
- University of Kentucky (62)
- University of Nebraska - Lincoln (59)
- University of South Florida (50)
- Edith Cowan University (46)
- University of Tennessee, Knoxville (43)
- Purdue University (40)
- University of Nevada, Las Vegas (40)
- University of Denver (39)
- Clemson University (38)
- University of Central Florida (37)
- Chinese Academy of Sciences (35)
- Dartmouth College (35)
- New Jersey Institute of Technology (34)
- University of Windsor (33)
- Kennesaw State University (32)
- Portland State University (32)
- Keyword
-
- Machine learning (517)
- Artificial intelligence (467)
- Deep learning (336)
- Machine Learning (298)
- Artificial Intelligence (216)
-
- Deep Learning (173)
- Simulation (152)
- Computer vision (118)
- AI (115)
- Neural networks (113)
- Reinforcement learning (96)
- Robotics (86)
- Optimization (75)
- Natural language processing (68)
- Natural Language Processing (67)
- Classification (64)
- Virtual reality (60)
- Neural network (55)
- Computer Vision (53)
- Neural Networks (53)
- Reinforcement Learning (52)
- Computer Science (49)
- Genetic algorithm (48)
- Path planning (48)
- Modeling (47)
- Algorithms (45)
- ChatGPT (41)
- Numerical simulation (41)
- Scheduling (41)
- Visualization (40)
- Publication Year
- Publication
-
- Journal of System Simulation (3382)
- Research Collection School Of Computing and Information Systems (989)
- Master's Projects (246)
- Theses and Dissertations (145)
- Electronic Thesis and Dissertation Repository (132)
-
- Electronic Theses and Dissertations (115)
- Computer Vision Faculty Publications (98)
- Conference papers (88)
- Machine Learning Faculty Publications (86)
- Marcel Adam Just (78)
- Computer Science Faculty Publications (68)
- Faculty Scholarship (63)
- Master's Theses (60)
- Doctoral Dissertations (53)
- Jeremy Straub (52)
- Faculty Publications (50)
- Dissertations (48)
- Articles (47)
- Electrical & Computer Engineering Faculty Publications (46)
- Natural Language Processing Faculty Publications (46)
- Dissertations, Theses, and Capstone Projects (44)
- Theses and Dissertations--Computer Science (43)
- USF Tampa Graduate Theses and Dissertations (41)
- Publications and Research (36)
- Bulletin of Chinese Academy of Sciences (Chinese Version) (35)
- Electrical & Computer Engineering Theses & Dissertations (32)
- Graduate Theses and Dissertations (31)
- Masters Theses (31)
- Rudolf Kaehr (31)
- Honors Theses (30)
- Publication Type
Articles 1021 - 1050 of 8513
Full-Text Articles in Physical Sciences and Mathematics
Target Search Planning And Algorithm For Monitoring Of Polar Disaster Areas, Fei Ding, Meinan Zhang, Hengheng Zhuang, Hairong Ma
Target Search Planning And Algorithm For Monitoring Of Polar Disaster Areas, Fei Ding, Meinan Zhang, Hengheng Zhuang, Hairong Ma
Journal of System Simulation
Abstract: Aiming at improving the ability of safe navigation route planning and risk assessment of ships in polar waters, a target search model and method based on clustering and efficient indexing of monitoring center are proposed. By constructing a disaster monitoring scenario based on the current navigation area of the ship, a virtual electronic fence is introduced to define the monitoring area. Spectral clustering algorithm is used to divide the risk level of the fence area, extract high-risk areas, and optimize the generation of target search scenarios; Efficient determination of the matching relationship between the target vessel and the fence …
Improved Particle Swarm Algorithm Of Unrelated Parallel Batch Scheduling Optimization, Lizhen Du, Tao Ye, Yuhao Wang, Yajun Zhang
Improved Particle Swarm Algorithm Of Unrelated Parallel Batch Scheduling Optimization, Lizhen Du, Tao Ye, Yuhao Wang, Yajun Zhang
Journal of System Simulation
Abstract: To address the problems of population diversity loss and the tendency to fall into local optimality in the PSO (particle swarm optimization)algorithm in dealing with unrelated parallel batch scheduling problems, an improved scheduling optimization algorithm for PSO is proposed for minimizing the maximum completion time solution. A real number encoding based on the sequence of artifacts is used for the encoding operation. A new strategy based on J_B local search is designed based on the mixed integer programming model of the problem. The Metropolis criterion of the simulated annealing algorithm isintroduced into the individual extreme value search of the …
Modeling And Analysis Of Metro Emergency Decision Based On Logical Game Probability Petri Net, Zhe Yan, Wei Liu, Yuyue Du
Modeling And Analysis Of Metro Emergency Decision Based On Logical Game Probability Petri Net, Zhe Yan, Wei Liu, Yuyue Du
Journal of System Simulation
Abstract: In order to solve the problem that logical Petri net can not describe dynamic game process well, logical game probabilistic Petri net is proposed. The four elements of the game are integrated into the logical Petri net, and the players of the game are defined as an attribute of Token, for which the strategy set and utility function are defined, and the information database is introduced. Probability change and vector are introduced to represent the transformation relationship of empirical probability in the process of game, and fuzzy theory is introduced on the basis of Bayes formula to solve the …
Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao
Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao
Journal of System Simulation
Abstract: Aiming at the autonomous decision-making difficulty of mobile picking robots in random and changeable complicated path environment during field operations, an autonomous obstacle avoidance path planning method based on deep reinforcement learning is propose. By setting the state space and action space and using the artificial potential field method to design the reward function, an obstacle penalty coefficient setting method based on collision cone collision avoidance detection is proposed to improve the autonomous collision avoidance ability. A virtual simulation system is constructed, in which the learning and training of the mobile picking robot is carried out and verified by …
Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang
Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang
Journal of System Simulation
Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.
Monitoring Method Research On Passenger Behavior On Escalator Based On Digital Twin, Nan Lü, Qibing Wang, Lu Jiawei, Juntong Chen, Gang Xiao
Monitoring Method Research On Passenger Behavior On Escalator Based On Digital Twin, Nan Lü, Qibing Wang, Lu Jiawei, Juntong Chen, Gang Xiao
Journal of System Simulation
Abstract: In order to solve the problems that the traditional escalator cannot be monitored and analyzed in real time during operation, the management and maintenance only on escalator equipment side, and the lack of monitoring passenger dangerous behavior, a monitoring method of passenger behavior on escalator based on digital twin is proposed. By constructing the digital twin of escalators, a visual interface is designed to map the escalator running status and passenger behavior data. Through passenger video surveillance, the improved OpenPose posture recognition algorithm is used to obtain the key point data of human body. Posture recognition is classified to …
Lidar Slam Mapping Method Adapted To Environmental Spatial Changes, Songming Jiao, Xin Yao, Hui Ding, Yufei Zhong
Lidar Slam Mapping Method Adapted To Environmental Spatial Changes, Songming Jiao, Xin Yao, Hui Ding, Yufei Zhong
Journal of System Simulation
Abstract: In the environment with obvious changes in space size, aiming at the drift and other problems of the existing algorithm, Adp-lio-sam mapping method is proposed to adapt to the environment space changes, and improve the generality of lio-sam algorithm. Point cloud dewarping method is improved, and Kalman filter algorithm is used to carry out the motion compensation data by fusing lidar interframe pose interpolation and IMU interpolation. Fuzzy algorithm is used to adapt different points filtering thresholds for different spatial environments and the constraints of loop closure detection are optimized. Experimental results show that, compared with the existing …
Two-Stage Robust Optimization-Based Economic Dispatch Of Virtual Power Plants Considering Cogeneration, Jinpeng Liu, Peng Jinchun, Jiaming Deng, Hushihan Liu
Two-Stage Robust Optimization-Based Economic Dispatch Of Virtual Power Plants Considering Cogeneration, Jinpeng Liu, Peng Jinchun, Jiaming Deng, Hushihan Liu
Journal of System Simulation
Abstract: With continuous enrichment of resources of the supply side and flexible and changeable load of the demand side of energy system to effectively cope with the complexity of system operation optimization and resource allocation, a robust optimization model of virtual power plant considering the interaction between electric and thermal units is proposed. Considering the uncertainty of renewable energy and load in virtual power plant, a two-stage robust optimization model of min-max-min structure is established, and the optimal operation economy dispatching scheme in the worst scenario is obtained. Robustness coefficient is introduced to flexibly adjust the conservativeness of the optimization …
Machine Learning-Based Classification Of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging, Jennifer Muller, Ruixuan Wang, Devon Middleton, Mahdi Alizadeh, Kichang Kang, Ryan Hryczyk, George Zabrecky, Chloe Hriso, Emily Navarreto, Nancy Wintering, Anthony J. Bazzan, Chengyuan Wu, Daniel A. Monti, Xun Jiao, Qianhong Wu, Andrew B. Newberg, Feroze Mohamed
Machine Learning-Based Classification Of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging, Jennifer Muller, Ruixuan Wang, Devon Middleton, Mahdi Alizadeh, Kichang Kang, Ryan Hryczyk, George Zabrecky, Chloe Hriso, Emily Navarreto, Nancy Wintering, Anthony J. Bazzan, Chengyuan Wu, Daniel A. Monti, Xun Jiao, Qianhong Wu, Andrew B. Newberg, Feroze Mohamed
Marcus Institute of Integrative Health Faculty Papers
BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.
MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging …
Connectome-Constrained Artificial Neural Networks, Jacob Morra
Connectome-Constrained Artificial Neural Networks, Jacob Morra
Electronic Thesis and Dissertation Repository
In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron …
Predicting Network Failures With Ai Techniques, Chandrika Saha
Predicting Network Failures With Ai Techniques, Chandrika Saha
Electronic Thesis and Dissertation Repository
Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to …
Burstormer: Burst Image Restoration And Enhancement Transformer, Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming Hsuan Yang
Burstormer: Burst Image Restoration And Enhancement Transformer, Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming Hsuan Yang
Computer Vision Faculty Publications
On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain multiple degradations. The challenge is to properly align the successive image shots and merge their complementary information to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication …
Clip2protect: Protecting Facial Privacy Using Text-Guided Makeup Via Adversarial Latent Search, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
Clip2protect: Protecting Facial Privacy Using Text-Guided Makeup Via Adversarial Latent Search, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
Computer Vision Faculty Publications
The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate 'naturalistic' images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low- dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the …
Multiclass Confidence And Localization Calibration For Object Detection, Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan
Multiclass Confidence And Localization Calibration For Object Detection, Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan
Computer Vision Faculty Publications
Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make over-confident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks and restricted to calibrating in-domain predictions. Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications. In this paper, we propose a new train-time technique for calibrating modern object detection methods. It is capable of jointly calibrating multiclass confidence and …
3d-Aware Multi-Class Image-To-Image Translation With Nerfs, Senmao Li, Joost Van De Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang
3d-Aware Multi-Class Image-To-Image Translation With Nerfs, Senmao Li, Joost Van De Weijer, Yaxing Wang, Fahad Shahbaz Khan, Meiqin Liu, Jian Yang
Computer Vision Faculty Publications
Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multiclass image-to-image (3D-aware 121) translation. Naively using 2D-121 translation methods suffers from unrealistic shape/identity change. To perform 3D-aware multiclass 121 translation, we decouple this learning process into a multiclass 3D-aware GAN step and a 3D-aware 121 translation step. In the first step, we propose two novel techniques: a new conditional architecture and an effective training strategy. In the second step, based on the well-trained multiclass 3D-aware GAN architecture, that preserves view-consistency, we …
Discriminative Co-Saliency And Background Mining Transformer For Co-Salient Object Detection, Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan
Discriminative Co-Saliency And Background Mining Transformer For Co-Salient Object Detection, Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan
Computer Vision Faculty Publications
Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignore explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation …
Dynamic Graph Enhanced Contrastive Learning For Chest X-Ray Report Generation, Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang
Dynamic Graph Enhanced Contrastive Learning For Chest X-Ray Report Generation, Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang
Computer Vision Faculty Publications
Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes …
3d Semantic Segmentation In The Wild: Learning Generalized Models For Adverse-Condition Point Clouds, Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing
3d Semantic Segmentation In The Wild: Learning Generalized Models For Adverse-Condition Point Clouds, Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing
Computer Vision Faculty Publications
Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. We study all-weather 3DSS modeling under two setups: 1) domain adaptive 3DSS that adapts from normal-weather data to adverse-weather data; 2) domain generalizable 3DSS that learns all-weather 3DSS models from normal-weather data. Our studies reveal …
Data-Driven Exploration Of Coarse-Grained Equations: Harnessing Machine Learning, Elham Kianiharchegani
Data-Driven Exploration Of Coarse-Grained Equations: Harnessing Machine Learning, Elham Kianiharchegani
Electronic Thesis and Dissertation Repository
In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in …
N-Shot Benchmarking Of Whisper On Diverse Arabic Speech Recognition, Bashar Talafha, Abdul Waheed, Muhammad Abdul-Mageed
N-Shot Benchmarking Of Whisper On Diverse Arabic Speech Recognition, Bashar Talafha, Abdul Waheed, Muhammad Abdul-Mageed
Natural Language Processing Faculty Publications
Whisper, the recently developed multilingual weakly supervised model, is reported to perform well on multiple speech recognition benchmarks in both monolingual and multilingual settings. However, it is not clear how Whisper would fare under diverse conditions even on languages it was evaluated on such as Arabic. In this work, we address this gap by comprehensively evaluating Whisper on several varieties of Arabic speech for the ASR task. Our evaluation covers most publicly available Arabic speech data and is performed under n-shot (zero-, few-, and full) finetuning. We also investigate the robustness of Whisper under completely novel conditions, such as in …
Impact Analysis Of Gpt Technology Revolution On Fundamental Scientific Research, Mengge Sun, Tao Han, Yanpeng Wang, Yuxin Huang, Xiwen Liu
Impact Analysis Of Gpt Technology Revolution On Fundamental Scientific Research, Mengge Sun, Tao Han, Yanpeng Wang, Yuxin Huang, Xiwen Liu
Bulletin of Chinese Academy of Sciences (Chinese Version)
The generative large model GPT represented by ChatGPT is developing rapidly, which has aroused extensive discussion in academic circle and the industry and has an incalculable impact on foundational scientific research development. The study first sorts out the development of the GPT technological revolution, and discusses the new changes brought about by this technology in scientific research. Then, based on the three aspects of application status, core principles and innovation subjects, the impact of the GPT technological revolution on basic scientific research and its development suggestions for China are discussed. The study believes that GPT technology can certainly play a …
Vision Language Navigation With Knowledge-Driven Environmental Dreamer, Fengda Zhu, Vincent C.S. Lee, Xiaojun Chang, Xiaodan Liang
Vision Language Navigation With Knowledge-Driven Environmental Dreamer, Fengda Zhu, Vincent C.S. Lee, Xiaojun Chang, Xiaodan Liang
Computer Vision Faculty Publications
Vision-language navigation (VLN) requires an agent to perceive visual observation in a house scene and navigate step-by-step following natural language instruction. Due to the high cost of data annotation and data collection, current VLN datasets provide limited instruction-trajectory data samples. Learning vision-language alignment for VLN from limited data is challenging since visual observation and language instruction are both complex and diverse. Previous works only generate augmented data based on original scenes while failing to generate data samples from unseen scenes, which limits the generalization ability of the navigation agent. In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a …
Threads, Buckets, And Impact: A Framework For Tool Accelerated Machine Learning Courses, Jonathan Adam Niemirowski
Threads, Buckets, And Impact: A Framework For Tool Accelerated Machine Learning Courses, Jonathan Adam Niemirowski
Doctoral Dissertations
Artificial intelligence and machine learning (ML) have exploded in use, accessibility, and awareness in the past few years, particularly with the release of ChatGPT in late 2022. Advances in end-user ML tools are accelerating the development of ML applications, lowering the technical barrier of entry for users outside of the computer science (CS) community. Access to ML education within STEM is mostly limited to upper-level computer science courses that have deep pre-requisite requirements or to introductory workshops that yield limited ML skills. Despite the critical need for ML education, there is a lack of guidance in instructional design for applied …
Accessible Autonomy: Exploring Inclusive Autonomous Vehicle Design And Interaction For People Who Are Blind And Visually Impaired, Paul D. S. Fink
Accessible Autonomy: Exploring Inclusive Autonomous Vehicle Design And Interaction For People Who Are Blind And Visually Impaired, Paul D. S. Fink
Electronic Theses and Dissertations
Autonomous vehicles are poised to revolutionize independent travel for millions of people experiencing transportation-limiting visual impairments worldwide. However, the current trajectory of automotive technology is rife with roadblocks to accessible interaction and inclusion for this demographic. Inaccessible (visually dependent) interfaces and lack of information access throughout the trip are surmountable, yet nevertheless critical barriers to this potentially lifechanging technology. To address these challenges, the programmatic dissertation research presented here includes ten studies, three published papers, and three submitted papers in high impact outlets that together address accessibility across the complete trip of transportation. The first paper began with a thorough …
Reinforcement Learning Approach To Stochastic Vehicle Routing Problem With Correlated Demands, Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
Reinforcement Learning Approach To Stochastic Vehicle Routing Problem With Correlated Demands, Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
Machine Learning Faculty Publications
We present a novel end-to-end framework for solving the Vehicle Routing Problem with stochastic demands (VRPSD) using Reinforcement Learning (RL). Our formulation incorporates the correlation between stochastic demands through other observable stochastic variables, thereby offering an experimental demonstration of the theoretical premise that non-i.i.d. stochastic demands provide opportunities for improved routing solutions. Our approach bridges the gap in the application of RL to VRPSD and consists of a parameterized stochastic policy optimized using a policy gradient algorithm to generate a sequence of actions that form the solution. Our model outperforms previous state-of-the-art metaheuristics and demonstrates robustness to changes in the …
Weakly-Supervised Anomaly Detection In Surveillance Videos Based On Two-Stream I3d Convolution Network, Sareh Soltani Nejad
Weakly-Supervised Anomaly Detection In Surveillance Videos Based On Two-Stream I3d Convolution Network, Sareh Soltani Nejad
Electronic Thesis and Dissertation Repository
The widespread adoption of city surveillance systems has led to an increase in the use of surveillance videos for maintaining public safety and security. This thesis tackles the problem of detecting anomalous events in surveillance videos. The goal is to automatically identify abnormal events by learning from both normal and abnormal videos. Most of previous works consider any deviation from learned normal patterns as an anomaly, which may not always be valid since the same activity could be normal or abnormal under different circumstances. To address this issue, the thesis utilizes the Two-Stream Inflated 3D (I3D) Convolutional Networks to extract …
Ocr Post-Processing Using Large Language Models, Mahdi Hajiali
Ocr Post-Processing Using Large Language Models, Mahdi Hajiali
UNLV Theses, Dissertations, Professional Papers, and Capstones
Optical Character Recognition (OCR) technology transforms textual visuals into an electronically readable, non-graphical format of the text. This allows the editing and other text manipulation of the content by language technology software such as machine translation, text comprehension, query-answering systems, and search engines. While Optical Character Recognition (OCR) systems continually progress towards greater precision, several complications persist when dealing with low-resolution source images or those with multicolored backgrounds. Consequently, the text derived from OCR necessitates additional refinement to optimize accuracy, beneficial for various subsequent applications. It is recognized that the character accuracy of OCR-generated text may influence certain natural language …
A Multi-Layer Information Dissemination Model And Interference Optimization Strategy For Communication Networks In Disaster Areas, Yuexia Zhang, Yang Hong, Mohsen Guizani, Sheng Wu, Peiying Zhang, Ruiqi Liu
A Multi-Layer Information Dissemination Model And Interference Optimization Strategy For Communication Networks In Disaster Areas, Yuexia Zhang, Yang Hong, Mohsen Guizani, Sheng Wu, Peiying Zhang, Ruiqi Liu
Machine Learning Faculty Publications
The communication network in disaster areas (CNDA) can disseminate the key disaster information in time and provide basic information support for decision-making and rescuing. Therefore, it is of great significance to study the information dissemination mechanism of CNDA. However, a CNDA is vulnerable to interference, which affects information dissemination and rescuing. To solve this problem, this paper established a multi-layer information dissemination model of CNDA (MMND) which models the CNDA from the perspective of degree distribution of nodes. The information dissemination process and equilibrium state in CNDA is analyzed by an improved dynamic dissemination method. Then, the effects of the …
Autonomous Shipwreck Detection & Mapping, William Ard
Autonomous Shipwreck Detection & Mapping, William Ard
LSU Master's Theses
This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …
Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen
Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen
Dartmouth College Ph.D Dissertations
Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …