Parallax-Tolerant Image Stitching With Geometric Structure Protection For Unmanned Ship Visual Perception,
2024
Key Lab. of Marine Simulation and Control, Dalian Maritime University, Dalian 116026, China
Parallax-Tolerant Image Stitching With Geometric Structure Protection For Unmanned Ship Visual Perception, Zhilin Yang, Yong Yin, Rukai Zhang, Qianfeng Jing, Sen Jiang, Wenfeng Zhu
Journal of System Simulation
Abstract: In order to address the artifacts caused by misalignment in maritime image stitch with low texture and large parallax, a geometric structure parallax-tolerant image stitch algorithm based on point-line feature registration and optimal seam fusion is proposed. Line segment features are introduced into the traditional homography transformation based on point features, and potential coplanar local line segments are merged into global line segments to provide accurate alignment conditions for seam line fusion. In the image fusion stage, the energy function of seam cutting method is designed by using the color difference and gradient difference of tanh measure and introducing …
Fusion Of Improved A* And Dynamic Window Approach For Mobile Robot Path Planning,
2024
Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China
Fusion Of Improved A* And Dynamic Window Approach For Mobile Robot Path Planning, Rongshen Lai, Lei Dou, Zhiyong Wu, Shuai Sun
Journal of System Simulation
Abstract: The traditional A* algorithm is computationally simple and has short planning paths, but it still suffers from redundancy of inflection points, low search efficiency and zigzagging planning paths. Aiming at the above problems, a fusion algorithm combining the improved A* algorithm and the improved dynamic window approach is proposed for the path planning of mobile robots. For the problem of redundant inflection points, the key nodes are extracted to effectively remove the useless inflection points; for the problem of low search efficiency, a dynamic weighting factor is introduced into the heuristic function of the evaluation function, which changes the …
A Highly Robust Target Tracking Algorithm Merging Cnn And Transformer,
2024
School of Mechatronic Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
A Highly Robust Target Tracking Algorithm Merging Cnn And Transformer, Peijin Liu, Xuefeng Fu, Haofeng Sun, Lin He, Shujie Liu
Journal of System Simulation
Abstract: To address the performance degradation of target tracking algorithms caused by target object deformation, scale variation, fast motion, and occlusion, a highly robust target tracking algorithm that Merging a CNN and Transformer is proposed based on siamese network architecture. In the feature extraction stage, standard convolutions are employed to extract shallow local feature information, while a convolution-like Transformer module is designed in the deep network to model global information. The pixel values in the Transformer are computed using a sliding window significantly reducing computational complexity. In the feature aggregation stage, a multi-head cross-attention module is utilized to construct a …
Study On Robust Chance Constrained Optimization Of Multi-Energy Supply System Based On Wind And Solar Power Combined Output Simulation,
2024
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
Study On Robust Chance Constrained Optimization Of Multi-Energy Supply System Based On Wind And Solar Power Combined Output Simulation, Zhe Bao, Wei Li, Xiaofang Zhang, Zongyuan An, Ye Xu
Journal of System Simulation
Abstract: In order to effectively avoid potential imbalance between supply and demand caused by the uncertainty of wind and solar power outputs, and promote the sustained development of the multi-energy supply system, a robust chance-constrained optimization model is developed for identifying optimal operation strategies under complexities and uncertainties through incorporating Copula theory, chance-constrained programming, and robust programming within a general framework. The results show that this model can not only accurately characterize the distribution probability of combined outputs of wind and solar power and formulate the operational strategies under low default risk conditions, but also reduce the proportion of highrisk …
Gpu Parallel Acceleration Framework For Heuristic Optimization Algorithm,
2024
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Gpu Parallel Acceleration Framework For Heuristic Optimization Algorithm, Dongjie Wang, Sixin Wen, Wanzhi Meng, Di Wu
Journal of System Simulation
Abstract: Heuristic optimization algorithm are a type of algorithm that uses large-scale populations for iterative calculations and are widely used to solve all kinds of complex optimization problems. However, such algorithm have the disadvantages of large calculation and long time consumption. To solve this problem, heuristic optimization algorithms are parallelized using GPU and compute unified device architecture (CUDA) to substantially improve computational efficiency. A GPU parallel framework for heuristic optimization algorithm is proposed, which designs an information interaction framework and algorithm parallel optimization strategy with a parallel logical structure, and solves the problem of the dissimilarity of the logical structure …
An Improved Cat Swarm Optimization For Heterogeneous Multiple Mobile Robots,
2024
Engineering Training and Innovation Education Center, Shanghai Polytechnic University, Shanghai 201209, China
An Improved Cat Swarm Optimization For Heterogeneous Multiple Mobile Robots, Liang Kang, Yi Du, Lihua Yin
Journal of System Simulation
Abstract: At present, it is difficult to achieve the real homogeneity of the members of the multiple mobile robots. The existing swarm intelligence algorithm is also difficult to accommodate the heterogeneity of the team. Focusing on the heterogeneous cooperation of multiple mobile robots, the concept of mother and child robots is proposed. In order to realize the application of swarm intelligence algorithm in heterogeneous multiple mobile robots, the basic cat swarm algorithm is improved. The subdomain and neighborhood of cat swarm are defined, and eight improvements of cat swarm algorithm are proposed, including priority of search direction, extended trajectory tracking …
Digital Twin-Driven Structural Thermal Deformation Compensation System For Radio Telescopes,
2024
School of Construction Machinery, Chang'an University, Xi'an 710064, China; The 39th Research Institute of China Electronics Technology Group Corporation, Xi'an 710006, China
Digital Twin-Driven Structural Thermal Deformation Compensation System For Radio Telescopes, Zhen Lei, Yuhua Liu, Kai Ding, Haoxiang Chen, Dongwei Li
Journal of System Simulation
Abstract: The structural thermal deformation of large-scale radio telescopes under solar thermal load cannot be measured in real-time and compensated dynamically. To solve this problem, a digital twin-driven structural thermal deformation compensation method and system is studied. Based on the fusion of measured data and simulation data, a temperature field modeling method is proposed. A simulation and prediction model of structural thermal deformation is established, and a dynamic compensation model of structural thermal deformation is built. A digital twin-driven dynamic structural thermal deformation compensation system for radio telescopes is developed. A micro-experimental model is studied to verify the effectiveness of …
Research On Path Optimization Algorithm In Dynamic Routing Environment,
2024
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Research On Path Optimization Algorithm In Dynamic Routing Environment, Xin Xie, Xiaobing Hu, Hang Zhou
Journal of System Simulation
Abstract: In a real dynamic routing environment, static path optimization (SPO) and traditional dynamic path optimization (DPO) tend to encounter issues such as detours, reversals and high computational complexity due to frequent real-time optimization calculation. To address these problems, a novel restart co-evolutionary path optimization (RCEPO) method based on the ripple-spreading algorithm (RSA) is proposed. This method integrates the path optimization process with the dynamic changes of the routing network environment to enhance the effectiveness of path optimization. Moreover, the path reoptimization calculation is performed only when the dynamic changes in the routing environment exceed the predicted range, thereby reducing …
Estimation Of The Berthing Parameter Of Unmanned Surface Vessels Based On 3d Lidar,
2024
Key Laboratory of Marine Simulation &Control for Ministry of Transportation, Dalian Maritime University, Dalian 116026, China
Estimation Of The Berthing Parameter Of Unmanned Surface Vessels Based On 3d Lidar, Haichao Wang, Yong Yin, Qianfeng Jing, Lin Cong
Journal of System Simulation
Abstract: Accurate estimation of berthing parameters is a prerequisite for unmanned surface vessel autonomous berthing. A method for berthing parameter estimation is proposed based on shipborne 3D LiDAR. The method consists of two main modules: ship pose estimation and berthing state estimation. In the berthing position estimation module, raw point cloud data undergoes preprocessing algorithms aims at downsampling and removing outliers. Point cloud registration algorithms are employed to determine the vessel's position during the berthing process. The berthing state estimation module extracts berth boundary information by using the MSAC algorithm, and on the basis of this information, calculates the berthing …
Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma,
2024
The Texas Medical Center Library
Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma, Xiaoqian Jiang, Pratip K Bhattacharya, Shyan Shams
Faculty, Staff and Student Publications
Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs …
In Reply: Can Artificial Intelligence Make The Cut? Dissecting Large Language Model’S Surgical Exam Performance,
2024
Thomas Jefferson University
In Reply: Can Artificial Intelligence Make The Cut? Dissecting Large Language Model’S Surgical Exam Performance, Adam M. Ostrovsky, Joshua R. Chen, Vishal N. Shah, Babak Abai
Department of Surgery Faculty Papers
No abstract provided.
Groundwater Modeling Of The Ogallala Aquifer: Use Of Machine Learning For Model Parameterization And Sustainability Assessment,
2024
University of Nebraska-Lincoln
Groundwater Modeling Of The Ogallala Aquifer: Use Of Machine Learning For Model Parameterization And Sustainability Assessment, Tewodros Aboret Tilahun
Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–
Addressing groundwater depletion problems in heterogeneous aquifer systems is a challenge. The heterogeneous Ogallala Aquifer, a critical source of groundwater in the central United States, has undergone decades of decline in water levels due to pumping. This project aims to build a robust groundwater model to evaluate optimal scenarios for sustainable use of the groundwater resource within a section of the Ogallala aquifer located in the Middle Republican Natural Resources District (MRNRD). This study follows a comprehensive approach involving parameterization, construction, and optimization. The model is parametrized using hydraulic conductivity and recharge values obtained from a random forest-based machine learning …
Digital Scribes: A Possible Solution For Provider Burnout By Reducing Provider Workload,
2024
Augsburg University
Digital Scribes: A Possible Solution For Provider Burnout By Reducing Provider Workload, Shannon Storley
Theses and Graduate Projects
Background: Provider burnout is continuing to be a massive problem for our healthcare industry. One major contributor to provider burnout is burdensome administrative tasks associated with documentation of electronic medical records (EMR). This review aims to uncover the applications for artificially intelligent digital scribes as a solution to reduce EMR documentation burden. Purpose: Provider burnout has shown to increase the incidence of major mistakes and decreased patient safety grades. Digital scribes could be a solution in reducing provider burnout by reducing the administrative burden of EMR documentation. Methods: A comprehensive literature review was conducted using articles from PubMed using search …
Enhancing Clinical Relevance Of Pretrained Language Models Through Integration Of External Knowledge: Case Study On Cardiovascular Diagnosis From Electronic Health Records,
2024
The Texas Medical Center Library
Enhancing Clinical Relevance Of Pretrained Language Models Through Integration Of External Knowledge: Case Study On Cardiovascular Diagnosis From Electronic Health Records
Faculty, Staff and Student Publications
Background: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. …
Reless: A Framework For Assessing Safety In Deep Learning Systems,
2024
CUNY Graduate Center
Reless: A Framework For Assessing Safety In Deep Learning Systems, Nan Jia, Anita Raja, Raffi T. Khatchadourian
Publications and Research
Traditionally, software refactoring helps to improve a system's internal structure and enhance its non-functional features, such as reliability and run-time performance, while preserving external behavior including original program semantics. However, in the context of learning-enabled software systems (LESS), e.g., Machine Learning (ML) systems, it is unclear which portions of a software's semantics require preservation at the development phase. This is mainly because (a) the behavior of the LESS is not defined until run-time; and (b) the inherently iterative and non-deterministic nature of ML algorithms. Consequently, there is a knowledge gap in what refactoring truly means in the context of LESS …
Development And Optimization Of A 1-Dimensional Convolutional Neural Network-Based Keyword Spotting Model For Fpga Acceleration,
2024
Grand Valley State University
Development And Optimization Of A 1-Dimensional Convolutional Neural Network-Based Keyword Spotting Model For Fpga Acceleration, Trysten E. Dembeck
Masters Theses
Spoken Keyword Spotting (KWS) has steadily remained one of the most studied and implemented technologies in human-facing artificially intelligent systems and has enabled them to detect specific keywords in utterances. Modern machine learning models, such as the variants of deep neural networks, have significantly improved the performance and accuracy of these systems over other rudimentary techniques. However, they often demand substantial computational resources, use large parameter spaces, and introduce latencies that limit their real-time applicability and offline use. These speed and memory requirements have become a tremendous problem where faster and more efficient KWS methods dominate and better meet industry …
Contrastive General Graph Matching With Adaptive Augmentation Sampling,
2024
Singapore Management University
Contrastive General Graph Matching With Adaptive Augmentation Sampling, Jianyuan Bo, Yuan Fang
Research Collection School Of Computing and Information Systems
Graph matching has important applications in pattern recognition and beyond. Current approaches predominantly adopt supervised learning, demanding extensive labeled data which can be limited or costly. Meanwhile, self-supervised learning methods for graph matching often require additional side information such as extra categorical information and input features, limiting their application to the general case. Moreover, designing the optimal graph augmentations for self-supervised graph matching presents another challenge to ensure robustness and effcacy. To address these issues, we introduce a novel Graph-centric Contrastive framework for Graph Matching (GCGM), capitalizing on a vast pool of graph augmentations for contrastive learning, yet without needing …
Artificial Intelligence, Work, And The Future Of Education,
2024
Lincoln Memorial University
Artificial Intelligence, Work, And The Future Of Education, Daniel Brown
Library Presentations
No abstract provided.
Contemplating Existence: Ai And The Meaning Of Life,
2024
Capitol Technology University
Contemplating Existence: Ai And The Meaning Of Life, Emily Barnes, James Hutson
Faculty Scholarship
This article explores the intersection of artificial intelligence (AI) with existential philosophy, examining how AI technologies influence human conceptualizations of purpose and meaning. Despite rapid advancements in AI, the domain's implications for existential thought remain underexplored. By integrating interdisciplinary perspectives from psychology, philosophy, and AI ethics, this study elucidates how AI can shape, challenge, or enhance our understanding of life's purpose. It investigates theoretical frameworks and practical implementations of AI engaging in existential questions, analyzing both the capabilities and limitations of AI systems such as ChatGPT in simulating human existential thought. The ethical implications of AI's role in existential inquiries …
Offensive Content Detection In Online Social Platforms,
2024
Clemson University
Offensive Content Detection In Online Social Platforms, Ebuka Okpala
All Dissertations
Online social platforms enable users to connect with large, diverse audiences and the ability for a message or content to flow from one user to another user, user to followers, followers to user, and followers to followers. Of course, the advantages of this are apparent, and the dangers are also clearly obvious. The user-generated content could be abusive, offensive, or hateful to other users, possibly leading to adverse health effects or offline harm. As more of society's public discourse and interaction move online and these platforms grow and increase their reach, it is inherently important to protect the safety of …
