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Automated Extraction from Aerospace engineering Diagrams Using Computer Vision Techniques 5

Automated Extraction from Aerospace Engineering Diagrams Usi...

引用

5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Manepalli, Shreya Karanam, Ashutosh Bysani, Hemanth Pes University Department of Computer Science Bengaluru India Bits Pilani Department of Electronics and Communication Bengaluru India Amrita Vishwa Vidyapeetham Department of Computer Science Bengaluru India

ISBN: (纸本)9798331528539

Aerospace engineering design and manufacturing greatly rely on accurate and efficient plane wing diagram data extraction. this paper introduces an innovative technique for automating the process using state-of-the-art computer vision approaches. Cutting edge image processing technologies extract vital technical metrics from these schematics. Experimental results demonstrate this methodology extracts information approximately four times faster than human input and with considerably higher precision. Improved design workflows and dependable engineering results are encouraged by such a feature. Collectively, these initiatives moreover offer insight into how computer vision may revive outdated methods in aeronautical engineering. Streamlined yet trustworthy design processes and results are made possible through such automated and precise data extraction from schematics. Meanwhile, revived methods hold potential to re-energize the field and ensure continued advancement. © 2024 IEEE.

关键词： Optical character recognition

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion 4

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

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4th international conference on artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

the landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. the integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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the Impact of Vectorization on the Efficiency of a Parallel PIC Code for Numerical Simulation of Plasma Dynamics in Open Trap 4th

The Impact of Vectorization on the Efficiency of a Para...

引用

4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Chernykh, Igor Kulikov, Igor Vshivkov, Vitaly Efimova, Anna Weins, Dmitry Chernoshtanov, Ivan Boronina, Marina Institute of Computational Mathematics and Mathematical Geophysics Siberian Branch of the RAS 6 Ac. Lavrentieva ave. Novosibirsk Russia

ISBN: (纸本)9783031812460

Last decades, we have seen a growing interest in thermonuclear fusion. there are a lot of installation prototypes created for controlled thermonuclear fusion. Our group developing software for numerical simulation of plasma physics processes in an open magnetic trap, which can be used for controlled thermonuclear fusion. this code is Fortran-based code. Parallel implementation using MPI for parallel computations. this parallel implementation is optimized for using CPUs with advanced vectorization instructions such as AVX2/AVX512. In this paper, we will show some optimization techniques for maximizing the performance of our parallel Particle-in-Cell (PIC) code using data alignment. this feature helps the compiler to optimize data and cycles in code during the compilation and building of the program. Also, we will show the performance comparison between optimized and nonoptimized programs and the effect of vector instructions utilization on the total code performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： FORTRAN (programming language)

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A Contour-Constraint Neural Network with Hierarchical Feature Learning for Lung Nodule Segmentation in 3D CT Images 4

A Contour-Constraint Neural Network with Hierarchical Featur...

引用

4th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Qiu, Junrong Li, Bin Liao, Riqiang Mo, Hongqiang Tian, Lianfang School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Lung Cancer Institute/Center of Cancer Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Southern Medical University Department of Pulmonary Surgery/Guangdong Guangzhou510080 China

ISBN: (纸本)9798350343113

Current convolutional neural network (CNN) based lung nodule segmentation methods suffer from the loss of spatial information during the down-sampling procedure and the heterogeneity of lung nodules (texture, size, etc.), which leads to inaccurate segmentation results. To solve the problems, a contour-constraint neural network with hierarchical feature learning is proposed in this work. A contour detection module is introduced to extract rich edge information as prior knowledge and predict edge explicitly from the shallow layers, while a hierarchical feature module is suggested to utilize multi-scale features and produce object masks in the deep layers. A differentiable edge extractor is designed to extract the edge from the predicted mask to maintain edge consistency. A contour-constraint loss function is proposed to exploit the contour information produced by the contour detection module and constrain the predicted mask reversely. In this roundabout way, the learning of contour information is enhanced, allowing more accurate segmentation. Extensive experiments are conducted on the widely-used dataset LIDC-IDRI, and our method achieves competitive results compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Textures

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State of the art Review in Skin Lesion Image Processing Analysis 4

State of the Art Review in Skin Lesion Image Processing Anal...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Muthamil Selvan, S. Dilip, Golda Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9781665401180

Skin illnesses are common in general practise and are a common occurrence. Access to medical care and high diagnostic accuracy are critical challenges, especially when separating benign illnesses from malignancies like melanomas, which require immediate diagnosis and treatment. Dermatologists use visual inspection to diagnose skin lesions based on dermoscopic or clinical imaging. To support the diagnostic process, which may be hampered by increasing workloads and a shortage of skilled workers in certain parts of the world, computer-aided detection and computer-aided diagnosis methods have been developed for image analysis. of skin lesions. Advanced machine learning, and in particular deep learning techniques, have achieved dermatological categorization of skin lesions using dermoscopic and non-dermoscopic images, which is generating great hope in this area. the objective of this study is to present a complete roadmap and the latest lines of research in the analysis of images of skin lesions. © 2022 IEEE

关键词： Image analysis

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Multi-focus image fusion based on hybrid feature extraction and interactive fusion 4

Multi-focus image fusion based on hybrid feature extraction ...

引用

4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Jiang, Jinyuan College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510686106

Multi-focus image fusion is the process of synthesizing multiple images with different focal points into a single image with higher clarity and a wider depth of field. However, existing methods face challenges when dealing with complex scenes, such as information loss, insufficient extraction of image details, and inaccurate fusion. therefore, this paper proposes a multi-focus image fusion method based on hybrid feature extraction and interactive fusion. By enhancing the network's ability to capture image details through mixed extraction blocks, feature information is effectively extracted. Subsequently, interactive fusion blocks enhance the interaction between features from different sources, making feature fusion more natural and effective. Finally, a feature reconstruction decoder is designed using convolutional layers and reverse bottleneck structures to effectively avoid information loss and maintain the richness and integrity of features. Extensive experiments indicate that, compared to 10 other state-of-the-art methods, the proposed approach in this paper exhibits superior performance in multi-focus image fusion tasks. © 2024 SPIE.

关键词： Image fusion

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cuQuantum SDK: A High-Performance Library for Accelerating Quantum science 4

cuQuantum SDK: A High-Performance Library for Accelerating Q...

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4th IEEE international conference on Quantum Computing and engineering, QCE 2023

作者： Bayraktar, Harun Charara, Ali Clark, David Cohen, Saul Costa, Timothy Fang, Yao-Lung L. Gao, Yang Guan, Jack Gunnels, John Haidar, Azzam Hehn, Andreas Hohnerbach, Markus Jones, Matthew Lubowe, Tom Lyakh, Dmitry Morino, Shinya Springer, Paul Stanwyck, Sam Terentyev, Igor Varadhan, Satya Wong, Jonathan Yamaguchi, Takuma Nvidia Corp Santa ClaraCA United States

ISBN: (纸本)9798350343236

We present the NVIDIA cuQuantum SDK [1], a state-of-the-art library of composable primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices continues to increase, making their classical simulation progressively more difficult, the availability of fast and scalable quantum circuit simulators becomes vital for quantum algorithm developers, as well as quantum hardware engineers focused on the validation and optimization of quantum devices. the cuQuantum SDK was created to accelerate and scale up quantum circuit simulators developed by the quantum information science community by enabling them to utilize efficient scalable software building blocks optimized for NVIDIA GPU-based platforms. the functional building blocks provided cover the needs of both state vector- and tensor network- based simulators, including approximate tensor network simulation methods based on matrix product state, projected entangled pair state, and other factorized tensor representations. By leveraging the enormous computing power of the latest NVIDIA GPU architectures, quantum circuit simulators that have adopted the cuQuantum SDK demonstrate significant acceleration, compared to CPU-only execution, for both the state vector and tensor network simulation methods. Furthermore, by utilizing the parallel primitives available in the cuQuantum SDK, one can easily transition to distributed GPU-accelerated platforms, including those furnished by cloud service providers and high-performance computing systems deployed by supercomputing centers, extending the scale of possible quantum circuit simulations. the rich capabilities provided by the cuQuantum SDK are conveniently made available via both Python and C application programming interfaces, where the former is directly targeting a broad Python quantum community and the latter allows tight integration with simulators written in any programming language. © 2023 IEEE.

关键词： Tensors

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A multi-focus image fusion method based on boundary-aware and adaptive fusion of experts 4

A multi-focus image fusion method based on boundary-aware an...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Chen, Peng College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510686106

the purpose of multi-focus image fusion is to extract all the features of complementary images to obtain a fusion image with complete image information. the fusion method based on decision map is widely used in multi-focus image fusion, because it can retain the information of the original image to the maximum extent. However, the method based on decision map often has the problem of misjudgment at the edge of clear region and fuzzy region, which affects the accuracy of fusion image. In order to solve the problem of misjudgment in the edge region based on the decision map scheme, we introduce differential convolution to improve the sensitivity of the network to the edge gradient, improve the judgment ability of the network in the edge region, and improve the accuracy of the decision map. Moreover, an efficient feature fusion method is essential for improving network performance. To ensure the quality of feature fusion results, we introduce a mixture fusion expert to guide the image feature fusion process, whose unique gating mechanism guarantees the efficiency of feature fusion. Extensive experiments have demonstrated that our proposed method achieves superior results compared to most existing state-of-the-art methods. © 2024 SPIE.

关键词： Image fusion

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Autonomous Wayfinding Simulation of Quadrotor UAV Cluster Based on Unity3D 4

Autonomous Wayfinding Simulation of Quadrotor UAV Cluster Ba...

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4th international conference on Computer science and Management Technology, ICCSMT 2023

作者： Wu, Jingjing Liu, Fengli Department of Mechanical Engineering Shenyang Ligong University Shenyang110158 China

ISBN: (纸本)9798400709517

Aiming at the effect and difficulty of different algorithms for autonomous wayfinding between forests for multiple quadcopter UAVs, unity3D engine was applied to carry out visual analysis of these algorithms. By collecting related algorithms, literature research and review, this paper studies the application cases of autonomous wayfinding for multiple quadrotor UAVs. According to the principle and implementation method of the selected algorithm, the corresponding algorithm script was written using C# and other programming languages, and it was connected with the UAV model in the unity3D scene. through visual analysis, the scenarios and algorithm scripts were run in the unity3D engine to observe the flight state and path planning effects of different algorithms. the A*algorithm has better effect in path planning and lower implementation difficulty and complexity than other algorithms, and is suitable for rapid deployment in practical applications. © 2023 ACM.

关键词： Unmanned aerial vehicles (UAV)

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AI-Powered Waste Management: A Case Study on the Application of YOLOv5 and K-Means Clustering in Urban Communities 4

AI-Powered Waste Management: A Case Study on the Application...

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4th international conference on Signal Processing and Computer science, SPCS 2023

作者： Tan, Tianzhuzi Dong, Xueying Xu, Qishun Zou, Nuo School of Information Science and Technology Beijing Forestry University Beijing China School of Management Henan University of Technology Zhengzhou China

ISBN: (纸本)9781510672505

Urban waste management is a pressing global issue that demands innovative and universally applicable solutions. this paper introduces a revolutionary approach to waste management that harnesses the power of artificial intelligence (AI) and community engagement. By integrating the YOLOv5 image detection algorithm, K-Means clustering, and integer programming within a community-based framework, we present a method that transcends geographical and cultural boundaries, offering a universally applicable solution to waste management. the potential implications of this approach are profound. Not only does it offer a practical solution to waste management in urban communities, but it also fosters community engagement and environmental awareness. this study, therefore, serves as a beacon for future research and practice in sustainable urban living, demonstrating the transformative potential of AI when applied to real-world challenges. © 2023 SPIE.

关键词： Monte Carlo methods

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