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Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

Computers, Materials & Continua 2025年第3期82卷 5239-5256页

作者： Ruizhe Yu Tingrui Yue Lei Liang Zhisheng Gao School of Computer and Software Engineering Xihua UniversityChengdu610039China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210016China Low Speed Aerodynamics Institute China Aerodynamics Research and Development CenterMianyang621000China

In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image *** deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational *** address these issues,this study proposes a deep learning-based approach tailored for PSP image *** that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based *** this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance ***,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow *** preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image *** integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data *** evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.

关键词： Pressure-sensitive paint deep learning image deblurring typeset variable attention convolution

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ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure

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Deep Underground Science and engineering 2025年第1期4卷 72-82页

作者： Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina

It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

关键词： autoencoder deep learning structural health monitoring time-series prediction

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Self-Supervised Learning for Precise Individual Tree Segmentation in Airborne LiDAR Point Clouds

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IEEE Access 2025年 13卷 70895-70908页

作者： Shaheen, Lama Rasheed, Bader Mazzara, Manuel Innopolis University Institute of Software Development and Engineering Innopolis420500 Russia Innopolis University Research Center for Artificial Intelligence Innopolis420500 Russia Innopolis University Laboratory of Innovative Technologies for Processing Video Content Innopolis420500 Russia

Segmenting individual trees from airborne LiDAR point cloud data is critical for forest management, urban planning, and ecological monitoring but remains challenging due to complex natural environments, diverse tree architectures, and dense canopies. Traditional supervised methods rely on extensive, manually annotated datasets that are often impractical to obtain. In this study, we propose a novel self-supervised learning framework that eliminates the need for manual labeling by integrating transformation-invariant feature extraction, an energy-based segmentation loss, and soft clustering. The framework operates in two stages: a pretext task applies geometric transformations—rotation (from –45° to +45°), translation (between –1 and 1 units), and scaling (between 0.5 and 2.0)—to learn robust features, while an unsupervised segmentation step leverages an energy function that combines height, density, and slope attributes to cluster points corresponding to individual trees. We evaluated our approach on a high-density LiDAR dataset acquired from the Estonian Land Board (LAS format, version 1.4) comprising over 850,000 points. Our method achieves up to 87% convexity, 78% solidity, and an elliptical fit error as low as 0.12, substantially reducing over-segmentation compared to baseline clustering techniques. These results demonstrate that our self-supervised framework offers a scalable, label-free solution for precise tree segmentation, with significant advantages in accuracy and efficiency over traditional supervised methods. © 2013 IEEE.

关键词： Data reduction

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DiffFormer: A Differential Spatial-Spectral Transformer for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10419-10428页

作者： Ahmad, Muhammad Mazzara, Manuel Distefano, Salvatore Khan, Adil Mehmood Ullo, Silvia Liberata Dhahran31261 Saudi Arabia Innopolis University Institute of Software Development and Engineering Innopolis420500 Russia University of Messina Dipartimento di Matematica e Informatica-MIFT Messina98121 Italy University of Hull School of Computer Science HullHU6 7RX United Kingdom University of Sannio Engineering Department Benevento82100 Italy

Hyperspectral image classification (HSIC) presents significant challenges due to spectral redundancy and spatial discontinuity, both of which can negatively impact classification performance. To mitigate these issues, this work proposes the differential spatial-spectral transformer (DiffFormer), a novel framework designed to enhance feature discrimination and improve classification accuracy. At its core, DiffFormer incorporates a differential multihead self-attention mechanism, which accentuates subtle spectral-spatial variations by applying differential attention across neighboring patches. The architecture integrates spectral-spatial tokenization, utilizing 3-D convolution-based patch embeddings, positional encoding, and a stack of transformer layers augmented with the SwiGLU activation function—a variant of the gated linear unit—to enable efficient and expressive feature extraction. In addition, a token-based classification head ensures robust representation learning, facilitating precise pixelwise labeling. Extensive experiments on benchmark hyperspectral datasets demonstrate that DiffFormer consistently outperforms state-of-the-art methods in classification accuracy, computational efficiency, and generalizability. © 2008-2012 IEEE.

关键词： Image classification

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of Computer Science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Research on the structure of a cloud field distribution measurement system in a high-speed icing wind tunnel

Research on the structure of a cloud field distribution meas...

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2024 International Conference on Optoelectronic Materials and Devices, ICOMD 2024

作者： Zhang, Quan Qing, Chao Zuo, Chenglin Hu, Jiahui Li, Yan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Low Speed Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan Mianyang621000 China Key Laboratory of Icing and Anti/De-Icing China Aerodynamics Research and Development Center Mianyang621000 China Intelligent Oil and Gas Laboratory Southwest Petroleum University Chengdu610500 China

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

This study addresses the need for cloud uniformity measurement in icing wind tunnel experiments by designing and developing a laser extinction tomography system for circular icing wind tunnels. The system is aimed at enhancing the accuracy of wind tunnel experiments simulating aircraft icing behavior, providing precise data for research on aircraft icing and anti-icing technologies. The system consists of 24 radiation sources and 24 detectors, uniformly distributed around a circular ring with a diameter of 800 mm, enabling real-time monitoring and analysis of cloud field uniformity. Using COMSOL multiphysics simulation software, the propagation paths of the laser through the cloud field were simulated, optimizing the arrangement of radiation sources and detectors to improve measurement accuracy. By integrating the Algebraic Reconstruction Technique (ART) with regularization methods, the image reconstruction algorithm enhances both precision and stability, effectively capturing the spatial distribution of the cloud field. Experimental results demonstrate that the system accurately reflects the cloud field distribution, with the error ratio controlled within acceptable limits, validating the system’s accuracy and reliability. This system provides a high-precision measurement platform for aircraft icing research and anti-icing technology experiments. © 2025 SPIE.

关键词： Tomography

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Simulation and analysis of a tomographic imaging system for cloud field measurement in rectangular icing wind tunnels

Simulation and analysis of a tomographic imaging system for ...

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2024 International Conference on Optoelectronic Materials and Devices, ICOMD 2024

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

The uniformity of cloud distribution is a critical metric for evaluating the performance of icing wind tunnels, directly affecting the accuracy of experimental results. To address the challenge of measuring cloud distribution in wind tunnels, this paper proposes a solution based on extinction tomography. A rectangular cross-sectional imaging system was developed specifically for rectangular wind tunnel structures, overcoming the limitations of traditional ring-shaped systems. To resolve the issue of insufficient projections, an iterative reconstruction algorithm combined with regularization techniques was employed for accurate image reconstruction. Additionally, the BM3D denoising algorithm was applied to enhance image quality. The results of simulation experiments demonstrate that the system effectively improves the PSNR and SSIM of reconstructed images under both Gaussian and speckle noise conditions, significantly enhancing the accuracy of cloud distribution measurements. © 2025 SPIE.

关键词： Image reconstruction

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Structure Enhancement Network Intrusion Detection Based on Graph Neural Network 19th

Structure Enhancement Network Intrusion Detection Based on G...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Li, Qi Sun, LiJun Tang, Bin Lu, Hao Du, JunWei Yu, Xu Qingdao University of Science and Technology Qingdao China Qingdao Innovation and Development Center of Harbin Engineering University Qingdao China Qingdao Institute of Software China University of Petroleum Qingdao China

ISBN: (纸本)9789819623754

With the rapid development of the Industrial Internet of Things (IIoT), industrial networks are encountering increasingly frequent and complex network attacks, leading to significant economic losses to production environments. Traditional intrusion detection methods have shown significant limitations in dealing with new types of attacks or their variants. Although deep learning technology has brought new possibilities to network intrusion detection, it still cannot effectively capture structural information and potential patterns in network data (such as the camouflage of attack flow). To address these challenges effectively, this paper presents a novel approach for network intrusion detection by leveraging Graph Enhanced Graph Neural Networks (GEGNN). After thoroughly studying the complex network topology and interaction capabilities between individual IP addresses, we propose a concise and efficient graph structure model. Meanwhile, we design a structure enhancement model, which, through research on both local enhancement and global processing, enables the model to better cope with the instability in network data and improve its ability to recognize complex structures. We comprehensively evaluate the proposed Network Intrusion Detection System (NIDS) model on two latest intrusion detection datasets. Experimental results demonstrate that our model outperforms existing methods in key metrics such as detection accuracy and F1 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Identification of Critical Test Scenario Groups for Aircraft Functions Based on Operational Design Domains

Identification of Critical Test Scenario Groups for Aircraft...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Krause, S. Reglitz, S. Hillig, D. Thielecke, F. Franke, M. Automated Driving & Software Solutions dSPACE GmbH NRW Paderborn33102 Germany Real-Time Test & Development Solutions dSPACE GmbH NRW Paderborn33102 Germany Institute of Aircraft Systems Engineering Hamburg University of Technology Hamburg21129 Germany Division of ICT Applications for Productio Bremer Institut für Produktion und Logistik GmbH Bremen28359 Germany

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The development of aircraft systems requires robust verification and validation processes to ensure safety, reliability, and functionality. The rapid advancements in system automation have introduced a level of complexity where traditional testing methods reach their limits. Such methods often encounter challenges in identifying critical test scenarios early in the development cycle, which can cause delays. This delay can lead to significant cost implications due to late error detection and subsequent redevelopments. The primary objective of this research is to develop a method that enhances the early identification of critical test scenarios, thereby improving the efficiency and effectiveness of the testing process by using the concept of operational design domains (ODD) as a framework. By employing a hierarchical ODD approach, this study creates smaller, priority test groups that can be continuously used and shared among stakeholders throughout the testing process. As a further step, a stochastic test exploration method is used to generate a variety of scenario-based test cases, including critical test cases. These test cases, written in TA-SCXML, are analyzed using ODD-based observers within a virtual integration environment. To identify test cases to be shared between stakeholders, a grouping concept is proposed. The groups contain critical test cases that verify the behavior as described by the ODD, as well as test cases that explore behavior outside of the ODD. The method is applied on a flight control use case involving a virtually integrated software prototype for investigating new flight automation concepts for an UAV. The proposed method successfully identifies critical scenario-based test cases, which are then grouped and provided to suppliers and other stakeholders. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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