检索结果-内蒙古大学图书馆

DF-CDM: conditional diffusion model with data fusion for structural dynamic response reconstruction

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 222卷

作者： Shu, Jiangpeng Yu, Hongchuan Liu, Gaoyang Duan, Yuanfeng Hu, Hao Zhang, He Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Ctr Balance Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta Jiaxing 314100 Peoples R China Shaoxing Univ Sch Civil Engn Shaoxing 312000 Peoples R China Shaoxing Univ Inst Artificial Intelligence Shaoxing 312000 Peoples R China Zhejiang Sci Res Inst Transport Hangzhou 310013 Peoples R China

In structural health monitoring (SHM) systems, data loss inevitably occurs and reduces the applicability of SHM techniques, such as condition assessment and damage identification. The current mainstream data-driven method, generative adversarial networks (GAN), suffers from convergence difficulty, limiting the accuracy and efficiency of response reconstruction. In this study, a conditional diffusion model with data fusion (DF-CDM) is proposed for structural dynamic response reconstruction. The original unsupervised diffusion model is improved by introducing the conditional input and modifying the deep denoising neural network to achieve supervised learning. Besides, data fusion is developed to further utilize the frequency-domain information and improve the reconstruction quality of the time-domain signals. The proposed model is validated on a three-span continuous bridge. Results show that the diffusion model with data fusion achieves the highest accuracy on the test set with R2, RMSE and MAE equaling 0.821, 0.0053 and 0.0042 respectively. Compared with the state-of-the-art GAN model, the diffusion model without the adversarial modules has a more stable training process and better reconstruction performance in both time and frequency domains. The modal identification and robustness analysis further verify the effectiveness of the proposed model. The proposed diffusion model with data fusion achieves high-quality structural response reconstruction, guaranteeing the applicability and reliability of subsequent response-based structural analysis.

关键词： Structural response reconstruction conditional diffusion model Deep learning Data fusion Modal analysis Structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

Enhancement of Image Reconstruction in Orthogonal Frequency-Division Multiplexing (OFDM)-Based Communication System Using conditional diffusion model of Generative AI

引用

APPLIED SCIENCES-BASEL 2025年第6期15卷

作者： Kim, Soohyun Kim, Jinwook Sun, Youngghyu Seon, Joonho Lee, Seongwoo Hwang, Byungsun Kim, Jeongho Kim, Kyounghun Kim, Jinyoung Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea SMARTEVER Co Ltd Res & Dev Dept Seoul 01886 South Korea

The orthogonal frequency-division multiplexing (OFDM) transmission technique is well known to be efficient for data transmission but is susceptible to performance degradation due to factors such as high-order modulation schemes, multipath fading, and noise. In this paper, an approach for reconstructing images received by the OFDM transmission technique is proposed based on generative AI. The approach exploits a conditional diffusion model (CDM) that incorporates conditional factors reflecting the degree of distortion in the received images by the OFDM technique. Additionally, it employs a method to learn the variance in the reverse process during training, considering the level of distortion associated with various modulation schemes. Through this adaptability, the proposed model is experimentally demonstrated to optimize image reconstruction performance under various modulation schemes in low-SNR environments. The proposed conditional diffusion model can enhance the PSNR of OFDM-based received images by up to 8 dB in low-SNR conditions with various modulation schemes.

关键词： orthogonal frequency-division multiplexing (OFDM) generative AI conditional diffusion model OFDM-based image reconstruction

来源：评论

学校读者我要写书评

暂无评论

A New Hyperspectral Reconstruction Method With conditional diffusion model for Snapshot Spectral Compressive Imaging

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Si, Yifan Lin, Zijian Wang, Xiaodong He, Sailing Zhejiang Univ Natl Engn Res Ctr Opt Instruments Ctr Opt & Electromagnet Res Zhejiang Prov Key Lab Sensing Technol Hangzhou 310058 Peoples R China Zhejiang Univ Shanghai Inst Adv Study Shanghai 201203 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China KTH Royal Inst Technol Sch Elect Engn Dept Electromagnet Engn S-10044 Stockholm Sweden

In the coded aperture snapshot spectral imaging (CASSI) system, the coded and compressed single-channel measurements need to be reconstructed into hyperspectral cubes. Existing discriminative models reconstruct the spectral cube by optimizing the mean squared error (MSE) between the ground truth and the predicted image, employing peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as metrics to gauge the quality of reconstruction. However, these indicators often possess significant limitations in mimicking human visual perception and in discerning the impact of image distortions on perceived visual quality. In this article, a new model named CASSIDiff is proposed to reconstruct CASSI measurements, achieving advanced results in perceptual loss-based evaluation metrics such as learned perceptual image patch similarity (LPIPS) and Fr & eacute;chet inception distance (FID). The diffusion model, which enjoys high accuracy and reliability in generative tasks, is used for the first time for the hyperspectral reconstruction task. A feature fusion mechanism based on discrete wavelet transform (DWT) is used to weaken the noise interference effect in the conditional diffusion model. Considering the interspectra similarity and long-range dependencies of hyperspectral data, the spatial-spectral attention mechanism is also introduced. Experiments show that CASSIDiff not only outperforms most existing algorithms in simulation datasets but also shows robustness to real data published and collected in our home-built CASSI system. The code and models are publicly available at: https://***/YifanSi/CASSIDiff.

关键词： Hyperspectral imaging Image reconstruction diffusion models Noise Imaging Feature extraction Attention mechanisms Optical imaging Visualization Visual perception Coded aperture snapshot spectral imaging (CASSI) conditional diffusion model Fr & eacute chet inception distance (FID) hyperspectral reconstruction learned perceptual image patch similarity (LPIPS)

来源：评论

学校读者我要写书评

暂无评论

EDG-CDM: A New Encoder-Guided conditional diffusion model-Based Image Synthesis Method for Limited Data

引用

IET COMPUTER VISION 2025年第1期19卷

作者： Lei, Haopeng Yin, Hao Liang, Kaijun Wang, Mingwen Zeng, Jinshan Luo, Guoliang Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Peoples R China Yuquan Sch Huangpu Dist Guangzhou Peoples R China East China Jiaotong Univ Virtual Real & Interact Tech Inst Nanchang Peoples R China

The diffusion Probabilistic model (DM) has emerged as a powerful generative model in the field of image synthesis, capable of producing high-quality and realistic images. However, training DM requires a large and diverse dataset, which can be challenging to obtain. This limitation weakens the model's generalisation and robustness when training data is limited. To address this issue, EDG-CDM, an innovative encoder-guided conditional diffusion model was proposed for image synthesis with limited data. Firstly, the authors pre-train the encoder by introducing noise to capture the distribution of image features and generate the condition vector through contrastive learning and KL divergence. Next, the encoder undergoes further training with classification to integrate image class information, providing more favourable and versatile conditions for the diffusion model. Subsequently, the encoder is connected to the diffusion model, which is trained using all available data with encoder-provided conditions. Finally, the authors evaluate EDG-CDM on various public datasets with limited data, conducting extensive experiments and comparing our results with state-of-the-art methods using metrics such as Fr & eacute;chet Inception Distance and Inception Score. Our experiments demonstrate that EDG-CDM outperforms existing models by consistently achieving the lowest FID scores and the highest IS scores, highlighting its effectiveness in generating high-quality and diverse images with limited training data. These results underscore the significance of EDG-CDM in advancing image synthesis techniques under data-constrained scenarios.

关键词： conditional diffusion model encoder guidance image synthesis limited data

来源：评论

学校读者我要写书评

暂无评论

MCDiff: A Multilevel conditional diffusion model for PolSAR Image Classification

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6721-6737页

作者： Zhang, Qingyi Fang, Xiaoxiao Liu, Tao Wu, Ronghua Liu, Liguo He, Chu Naval Univ Engn Sch Elect Engn Wuhan 430033 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430079 Peoples R China

With the swift advancement of deep learning, significant strides have been made in polarimetric synthetic aperture radar (PolSAR) image classification, particularly with the advent of diffusion models that allow for explicit class probability modeling. However, existing diffusion models have yet to fully leverage the rich polarimetric characteristics of PolSAR images. To address this, we propose the multilevel conditional diffusion (MCDiff) model for PolSAR image classification, incorporating three key strategies. First, a prior learning module is constructed to capture scattering characteristics across all three polarization basis parameter spaces, providing conditional guidance for the diffusion model. Second, a multiscale and multidimensional noise prediction module is designed to reduce the information loss when noisy labels and image features of different dimensions are fused to predict noise. Finally, a multilevel high-order statistical feature learning module is introduced to aid in the additive Gaussian noise prediction of noisy labels while mitigating the impact of PolSAR images' multiplicative speckle noise on the prediction. Experimental results on three benchmark datasets confirm MCDiff's ability to achieve high-performance explicit class probability modeling for PolSAR images among the compared methods.

关键词： diffusion models Noise Feature extraction Radar Solid modeling Radar imaging Radar polarimetry Radar scattering Electromagnetic scattering Noise measurement conditional diffusion model image classification polarimetric synthetic aperture radar (PolSAR) scattering feature statistical feature

来源：评论

学校读者我要写书评

暂无评论

MAFCD: Multi-level and adaptive conditional diffusion model for anomaly detection

引用

INFORMATION FUSION 2025年 118卷

作者： Wu, Zhichao Zhu, Li Yin, Zitao Xu, Xirong Zhu, Jianmin Wei, Xiaopeng Yang, Xin Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Social Comp & Cognit Intelligence Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Liao Ning Oxiranchem Inc Liaoyang 111003 Peoples R China

In the real-world Internet of Things (IoT) systems, a variety of Internet-connected sensory devices, spanning from chemical processing equipment to material handling machinery and server machines are typically monitored with multivariate time series. Anomaly detection in these systems is pivotal for identifying potentially dangerous or unsafe conditions and implementing timely preventive measures. However, the complex contextual dependencies and diversified patterns inherent multivariate time series, such as seasonal fluctuations and trends in industrial processes, present significant challenges for existing anomaly detection methods, which strike a balance between fidelity and diversity in multivariate time series analysis. To address these issues, a novel Multi-level and Adaptive conditional diffusion model, called MAFCD, is proposed for anomaly detection across various industrial devices. The architecture of MAFCD is built upon a conditional diffusion model framework, guaranteeing both high-fidelity and diversity in generated multiple time series samples through adaptive fusion strategy and multi-level feature information. In particular, the model offers real-time anomaly occurrences by dynamically adjusting fusion weights across multiple features. Moreover, to enhance model stability, anomaly recognition results undergo weighted aggregation using exponential and symbolic ensemble function through multi-step sampling. Empirical evaluation across four public datasets and its application in an ethylene oxide production process demonstrates the superior performance and practical utility of the proposed MAFCD, underscoring its robust generalization ability.

关键词： Multi-level feature Adaptive feature fusion conditional diffusion model Multi-step sampling ensemble

来源：评论

学校读者我要写书评

暂无评论

A conditional diffusion model With Fast Sampling Strategy for Remote Sensing Image Super-Resolution

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Meng, Fanen Chen, Yijun Jing, Haoyu Zhang, Laifu Yan, Yiming Ren, Yingchao Wu, Sensen Feng, Tian Liu, Renyi Du, Zhenhong Zhejiang Univ Sch Earth Sci Zhejiang Prov Key Lab GIS Hangzhou 310058 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Natl Engn Res Ctr Geomat Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Zhejiang Univ Sch Software Technol Ningbo Peoples R China

Conventional deep learning-based methods for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution (SR) outputs of these methods are yet to become sufficiently satisfactory in visual quality. Recent diffusion model-based generative deep learning models are capable to enhance the visual quality of output images, but this capability is limited due to their sampling efficiency. In this article, we propose FastDiffSR, an SRSISR method based on a conditional diffusion model. Specifically, we devise a novel sampling strategy to reduce the number of sampling steps required by the diffusion model while ensuring the sampling quality. Meanwhile, the residual image is adopted to reduce computational costs, demonstrating that integrating channel attention and spatial attention begets a further improvement in the visual quality of output images. Compared to the state-of-the-art (SOTA) convolutional neural network (CNN)-based, GAN-based, and Transformer-based SR methods, our FastDiffSR improves the learned perceptual image patch similarity (LPIPS) by 0.1-0.2 and achieves better visual results in some real-world scenes. Compared with existing diffusion-based SR methods, our FastDiffSR achieves significant improvements in pixel-level evaluation metric peak signal-noise ratio (PSNR) while having smaller model parameters and obtaining better SR results on Vaihingen data with faster inference time by 2.8-28 times, showing excellent generalization ability and time efficiency. Our code will be open source at https://***/Meng-333/FastDiffSR.

关键词： diffusion models Transformers Superresolution Remote sensing Visualization Computational modeling Training conditional diffusion model deep learning generative models remote sensing super-resolution (SR)

来源：评论

学校读者我要写书评

暂无评论

A conditional diffusion model for probabilistic estimation of traffic states at sensor-free locations

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 166卷

作者： Lei, Da Xu, Min Wang, Shuaian Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Hung Hom Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Hong Kong Peoples R China

Transportation administrators and urban planners rely on accurate network-wide traffic state estimation to make well-informed decisions. However, due to insufficient sensor coverage, traffic state estimation at sensor-free locations (TSES) poses significant challenges for downstream network-wide traffic analysis. This is because direct observations are not available at these sensor-free locations. Most existing traffic state estimation (TSE) research focuses on inferring several unknown time points based on observed historical data using deterministic models. In contrast, TSES is to infer the entire unknown traffic time series of a given sensor-free node, thereby presenting high predictive difficulty, as we could not learn any historical traffic patterns locally. In this study, we introduce a novel probabilistic model - the conditional diffusion framework with spatio-temporal estimator (CDSTE) - to tackle the TSES problem. When dealing with TSES, deterministic models can only produce point value estimates, which may substantially deviate from the actual traffic states of sensor-free locations. To mitigate this, the proposed CDSTE integrates the conditional diffusion framework with cutting-edge spatio-temporal networks to extract the underlying dependencies in traffic states between sensor-free and sensor-equipped nodes. This integration enables reliable probabilistic traffic state estimations for sensor-free locations, which can be used to quantify the variability of estimations in TSES to support flexible and robust decision-making processes for traffic management and control. Extensive numerical experiments on real-world datasets demonstrate the superior performance of CDSTE for TSES over five widely-used baseline models.

关键词： conditional diffusion model Probabilistic estimation Network-wide estimation Insufficient sensor coverage Spatio-temporal estimator

来源：评论

学校读者我要写书评

暂无评论

diffusionAAE: Enhancing hyperspectral image classification with conditional diffusion model and Adversarial Autoencoder

引用

ECOLOGICAL INFORMATICS 2025年 87卷

作者： Cao, Zeyu Li, Jinhui Xu, Xiangrui Hangzhou City Univ Sch Spatial Planning & Design Hangzhou 310015 Peoples R China Hangzhou City Univ Res Inst Spatial Planning & Design Hangzhou 310015 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

Hyperspectral image (HSI) classification is essential for ecological monitoring, but faces significant challenges due to high dimensionality, complex spectral-spatial relationships, and limited labeled data. This study introduces diffusionAAE, a novel framework that uniquely combines Adversarial Autoencoders (AAE) with conditional diffusion models to address these challenges. Unlike existing approaches, diffusionAAE incorporates spectral similarity constraints and class label guidance into the diffusion process, ensuring the generation of physically realistic synthetic samples. Our framework's key innovation lies in its two-stage architecture: first, an AAE extracts robust latent features capturing intricate spectral-spatial relationships;second, a conditional diffusion model refines these features through progressive denoising, enabling class-specific feature generation while maintaining physical constraints inherent to hyperspectral data. Comprehensive experiments on three benchmark datasets demonstrate diffusionAAE's superior performance: compared to state-of-theart methods, our approach achieves significant improvements with an overall accuracy (OA) of 96.77% on Indian Pines (3.21% higher than CNN-based methods), 99.56% on University of Pavia (1.24% improvement over Transformer-based approaches), and 99.62% on Salinas (0.98% better than the best competing method). Notably, diffusionAAE shows remarkable performance on minority classes, with an average 7.35% accuracy improvement across underrepresented classes in the Indian Pines dataset. The framework demonstrates particular strength in scenarios with limited training data, maintaining 95.3% accuracy even when using only 5% of available labeled samples. These results establish diffusionAAE as a significant advancement for ecological informatics applications, especially for biodiversity monitoring and land cover classification where labeled data scarcity and class imbalance are prevalent challenges.

关键词： Land cover classification Generative model Feature extraction Hyperspectral image classification conditional diffusion model Adversarial autoencoder

来源：评论

学校读者我要写书评

暂无评论

DiffCrime: A Multimodal conditional diffusion model for Crime Risk Map Inference 24

DiffCrime: A Multimodal Conditional Diffusion Model for Crim...

引用

30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Wang, Shuliang Pan, Xinyu Ruan, Sijie Han, Haoyu Wang, Ziyu Yuan, Hanning Zhu, Jiabao Li, Qi Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798400704901

Crime risk map plays a crucial role in urban planning and public security management. Traditionally, it is obtained solely from historical crime incidents or inferred from limited environmental factors, which are not sufficient to accurately model the occurrences of crimes over the geographical space well. Motivated by the impressive and realistic conditional generating power of diffusion models, in this paper, we propose a multimodal conditional diffusion method, namely, DiffCrime, to infer the crime risk map based on datasets in various domains, i.e., historical crime incidents, satellite imagery, and map imagery. It is equipped with a history-gated multimodal denoising network, i.e., HamNet, dedicated to the crime risk map inference. HamNet emphasizes the importance of historical crime data via a Gated-based History Fusion (GHF) module and adaptively controls multimodal conditions to be fused across different diffusion time steps via a Time step-Aware Modality Fusion (TAMF) module. Extensive experiments on two real-world datasets demonstrate the effectiveness of DiffCrime, which outperforms baselines by at least 43% and 31% in terms of RMSE, respectively.

关键词： crime risk map inference conditional diffusion model multimodal learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：