Applied Intelligence最新文献_第10页

MKDFusion: modality knowledge decoupled for infrared and visible image fusion MKDFusion：用于红外和可见光图像融合的解耦模态知识

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-14 DOI: 10.1007/s10489-025-06470-w

Yucheng Zhang, You Ma, Lin Chai

{"title":"MKDFusion: modality knowledge decoupled for infrared and visible image fusion","authors":"Yucheng Zhang, You Ma, Lin Chai","doi":"10.1007/s10489-025-06470-w","DOIUrl":"10.1007/s10489-025-06470-w","url":null,"abstract":"<div><p>The purpose of infrared and visible fusion is to integrate useful information from both infrared and visible images into a single image. The fused image should possess rich texture details and salient target information of the two images. Current image fusion algorithms primarily face two limitations: 1) The lack of decoupling between modality-agnostic and modality-specific knowledge during the feature extraction stage hinders the alignment of modality-agnostic knowledge and the differentiation of modality-specific knowledge. 2) The interaction between modality features is not sufficiently explored in the feature fusion stage, which inhibits the exploitation of complementary information. To address the above challenges, we propose a Modality Knowledge Decoupled (MKD) module in the feature extraction stage and a Cross-Modality Mamba Fusion (CMF) module in the feature fusion stage. In MKD, we first utilize a dual-branch network to extract modality-agnostic and modality-specific knowledge separately. Then, a pair of Knowledge Discriminators (KD) is constructed to minimize inter-modality irrelevant knowledge and maximize inter-modality relevant knowledge. In CMF, the interactions between different modality knowledge are learnt in a hidden state space, which not only reduces the inter-modality knowledge differences but also enhances the texture information of the image. Experiments on three datasets demonstrate that our method outperforms existing methods, highlighting less salient targets and texture information more effectively. In addition, MKDFusion has demonstrated excellent generalization performance and enormous potential in high-level vision tasks in medical image fusion and object detection applications. The code is available at https://github.com/SEU-ZYC/MKDFusion.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-shot segmentation combined with domain adaptation: a flexible paradigm for parsing astronaut work environments 结合领域适应的少镜头分割：一种分析宇航员工作环境的灵活范例

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-14 DOI: 10.1007/s10489-025-06508-z

Qingwei Sun, Jiangang Chao, Wanhong Lin, Wei Chen, Zhenying Xu, Jin Yang

{"title":"Few-shot segmentation combined with domain adaptation: a flexible paradigm for parsing astronaut work environments","authors":"Qingwei Sun, Jiangang Chao, Wanhong Lin, Wei Chen, Zhenying Xu, Jin Yang","doi":"10.1007/s10489-025-06508-z","DOIUrl":"10.1007/s10489-025-06508-z","url":null,"abstract":"<div><p>The capacity to perform few-shot segmentation of the astronaut work environment (AWE) is of critical importance, especially for tasks that cannot be predetermined. The challenging task of transferring FSS models, which are trained on natural datasets, to the AWE—referred to as cross-domain few-shot segmentation (CD-FSS)—holds substantial importance. Rather than devising an entirely novel model, we propose an approach that integrate domain adaptation (DA) with extant FSS models, herein termed meta learners. Specifically, a prior learner based on generative adversarial networks (GAN) is devised to impart semantic guidance to the meta learner. To discern challenging samples, a loss function incorporating a scaling factor is employed during the training stage of the prior learner. Furthermore, a metric-based fusion module is proposed to mitigate bias in accordance with the association between the prior learner and the meta learner. The results evince that our method can be seamlessly integrated with different types of existing FSS models, thereby enhancing their cross-domain performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MGMP: Multi-granularity semantic relation learning and meta-path structure interaction learning for fake news detection MGMP：基于多粒度语义关系学习和元路径结构交互学习的假新闻检测

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-14 DOI: 10.1007/s10489-025-06560-9

Baozhen Lee, Dandan Cao, Tingting Zhang

引用次数: 0

Intelligent fault diagnosis method based on data generation and long-patch vision transformer under small samples 小样本下基于数据生成和长片视觉变压器的智能故障诊断方法

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-11 DOI: 10.1007/s10489-025-06535-w

Jian Cen, Weiwei Si, Xi Liu, Bichuang Zhao, Hankun Huang, Junfu Liu

{"title":"Intelligent fault diagnosis method based on data generation and long-patch vision transformer under small samples","authors":"Jian Cen, Weiwei Si, Xi Liu, Bichuang Zhao, Hankun Huang, Junfu Liu","doi":"10.1007/s10489-025-06535-w","DOIUrl":"10.1007/s10489-025-06535-w","url":null,"abstract":"<div><p>Rotating machinery is an important part of modern industry, and bearings are one of the most important things. However, bearing fault data are difficult to collect, and bearing fault diagnosis under small samples has significant research potential. In this paper, we proposed a fault diagnosis framework that combines diffusion modeling and improved Vision Transformer. First, the short-time Fourier transform is applied to the original one-dimensional vibration signals to convert the data into time-frequency maps. Second, the conditional diffusion model was applied to generate the required samples and expand the dataset. Finally, the Long-patch Vision Transformer (LVT) proposed in this paper is used to classify the mixed samples. LVT designs a long-patch division method for time-frequency maps with dense transverse features. The LVT contains denser features in each patch, and this method is more suitable for time-frequency maps. Validating the method proposed in this paper on two datasets and comparing it with other methods, our method achieved the highest accuracy among the compared methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TRSD: tensor spatial reconstruction and spectral metric decision fusion for hyperspectral anomaly detection with noise 含噪声高光谱异常检测的张量空间重构与光谱度量决策融合

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-10 DOI: 10.1007/s10489-025-06504-3

Zhenhua Mu, Yihan Wang, Xianghai Wang

{"title":"TRSD: tensor spatial reconstruction and spectral metric decision fusion for hyperspectral anomaly detection with noise","authors":"Zhenhua Mu, Yihan Wang, Xianghai Wang","doi":"10.1007/s10489-025-06504-3","DOIUrl":"10.1007/s10489-025-06504-3","url":null,"abstract":"<div><p>The unique and detailed spectral information in hyperspectral images (HSI) provides an advantage for distinguishing different targets in anomaly detection (AD). However, most traditional HSI-AD methods primarily focus on the inherent spectral structure information, often overlooking the strong spatial-spectral synergy present in HSI. An increase in spectral resolution typically leads to a decrease in the number of photons received per channel, which increases the likelihood of correlated noise during image formation. To address these issues and significantly improve detection performance, a method called Tensor Space Reconstruction and Spectral Local Correlation Metric Decision Fusion (TRSD) is proposed for HSI-AD in the presence of noise. First, three-dimensional principal component (PC) extraction, based on information entropy, is performed to obtain a denoised purified image for reconstruction. The initial feature detection image is generated by calculating the purified image using the local Mahalanobis distance. To compensate for the loss of spectral information caused by PC analysis in the spectral dimension during Tucker reconstruction, the feature map is extracted using the local spectral correlation metric. Finally, the two detection feature images are adaptively fused to generate the final AD image, which highlights anomaly targets and improves detection accuracy.The proposed algorithm is experimentally validated through comparisons with current typical AD algorithms, using real HSIs captured in four different complex noise-added scenarios. The effectiveness of the algorithm is demonstrated through experiments. The source code for TRSD will be made publicly available at https://github.com/muzhenhuam/TRSD.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diagnosis test selection for distributed systems under communication and privacy constraints 通信和隐私约束下分布式系统的诊断测试选择

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-10 DOI: 10.1007/s10489-025-06543-w

Anna Sztyber-Betley, Elodie Chanthery, Louise Travé-Massuyès, Gustavo Pérez-Zuñiga

{"title":"Diagnosis test selection for distributed systems under communication and privacy constraints","authors":"Anna Sztyber-Betley, Elodie Chanthery, Louise Travé-Massuyès, Gustavo Pérez-Zuñiga","doi":"10.1007/s10489-025-06543-w","DOIUrl":"10.1007/s10489-025-06543-w","url":null,"abstract":"<div><p>Distribution is often necessary for large-scale systems because it makes monitoring and diagnosis more manageable from both computational and communication costs perspectives. Decomposing the system into subsystems may also be required to satisfy geographic, functional, or privacy constraints. The selection of diagnosis tests guaranteeing some level of diagnosability must adhere to this decomposition by remaining as local as possible in terms of the required sensor variables. This helps minimize communication costs. In practical terms, this means that the number of interconnections between subsystems should be minimized while keeping diagnosability, i.e., fault isolation capability, at its maximum. This paper differentiates itself from existing literature by leveraging flexibility in forming the subsystems. Through structural analysis and graph partitioning, we address the combined challenges of constrained decomposition of a large-scale system into subsystems and the selection of diagnosis tests that achieve maximal diagnosability with minimal subsystem interconnection. The proposed solution is implemented through an iterative algorithm, which is proven to converge. Its efficiency is demonstrated using a case study in the domain of water networks.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Power-GNN: a graph over-sampling method to mitigate power-law distribution in graph neural networks Power-GNN：一种缓解图神经网络中幂律分布的图过采样方法

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-10 DOI: 10.1007/s10489-025-06421-5

Peidong Li, Zhenghong Zhong, Yangguang Zhao, Changheng Shao, Yi Sui, Rencheng Sun

{"title":"Power-GNN: a graph over-sampling method to mitigate power-law distribution in graph neural networks","authors":"Peidong Li, Zhenghong Zhong, Yangguang Zhao, Changheng Shao, Yi Sui, Rencheng Sun","doi":"10.1007/s10489-025-06421-5","DOIUrl":"10.1007/s10489-025-06421-5","url":null,"abstract":"<div><p>Since the advent of Graph Neural Networks (GNNs), they have been widely applied in the analysis and processing of graph data, especially demonstrating outstanding performance in semi-supervised node classification tasks. However, the class distribution in real-world graph data often exhibits a long-tail, imbalanced distribution, posing significant challenges to the classification performance of GNNs. Graph over-sampling methods address this by synthesizing new nodes for minority classes and creating corresponding edges, thus aiming to balance class representation and enhance model accuracy. Nonetheless, the degree distribution of nodes in reality also follows a power-law distribution, leading to synthesized nodes becoming low-degree tail nodes under existing edge construction strategies. This restricts their ability to acquire sufficient aggregation information, thereby degrading their representation quality and impacting classification outcomes. To address these challenges, this paper introduces Power-GNN, a novel graph data over-sampling framework tailored to tackle the dual challenges of imbalanced class distribution and the power-law distribution of node degrees. Power-GNN innovatively utilizes the power-law distribution of node degrees in a reverse manner. It strategically adds edges with high similarity to nodes with fewer connections, thereby amplifying the aggregation capability of synthesized nodes and boosting overall model performance. Through evaluations on multiple public benchmark datasets, Power-GNN has demonstrated superior performance over existing baselines across three common GNN architectures.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An innovative deep learning-driven technique for restoration of lost high-density surface electromyography signals 一种创新的深度学习驱动技术，用于恢复丢失的高密度表面肌电信号

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-09 DOI: 10.1007/s10489-025-06471-9

Juzheng Mao, Honghan Li, Yongkun Zhao

{"title":"An innovative deep learning-driven technique for restoration of lost high-density surface electromyography signals","authors":"Juzheng Mao, Honghan Li, Yongkun Zhao","doi":"10.1007/s10489-025-06471-9","DOIUrl":"10.1007/s10489-025-06471-9","url":null,"abstract":"<div><p>High-density surface electromyography (HD-sEMG) plays a crucial role in medical diagnostics, prosthetic control, and human-machine interactions. Compared to traditional bipolar sEMG, HD-sEMG employs smaller electrode spacing and sizes. This configuration not only reduces the signal collection area but also increases sensitivity to individual variations in skin impedance. Additionally, smaller high-density electrodes are more susceptible to environmental electromagnetic interference, thereby increasing the risk of signal loss and limiting the further development and application of HD-sEMG technology. To address this issue, this study introduces a novel deep learning-based technique specifically designed to restore lost HD-sEMG signals. Through an improved novel convolutional neural network (CNN), our method can reconstruct HD-sEMG signals both efficiently and accurately. Experimental results demonstrate that the proposed CNN algorithm effectively reconstructs lost HD-sEMG signals with high fidelity. The average root mean square error (RMSE) across all participants was 0.108, the mean absolute error (MAE) was 0.070, and the coefficient of determination (<span>(R^2)</span>) was 0.98. Furthermore, the model achieved an average structural similarity index measure (SSIM) of 0.96 and a peak signal-to-noise ratio (PSNR) of 29.13 dB, indicating high levels of structural similarity and signal clarity in the reconstructed data. These findings highlight the robustness and effectiveness of our method, suggesting its potential for enhancing the reliability and utility of HD-sEMG signals in real applications.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06471-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward imperceptible and robust image watermarking against screen-shooting with dense blocks and CBAM 基于密集块和CBAM的防截屏图像水印研究

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-09 DOI: 10.1007/s10489-025-06496-0

Jiamin Wang, Xiaobing Kang, Wei Li, Jing Geng, Yalin Miao, Yajun Chen

{"title":"Toward imperceptible and robust image watermarking against screen-shooting with dense blocks and CBAM","authors":"Jiamin Wang, Xiaobing Kang, Wei Li, Jing Geng, Yalin Miao, Yajun Chen","doi":"10.1007/s10489-025-06496-0","DOIUrl":"10.1007/s10489-025-06496-0","url":null,"abstract":"<div><p>In cross-media information communication, it is essential to embed watermarks imperceptibly while also robustly resisting screen- shooting attacks. However, existing robust watermarking methods often struggle to achieve both objectives simultaneously. Therefore, this paper proposes a novel end-to-end screen-shooting resistant image watermarking method based on dense blocks and the convolutional block attention module (CBAM) attention mechanism. In the watermark embedding phase, an encoder that integrates dense connections and CBAM is employed. This approach effectively extracts features from the cover image, enhancing the visual quality of watermarked images while ensuring a certain level of robustness. The noise layer simulated by differentiable function not only contains moiré patterns, illumination, and perspective distortions—factors that significantly impact the screen-shooting process—but also encompasses Gaussian noise, which is commonly present. During the watermark extraction phase, a gradient mask is utilized to guide the encoder in generating watermarked images that facilitate more effective decoding, thereby enabling accurate extraction of the watermark. Ultimately, the robustness is improved by the encoder, the introduced noise layer, and the decoder through joint training. Experimental results demonstrate that the proposed method not only achieves excellent visual quality, with a PSNR value of 36.04 dB for the watermarked images, but also maintains a watermark extraction rate exceeding 95% under various shooting conditions (including different distances, angles, and devices). Notably, the extraction rate reaches 100% at shooting distances of 20 cm and 30 cm, showcasing strong robustness.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PSCNet: Long sequence time-series forecasting for photovoltaic power via period selection and cross-variable attention 基于周期选择和交叉变量关注的长序列时间序列光伏发电预测

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-04-09 DOI: 10.1007/s10489-025-06526-x

Hao Tan, Jinghui Qin, Zizheng Li, Weiyan Wu

{"title":"PSCNet: Long sequence time-series forecasting for photovoltaic power via period selection and cross-variable attention","authors":"Hao Tan, Jinghui Qin, Zizheng Li, Weiyan Wu","doi":"10.1007/s10489-025-06526-x","DOIUrl":"10.1007/s10489-025-06526-x","url":null,"abstract":"<div><p>With the continuous expansion of photovoltaic installation capacity, accurate prediction of photovoltaic power generation is crucial for balancing electricity supply and demand, optimizing energy storage systems, and improving energy efficiency. With the help of deep learning technologies, the stability and reliability of the photovoltaic power generation prediction have been significantly improved. However, existing methods primarily focus on temporal dependencies and often fall short in capturing the multivariate correlations between variables. In this paper, we propose a novel long-sequence time-series forecasting network for photovoltaic power via period selection and Cross-variable attention, named PSCNet. Specifically, we first propose the Top-K periodicity selection module (TPSM) to identify the Top-K principal periods for decoupling overlapped multi-periodic patterns, enabling the model to attend to periodic changes across different scales simultaneously. Then, we design a time-variate cascade perceptron to capture both temporal change patterns and variate change patterns in the time series. It contains two elaborate modules named Time-mixing MLP (TM-MLP) and Cross-variable Attention Module (CvAM). The former module aims to capture long-short term variations in time series while the latter one integrates the effective information from different auxiliary variates that have an impact on photovoltaic power forecasting to enhance the feature representation for better power prediction. Extensive experiments on the DKASC, Alice Springs dataset demonstrate that our model can outperform existing state-of-the-art photovoltaic power forecasting methods in terms of three common-used metrics including Mean Average Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE).</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0