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微内核在安全性、稳定性和模块化方面相比于宏内核有着极大的优势.然而以seL4为代表的现代微内核在设计上有3点缺陷：1)在支持同步进程间通信(IPC)的情况下冗余地支持了异步通知,这违背了微内核的最小化原则;2)通知机制依赖内核的转发;3)系统调用和同步IPC需要频繁地进出内核,后… … 相似文献
廖东海;陆慧梅;陈伟豪;赵方亮;向勇 《小型微型计算机系统》2026,(3)
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针对社交媒体环境中舆情传播的动态过程,探讨了信息传播与消亡过程中存在的时间滞后对官方-民间双层耦合网络舆情演化的影响。首次建立官方层（OLN）与民间层（CLN）耦合的双层网络传播动力学框架,并引入时滞参数τ刻画权威信息发布与公众响应的延迟过程,通过稳定性理论及Hopf分岔分析,研究系统平衡点的存在性与稳定性。时滞τ存在一个临界值τc。当τ<τc时,系统渐近稳定,舆情逐渐消亡;当τ>τc时,系统失稳并发生Hopf分岔,舆情将呈现周期性波动。OLN的阻止率和推进率均能降低传播阈值R0,其中阻止率对控制舆情传播作用更为显著。时滞是影响双层网络舆情稳定性的关键因素,官方迅速捕捉舆情动态并及时干预能够降低舆情传播风险。… … 相似文献
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ObjectiveBenefiting from their high-resolution spectral information and large-scale spatial information, hyperspectral images （HSIs） have demonstrated exceptional capabilities in numerous remote sensing applications. Over the past few decades, hyperspectral image classification （HIC） has attracted considerable research attention. Many machine learning-based HIC methods have been proposed; however, these approaches require a sufficient number of labeled samples to achieve ideal classification accuracy. Unfortunately, the high cost and effort associated with labeling HSIs often results in a scarcity of labeled data for many newly acquired HSIs. Therefore, researchers have introduced the cross-domain HIC （CD-HIC） mechanism, which uses a hyperspectral image with sufficient labeled samples （source domain） to assist in classifying a hyperspectral image with limited labeled samples （target domain）. However, cross-domain classification is one of the major challenges in HIC due to feature and class distribution differences between source and target domains. The cross-domain few shot learning （CDFSL） methods, which integrated domain adaptation with few-shot learning, have been widely applied to the CD-HIC problem. Owing to the difficulty of spectral sequence encoding and the spectral similarity between classes, most existing CDFSL methods use convolutional neural network （CNN） or other remarkable spatial feature extractors to obtain spatial information, thereby improving classification accuracy. However, extracting spatial features often leads to distortion in the distribution of ground objects and their class boundaries. Aiming to address this issue, a lightweight Res-3D-CNN with embedded Transformer layers （LRCT） has been designed for feature extraction in CD-HIC. LRCT effectively captures long-term dependencies of the spectrum while simultaneously extracting spatial information, thereby notably improving the performance of spectral feature-based methods.MethodIn this study, a simple and effective deep learning network is proposed for feature extraction from HSIs. In CNNs, the convolution （Conv） captures high-frequency features of images by employing a weight-sharing mechanism within local receptive fields. In contrast, Transformers model long-range dependencies between features using self-attention mechanisms and adaptively focus on key areas. Moreover, the Transformer exhibits low-pass filtering characteristics, which primarily captures the low-frequency global information of images. Considering the complementary characteristics of Conv and Transformer, the Transformer layer is embedded into Res-3D-CNN to establish a lightweight dual-stream feature extraction network to perform feature extraction on the source and target domains. Furthermore, the CDFSL method is adopted to learn general information from the extracted features of source class data, which helps in target class data prediction with only very few or no labeled data. This approach helps achieve outstanding classification performance in the subsequent CD-HIC. The LRCT-CDFSL method comprises four main aspects as follows： 1） Data preprocessing： using a mapping layer to combine the dimensions of the original his; 2） Feature learning based on lRCT： employing a deep neural network for feature learning to enhance the representation capability for HSI data; 3） Few-shot learning： enhancing intra-class compactness and inter-class separability by calculating the Euclidean distance between labeled and unlabeled samples, thereby effectively adapting to scenes with limited samples; 4） Domain adaptation： using domain adaptation techniques to enable the feature extractor to generate highly generalized features, thereby improving the generalization capability of the model across different domains.ResultUsing Chikusei data as the source domain, and Indian Pines, Salinas, and Pavia University data as the target domains, extensive experiments are conducted to validate the performance of the proposed LRCT-CDFSL model and ensure fair comparison with advanced methods. Moreover, the effectiveness of the proposed LRCT-CDFSL is respectively tested by using the Indian Pines, Salinas, and Pavia University datasets as the target domain. The experimental results demonstrate that the LRCT-CDFSL method achieves faster and more accurate classification performance compared to existing methods. When only five labeled samples per class are available, the LRCT-CDFSL method achieves overall accuracy （OA） scores of 71.01% and 92.06%, and 84.14% on the respective target domain datasets. Compared to current mainstream cross-domain few-shot HIC methods, the LRCT-CDFSL method shows superior classification performance across various target domain datasets. Specifically, LRCT-CDFSL improves OA by 7.57%, 3.35%, and 2.77%, respectively, and reduces training time by 36%, 37%, and 30%, respectively.ConclusionA deep transfer learning network called LRCT network is introduced by embedding Transformer layer into a residual three-dimensional CNN （Res-3D-CNN）. In Res-3D-CNN, a 1 × 1 convolution kernel is incorporated to adjust the number of channels in the feature map, reduce the model parameters, and accelerate the training. In addition, after ![]()
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《中国图象图形学报》2026,31(3):927-943
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本系统由:北京勤云科技发展有限公司提供技术支持
