Table of Content

30 May 2026, Volume 44 Issue 3

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Advanced Communications

Compressed Sensing-Based Approach for Reducing Wear in AFM Fiber Probes

YE Shuai, SHANG Yana, CHEN Na, LIU Shupeng, LIU Yong

2026, 44(3):  345-357.  doi:10.3969/j.issn.0255-8297.2026.03.001

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To address the issue of tip wear in atomic force microscope(AFM) fiber probe scanning imaging, an undersampling scanning method based on compressed sensing(CS)was proposed, which reduced the number of contacts between the fiber probe and the sample and effectively prolonged the probe lifetime. Complete AFM images were reconstructed using the CS algorithm. A convolutional neural network(CNN) was introduced to optimize the quality of CS-reconstructed images and mitigate the degradation of image quality caused by undersampling. Fiber probe wear was therefore reduced while maintaining high-quality imaging. In the experiments, when the scanning frequency was 0.3 Hz and the number of scanning points was reduced from 200 × 200 to 100 × 100, the tip wear was reduced from 78 nm to 13 nm, and the imaging time was reduced to one quarter of the original imaging time. After optimization using the CNN, the AFM images had a peak signal-to-noise ratio(PSNR) of 30.12 dB and a structural similarity(SSIM) of 0.96. The results demonstrate that low-wear, fast, and high-quality AFM imaging with fiber probes can be achieved.

Intelligent Information Processing

Uncertainty Modeling-Driven LiDAR-Vision Adaptive Fusion for Dynamic Obstacle Detection

ZHU Lei, ZHONG Ruofei, YUAN Xinze, FAN Hongchao, SUN Zhenxing

2026, 44(3):  358-376.  doi:10.3969/j.issn.0255-8297.2026.03.002

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Accurate and reliable dynamic obstacle perception is a crucial prerequisite for the safe navigation of autonomous mapping UAVs in confined and spatially constrained environments. However, existing LiDAR-vision fusion methods often struggle to cope with significant variations in sensor reliability under degraded conditions, such as poor illumination, reflection interference, or motion blur. To address these challenges, this paper proposed an uncertainty-modeling-driven LiDAR-vision fusion framework for dynamic obstacle detection, which adaptively adjusted sensor contributions by explicitly modeling sensor observation uncertainty. Based on probabilistic models, the framework performed real-time uncertainty quantification for both LiDAR point clouds and RGB images and introduced an adaptive sensor reliability score(ASRS) mechanism to guide fusion decisions and subsequent object tracking. Experiments were conducted on a self-constructed multi-condition dataset, and the results show that the proposed method improves the F1-score by approximately 15%-20% compared with existing methods in challenging scenarios involving low illumination, glass reflections, and motion blur. Furthermore, it maintains real-time processing performance at approximately 25 Hz on embedded platforms, validating the method's robustness and engineering feasibility in complex degraded environments.

Method for Extracting Chinese-Burmese Parallel Sentence Pairs Based on Language Feature Enhancement

ZHAO Zixiao, WANG Hao, SHEN Tao, JIANG Shuting, ZHANG Siqi, LAI Hua, HUANG Yuxin, YU Zhengtao

2026, 44(3):  377-389.  doi:10.3969/j.issn.0255-8297.2026.03.003

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To address the scarcity of labeled resources and the limited representational capacity of models in extracting parallel sentence pairs in low-resource languages, this paper proposed a language-feature-enhanced method for Chinese-Burmese parallel sentence pair extraction. The method was optimized from three aspects: data augmentation, model architecture, and training mechanism. First, a Chinese-Burmese dual encoder based on a Siamese network was constructed to build a cross-lingual semantic representation space.Second, an information-content evaluation mechanism based on the L₂ norm of word vectors was introduced to replace high-information features and perform sample augmentation,thus alleviating the data sparsity problem under low-resource conditions. Finally, positive and negative samples were constructed and dynamically modeled through contrastive learning to optimize sample boundaries and achieve more accurate Chinese-Burmese semantic alignment. Experimental results show that the proposed method achieves an F1 score of 95.03% on the Chinese-Burmese parallel sentence pair extraction task, outperforming the baseline model. In addition, this paper constructs a high-quality general-domain ChineseBurmese dataset containing 5 × 10⁵ sentence pairs, providing data support for research on low-resource languages.

Combustion Chamber Ignition Prediction Algorithm Based on AttResVGG Model

LIAO Qing, CHEN Hongyou, LIU Chongyang, QU Lingfeng, DUAN Jiping, XIA Ping, TIAN Baodan, FAN Yong

2026, 44(3):  390-408.  doi:10.3969/j.issn.0255-8297.2026.03.004

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To address the challenges posed by complex operating conditions, limited training samples, data imbalance, and the inability of conventional deep learning models to meet the requirements of ignition prediction for aero-engine combustion chambers, a network model integrating residual connections and a self-attention mechanism, called the attention residual visual geometry group network(AttResVGG), was proposed. The model used a multi-head self-attention mechanism to capture dependencies among operating condition parameters and establish a mapping between these parameters and ignition status. To address insufficient data size and class imbalance, a physically constrained data augmentation strategy was designed to synthesize new operating condition samples while maintaining key physical parameter relationships, such as the fuel-air ratio and temperature-pressure ratio.In addition, an automated machine learning algorithm based on Bayesian optimization was designed to optimize model hyperparameters, further enhancing the model's predictive performance. To validate the effectiveness of this model, experiments on two datasets show that the accuracy of the AttResVGG model reaches 97.67% and 90.48%, and the Kappa coefficients reach 0.950 5 and 0.834 6, respectively, which are better than those of the compared models.

Analysis on the External Action Intensity and Spatial Network Relationship of Urban Green Competitiveness: A Case Study of Liaoning Province

GUO Lina, YANG Yueheng, ZHAO Yanxia

2026, 44(3):  409-421.  doi:10.3969/j.issn.0255-8297.2026.03.005

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With the continued expansion of cities and the rapid growth of urban agglomerations, problems such as unbalanced spatial development and uneven resource allocation have emerged. Optimizing urban spatial structure has become a key issue. This study used principal component analysis (PCA) to measure the quality level of urban green competitiveness in Liaoning Province from 2015 to 2020 and applied a modified gravity model to analyze external action intensity and spatial network relationships. The results are as follows. 1) The overall quality level of green competitiveness of urban agglomerations in Liaoning Province is constantly improving, but the pattern of imbalance between cities with strong and weak competitiveness has not been significantly improved. 2) The external action intensity of cities in Liaoning Province has improved to a certain extent, but differences remain among cities, especially in Shenyang and Benxi, which have values far higher than those of other cities. 3) The network structure of the overall interaction intensity among urban agglomerations in Liaoning Province is relatively dense, with frequent and dynamic interactions among cities.

A Low-Light Few-Shot Object Detection Method Based on Feature Optimization

JIANG Zetao, JIN Xin, LENG Lu, ZHU Wencai

2026, 44(3):  422-436.  doi:10.3969/j.issn.0255-8297.2026.03.006

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To address the scarcity of samples for low-light object detection in certain environments, this paper proposed a low-light few-shot object detection method based on feature optimization. The method designed a denoising Wasserstein autoencoder(DNWAE)module and an adaptive variational feature aggregation(AVFA) module to address the problem of weak image feature information under low-light conditions and enhance important features. To reduce object classification confusion caused by limited training samples in few-shot learning, the paper designed a category information guided detection head(CIGDH) module to improve detection accuracy. Experimental results show that, compared with the selected mainstream few-shot object detection algorithms, this method achieves an average improvement of 9.3%–19.2% in detection accuracy after being trained on low-light datasets. Moreover, after being trained on normal-light datasets, this method achieves an average improvement of 3.0% in detection accuracy compared with the current state-of-the-art algorithm. The proposed algorithm is meaningful and has good application value for few-shot object detection under low-light conditions.

Dual Attention-Incorporated Lightweight U-shaped Network for Lung Lesion Image Segmentation

HE Xiaochen, DING Derui, LI Ming, WANG Fei, WANG Bo

2026, 44(3):  437-451.  doi:10.3969/j.issn.0255-8297.2026.03.007

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To address the problems of low contrast, fuzzy texture details, and inadequate edge feature extraction in lung lesion images, this paper proposed a novel lightweight U-shaped network incorporating dual attention, termed DALU-Net. First, an attentionbased two-branch fusion module was designed. During encoding, the two branches focused on global and local information, respectively, to capture global localization information and lesion edge features. Then, a parallel texture enhancement module was introduced,and a statistical feature histogram was obtained using a quantization counting operator to enhance the texture features extracted by the shallow network and address the challenge of low contrast. Finally, a reverse-attention dual-interference refinement module was developed to enable the network to focus on and process mis-segmentation information during the decoding stage, thus simultaneously eliminating false-positive and false-negative features in the reconstructed image. The effectiveness of the network was verified on two lung lesion datasets: COVID-19 CT scan and MS COVID-19. Compared with existing networks,the proposed network achieves the best results on all five evaluation metrics, with a Dice score that is 1.42% higher than that of the second-best model, UNeXt, while also using fewer parameters.

Light Field Image Compression Based on Implicit Disparity Compensation

LU Yongjie, AN Ping, HUANG Xinpeng, YANG Chao

2026, 44(3):  452-464.  doi:10.3969/j.issn.0255-8297.2026.03.008

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Light field(LF) imaging captures both the positional and angular information of light rays, leading to a significant increase in LF data volume due to its high-dimensional characteristics. As a result, efficient LF compression techniques have become an important research focus in this field. In recent years, researchers have proposed various deep learningbased methods for LF compression. However, these methods often struggle to achieve endto-end joint optimization and require the explicit transmission of disparity or geometric information, which significantly increases the complexity of the coding scheme. To address this issue, this paper proposed a novel end-to-end LF compression model. The model utilized disparity relationships among LF views and used a deformable attention mechanism for disparity compensation, enabling effective LF compression by encoding and decoding disparity features and residuals. Experimental results show that the proposed method outperforms other LF compression methods in rate-distortion performance and achieves state-of-the-art performance in mid-to-high bitrate coding.

Artiflcial Intelligence Technology and Applications

Split-Chain Trading System Design for Multi-source Heterogeneous Data Assets

LI Shouwei, JIANG Yimin, ZHANG Jiazheng

2026, 44(3):  465-485.  doi:10.3969/j.issn.0255-8297.2026.03.009

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This paper proposed a split-chain trading system combining a consortium chain and a sidechain for multi-source heterogeneous data assets to address challenges in data asset trading. These challenges include fragmented enterprise and individual trading environments, high risks of privacy leakage, and insufficient system throughput. The transaction characteristics of multi-source heterogeneous data assets were analyzed, and a user-type-aware automatic split-chain routing mechanism was designed. The consortium chain ensures strong-consistency auditing for enterprise data, while the sidechain enables high-concurrency processing of personal data. Trusted computing technology was also introduced to construct a hardware-level privacy protection solution, allowing data to be used without being exposed or copied. The experimental results show that the split-chain architecture increases the peak system throughput to 2 400 TPS, representing an improvement of approximately 118% compared with traditional single-chain systems. Meanwhile, it maintains transaction latency below 2 s under high-concurrency conditions, with the added security overhead remaining at the millisecond level. This system effectively balances security and transaction efficiency while enabling integrated trading of heterogeneous data.It also integrates the trading of enterprise and personal data assets into a standardized transaction system while protecting data privacy, thus providing an engineering solution for data circulation in the digital economy.

Cross-View Group Recommendation Algorithm Driven by Hypergraph Neural Networks

GUO Yan, WANG Haoran, HOU Songsong, DUAN Xuliang, MU Jiong

2026, 44(3):  486-502.  doi:10.3969/j.issn.0255-8297.2026.03.010

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In recent years, with the development of information technology and the widespread popularity of online social networking, traditional recommender systems have gradually exposed limitations in characterizing complex user needs, thus drawing increasing attention to group recommendation systems. However, most existing group recommendation methods rely on simple aggregation of individual members' preferences, making it difficult to capture implicit consensus in group behavior. To address this problem, this paper proposed IConcen, a hypergraph neural network-based group recommendation model that integrated multi-view information. The model captured group interactions from three complementary perspectives: member level, item level, and group level. It also introduced an adaptive fusion module that dynamically balances the weights of these views to generate highly expressive fused features, effectively improving recommendation performance.Experimental results on the Mafengwo and CAMRa2011 datasets show that the proposed method outperforms mainstream models such as HCR in both group recommendation and user recommendation tasks. Specifically, on the Mafengwo dataset, the proposed method improves HR@5 and NDCG@5 by more than 24.3% and 28.2%, respectively, compared with the S2-HHGR, Agree, and HCR models, verifying its effectiveness and superiority.

Multi-modal Rumor Detection Method Fusing Image and Text Features

GAO Guangliang, LIANG Weichao, ZHU Tao, HONG Lei, XIA Lingling

2026, 44(3):  503-514.  doi:10.3969/j.issn.0255-8297.2026.03.011

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To further improve the effectiveness and stability of rumor detection, a multimodal rumor detection method was proposed that integrated image and text features with adaptive noise resistance and semantic restoration. First, a three-stage processing pipeline consisting of pretrained encoding, dynamic pooling, and multi-head enhancement was designed to encode rumor-related texts and comments into semantic vectors. Then,two parallel modules were constructed: one for visual feature extraction and the other for optical character feature extraction. These modules encoded rumor images and explicit text within them into complementary enhanced image vectors. Finally, adaptive gating and dynamic cross-attention mechanisms were used to filter noise and enhance semantics,achieving local alignment and global integration of image and text information. Experimental results show that, compared with baseline algorithms, the proposed method can effectively capture deep correlations between image and text information and improve credibility and practicality of rumor detection results.