Expert Systems with Applications最新文献_第3页

ITAdapter: Image-Tag adapter framework with retrieval knowledge enhancer for radiology report generation ITAdapter：带有检索知识增强器的图像标签适配器框架，用于放射学报告生成

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-06 DOI: 10.1016/j.eswa.2026.131494

Shuaipeng Ding , Jianan Shui , Mingyuan Ge , Mengnan Fan , Xin Li , Yijie Zhu , Mingyong Li

Automated radiology report generation has emerged as a crucial technology for improving clinical workflow efficiency and alleviating the documentation burden on radiologists. Current approaches predominantly employ encoder-decoder architectures, they often overemphasize text generation while neglecting two critical issues: inherent biases in textual data distribution that limit abnormal region descriptions, and inadequate cross- modal interaction. To address these challenges, we propose an innovative Image-Tag Adapter (ITAdapter) framework that dynamically balances visual information and diagnostic information during decoding, with particular attention to optimizing feature selection for different types of generated words. The framework incorporates two key components: a Retrieval Knowledge Enhancer (RKE) that utilizes pre-trained CLIP models’ cross-modal retrieval capability to obtain relevant clinical reports as diagnostic references, and an Image-Tag Adapter (ITA) that intelligently fuses visual information with diagnostic information from disease tags. For model optimization, we combine reinforcement learning with knowledge distillation to enable effective knowledge transfer through iterative training. Extensive experiments on IU X-ray and MIMIC-CXR benchmark datasets demonstrate our method’s effectiveness in generating more accurate and clinically relevant reports, achieving the highest performance scores: on IU X-ray, BLEU-1 = 0.536, BLEU-4 = 0.206 and METEOR = 0.220; on MIMIC-CXR, BLEU-1 = 0.411, BLEU-4 = 0.141 and METEOR = 0.152.

自动化放射学报告生成已经成为提高临床工作流程效率和减轻放射科医生文档负担的关键技术。当前的方法主要采用编码器-解码器架构，它们往往过分强调文本生成，而忽略了两个关键问题：文本数据分布中的固有偏差限制了异常区域描述，以及不充分的跨模态交互。为了解决这些挑战，我们提出了一个创新的图像标签适配器（ITAdapter）框架，该框架在解码过程中动态平衡视觉信息和诊断信息，特别注意优化不同类型生成词的特征选择。该框架包含两个关键组件：检索知识增强器（RKE）利用预先训练的CLIP模型的跨模式检索能力获取相关临床报告作为诊断参考，图像标签适配器（ITA）智能地将视觉信息与疾病标签的诊断信息融合在一起。在模型优化方面，我们将强化学习与知识蒸馏相结合，通过迭代训练实现有效的知识迁移。在IU x射线和MIMIC-CXR基准数据集上的大量实验表明，我们的方法在生成更准确和临床相关的报告方面是有效的，并获得了最高的性能分数：在IU x射线上，BLEU-1 = 0.536, BLEU-4 = 0.206和METEOR = 0.220；在MIMIC-CXR上，BLEU-1 = 0.411, BLEU-4 = 0.141, METEOR = 0.152。

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引用次数: 0

CNN-DET: A hybrid deep learning architecture for emotion recognition CNN-DET：一种用于情感识别的混合深度学习架构

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-06 DOI: 10.1016/j.eswa.2026.131377

Berrouachedi Abdelkader, Jaziri Rakia, Bernard Gilles

Emotion recognition plays a crucial role in various biometric applications, including human-computer interaction, healthcare, and security. This paper presents CNN-DET, a novel hybrid approach that integrates Convolutional Neural Networks (CNNs) with Deep Extra-Trees (DETs) for robust facial emotion recognition. The proposed methodology leverages hierarchical feature extraction through pre-trained CNN models combined with ensemble-based classification using DETs to accurately detect and classify emotions from facial expressions. Comprehensive evaluation on benchmark datasets demonstrates the superior performance of our approach. On the FER-2013 dataset, CNN-DET achieves 98.16% accuracy in 10-fold cross-validation and 85.32% accuracy on the standard test set, with precision of 85.7%, recall of 85.3%, and F1-score of 85.4%. The model maintains strong performance across diverse conditions, achieving 91.2% accuracy on AffectNet and 89.7% accuracy on RAF-DB, confirming its generalization capability. Extensive experiments reveal that our method reduces misclassification between visually similar emotions by 23.4% compared to traditional CNN approaches and shows 15.8% improvement in robustness under varying lighting conditions. The proposed approach not only accurately recognizes emotions but also demonstrates consistent performance across different demographic groups, with less than 3.2% performance variance across age and ethnicity subgroups. These findings highlight the significant potential of deep learning techniques for emotion recognition in biometric applications, providing valuable insights for developing more intelligent and interactive systems. Future research will focus on multimodal data fusion and temporal modeling to further enhance recognition accuracy and real-time performance.

情感识别在各种生物识别应用中起着至关重要的作用，包括人机交互、医疗保健和安全。本文提出了CNN-DET，一种新颖的混合方法，将卷积神经网络（cnn）与深度额外树（det）相结合，用于鲁棒的面部情绪识别。该方法通过预训练的CNN模型进行分层特征提取，并结合基于集成的分类，使用det从面部表情中准确地检测和分类情绪。对基准数据集的综合评估证明了我们的方法的优越性能。在FER-2013数据集上，CNN-DET在10倍交叉验证中准确率达到98.16%，在标准测试集上准确率达到85.32%，准确率为85.7%，召回率为85.3%，f1得分为85.4%。该模型在不同条件下保持了较强的性能，在AffectNet上达到91.2%的准确率，在RAF-DB上达到89.7%的准确率，证实了其泛化能力。大量的实验表明，与传统的CNN方法相比，我们的方法减少了23.4%的视觉相似情绪之间的错误分类，并且在不同光照条件下的鲁棒性提高了15.8%。所提出的方法不仅能准确地识别情绪，而且在不同的人口群体中表现出一致的表现，在不同年龄和种族的子群体中表现差异小于3.2%。这些发现突出了深度学习技术在生物识别应用中情感识别的巨大潜力，为开发更智能和互动的系统提供了有价值的见解。未来的研究将集中在多模态数据融合和时间建模方面，以进一步提高识别精度和实时性。

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引用次数: 0

MGPNet: A multi-modal geo-physical production network for reservoir yield forecasting MGPNet：用于油藏产量预测的多模态地球物理生产网络

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-06 DOI: 10.1016/j.eswa.2026.131407

Qianlin Qiao , Ying Qiao , Xin Sun , Qiaomu Wen , Jian Lei

Accurate reservoir production prediction often depends on a single data source or simplified physical assumptions, limiting the ability to fully capture complex reservoir heterogeneity and multi-physical coupling processes. To address these challenges, this paper proposes MGPNet, a multi-modal reservoir production prediction network that integrates seismic images, logging curves, and historical production sequences. The network incorporates the GeoFE-AE module, which uses adversarial feature enhancement and semantic alignment mechanisms to achieve deep feature extraction and collaborative representation across modalities. Furthermore, it introduces a Multi-scale Cross-Attention Mechanism (MCAM) to enable effective feature fusion across different modalities at multiple semantic granularities, and employs a bidirectional Transformer decoding and prediction module (BiAD) to accurately forecast future production sequences. Using a high-fidelity synthetic multi-modal dataset, experimental results demonstrate that MGPNet significantly outperforms existing mainstream methods across several key metrics: Mean Absolute Error (MAE) of 2.12, Root Mean Square Error (RMSE) of 5.87, Coefficient of Determination (R²) of 0.987, and Explained Variance Score (EVS) of 0.971. These results validate the model’s comprehensive strengths in accuracy, stability, and robustness to noise. Furthermore, transfer-learning evaluations on real wells from the Volve oilfield confirm the model’s practical applicability and strong cross-well generalization capability. This research offers a promising technical approach for deep fusion modeling of multi-source reservoir data, with substantial potential for practical engineering applications and further academic exploration.

准确的储层产量预测通常依赖于单一数据源或简化的物理假设，这限制了充分捕捉复杂储层非均质性和多物理耦合过程的能力。为了应对这些挑战，本文提出了MGPNet，这是一种集成了地震图像、测井曲线和历史生产序列的多模式油藏产量预测网络。该网络结合了GeoFE-AE模块，该模块使用对抗特征增强和语义对齐机制来实现深度特征提取和跨模态的协同表示。此外，它还引入了一种多尺度交叉注意机制（MCAM）来实现多语义粒度下不同模式的有效特征融合，并采用双向变压器解码和预测模块（BiAD）来准确预测未来的生产序列。实验结果表明，MGPNet在平均绝对误差（MAE）为2.12、均方根误差（RMSE）为5.87、决定系数（R2）为0.987、解释方差评分（EVS）为0.971等关键指标上显著优于现有主流方法。这些结果验证了该模型在准确性、稳定性和对噪声的鲁棒性方面的综合优势。通过对Volve油田实际井的迁移学习评价，验证了该模型的实用性和较强的井间推广能力。该研究为多源油藏数据的深度融合建模提供了一种很有前景的技术方法，具有实际工程应用和进一步的学术探索潜力。

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引用次数: 0

A robust model on the location of temporary medical centers considering secondary disasters 考虑二次灾害的临时医疗中心位置的鲁棒模型

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131532

Hongmei Li , Dongxia Qu , Taibo Luo

Natural disasters occur frequently in the real world, and the secondary disasters they trigger can also result in significant losses. Well-organized disaster response is essential for providing timely, effective medical services in such emergencies. This paper addresses a post-disaster emergency response problem involving the determination of the location and number of temporary medical centers (TMCs) and the planning of casualty transportation, considering both primary and secondary disasters under uncertainty. A two-stage robust optimization model based on the minimax regret criterion is proposed. In the first stage, the locations and allocations for primary disaster casualties are determined prior to the occurrence of secondary disasters. In the second stage, casualties resulting from secondary disasters are transported to the established TMCs. To handle uncertainty in the number and spatial distribution of secondary disaster casualties, a minimax regret approach is employed across a set of predefined scenarios. To enhance computational efficiency, a scenario relaxation algorithm based on row generation is developed. Case studies based on the Lushan Earthquake are conducted to validate the feasibility and effectiveness of the model. Results demonstrate that incorporating secondary disasters significantly improves the efficiency of casualty treatment compared to models considering only primary disasters. Under the uncertainty of secondary disasters, constructing a limited number of critical scenarios is sufficient, and large-capacity TMCs are more recommended.

自然灾害在现实世界中频繁发生，其引发的次生灾害也会造成重大损失。组织良好的灾害应对对于在此类紧急情况下提供及时有效的医疗服务至关重要。本文研究了在不确定性条件下，考虑初级灾害和次级灾害的灾后应急响应问题，涉及临时医疗中心（tmc）的位置和数量的确定以及伤员运输的规划。提出了一种基于极大极小后悔准则的两阶段鲁棒优化模型。在第一阶段，在次生灾害发生前确定一次灾害伤亡的地点和分配。在第二阶段，由次生灾害造成的伤亡人员被运送到已建立的灾害管理中心。为了处理次生灾害伤亡人数和空间分布的不确定性，在一组预定义的场景中采用了最小最大遗憾方法。为了提高计算效率，提出了一种基于行生成的场景松弛算法。以芦山地震为例，验证了模型的可行性和有效性。结果表明，与只考虑初级灾害的模型相比，纳入次生灾害的模型显著提高了伤亡处理的效率。在次生灾害的不确定性下，构建有限数量的关键场景就足够了，更推荐大容量的tmc。

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引用次数: 0

Breaking the low-cost barrier: a memory-augmented reactive navigation system for UAVs in cluttered indoor environments 打破低成本障碍：用于杂乱室内环境中的无人机的记忆增强反应导航系统

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131469

Jiale Quan , Weijun Hu , Xianlong Ma , Gang Chen

Achieving robust indoor autonomous flight for Unmanned Aerial Vehicles (UAVs) under strict hardware and computational constraints remains a formidable challenge. Conventional solutions relying on high-end sensors or global mapping are often inapplicable to resource-constrained micro-UAVs. In this paper, we propose a mapless integrated navigation framework aimed at achieving stable flight using a low-cost single-line 2D LiDAR. To address the limitations of sparse sensing, we propose a window-neighborhood-based denoising filtering algorithm and a velocity estimation-based motion distortion correction module. The system combines a risk-aware local planner and a short-sighted trajectory memory mechanism to navigate through cluttered spaces. The system operates in an O(N) loop with sub-millisecond latency. To overcome the local minima inherent in reactive planning, a deadlock escape layer is introduced, which formalizes navigation difficulty through trajectory entropy analysis, and generates recovery waypoints using discrete polar coordinate search. Validation through high-fidelity simulations and real-world experiments show that the system is capable of collision-free navigation at speeds up to 6 m/s, using low-cost sensors. This work provides an efficient solution for deploying intelligent aerial robots in perception-constrained indoor environments.

在严格的硬件和计算限制下，实现无人飞行器（uav）的强大室内自主飞行仍然是一个艰巨的挑战。依靠高端传感器或全局映射的传统解决方案往往不适用于资源受限的微型无人机。在本文中，我们提出了一种无地图集成导航框架，旨在使用低成本单线2D激光雷达实现稳定飞行。为了解决稀疏感知的局限性，我们提出了一种基于窗邻域的去噪滤波算法和一种基于速度估计的运动畸变校正模块。该系统结合了具有风险意识的本地规划师和短视轨迹记忆机制，可以在杂乱的空间中导航。系统运行在一个O(N)循环与亚毫秒的延迟。为了克服响应式规划固有的局部最小值问题，引入了死锁逃逸层，通过轨迹熵分析形式化导航难度，并利用离散极坐标搜索生成恢复路点。通过高保真仿真和真实世界实验验证，该系统能够使用低成本传感器，以高达6米/秒的速度进行无碰撞导航。这项工作为在感知受限的室内环境中部署智能空中机器人提供了一种有效的解决方案。

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引用次数: 0

Safety-assured decision support for ASV navigation via hybrid graph planning and timed automata verification 通过混合图形规划和定时自动机验证的ASV导航安全决策支持

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131367

Huilin Ge , Meng Li , Guanghui Wen , Yu Lu

Autonomous surface vehicles (ASVs) require reliable safety assurance to operate in complex and dynamic marine environments. This paper develops an integrated decision-support framework that couples hybrid graph-based path planning with formal verification to guarantee safe and reachable navigation. A composite roadmap is generated using the proposed HVV-E planner, which combines Voronoi-based global planning and visibility-graph refinement to produce collision-free and energy-aware trajectories. To ensure the trustworthiness of candidate routes, the navigation process is abstracted into a Linearly Priced Timed Automata (LPTA) model and formally verified using UPPAAL. Both qualitative properties, including obstacle avoidance and deadlock-freeness, and quantitative mission constraints, such as bounded travel time and energy consumption, are examined. The verification results provide explicit explanations of why a route is safe or unsafe, enabling early identification of infeasible or risky mission configurations. Experiments conducted in realistic Singapore Strait scenarios demonstrate that the proposed framework delivers transparent, safety-assured, and energy-aware navigation support for real-world ASV missions. The results highlight the value of integrating formal reasoning with intelligent planning to advance explainable and trustworthy autonomous maritime systems.

自动水面车辆（asv）需要可靠的安全保证才能在复杂和动态的海洋环境中运行。本文开发了一种集成的决策支持框架，将基于混合图的路径规划与形式验证相结合，以保证安全可达的导航。利用提出的HVV-E规划器生成复合路线图，该规划器结合了基于voronoi的全局规划和可见性图的改进，以生成无碰撞和能量感知的轨迹。为了保证候选路径的可信度，将导航过程抽象为线性定价时间自动机（LPTA）模型，并使用UPPAAL进行形式化验证。定性性质，包括避障和无死锁，定量任务约束，如有限的旅行时间和能量消耗，进行了检查。验证结果明确解释了路线安全或不安全的原因，从而能够及早识别不可行或有风险的任务配置。在现实的新加坡海峡场景中进行的实验表明，所提出的框架为现实世界的ASV任务提供了透明、安全、节能的导航支持。结果强调了将形式推理与智能规划相结合以推进可解释和可信赖的自主海事系统的价值。

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引用次数: 0

Temporal three-way decision-making for emergency admission integrating multigranulation neighborhood rough set with Gaussian mixture-hidden Markov model 基于高斯混合-隐马尔可夫模型的多粒邻域粗糙集急诊入院时间三向决策

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131458

Meng Zhang, Jianghua Zhang, Dongchen Gao, Weibo Liu

Accurate and timely admission prediction in emergency departments is essential for improving resource allocation, enhancing patient outcomes, and mitigating overcrowding. However, the progression of emergency patients often exhibits strong temporal dynamics, and clinical decisions typically involve not only admission and non-admission but also an intermediate state of wait-and-see. To address this challenge, this study proposes a novel temporal three-way decision-making method that integrates Temporal Feature-based Multigranulation Neighborhood Rough Set (TMNRS) with Gaussian Mixture-Hidden Markov Model (GMM-HMM). Specifically, TMNRS is utilized to quantify and characterize the initial distribution of patient states from both theoretical and data-driven perspectives, thereby providing parameter support for subsequent modeling. Building on this foundation, GMM-HMM is employed to capture the dynamic evolution of patients’ conditions across three states over time. This integration facilitates interpretable state representation of the model. Finally, comprehensive experiments conducted on real-world clinical data, including comparisons with multiple benchmark models, demonstrate competitive and rob ust performance of the proposed approach in supporting temporal three-way admission decision-making for emergency patients.

在急诊科准确和及时的入院预测是必不可少的，以改善资源分配，提高患者的结果，并缓解过度拥挤。然而，急诊患者的进展往往表现出强烈的时间动态，临床决策通常不仅涉及入院和不入院，还包括观望的中间状态。为了解决这一挑战，本研究提出了一种新的时间三向决策方法，该方法将基于时间特征的多粒邻域粗糙集（TMNRS）与高斯混合-隐马尔可夫模型（GMM-HMM）相结合。具体而言，利用TMNRS从理论和数据驱动的角度对患者状态的初始分布进行量化和表征，从而为后续建模提供参数支持。在此基础上，GMM-HMM被用来捕捉三个州的患者病情随时间的动态演变。这种集成促进了模型的可解释状态表示。最后，在现实世界的临床数据上进行了全面的实验，包括与多个基准模型的比较，证明了所提出的方法在支持急诊患者的时间三方入院决策方面的竞争性和公平性。

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引用次数: 0

NeuroVision: EEG-to-image reconstruction via progressive neural encoding and cross-modal distillation 神经视觉：通过渐进式神经编码和跨模态蒸馏的脑电图到图像的重建

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131526

Tianwei Qu , Zexue Yang , Qixian Zhang

Reconstructing visual imagery from electroencephalography (EEG) signals represents a fundamental challenge in brain-computer interfaces, as EEG recordings capture rich neural information about visual perception but lack the spatial resolution necessary for direct image reconstruction. Existing approaches typically employ diffusion models to synthesize images from EEG embeddings, yet these methods often fail to preserve the semantic content encoded in neural signals due to information loss during the encoding-decoding process. In this paper, we propose NeuroVision, a novel framework that adapts neural signal encoders for unified EEG understanding and visual reconstruction through progressive neural encoding and cross-modal distillation. Our approach addresses the fundamental trade-off between preserving EEG semantic information and achieving high-quality image reconstruction by introducing a three-stage progressive training scheme that gradually enhances reconstruction capability while maintaining original neural signal understanding. We develop a temporal-spatial attention fusion mechanism to capture multi-channel temporal dynamics in EEG signals, coupled with adaptive feature alignment that dynamically maps EEG representations to visual feature spaces. Furthermore, we introduce a semantic-preserving loss function that ensures reconstructed images faithfully reflect the semantic content of neural activity rather than generating visually plausible but semantically inconsistent outputs. Extensive experiments demonstrate that NeuroVision achieves superior reconstruction quality compared to existing diffusion-based approaches while significantly better preserving semantic correspondence between neural signals and visual content, establishing a new paradigm for EEG-to-image reconstruction that prioritizes semantic fidelity alongside visual quality.

从脑电图（EEG）信号中重建视觉图像是脑机接口的一个基本挑战，因为脑电图记录捕获了关于视觉感知的丰富神经信息，但缺乏直接图像重建所需的空间分辨率。现有方法通常采用扩散模型从脑电信号嵌入中合成图像，但由于编码-解码过程中的信息丢失，这些方法往往不能保留神经信号中编码的语义内容。在本文中，我们提出了一种新的神经视觉框架，该框架采用神经信号编码器，通过渐进式神经编码和跨模态蒸馏来实现统一的EEG理解和视觉重建。我们的方法解决了保留EEG语义信息和实现高质量图像重建之间的基本权衡，通过引入三阶段渐进式训练方案，在保持原始神经信号理解的同时逐步增强重建能力。我们开发了一种时空注意力融合机制来捕捉脑电图信号中的多通道时间动态，再加上自适应特征对齐，将脑电图表征动态映射到视觉特征空间。此外，我们引入了一个语义保持损失函数，以确保重建图像忠实地反映神经活动的语义内容，而不是产生视觉上似是而非语义上不一致的输出。大量实验表明，与现有的基于扩散的方法相比，NeuroVision实现了更好的重建质量，同时更好地保留了神经信号和视觉内容之间的语义对应关系，为脑电图到图像的重建建立了一个新的范例，优先考虑语义保真度和视觉质量。

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引用次数: 0

DAWN: Dimension-aware graph contrastive learning for few-shot dissolved gas analysis DAWN：用于少量溶解气体分析的维度感知图对比学习

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131504

Jiyuan Sun , Huifang Ma , Shuai Yang , Rui Bing , Zhixin Li

Dissolved Gas Analysis (DGA) is a widely used technique for identifying characteristic gas signatures indicative of transformer faults. However, traditional DGA methods struggle to capture the complex interdependencies among gases. Although deep learning has shown promise in this domain, existing approaches face significant limitations under few-shot learning scenarios, primarily due to severe class imbalance and high inter-class similarity, which lead to diagnostic ambiguity. Moreover, these methods often overlook critical inter-gas relationships that are essential for understanding underlying fault mechanisms. To address these challenges, we propose DAWN (Dimension-AWare Graph CoNtrastive Learning), a novel framework that integrates two key components: (1) a contrastive few-shot learning module with clustering consistency loss to enhance the discriminability of similar fault categories through lightweight fine-tuning, and (2) a knowledge-enhanced dimension graph that explicitly models structural dependencies among gas features by combining statistical correlations with expert domain knowledge. Extensive evaluations on DGA datasets demonstrate that DAWN achieves state-of-the-art performance, improving rare fault detection accuracy by over 15% compared to conventional methods. To the best of our knowledge, this work represents the first contrastive few-shot learning framework tailored for DGA-based fault diagnosis.

溶解气体分析（DGA）是一种广泛应用于变压器故障特征气体特征识别的技术。然而，传统的DGA方法难以捕捉气体之间复杂的相互依赖关系。尽管深度学习在这一领域显示出了前景，但现有的方法在少数学习场景下面临着显著的局限性，主要是由于严重的类不平衡和高类间相似性，这导致了诊断的模糊性。此外，这些方法往往忽略了对理解潜在断层机制至关重要的气体间关系。为了解决这些挑战，我们提出了一个新的框架DAWN (dimension - aware Graph CoNtrastive Learning)，该框架集成了两个关键组件：(1)具有聚类一致性损失的对比少shot学习模块，通过轻量级微调增强相似故障类别的可分辨性；(2)通过结合统计相关性和专家领域知识来显式建模天然气特征之间的结构依赖关系的知识增强维图。对DGA数据集的广泛评估表明，DAWN达到了最先进的性能，与传统方法相比，将罕见故障检测准确率提高了15%以上。据我们所知，这项工作代表了第一个为基于遗传算法的故障诊断量身定制的对比少镜头学习框架。

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引用次数: 0

A dynamic model of rumor propagation based on adversarial behavior and evolutionary games 基于对抗行为和进化博弈的谣言传播动态模型

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131490

Chaolong Jia , Guicai Deng , Xiaochuan Chen , Kangle Chen , Rong Wang , Tun Li , Yunpeng Xiao

Rumors pose a serious threat to social stability across modern social networks. To address the ongoing confrontation between rumor and anti-rumor groups, this study proposes a framework for analyzing rumor dissemination based on confrontational behavior and evolutionary game theory. This study considers differences in users’ real-time preferences for rumors relative to anti-rumors, which significantly influence rumor spread and are complex to measure. Multivariate linear regression is applied to develop a real-time preference metric and identify multiple factors influencing user preferences. Evolutionary game theory is then employed to construct a game-theoretic mechanism for anti-rumor behavior. The coexistence and confrontation between the two groups were analyzed using the Rosenzweig–MacArthur equations to characterize overall rumor propagation dynamics. For individual users, three states are defined after exposure to both rumor types: a wavering state (I), a rumor-adopting state (P), and an anti-rumor state (O), which form the basis of a rumor dissemination dynamics model. Experimental results show that the proposed model achieves an average SMAPE of 16%, while complementary metrics such as RMSE and AUC further confirm its reliable performance across different evaluation dimensions.

在现代社交网络中，谣言对社会稳定构成严重威胁。针对谣言与反谣言群体之间持续的对抗，本研究提出了一个基于对抗行为和进化博弈论的谣言传播分析框架。本研究考虑了用户对谣言和反谣言的实时偏好的差异，这对谣言的传播有显著影响，并且测量起来很复杂。应用多元线性回归建立实时偏好度量，识别影响用户偏好的多个因素。运用进化博弈论构建了反谣言行为的博弈论机制。使用Rosenzweig-MacArthur方程分析两组之间的共存和对抗，以表征谣言的整体传播动态。对于个人用户而言，在接触两种谣言类型后，定义了三种状态：摇摆状态(I)、接受谣言状态(P)和反谣言状态(O)，这三种状态构成了谣言传播动力学模型的基础。实验结果表明，该模型的平均SMAPE达到了16%，而RMSE和AUC等互补指标进一步证实了其在不同评估维度上的可靠性能。

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引用次数: 0