Engineering Applications of Artificial Intelligence最新文献_第2页

Separable physical spatiotemporal graph message aggregation for fault diagnosis 面向故障诊断的可分离物理时空图信息聚合

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-13 DOI: 10.1016/j.engappai.2026.114109

Kuangchi Sun , Aijun Yin , Yihua Hu

Spatiotemporal graph has become a research hotspot for it can excavate spatiotemporal information in multi-sensor fault diagnosis. However, the existing methods do not fully consider the physical attenuation characteristics in edge when the fault features are transmitted to the next sensor in the case of cross-sensor spatial temporal correlation. Besides, existing spatiotemporal convolutional networks pay much attention to the integration of all nodes for information update and the network structure design without realize the aggregation of edge information with different attributes. To address these issues, we propose Separable Physical Spatiotemporal Graph Message Aggregation (SPSGMA) for Fault Diagnosis. Firstly, a spatiotemporal graph of physical connection properties across sensors is proposed to assign different properties to different edges. Then, a novel wavelet frequency selection method is proposed for node feature extraction of different physical edge. Finally, a separable message aggregation network is designed to realize aggregation of frequency messages on different physical edges and classification rather than unified feature extraction. Three different datasets are used to verify the effectiveness of SPSGMA. Compared with other methods, SPSGMA achieves the best diagnostic performance in long chain sensor data diagnosis, and its average diagnosis accuracy in different diagnosis respectively are 99.99%, 98.59%, and 99.93%.

时空图由于能够挖掘多传感器故障诊断中的时空信息而成为研究热点。然而，现有方法在跨传感器时空相关的情况下，没有充分考虑故障特征传递到下一个传感器时边缘的物理衰减特性。此外，现有的时空卷积网络注重对所有节点进行信息更新和网络结构设计的整合，没有实现不同属性边缘信息的聚合。为了解决这些问题，我们提出了用于故障诊断的可分离物理时空图消息聚合（SPSGMA）。首先，提出了传感器间物理连接属性的时空图，为不同的边缘分配不同的属性；然后，提出了一种新的小波频率选择方法，用于不同物理边缘的节点特征提取。最后，设计了一个可分离的消息聚合网络，实现了不同物理边缘上频率消息的聚合和分类，而不是统一的特征提取。使用三个不同的数据集来验证SPSGMA的有效性。与其他方法相比，SPSGMA在长链传感器数据诊断中获得了最好的诊断性能，其在不同诊断中的平均诊断准确率分别为99.99%、98.59%和99.93%。

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引用次数: 0

Explainable artificial intelligence-Infused hybrid transfer learning framework with multiscale feature fusion for brain tumor detection and classification 基于多尺度特征融合的可解释人工智能混合迁移学习框架用于脑肿瘤检测与分类

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-13 DOI: 10.1016/j.engappai.2026.114128

Shahid Mohammad Ganie , Rama Chaithanya Tanguturi , Manahil Mohammed Alfuraydan

Brain tumors represent a significant health issue and are a leading cause of cancer-related fatalities globally. Early detection and accurate classification approaches are essential for addressing this critical health issue. This study proposes a novel hybrid deep multiscale integration network (DMI-Net) model for brain tumor diagnosis using magnetic resonance imaging (MRI) dataset. Image preprocessing included resizing, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and Gaussian filtering to enhance image quality. A lightweight parallel depthwise separable convolutional neural network (PD-CNN) is designed to extract multiscale relevant features with minimum computational resources. Principal component analysis (PCA), linear discriminant analysis (LDA), uniform manifold approximation and projection (UMAP), and t-distributed stochastic neighbor embedding (t-SNE) were used to visualize and validate the class-separable structure of the feature space in interpretability assessment. The hybrid framework was developed by stacking and concatenating three top-performing transfer learning (TL) models and integrating them with the PD-CNN architecture. Evaluation was conducted using standard performance metrics. For interpretability in clinical decision-support, model outputs were analyzed using shapley additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) and its variants. The DMI-Net model demonstrated superior results compared with eight TL models, achieving an accuracy of 99.24%, precision of 99.00%, recall of 98.42%, F1-score of 98.54%, and area under the receiver operating characteristic curve of 98.85%. It outperformed existing state-of-the-art studies in the literature. The results indicate the potential utility of the proposed model for increasing confidence in diagnosing brain tumors, supporting clinical decision-making.

脑瘤是一个重大的健康问题，也是全球癌症相关死亡的主要原因。早期发现和准确分类方法对于解决这一严重的健康问题至关重要。本研究提出了一种基于磁共振成像（MRI）数据集的新型混合深度多尺度集成网络（DMI-Net）脑肿瘤诊断模型。图像预处理包括调整大小、使用对比度限制自适应直方图均衡化（CLAHE）增强对比度、归一化和高斯滤波来增强图像质量。设计了一种轻量级并行深度可分卷积神经网络（PD-CNN），以最小的计算资源提取多尺度相关特征。采用主成分分析（PCA）、线性判别分析（LDA）、均匀流形逼近与投影（UMAP）和t分布随机邻居嵌入（t-SNE）对可解释性评价中特征空间的可分类结构进行可视化和验证。混合框架是通过堆叠和连接三个表现最好的迁移学习（TL）模型并将它们与PD-CNN架构集成而成的。使用标准性能指标进行评估。为了临床决策支持的可解释性，使用shapley加性解释（SHAP）和梯度加权类激活映射（Grad-CAM）及其变体分析模型输出。DMI-Net模型的准确率为99.24%，精密度为99.00%，召回率为98.42%，f1评分为98.54%，受试者工作特征曲线下面积为98.85%。它优于文献中现有的最先进的研究。结果表明，所提出的模型在提高诊断脑肿瘤的信心，支持临床决策方面具有潜在的效用。

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引用次数: 0

Prediction of flutter derivatives for closed-box bridge girder: A feature-fusion residual neural network algorithm 闭箱梁颤振导数的特征融合残差神经网络预测

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-13 DOI: 10.1016/j.engappai.2026.114142

Chuanting Liu , Genshen Fang , Zuopeng Wen , Ke Li , Yaojun Ge

Flutter derivatives are crucial parameters for aerodynamic performance analysis of long-span bridges, which are typically identified through time-consuming and costly methods such as wind tunnel tests or computational fluid dynamics (CFD). This study proposes a deep learning approach for the rapid identification of the flutter derivatives of closed-box girders, utilizing feature-fusion residual network architecture (FF-ResNet). We construct a dataset comprising flutter derivatives of 113 cross-sections at eight reduced wind speeds, and the flutter derivatives are identified via multi-frequency forced vibration CFD simulations. Then, the reduced wind speed and a pre-processed image of the cross-section are used as inputs, and the model is trained to learn multi-modal features. Bayesian optimization is employed to enhance predictive accuracy for flutter derivatives, with the model achieving r-squared (

R^{2}

) values exceeding 0.97 on the training set and 0.92 on the validation set; in 10-fold cross-validation, the average

R^{2}

of the validation set across ten folds also exceeds 0.92, demonstrating high accuracy. Next, the model is used to analyze the variation of flutter derivatives across the aerodynamic shape range, and the SHapley Additive exPlanations (SHAP) algorithm is applied to investigate the importance of the geometric parameters. The predicted flutter derivatives are then employed to compute the critical wind speed distribution over the range of considered cross-section variations.

颤振导数是大跨度桥梁气动性能分析的关键参数，通常通过风洞试验或计算流体动力学（CFD）等耗时且昂贵的方法进行识别。本研究提出了一种基于特征融合残差网络架构（FF-ResNet）的闭箱梁颤振导数快速识别的深度学习方法。本文构建了一个包含8种降低风速下113个截面颤振导数的数据集，并通过多频强迫振动CFD模拟来识别颤振导数。然后，将降低后的风速和预处理后的横截面图像作为输入，训练模型学习多模态特征。采用贝叶斯优化方法提高了颤振导数的预测精度，模型在训练集和验证集上的r-squared （R2）值分别超过0.97和0.92；在10次交叉验证中，验证集跨10次的平均R2也超过0.92，显示出较高的准确性。其次，利用该模型分析了颤振导数在气动形状范围内的变化，并采用SHapley加性解释（SHAP）算法研究了几何参数的重要性。然后利用预测的颤振导数计算在考虑的截面变化范围内的临界风速分布。

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引用次数: 0

Transformer-based offline-to-online reinforcement learning for decision-making and control in autonomous driving 基于变压器的离线到在线的自动驾驶决策控制强化学习

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-13 DOI: 10.1016/j.engappai.2026.114139

Feihong Tan , Ping Lu , Fulin Zhang , Xin Ye , Bo Hu , Xing Shu

Developing robust decision-making and control systems for autonomous driving in complex, dynamic environments involving multi-vehicle interactions at intersections, roundabouts, and merging ramps remains a significant hurdle. In this context, Reinforcement Learning (RL) emerges as a highly promising approach. The primary methods for applying RL, however, present a core dilemma. On one hand, offline RL cannot adapt well to real-world conditions because it learns from a fixed dataset. On the other hand, online RL requires learning through real-world interaction, which is inherently unsafe for driving. To address these issues, this paper proposes a Transformer-based Offline-to-online Reinforcement Learning (TORL) framework. Firstly, the framework's offline learning paradigm integrates a Transformer architecture with a maximum entropy mechanism. This synergistic approach allows the model to capture long-term temporal dependencies for high-performance decision-making and control while ensuring the initial policy is robust and generalizable. Building on this foundation, the framework employs a trifecta of synergistic mechanisms during online fine-tuning, including Human-in-the-Loop (HITL) safe exploration, a hybrid replay buffer, and a mixed data-source learning approach, to simultaneously mitigate performance degradation from distributional shifts and neutralize the critical safety risks of online exploration. Comprehensive experiments conducted in the MetaDrive simulation environment demonstrate that TORL surpasses baseline methods, achieving an absolute increase of approximately 29.4% in normalized return and 46.1% in task success rate, while maintaining a zero-collision record. Furthermore, the framework's real-time feasibility was validated on an experimental autonomous vehicle platform, demonstrating low computational latency suitable for practical deployment. This study demonstrates that the proposed offline-to-online RL paradigm offers a robust and effective solution for developing high-performance decision-making and control systems for autonomous vehicles.

在复杂的动态环境中，包括交叉路口、环形交叉路口和合并坡道上的多车交互，为自动驾驶开发强大的决策和控制系统仍然是一个重大障碍。在这种情况下，强化学习（RL）成为一种非常有前途的方法。然而，应用强化学习的主要方法存在一个核心难题。一方面，离线强化学习不能很好地适应现实世界的条件，因为它从固定的数据集学习。另一方面，在线强化学习需要通过现实世界的互动进行学习，这对驾驶来说本质上是不安全的。为了解决这些问题，本文提出了一种基于变压器的离线到在线强化学习（TORL）框架。首先，该框架的离线学习范式集成了具有最大熵机制的Transformer体系结构。这种协同方法允许模型捕获长期的时间依赖性，以便进行高性能的决策和控制，同时确保初始策略是健壮的和可推广的。在此基础上，该框架在在线微调期间采用了三种协同机制，包括人在环（HITL）安全探索、混合重播缓冲区和混合数据源学习方法，以同时减轻分布转移带来的性能下降，并消除在线探索的关键安全风险。在MetaDrive仿真环境中进行的综合实验表明，TORL超越了基线方法，在保持零碰撞记录的同时，实现了约29.4%的归一化收益率和46.1%的任务成功率的绝对增长。此外，在实验自动驾驶汽车平台上验证了该框架的实时性可行性，证明了适合实际部署的低计算延迟。该研究表明，提出的离线到在线RL范式为开发高性能的自动驾驶车辆决策和控制系统提供了一个鲁棒和有效的解决方案。

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引用次数: 0

Theory-guided data-driven based on the learning curve for fracturing performance prediction 基于学习曲线的理论导向数据驱动压裂动态预测

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-13 DOI: 10.1016/j.engappai.2026.114163

Yunjin Wang , Leyi Zheng , Gong Chen , Jianlong Zhang , Hao Bai , Hanxuan Song , Tingxue Jiang , Fujian Zhou

Accurate and robust prediction of fracturing performance is essential for optimizing fracturing strategies. Here, a fracturing learning curve is proposed based on the fracturing characteristics in Gimsar shale oil, and is used as a theoretical guide to build a theory-guided data-driven (TgDD) model to predict the fracturing performance. The fracturing learning curve is further decomposed into dimensionless trends and local fluctuations. Convolutional neural network (CNN) and gated recurrent unit (GRU) were combined to build a CNN-GRU to predict the dimensionless trend. Using adaptive boosting (AdaBoost) integrated random forest (RF) to build an AdaBoost-RF to predict the local fluctuations. The results show that dimensionless trend has time series characteristics. CNN-GRU can extract and select the features, and its prediction ability is 28.1 % and 12.9 % higher than that of CNN and GRU. AdaBoost-RF can dynamically adjust the weights, and its prediction ability is about 37% higher than that of the RF. TgDD is more sensitive to engineering parameters. Relative to the direct prediction, the prediction accuracy of the TgDD is improved by 47.6 %. There are two main reasons for the higher prediction accuracy of TgDD. One is that the dimensionless trend belongs to the time series data, for which the established CNN-GRU model has an extremely strong prediction ability. The second is that the fluctuation amplitude of local fluctuations is reduced, which improves the data quality. The engineering parameters of the newly fractured wells were optimized using TgDD, and its estimated ultimate recovery was improved from 0.4847 to 0.4917.

准确、可靠的压裂性能预测对于优化压裂策略至关重要。基于Gimsar页岩油的压裂特征，提出了压裂学习曲线，并将其作为理论指导，建立理论指导数据驱动（TgDD）模型，预测压裂性能。压裂学习曲线进一步分解为无量纲趋势和局部波动。将卷积神经网络（CNN）和门控循环单元（GRU）相结合，构建了CNN-GRU模型，用于预测无量纲趋势。利用自适应增强（AdaBoost）集成随机森林（RF）构建AdaBoost-RF预测局部波动。结果表明，无量纲趋势具有时间序列特征。CNN-GRU能够对特征进行提取和选择，预测能力分别比CNN和GRU高28.1%和12.9%。AdaBoost-RF可以动态调整权重，其预测能力比RF提高约37%。TgDD对工程参数更为敏感。与直接预报相比，TgDD的预报精度提高了47.6%。TgDD预测精度较高的原因主要有两点。一是无量纲趋势属于时间序列数据，对此所建立的CNN-GRU模型具有极强的预测能力。二是降低了局部波动的波动幅度，提高了数据质量。利用TgDD对新压裂井的工程参数进行了优化，最终采收率由0.4847提高到0.4917。

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引用次数: 0

Multimodal emotion recognition from complete modality to missing modality based on text, audio, and visual: A review 基于文本、音频和视觉的多模态情感识别：从完整情态到缺失情态

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-13 DOI: 10.1016/j.engappai.2026.114127

Huihui Li , Huiqi Han , Chunlin Xu , Tongbao Chen , Xiaoyong Liu , Guihua Wen

Multimodal Emotion Recognition (MER), as a key component of affective computing, significantly improves the accuracy and robustness of emotion recognition by integrating multiple modalities such as text, audio, and visual information. However, most existing studies are based on the assumption of data integrity, while missing modality data is inevitable in practical applications, which poses new challenges to MER. This paper, for the first time, conducts a comprehensive and systematic review of MER methods from complete modality to missing modality, covering the analysis of common datasets, feature extraction techniques, information fusion mechanisms, and the latest methods. In particular, we elaborate on the construction methods of missing modality data and conduct a comprehensive comparison of MER methods under both complete and missing modalities. Furthermore, we summarize the common evaluation metrics in the field of MER, deeply discuss the core challenges currently faced, and prospect the future research directions. This review aims to provide researchers with a comprehensive understanding of the state of MER technology, thereby offering directional suggestions for subsequent research.

多模态情感识别（MER）作为情感计算的重要组成部分，通过整合文本、音频和视觉等多模态信息，显著提高了情感识别的准确性和鲁棒性。然而，现有的研究大多是建立在数据完整性假设的基础上，而在实际应用中，模态数据的缺失是不可避免的，这对MER提出了新的挑战。本文首次从完整模态到缺失模态对MER方法进行了全面系统的综述，涵盖了常用数据集的分析、特征提取技术、信息融合机制以及最新方法。我们特别阐述了缺失模态数据的构建方法，并对完整模态和缺失模态下的MER方法进行了全面比较。在此基础上，总结了市场营销领域常用的评价指标，深入探讨了当前面临的核心挑战，并对未来的研究方向进行了展望。本文旨在让研究者对MER技术的现状有一个全面的了解，从而为后续的研究提供方向性的建议。

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引用次数: 0

Maximal margin hyper-ellipsoid support vector machine for multi-class classification 多类分类的最大边界超椭球支持向量机

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-12 DOI: 10.1016/j.engappai.2026.114055

Ting Ke, Mingzhu Meng, Feifei Yin

To address the low efficiency issues of support vector machine (SVM)-based multi-classification methods, the hyper-sphere support vector machine has been widely adopted. However, it still suffers from challenges such as feature correlation and inconsistent feature scales. To overcome these limitations, this paper proposes a maximal margin hyper-ellipsoid support vector machine (M³HE-SVM) approach. Unlike conventional methods that use Euclidean distance, this approach employs Mahalanobis distance for optimal margin measurement, aimed at not only decorrelating features, eliminating dimensional discrepancies, and achieving implicit feature selection, but also further capturing the geometric information of data and the probability distribution of the population. Extensive experiments are conducted on three categories of datasets: (1) a variety of representative synthetic datasets covering scenarios with linear separability, nonlinear distributions, class imbalance, non-spherical structures, and high-dimensional multi-class data; (2) multiple real-world datasets from the University of California, Irvine (UCI) Machine Learning Repository; and (3) large-scale real-world datasets and NDC datasets. Experimental results demonstrate that M³HE-SVM consistently outperforms the maximal margin hypersphere support vector machine (M³HS-SVM) and other traditional methods in both classification accuracy and testing efficiency, exhibiting strong robustness and generalization ability.

为了解决基于支持向量机（SVM）的多分类方法效率低的问题，超球面支持向量机被广泛采用。然而，它仍然面临着特征相关性和特征尺度不一致等挑战。为了克服这些局限性，本文提出了一种极大余量超椭球支持向量机（M3HE-SVM）方法。与传统方法使用欧几里得距离不同，该方法采用马氏距离进行最优边缘度量，不仅可以去除相关特征，消除维度差异，实现隐式特征选择，而且还可以进一步捕获数据的几何信息和总体的概率分布。在三类数据集上进行了大量的实验：(1)涵盖线性可分性、非线性分布、类不平衡、非球形结构和高维多类数据场景的各种具有代表性的合成数据集；(2)来自加州大学欧文分校（UCI）机器学习存储库的多个真实数据集；(3)大规模真实世界数据集和NDC数据集。实验结果表明，M3HE-SVM在分类精度和测试效率上均优于M3HS-SVM等传统方法，具有较强的鲁棒性和泛化能力。

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引用次数: 0

Self-discharge estimation for lithium-ion batteries based on formation data in production 基于生产地层数据的锂离子电池自放电估计

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-12 DOI: 10.1016/j.engappai.2026.114180

Haoyuan Zheng , Shaobin Yang , Weihua Xue , Shouzhen Xiao , Ding Shen , Wei Dong , Xu Zhang

Global annual shipments of lithium-ion batteries reached 1545.1 GW-hours (GWh) in 2024, representing a substantial increase. Notably, the energy-storage segment alone experienced a year-on-year growth of 64.9 %. Prior to dispatch, lithium-ion batteries must undergo self-discharge testing to ensure safety and reliability. In practice, identifying the approximately 2% of batteries exhibiting excessive self-discharge requires a prolonged resting period (10-30 days) to track self-discharge voltage drop (SDV-drop), which accounts for nearly two-thirds of the overall production cycle and severely limits manufacturing efficiency. Rapid and accurate prediction of self-discharge behavior has thus become a pressing engineering challenge. This study presents an artificial intelligence enabled framework that predicts a 28-day voltage drop using formation-stage data, thereby obviating the prolonged rest period. The approach integrates latent feature extraction from charge-discharge curves, unsupervised clustering, and transfer learning. Specifically, both comprehensive temporal and static features are automatically extracted from current, voltage, and capacity trajectories, along with scalar performance indicators. A hybrid K-means-t-distributed stochastic neighbor embedding (t-SNE) algorithm partitions the dataset into internally homogeneous clusters, enhancing intra-cluster consistency and inter-cluster separability. During transfer learning, maximum mean discrepancy aligns feature distributions between source and target domains, while a feature-label consistency constraint further mitigates domain shift and improves generalization. Comparative experiments demonstrate that the proposed model markedly outperforms state-of-the-art baselines in predicting SDV-drop. This framework thus provides a theoretical foundation and practical pathway for rapid self-discharge assessment, which enables significant reductions in production cycle time and improves manufacturing efficiency.

2024年，全球锂离子电池年出货量达到1545.1吉瓦时（GWh），大幅增长。值得注意的是，仅储能部分就经历了64.9%的同比增长。锂离子电池在出厂前必须进行自放电测试，以确保安全性和可靠性。在实践中，识别大约2%的电池表现出过度自放电，需要很长时间（10-30天）来跟踪自放电电压降（SDV-drop），这占整个生产周期的近三分之二，严重限制了制造效率。因此，快速准确地预测自放电行为已成为一项紧迫的工程挑战。该研究提出了一种人工智能框架，可以使用地层阶段数据预测28天的电压降，从而避免了长时间的休息时间。该方法集成了从充放电曲线中提取潜在特征、无监督聚类和迁移学习。具体来说，从电流、电压和容量轨迹以及标量性能指标中自动提取综合的时间和静态特征。混合k均值-t分布随机邻居嵌入（t-SNE）算法将数据集划分为内部均匀的簇，增强了簇内一致性和簇间可分离性。在迁移学习过程中，最大平均差异使源域和目标域之间的特征分布保持一致，而特征标签一致性约束进一步减轻了域移动并提高了泛化。对比实验表明，所提出的模型在预测sdv下降方面明显优于最先进的基线。该框架为快速自放电评估提供了理论基础和实践途径，可显著缩短生产周期时间，提高制造效率。

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引用次数: 0

Transformers meet hyperspectral imaging: A comprehensive study of models, challenges and open problems 变压器满足高光谱成像：模型，挑战和开放问题的综合研究

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-12 DOI: 10.1016/j.engappai.2026.113947

Guyang Zhang, Waleed Abdulla

Transformers have become the architecture of choice for learning long-range dependencies, yet their adoption in hyperspectral imaging (HSI) is still emerging. We reviewed more than 300 papers published up to 2025 and present the first end-to-end survey dedicated to Transformer-based HSI classification. The study categorizes every stage of a typical pipeline—pre-processing, patch or pixel embedding, positional encoding, spatial–spectral feature extraction, multi-head self-attention variants, skip connections, and loss design—and contrasts alternative design choices with the unique spatial–spectral properties of HSI. We map the field’s progress against persistent obstacles: scarce labeled data, extreme spectral dimensionality, computational overhead, and limited model explainability. Finally, we outline a research agenda prioritizing valuable public data sets, lightweight on-edge models, illumination and sensor shifts robustness, and intrinsically interpretable attention mechanisms. Our goal is to guide researchers in selecting, combining, or extending Transformer components that are truly fit for purpose for next-generation HSI applications.

变形器已经成为学习远程依赖关系的首选架构，但它们在高光谱成像（HSI）中的应用仍在兴起。我们回顾了截至2025年发表的300多篇论文，并提出了第一个致力于基于变压器的HSI分类的端到端调查。该研究对典型管道的每个阶段进行了分类——预处理、补丁或像素嵌入、位置编码、空间光谱特征提取、多头自关注变体、跳过连接和损耗设计——并将不同的设计选择与HSI独特的空间光谱特性进行了对比。我们将该领域的进展映射到持续存在的障碍：稀缺的标记数据、极端的光谱维度、计算开销和有限的模型可解释性。最后，我们概述了一个研究议程，优先考虑有价值的公共数据集、轻量级边缘模型、照明和传感器转换的鲁棒性，以及内在可解释的注意力机制。我们的目标是指导研究人员选择、组合或扩展真正适合下一代HSI应用的Transformer组件。

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引用次数: 0

Multi-axial vibration fatigue optimization strategy based on the artificial intelligence algorithm 基于人工智能算法的多轴振动疲劳优化策略

IF 8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2026-02-12 DOI: 10.1016/j.engappai.2026.114084

Xiaopeng Zhang , Boqiang Zhang , Dengfeng Wang , Zihao Meng , Fengmin Lian , Jialin Dong , Shang Zhang , Hongli Chen , Haijun Ruan

To reduce the computational cost in the multi-criteria optimization process based on multi-axial vibration fatigue life and improve the fairness of multi-criteria decision-making, this paper proposes a multi-axial vibration fatigue optimization strategy based on artificial intelligence. This strategy combines a hybrid surrogate model using Random Forest (RF) and Radial Basis Function Neural Network (RBFNN), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and a Modified Preference Selection Index (MPSI). The multiaxial vibration fatigue formula is derived for calculating the fatigue life of structures. The RF-RBFNN hybrid surrogate model is used to fit the relationship between variables and responses. The NSGA-II is employed to mine the Approximation set from the hybrid surrogate model, and the Modified Preference Selection Index method is used to determine the best compromise solution. The research results indicate that, while satisfying all constraint indicators, the weight reduction rate of the vehicle frame is 4.9%, and the prediction accuracy of the fatigue life surrogate model is 97.015%. The main contribution of this paper is to extend the artificial intelligence algorithm to the field of multi-axis vibration fatigue optimization and apply it to the solution process of the fatigue life of the tractor frame.

为了降低基于多轴振动疲劳寿命的多准则优化过程中的计算成本，提高多准则决策的公平性，提出了一种基于人工智能的多轴振动疲劳优化策略。该策略结合了随机森林（RF）和径向基函数神经网络（RBFNN）的混合代理模型、非支配排序遗传算法II （NSGA-II）和改进的偏好选择指数（MPSI）。推导了计算结构疲劳寿命的多轴振动疲劳公式。采用RF-RBFNN混合代理模型拟合变量与响应之间的关系。采用NSGA-II算法从混合代理模型中挖掘近似集，并采用修正偏好选择指数法确定最佳折衷方案。研究结果表明，在满足所有约束指标的情况下，车架减重率为4.9%，疲劳寿命替代模型预测精度为97.015%。本文的主要贡献是将人工智能算法扩展到多轴振动疲劳优化领域，并将其应用于拖拉机车架疲劳寿命的求解过程。

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