Engineering Applications of Artificial Intelligence最新文献

{"title":"Transformer-based offline-to-online reinforcement learning for decision-making and control in autonomous driving","authors":"Feihong Tan ,&nbsp;Ping Lu ,&nbsp;Fulin Zhang ,&nbsp;Xin Ye ,&nbsp;Bo Hu ,&nbsp;Xing Shu","doi":"10.1016/j.engappai.2026.114139","DOIUrl":"10.1016/j.engappai.2026.114139","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"170 ","pages":"Article 114139"},"PeriodicalIF":8.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161831","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}

{"title":"A quantum group decision-making model for patient-capital project selection integrating cumulative prospect theory under linear Diophantine fuzzy uncertainty","authors":"Wen Li ,&nbsp;Fanfan Mu ,&nbsp;Zhiliang Ren ,&nbsp;Obaid Ur Rehman","doi":"10.1016/j.engappai.2026.114169","DOIUrl":"10.1016/j.engappai.2026.114169","url":null,"abstract":"<div><div>The effective selection of patient-capital projects is crucial for promoting China’s transition toward low-carbon development, technological innovation, and sustainable value creation. However, such decision-making processes typically involve multiple experts whose assessments are affected by individual risk preferences and inter-expert opinion interference—factors that are seldom modeled simultaneously yet jointly exert significant influence in existing studies. To address these limitations, this study develops an integrated multi-criteria quantum group decision-making framework for patient-capital project selection, which combines linear Diophantine fuzzy sets (LDFSs), cumulative prospect theory (CPT), quantum probability theory (QPT), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, a linear Diophantine fuzzy Deng entropy (LDFDE) method is proposed to characterize the information uncertainty inherent in LDFSs. Second, a personalized criterion-weighting approach that integrates LDFDE with the classical entropy weighting method is developed to reflect both objective data variability and subjective expert preferences. Third, an integrated quantum CPT-TOPSIS model is constructed, in which the CPT-TOPSIS component captures behavioral biases and risk attitudes, while the QPT-based aggregation component models opinion interference to achieve more consistent and robust group decisions. Finally, a real-world case study on patient-capital project selection demonstrates the practicality and effectiveness of the proposed framework. We also report sensitivity and comparative analyses to validate that the proposed approach enhances ranking stability and reliability. In summary, the findings underscore the potential of the model as a reliable decision-support tool for complex, uncertain, and psychologically driven investment scenarios.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"170 ","pages":"Article 114169"},"PeriodicalIF":8.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146193014","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}

{"title":"Theory-guided data-driven based on the learning curve for fracturing performance prediction","authors":"Yunjin Wang ,&nbsp;Leyi Zheng ,&nbsp;Gong Chen ,&nbsp;Jianlong Zhang ,&nbsp;Hao Bai ,&nbsp;Hanxuan Song ,&nbsp;Tingxue Jiang ,&nbsp;Fujian Zhou","doi":"10.1016/j.engappai.2026.114163","DOIUrl":"10.1016/j.engappai.2026.114163","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"170 ","pages":"Article 114163"},"PeriodicalIF":8.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146193284","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}

{"title":"Multimodal emotion recognition from complete modality to missing modality based on text, audio, and visual: A review","authors":"Huihui Li ,&nbsp;Huiqi Han ,&nbsp;Chunlin Xu ,&nbsp;Tongbao Chen ,&nbsp;Xiaoyong Liu ,&nbsp;Guihua Wen","doi":"10.1016/j.engappai.2026.114127","DOIUrl":"10.1016/j.engappai.2026.114127","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"170 ","pages":"Article 114127"},"PeriodicalIF":8.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161878","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}

{"title":"Forecast-enhanced bilevel real-time pricing for microgrids via hybrid-action reinforcement learning","authors":"Jingqi Wang ,&nbsp;Yan Gao ,&nbsp;Youmeng He","doi":"10.1016/j.engappai.2026.114195","DOIUrl":"10.1016/j.engappai.2026.114195","url":null,"abstract":"<div><div>The integration of distributed energy resources into microgrids faces many complex challenges, including renewable intermittency, hybrid decision-making, and hierarchical coordination. This paper presents a forecast-enhanced bilevel real-time pricing framework using a hybrid-action deep reinforcement learning (DRL) algorithm with Gumbel-Softmax reparameterization. The framework manages both discrete generator commitment and continuous pricing decisions through integrated optimization. Our approach integrates Long Short-Term Memory (LSTM) forecasting to enhance proactive scheduling, while coordinating microgrid agents through a bilevel optimization architecture. The main innovations include: a hybrid-action DRL algorithm integrating Gumbel-Softmax reparameterization for joint discrete–continuous optimization; LSTM-based renewable forecasting integrated into state representation. Our DRL approach shows enhanced system performance with improved constraint satisfaction and operational efficiency, offering a practical solution for complex hybrid-action energy optimization problems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"170 ","pages":"Article 114195"},"PeriodicalIF":8.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146193015","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}

{"title":"Maximal margin hyper-ellipsoid support vector machine for multi-class classification","authors":"Ting Ke,&nbsp;Mingzhu Meng,&nbsp;Feifei Yin","doi":"10.1016/j.engappai.2026.114055","DOIUrl":"10.1016/j.engappai.2026.114055","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114055"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174799","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}

{"title":"Self-discharge estimation for lithium-ion batteries based on formation data in production","authors":"Haoyuan Zheng ,&nbsp;Shaobin Yang ,&nbsp;Weihua Xue ,&nbsp;Shouzhen Xiao ,&nbsp;Ding Shen ,&nbsp;Wei Dong ,&nbsp;Xu Zhang","doi":"10.1016/j.engappai.2026.114180","DOIUrl":"10.1016/j.engappai.2026.114180","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114180"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174800","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}

{"title":"Bidirectional encoder representations from transformer fusion quantum dual-stage attention bidirectional gated recurrent unit and diffusion method for short-term wind power prediction","authors":"Linfei Yin,&nbsp;Yufeng Liu","doi":"10.1016/j.engappai.2026.114095","DOIUrl":"10.1016/j.engappai.2026.114095","url":null,"abstract":"<div><div>With wind energy increasing proportion in renewable energy structure, wind energy is already a backbone in low carbon energy structure. Short-term wind power prediction can assist the demand for real-time dispatching of wind farms and power grids. Regard to the problems of low prediction accuracy and long training time of existing prediction models for short-term wind power prediction, this study proposes a large-model bidirectional encoder representations from Transformer fusion quantum dual-stage attention bidirectional gated recurrent unit and diffusion method for short-term wind power prediction. The proposed method utilizes improved complete ensemble empirical mode decomposition with adaptive noise to decompose the wind power, and then the decomposed data are input into the quantum dual-stage attention bidirectional gated recurrent unit and quantum diffusion model for training prediction; then, the bidirectional encoder representations from Transformer provides final wind power prediction. Compared with 52 prediction algorithms, the average absolute error of the proposed method is more than 30.57% less. Furthermore, the addition of parameterized quantum circuits shortens training prediction time by nearly 25%.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114095"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174803","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}

{"title":"Exploring the sustainable development path of global digital service trade stability: A hybrid approach perspective","authors":"Shikang Kang,&nbsp;Yu Shang","doi":"10.1016/j.engappai.2026.114172","DOIUrl":"10.1016/j.engappai.2026.114172","url":null,"abstract":"<div><div>As the digitization process accelerates, unbalanced sustainable development conditions exacerbate the instability and risks of global digital service trade. Based on the theoretical framework of sustainable development, this study takes panel data of 161 economies from 2014 to 2023 as a sample and employs a hybrid approach of empirical analysis-dynamic fuzzy set qualitative comparisons (dynamic QCA)-artificial neural network (ANN) to identify the sustainability capabilities that drive the digital service trade stability (Dts), and to explore the sustainability portfolio paths that generate high Dts and the cases. The results show that 11 drivers are significantly and positively correlated with Dts and that a single sustainability capability does not constitute high Dts. Three combination paths exist to achieve high Dts, with industrialized innovation capabilities distributed across each path. Economic coherence, ecological sustainability, social peace and inclusion, and sustainable health and well-being as alternatives to the combination paths. The most influential antecedent condition is industrialization innovation capacity, followed by ecological sustainability. The findings demonstrate that tailored combinations of sustainable capabilities, rather than any single factor, underpin trade resilience. This study proposes and validates a hybrid research framework for artificial intelligence (AI) empowerment. This framework not only reveals the multiple driving paths of the stability of digital service trade, enriches the research of sustainable development, but also provides a new AI methodology paradigm for the interpretable causal discovery of complex socio-economic systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 114172"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174864","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}

{"title":"Transformers meet hyperspectral imaging: A comprehensive study of models, challenges and open problems","authors":"Guyang Zhang,&nbsp;Waleed Abdulla","doi":"10.1016/j.engappai.2026.113947","DOIUrl":"10.1016/j.engappai.2026.113947","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"169 ","pages":"Article 113947"},"PeriodicalIF":8.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174847","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}