Advanced Engineering Informatics最新文献

{"title":"Helicopter trajectory planning method based on improved IRRT*-D* algorithm in forest fire rescue scenarios","authors":"Jia Yuan ,&nbsp;Quan Shao ,&nbsp;Jianhong Sun","doi":"10.1016/j.aei.2025.103947","DOIUrl":"10.1016/j.aei.2025.103947","url":null,"abstract":"<div><div>Helicopters are widely employed in forest fire rescue operations due to their strong manoeuvrability, rapid response capabilities, and fewer terrain constraints. However, because of the dynamic and unpredictable nature of forest fire environments, helicopters may inadvertently enter high-risk areas—such as zones with extreme temperatures and dense smoke—when following pre-planned routes, thereby compromising flight safety. To address the challenges associated with helicopter firefighting and rescue operations in dynamic fire scenarios, this paper firstly constructs a multi-threat environment model tailored to forest fire conditions. Considering the performance constraints of various rescue helicopters, an improved IRRT*-D* trajectory planning algorithm is proposed that accounts for both helicopter heterogeneity and environmental dynamics. This integrated trajectory planning algorithm combines the rapid search capability of the Informed-RRT* algorithm with the path optimization strength of the D* Lite algorithm, ensuring that safe and effective flight paths are generated in real time for helicopters operating in dynamic fire environments. Finally, the effectiveness of the proposed method is evaluated through simulation experiments conducted in different forest fire scenarios, with results compared to those of other trajectory planning algorithms. The simulation outcomes demonstrate that the improved IRRT*-D* algorithm exhibits fast convergence, optimal path quality, and algorithmic stability when addressing helicopter trajectory planning in complex fire environments, thereby ensuring the safe operation of helicopters in high-risk forest fire scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103947"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-augmented contrastive learning framework for quantitative diagnosis under limited data conditions","authors":"Kexin Yin ,&nbsp;Chunjun Chen ,&nbsp;Fengyu Ou ,&nbsp;Boyuan Mu ,&nbsp;Lu Yang ,&nbsp;Yaowen Zhang","doi":"10.1016/j.aei.2025.103930","DOIUrl":"10.1016/j.aei.2025.103930","url":null,"abstract":"<div><div>Quantitative diagnosis of bearing faults is essential for ensuring the safe and reliable operation of mechanical transmission systems, especially under complex and variable operating conditions. However, in real-world scenarios, collecting sufficient labeled fault data remains a major challenge, hindering accurate fault classification and severity estimation. While diffusion models have shown strong potential in data generation, existing methods primarily use them to expand training sets without explicitly modeling fault semantics or guiding the learning process. To address these limitations, we propose a novel Diffusion-Augmented Contrastive Learning (DiCL) framework for quantitative bearing fault diagnosis under limited data conditions. First, a fault-controllable denoising diffusion probabilistic model (DDPM) is developed to generate class-conditional synthetic signals across various fault types and severity levels. These synthetic samples are further used to construct compound fault labels that reflect complex or multi-fault conditions. Second, a dual-branch contrastive learning strategy is adopted, where two input samples are jointly processed to form contrastive pairs. This mechanism enables the feature extraction network to learn fault-discriminative representations by reinforcing shared fault characteristics and suppressing irrelevant variations. Third, a cycle-consistency constraint is introduced via a composite loss function to enforce semantic alignment among samples of the same fault class. The proposed DiCL framework is evaluated on two bearing fault datasets: the Paderborn University bearing dataset and a laboratory-scale mechanical transmission system dataset. Experimental results demonstrate that DiCL achieves high-fidelity data generation and superior diagnostic performance. Notably, even with only 5% of the fault training set, DiCL attains over 80% classification accuracy on both benchmark datasets, significantly outperforming state-of-the-art baseline methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103930"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCMDA: A continual learning and frugal AI based quality inspection mechanism for edge computing platforms","authors":"Garima Nain ,&nbsp;K.K. Pattanaik ,&nbsp;G.K. Sharma ,&nbsp;Himanshu Gauttam","doi":"10.1016/j.aei.2025.103929","DOIUrl":"10.1016/j.aei.2025.103929","url":null,"abstract":"<div><div>Industrial-edge empowered Deep Learning (DL) solutions facilitate indigenous Predictive Quality Inspection (PQI) of Mass Production (MP) processes and products. However, the existing DL-based PQI systems fail in Mass Customized and Personalized Product (MCPP) setups. DL models mandate maintenance to ensure sustainable solutions and challenges faced are (i) unavailability of previous product data, (ii) limited data availability of new MCPPs, and (iii) resource-efficiency and near-real-time execution of these mechanisms at the industrial edge. To address aforementioned issues, this paper proposes a Memory Aware Synapses (MAS) and frugal Artificial Intelligence (AI) solution named <em><strong>M</strong>AS-<strong>C</strong>loning over <strong>M</strong>ixUp-based <strong>D</strong>ata <strong>A</strong>ugmentation (<strong>MCMDA</strong>)</em>. The MAS scheme, a regularization-based continual learning scheme, ensures the learning of new MCPPs while preserving the past information under unavailability of previous product data. The limited data availability of new MCPPs’ is resolved via Frugal-AI solutions such as knowledge transfer in better weight initialization of new output heads (weight cloning) and MixUp-based data augmentation for less enchanted predictive/regression tasks. A mechanism to generate optimal synthetic data using MixUp-based data augmentation is incorporated for supreme DL-model performance. Compared to state-of-the-art schemes, <em>MCMDA</em> enhances model performance by 71.3%, reduces storage necessity by 32.92%, and minimizes training cost by 8.71%–57.71% for real-world injection molding use-case.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103929"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable eye-tracking-based framework for enhanced level-specific situational awareness recognition in air traffic control","authors":"Xing Yao ,&nbsp;Chun-Hsien Chen ,&nbsp;Bufan Liu ,&nbsp;Guorui Ma ,&nbsp;Xiaoqing Yu","doi":"10.1016/j.aei.2025.103928","DOIUrl":"10.1016/j.aei.2025.103928","url":null,"abstract":"<div><div>Situational awareness (SA) recognition is essential for air traffic controllers (ATCOs) to ensure operational safety in human-AI collaborative environments. The existing studies have primarily focused on overall SA assessment, neglecting its three distinct levels: perception (SA1), comprehension (SA2), and projection (SA3). This study presents an explainable eye-tracking-based three-phase framework for SA recognition. In Phase 1, an unsupervised learning approach was employed to annotate SA levels from behavioral data. Phase 2 involved statistical analysis to extract salient eye-tracking features associated with each SA level. In Phase 3, an ensemble model was developed by integrating the most effective classical algorithms to perform level-specific SA recognition with enhanced robustness and accuracy; SHAP (SHapley Additive exPlanations) values were further employed to interpret feature contributions for the best-performing model at each SA level. To validate the proposed framework, a simulated air traffic control (ATC) radar monitoring experiment incorporating three-level SA-probe tests was conducted with 18 participants. Five-fold cross-validation assessed overall model performance, while Leave-One-Subject-Out (LOSO) evaluated its generalizability across individuals. The ensemble model achieved consistently high accuracy across all SA levels under both evaluation strategies. SHAP analysis highlighted fixation duration, fixation count, and saccade count as key features, with their contributions varying by SA level. These findings demonstrate the need for level-specific SA recognition and lay the foundation for accurate SA monitoring in ATC and other high-risk domains, improving model transparency and interpretability for enhanced operational safety.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103928"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidence-V2X: Confidence-driven sparse communication for efficient V2X cooperative perception","authors":"Xiaojun Tan ,&nbsp;Rui Wang ,&nbsp;Jinping Wang ,&nbsp;Shuai Wang ,&nbsp;Xu Wang ,&nbsp;Dongsheng Wu","doi":"10.1016/j.aei.2025.103914","DOIUrl":"10.1016/j.aei.2025.103914","url":null,"abstract":"<div><div>Collaborative perception enables agents to enhance their perceptual capabilities by exchanging feature messages with others. However, prior work has typically focused on individual aspects, such as independently investigating improvements in joint perception or reducing communication burdens, often at the expense of achieving strong overall system performance across varying environments. To address the problem of achieving an optimal balance between the object detection ability and communication load of a model, we propose Confidence-V2X, a novel cooperative perception method, that emphasizes the dynamic gating of the feature exchange strategy as well as the optimization of sparse feature refinement and fusion techniques. In Confidence-V2X, we first refine the given raw perceptual features using confidence maps and perform structured packaging to fully prepare for the subsequent process. Next, the outbound interagent communication procedure for compact data exchange is dynamically gated and uniformly scheduled based on a whitelist. Finally, agents update the sparse features along the temporal dimension and adaptively fuse them in the spatial dimension based on confidence information to obtain the final cooperative perception result. Extensive experiments conducted on three datasets demonstrate that Confidence-V2X achieves superior performance to that of the existing methods across multiple metrics while markedly reducing the imposed communication overhead. Our corresponding code will be released on <span><span>https://github.com/Rwang0208/Confidence-V2X</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103914"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-centric proactive design for manufacturing with deep generative modeling in Industry 5.0","authors":"Yanzhen Jing ,&nbsp;Guanghui Zhou ,&nbsp;Chao Zhang ,&nbsp;Fengtian Chang","doi":"10.1016/j.aei.2025.103952","DOIUrl":"10.1016/j.aei.2025.103952","url":null,"abstract":"<div><div>In Industry 5.0, human-centric smart manufacturing prioritizes the needs of technologists to help enterprises sustain competitive advantages. In this context, design for manufacturing (DFM) plays an essential role, as it ensures the manufacturability of digital designs to deliver high-quality products. Due to novice designers’ limited manufacturing knowledge, the implementation of DFM depends on repeated and passive design iterations, placing a heavy burden on designers. Existing research on improving DFM focuses on manufacturability analysis, which only provides analysis results but ignores novice designers’ manufacturability needs for design modifications. To bridge the gap, this paper proposes a novel human-centric proactive DFM approach that aims to address designers’ manufacturability needs throughout the design process to reduce passive iterations and meet evolving industry demands. Specifically, considering multiple design parameters, a deep learning network is trained for 3D model generation and similarity calculation. Next, the learned network can support human-centric proactive DFM, which includes two parts: automated manufacturability guidance for incomplete designs and manufacturability analysis for complete designs. Through 3D model generation, incomplete designs can be completed and unmanufacturable designs can be modified. Furthermore, similarity calculation facilitates historical manufacturable case recommendation to meet designers’ needs in their decision-making. Experimental results show the efficacy of the approach, achieving accuracy improvements of 4.17% on the impeller dataset and 4% on the manufacturing feature dataset in manufacturability analysis, compared with state-of-the-art approaches. Application examples demonstrate its effectiveness to assist novice designers to proactively improve product manufacturability.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103952"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A causal saliency enhancement and Mamba-based multi-scale feature fusion encoding framework for railway fastener fault diagnosis","authors":"Quanyu Long ,&nbsp;Dechen Yao ,&nbsp;JianWei Yang ,&nbsp;Jia Dong ,&nbsp;Bin Zhu ,&nbsp;YuanTing Dai","doi":"10.1016/j.aei.2025.103933","DOIUrl":"10.1016/j.aei.2025.103933","url":null,"abstract":"<div><div>Fault detection of railway fasteners is vital for ensuring train operation safety within intelligent rail maintenance systems. However, existing Transformer-based detectors often incur high computational overhead and exhibit limited adaptability to multi-scale variations and complex background interference. To address these limitations, we propose SaM-DETR, a novel object detection framework that deeply couples causal saliency enhancement with a Mamba-based Multi-scale State Space Module (MSSM). Specifically, we introduce a differentiable Top-K selection mechanism combined with a causal invariance loss to adaptively learn the optimal foreground–background ratio, effectively suppressing spurious correlations and enhancing salient feature extraction. Furthermore, we replace the vanilla transformer encoder with a linear-complexity Mamba-based MSSM encoder, enabling efficient long-range feature fusion while dramatically reducing giga floating-point operations (GFLOPs). Extensive experiments on two real-world railway fastener datasets with different resolutions demonstrate that SaM-DETR achieves a balanced trade-off among detection accuracy, computational efficiency, and inference speed, outperforming mainstream DEtection TRansformer (DETR)-based models. These results validate the robustness and lightweight advantages of its novel causal-saliency—driven multi-scale design in multi-resolution industrial scenarios.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103933"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing aero-engine blade few-shot anomaly detection with visual-language multi-modal models under domain shift conditions","authors":"Jiafeng Tang,&nbsp;Kunpeng Tan,&nbsp;Zhibin Zhao,&nbsp;Xingwu Zhang,&nbsp;Chuang Sun,&nbsp;Xuefeng Chen","doi":"10.1016/j.aei.2025.103953","DOIUrl":"10.1016/j.aei.2025.103953","url":null,"abstract":"<div><div>The blade is a critical component of the aero-engines, and the regular inspection of blades is essential for the healthy operation of aero-engines. Data-driven deep learning (DL) methods for blade anomaly detection gain significant success. However, the nature of data-hungry hampers the further development of general DL-based methods. In light of the insights garnered from the intricate feature embedding of the recent large-scale models, we discern their aptitude for the extensive potential in blade anomaly detection (BAD), notably within contexts constrained by scant sample sizes. Additionally, we are aware of the real-world domain shift problems caused by complex circumstances in aero-engine BAD highly cripple the performance of large-scale models. Thus, we propose a domain semantic perception for blade anomaly detection (DSP4BAD), a few-shot BAD method based on the visual-language large-scale model (CLIP), for exploring the potential of large-scale models and mitigating the above existing challenges. In general, benefiting from the CLIP’s extensive pre-training knowledge in large-scale datasets, DSP4BAD can obtain excellent anomaly detection performance by embracing the tailored knowledge from few-shot samples. To this end, we develop the domain-state prompt augmentation template (DSPAT) and the attention-guided feature adaptation module (AGFAM) to facilitate the adaptation of CLIP’s general knowledge to the domain-specific ones of BAD. Meanwhile, given the redundancy of in-context information in CLIP, an in-context semantic refinement module (ISRM) is devised for purifying the context semantic about anomaly to further alleviate the domain shift issues. Extensive experiment results demonstrate that our DSP4BAD achieves the state-of-the-art performance of anomaly detection with few-shot samples, which provides a promising tack for applications of large-scale models toward real-world blade inspection.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103953"},"PeriodicalIF":9.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implicitly inspired prediction approach for design thinking with multi-domain analogical knowledge driven by electroencephalogram data","authors":"Liting Jing ,&nbsp;Jianglong Du ,&nbsp;Yubo Dou ,&nbsp;Chulin Tian ,&nbsp;Di Feng ,&nbsp;Shaofei Jiang","doi":"10.1016/j.aei.2025.103949","DOIUrl":"10.1016/j.aei.2025.103949","url":null,"abstract":"<div><div>Explaining the potential relationship between analogical knowledge and target design problems is vital in analogical design. Existing studies neglect the role of multi-domain analogical knowledge in stimulating innovative design thinking. Furthermore, when data mining methods are used to evaluate the inspirational effect of analogical knowledge, the cognitive psychological state of designers is not fully considered. To address these issues, an implicitly inspired prediction approach for design thinking with multi-domain analogical knowledge driven by electroencephalogram (EEG) data is proposed. First, the fuzzy best–worst-method (BWM) model is used to screen analogical knowledge across three domains, namely, biology, abstract principles, and engineering case knowledge, which are retrieved from the AskNature platform, TRIZ effect webpage, and patent database, respectively, and then the transfer characteristics and semantic similarity of analogical knowledge are defined to support encoding. Second, an EEG experiment is designed. In the experiment, analogical knowledge from different domains serves as target stimuli, and the subjects are required to conduct knowledge transfer reasoning and scheme evaluation on the analogical knowledge presented in sequence. By collecting EEG data and mining the power density indicators of the frequency-domain features, the cognitive preferences of the subjects toward analogical knowledge are analyzed. Third, a support vector machine (SVR) model is constructed to predict the inspirational effect of analogical knowledge, after which the most suitable analogical knowledge is screened. A practical case study of a metal ore crushing and separation device is employed to validate the proposed approach. The validation results confirm that mining EEG data can explore the inspirational effect of analogical knowledge and parse designers’ psychological states during the design process.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103949"},"PeriodicalIF":9.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic perception of potential safety hazards: A cross-modal multi-task framework for feature alignment, image classification and captioning","authors":"Yanjun Guo ,&nbsp;Xinbo Ai ,&nbsp;Mingxiu Guo ,&nbsp;Shaoyang Cheng","doi":"10.1016/j.aei.2025.103919","DOIUrl":"10.1016/j.aei.2025.103919","url":null,"abstract":"<div><div>Automatic perception of potential safety hazards (PSHs) is critical for ensuring workplace safety and protecting property against significant threats. PSHs perception involves determining whether hazards exist, capturing on-site images, and completing inspection reports, which are critical for mitigating these risks. Though computer vision techniques like image classification and image captioning offer promising alternatives for PSHs perception. However, comprehensive hazard perception requires not only hazard identification but also semantic relationship comprehension among scene entities to formulate descriptive safety reports. To address the multifaceted nature of PSHs perception, this study proposes a cross-modal multi-task learning (MTL) method named Hazard-MTL, which jointly optimizes three synergistic tasks: feature alignment (image–text), binary image classification, and image captioning. Specifically, our approach employs a scene graph-guided chain-of-thought data augmentation method that integrates knowledge prompts and multi-task contextual reasoning to produce semantically coherent and informationally complete risk descriptions. To improve the model robustness, a bidirectional contrastive loss was designed to suppress irrelevant cross-modal similarities. Additionally, a dynamic joint training strategy is introduced that combines progressive teacher forcing with adaptive loss weighting to achieve harmonized multi-task optimization. Our model outperforms single-task baselines with 72.7% <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score (+10.7%) for PSHs classification and 1.575 CIDEr (+0.619) for description generation. Hazard-MTL advances holistic scene understanding by integrating MTL, offering a safer automated solution for enterprise and construction safety management.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103919"},"PeriodicalIF":9.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}