Advanced Engineering Informatics最新文献

{"title":"Font conversion for steel product number recognition: A conditioned diffusion model approach","authors":"Taehan Lee ,&nbsp;Hyeyeon Choi ,&nbsp;Bum Jun Kim ,&nbsp;Hyeonah Jang ,&nbsp;Donggeon Lee ,&nbsp;Sang Woo Kim","doi":"10.1016/j.aei.2025.103368","DOIUrl":"10.1016/j.aei.2025.103368","url":null,"abstract":"<div><div>In the steel manufacturing industry, it is crucial to automatically recognize semi-finished product numbers to avoid mix-ups and ensure that each product is processed according to its specific material properties. The advancement of deep learning has significantly improved the recognition of steel product numbers, particularly those printed by machines with consistent thickness and spacing, resulting in high recognition accuracy. Conversely, handwritten numbers by workers are often challenging to recognize due to varying thickness, spacing, being too thin, partially erased, or overwritten with scribbles. This inconsistency causes low recognition accuracy of steel product number recognition models for fonts with insufficient training data or fonts not seen during training. The models must be updated periodically whenever a new font is used and remain vulnerable to new fonts until sufficient data is accumulated and updated. In this paper, we propose a Font Changer that converts various fonts into a representative font to address these issues. Font Changer is designed to learn the trajectory from a Gaussian distribution to the data distribution of images generated in a representative font with clean background. Font Changer, composed of a conditional image encoder and a diffusion model, extracts location, size, and number information from the original image containing the steel product number. The extracted information is then used as a condition for the diffusion model, allowing it to generate the closest sample within the data distribution. Images processed by the Font Changer exhibit uniformity, ensuring the consistency of steel product number images. Experiments demonstrate that the Font Changer enhances number recognition by removing background noise and converting even messy and damaged images into a consistent representative font. Our proposed method advances the steel manufacturing industry by standardizing fonts in work environments with diverse handwritten fonts.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103368"},"PeriodicalIF":8.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882540","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":"Robust support vector machine based on the bounded asymmetric least squares loss function and its applications in noise corrupted data","authors":"Jiaqi Zhang,&nbsp;Hu Yang","doi":"10.1016/j.aei.2025.103371","DOIUrl":"10.1016/j.aei.2025.103371","url":null,"abstract":"<div><div>The support vector machine (SVM) is a popular machine learning tool that has achieved great success in various fields, but its performance is significantly disturbed on noise corrupted datasets. In this paper, motivated by the bounded quantile loss function, based on the relationship of the expectile and asymmetric least squares loss function, we propose the bounded asymmetric least squares loss function (<span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>b</mi><mi>a</mi><mi>l</mi><mi>s</mi></mrow></msub></math></span> loss function). The <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>b</mi><mi>a</mi><mi>l</mi><mi>s</mi></mrow></msub></math></span> loss function is an extension of the asymmetric least squares loss function. <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>b</mi><mi>a</mi><mi>l</mi><mi>s</mi></mrow></msub></math></span> loss function inherits the good properties from the asymmetric least squares loss function, such as asymmetric and differentiable. Further, <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>b</mi><mi>a</mi><mi>l</mi><mi>s</mi></mrow></msub></math></span> loss function is more robust to noise in classification and regression problems. Next, we propose two models based on <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>b</mi><mi>a</mi><mi>l</mi><mi>s</mi></mrow></msub></math></span> loss function, namely, BALS-SVM and BALS-SVR. The <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>b</mi><mi>a</mi><mi>l</mi><mi>s</mi></mrow></msub></math></span> loss function is a non-convex loss function which makes it difficult to optimize. Thus, we design a clipping dual coordinate descent (clipDCD) based half-quadratic algorithm for solving the proposed models. We further find that BALS-SVM and BALS-SVR can be viewed as iterative weighted asymmetric least squares loss function based support vector machines and support vector regression, which enhances the interpretability of the models. Finally, we provide a theoretical analysis of the model based on a general framework of bounded loss function, mainly including Fisher consistency and noise insensitivity. Meanwhile, theoretical guarantees are provided for the proposed models. The results on the simulated dataset and the 14 classification and 11 regression benchmark dataset show that our method is superior compared to the classical methods and some state-of-the-art methods, especially on the noise corrupted dataset. The statistical tests further confirm this fact. Experiments on the Fashion MNIST dataset and gene expression dataset further illustrate that our proposed model also performs well in real environments.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103371"},"PeriodicalIF":8.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882538","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":"Climate-resilient epoxy asphalt mixture design: An intelligent framework","authors":"Ke Zhang ,&nbsp;Zhaohui Min ,&nbsp;Wei Huang ,&nbsp;Theunis F.P. Henning ,&nbsp;Xiatong Hao ,&nbsp;Kaimo Shao","doi":"10.1016/j.aei.2025.103395","DOIUrl":"10.1016/j.aei.2025.103395","url":null,"abstract":"<div><div>Epoxy asphalt mixture exhibits excellent durability, crack resistance and high-temperature stability, making it an ideal choice for climate-resilient pavement materials. In order to expand its application scope and maximize its advantage, it is necessary to propose more advanced mixture design method. This study proposed an intelligent design framework combining machine learning and metaheuristic algorithms for developing epoxy asphalt mixture. First, high-accuracy prediction models of the performance of epoxy asphalt mixture under high and low-temperature environments were established using Extreme Gradient Boosting optimized by Particle Swarm Optimization (PSO-XGBoost). Then, interpretability analysis, including feature importance and accumulated local effects, was conducted based on these models to identify the key design features of epoxy asphalt mixture and determine their empirical value ranges to achieve satisfactory mixture performance. Next, diversified strategies were determined to meet engineering needs, including high performance, low cost and carbon emissions, as well as a comprehensive strategy that incorporates all these objectives. Subsequently, multi-objective optimization models considering these strategies were established, and the optimal solutions were generated based on the Third Generation of Non-dominated Sorting Genetic Algorithm (NSGA-III) and TOPSIS. Finally, the practical feasibility of these solutions was confirmed through laboratory tests. Based on the proposed framework, high-performance, cost-effective, and environmentally sustainable epoxy asphalt mixtures can be obtained. This study sets a new benchmark for future research in the intelligent design of sustainable pavement materials, emphasizing the practical and theoretical implications of integrating advanced computational tools in pavement material science.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103395"},"PeriodicalIF":8.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882537","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":"Coupled prediction method for assembly precision and performance of composite structures based on a hybrid saint-venant’s principle and neural network approach","authors":"Xin Tong,&nbsp;Jianfeng Yu,&nbsp;Dong Xue,&nbsp;He Zhang,&nbsp;baihui Gao,&nbsp;Jie Zhang,&nbsp;Yuan Li","doi":"10.1016/j.aei.2025.103401","DOIUrl":"10.1016/j.aei.2025.103401","url":null,"abstract":"<div><div>The application of composite materials and interference fit technology in aerospace products presents new challenges to assembly quality requirements: specifically, the demand for higher assembly precision and reduced assembly stress, as these factors directly impact the aerodynamic performance and service life of the product. Consequently, a large number of assembly deviation and stress predictions are necessary during the aircraft structure design process. To meet the requirements for prediction accuracy and efficiency under the constraints of large data volumes and high computational costs, this study proposes an innovative method for the rapid prediction of assembly precision and performance coupling (CPAP) in composite structures. This method combines Saint-Venant’s principle with finite element analysis (FEA) to create an efficient sample generation technique that can quickly provide key data on assembly deviations and stress around the interference fit holes (SAH). Additionally, dimensionality reduction techniques are incorporated into the metamodel (MM), effectively capturing the nonlinear relationships between assembly process parameters and both assembly precision and performance. This results in a predictive coupling model with statistical analysis capabilities. Case studies demonstrate that the method proposed in this study significantly improves prediction efficiency compared to traditional approaches. Furthermore, the results highlight the substantial influence of interference fit process parameters on the assembly accuracy and performance of single longitudinal splicing (SLS) joint structures. This research offers an effective tool for controlling the assembly quality of aerospace products, contributing to technological innovation and advancements in the aerospace industry.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103401"},"PeriodicalIF":8.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878895","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":"Concise belief rule base with credibility decay for system performance prediction","authors":"Jie Wang ,&nbsp;Yaqian You ,&nbsp;Zhijie Zhou ,&nbsp;Peng Zhang","doi":"10.1016/j.aei.2025.103385","DOIUrl":"10.1016/j.aei.2025.103385","url":null,"abstract":"<div><div>In engineering scenarios, the performance of industrial systems varies continuously, making it necessary to develop a prediction model to track system performance. Recently, a modeling approach known as the concise belief rule base (CBRB) has provided an effective reference for performance prediction. However, CBRB ignores the decay phenomenon of information credibility during the prediction process, leading to suboptimal output accuracy. To address this limitation, a novel performance prediction model based on the concise belief rule base with credibility decay (CBRB-CD) is put forward. The proposed model incorporates a decay factor to reflect the property that the credibility of belief rules decays over time. Meanwhile, the decay factor is aggregated into the fusion process of belief rules, from which the prediction results are generated. Furthermore, a stability analysis of the prediction model is carried out by introducing external perturbations to validate the prediction results. The analysis results quantitatively reveal the changing patterns of prediction results under perturbed environments. Finally, real-world experiments on aerospace relays demonstrate the feasibility of the proposed model.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103385"},"PeriodicalIF":8.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877322","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":"Collaborative garment design through group chatting with generative industrial large models","authors":"Arjun Rachana Harish ,&nbsp;Zhaolin Yuan ,&nbsp;Ming Li ,&nbsp;Hongxia Yang ,&nbsp;George Q. Huang","doi":"10.1016/j.aei.2025.103366","DOIUrl":"10.1016/j.aei.2025.103366","url":null,"abstract":"<div><div>The collaborative garment designing lifecycle involves stages such as designing, styling, and patterning. Some of these stages can be partially or fully automated using industrial large models (LMs), such as generative and large language models. The key to quick and cost-effective order fulfillment is the orchestration of group interactions, or a group chat, between the stakeholders and LMs in garment design. However, certain unaddressed aspects, such as knowledge retention, generalization, and complexity of group interaction, are critical to realizing group chat for garment design. This study proposes a framework called ChatFashion for group chat in garment design. Transformer, a core construct of the proposed framework, orchestrates interaction among stakeholders and industrial LMs. It undergoes an evolution with the intelligence it picks up from its interaction with diverse stakeholders and industrial LMs, allowing it to act as a one-stop solution for multidisciplinary design needs. This study contributes to theory in the following aspects. First, it proposes transformers to eliminate concerns about knowledge retention by industrial LMs. Second, while other studies focus on the benefits of industrial LMs to simplify individual stages in garment design, this study introduces the design and demonstration of a ChatFashion framework for collaborative garment designing using industrial LMs. Finally, this study advances the literature on prompt engineering of industrial LMs by utilizing collaborative learning (or models learning from each other) to capture and orchestrate the group chat among stakeholders, signifying its practicality and value for research in garment design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103366"},"PeriodicalIF":8.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877324","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":"GLoU-MiT: Lightweight Global-Local Mamba-Guided U-mix transformer for UAV-based pavement crack segmentation","authors":"Jinhuan Shan ,&nbsp;Yue Huang ,&nbsp;Wei Jiang ,&nbsp;Dongdong Yuan ,&nbsp;Feiyang Guo","doi":"10.1016/j.aei.2025.103384","DOIUrl":"10.1016/j.aei.2025.103384","url":null,"abstract":"<div><div>The utility of Unmanned Aerial Vehicles (UAVs) for routine pavement distresses inspection has been increasingly recognized due to their efficiency, flexibility, safety, and low-cost automation. However, UAV-acquired high-altitude images present unique challenges for deep learning-based semantic segmentation models, such as minute crack details, blurred boundaries, and high levels of environmental noise. We propose GLoU-MiT, a lightweight segmentation model designed to address the difficulties of UAV-based pavement crack segmentation. Our model integrates a U-shaped Mix Transformer architecture for efficient hierarchical feature extraction, a Global-Local Mamba-Guided Skip Connection for improved feature alignment and computational efficiency, and a Boundary / Semantic Deep Supervision Refinement Module to enhance segmentation precision in complex scenarios. Extensive experiments on UAV-Crack500, CrackSC and Crack500 datasets demonstrate that GLoU-MiT effectively improves segmentation accuracy, particularly in low-contrast and complex background environments, making it a robust solution for UAV-based pavement crack inspection tasks. Furthermore, inference speed and energy consumption evaluations conducted on the Jetson Orin Nano (8 GB) show that our model achieves an excellent balance between accuracy, energy efficiency, and speed. The code will be released at: <span><span>https://github.com/SHAN-JH/GLoU-MiT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103384"},"PeriodicalIF":8.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877323","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":"ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades","authors":"Mariam Hassan ,&nbsp;Florent Forest ,&nbsp;Olga Fink ,&nbsp;Malcolm Mielle","doi":"10.1016/j.aei.2025.103345","DOIUrl":"10.1016/j.aei.2025.103345","url":null,"abstract":"<div><div>Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, incorporating thermal data only through a post-hoc projection. Due to the lower resolution of thermal cameras and the challenges of RGB/Thermal camera calibration, this post-hoc projection often results in spatial discrepancies between temperatures projected onto the 3D model and real temperatures at the surface. We propose ThermoNeRF, a novel multimodal Neural Radiance Fields (NerF) that renders new RGB and thermal views of a scene with joint optimization of the geometry and thermal information while preventing cross-modal interference. To compensate for the lack of texture in thermal images, ThermoNeRF leverages paired RGB and thermal images to learn scene geometry while maintaining separate networks for reconstructing RGB color and temperature values, ensuring accurate and modality-specific representations. We also introduce ThermoScenes, a dataset of paired RGB+thermal images comprising 8 scenes of building facades and 8 scenes of everyday objects enabling evaluation in diverse scenarios. On ThermoScenes, ThermoNeRF achieves an average mean absolute error of 1.13 °C for buildings and 0.41 °C for other scenes when predicting temperatures of previously unobserved views. This improves accuracy by over 50% compared to concatenated RGB+thermal input in standard NeRF. While ThermoNeRF performs well on aligned RGB-thermal images, future work could address misaligned or unpaired data for better generalization. <span><span>Code</span><svg><path></path></svg></span> and <span><span>dataset</span><svg><path></path></svg></span> are available online.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103345"},"PeriodicalIF":8.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874436","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":"Streamlining design-to-manufacturing for assembly-based robotics in wood panel framing tasks of industrialized construction: Introducing a BIM-to-BoT (B2B) framework","authors":"Behnam M. Tehrani,&nbsp;Aladdin Alwisy","doi":"10.1016/j.aei.2025.103393","DOIUrl":"10.1016/j.aei.2025.103393","url":null,"abstract":"<div><div>Robotics hold immense potential to transform the industrialized construction (IC) sector by enhancing productivity, drawing on its similarities with the manufacturing industry. However, the unique nature of construction projects and the demand for customized building designs present substantial technological hurdles for incorporating robotics into IC activities. The time and cost burdens of the project- and task-driven reprogramming of robotic systems can outweigh the perceived benefits of robotics-based automation. Those burdens are further compounded by the notable lack of research addressing the technical collaboration issues between virtual design and construction software, specifically Building Information Modeling (BIM) tools, and commercially available robotic software suites. This research aims to promote the adoption of construction robotics by streamlining the design-to-manufacturing processes of framing tasks in IC. The proposed framework is integrated into a software platform that establishes a collaborative environment for BIM and assembly-based robotics in panel framing tasks of IC, referred to as BIM-to-Bot (B2B). This platform uses architectural BIM models from Revit and automatically converts building design information into shop drawings and robotics-based manufacturing procedures for a multi-robot system designed specifically for framing tasks in IC. The proposed framework was evaluated through a case study revealing significant efficiency improvements. The drafting process improved by 93.33%, and the robotic programming process improved by 88.66% when using the proposed method compared to manual drafting and programming, resulting in an 89.34% time savings in BIM-to-Robot processes. By enabling the integration of robotics into panel framing tasks, this research bridges the crucial gap between building designs and robotic assembly. The enhanced efficiency associated with the proposed streamlined design-to-manufacturing processes is expected to pave the way for the broader adoption of robotics in the construction industry, heralding a new era of IC.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103393"},"PeriodicalIF":8.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874877","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":"Collaborative 3D object detection by smart vehicles considering semantic information and agent heterogeneity","authors":"Zhe Huang ,&nbsp;Yongcai Wang ,&nbsp;Deying Li ,&nbsp;Yunjun Han ,&nbsp;Lei Wang","doi":"10.1016/j.aei.2025.103351","DOIUrl":"10.1016/j.aei.2025.103351","url":null,"abstract":"<div><div>Collaborative perception can significantly enhance perception performance through information sharing among multiple smart vehicles and roadside perception systems. Existing methods typically require sharing and aggregating all information from other collaborators, which can significantly reduce collaborative performance in detecting distant or occluded objects due to the large amount of redundant information in the final perception. To address this issue, we propose a novel collaborative framework, named the Semantic Aware Heterogeneous Network (SAHNet), which extracts, shares and fuses perceptually crucial and useful information among heterogeneous collaborators to improve the performance of 3D object detection. Specifically, we first design a Foreground and Boundary Feature Selection (FBFS) to enhance meaningful feature extraction. Then a Heterogeneous Feature Transfer module (HFF) is then proposed to account for collaborators’ heterogeneity to better transfer perception-critical features. Finally, we introduce a Semantic Feature Fusion module (SFF) that effectively aggregates features using semantic information. The proposed framework has been extensively compared and evaluated on two simulation datasets and one real-world dataset. The experimental results demonstrate that SAHNet consistently outperforms existing methods in collaborative object detection, demonstrating strong robustness even under conditions with localization noise and time delays. Additionally, we have provided a comprehensive ablation study to illustrate the effectiveness of each module within our framework.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873669","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}