Advanced Engineering Informatics最新文献

{"title":"Optimized UAV view planning for high-quality 3D reconstruction of buildings using a modified sparrow search algorithm","authors":"Zhenyu Liang ,&nbsp;Liu Yang ,&nbsp;Zhaolun Liang ,&nbsp;Jeff Chak Fu Chan ,&nbsp;Zhaojie Zhang ,&nbsp;Mingzhu Wang ,&nbsp;Jack C.P. Cheng","doi":"10.1016/j.aei.2025.103344","DOIUrl":"10.1016/j.aei.2025.103344","url":null,"abstract":"<div><div>High-quality 3D reconstruction of existing buildings is essential for their maintenance, restoration, and management. Effective view planning for image collection significantly impacts the quality of photogrammetry-based 3D reconstruction. Intricate building structures, such as the overhangs, protrusions, and concave regions, can lead to under-sampled regions with traditional view planning methods, while excessively increasing the number of views require substantial computational resources and data collection efforts. To address these issues, this paper proposes a novel exploration-then-exploitation view planning strategy to achieve high-quality building reconstruction with minimal views. Firstly, the UAV no-fly regions and building attention regions are identified through semantic and geometric analysis of the images and coarse model during the exploration stage. Then, a novel optimization fitness function is mathematically formulated, considering building attention regions and reconstruction influential factors, including distance, incidence angle, parallax angle, and overlap. Furthermore, a modified sparrow search algorithm is proposed with the improved optimization mechanism and the integration of view planning physical model, enabling effective generation of optimal viewpoint set. Finally, the collision-free shortest trajectory is designed, allowing the UAV to collect images and reconstruct a high-quality model during exploitation stage. Experiments in virtual and real-world scenarios validate the effectiveness of our proposed modified SSA mechanism and the view planning strategy. Results demonstrate that the modified SSA achieves higher convergence accuracy and speed compared to the original SSA, PSO and GA. Our strategy can generate more accurate and complete 3D reconstruction models with the same or fewer captured images compared to commonly used and state-of-the-art strategies.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103344"},"PeriodicalIF":8.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842568","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":"Pythagorean fuzzy comprehensive distance-based ranking approach for assessing industry 4.0 adoption strategies in the automotive manufacturing sector","authors":"Pratibha Rani ,&nbsp;Arunodaya Raj Mishra ,&nbsp;Erfan Babaee Tirkolaee ,&nbsp;Ahmad M. Alshamrani ,&nbsp;Adel Fahad Alrasheedi","doi":"10.1016/j.aei.2025.103359","DOIUrl":"10.1016/j.aei.2025.103359","url":null,"abstract":"<div><div>The automotive sector is experiencing a robust boom, driven by technological advancements, increased customers’ demand, and a growing focus on sustainable development goals. Industry 4.0 (I4.0) adoption in this sector leads to the development of data-driven solutions, manufacturing innovations, higher demand for newer services, and improved operational efficiency. For the successful adoption of I4.0, their strategies should be evaluated with respect to certain criteria. To this aim, this study introduces an integrated Pythagorean fuzzy Comprehensive Distance-Based Ranking (COBRA) approach to evaluate and prioritize the adoption strategies in the automotive manufacturing sector. The proposed framework is divided into four phases. In the first phase, the decision experts’ (DEs) weights are computed with the use of the score function and rank sum model (RSM). In the next phase, an aggregated Pythagorean fuzzy decision matrix is created through a fairly power-weighted operator. For this purpose, the Pythagorean fuzzy fairly power-weighted aggregation operators are introduced to combine individual Pythagorean Fuzzy Information (PFI). In the third phase, the criteria weights are obtained through a combined weighting procedure involving the objective weight by standard deviation (SD)-based method and the subjective weight via Stepwise Weight Assessment Ratio Analysis (SWARA) tool. Based on these phases, a novel Pythagorean fuzzy COBRA approach is developed to deal with the I4.0 adoption strategies evaluation problem. A novel distance measure is also offered to describe the degree of dissimilarity between Pythagorean fuzzy sets (PFSs). Moreover, a comparison with existing distances is discussed to demonstrate the efficiency of the developed distance measure. The suggested methodology is then applied to a case study of the I4.0 adoption strategy selection problem within the context of PFI. Finally, sensitivity and comparative investigations are made to assess the rationality of obtained results.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103359"},"PeriodicalIF":8.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842464","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":"EEG right & left voluntary hand movement-based virtual brain-computer interfacing keyboard using hybrid deep learning approach","authors":"Biplov Paneru ,&nbsp;Bipul Thapa ,&nbsp;Bishwash Paneru ,&nbsp;Sanjog Chhetri Sapkota","doi":"10.1016/j.aei.2025.103304","DOIUrl":"10.1016/j.aei.2025.103304","url":null,"abstract":"<div><div>Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persist due to the complexity and variability of brain activity. Current EEG-based BMIs face limitations in adaptability, usability, and robustness, especially in applications like virtual keyboards, as traditional machine-learning models struggle to handle high-dimensional EEG data effectively. To address these gaps, we developed an EEG-based BMI system capable of accurately identifying voluntary keystrokes, specifically leveraging right and left voluntary hand movements. Using a publicly available EEG dataset, the signals were pre-processed with band-pass filtering, segmented into 22-electrode arrays, and refined into event-related potential (ERP) windows, resulting in a 19x200 feature array categorized into three classes: resting state (0), ’d’ key press (1), and ’l’ key press (2). Our approach employs a hybrid neural network architecture with BiGRU-Attention as the proposed model for interpreting EEG signals, achieving superior test accuracy of 90% and a mean accuracy of 91% in 10-fold stratified cross-validation. This performance outperforms traditional ML methods like Support Vector Machines (SVMs) and Naive Bayes, as well as advanced architectures such as Transformers, CNN-Transformer hybrids, and EEGNet. Finally, the BiGRU-Attention model is integrated into a real-time graphical user interface (GUI) to simulate and predict keystrokes from brain activity. Our work demonstrates how deep learning can advance EEG-based BMI systems by addressing the challenges of signal interpretation and classification. By providing a comparative analysis of multiple models and implementing a real-time application, this research highlights the feasibility and reliability of BMIs in improving accessibility for individuals with motor impairments.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103304"},"PeriodicalIF":8.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842567","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":"Multivariate probabilistic prediction of dam displacement behaviour using extended Seq2Seq learning and adaptive kernel density estimation","authors":"Minghao Li ,&nbsp;Qiubing Ren ,&nbsp;Mingchao Li ,&nbsp;Zhiyong Qi ,&nbsp;Dawen Tan ,&nbsp;Hai Wang","doi":"10.1016/j.aei.2025.103343","DOIUrl":"10.1016/j.aei.2025.103343","url":null,"abstract":"<div><div>Displacement prediction is an essential component of concrete dam health monitoring. However, considering the inherent uncertainties that exist within the structural responses to ever-fluctuating environmental factors like water levels and temperatures, which directly affect dam displacement behaviour, it is clear that mere point predictions are insufficient to accurately quantify the resultant uncertainty. To address this issue, we propose a multivariate probabilistic prediction framework that integrates the extended matrix long short-term memory-embedded attention sequence-to-sequence (Att-S2S-mLSTM) model with the density-based adaptive kernel density estimation (DAKDE). Specifically, the designed mLSTM incorporates a matrix memory and a covariance update rule to effectively simulate the complex nonlinear relationship between environmental factors and dam displacements. The Att-S2S architecture dynamically models sequential monitoring data of varying lengths, where the attention mechanism further augments its capacity to capture long-range dependencies, consequently generating displacement predictions with minimized errors. The DAKDE method then exploits the local density of prediction residuals obtained from Att-S2S-mLSTM to adaptively construct probability density functions. In this case, reliable prediction intervals are derived at various confidence levels, which are regarded as probabilistic predictions and serve as uncertainty quantification associated with displacement predictions. The large amount of monitoring data collected from a real-world concrete dam is employed to verify the effectiveness and superiority of the proposed framework. Moreover, a set of comparisons made among our proposed model and other advanced methods demonstrate that our methodology is more accurate and robust than other baselines in making both point and probabilistic predictions for dam displacement behaviour.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103343"},"PeriodicalIF":8.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843267","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 strategic conflict management for 4D trajectories under weather forecast uncertainty","authors":"Yi Zhou ,&nbsp;Minghua Hu ,&nbsp;Daniel Delahaye ,&nbsp;Ying Zhang ,&nbsp;Lei Yang","doi":"10.1016/j.aei.2025.103293","DOIUrl":"10.1016/j.aei.2025.103293","url":null,"abstract":"<div><div>The design of decision support tools for strategic conflict management (SCM) needs to integrate and manage uncertainty while accommodating the diverse performance preferences of multiple stakeholders. This paper proposes a novel collaborative SCM approach for four-dimensional (4D) trajectories under weather forecast uncertainty, integrating trajectory prediction, strategic conflict detection and resolution, and collaborative decision-making. A 4D grid-based conflict risk assessment method is introduced for trajectories generated by the ensemble trajectory predictor, incorporating weather uncertainty from ensemble forecasts. A multi-objective optimization model is formulated to reorganize aircraft trajectories within free route airspace, employing rerouting, flight level allocation, and speed control to optimize safety, efficiency, and predictability. Predictability is explicitly considered to enhance adherence to planned trajectories and reduce operational uncertainty, while equity is incorporated as a constraint to ensure a fair distribution of trajectory adjustments. To efficiently solve this large-scale multi-objective SCM problem, a decomposition-based memetic algorithm (DMA) is proposed. The DMA combines a decomposition-based global search framework with local refinement via a hybridization strategy to achieve a good balance between exploration and exploitation. The effectiveness of the proposed method is validated using a simulation scenario featuring 760 flights in the high-density western China airspace. Results demonstrate that the approach effectively identifies trade-offs between different stakeholder objectives and provides optimized solutions that support collaborative decision-making in strategic conflict management.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103293"},"PeriodicalIF":8.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828997","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":"Zero-shot fault diagnosis using soft semantic embedding of diffusion-encoded probability","authors":"Chuan Li ,&nbsp;Lijuan Yan ,&nbsp;Ping Wang ,&nbsp;Jianyu Long ,&nbsp;Ziqiang Pu","doi":"10.1016/j.aei.2025.103319","DOIUrl":"10.1016/j.aei.2025.103319","url":null,"abstract":"<div><div>Fault diagnosis is essential for ensuring the stable operation of industrial equipment. However, it faces significant challenges due to insufficient fault samples and the occurrence of unseen faults during operation. For this reason, a novel semantic embedding of diffusion-encoded probability (SEDEP) is proposed for zero-shot fault diagnosis (ZSFD) in this work. A diffusion-encoded convolutional autoencoder is first trained on abundant normal data to extract features for capturing essential fault patterns from raw data. A soft semantic learning network is then constructed using a Gaussian mixture model to generate probabilities as semantic representations. A variational autoencoder-based zero-shot learning framework incorporating cross-alignment loss and distribution-alignment loss is employed to accommodate diffusion-encoded features and soft semantic information. By training on normal data to increase inter-class distance among features and enhancing semantic representation through soft semantic learning, the proposed SEDEP has outstanding nature in ZSFD. Experiments on a benchmark bearing setup and a beam chopper bearing setup achieved accuracy values of 90.15% and 94.96%, respectively. This demonstrates its robustness across different fault scenarios. Compared to state-of-the-art methods, SEDEP provides a generalizable and effective approach for addressing ZSFD tasks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103319"},"PeriodicalIF":8.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828996","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 cost-efficiency analysis of drones in revolutionizing intra-city express services","authors":"Xiao Chu ,&nbsp;Shuiwang Chen ,&nbsp;Kai Wang ,&nbsp;Lingxiao Wu ,&nbsp;Gangyan Xu","doi":"10.1016/j.aei.2025.103324","DOIUrl":"10.1016/j.aei.2025.103324","url":null,"abstract":"<div><div>Drones have emerged as promising transportation tools in the urban logistics industry, particularly for short-distance transportation, such as last-mile delivery and food delivery. With advancements in coverage range and payload capacity, medium drones are poised to open up a medium-range urban logistics market known as intra-city express service (ICES). This study aims to examine the benefits of deploying drones into ICES by comparing the costs and efficiency of current truck-based ICES with four potential drone-based modes: station-to-station (S-S), vertiport-to-vertiport (V-V), station-to-hub-to-station (S-H-S, with hub denoting distribution center), and vertiport-to-hub-to-vertiport (V-H-V). Based on the dataset from a courier in China, a thorough examination of the cost- and time-saving advantages associated with drone-based modes is conducted. The results demonstrate the promising potential of deploying drones in the ICES industry to save costs and enhance efficiency, but these benefits are highly dependent on the operational mode of drones. Among the proposed modes, none emerges as a dominant option across both metrics. Nevertheless, the V-V and V-H-V stand out as non-dominant modes, with V-V offering the highest time efficiency and V-H-V proving to be the most cost-efficient. Vertiports and distribution centers are crucial facilities for achieving cost reduction as they facilitate parcel consolidation. These facilities aggregate demands and strong the flow rate However, distribution centers may not necessarily enhance efficiency since they can introduce additional waiting time and detours. Overall, the managerial insights and policy implications proposed in this study demonstrate significant potential for the utilization of drones in intra-city express service.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103324"},"PeriodicalIF":8.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828998","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":"Identification of stochastic disturbance sources of air doors in mine ventilation systems","authors":"Yonghong Liu ,&nbsp;Ziming Wang ,&nbsp;De Huang","doi":"10.1016/j.aei.2025.103356","DOIUrl":"10.1016/j.aei.2025.103356","url":null,"abstract":"<div><div>Identifying stochastic disturbance sources at air doors in mine ventilation systems is essential for ensuring both safety and operational efficiency. To address this issue, a time-dependent disturbance model, based on the Navier-Stokes equations, was developed to facilitate the rapid and accurate localization of disturbances. Particle Image Velocimetry (PIV) experiments were performed to investigate the characteristics of air door disturbances and their dynamic effects on airflow. A novel approach for identifying disturbance sources, based on time-series monitoring signals, was proposed, wherein air volume and air pressure serve as key features for classifying disturbance locations. The PIV experiments revealed both logarithmic and linear relationships between tunnel wind speed and contraction wind speed, as well as between the air door opening ratio and the contraction coefficient. By leveraging unsteady disturbance theory, training samples were generated for the stochastic disturbance source identification task. Experimental results demonstrated that Long Short-Term Memory (LSTM) networks achieved a high accuracy of 90.28% in identifying disturbances based solely on air volume. In both T-type and complex ventilation systems, the inclusion of air pressure features significantly improved identification accuracy by 9.66% and 9.16%, respectively. However, the combination of air volume and air pressure features resulted in minimal additional improvements (0.03% and 0.12%). The evaluation of model performance confirmed that the LSTM-based framework, supported by unsteady disturbance theory, is highly effective in identifying stochastic disturbance sources within mine ventilation systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103356"},"PeriodicalIF":8.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830236","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 decoding-priority-based self-adaptive prairie dog optimizer for cyclic cross-period bidirectional milk-run vehicle scheduling problem","authors":"Kaiyuan Zhang,&nbsp;Binghai Zhou","doi":"10.1016/j.aei.2025.103311","DOIUrl":"10.1016/j.aei.2025.103311","url":null,"abstract":"<div><div>In the context of mass customization, the milk-run model has emerged as a predominant logistics strategy. However, traditional milk-run models fail to exploit the potential of integrating transportation tasks across multiple periods. Additionally, growing environmental concerns necessitate the inclusion of reverse logistics. This paper investigates a Cyclic Cross-Period Bidirectional Milk-run Vehicle Scheduling Problem (CCBMVSP) that aims to minimize both economic costs (total operational cost) and service levels (total earliness and tardiness) simultaneously. To address this problem, we develop a mixed-integer programming model and apply the epsilon-constraint method to obtain exact solutions for small-scale instances. Given the NP-hard nature of the problem, we propose a multi-objective optimization algorithm, the Decoding-priority-based Self-adaptive Prairie Dog Optimizer (DSPDO). The encoding and decoding procedures are specifically designed with a constraint-oriented solution repair strategy. Chaotic mapping is introduced to enhance the diversity of the initial population. Moreover, we propose a multi-elite iteration strategy and a variable neighborhood search strategy to strengthen the algorithm’s exploration and exploitation capabilities. Based on decoding priority, an adaptive exploration–exploitation balance strategy is also introduced. Finally, extensive numerical experiments demonstrate that the proposed algorithm outperforms benchmark methods when solving larger-scale instances.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103311"},"PeriodicalIF":8.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825579","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":"Time-frequency self-similarity enhancement network and its application in wind turbines fault analysis","authors":"Dezun Zhao ,&nbsp;Depei Shao ,&nbsp;Tianyang Wang ,&nbsp;Lingli Cui","doi":"10.1016/j.aei.2025.103322","DOIUrl":"10.1016/j.aei.2025.103322","url":null,"abstract":"<div><div>The data-driven time–frequency analysis (TFA) method has garnered widespread attention due to its robust feature learning and representation capabilities. However, existing methods still require further development in characterizing nonstationary signals with closely-spaced and crossing frequencies generated from wind turbines, and realizing mechanical fault detection. To this end, a novel method, termed time–frequency self-similarity enhancement network (TFSSEN), is proposed. First, an adaptive time–frequency characterizing module (ATFCM), consisting of the time–frequency convolutional layer and adaptive convolutional pooling unit, is designed to represent random scale vibration signals to an appropriate scale time–frequency representation (TFR). Second, a non-local and global attention residual group (NGARG) is constructed, where a single-scale self-similarity exploitation module is introduced to calculate feature correlations within single-scale TFR, and an improved-global context attention mechanism is developed to explore the most informative components in multi-scale time–frequency features, thereby achieving precise feature reconstruction. Finally, the self-similarity mixed-scale time–frequency enhancement module (SMTEM) is constructed by multiple cascaded NGARGs, and it can extract frequency information from similar time–frequency features and gradually enhance energy concentration. Simulation results show that the TFSSEN can effectively characterize nonstationary signal with closely-spaced and crossing frequencies. The comprehensive experiment analysis on the wind turbine planetary gearbox and bearings further demonstrates that the TFSSEN exhibits superior performance for characterizing nonstationary fault characteristic frequencies (FCFs) compared with advanced TFA methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103322"},"PeriodicalIF":8.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828995","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}