Advanced Engineering Informatics最新文献

{"title":"Addressing unknown faults diagnosis of transport ship propellers system based on adaptive evolutionary reconstruction metric network","authors":"Changdong Wang ,&nbsp;Xiaofei Liu ,&nbsp;Jingli Yang ,&nbsp;Huamin Jie ,&nbsp;Tianyu Gao ,&nbsp;Zhenyu Zhao","doi":"10.1016/j.aei.2025.103287","DOIUrl":"10.1016/j.aei.2025.103287","url":null,"abstract":"<div><div>Accurate fault diagnosis of the propeller supports the normal operation of each ship and aids in the decision-making for maintenance and industrial dispatch. However, the unpredictable status presents challenges for effective fault diagnosis in real applications, particularly involving unknown operating conditions and fault modes. Therefore, this paper proposes a single-source domain generalization diagnostic method based on an adaptive evolutionary reconstruction metric network, achieving high diagnostic precision for propellers. Specifically, an embedded self-evolution regularization strategy is designed to compel the model to learn the residual label-related features, thereby enhancing the model’s generalization capabilities. Moreover, a reinforcement learning-based adaptive threshold mechanism is built to reinforce the model’s adaptability when facing unknown faults. Relying on a real-world data collection platform for transport ship propellers and a public dataset, the superiorities of the proposed AERMN are demonstrated by comparing it with several strong baseline and cutting-edge methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103287"},"PeriodicalIF":8.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786038","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":"Prediction of compression coefficient of Nanjing floodplain soft soil based on explainable artificial intelligence","authors":"Bin Ruan ,&nbsp;Chongjin Liu ,&nbsp;Zhenglong Zhou ,&nbsp;Jianxiong Miao ,&nbsp;Hao Huang","doi":"10.1016/j.aei.2025.103308","DOIUrl":"10.1016/j.aei.2025.103308","url":null,"abstract":"<div><div>The low bearing capacity and high compressibility of soft soils significantly influence the design of building foundations. Consequently, accurate prediction of the compression coefficient is essential for ensuring the stability and safety of structures. This study established a database consisting of 699 samples of Nanjing floodplain soft soil and developed a hybrid machine learning model, CNN (Convolutional Neural Network) − CatBoost (a gradient algorithm utilizing symmetric decision trees), which utilizes deep feature extraction through CNN to accurately predict the compression coefficient of Nanjing floodplain soft soil. The coefficients of determination (R²) for the training and testing sets were 0.965 and 0.933, respectively. In comparison to traditional models, the hybrid model demonstrated significant advantages in prediction accuracy and error management, exhibiting improved fitting and generalization capabilities. Furthermore, SHAP and PDP analyses were conducted to evaluate the influence of five input features—wet density, plastic limit, plasticity index, liquidity index, and depth—on the output results, indicating that the plasticity index had the most substantial effect on the compression coefficient estimated by the hybrid model. This model offers a promising tool for advancing geotechnical engineering applications, enhancing prediction accuracy and decision-making in foundation design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103308"},"PeriodicalIF":8.0,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783634","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":"Parameter inverse analysis of high rockfill dams considering material uncertainty based on the EJaya-SESM model","authors":"Qiubing Ren ,&nbsp;Qin Ke ,&nbsp;Yinpeng He ,&nbsp;Mingchao Li ,&nbsp;Lei Xiao ,&nbsp;Heng Li","doi":"10.1016/j.aei.2025.103306","DOIUrl":"10.1016/j.aei.2025.103306","url":null,"abstract":"<div><div>Uncertainties of rockfill material can significantly impact the structural behavior of rockfill dams. Identifying such uncertainties is thus essential to rockfill dam behavior analysis. Traditional material uncertainty identification using Monte Carlo-Stochastic Finite Element (MC-SFE) calculation is extremely tedious and time-consuming. To alleviate the computational burden, this work presents an efficient machine learning method for inverse calculation of rockfill dam material uncertainties based on the Stacking Ensemble Surrogate Model (SESM) and Enhanced Jaya (EJaya) algorithm. In this methodology, a multi-parameter and multi-zone random field is firstly introduced to describe the spatial heterogeneity of rockfill material in the dam body field. Then, MC-SFE is replaced by the stacking ensemble learning-based surrogate model to explore the complex mapping relationships between rockfill material parameters and dam settlement responses. Subsequently, a novel optimization algorithm called EJaya is developed to minimize the objective function for material parameters' inversion calculations. The implementation of the EJaya-SESM model is demonstrated on a real-world high rockfill dam in service as an illustrative example. Through comprehensive forward analysis, the effectiveness and rationality of inversion calculations are further verified. The numerical results show that the stacking ensemble strategy can greatly improve the surrogate model's accuracy with reduced computational time versus MC-SFE calculation, and the inversion outcomes derived from the EJaya algorithm demonstrate superior precision compared to those attained by several commonly used metaheuristic techniques. This study provides an advanced means to achieve excellent performance in parameter inverse analysis of rockfill dams at a low computation cost.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103306"},"PeriodicalIF":8.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776331","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 improved elitist-Q-Learning path planning strategy for VTOL air-ground vehicle using convolutional neural network mode prediction","authors":"Jing Zhao,&nbsp;Chao Yang,&nbsp;Weida Wang,&nbsp;Ying Li,&nbsp;Tianqi Qie,&nbsp;Bin Xu","doi":"10.1016/j.aei.2025.103316","DOIUrl":"10.1016/j.aei.2025.103316","url":null,"abstract":"<div><div>Vertical take-off and landing (VTOL) air-ground integrated vehicles have received extensive attention in rescue, transportation, and other task fields. To further improve the task efficiency in complex environments such as post-disaster cities and scrubland, this vehicle requires efficient and rational path planning. In above environments, it is difficult to obtain complete and accurate obstacle information. The planning process faces the technical difficulties of using the limited obstacle perception information to switch air-ground modes and fast acquire the optimal planning trajectory with the shortest distance. To address the above issues, this paper proposes an improved elitist-Q-Learning path planning strategy for the VTOL air-ground vehicle using convolutional neural network mode prediction. Firstly, to predict the mode switching actions, a convolutional neural mode prediction network is constructed with local obstacle information as input data. Secondly, based on the above predicted actions, an elitist-Q-Learning (EQL) multi-mode planning algorithm is designed. A new reward function considering the multi-mode actions is proposed. On this basis, heuristic correction and elitist adjusting factors replace the fixed rewards of traditional Q-Learning with dynamically adjusted rewards during the iterative process. The Q table is quickly updated to converge to optimal values. Finally, this proposed strategy is verified in randomly generated maps of 1000 m*1000 m. Results show that the prediction accuracy can be maintained over 93 %. Its path distance is reduced by 4.56 % and 1.75 % compared to that of traditional Q-Learning and A* with mode prediction, respectively. It has the same path distance as BAS-A*, LPA*, and D* Lite with mode prediction. Compared to traditional Q-Learning, it reduces computational time by 36.61 %. When converged, its iterative numbers are 58.9 % less than those of traditional Q-Learning.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":""},"PeriodicalIF":8.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776615","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 transfer learning-based calibration-free model for blood pressure prediction using smart monitors","authors":"Min-Syuan Wu ,&nbsp;Yuan-Yuan Liu ,&nbsp;Kuo-Hao Chang","doi":"10.1016/j.aei.2025.103291","DOIUrl":"10.1016/j.aei.2025.103291","url":null,"abstract":"<div><div>Blood pressure monitoring is critical because it enables effective management of hypertension, empowering individuals to take control of their cardiovascular health and prevent serious health complications. In recent years, smart blood pressure monitors have been gradually replacing traditional ones due to their convenience. Collaborating with a company manufacturing smart blood pressure monitors, we develop a calibration-free blood pressure prediction model using electrocardiogram (ECG) and photoplethysmogram (PPG) signals, thereby eliminating the need for initial cuff-based measurement in smart blood pressure monitors. Initially, a pre-trained blood pressure prediction model is established using the publicly available Medical Information Mart for Intensive Care (MIMIC-III) dataset. The pre-trained model, which employs a ResNet deep learning model, achieves a mean absolute error (MAE) of 3.60 for systolic blood pressure (SBP) and 2.97 for diastolic blood pressure (DBP). Subsequently, to ensure the capability of the model in predicting blood pressure based on limited signal data from a smart electronic blood pressure monitor, a novel transfer learning approach known as TL-SQEBPP (Transfer Learning-based Signal Quality Enhanced Blood Pressure Prediction model) is adopted. This framework utilizes the ResNet deep learning model for blood pressure prediction while also incorporating a signal quality model based on the autoencoder as well as an adaptation layer which minimizes the gap between the source domain (MIMIC-III) and the target domain data. Target domain data includes both company-provided and experimental data gathered from subjects using the smart blood pressure monitor. Transfer learning using the target domain data is applied to test and validate the TL-SQEBPP model. The results demonstrate that our proposed method performs well, with TL-SQEBPP achieving an MAE of 4.9 for SBP and 4.19 for DBP with transfer learning applied based on the experimental data. In addition, when transfer learning was applied using the company-provided data, TL-SQEBPP was shown to yield MAEs for SBP and DBP substantially lower compared to alternative versions of the architecture in which the signal quality model and/or the adaptation layer were not included.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103291"},"PeriodicalIF":8.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783635","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":"Structural precision control with manufacturability-performance balancing for metallic thin-walled ring","authors":"Binyu Yan ,&nbsp;Bao Meng ,&nbsp;Yao Ma ,&nbsp;Xinzhou Wu ,&nbsp;Yubo He ,&nbsp;Min Wan","doi":"10.1016/j.aei.2025.103307","DOIUrl":"10.1016/j.aei.2025.103307","url":null,"abstract":"<div><div>The structural dimensions of thin-walled components with irregular cross-sectional geometries have a significant impact on their service performances. The interaction of deviations across multi-dimensions during manufacturing introduces substantial challenges in achieving precise performance control. To ensure the superiority and stability of the rebound performance of metallic seal rings, this study presented a structural precision control method to harmonize the manufacturability, performances and manufacturing cost for complex components with multiple structures. Using the multi-structured metallic seal rings as application case, the influence of structural variables on rebound performance was analyzed and four factors were identified as significant factors. With the response surface method, a quantitative relationship between significant factors and rebound rate was established. Considering the structural manufacturability, high performance and cost, a structural group was selected for precision control. Introducing deviation variables to the quantitative function of rebound rate, the boundary constraints of the tolerance intervals were solved under performance goal and manufacturability accounting for multi-stage fabrication. With objective functions, the optimal tolerance intervals were iteratively calculated through a genetic algorithm. Experimental results demonstrated that all the rebound rates exceeded 95% with the dimensional precision in the constraint intervals. Furthermore, the developed rebound rate prediction model exhibits high accuracy, with a maximum error below 5%. With the service performance and cost assured, through the application of the strategic dimensional reconciliation of manufacturing tolerance control framework, the complexities in maintaining structural precision across the various stages of fabricating components with intricate geometries have been substantially reduced.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103307"},"PeriodicalIF":8.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767894","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":"Leveraging large self-supervised time-series models for transferable diagnosis in cross-aircraft type Bleed Air System","authors":"Yilin Wang ,&nbsp;Peixuan Lei ,&nbsp;Xuyang Wang ,&nbsp;Liangliang Jiang ,&nbsp;Liming Xuan ,&nbsp;Wei Cheng ,&nbsp;Honghua Zhao ,&nbsp;Yuanxiang Li","doi":"10.1016/j.aei.2025.103275","DOIUrl":"10.1016/j.aei.2025.103275","url":null,"abstract":"<div><div>Bleed Air System (BAS) is critical for maintaining flight safety and operational efficiency, supporting functions such as cabin pressurization, air conditioning, and engine anti-icing. However, BAS malfunctions, including overpressure, low pressure, and overheating, pose significant risks such as cabin depressurization, equipment failure, or engine damage. Current diagnostic approaches face notable limitations when applied across different aircraft types, particularly for newer models that lack sufficient operational data. To address these challenges, this paper presents a self-supervised learning-based foundation model that enables the transfer of diagnostic knowledge from mature aircraft (e.g., A320, A330) to newer ones (e.g., C919). Leveraging self-supervised pretraining, the model learns universal feature representations from flight signals without requiring labeled data, making it effective in data-scarce scenarios. This model enhances both anomaly detection and baseline signal prediction, thereby improving system reliability. The paper introduces a cross-model dataset, a self-supervised learning framework for BAS diagnostics, and a novel Joint Baseline and Anomaly Detection Loss Function tailored to real-world flight data. These innovations facilitate efficient transfer of diagnostic knowledge across aircraft types, ensuring robust support for early operational stages of new models. Additionally, the paper explores the relationship between model capacity and transferability, providing a foundation for future research on large-scale flight signal models.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103275"},"PeriodicalIF":8.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767799","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":"Sustainable foundation design: Hybrid TLBO-XGB model with confidence interval enhanced load–displacement prediction for PGPN piles","authors":"Tram Bui-Ngoc ,&nbsp;Duy-Khuong Ly ,&nbsp;Tan Nguyen ,&nbsp;T. Nguyen-Thoi","doi":"10.1016/j.aei.2025.103288","DOIUrl":"10.1016/j.aei.2025.103288","url":null,"abstract":"<div><div>Pre-bored Grouted Planted Nodular (PGPN) piles have emerged as a cost-effective and environmentally sustainable solution in pile foundation engineering. However, their unique load–displacement behavior, influenced by a cemented soil layer between the pile and the natural soil, remains less understood compared to conventional piles. This study addresses this knowledge gap by developing a robust predictive model for the axial load-bearing behavior of PGPN piles. The model utilizes a novel hybrid approach that combines eXtreme Gradient Boosting (XGB) with Teaching–Learning-Based Optimization (TLBO) to predict pile head settlement. With a dataset of 1,209 field samples collected across Vietnam, the model incorporates pile properties, Standard Penetration Test (SPT) data, and applied loads. To quantify prediction reliability, Monte Carlo Simulation (MCS) is employed to generate confidence intervals under varying loading scenarios, reflecting a range of plausible displacement values for each load increment. Comparative analysis demonstrates that the TLBO-XGB model significantly outperforms other machine learning models and metaheuristic algorithms. Feature importance analysis and Partial Dependence Plots (PDPs) elucidate the physical relevance of input parameters. This study offers a pioneering approach to predicting the load–displacement behavior of PGPN piles, with a Graphical User Interface (GUI) designed to support the safe and cost-effective design of these advanced foundation systems, balancing precision and safety through confidence interval insights.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103288"},"PeriodicalIF":8.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767800","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":"Dual-perspective safety driver secondary task detection method based on swin-transformer and cross-attention","authors":"Qingchao Liu ,&nbsp;Siqi Chen ,&nbsp;Guoqing Liu ,&nbsp;Lie Yang ,&nbsp;Quan Yuan ,&nbsp;Yingfeng Cai ,&nbsp;Long Chen","doi":"10.1016/j.aei.2025.103320","DOIUrl":"10.1016/j.aei.2025.103320","url":null,"abstract":"<div><div>With the rapid development of autonomous driving technology, the safety of autonomous vehicles has attracted widespread attention. However, existing research primarily focuses on traditional driver behavior detection and typically adopts a single perspective for analysis, lacking a comprehensive study of the safety driver’s status in autonomous driving scenarios. Therefore, this study proposes an autonomous driving safety driver secondary task detection method (ST-CAFL) based on dual perspectives using Swin-Transformer (Swin-T) and cross-attention (CA) to improve the accuracy and robustness of secondary task detection. Firstly, the ST-CAFL framework efficiently captures multi-scale spatial information through Swin-T’s hierarchical feature extraction mechanism. Secondly, the CA mechanism effectively integrates information from dual perspectives, providing a more comprehensive capture of the safety driver’s behavioral characteristics. Additionally, this study introduces center loss to improve classification accuracy and enhance the model’s ability to recognize secondary tasks performed by the safety driver, thereby achieving more comprehensive and accurate detection. To evaluate the effectiveness of the proposed method, we created a dual-perspective safety driver behavior detection (ASD) dataset and conducted extensive experiments on this dataset. The results indicate that the ST-CAFL framework achieved an accuracy of 96.15% on the SAM dataset and 94.39% on the ASD dataset. To further validate the applicability of this method, we conducted extensive experiments across both datasets. Moreover, ST-CAFL outperformed several existing methods in terms of detection performance in comparative evaluations. This research fills the gap in the autonomous driving safety driver secondary task detection field and provides an essential reference for the design and implementation of future autonomous driving systems, possessing broad application value and research significance.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103320"},"PeriodicalIF":8.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767892","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":"Dynamic gesture recognition during human–robot interaction in autonomous earthmoving machinery used for construction","authors":"Shiwei Guan,&nbsp;Jiajun Wang,&nbsp;Xiaoling Wang,&nbsp;Chen Ding,&nbsp;Hongyang Liang,&nbsp;Qi Wei","doi":"10.1016/j.aei.2025.103315","DOIUrl":"10.1016/j.aei.2025.103315","url":null,"abstract":"<div><div>Effective interaction between operators and autonomous earthmoving machinery can accurately convey the rich engineering experience of operators to machines, ensuring efficient human–robot collaboration in construction. In this study, we propose a pipeline for dynamic gesture interaction between authorised operators and autonomous earthmoving machinery. Initially, the autonomous earthmoving machinery preprocessed the video stream using video restoration algorithms if it operated under harsh environmental conditions. Subsequently, the machinery used a safety helmet colour detection algorithm based on YOLOv8 to determine whether an operator has the authorisation to interact with it by recognising the colour of the safety helmet worn by the operator, thereby preventing incorrect operations of the machinery from unauthorised operators. Finally, the autonomous earthmoving machinery utilised the proposed video swin transformer with Adapt multilayer perceptron (AdaptViSwT) dynamic gesture recognition algorithm to recognise dynamic gesture instructions provided by authorised operators and execute the corresponding operations, enabling human–robot collaboration under complex construction conditions. To train the proposed AdaptViSwT effectively, we established a dynamic gesture interaction dataset comprising 6,502 videos that contained nine commonly used instructions for commanding earthmoving machinery. The experiments verified that, on construction-site datasets, the proposed pipeline achieved 91.2% accuracy in detecting authorised worker. In dynamic gesture recognition, it achieved 98.32% accuracy and 98.44% F1-score. These results effectively ensure the safety and reliability of human-robot collaborative construction.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103315"},"PeriodicalIF":8.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767895","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}