Advanced Engineering Informatics最新文献

{"title":"Product carbon emissions estimation method in the early design stage based on multi-perspective similarity matching of design scenarios","authors":"Lin Kong ,&nbsp;Yanyan Nie ,&nbsp;Liming Wang ,&nbsp;Fangyi Li ,&nbsp;Lirong Zhou ,&nbsp;Geng Wang ,&nbsp;Haiyang Lu ,&nbsp;Xingyuan Xiao ,&nbsp;Weitong Liu ,&nbsp;Yan Ma","doi":"10.1016/j.aei.2024.103094","DOIUrl":"10.1016/j.aei.2024.103094","url":null,"abstract":"<div><div>Carbon emissions estimation of design schemes during the early design stage enables thorough consideration of environmental issues from the source, which holds critical significance for carbon reduction and emission mitigation. Nevertheless, the scarcity of life cycle inventory data, coupled with the intricacies involved in the collection, presents a formidable challenge to conducting precise carbon emissions assessment. To address this issue, this research proposes a product carbon emissions estimation method in the early design stage based on multi-perspective similarity matching of design scenarios, which utilizes the idea of knowledge reuse through case-based reasoning. Specifically, the case-based reasoning framework encompassing case base construction, case retrieval, reuse, and revision has been outlined, which standards the procedure for obtaining the most similar case. Moreover, the design scenario is defined to comprehensively describe all life cycle activities that influence product carbon emissions, and the design scenario-based multi-layer model is constructed that encompasses the product’s lifecycle-related design information pertinent to carbon emissions, along with its intricate interrelationships, serving as the input information for precise case retrieval. Subsequently, a multi-perspective similarity matching strategy that integrates both the attribute and correlation information of design scenarios is developed, which accurately identifies the most similar case in the case base, enabling the efficient reuse of historical data. An example of the wind turbine gearbox is given as an example, the results indicating that the proposed carbon emission estimation method aligns most closely with actual machining conditions, achieving a minimal error of 2.75%, thereby unequivocally validating its effectiveness and reliability. This work provides designers with a targeted strategy for obtaining carbon emissions during the early design stage, thereby facilitating optimized decision-making for low-carbon design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103094"},"PeriodicalIF":8.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129626","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 rollout heuristic-reinforcement learning hybrid algorithm for disassembly sequence planning with uncertain depreciation condition and diversified recovering strategies","authors":"Yaping Ren ,&nbsp;Zhehao Xu ,&nbsp;Yanzi Zhang ,&nbsp;Jiayi Liu ,&nbsp;Leilei Meng ,&nbsp;Wenwen Lin","doi":"10.1016/j.aei.2024.103082","DOIUrl":"10.1016/j.aei.2024.103082","url":null,"abstract":"<div><div>Disassembly is one of the crucial aspects of green manufacturing. For the end-of-life products, an effective disassembly sequence planning method can enhance recovery value and mitigate the negative consequences of resource depletion and waste generation. However, both the uncertainty of product depreciation condition and the NP-hard characteristics (including the determination of disassembly sequences and the selection of recovering strategies of subassemblies) of the disassembly sequence planning results in difficulties to determine the optimal/near-optimal disassembly solutions. To address these challenges, this work establishes an extended Petri net that considers diversified recovering strategies of each subassembly caused by uncertain product depreciation condition. Then, a rollout heuristic-reinforcement learning hybrid algorithm that integrates a rollout decision rule into the reinforcement learning procedure is proposed to rapidly find the high-quality disassembly solutions based on the extended Petri net, in which the uncertainty of disassembly information is tackled by training disassembly samples and the global exploration capability of the learning procedure is significantly improved by using the rollout decision rule. Finally, three products with different complexities and sizes are used to verify the performance of the proposed algorithm, and the experimental results indicate that our proposed rollout heuristic-reinforcement learning hybrid algorithm can efficiently compute the high-quality disassembly sequences under various disassembly environments.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103082"},"PeriodicalIF":8.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129690","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 self-supervised masked spatial distribution learning method for predicting machinery remaining useful life with missing data reconstruction","authors":"Ben Niu ,&nbsp;Yi Xiao ,&nbsp;Qinge Xiao ,&nbsp;Yang Liu ,&nbsp;Tao Peng ,&nbsp;Zhile Yang","doi":"10.1016/j.aei.2024.102938","DOIUrl":"10.1016/j.aei.2024.102938","url":null,"abstract":"<div><div>Accurately predicting the remaining useful life (RUL) of machines is vital for assessing machine health and minimizing economic losses resulting from downtime in sensor-equipped machines. However, real-world applications often encounter challenges such as rapid production cycles and unstable network conditions, inevitably leading to significant amounts of missing data. This challenges data-driven machinery RUL prediction, as conventional deep learning methods may struggle with missing data, impacting prediction accuracy. To address the issue, a missing data reconstruction method based on self-learning of mask spatial distribution is proposed. The structured spatial distribution characteristics of the mask, learned by the autoencoder, serve as self-supervised information for the imputation network to improve the data reconstruction performance. Meanwhile, a multi-task learning-enhanced prediction network architecture with adaptive weight adjustment is designed, defining tasks by RUL prediction under different data reconstruction accuracies. After pre-training on multiple tasks, the prediction network’s learning efficiency benefits from incorporating both common and task-specific rules for feature extraction from similar reconstructed data distributions. The proposed method is evaluated through ablation and comparative tests on application scenarios and standard datasets. Experimental results show that the proposed algorithm performs competitively against state-of-the-art data reconstruction algorithms on these test suites.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 102938"},"PeriodicalIF":8.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129741","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":"Semantic elaboration of low-LOD BIMs: Inferring functional requirements using graph neural networks","authors":"Suhyung Jang ,&nbsp;Ghang Lee ,&nbsp;Minkyeong Park ,&nbsp;Jaekun Lee ,&nbsp;Seungah Suh ,&nbsp;Bonsang Koo","doi":"10.1016/j.aei.2024.103100","DOIUrl":"10.1016/j.aei.2024.103100","url":null,"abstract":"<div><div>This study proposes a method to automatically subcategorize early object types in low levels of development (LODs) into detailed types (i.e., subtypes) with distinct functional requirements, such as insulation, waterproofing, and load-bearing. While rough cost estimation is possible in the early design phase without detailed object classifications, its accuracy is often limited. Subcategorizing generic objects like walls and columns into more detailed types enhances the precision of early-stage engineering analyses, including cost estimation, load assessments, and material takeoffs. Existing automated object subclassification methods rely on information extracted from highly detailed models, which are unavailable in early-stage building information models (BIMs) due to a lack of geometric and attributive distinctions. This study addresses these limitations by leveraging functional requirements inferred from object connections and placement in early BIMs, achieved using a graph neural network (GNN). To convert BIMs into graphs, a novel threshold-enhanced triangle intersection (TETI) algorithm is introduced, overcoming inaccuracies and exception-handling issues in existing methods. The study explores two GNN-based approaches: node property prediction and node prediction. The former distinguished generic object types into 14 detailed categories, but cost estimation required greater specificity. The latter successfully classified objects into 42 subtypes, with the best results achieved using semantically rich embeddings from a large language model (LLM) and GraphSAGE with three SAGE convolution layers, three hops, and 1,024 dimensions, yielding a weighted F1-score of 0.8766. This approach significantly reduces input data requirements compared to existing methods, enabling more accurate early identification of functional requirements in low-LOD BIMs and supporting both early engineering analyses and detailing processes.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103100"},"PeriodicalIF":8.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129689","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":"Hybrid large language model approach for prompt and sensitive defect management: A comparative analysis of hybrid, non-hybrid, and GraphRAG approaches","authors":"Kahyun Jeon,&nbsp;Ghang Lee","doi":"10.1016/j.aei.2024.103076","DOIUrl":"10.1016/j.aei.2024.103076","url":null,"abstract":"<div><div>This study aims to propose a large language model (LLM)-enhanced defect question-answering (QA) method that can secure private and sensitive data while yielding high performance. Prompt responses to residents’ complaints are crucial for preventing recurring defects. However, traditional defect analysis and response methods rely on the expertise of a few skilled workers, making it difficult to ensure timely responses. The rapid advancement of LLMs offers a potential solution for improving defect QA tasks. However, many companies prohibit the use of closed-source LLM services, such as ChatGPT, due to concerns about potential data breaches. One possible solution is to use open-source LLMs like Llama and BERT, which can be locally installed and used. However, open-source LLMs typically perform worse than closed-source LLMs. Although the performance of open-source LLMs can be greatly improved through fine-tuning, the preparation of training datasets requires a significant amount of time and labor. To address these challenges, this study proposes a hybrid defect QA method that deploys an open-source LLM for defect management to secure sensitive information, and a closed-source LLM for generating a training dataset to reduce both the time and labor required. To validate the proposed method, we compare it to the state-of-the-art LLMs, GPT-4o and Llama 3, as well as graph retrieval-augmented generation (GraphRAG)-based QA systems, which have been extensively studied recently. Our results show that the hybrid LLM-based QA method achieved the highest ROUGE score of 81.6%. These findings demonstrate superior practical applicability, enabling cost-effective data generation and reliable domain adaptation within a secure data environment. This approach is beneficial for domain-specific tasks beyond defect management, where the accurate provision of specialized information and integration of historical knowledge are essential.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103076"},"PeriodicalIF":8.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129688","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":"Multi-source fault data fusion diagnosis method based on hyper-feature space graph collaborative embedding","authors":"Xiaoxin Dong ,&nbsp;Hua Ding ,&nbsp;Dawei Gao ,&nbsp;Guangyu Zheng ,&nbsp;Jiaxuan Wang ,&nbsp;Qifa Lang","doi":"10.1016/j.aei.2024.103092","DOIUrl":"10.1016/j.aei.2024.103092","url":null,"abstract":"<div><div>Rotating machinery fault diagnosis based on multi-source sensor monitoring presents high dimensionality, high sampling frequency, and nonlinearity problems, making it challenging to accurately and timely determine the true health status of the equipment. Moreover, existing methods, such as deep learning models, face issues like a large number of training parameters and limited interpretability, which hinder their application in engineering practice, especially in scenarios that require fast diagnostic performance and ease of deployment. To address this problem, a novel fault diagnosis framework based on hyper-feature space graph collaborative embedding (HFSGCE) is proposed in this paper to improve the health status identification efficiency. Firstly, the algorithm realizes the preservation of the near-neighbor structure of the data by establishing a hyper-feature space embedding graph model corresponding to different types of sensor data. Secondly, a fused hyper-Laplacian scatter matrix is established based on the graph structure model to achieve feature-level fusion of multi-source data. Finally, the dimensionality-reduced multi-source monitoring data is fed into the classifier for pattern recognition. The algorithm was experimentally validated using two types of bearing fault simulation data from Paderborn University and our laboratory. The results demonstrate that the algorithm effectively eliminates redundant information from large volumes of low-value-density monitoring data, providing a new insight for rotating machinery fault diagnosis in the context of big data.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103092"},"PeriodicalIF":8.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129740","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":"Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy","authors":"Zhenya Wang ,&nbsp;Pan Liang ,&nbsp;Rengui Bai ,&nbsp;Yaming Liu ,&nbsp;Jingshan Zhao ,&nbsp;Ligang Yao ,&nbsp;Jun Zhang ,&nbsp;Fulei Chu","doi":"10.1016/j.aei.2024.102972","DOIUrl":"10.1016/j.aei.2024.102972","url":null,"abstract":"<div><div>Aero-engines, pumps, and trains are widely used in transportation, maritime, aerospace, and other industries. However, these devices often operate in harsh and complex environments, making their internal components prone to failure. Thus, constructing a highly accurate fault diagnosis model is essential for ensuring the safe and reliable operation of machinery. However, most existing models require many labeled samples to build accurate training models, which is both expensive and difficult to achieve. Moreover, some models lack adaptability and often require adjustments to their structure or hyperparameters to suit new diagnostic tasks. To address these challenges, this paper proposes a few-shot fault diagnosis model based on multi-scale perception multi-level feature fusion image quadrant entropy (MPMFFIQE). The MPMFFIQE method uses the gramian angle summation field (GASF) to convert transient signals into images, preserving more detailed information about the mechanical state. The multi-scale perception multi-level feature strategy is then applied to sequentially enlarge and reconstruct feature maps at various levels, maximizing the extraction of fault-related information. Afterward, the fusion image quadrant entropy technique is proposed to combine nonlinear dynamic features from these feature maps, forming the mechanical MPMFFIQE feature set. Finally, this set is input into the harris hawks optimization support vector machine (HHOSVM) classifier to achieve fault identification. Results from three real-world case studies demonstrate that the proposed MPMFFIQE method improves accuracy by up to 12.90% in comparison with six feature extraction techniques. Furthermore, the proposed model achieves an accuracy rate exceeding 98.10% with just five training samples per state, representing up to a 27.48% improvement over six existing models. These findings confirm that the developed model can effectively and accurately diagnose mechanical faults in industrial applications using only a small number of training samples. Additionally, the model shows strong generalization across different mechanical equipment, highlighting its significant practical value.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"63 ","pages":"Article 102972"},"PeriodicalIF":8.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132008","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":"Fault diagnosis of high-speed train suspension systems under variable speeds based on dynamic transfer loss weight-deep subdomain adaptation network","authors":"Funing Yang,&nbsp;Chunrong Hua,&nbsp;Junyi Mu,&nbsp;Yan Huang,&nbsp;Weiqun Liu,&nbsp;Dawei Dong","doi":"10.1016/j.aei.2024.103091","DOIUrl":"10.1016/j.aei.2024.103091","url":null,"abstract":"<div><div>Fault diagnosis of suspension systems under variable speeds is crucial for the safe operation of high-speed trains. However, the machine learning-based fault diagnosis of suspension systems is hindered by the requirements for multiple sensors, supervised training, and reliance on expert experience for setting the transfer loss weight in domain adaptation methods. Using the wavelet packet energy method and the coefficient of variation, this study developed a sensitivity indicator for vibration signals at different locations to suspension components faults, and determined the center of the bogie frame as the optimal single sensor location. Based on the Euclidean distance between feature tensors output by the backbone network, an approach to dynamically update the transfer loss weight suitable for domain adaptation was proposed. A dynamic transfer loss weight-deep subdomain adaptation network (DTLW-DSAN) was constructed to dynamically adjust the network’s focus on source domain feature extraction and target domain adaptation, which can realize accurate and rapid network fitting. The proposed DTLW-DSAN achieved an average diagnosis accuracy of 93.09 % for the primary cylindrical spring, primary damper, and air spring of a high-speed train suspension system under wide-range speed variations (between 100, 200, and 300 km/h). The DTLW-DSAN’s generality was validated using the visual transfer learning public dataset Office-31.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"64 ","pages":"Article 103091"},"PeriodicalIF":8.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129739","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":"Cooperative dual-actor proximal policy optimization algorithm for multi-robot complex control task","authors":"Jacky Baltes,&nbsp;Ilham Akbar,&nbsp;Saeed Saeedvand","doi":"10.1016/j.aei.2024.102960","DOIUrl":"10.1016/j.aei.2024.102960","url":null,"abstract":"<div><div>This paper introduces a novel multi-agent Deep Reinforcement Learning (DRL) framework named the Cooperative Dual-Actor Proximal Policy Optimization (CDA-PPO) algorithm, designed to address complex humanoid robot cooperative learning control tasks. Effective cooperation among multiple humanoid robots, particularly in scenarios involving complex walking gait control and external disturbances in dynamic environments, is a critical challenge. This is especially pertinent for tasks requiring precise coordination and control, such as joint object transportation. In various real-life scenarios, humanoid robots might need to cooperate to carry large objects in many scenarios. This capability is crucial for logistics, manufacturing, intelligent transportation, and search-and-rescue missions applications. Humanoid robots have gained significant popularity, and their use in these cooperative tasks is becoming more common. To address this challenge, we propose CDA-PPO, which introduces a learning-based communication platform between agents and employs two distinct policy networks for each agent. This dual-policy approach enhances the robots’ ability to adapt to complex interactions and maintain stability while performing intricate tasks. We demonstrate the efficacy of CDA-PPO in a cooperative object-transportation scenario, where two humanoid robots collaborate to carry a table. The experimental results show that CDA-PPO significantly outperforms traditional methods, such as Independent PPO (IPPO), Multi-Agent PPO (MAPPO), and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3), in terms of training efficiency, stability, reward acquisition, and humanoid robot cooperative balance control with effective coordination between robots. The findings underscore the potential of CDA-PPO to advance the field of cooperative multi-agent control problems, proposing the way for future research in complex robotics applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"63 ","pages":"Article 102960"},"PeriodicalIF":8.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132011","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":"Research on multi-stage topology optimization method based on latent diffusion model","authors":"Wei Zhang ,&nbsp;Guodong Zhao ,&nbsp;Lijie Su","doi":"10.1016/j.aei.2024.102966","DOIUrl":"10.1016/j.aei.2024.102966","url":null,"abstract":"<div><div>Topology optimization aims to identify the optimal material distribution to enhance structural performance. Traditional methods such as Solid Isotropic Material with Penalization (SIMP) involve extensive finite element iterative calculations, limiting their ability to address complex or large-scale problems. This paper introduces a novel topology optimization method based on latent diffusion models (TOLDM). It is a multi-stage topology optimization approach that incorporates latent diffusion models with the SIMP method. TOLDM reduces the dimensionality and memory usage of data processing by shifting the diffusion process from the image space to a lower-dimensional latent space, decreasing the reliance on high-performance computing resources and accelerating the training and inference processes. Moreover, by incorporating a cross-attention module, the proposed method introduces physical conditions of various modalities into the diffusion process, enhancing the feasibility and manufacturability of the generated topological structures. This integrated optimization strategy not only improves the diversity and efficiency of structural designs but also achieves an optimized balance between resource consumption and performance. We have also compared TOLDM with other methods based on deep generative models, demonstrating its superiority. Furthermore, by incorporating transfer learning, TOLDM was successfully applied to the structural optimization of automobile engine hoods, enabling the generation of efficient and manufacturable topological structures. This indicates that TOLDM has high applicability in resource-constrained engineering fields, effectively advancing the practical implementation of topology optimization technology and expanding its technical boundaries.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"63 ","pages":"Article 102966"},"PeriodicalIF":8.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132012","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}