Advanced Engineering Informatics最新文献_第10页

Mathematical formulation and hybrid algorithm for a three-dimensional parallel row ordering problem considering adaptive material handling points and obstacles 考虑自适应搬运点和障碍物的三维平行行排序问题的数学公式及混合算法

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-10 DOI: 10.1016/j.aei.2025.103636

Haojie Ma , Zeqiang Zhang , Zongxing He , Dan Ji , Jie Li

{"title":"Mathematical formulation and hybrid algorithm for a three-dimensional parallel row ordering problem considering adaptive material handling points and obstacles","authors":"Haojie Ma , Zeqiang Zhang , Zongxing He , Dan Ji , Jie Li","doi":"10.1016/j.aei.2025.103636","DOIUrl":"10.1016/j.aei.2025.103636","url":null,"abstract":"<div><div>A well-designed layout can significantly improve operational efficiency, reduce costs, and enhance competitiveness. The parallel row ordering problem is an important component of facility layout problems. To address the limitations of current parallel row layout research, which often overlooks the presence of obstacles and fixed material handling points, this study proposes a three-dimensional (3D) parallel row ordering problem that considers adaptive material handling points and obstacles based on the actual conditions of manufacturing workshops. This problem focuses on determining the exact final positions of material handling points in 3D space. Subsequently, we developed a mixed-integer linear programming model to minimise material handling costs. For small-scale instances, a commercial solver was used to determine the exact solutions. In contrast, for large-scale problems where commercial solvers exhibit limited performance, a two-stage method was proposed. In the first stage, an improved genetic algorithm combined with a tabu search was employed to identify a high-quality facility sequence. In the second stage, a commercial solver was utilised to optimise the specific locations of material handling points to achieve lower material handling costs. The correctness of the proposed model and two-stage method was validated through benchmark instances. Then, the versatility of the proposed algorithm was demonstrated by comparing the obtained computational results of solving basic parallel row ordering problem instances with those of various algorithms. Finally, two actual cases were used to confirm the practical applicability of the algorithm.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103636"},"PeriodicalIF":8.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588535","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}

引用次数: 0

Generating command modeling and design graphs with data augmentation for enhanced 3D modeling support 使用数据增强功能生成命令建模和设计图形，以增强3D建模支持

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-10 DOI: 10.1016/j.aei.2025.103644

Yugyeong Jang, Kyung Hoon Hyun

引用次数: 0

Adaptive single-source open domain generalization network: a novel physical information fusion framework for fault diagnosis based on physically embedded autoencoder and adaptive open-set feature separation 自适应单源开放域泛化网络：一种基于物理嵌入式自编码器和自适应开集特征分离的故障诊断物理信息融合框架

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-10 DOI: 10.1016/j.aei.2025.103632

Feiyu Lu , Qingbin Tong , Xuedong Jiang , Jianjun Xu , Xin Du , Jingyi Huo , Zengqiang Ma

{"title":"Adaptive single-source open domain generalization network: a novel physical information fusion framework for fault diagnosis based on physically embedded autoencoder and adaptive open-set feature separation","authors":"Feiyu Lu , Qingbin Tong , Xuedong Jiang , Jianjun Xu , Xin Du , Jingyi Huo , Zengqiang Ma","doi":"10.1016/j.aei.2025.103632","DOIUrl":"10.1016/j.aei.2025.103632","url":null,"abstract":"<div><div>Recently, fault diagnosis models based on domain generalization have been well studied. However, most methods typically assume access to multiple source domain data, and the label types of the source and target domain data are consistent. This is difficult to address in practical engineering problems because of the probabilistic nature and uncertainty of fault occurrences, which may arise from only a single source domain data being available and the emergence of new data with some unknown fault types, defining it as a Single-source Open Domain Generalization Fault Diagnosis (SODGFD) task. To address the important and challenging practical issues, this paper proposes an Adaptive Single-source Open Domain Generalization Network (ASODGN). Firstly, a physically embedded autoencoder (PEA) is proposed, trained using single-source domain data. The original data is processed through a novel wavelet packet energy encoder, embedding wavelet packet energy into a low-dimensional feature space consistent with the data feature dimensions, and dynamically adjusting the wavelet packet energy and data features using a dynamic weight mechanism. Secondly, a sparse regularized activation function (SRA) with strong feature constraint capability is adopted to form a classifier, achieving data classification functionality. Finally, an Adaptive Open-set Feature Separation algorithm (AOFS) is developed, which comprehensively considers both the data information and positional information and utilizes a hard threshold to partition open-set data in the test set. Sixteen SOFD tasks are conducted on constant speed and time-varying speed datasets, and the results demonstrate that ASODGN is a highly effective fault diagnosis model compared to twenty related methods.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103632"},"PeriodicalIF":8.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604546","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}

引用次数: 0

Uncertainty reports as explainable AI: A cognitive-adaptive framework for human-AI decision systems in context tasks 不确定性报告作为可解释的人工智能：上下文任务中人类-人工智能决策系统的认知适应框架

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-09 DOI: 10.1016/j.aei.2025.103634

Lin Sheng, Fangyuan Chang, Qinghua Sun, Danba Wangzha, Zhenyu Gu

{"title":"Uncertainty reports as explainable AI: A cognitive-adaptive framework for human-AI decision systems in context tasks","authors":"Lin Sheng, Fangyuan Chang, Qinghua Sun, Danba Wangzha, Zhenyu Gu","doi":"10.1016/j.aei.2025.103634","DOIUrl":"10.1016/j.aei.2025.103634","url":null,"abstract":"<div><div>As large language models (LLMs) increasingly support high-stakes human-AI decision-making, understanding how humans interpret LLM-generated uncertainty becomes critical. Existing explainable AI (XAI) methods for LLMs focus on post-hoc, model-centric explanations, often overlooking human cognitive responses and contextual demands. This paper proposes a Cognitive-Adaptive Framework for Human-AI Decision Systems, operationalized through a layered heterogeneous network system that employs a dual-decomposition structure to integrate user feedback, contextual variables, and LLM-generated uncertainty cues. The framework decomposes uncertainty processing into three stages—Qualification, Delivery, and Adaptation—and analyzes human responses through multi-level statistical, mediation, and causal inference. Empirical results from a controlled experiment show that the direction of uncertainty (certainty vs. uncertainty) exerts the strongest influence on decision efficiency and trust; ambiguity, more than information volume, mediates trust and accuracy; and task-required expertise modulates users’ preferences for different uncertainty formats. The proposed system demonstrates high computational efficiency, structural robustness, and behavioral interpretability. These findings offer practical strategies for user-aligned LLM uncertainty communication and lay a foundation for adaptive, context-sensitive XAI systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103634"},"PeriodicalIF":8.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579662","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}

引用次数: 0

A novel multi-sensor information fusion method for fault diagnosis of rotating machinery with missing signals 基于多传感器信息融合的旋转机械缺失信号故障诊断方法

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-09 DOI: 10.1016/j.aei.2025.103595

Tantao Lin , Zhijun Ren , Kai Huang , Yongsheng Zhu , Hamid Reza Karimi

{"title":"A novel multi-sensor information fusion method for fault diagnosis of rotating machinery with missing signals","authors":"Tantao Lin , Zhijun Ren , Kai Huang , Yongsheng Zhu , Hamid Reza Karimi","doi":"10.1016/j.aei.2025.103595","DOIUrl":"10.1016/j.aei.2025.103595","url":null,"abstract":"<div><div>Multi-sensor systems are a cornerstone of rotating machinery diagnostics, with information fusion significantly enhancing diagnostic accuracy and robustness. However, many existing methods fail to balance the consistency and specificity of sensor signals, resulting in incomplete fault information extraction. Moreover, these methods often assume the availability of all sensor signals, rendering them ineffective when signals are missing. To address these limitations, this paper proposes a novel multi-sensor information fusion fault diagnosis (MSIFFD) method designed specifically for scenarios with missing signals. The method employs a feature encoder that integrates private and shared features to extract comprehensive fault information. A learnable prompt-based module reconstructs missing features by integrating existing signals with prior knowledge. Additionally, intra-source and inter-source fusion modules are utilized to further enhance feature integration. Experimental results demonstrate the superior performance of the proposed method. Even in tasks where three sensor channels are missing, the method achieves an impressive accuracy of 97.65%, providing a robust solution for fault diagnosis in complex industrial environments.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103595"},"PeriodicalIF":8.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579663","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}

引用次数: 0

Aircraft EWIS safety risk level classification based on Multi-EDA and MHATT-BiLSTM 基于Multi-EDA和mhat - bilstm的飞机EWIS安全风险等级分类

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-09 DOI: 10.1016/j.aei.2025.103637

Yiqin Sang, Hongjuan Ge, Huang Li, Shijia Li

{"title":"Aircraft EWIS safety risk level classification based on Multi-EDA and MHATT-BiLSTM","authors":"Yiqin Sang, Hongjuan Ge, Huang Li, Shijia Li","doi":"10.1016/j.aei.2025.103637","DOIUrl":"10.1016/j.aei.2025.103637","url":null,"abstract":"<div><div>The Electrical Wiring Interconnection System (EWIS) plays a critical role in aircraft operational safety, as its failures may lead to severe system malfunctions and compromise overall flight safety. However, existing research has rarely focused on EWIS safety risk analysis using textual reports, which often contain rich failure-related information that remains underexplored. The objective of this study is to improve the automatic classification accuracy of EWIS safety risk levels using narrative reports, addressing challenges such as data sparsity and semantic complexity. To this end, this study proposes an innovative model fusion strategy to construct a robust and accurate EWIS safety risk level classification model. Specifically, Multidimensional Easy Data Augmentation (Multi-EDA) enhances data diversity and coverage. Word2Vec is then employed to transform the preprocessed text into dense semantic vectors. Subsequently, a Bidirectional Long Short-Term Memory Neural Network with a Multi-Head Attention Mechanism (MHATT-BiLSTM) captures both bidirectional dependencies and long-range semantic relationships within EWIS texts. The model is evaluated using seven evaluation metrics: Accuracy, Precision, Recall, F1-score, Macro F1, Micro F1, and Weighted F1. Experimental results demonstrate that the proposed strategy consistently outperforms various benchmark and state-of-the-art models across all metrics. This research contributes a domain-adapted risk classification framework tailored to the linguistic and structural features of EWIS narratives, which enhances the modeling of professional aviation texts. The proposed method provides actionable insights for data-driven safety risk monitoring in civil aviation, enabling more effective risk prioritization, decision support, and resource allocation in EWIS maintenance and safety assurance practices.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103637"},"PeriodicalIF":8.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588624","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}

引用次数: 0

High-performance manufacturing systems: concepts, performance metrics, enablers, challenges, and research directions 高性能制造系统：概念、性能指标、推动因素、挑战和研究方向

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-09 DOI: 10.1016/j.aei.2025.103617

Jiewu Leng , Caiyu Xu , Xueguan Song , Qiang Liu , Xin Chen , Weiming Shen , Lihui Wang

{"title":"High-performance manufacturing systems: concepts, performance metrics, enablers, challenges, and research directions","authors":"Jiewu Leng , Caiyu Xu , Xueguan Song , Qiang Liu , Xin Chen , Weiming Shen , Lihui Wang","doi":"10.1016/j.aei.2025.103617","DOIUrl":"10.1016/j.aei.2025.103617","url":null,"abstract":"<div><div>In today’s market, there is a growing appetite for personalized services and premium quality. This evolving landscape has driven manufacturers to prioritize the capabilities of their manufacturing systems, such as flexibility, reconfigurability, resilience, and reliability. This paper introduces a manufacturing system paradigm named High-Performance Manufacturing System (HPMS), which serves as a guide for the design, manufacturing, configuration, and operation to satisfy the demand for system performance. Drawing on this paradigm, the paper conducts an exhaustive examination of the performance metrics together with their interrelations. The paper’s contributions are threefold. Firstly, it provides an in-depth review of the definitions and assessment methods for manufacturing system performance metrics from a holistic perspective. Secondly, it analyzes the intricate relationships among multiple performance metrics. Lastly, the paper explores potential key enablers, challenges, and research directions in the lifecycle management of HPMSs. It is anticipated to help practitioners be clearer in balancing their goals in the lifecycle management of their manufacturing systems, evolving into a value-oriented paradigm towards Industry 5.0.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103617"},"PeriodicalIF":8.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579664","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}

引用次数: 0

Compound fault diagnosis of hydraulic system based on sample screening and joint analysis with pressure sensors pairs 基于样本筛选和压力传感器对联合分析的液压系统复合故障诊断

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-09 DOI: 10.1016/j.aei.2025.103621

Weinan Xu , Jianguo Zhang , Huiqiang Li

{"title":"Compound fault diagnosis of hydraulic system based on sample screening and joint analysis with pressure sensors pairs","authors":"Weinan Xu , Jianguo Zhang , Huiqiang Li","doi":"10.1016/j.aei.2025.103621","DOIUrl":"10.1016/j.aei.2025.103621","url":null,"abstract":"<div><div>Hydraulic systems play a crucial role in industrial control technology. This article proposes a compound fault diagnosis approach for hydraulic systems using a two-stage attention-multiscale convolutional neural network (CNN) framework paired with prediction interval estimation (PIE). Specifically, a model-agnostic PIE method constructs pseudo-label sets from non-stationary samples across various equipment failure modes. For each failure mode, the intersection of prediction interval sets from multiple components is taken to obtain conservative and credible samples. Sensors are then selected based on hydraulic circuit topology and fault impact analysis. Next, a dedicated network model is established for each component. Within the encoder module, dimensional segmental embedding (DSW) is applied to sensor data, followed by feature mapping using cross-time and cross-dimensional attention mechanisms. The extracted features are fused via a multiscale CNN. Experimental results show diagnosis accuracy for four components reaching 99.88 %, 97.44 %, 100 %, and 98.55 %, respectively. Furthermore, significant improvement is observed on the dataset optimized by PIE-based screening, demonstrating the method’s effectiveness in identifying compound faults even when using only two pressure sensors with low fault-mode correlation scores. This approach enhances the reliability and robustness of hydraulic system condition monitoring.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103621"},"PeriodicalIF":8.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588623","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}

引用次数: 0

Dynamic adaptive fault diagnosis using multi-channel image fusion and deep learning in channel failure occasions on rolling bearings 基于多通道图像融合和深度学习的滚动轴承通道故障动态自适应诊断

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-09 DOI: 10.1016/j.aei.2025.103631

Binbin Qiu , Siqi Liu , Weidong Li , Xi Vincent Wang , Lihui Wang

{"title":"Dynamic adaptive fault diagnosis using multi-channel image fusion and deep learning in channel failure occasions on rolling bearings","authors":"Binbin Qiu , Siqi Liu , Weidong Li , Xi Vincent Wang , Lihui Wang","doi":"10.1016/j.aei.2025.103631","DOIUrl":"10.1016/j.aei.2025.103631","url":null,"abstract":"<div><div>Rolling bearings are widely used in various large and complex mechanical equipment, but they often face high incidental failures in harsh working environments. Therefore, it is crucial to ensure their safety and reliability. To address the inefficiency of feature extraction, inconspicuous features of one-dimensional bearing vibration signals, and channel failure due to sensor damage and equipment failure, this paper proposes a dynamic adaptive rolling bearing fault diagnosis method that combines image fusion with deep learning. The method includes the following steps: (1) A threshold-based evaluation criterion is used to adaptively exclude failed channels and a bagging algorithm is applied to select high-quality signal channels. (2) to effectively capture complex fault features, a strategy based on ensemble learning bagging algorithm and Gramian angular field image fusion is developed. (3) In order to reduce computational complexity and improve diagnosis accuracy, a convolutional neural network is used for feature extraction and dimensionality reduction of compressed two-dimensional images, and a BiLSTM network is used to create a mapping between fault features and fault categories. To verify the feasibility of the proposed method, fault diagnosis analysis was conducted using a bearing fault dataset from the vibration platform at the University of Shanghai for Science and Technology. Its average fault diagnosis accuracy reached 99.48%, which is much higher than the CNN, LSTM, BiLSTM and CNN-LSTM methods. Experimental results show that this new method is expected to improve the accuracy of bearing fault diagnosis in practical engineering applications, and provide reliable technical support for the safe operation of large-scale and complex mechanical equipment.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103631"},"PeriodicalIF":8.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588625","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}

引用次数: 0

Integrated predictive motion control for electric vehicles: A fast solution for safety and control-constrained optimization 电动汽车集成预测运动控制：安全和控制约束优化的快速解决方案

IF 8 1区工程技术

Advanced Engineering Informatics Pub Date : 2025-07-08 DOI: 10.1016/j.aei.2025.103597

Zihan Li , Ping Wang , Hanghang Liu , Yunfeng Hu

{"title":"Integrated predictive motion control for electric vehicles: A fast solution for safety and control-constrained optimization","authors":"Zihan Li , Ping Wang , Hanghang Liu , Yunfeng Hu","doi":"10.1016/j.aei.2025.103597","DOIUrl":"10.1016/j.aei.2025.103597","url":null,"abstract":"<div><div>Electric vehicles equipped with flexible and independent motors is promising to make rapid and precise adjustments in vehicle motion, thereby enhancing active safety. However, coupled dynamics inevitably leads to coordinated optimization with multiple constraints, especially under extreme conditions on rough terrain, which also imposes greater demands on the real-time capability of the on-board control system. To address the above challenge, this paper proposes an integrated hierarchical control strategy to enhance overall vehicle stability and safety, utilizing a fast iterative optimization solution that incorporates both state and control constraints. At the high level, a nonlinear model predictive controller (NMPC) is designed to meet multiple safety control requirements, employing tire slip ratios solved as virtual control inputs. The oriented prediction model captures road unevenness, tire combined-slip characteristics, and dynamic loads. To enhance computational efficiency, a Pontryagin’s Minimum Principle (PMP)-based fast-solving algorithm is employed, reformulating the nonlinear optimization problem into a two-point boundary value problem and addressing original hard constraints through terminal covariate adjustments. At the low level, another predictive controller is implemented to regulate motor torques, ensuring accurate tracking of the desired tire slip ratios. Hardware-in-the-loop test results demonstrate improved overall vehicle stability under challenging driving conditions, with the millisecond-level solution effectively achieving more constrained and satisfactory performance. Additionally, the designed PMP-based fast solution method achieves a nearly 60% speedup compared to the traditional sequential quadratic programming (SQP).</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"68 ","pages":"Article 103597"},"PeriodicalIF":8.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579661","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}

引用次数: 0