Reliability Engineering & System Safety最新文献_第10页

Agent-based fire evacuation model using social learning theory and intelligent optimization algorithms

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.111000

Peng Lu , Yufei Li

{"title":"Agent-based fire evacuation model using social learning theory and intelligent optimization algorithms","authors":"Peng Lu , Yufei Li","doi":"10.1016/j.ress.2025.111000","DOIUrl":"10.1016/j.ress.2025.111000","url":null,"abstract":"<div><div>Fire incidents often lead to a series of social problems. Therefore, it is particularly important to optimize evacuation strategies and promote relevant social safety knowledge. Based on this, the study proposes a fire evacuation model that integrates the Fire Dynamics Simulator (FDS) with Agent-Based Modeling (ABM) to simulate a bar fire scenario. In this model, the concept of social learning is introduced, and multiple factors such as evacuation time, trampling risk, and pedestrian health are considered as risk evaluation indicators. Machine learning combined with intelligent optimization methods is applied to optimize evacuation strategies. <strong>First</strong>, we validate the effectiveness of the model by comparing the averaged simulation results with real-world data. The results demonstrate that the simulation outcomes of our model exhibit good accuracy and robustness. <strong>Secondly</strong>, we analyze the importance of the second-floor safety exit. When the second-floor safety exit remains unobstructed, evacuation efficiency and casualty risk can be significantly improved. <strong>Then</strong>, we examine the role of social knowledge. When people are aware of the fire risk and choose to evacuate immediately, casualties can be significantly reduced. <strong>Finally</strong>, we study the effectiveness of phased evacuation in enhancing crowd safety. By employing a method that combines Random Forest and the Particle Swarm Optimization-Genetic Algorithm (PSO-GA), phased evacuation strategies are optimized, resulting in definitive strategies to reduce evacuation risks. This finding further expands social knowledge, indicating that when the proportion of staggered evacuation is appropriate, evacuation risks can be significantly reduced. Our research contributes to the development of social safety knowledge and provides methodological references for formulating evacuation strategies in different settings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111000"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563192","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

Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.111001

Alfian Tan , Rasa Remenyte-Prescott , Joy Egede , Don Sharkey , Michel Valstar

{"title":"Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure","authors":"Alfian Tan , Rasa Remenyte-Prescott , Joy Egede , Don Sharkey , Michel Valstar","doi":"10.1016/j.ress.2025.111001","DOIUrl":"10.1016/j.ress.2025.111001","url":null,"abstract":"<div><div>About 10 % of newborns need a life support procedure following birth. However, this procedure has a considerable error rate of more than 25 %, which may compromise its safety and reliability. Continuous studies to improve its performance are carried out, but in-field studies can be expensive and not always feasible. Hence, a modelling approach is proposed. Studies have shown how variations and errors in this procedure are associated with technical and non-technical factors. Thus, the proposed approach includes these aspects by considering different settings of thermal care, the experience of the doctor, types of respiratory devices, and the ability of the clinical staff to cope with stress. The Coloured Petri Nets (CPNs) approach is used to model the characteristics of this Newborn Life Support (NLS) procedure. This technique can facilitate complex system modelling with a compact representation. The dynamic characteristics of the procedure are implemented during a simulation of the CPNs model. These relate to the duration of steps, the baby's physical response, and variations or errors from the required protocol. This paper demonstrates how risks in the protocol relating to the baby's final condition and clinical decision inaccuracies can be quantified by the approach.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111001"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.111002

Chengfei Liu , Enyuan Wang , Zhonghui Li , Zesheng Zang , Baolin Li , Shan Yin , Chaolin Zhang , Yubing Liu , Jinxin Wang

{"title":"Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning","authors":"Chengfei Liu , Enyuan Wang , Zhonghui Li , Zesheng Zang , Baolin Li , Shan Yin , Chaolin Zhang , Yubing Liu , Jinxin Wang","doi":"10.1016/j.ress.2025.111002","DOIUrl":"10.1016/j.ress.2025.111002","url":null,"abstract":"<div><div>The reliable early warning of risks associated with gas, fire, dust, and roof hazards is crucial for the safe mining of coal mines. Traditional warning methods suffer from singular disaster risk warnings, low integration of risk information across different indicators, and insufficient perception of multi-hazard coupling relationships. To address these challenges, this paper proposes a method for adaptive integration of risk warnings that quantitatively learns the relationships between various indicators and warning tasks. Anomaly-transformer and E<sup>2</sup>GAN models are first employed to detect anomalies and impute missing values in time-series data. Subsequently, an improved MMoE model is used for multi-indicator fusion and prediction, allowing the simultaneous forecasting of future trends for all early-warning indicators. Finally, an adaptive multi-hazard risk integration warning model is developed, utilizing original and predicted data to calculate the current and future risk probabilities for various hazards. Comprehensive risk identification and warning are then performed using a multi-hazard grading identification. Experimental results show that the improved MMoE model outperforms LSTNet and TCN in prediction accuracy, and the integration model exceeds CNN and GRU in warning performance. Field validation confirms that this approach effectively identifies risks and enhances the reliability of intelligent early warning systems, ensuring coal mining safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111002"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592800","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 new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.110966

Liangqi Wan , Yumeng Wei , Qiaoke Zhang , Lei Liu , Yuejian Chen

{"title":"A new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis","authors":"Liangqi Wan , Yumeng Wei , Qiaoke Zhang , Lei Liu , Yuejian Chen","doi":"10.1016/j.ress.2025.110966","DOIUrl":"10.1016/j.ress.2025.110966","url":null,"abstract":"<div><div>The Kriging model-assisted reliability analysis method is widely recognized as an effective way to evaluate structural failure probability. However, accurately estimating failure probability is challenging due to the inherent limitations of the Kriging model in accounting for response noise during the modeling process. This limitation undermines the accuracy of emulation in reliability analysis, significantly reducing the confidence of the reliability evaluation. To overcome this challenge, this paper proposes an active learning Lasso-based multiple stochastic Kriging model-Monte Carlo simulation method. First, a Voronoi-based adaptive proximity-guided sampling strategy is presented to sample important MCS points near the limit state surface by continuously identifying sensitive Voronoi cells. These identified MCS points are then used to select the stochastic Kriging model components, thereby ensuring that the selection process prioritizes the most informative regions. Second, a Lasso-based model selection strategy is proposed to account for the model-form uncertainty in the multiple stochastic Kriging modeling process, which optimizes and selects the best ensemble of multiple stochastic Kriging model components for the framework of the surrogate ensemble-assisted reliability analysis method. The effectiveness of the proposed method is demonstrated through numerical and engineering case studies. Results show that the proposed method provides more accurate failure probability estimation with fewer calls to limit state functions compared to existing methods, improving predictive accuracy and computational efficiency in structural reliability analysis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110966"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563780","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 global attention based gated temporal convolutional network for machine remaining useful life prediction

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.110997

Xu Xinyao , Zhou Xiaolei , Fan Qiang , Yan Hao , Wang Fangxiao

{"title":"A global attention based gated temporal convolutional network for machine remaining useful life prediction","authors":"Xu Xinyao , Zhou Xiaolei , Fan Qiang , Yan Hao , Wang Fangxiao","doi":"10.1016/j.ress.2025.110997","DOIUrl":"10.1016/j.ress.2025.110997","url":null,"abstract":"<div><div>As the core technique of the prognostic and health management field, data-driven remaining useful life (RUL) prediction generally requires abundant data to construct reliable mappings from monitoring data to machines’ RUL labels. However, the diverse working conditions of machines can lead to their different degradation trajectories, which makes similar data indicate diverse RULs of different machines. When predicting RULs with monitoring data, the phenomenon causes a severe label confusion problem and limits the performance of data-driven RUL prediction methods. In this paper, a new gated-temporal-convolutional-network-based method is proposed for RUL prediction tasks of machines. To handle the label confusion problem, a novel global attention mechanism is proposed, which enables the proposed model to identify confused data by the difference in machines’ global degradation tendencies. Besides, a new temporal convolutional network with a gating mechanism is proposed for better feature extraction performance. Moreover, a new nearest-neighbor-based data compensation strategy is designed to simplify data distributions. Both strategies also contribute to the solution of the problem. The proposed method is verified on an aircraft turbofan engine dataset and a bearing dataset. The experiment results show the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110997"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683421","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

Remaining useful life prediction using a hybrid transfer learning-based adaptive Wiener process model

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.110975

Xiaowu Chen , Zhen Liu , Kunping Wu , Hanmin Sheng , Yuhua Cheng

{"title":"Remaining useful life prediction using a hybrid transfer learning-based adaptive Wiener process model","authors":"Xiaowu Chen , Zhen Liu , Kunping Wu , Hanmin Sheng , Yuhua Cheng","doi":"10.1016/j.ress.2025.110975","DOIUrl":"10.1016/j.ress.2025.110975","url":null,"abstract":"<div><div>Because of the characteristics of uncertainty description and interpretability, Wiener process (WP) has found extensive application in the domain of forecasting remaining useful life (RUL). Nevertheless, most existing WP often require selecting the suitable deterioration function and drift coefficient types based on the deterioration characteristics of target sample, which greatly limits their universality and feasibility in practical engineering. In order to address this issue, a hybrid adaptive WP based on transfer learning is presented to dynamically model the deterioration process of products with different deterioration features. The Brownian motion-based drift coefficient is applied to improve the adaptive characteristics of WP on the time-variant deterioration rate. A transfer learning-based long short-term memory (LSTM) model is utilized to adaptively track the dynamic nonlinear characteristics. According to the notion of first arrival time, we have successfully derived the explicit formula for the probability density function, so that the uncertainty contained in predicted results can be directly characterized. By using two capacity datasets and one torque bar deterioration dataset exhibiting distinct deterioration features, comparative experiments with eight different existing models have proven the universality and superiority of our model in forecasting RUL.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110975"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592798","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 credible interval model updating method for structural population analysis and design stages based on small samples

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110996

Yang Cao, Xiaojun Wang

{"title":"A credible interval model updating method for structural population analysis and design stages based on small samples","authors":"Yang Cao, Xiaojun Wang","doi":"10.1016/j.ress.2025.110996","DOIUrl":"10.1016/j.ress.2025.110996","url":null,"abstract":"<div><div>In practical engineering, a persistent discrepancy exists between numerical simulations and real responses. This gap significantly undermines reliability in the established models and spurs the development of model updating. Yet, during the structural analysis and design phases, the focus of model updating often extends beyond the current structure to encompass the same type of structural population, so this paper proposes a credible interval model updating method for addressing the issue of uncertain model updating. This method divides the uncertain model updating problem into two subgoals: ensuring that the experimental responses credibly describe the real responses and that the simulation responses accurately fit experimental responses. For the first subgoal, the non-probabilistic credible convex sets for multi-type responses are established by introducing the concepts of multidimensional response space and credibility level. For the second subgoal, this paper categorizes model parameters into uncertain parameters and updating parameters, allowing the simulation model to fully consider prior information and be more generally applicable to the uncertain conditions of structural population. Particularly, the comparison between the predictions of the updated model and experimental results from other operating conditions highlights the robustness of the updated model and the advancement of the methodology.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110996"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628421","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

Efficient global reliability sensitivity method by combining dimensional reduction integral with stochastic collocation

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110993

Xiaomin Wu, Zhenzhou Lu

{"title":"Efficient global reliability sensitivity method by combining dimensional reduction integral with stochastic collocation","authors":"Xiaomin Wu, Zhenzhou Lu","doi":"10.1016/j.ress.2025.110993","DOIUrl":"10.1016/j.ress.2025.110993","url":null,"abstract":"<div><div>Defined as the mean square difference between unconditional failure probability (FP) and conditional FP on fixed input realization, global reliability sensitivity (GRS) can quantify the effect of random input on FP. For efficiently estimating the GRS, a novel method is proposed by combining truncated <strong>d</strong>imensional <strong>r</strong>eduction <strong>i</strong>ntegral with <strong>s</strong>tochastic <strong>c</strong>ollocation (DRI-SC). In the DRI-SC, the unconditional and conditional FPs are equivalently converted into the expected cumulative distribution function (CDF) of a selected reduction input. Then, using the continuity of CDF, a truncated DRI is combined with SC to efficiently estimate the expected CDF. To further enhance the efficiency of DRI-SC, an adaptive Kriging model is trained to provide the integrand CDF values at the SC nodes. The novelties of the DRI-SC include deriving the unconditional and conditional FPs required by GRS as the expected CDF, designing an SC node-sharing strategy, and training the Kriging model in the SC node set. DRI-SC inherits the universality of numerical simulation but avoids its prohibitive computation, and the DRI-SC maintains the efficiency of the existing SC-based GRS methods but avoids the density fitting. The superiority of the DRI-SC over existing methods is verified by the presented examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110993"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578813","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

Enhanced risk assessment framework for complex maritime traffic systems via data driven: A case study of ship navigation in Arctic

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110991

Shenping Hu , Cuiwen Fang , Jianjun Wu , Cunlong Fan , Xinxin Zhang , Xue Yang , Bing Han

{"title":"Enhanced risk assessment framework for complex maritime traffic systems via data driven: A case study of ship navigation in Arctic","authors":"Shenping Hu , Cuiwen Fang , Jianjun Wu , Cunlong Fan , Xinxin Zhang , Xue Yang , Bing Han","doi":"10.1016/j.ress.2025.110991","DOIUrl":"10.1016/j.ress.2025.110991","url":null,"abstract":"<div><div>The era of big data has been characterized by an increasing diversity of information and a deeper application of system safety. In this context, this study proposes an enhanced risk assessment (ERA) framework to estimate traffic risk from massive data obtained in complex maritime traffic systems. The ERA framework adopts a 4R model that includes risk perception, risk cognition, risk reasoning, and risk control. The ERA framework integrates the Systems Theoretic Accident Model and Process and Stochastic Petri Nets to analyze the ship traffic process and develop risk control schemes. The feasibility of the proposed framework is demonstrated by a case study in Arctic waters. The results indicate that ice concentration represents a key factor for ship traffic in Arctic waters and that the risk control scheme type is related to the ice resistance level of ships. Accordingly, for ships with low ice resistance or no ice-class ships, the traffic risk is high when they are passing through the East Siberian, Laptev, Kara Sea, and the Vilkitskogo Strait, and icebreakers are required in July and October. In contrast, for ships with a higher ice resistance, regular traffic is generally possible for the East Siberian and Laptev Seas.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110991"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563191","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

DCAGGCN: A novel method for remaining useful life prediction of bearings

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-03 DOI: 10.1016/j.ress.2025.110978

Deqiang He , Jiayang Zhao , Zhenzhen Jin , Chenggeng Huang , Cai Yi , Jinxin Wu

{"title":"DCAGGCN: A novel method for remaining useful life prediction of bearings","authors":"Deqiang He , Jiayang Zhao , Zhenzhen Jin , Chenggeng Huang , Cai Yi , Jinxin Wu","doi":"10.1016/j.ress.2025.110978","DOIUrl":"10.1016/j.ress.2025.110978","url":null,"abstract":"<div><div>Accurate prediction of Bearings' remaining useful life (RUL) is crucial in equipment operation and maintenance. The bearing RUL prediction technology based on GCN has recently been widely used. However, the existing GCN-based RUL prediction results are limited by two aspects : (1) GCN usually uses the predefined adjacency matrix to define the graph, which makes the graph unable to track the real-time correlation of degradation features in time. (2) Existing GCN uses only one to two layers of graph convolution and cannot extract deep features. Based on the issues above, this paper proposes a bearing RUL prediction model that utilizes a Dual-correlation adaptive gated graph convolutional network (DCAGGCN). Firstly, a predefined double correlation graph is proposed and obtained by feature channel data. Next, an adaptive graph is created by transforming a source matrix and a target matrix, and then integrating it with a predefined graph. This allows the network to consider two types of correlation and adaptively adjust the graph's topology. In addition, this paper proposes a gated convolution layer, which can greatly alleviate the over-smoothing problem caused by the stacking of graph convolution layers. The effectiveness of the proposed method is verified by two public datasets.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110978"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578816","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