资源类型

期刊论文 85

年份

2024 1

2023 7

2022 13

2021 8

2020 6

2019 7

2018 4

2017 14

2016 5

2015 2

2013 3

2012 2

2010 1

2008 2

2007 3

2006 4

2003 1

2000 1

展开 ︾

关键词

机器学习 2

贝叶斯优化 2

CCUS 1

Gibbs抽样 1

K平均聚类 1

L唱M算法 1

MCMC模拟 1

Weibull分布 1

不确定性评估 1

井壁稳定 1

人工智能 1

人工智能科学 1

人工神经网络 1

代表帧 1

仿生纺丝 1

优化算法 1

供应商协同管理 1

先天性心脏病 1

展开 ︾

检索范围：

排序： 展示方式：

Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

《结构与土木工程前沿（英文）》 2013年 第7卷 第3期   页码 276-287 doi: 10.1007/s11709-013-0207-9

摘要： This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines. The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter. The measurement errors associated with the ILI tools are considered in the Bayesian inference. The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta, Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

关键词： pipeline     metal-loss corrosion     inverse Gaussian process     measurement error     hierarchical Bayesian     Markov Chain Monte Carlo (MCMC)    

HTML PDF 收藏

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

《结构与土木工程前沿（英文）》 2021年 第15卷 第1期   页码 80-98 doi: 10.1007/s11709-021-0682-3

摘要： Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause–effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

关键词： Bayesian belief network     seismically induced soil liquefaction     interpretive structural modeling     lateral displacement    

HTML PDF 收藏

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

《工程管理前沿（英文）》 2017年 第4卷 第4期   页码 483-489 doi: 10.15302/J-FEM-2017023

摘要： Bi-directional pedestrian flows are common at crosswalks, footpaths, and shopping areas. However, the properties of pedestrian movement may vary in urban areas according to the type of walking facility. In recent years, crowd movements at carnival events have attracted the attention of researchers. In contrast to pedestrian behavior in other walking facilities, pedestrians whose attention is attracted by carnival displays or activities may slow down and even stop walking. The Lunar New Year Market is a traditional carnival event in Hong Kong held annually one week before the Lunar New Year. During the said event, crowd movements can be easily identified, particularly in Victoria Park, where the largest Lunar New Year Market in Hong Kong is hosted. In this study, we conducted a video-based observational survey to collect pedestrian flow and speed data at the Victoria Park Lunar New Year Market on the eve of the Lunar New Year. Using the collected data, an extant mathematical model was calibrated to capture the relationships between the relevant macroscopic quantities, thereby providing insight into pedestrian behavior at the carnival event. Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled experiment. Results obtained enhance our understanding of crowd behavior under different conditions at carnival events, thus facilitating the improvement of the safety and efficiency of similar events in the future.

关键词： pedestrian flow model     bi-directional interactions     empirical studies     Bayesian inference    

HTML PDF 收藏

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

《能源前沿（英文）》 2022年 第16卷 第2期   页码 277-291 doi: 10.1007/s11708-021-0731-6

摘要： An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.

关键词： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate formulation    

HTML PDF 收藏

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

《环境科学与工程前沿（英文）》 2023年 第17卷 第7期 doi: 10.1007/s11783-023-1685-1

摘要：

● A hydrodynamic-Bayesian inference model was developed for water pollution tracking.

关键词： Identification of pollution sources     Water quality restoration     Bayesian inference     Hydrodynamic model     Inverse problem    

HTML PDF 收藏

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

HTML PDF 收藏

Combined reticular blind drainage and vertical hierarchical drainage system for landfills located in

Wenjing LU,Zhonge FU,Yan ZHAO

《环境科学与工程前沿（英文）》 2016年 第10卷 第1期   页码 177-184 doi: 10.1007/s11783-014-0710-9

摘要： A novel water control technology that combines the features of a reticular blind drainage system and a vertical hierarchical drainage system is developed and applied in the Yanziyan Sanitary Landfill, which is located at an area (Loudi City, Hunan Province, China) with high rainfall and high groundwater level. The reticular blind drain system, which was installed on the bottom and side walls of the landfill site, can conveniently guide the flow of groundwater out of the site while preventing a disorganized flow of groundwater. The vertical hierarchical drainage system was installed to separate rainfall water and leachate in the landfill site, thus efficiently reducing the pressure of leachate treatment. The whole drainage system plays a key role in foundation stabilization by seepage control and separation and in the instant drainage of rainfall water. The leachate reduction efficiency of the drainage technology was calculated in terms of leachate production before (336519 m ) and after (29664 m ) technology application. Over 90% of leachate derived from rainfall water and groundwater inflow was avoided upon installation of the vertical hierarchical drainage and reticular blind drainage systems. The technology can thus be popularized and applied for water control in landfills located in areas with high rainfall and high groundwater level. The proposed technology can be used to alleviate the pressure of leachate treatment and to reduce the risk of instability.

关键词： landfill     reticular blind drain     vertical hierarchical drain     guidance and drainage     impermeable layer    

HTML PDF 收藏

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第8期   页码 617-633 doi: 10.1631/FITEE.1400263

摘要： A local discriminant regularized soft -means (LDRSKM) method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft -means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

关键词： Multimode process monitoring     Local discriminant regularized soft k-means clustering     Kernel support vector data description     Bayesian inference     Tennessee Eastman process    

HTML PDF 收藏

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

《化学科学与工程前沿（英文）》 2017年 第11卷 第3期   页码 414-428 doi: 10.1007/s11705-017-1663-x

摘要： Root cause analysis (RCA) of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

关键词： abnormal aluminum electrolysis cell condition     Fuzzy-Bayesian network     multi-source knowledge solidification and reasoning     root cause analysis    

HTML PDF 收藏

Effect of hierarchical ZSM-5 zeolite crystal size on diffusion and catalytic performance of n-heptane

Shuman Xu, Xiaoxiao Zhang, Dangguo Cheng, Fengqiu Chen, Xiaohong Ren

《化学科学与工程前沿（英文）》 2018年 第12卷 第4期   页码 780-789 doi: 10.1007/s11705-018-1733-8

摘要： Hierarchical ZSM-5 zeolite aggregates with different sizes of nanocrystals were synthesized using different amounts of the mesoporogen 3-aminopropyltriethoxysilane. The effect of the crystal size on the catalytic cracking of -heptane was investigated and the Thiele modulus and effectiveness factor were used to determine the reaction rate-limiting step. The crystal size affected the textual properties of the catalysts but not the acidic properties of the catalysts. The reaction rate was first order with respect to the -heptane concentration. Cracking over hierarchical zeolites with nanocrystal sizes larger than about 50 nm took place under transition-limiting conditions, whereas the reaction over hierarchical zeolites with nanocrystal sizes of 15 or 30 nm proceeded under reaction control conditions. Hierarchical ZSM-5 zeolite aggregates with smaller nanocrystals had better selectivity for light olefins which can be ascribed to the shorter diffusion path lengths and lower diffusion resistance in these catalysts. Furthermore, these catalysts had lower coking levels which can be attributed to the substantial number of mesopores which prevent the formation of coke precursors.

关键词： hierarchical ZSM-5     crystal size     catalytic cracking     Thiele modulus     effectiveness factor    

HTML PDF 收藏

基于层次优化的图像网格化方法

Hao XIE,Ruo-feng TONG

《信息与电子工程前沿（英文）》 2016年 第17卷 第1期   页码 32-40 doi: 10.1631/FITEE.1500171

摘要：

矢量图形作为光栅图像的一种几何表示形式，具有许多优点，例如，定义独立性和编辑功能。将栅格图像转换为矢量图形的一种流行方法是图像网格划分，其目的是找到一个网格以尽可能忠实地表示图像。对于传统的网格划分算法，问题的症结主要在于目标的高非线性度和非平滑度，这使得很难找到理想的最优解。为了改善这种情况，我们提出了一种层次优化算法，解决了从较粗级别到较细级别的问题，并为每个级别提供了其较粗的提升的初始化功能。为了进一步简化问题，将原始的非凸问题转换为线性最小二乘，从而变成凸的，这使问题更容易解决。字典学习框架用于将几何和拓扑完美地结合在一起。然后采用交替方案来求解两个部分。实验表明，对于大多数图像，该算法运行速度快，效果优于现有图像。

关键词： 图像网格化；层次优化；凸化    

HTML PDF 收藏

Facile synthesis of hierarchical flower-like Ag/Cu

Mengyun Wang, Shengbo Zhang, Mei Li, Aiguo Han, Xinli Zhu, Qingfeng Ge, Jinyu Han, Hua Wang

《化学科学与工程前沿（英文）》 2020年 第14卷 第5期   页码 813-823 doi: 10.1007/s11705-019-1854-8

摘要： Novel, hierarchical, flower-like Ag/Cu O and Au/Cu O nanostructures were successfully fabricated and applied as efficient electrocatalysts for the electrochemical reduction of CO . Cu O nanospheres with a uniform size of ~180 nm were initially synthesized. Thereafter, Cu O was used as a sacrificial template to prepare a series of Ag/Cu O composites through galvanic replacement. By varying the Ag/Cu atomic ratio, Ag /Cu O, having a hierarchical, flower-like nanostructure with intersecting Ag nanoflakes encompassing an inner Cu O sphere, was prepared. The as-prepared Ag /Cu O samples presented higher Faradaic efficiencies (FE) for CO and relatively suppressed H evolution than the parent Cu O nanospheres due to the combination of Ag with Cu O in the former. Notably, the highest CO evolution rate was achieved with Ag /Cu O due to the larger electroactive surface area furnished by the hierarchical structure. The same hierarchical flower-like structure was also obtained for the Au /Cu O composite, where the FE (10%) was even higher than that of Ag /Cu O. Importantly, the results reveal that Ag /Cu O and Au /Cu O both exhibit remarkably improved stability relative to Cu O. This study presents a facile method of developing hierarchical metal-oxide composites as efficient and stable electrocatalysts for the electrochemical reduction of CO .

关键词： bimetallic nanostructure     hierarchical metal/oxide nanomaterial     galvanic replacement     electrochemical reduction of CO2    

HTML PDF 收藏

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Xueping PAN, Ping JU, Feng WU, Yuqing JIN

《机械工程前沿（英文）》 2017年 第12卷 第3期   页码 367-376 doi: 10.1007/s11465-017-0429-y

摘要：

A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive train system in a wind turbine generator (WTG) is proposed in this paper. Firstly, the parameters of the DFIG and the drive train are estimated locally under different types of disturbances. Secondly, a coordination estimation method is further applied to identify the parameters of the DFIG and the drive train simultaneously with the purpose of attaining the global optimal estimation results. The main benefit of the proposed scheme is the improved estimation accuracy. Estimation results confirm the applicability of the proposed estimation technique.

关键词： wind turbine generator     DFIG     drive train system     hierarchical parameter estimation method     trajectory sensitivity technique    

HTML PDF 收藏

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Haosen Cao,Bin-Bin Hu,Xiaoyu Mo,Duxin Chen,Jianxi Gao,Ye Yuan,Guanrong Chen,Tamás Vicsek,Xiaohong Guan,Hai-Tao Zhang,

《工程（英文）》 doi: 10.1016/j.eng.2023.06.011

摘要： Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks, and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower- to higher- level nodes, such as lagging birds’ howl in a flock or the opinions of lower-level individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of “bunching” effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks, which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability     Large hierarchical networks     Reverse edges     Information flows     Complex networks    

HTML PDF 收藏

Erratum to: Synthesis of hierarchical nanohybrid CNT@Ni-PS and its applications in enhancing the tribological

《化学科学与工程前沿（英文）》 2022年 第16卷 第10期   页码 1530-1530 doi: 10.1007/s11705-022-2240-5

HTML PDF 收藏

标题 作者 时间 类型 操作

Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

期刊论文

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD

期刊论文

A Bayesian modeling approach to bi-directional pedestrian flows in carnival events

S. Q. XIE, S. C. WONG, William H. K. LAM

期刊论文

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

期刊论文

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

期刊论文

Combined reticular blind drainage and vertical hierarchical drainage system for landfills located in

Wenjing LU,Zhonge FU,Yan ZHAO

期刊论文

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

期刊论文

A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

期刊论文

Effect of hierarchical ZSM-5 zeolite crystal size on diffusion and catalytic performance of n-heptane

Shuman Xu, Xiaoxiao Zhang, Dangguo Cheng, Fengqiu Chen, Xiaohong Ren

期刊论文

基于层次优化的图像网格化方法

Hao XIE,Ruo-feng TONG

期刊论文

Facile synthesis of hierarchical flower-like Ag/Cu

Mengyun Wang, Shengbo Zhang, Mei Li, Aiguo Han, Xinli Zhu, Qingfeng Ge, Jinyu Han, Hua Wang

期刊论文

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Xueping PAN, Ping JU, Feng WU, Yuqing JIN

期刊论文

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Haosen Cao,Bin-Bin Hu,Xiaoyu Mo,Duxin Chen,Jianxi Gao,Ye Yuan,Guanrong Chen,Tamás Vicsek,Xiaohong Guan,Hai-Tao Zhang,

期刊论文

Erratum to: Synthesis of hierarchical nanohybrid CNT@Ni-PS and its applications in enhancing the tribological

期刊论文