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期刊论文 31

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关键词

机器学习 2

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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    

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    

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    

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    

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    

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    

裂缝性储层数据驱动模型证伪与不确定性量化 Article

方军龄, 龚斌, Jef Caers

《工程(英文)》 2022年 第18卷 第11期   页码 116-128 doi: 10.1016/j.eng.2022.04.015

摘要:

天然裂缝的许多特性是不确定的,如裂缝的空间分布、岩石物理特性和流体流动性能。贝叶斯定理提供了一个框架来量化地质建模和流动模拟的不确定性,从而支持储层物性预测。贝叶斯方法在裂缝性储层中的应用大多局限于合成案例。然而,在现场应用中,一个主要问题是贝叶斯先验是被证伪的,因为它不能预测油气藏的生产历史。在本文中,我们展示了如何利用全局敏感性分析(GSA)来确定先验被证伪的原因。然后,我们采用近似贝叶斯计算(ABC)方法,结合基于决策树的代理模型来拟合生产历史。我们将这两种方法应用于一个复杂的裂缝性油气藏,其中综合考虑了所有不确定因素,包括油层物理特性、岩石物理特性、流体特性、离散裂缝参数以及压力和渗透率的动态变化。我们成功地找出了证伪的几个原因。结果表明,我们提出的方法可以有效地量化裂缝性储层建模和流动模拟的不确定性。此外,关键参数的不确定性,如裂缝开度和断层传导率,得到了降低。

关键词: 贝叶斯证据学习     证伪     裂隙性储层     随机森林     近似贝叶斯计算    

基于变分贝叶斯多稀疏成分提取的空间碎片超高速撞击损伤重构方法研究 Research Article

黄雪刚,石安华,罗庆,罗锦阳

《信息与电子工程前沿(英文)》 2022年 第23卷 第4期   页码 530-541 doi: 10.1631/FITEE.2000575

摘要: 为提高在轨航天器抵御空间碎片撞击的生存能力,提出一种撞击损伤评估方法。首先,建立一个针对红外热图像序列数据的多区域损伤挖掘模型,用于描述处于不同空间层的撞击损伤。采用变分贝叶斯推理来求解模型参数,从而有效地从红外热图像数据中识别不同类型撞击损伤。然后,提出一种图像处理框架,包括具有能量函数的图像分割算法和具有稀疏表示的图像融合方法,以消除变异贝叶斯误差并比较不同类型损伤的位置。在试验部分,将上述方法用于评估二次碎片云对Whipple防护结构的复杂撞击损伤。实验结果证明本文提出的方法可以对空间碎片超高速撞击造成的不同类型复杂损伤进行有效识别与评估。

关键词: 超高速撞击;变分贝叶斯;稀疏表示;损伤评估    

long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian

Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG

《环境科学与工程前沿(英文)》 2015年 第9卷 第3期   页码 534-544 doi: 10.1007/s11783-014-0660-2

摘要: In this study, the performance of nitrogen and phosphorus removal in a full-scale closed-loop bioreactor (oxidation ditch) system was simulated using the ASM2d model. Routine data describing the process for two years were compiled for calibration and validation. To overcome the identifiability problem, the classic Bayesian inference approach was utilized for parameter estimation. The calibrated model could describe the long-term trend of nutrient removal and short-term variations of the process performance, showing that the Bayesian method was a reliable and useful tool for the parameter estimation of the activated sludge models. The anoxic phosphate uptake by polyphosphate accumulating organisms (PAO) contributed 71.2% of the total Poly-P storage, which reveals the dominance of denitrifying phosphorus removal process under the oxygen limiting conditions. It was found that 58.7% of the anoxic Poly-P storage and denitrification by PAO in the reactor was achieved in the aerated compartment, implying that the PAO’s anoxic activity was significantly stimulated by the low dissolved oxygen (DO) level in this compartment due to the oxygen gradient caused by brush aerator.

关键词: activated sludge model     Bayesian inference     biological nutrient removal     closed-loop bioreactor     oxidation ditch     denitrifying polyphosphate accumulating organisms    

基于带约束最大间隔的贝叶斯分类器判别学习方法 None

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

《信息与电子工程前沿(英文)》 2018年 第19卷 第5期   页码 639-650 doi: 10.1631/FITEE.1700007

摘要: 提出一种新的面向贝叶斯模式分类的判别学习方法,称作“带约束的最大间隔(CMM)方法”。通过计算正样本最小决策值和负样本最大决策值的差异,定义类别之间的类别间隔。基于该类别间隔和正确分类的约束,将间隔函数学习问题转化为最大化类别间隔问题。利用序列无约束最小化技术解决该非线性规划问题。运用CMM方法得到基于高斯混合模型的贝叶斯分类器,并在10个UCI数据集上进行实验。结果表明,利用CMM方法得到的分类器分类性能,明显优于代表性的生成式学习方法期望最大化(EM)和判别式学习方法支持向量机(SVM),并且在多个数据集上取得了相比之前最优结果更好的效果。分类实验和分类器对比实验证明,CMM方法有效,具有一定应用前景。

关键词: 判别学习;统计建模;贝叶斯分类器;高斯混合模型;UCI数据集    

基于样地调查的地质碳储量的贝叶斯优化

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

《工程(英文)》 2022年 第18卷 第11期   页码 96-104 doi: 10.1016/j.eng.2022.06.011

摘要:

We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO2 trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO2 injection  strategies during field-scale CO2 sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO2 injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations. 

关键词: 组分流     贝叶斯优化     地质碳储量     CCUS     机器学习     人工智能科学    

IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

《工程管理前沿(英文)》 2017年 第4卷 第4期   页码 428-436 doi: 10.15302/J-FEM-2017048

摘要: In the EU Horizon 2020 Shift2Rail Multi-Annual Action Plan, the challenge of railway maintenance is generating knowledge from data and/or information. Therefore, we promote a novel concept called “IN2CLOUD,” which comprises three sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning, and 3) collaborative management using asset-related data acquired from the intelligent hybrid cloud. The concept is developed under the assumption that organizations want/need to learn from each other (including domain knowledge and experience) but do not want to share their raw data or information. IN2CLOUD will help the movement of railway industry systems from “local” to “global” optimization in a collaborative way. The development of cutting-edge intelligent hybrid cloud-based solutions, including information technology (IT) solutions and related methodologies, will enhance business security, economic sustainability, and decision support in the field of intelligent asset management of railway assets.

关键词: railway     intelligent asset management     collaborative learning     big data     hybrid cloud     Bayesian    

反应式容侵系统入侵预测的混合式贝叶斯网络方法

王良民,马建峰

《中国工程科学》 2008年 第10卷 第8期   页码 87-96

摘要: 提出了基于入侵模型的混合式贝叶斯网络(HyBN, hybrid bayesian network)模型,将入侵模型中攻击行为和系统安全状态节点分离为攻击层和状态层两个网络层次,两层间使用收敛连接,而两层内部的节点间使用连续连接

关键词: 容忍入侵     警报关联     入侵模型     入侵预测    

可靠性工程中参数的一种估计方法

韩明

《中国工程科学》 2003年 第5卷 第3期   页码 51-56

摘要:

提出了可靠性工程中参数的一种估计方法——新Bayes估计法,给出了失效概率、失效率的新Bayes估计的定义及其新Bayes估计。最后,结合实际问题的数据,进行了具体计算和分析,结果表明所提出的新Bayes估计法有效、可行,便于工程技术人员在工程中应用。

关键词: 可靠性工程     参数估计     新Bayes估计     失效概率    

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

《结构与土木工程前沿(英文)》 2021年 第15卷 第2期   页码 490-505 doi: 10.1007/s11709-020-0669-5

摘要: This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy ( ), precision, recall, , and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

关键词: seismic soil liquefaction     Bayesian belief network     cone penetration test     parameter learning     structural learning    

标题 作者 时间 类型 操作

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

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

裂缝性储层数据驱动模型证伪与不确定性量化

方军龄, 龚斌, Jef Caers

期刊论文

基于变分贝叶斯多稀疏成分提取的空间碎片超高速撞击损伤重构方法研究

黄雪刚,石安华,罗庆,罗锦阳

期刊论文

long-term nutrient removal in a full-scale closed-loop bioreactor for sewage treatment: an example of Bayesian

Zheng LI,Rong QI,Wei AN,Takashi MINO,Tadashi SHOJI,Willy VERSTRAETE,Jian GU,Shengtao LI,Shiwei XU,Min YANG

期刊论文

基于带约束最大间隔的贝叶斯分类器判别学习方法

Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG

期刊论文

基于样地调查的地质碳储量的贝叶斯优化

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

期刊论文

IN2CLOUD: A novel concept for collaborative management of big railway data

Jing LIN, Uday KUMAR

期刊论文

反应式容侵系统入侵预测的混合式贝叶斯网络方法

王良民,马建峰

期刊论文

可靠性工程中参数的一种估计方法

韩明

期刊论文

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU

期刊论文