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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

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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

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Time-series prediction based on global fuzzy measure in social networks

Li-ming YANG,Wei ZHANG,Yun-fang CHEN

《信息与电子工程前沿（英文）》 2015年第16卷第10期页码 805-816 doi: 10.1631/FITEE.1500025

摘要： Social network analysis (SNA) is among the hottest topics of current research. Most measurements of SNA methods are certainty oriented, while in reality, the uncertainties in relationships are widely spread to be overridden. In this paper, fuzzy concept is introduced to model the uncertainty, and a similarity metric is used to build a fuzzy relation model among individuals in the social network. The traditional social network is transformed into a fuzzy network by replacing the traditional relations with fuzzy relation and calculating the global fuzzy measure such as network density and centralization. Finally, the trend of fuzzy network evolution is analyzed and predicted with a fuzzy Markov chain. Experimental results demonstrate that the fuzzy network has more superiority than the traditional network in describing the network evolution process.

关键词： Time-series network Fuzzy network Fuzzy Markov chain

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基于灰色模糊数的公交线网优化研究

邓卫,胡启洲

《中国工程科学》 2007年第9卷第11期页码 21-25

摘要：

在考虑信息不完全的情况下，用灰色模糊数对公交线网优化问题进行了研究。在给出了公交线网优化的约束条件与优化目标的基础上，利用灰色模糊数建立了公交线网优化的决策模型。灰色模糊数是用三参数区间参与优化过程，在允许参数在一定范围内变化的情况下，计算后得到的结果是一个区间向量，其中向量值最大的为所求结果，适用于城市公交线网的优化问题。实例应用分析表明，优化后的公交线网效率提高，可达性良好，可满足城市公交可持续发展的要求。

关键词：公交线网优化灰色模糊数

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基于Rough集理论的模糊神经网络构造方法

黄显明,易继锴

《中国工程科学》 2004年第6卷第4期页码 44-50

摘要：

提出了在模糊神经网络中使用Rough集理论进行网络结构设计的方法。由于Rough集理论有强大的数值分析能力，而模糊神经网络具有准确的逼近收敛能力和较高的精度，所以通过两者的结合，可以得到一种可理解性好、计算简单、收敛速度快的神经网络模型。这种网络构造方法的主要过程为：首先，利用Rough集理论对给定数据集进行规则获取；然后，根据这些规则构造模糊神经网络各层的神经元个数及相关参数初始值；最后，用BP算法迭代求出网络的各种参数，完成网络的设计。给出了一个二维非线性函数拟合的实例，进一步验证了方法的正确性。

关键词：模糊神经网络 Rough集规则获取函数拟合

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高频真空木材干燥的模糊神经网络控制方法研究

姜滨,孙丽萍,曹军,周正

《中国工程科学》 2014年第16卷第4期页码 17-20

摘要：

高频真空木材干燥是一种干燥速度快、能源消耗低、环境污染小的新型联合干燥技术。在木材高频真空联合干燥过程的理论分析基础上，针对神经网络方法建立的木材干燥模型，设计了木材干燥模糊控制器和模糊神经网络控制器。对模糊控制和模糊神经网络两种控制方法进行了仿真实验，结果表明模糊神经网络方法控制效果更好，如温度上升快，控制精度高，稳定性好。模糊神经网络控制方法对实现木材干燥过程的全自动控制具有重要研究意义。

关键词：高频真空木材干燥模糊神经网络

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A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

《工程管理前沿（英文）》 2017年第4卷第3期页码 338-347 doi: 10.15302/J-FEM-2017032

摘要： This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies. Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation (SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.

关键词： supply chain network stochastic demand sampling average approximation Bayesian bootstrap Latin hypercube sampling

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工程网络计划的LR型模糊系数线性规划方法

高朋,冯俊文

《中国工程科学》 2009年第11卷第2期页码 70-74

摘要：

工程网络计划的基础是对各工序持续时间的估计，而导致工序工期不确定性的因素不仅具有随机性，通常也具有模糊性。文章提出一种具有LR型模糊数的线性规划模型，解决了工程网络计划的时间参数估计和关键路径识别问题，并通过引入λ截集来充分描述决策者在不同情形下对工序工期估计的可信程度。最后给出一实例详细说明了该方法的应用过程及有效性。

关键词：工程网络计划模糊线性规划工序

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

《结构与土木工程前沿（英文）》 2017年第11卷第1期页码 90-99 doi: 10.1007/s11709-016-0363-9

摘要： Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis (SA)

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Seismic vulnerability assessment of water supply network in Tianjin, China

Yanxi CHEN,Zhiguang NIU,Jiaqi BAI,Yufei WANG

《环境科学与工程前沿（英文）》 2014年第8卷第5期页码 767-775 doi: 10.1007/s11783-014-0632-6

摘要： The water supply network (WSN) system is a critical element of civil infrastructure systems. Its complexity of operation and high number of components mean that all parts of the system cannot be simply assessed. Earthquakes are the most serious natural hazard to a WSN, and seismic risk assessment is essential to identify its vulnerability to different stages of damage and ensure the system safety. In this paper, using a WSN located in the airport area of Tianjin in northern China as a case study, a quantitative vulnerability assessment method was used to assess the damage that the water supply pipelines would suffer in an earthquake, and the finite element software ABAQUS and fuzzy mathematic theory were adopted to construct the assessment method. ABAQUS was applied to simulate the seismic damage to pipe segments and components of the WSN. Membership functions based on fuzzy theory were established to calculate the membership of the components in the system. However, to consider the vulnerability of the whole system, fuzzy cluster analysis was used to distinguish the importance of pipe segments and components. Finally, the vulnerability was quantified by these functions. The proposed methodology aims to assess the performance of WSNs based on pipe vulnerabilities that are simulated and calculated by the model and the mathematical method based on data of damage. In this study, a whole seismic vulnerability assessment method for a WSN was built, and these analyses are expected to provide necessary information for a mitigation plan in an earthquake disaster.

关键词： water supply network seismic vulnerability assessment finite element fuzzy mathematics

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Understanding innovation diffusion and adoption strategies in megaproject networks through a fuzzy system

Yan ZHANG, His-Hsien WEI, Dong ZHAO, Yilong HAN, Jiayu CHEN

《工程管理前沿（英文）》 2021年第8卷第1期页码 32-47 doi: 10.1007/s42524-019-0082-8

摘要： Innovation and knowledge diffusion in megaprojects is one of the most complicated issues in project management. Compared with conventional projects, megaprojects typically entail large-scale investments, long construction periods, and conflicting stakeholder interests, which result in a distinctive pattern of innovation diffusion. However, traditional investigation of innovation diffusion relies on subjective feedback from experts and frequently neglects inter-organizational knowledge creation, which frequently emerges in megaprojects. Therefore, this study adopted project network theory and modeled innovation diffusion in megaprojects as intra- and inter-organizational learning processes. In addition, system dynamics and fuzzy systems were combined to interpret experts’ subject options as quantitative coefficients of the project network model. This integrated model will assist in developing an insightful understanding of the mechanisms of innovation diffusion in megaprojects. Three typical network structures, namely, a traditional megaproject procurement organization (TMO), the environ megaproject organization (EMO), and an integrated megaproject organization (IMO), were examined under six management scenarios to verify the proposed analytic paradigm. Assessment of project network productivity suggested that the projectivity of the TMO was insensitive to technical and administrative innovations, the EMO could achieve substantial improvement from technical innovations, and the IMO trended incompatibly with administrative innovations. Thus, industry practitioners and project managers can design and reform agile project coordination by using the proposed quantitative model to encourage innovation adoption and reduce productivity loss at the start of newly established collaborations.

关键词： megaproject innovation adoption project network system dynamic fuzzy logic

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基于自适应网络模糊推理系统的移动机器人导航控制器 Research Article

Panati SUBBASH, Kil To CHONG

《信息与电子工程前沿（英文）》 2019年第20卷第2期页码 141-151 doi: 10.1631/FITEE.1700206

摘要：在障碍物高度杂乱的未知环境中自主导航是移动机器人研究的一个基本问题。提出一种基于自适应网络模糊推理系统（ANFIS）的差分驱动移动机器人导航控制器，用超声波传感器捕捉移动机器人周围的环境信息。设计了一个基于模糊逻辑的导航控制器，用于获取数据集训练ANFIS控制器。在移动机器人导航过程中，考虑到环境噪声对传感器读数的影响，将加性高斯白噪声添加到传感器读数中并反馈给已训练的ANFIS控制器。在3种不同环境下对移动机器人进行导航，评价该导航控制器的鲁棒性。通过与已有移动机器人导航控制器（如神经网络、模糊逻辑）比较行程长度、行程效率、弯曲能量，验证ANFIS控制器性能。仿真结果表明，与其他控制器相比，ANFIS控制器具有更好性能，能够在不同环境中顺利导航且不与障碍物发生碰撞。

关键词：自适应网络模糊推理系统；加性高斯白噪声；自主导航；移动机器人

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yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-basedfuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam

《化学科学与工程前沿（英文）》 2013年第7卷第3期页码 357-365 doi: 10.1007/s11705-013-1336-3

摘要： Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

关键词： oil recovery artificial intelligence extraction neural networks supercritical extraction

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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