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分层自适应神经模糊推理系统控制器；晶闸管控制串联电容器补偿技术；自动发电控制（AGC）；多目标粒子群优化算法；电力系统动态稳定性；相互联系的多源电力系统 1

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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    

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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    

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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    

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

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    

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Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

《机械工程前沿（英文）》 2013年 第8卷 第4期   页码 429-442 doi: 10.1007/s11465-013-0277-3

摘要：

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

关键词： electrochemical machining process (ECM)     modeling     adaptive neuro-fuzzy inference system (ANFIS)     optimization     cuckoo optimization algorithm (COA)    

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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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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    

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falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference

《结构与土木工程前沿（英文）》   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

《结构与土木工程前沿（英文）》 2021年 第15卷 第1期   页码 61-79 doi: 10.1007/s11709-020-0684-6

摘要： Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS–particle swarm optimization (PSO), ANFIS–ant colony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C–O, C–W, C–F, O–W, O–F, and W–F), trivariate (C–O–W, C–W–F, O–W–F), and four-variate (C–O–W–F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS–DE– (O) (MP= 0.96), ANFIS–PSO– (C-O) (MP= 0.88), ANFIS–DE– (O–W–F) (MP= 0.94), and ANFIS–PSO– (C–O–W–F) (MP= 0.89), respectively. ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.

关键词： foamed concrete     adaptive neuro fuzzy inference system     nature-inspired algorithms     prediction of compressive strength    

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Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems

Abbas PARSAIE,Amir Hamzeh HAGHIABI,Mojtaba SANEIE,Hasan TORABI

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 111-122 doi: 10.1007/s11709-016-0354-x

摘要： Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.

关键词： weir-gate     soft computing     crest geometry     circular crest weir     cylindrical shape    

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from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference

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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Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

《信息与电子工程前沿（英文）》 2016年 第17卷 第5期   页码 389-402 doi: 10.1631/FITEE.1500385

摘要： Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) Global Positioning System (GPS) data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointing where people live at scale. We revisit this research topic and infer home location within 100 m×100 m squares at 70% accuracy for 76% and 71% of active users in New York City and the Bay Area, respectively. To the best of our knowledge, this is the first time home location has been detected at such a fine granularity using sparse and noisy data. Since people spend a large portion of their time at home, our model enables novel applications. As an example, we focus on modeling people’s health at scale by linking their home locations with publicly available statistics, such as education disparity. Results in multiple geographic regions demonstrate both the effectiveness and added value of our home localization method and reveal insights that eluded earlier studies. In addition, we are able to discover the real buzz in the communities where people live.

关键词： Home location     Mobility patterns     Healthcare    

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因果推理 Review

况琨, 李廉, 耿直, 徐雷,  张坤, 廖备水, 黄华新, 丁鹏, 苗旺, 蒋智超

《工程（英文）》 2020年 第6卷 第3期   页码 253-263 doi: 10.1016/j.eng.2019.08.016

摘要：

因果推理是解释性分析的强大建模工具，它可使当前的机器学习变得可解释。如何将因果推理与机器学习相结合，开发可解释人工智能（XAI）算法，是迈向人工智能2.0的关键步骤之一。为了将因果推理的知识带给机器学习和人工智能领域的学者，我们邀请从事因果推理的研究人员，从因果推理的不同方面撰写了本综述。本综述包括以下几个部分：况琨博士的“平均因果效应评估——简要回顾与展望”，李廉教授的“反事实推理的归因问题”，耿直教授的“Yule-Simpson悖论和替代指标悖论”，徐雷教授的“因果发现CPT方法”，张坤教授的“从观测数据中发现因果关系”，廖备水和黄华新教授的“形式论辩在因果推理和解释中的作用”，丁鹏教授的“复杂实验中的因果推断”，苗旺教授的“观察性研究中的工具变量和阴性对照方法”，蒋智超博士的“有干扰下的因果推断”。

关键词： 因果推断     指示变量     阴性对照     因果推理和解释     因果发现     反事实推理     治疗效果评价    

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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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连续生产线设备故障诊断专家系统的动态模糊推理机制的研究

谈理,刘谨,梅丽婷

《中国工程科学》 2005年 第7卷 第6期   页码 57-60

摘要：

针对连续生产线设备故障诊断专家系统的研制，阐述了在建立模糊推理机过程中引入具有实时性的动态模糊关系的思想，并构造一个随无故障时间变化的动态隶属度函数来实现。

关键词： 故障诊断     专家系统     模糊推理    

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标题 作者 时间 类型 操作

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

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

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

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

期刊论文

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

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

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

期刊论文

falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference

期刊论文

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

期刊论文

Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems

Abbas PARSAIE,Amir Hamzeh HAGHIABI,Mojtaba SANEIE,Hasan TORABI

期刊论文

from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference

J. Sargolzaei, A. Hedayati Moghaddam

期刊论文

Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

期刊论文

因果推理

况琨, 李廉, 耿直, 徐雷,  张坤, 廖备水, 黄华新, 丁鹏, 苗旺, 蒋智超

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

连续生产线设备故障诊断专家系统的动态模糊推理机制的研究

谈理,刘谨,梅丽婷

期刊论文