资源类型

期刊论文 777

年份

2024 2

2023 68

2022 74

2021 61

2020 48

2019 54

2018 54

2017 55

2016 30

2015 28

2014 26

2013 23

2012 17

2011 24

2010 30

2009 23

2008 39

2007 30

2006 16

2005 11

展开 ︾

关键词

机器学习 27

人工智能 8

深度学习 6

过程强化 6

优化 4

大数据 4

智能制造 4

三峡工程 3

不确定性 3

DSM（设计结构矩阵） 2

产业发展 2

代理模型 2

催化剂 2

催化裂化 2

凝固过程 2

增材制造 2

层次分析法 2

废水 2

时间序列 2

展开 ︾

检索范围：

排序： 展示方式：

operation in the presence of different random noises and uncertainty: Implementation of generalized Gaussianprocess regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

《机械工程前沿（英文）》 2015年 第10卷 第4期   页码 405-412 doi: 10.1007/s11465-015-0354-x

摘要：

The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines’ behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system’s variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system’s behavior over coldstart operations. Then, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of analysis of the engine’s behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.

关键词： automotive engine     calibration     coldstart operation     Gaussian process regression machine (GPRM)     uncertainty and random noises    

HTML PDF 收藏

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

《结构与土木工程前沿（英文）》 2013年 第7卷 第2期   页码 133-136 doi: 10.1007/s11709-013-0202-1

摘要： This article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parameter ( ) of unsaturated soil. GPR method proceeds by parameterising a covariance function, and then infers the parameters given the data set. Input variables of GPR are net confining pressure ( ), saturated volumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameter ( ). A comparative study has been carried out between the developed GPR and Artificial Neural Network (ANN) models. A sensitivity analysis has been done to determine the effect of each input parameter on . The developed GPR gives the variance of predicted . The results show that the developed GPR is reliable model for prediction of of unsaturated soil.

关键词： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network (ANN)     variance    

HTML PDF 收藏

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1083-1096 doi: 10.1007/s11709-020-0654-z

摘要： The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of , , and . Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria ( =0.841, =0.592, and =0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

关键词： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression     M5P model tree     random forest    

HTML PDF 收藏

prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussianprocess regression

《能源前沿（英文）》 doi: 10.1007/s11708-023-0906-4

摘要： Lithium-ion batteries (LIBs) are widely used in transportation, energy storage, and other fields. The prediction of the remaining useful life (RUL) of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery. In order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPR) is proposed. In the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neural network model, which is used to predict the initial cycle lifetime of 124 LIBs. The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space. The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated, and the shortest distance is considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model (DEM). In the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (ERUL). By testing four batteries under different working conditions, the RMSE of all capacity estimation is less than 1.2%, and the accuracy percentage (AP) of remaining life prediction is more than 98%. Experiments show that the method does not need human intervention and has high prediction accuracy.

关键词： lithium-ion batteries     RUL prediction     double exponential model     neural network     Gaussian process regression (GPR)    

HTML PDF 收藏

of driver-response relationships: identifying factors using a novel framework integrating quantile regressionwith interpretable machine learning

《环境科学与工程前沿（英文）》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1676-2

摘要：

● A novel framework integrating quantile regression with machine learning is proposed.

关键词： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression     Total phosphorus     Chlorophyll a    

HTML PDF 收藏

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

《结构与土木工程前沿（英文）》 2020年 第14卷 第1期   页码 185-198 doi: 10.1007/s11709-019-0591-x

摘要： The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like -value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient of determination ( ). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

关键词： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard penetration test    

HTML PDF 收藏

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

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

非线性计数系统的关键因子辨识方法 Research Article

张新民,王静波,魏驰航,宋执环

《信息与电子工程前沿（英文）》 2022年 第23卷 第1期   页码 123-133 doi: 10.1631/FITEE.2000324

摘要： 从数据中识别对系统输出产生较大影响的关键因子是科学和工程领域最具挑战性的任务之一。本文针对非线性计数系统，提出基于敏感性分析的广义高斯过程回归（SA-GGPR）建模方法，以识别影响系统输出的关键因子。SA-GGPR采用具有泊松似然的GGPR模型描述非线性计数系统。GGPR模型继承了非参数核学习和泊松分布的优点，可处理复杂非线性计数系统。然而，由于GGPR模型的非参数核学习架构，难以理解GGPR模型中输入和输出之间的关系。SA-GGPR方法通过定量评估不同输入对系统输出的影响来辨识影响系统输出的关键因子。在模拟非线性计数系统和实际钢铁轧制过程的应用结果表明，SA-GGPR方法在识别精度方面优于几种先进方法。

关键词： 关键因子；非线性计数系统；广义高斯过程回归；敏感性分析；钢铁轧制过程    

HTML PDF 收藏

Evaluation and prediction of slope stability using machine learning approaches

《结构与土木工程前沿（英文）》 2021年 第15卷 第4期   页码 821-833 doi: 10.1007/s11709-021-0742-8

摘要： In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.

关键词： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

HTML PDF 收藏

Development of machine learning multi-city model for municipal solid waste generation prediction

《环境科学与工程前沿（英文）》 2022年 第16卷 第9期 doi: 10.1007/s11783-022-1551-6

摘要：

● A database of municipal solid waste (MSW) generation in China was established.

关键词： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

HTML PDF 收藏

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

《结构与土木工程前沿（英文）》 2022年 第16卷 第10期   页码 1249-1266 doi: 10.1007/s11709-022-0858-5

摘要： The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f1(x) and the maximum displacement to self-weight ratio as f2(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f1(x) and f2(x).

关键词： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

HTML PDF 收藏

Simulation and analysis of grinding wheel based on Gaussian mixture model

Yulun CHI, Haolin LI

《机械工程前沿（英文）》 2012年 第7卷 第4期   页码 427-432 doi: 10.1007/s11465-012-0350-3

摘要：

This article presents an application of numerical simulation technique for the generation and analysis of the grinding wheel surface topographies. The ZETA 20 imaging and metrology microscope is employed to measure the surface topographies. The Gaussian mixture model (GMM) is used to transform the measured non-Gaussian field to Gaussian fields, and the simulated topographies are generated. Some numerical examples are used to illustrate the viability of the method. It shows that the simulated grinding wheel topographies are similar with the measured and can be effective used to study the abrasive grains and grinding mechanism.

关键词： grinding wheel     3D topographies measurement     Gaussian mixture model     simulation    

HTML PDF 收藏

基于混合驱动高斯过程学习的强机动多目标跟踪方法 Research Article

国强1,滕龙1,2,尹天祥3,郭云飞3,吴新良2,宋文明2

《信息与电子工程前沿（英文）》 2023年 第24卷 第11期   页码 1647-1656 doi: 10.1631/FITEE.2300348

摘要： 现有机动目标跟踪方法在杂波环境中强机动目标的跟踪性能并不令人满意。本文提出一种混合驱动方法，利用数据驱动和基于模型算法的优点跟踪多个高机动目标。将时变恒速（CV）模型集成到在线学习的高斯过程（GP）中，提高高斯过程的预测性能。进一步与广义概率数据关联（GPDA）算法相结合，实现多目标跟踪。通过仿真实验可知，与广泛使用的机动目标跟踪算法如交互式多模型（IMM）和数据驱动的高斯过程运动跟踪器（GPMT）相比，提出的混合驱动方法具有显著的性能优势。

关键词： 目标跟踪；高斯过程；数据驱动；在线学习；模型驱动；概率数据关联    

HTML PDF 收藏

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 274-287 doi: 10.1007/s11705-021-2043-0

摘要： Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

关键词： smart chemical process operations     data generation     hybrid method     machine learning     particle swarm optimization    

HTML PDF 收藏

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

《环境科学与工程前沿（英文）》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1735-8

摘要：

● Data-driven approach was used to simulate VFA production from WAS fermentation.

关键词： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

HTML PDF 收藏

标题 作者 时间 类型 操作

operation in the presence of different random noises and uncertainty: Implementation of generalized Gaussianprocess regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

期刊论文

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

期刊论文

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

期刊论文

prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussianprocess regression

期刊论文

of driver-response relationships: identifying factors using a novel framework integrating quantile regressionwith interpretable machine learning

期刊论文

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

期刊论文

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

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

期刊论文

非线性计数系统的关键因子辨识方法

张新民,王静波,魏驰航,宋执环

期刊论文

Evaluation and prediction of slope stability using machine learning approaches

期刊论文

Development of machine learning multi-city model for municipal solid waste generation prediction

期刊论文

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

期刊论文

Simulation and analysis of grinding wheel based on Gaussian mixture model

Yulun CHI, Haolin LI

期刊论文

基于混合驱动高斯过程学习的强机动多目标跟踪方法

国强1,滕龙1,2,尹天祥3,郭云飞3,吴新良2,宋文明2

期刊论文

Hybrid method integrating machine learning and particle swarm optimization for smart chemical process

Haoqin Fang, Jianzhao Zhou, Zhenyu Wang, Ziqi Qiu, Yihua Sun, Yue Lin, Ke Chen, Xiantai Zhou, Ming Pan

期刊论文

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

期刊论文