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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    

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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)    

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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    

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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)    

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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    

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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    

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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    

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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    

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一种基于高斯过程与粒子群算法的CNN超参数自动搜索混合模型优化算法 Research Article

闫涵,仲崇权,吴玉虎,张立勇,卢伟

《信息与电子工程前沿（英文）》 2023年 第24卷 第11期   页码 1557-1573 doi: 10.1631/FITEE.2200515

摘要： 卷积神经网络（CNN）在许多实际应用领域中有着快速发展。然而，CNN性能很大程度上取决于其超参数，而为CNN配置合适的超参数通常面临着以下3个挑战：（1）不同类型CNN超参数的混合变量编码问题；（2）评估候选模型的昂贵计算成本问题；（3）确保搜索过程中收敛速率和模型性能问题。针对上述问题，提出一种基于高斯过程（GP）和粒子群优化算法（PSO）的混合模型优化算法（GPPSO），用于自动搜索最优的CNN超参数配置。首先，设计一种新的编码方法高效编码CNN中不同类型的超参数。其次，提出一种混合代理辅助（HSA）模型降低评估候选模型的高计算成本。最后，设计一种新的激活函数改善模型性能并确保收敛速率。在图像分类基准数据集上进行了大量实验，验证GPPSO优于最先进的方法。以金属断口诊断为例，验证GPPSO算法在实际应用中的有效性。实验结果表明，GPPSO仅需0.04和1.70 GPU天即可在CIFAR-10和CIFAR-100数据集上实现95.26％和76.36％识别准确率。

关键词： 卷积神经网络；高斯过程；混合模型；超参数优化；混合变量；粒子群优化    

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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality

Xin Peng, Yang Tang, Wenli Du, Feng Qian

《化学科学与工程前沿（英文）》 2017年 第11卷 第3期   页码 429-439 doi: 10.1007/s11705-017-1675-6

摘要： In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving (MSAGL) projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methods.

关键词： non-Gaussian processes     subspace projection     independent component analysis     locality preserving projection     finite mixture model    

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Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infraredspectroscopy: Implications for enabling cell culture process analytical technology?

Huiquan Wu, Erik Read, Maury White, Brittany Chavez, Kurt Brorson, Cyrus Agarabi, Mansoor Khan

《化学科学与工程前沿（英文）》 2015年 第9卷 第3期   页码 386-406 doi: 10.1007/s11705-015-1533-3

摘要： Compared to small molecule process analytical technology (PAT) applications, biotechnology product PAT applications have certain unique challenges and opportunities. Understanding process dynamics of bioreactor cell culture process is essential to establish an appropriate process control strategy for biotechnology product PAT applications. Inline spectroscopic techniques for real time monitoring of bioreactor cell culture process have the distinct potential to develop PAT approaches in manufacturing biotechnology drug products. However, the use of inline Fourier transform infrared (FTIR) spectroscopic techniques for bioreactor cell culture process monitoring has not been reported. In this work, real time inline FTIR Spectroscopy was applied to a lab scale bioreactor mAb IgG3 cell culture fluid biomolecular dynamic model. The technical feasibility of using FTIR Spectroscopy for real time tracking and monitoring four key cell culture metabolites (including glucose, glutamine, lactate, and ammonia) and protein yield at increasing levels of complexity (simple binary system, fully formulated media, actual bioreactor cell culture process) was evaluated via a stepwise approach. The FTIR fingerprints of the key metabolites were identified. The multivariate partial least squares (PLS) calibration models were established to correlate the process FTIR spectra with the concentrations of key metabolites and protein yield of in-process samples, either individually for each metabolite and protein or globally for all four metabolites simultaneously. Applying the 2 derivative pre-processing algorithm to the FTIR spectra helps to reduce the number of PLS latent variables needed significantly and thus simplify the interpretation of the PLS models. The validated PLS models show promise in predicting the concentration profiles of glucose, glutamine, lactate, and ammonia and protein yield over the course of the bioreactor cell culture process. Therefore, this work demonstrated the technical feasibility of real time monitoring of the bioreactor cell culture process via FTIR spectroscopy. Its implications for enabling cell culture PAT were discussed.

关键词： process analytical technology (PAT)     Fourier-transform infrared (FTIR) spectroscopy     partial least squares (PLS) regression     mouse IgG3     bioreactor cell culture process     real time process monitoring    

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过程操作性能的在线评估与诊断

Sedghi Shabnam,Huang Biao

《工程（英文）》 2017年 第3卷 第2期   页码 214-219 doi: 10.1016/J.ENG.2017.02.004

摘要： 此外，稳态模态的多模态特性由混合概率主成分回归方法(mixture probabilistic principal component regression，MPPCR) 处理；动态主成分回归方法(dynamicprincipal component regression，DPCR) 被用来探究不同模态间的过渡状态的性能评估。

关键词： 最优性能评估     概率主成分回归     多模态过程    

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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

《能源前沿（英文）》 2013年 第7卷 第1期   页码 103-110 doi: 10.1007/s11708-012-0220-z

摘要： The energy consumption of office buildings in China has been growing significantly in recent years. Obviously, there are significant relationships between building envelope and the energy consumption of office buildings. The 8 key building envelope influencing factors were found in this paper to evaluate their effects on the energy consumption of the air-conditioning system. The typical combinations of the key influencing factors were performed in Trnsy simulation. Then on the basis of the simulated results, the multiple regression models were developed respectively for the four climates of China—hot summer and warm winter, hot summer and cold winter, cold, and severely cold. According to the analysis of regression coefficients, the appropriate building envelope design schemes were discussed in different climates. At last, the regression model evaluations consisting of the simulation evaluations and the actual case evaluations were performed to verify the feasibility and accuracy of the regression models. The error rates are within±5% in the simulation evaluations and within±15% in the actual case evaluations. It is believed that the regression models developed in this paper can be used to estimate the energy consumption of office buildings in different climates when various building envelope designs are considered.

关键词： regression model     energy consumption     building envelope     office building     different climates    

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 221-236 doi: 10.1007/s11705-021-2061-y

摘要： To study the dynamic behavior of a process, time-resolved data are collected at different time instants during each of a series of experiments, which are usually designed with the design of experiments or the design of dynamic experiments methodologies. For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modeling method, has been proposed. Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used in several of previous publications, and Lasso regression, which is newly incorporated in this paper for the estimation of DRSM models. Here, we show that both approaches yield similarly accurate models, while the computational time of Lasso is on average two magnitude smaller. Two case studies are performed to show the advantages of the proposed method. In the first case study, where the concentrations of different species are modeled directly, DRSM method provides more accurate models compared to the models in the literature. The second case study, where the reaction extents are modeled instead of the species concentrations, illustrates the versatility of the DRSM methodology. Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety of organic synthesis datasets.

关键词： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

《结构与土木工程前沿（英文）》 2021年 第15卷 第5期   页码 1181-1198 doi: 10.1007/s11709-021-0744-6

摘要： In the recent era, piled raft foundation (PRF) has been considered an emergent technology for offshore and onshore structures. In previous studies, there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study. Finite element (FE) models are prepared with various design variables in a double-layer soil system, and the load sharing and interaction factors of piled rafts are estimated. The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neural network (ANN) modeling, and some prediction models are proposed. ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factors through backpropagation technique. The factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be used for developing the design strategy of PRF.

关键词： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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标题 作者 时间 类型 操作

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

Pijush Samui, Jagan J

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

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

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

Hao QIN, Shenwei ZHANG, Wenxing ZHOU

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

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

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

一种基于高斯过程与粒子群算法的CNN超参数自动搜索混合模型优化算法

闫涵,仲崇权,吴玉虎,张立勇,卢伟

期刊论文

Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality

Xin Peng, Yang Tang, Wenli Du, Feng Qian

期刊论文

Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infraredspectroscopy: Implications for enabling cell culture process analytical technology?

Huiquan Wu, Erik Read, Maury White, Brittany Chavez, Kurt Brorson, Cyrus Agarabi, Mansoor Khan

期刊论文

过程操作性能的在线评估与诊断

Sedghi Shabnam,Huang Biao

期刊论文

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

期刊论文

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

期刊论文

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

期刊论文