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Weight Analysis of Impact Factors of Interbedded Anti-Inclined Slopes Block-Flexure Toppling Based on support vector regression

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Journal of Earth Science 2024年第2期35卷 568-582页

作者： Bocheng Zhang Huiming Tang Yibing Ning Kun Fang Ding Xia Department of Engineering Geology and Geotechnical Engineering Faculty of EngineeringChina University of GeosciencesWuhan430074China Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan430074China College of Geological Engineering and Geomatics Chang’an UniversityXi’an710054China

Block-flexure toppling failure is frequently encountered in interbedded anti-inclined rock(IAR)slopes,and seriously threatens the construction of hydropower infrastructure.In this study,we first investigated the Lean Reservoir area’s geological setting and the Linda landslide’s characteristics.Then,uniform design and random design were used to design 110 training datasets and 31 testing datasets,respectively.Afterwards,the toppling response was obtained by using the discrete element code.Finally,support vector regression was used to obtain the influence weights of 21 impact factors.The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96%and 17.28%,respectively,which are much greater than the other 19 impact factors within the research range.For the primary impact factors,the influence weight is sorted from large to small as slope geometry parameters,joints parameters,and rock mechanics parameters.Joints parameters,especially the geometric parameters,cannot be ignored when evaluating the stability of IAR slopes.Through numerical simulation,it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation.

关键词： interbedded anti-inclined slopes block-flexure toppling impact factors numerical simulation support vector regression engineering geology

Deformation resistance prediction of tandem cold rolling based on grey wolf optimization and support vector regression

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Journal of Iron and Steel Research(International) 2023年第9期30卷 1803-1820页

作者： Ze-dong Wu Xiao-chen Wang Quan Yang Dong Xu Jian-wei Zhao Jing-dong Li Shu-zong Yan National Engineering Technology Research Center of Flat Rolling Equipment University of Science and Technology BeijingBeijing 100083China

In the traditional rolling force model of tandem cold rolling mills,the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material,which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip head.The inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process,and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression(SVR)model.The grey wolf optimization(GWO)algorithm was used to optimize the hyperparameters in the SVR model,and a deformation resistance prediction model based on GWO–SVR was established.Compared with the traditional model,the GWO–SVR model shows different degrees of improvement in each stand,with significant improvement in stands S3–S5.The prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling mill.The head rolling force had a similar degree of improvement in accuracy to the deformation resistance,and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously improved.Meanwhile,the thickness quality and shape quality of the strip head were improved accordingly,and the application results were consistent with expectations.

关键词： Tandem cold rolling Cross-process data application Deformation resistance prediction support vector regression Grey wolf optimization Rolling force accuracy

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

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Building Simulation 2022年第3期15卷 317-331页

作者： Rui Tang Cheng Fan Fanzhe Zeng Wei Feng Institute for Environmental Design and Engineering University College LondonLondonUK Key Laboratory for Resilient Infrastructures of Coastal Cities(Shenzhen University) Ministry of EducationChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina School of Municipal and Environmental Engineering Shenyang Jianzhu UniversityShenyangChina China Energy Group Lawrence Berkeley National LaboratoryBerkeleyCAUSA

Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

关键词： demand response support vector regression machine learning building peak demand model predictive control smart grid

Rapid fatty acids detection of vegetable oils by Raman spectroscopy based on competitive adaptive reweighted sampling coupled with support vector regression

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Food Quality and Safety 2022年第4期6卷 545-554页

作者： Linjiang Pang Hui Chen Liqing Yin Jiyu Cheng Jiande Jin Honghui Zhao Zhihao Liu Longlong Dong Huichun Yu Xinghua Lu College of Food and Health Zhejiang A&F UniversityHangzhouChina College of Food&Bioengineering Henan University of Science and TechnologyLuoyangChina Zhejiang Provincial Grain Breau of Grain and Oil Reserve HangzhouChina

Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.

关键词： Raman spectroscopy fatty acid composition competitive adaptive reweighted sampling support vector regression

support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit

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Petroleum Science 2021年第4期18卷 1230-1239页

作者： Venkata Vijayan S Hare Krishna Mohanta Ajaya Kumar Pani Department of Chemical Engineering Birla Institute of Technology and SciencePilaniRajasthan333031India

Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point(IBP)and end boiling point(EBP)in crude distillation unit.In this work,adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning(JITL)approach.The different types of local models designed are locally weighted regression(LWR),multiple linear regression(MLR),partial least squares regression(PLS)and support vector regression(SVR).In addition to model development,the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated.Results show that the JITL model based on support vector regression with iterative single data algorithm optimization(ISDA)local model(JITL-SVR:ISDA)yielded best prediction accuracy in reasonable computation time.

关键词： Adaptive soft sensor Just in time learning regression support vector regression Naphtha boiling point

Weight Analysis of Impact Factors of Interbedded Anti-Inclined Slopes Block-Flexure Toppling Based on support vector regression

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Journal of Earth Science 2023年

作者： Bocheng Zhang Huiming Tang Yibing Ning Kun Fang Ding Xia Badong National Observation and Research Station of Geohazards China University of Geosciences College of Geological Engineering and Geomatics Chang'an University Department of Engineering Geology and Geotechnical Engineering Faculty of Engineering China University of Geosciences

Block-flexuretopplingfailureisfrequentlyencounteredininterbeddedanti-inclined rock (IAR) slopes, and seriously threatens the construction of hydropower infrastructure. In this study,we first investigated the Lean Reservoir area.s geological setting and the Linda landslide’s characteristics. Then, uniform design and random design were used to design 110 training datasets and 31 testing datasets,respectively.Afterwards,thetopplingresponsewasobtainedbyusingthediscreteelement code. Finally, support vector regression was used to obtain the influence weights of 21 impact factors.The results show that the influence weight of the slope angle and rock formation dip angle on the toppling deformation among tertiary impact factors is 25.96%and 17.28%, respectively, which are much greater than the other 19 impact factors within the research range. For the primary impact factors, the influenceweightissortedfromlargetosmallasslopegeometryparameters,jointsparameters,and rock mechanics parameters. Joints parameters, especially the geometric parameters, cannot be ignored when evaluating the stability of IAR slopes. Through numerical simulation, it was qualitatively determined that failure surfaces of slopes were controlled by cross joints and that the rocks in the slope toe play a role in preventing slope deformation.

关键词： interbedded anti-inclined slopes block-flexure toppling impact factors numericalsimulation support vector regression engineering geology

A Metamodeling Method Based on support vector regression for Robust Optimization

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Chinese Journal of Mechanical Engineering 2010年第2期23卷 242-251页

作者： XIANG Guoqi HUANG Dagui School of Mechatronics Engineering University of Electronic Science and Technology of China Chengdu 610054 China School of Mechanical Engineering Panzhihua University Panzhihua 617000 China

Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.

关键词： support vector regression metamodeling robust optimization genetic algorithm

Particle Swarm Optimization Based support vector regression for Blind Image Restoration

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Journal of Computer Science & Technology 2012年第5期27卷 989-995页

作者： Ratnakar Dash Pankaj Kumar Sa Banshidhar Majhi Computer Science and Engineering Department National Institute of Technology RourkelaRourkela 769008India

This paper presents a swarm intelligence based parameter optimization of the support vector machine （SVM） for blind image restoration. In this work, SVM is used to solve a regression problem. support vector regression （SVR） has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization （PSO） technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.

关键词： image restoration support vector regression particle swarm optimization

Predicting Chinese Abbreviations from Definitions:An Empirical Learning Approach Using support vector regression

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Journal of Computer Science & Technology 2008年第4期23卷 602-611页

作者：孙栩王厚峰王波 Institute of Computational Linguistics School of Electronics Engineering and Computer Science

In Chinese, phrases and named entities play a central role in information retrieval. Abbreviations, however make keyword-based approaches less effective. This paper presents an empirical learning approach to Chinese abbreviation prediction. In this study, each abbreviation is taken as a reduced form of the corresponding definition （expanded form）, and the abbreviation prediction is formalized as a scoring and ranking problem among abbreviation candidates, which are automatically generated from the corresponding definition. By employing support vector regression （SVR） for scoring, we can obtain multiple abbreviation candidates together with their SVR values, which are used for candidate ranking. Experimental results show that the SVR method performs better than the popular heuristic rule of abbreviation prediction. In addition, in abbreviation prediction, the SVR method outperforms the hidden Markov model （HMM）.

关键词： statistical natural language processing abbreviation prediction support vector regression word clustering