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Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression

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OPTIK 2019年 180卷 244-253页

作者： Lu, Shizeng Jiang, Mingshun Wang, Xiaohong Yu, Hongliang Su, Chenhui Univ Jinan Sch Elect Engn Jinan 250022 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

The assessment of structural damage is of great significance for ensuring the service safety of carbon fiber reinforced plastics (CFRP) structures. In this paper, the damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression was studied. The structural dynamic response signals were detected by fiber Bragg grating sensors. Then, the Fourier transform was used to extract the dynamic characteristics of the structure as the damage feature, and the damage feature dimensionality was reduced by using the RReliefF algorithm. On this basis, the damage degree prediction model of CFRP structure based on epsilon support vector regression was established. Finally, the method proposed in this paper was experimentally verified. The results showed that the epsilon-support vector regression model can accurately predict the damage degree of unknown samples, and the absolute relative error of 27 experiments was less than 10% for 30 testing experiments. This paper provided a feasible method for predicting the damage degree of CFRP structures.

关键词： Damage degree prediction Carbon fiber reinforced plastics Frequency response RReliefF epsilon-support vector regression

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Machine learning-based quantitative structure-retention relationship models for predicting the retention indices of volatile organic pollutants

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2022年第3期19卷 1457-1466页

作者： Sepehri, B. Ghavami, R. Farahbakhsh, S. Ahmadi, R. Univ Kurdistan Dept Chem Chemometr Lab Fac Sci POB 416 Sanandaj Iran

In this research, a dataset including 206 volatile organic compounds was used to develop quantitative structure-retention relationship models for predicting the retention indices of volatile organic compounds on DB-5 stationary phase. A total of 141 molecules were put in train set to build models and 65 molecules were put in test set to validate models, externally. By using stepwise-multiple linear regression, two descriptors including X1sol (solvation connectivity index chi-1) and AAC (mean information index on atomic composition) were selected to create linear and nonlinear quantitative structure-retention relationship models. Multiple linear regression, epsilon-support vector regression and deep learning-based artificial neural network were used as modeling techniques. All models were validated by calculating several statistical parameters for both train and test sets that show created models have high predictive power. R-2 values for the test set of multiple linear regression, epsilon-support vector regression and deep learning-based artificial neural network models were 0.90, 0.94 and 0.94, respectively. Results show the Van der Waals interactions of molecules with methyl groups in DB-5 stationary phase and the electrostatic interactions of atoms with partial negative charge in molecules with the hydrogen atoms of phenyl groups in DB-5 stationary phase are responsible for the separation of volatile organic compounds in DB-5 stationary phase. Finally, these created models were used to predict the retention indices of 694 volatile organic compounds that had no retention index data on DB-5 stationary phase.

关键词： Quantitative structure– retention relationship Volatile organic compounds epsilon-support vector regression Deep learning R software

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A New Adaptive Disturbance/Uncertainty Estimator Based Control Scheme For LTI Systems

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IEEE ACCESS 2022年 10卷 106849-106858页

作者： Bayrak, Abdurrahman Kurkcu, Burak Efe, Mehmet Onder Hacettepe Univ Grad Sch Sci & Engn TR-06800 Ankara Turkey ASELSAN Inc Unmanned & Autonomous Syst Engn Dept TR-06750 Ankara Turkey Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey

This paper introduces a machine learning assisted disturbance/uncertainty estimator based control scheme. The aim of the proposed method is to update the nominal model directly used by the conventional disturbance observer based control architecture and approximate it to the perturbed/uncertain system using machine learning approaches. This enhances the disturbance rejection performance of the system remarkably. The performance deterioration capacity of lumped disturbances, which are the mixed effect of disturbances entering through the control channels and modeling uncertainties, are decomposed in our approach and handled separately. For this study, harmonic disturbance model and constant unstructured uncertainty model are considered, and epsilon-support vector regression approach is used together with an online adaptation algorithm. A numerical example is given to demonstrate the merits and effectiveness of the proposed approach. Simulation results show that the proposed method outperforms the conventional disturbance/uncertainty estimator based control architecture by increasing disturbance estimation performance of the system.

关键词： Disturbance/uncertainty estimator disturbance observer machine learning robust control robustness epsilon-support vector regression

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SYSTEM IDENTIFICATION OF ABKOWITZ MODEL FOR SHIP MANEUVERING MOTION BASED ON ε-support vector regression 38

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38th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2019)

作者： Liu, B. Jin, Y. Mageet, A. R. Yiew, L. J. Zhang, S. Technol Ctr Offshore & Marine 12 Prince Georges Pk Singapore 118411 Singapore

ISBN: (纸本)9780791858844

System identification is crucial to predict the maneuverability of the ship. In this work, E-support vector regression (epsilon-SVR) is implemented to identify hydrodynamic derivatives of Abkowitz maneuver model. A proposed technique, batch learning, is implemented with the addition of Gaussian white noise to reconstruct the samples and alleviate the parameter drift in the system identification of the ship maneuvering model. The predicted results are compared with results obtained from Planar Motion Mechanism (PMM) test. Standard maneuvers, 35 turning circle, 10 degrees/10 degrees and 20 degrees/20 degrees zigzags, are simulated and compared with the predicted model by epsilon-SVR. The presented results show that the proposed batch learning technique with Gaussian white noise is an effective technique, which improves the accuracy and robustness of epsilon-SVR in system identification. The results obtained from the predicted model match well with the those obtained from PMM results, which shows its excellent generalization performance. The developed model is applied to understand control requirements for vessels under different conditions.

关键词： system identification Abkowitz model epsilon-support vector regression parameter drift batch learning

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An effective way to integrate ε-support vector regression with gradients

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 99卷 126-140页

作者： Zhou, XiaoJian Jiang, Ting Nanjing Univ Posts & Telecommun Sch Management Nanjing 210003 Jiangsu Peoples R China Penn State Univ Dept Stat University Pk PA 16801 USA Nanjing Univ Finance & Econ Sch Informat Engn Nanjing 210023 Jiangsu Peoples R China

epsilon-support vector regression (epsilon-SVR), as a direct implementation of the structural risk minimization principle rather than empirical risk minimization principle, is a new regression method with good generalization ability and can efficiently solve small-sample learning problems. In this work, through incorporating gradient information into the traditional epsilon-SVR, the gradient-enhanced epsilon-SVR (GESVR) is developed. The efficiency of GESVR is compared with the traditional epsilon-SVR by employing analytical function fitting, compared with the gradient-enhanced least square support vector regression (GELSSVR) by using two real-life examples, and tested in a scenario where the exact gradient information is unknown. The results show that GESVR provides more accurate prediction results than the traditional epsilon-SVR model, and outperforms GELSSVR in some real-life cases. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： epsilon-support vector regression Metamodel Gradient information Machine learning

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Research on PM2.5 estimation and prediction method and changing characteristics analysis under long temporal and large spatial scale - A case study in China typical regions

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 696卷 133983-000页

作者： Luo Yi Teng Mengfan Yang Kun Zhu Yu Zhou Xiaolu Zhang Miao Shi Yan Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Yunnan Peoples R China Yunnan Normal Univ GIS Technol Res Ctr Resource & Environm Western C Minist Educ Kunming 650500 Yunnan Peoples R China Texas Christian Univ Dept Geog Ft Worth TX 76129 USA Queens Univ Smith Sch Business Toronto ON Canada Informat Ctr Yunnan Power Co Ltd Kunming 650217 Yunnan Peoples R China

High concentration of fine particulatematter (PM2.5) has been shown to be a major contributor to hazeweather, which has been associated with an increased prevalence in lung cancer. An accurate estimation and predication of PM2.5 historical levels, and its spatial-temporal variability can assist in strategically improving regional air quality and reducing its harmful effects on population health. This paper targets Beijing, Tianjin, and Hebei province (BTH), three northeast province of china (TNPC), Yangtze river delta (YRD) and pearl river delta (PRD) as the study areas. Data used in this study include PM2.5 measurements from April 2013 to December 2016, MODIS AOD raster imageries and five meteorological factors from2000 to 2016. By combining back propagation artificial neural network (BPANN) and e-support vector regression (e-SVR), a novel hybrid model was constructed to impute the historical PM2.5 missing values in the long time series from 2000 to 2012, and to predict the concentration of PM2.5 from April 2014 to December 2017. The hybrid model produced results superior to BPANN and eSVR with a higher accuracy, lower error rate, and a stable performance. This model can be applied to the other four regions with consistent results. Results of spatial-temporal analysis indicated that the PM2.5 concentration has increased along with a pollution range expansion in BTH from 2000 to 2010. In addition, the PM2.5 concentration decreased slowly in PRD. The concentration and pollution range of PM2.5 in TNPC and YRD showed a stable trend. In 2012, the four research areas all showed decreased trend, and the pollution range narrowed. From 2013 to 2016, the PM2.5 concentration increased shortly then decreased;in particular, the high pollution areas saw a decrease in PM2.5 concentration, which correlated with control measures adopted by the state during the same time period. The hot spots of PM2.5 were mainly distributed in the inland cities. (C) 2019 Elsevier B.V. All rights re

关键词： Fine particulate matter Back propagation artificial neural network epsilon-support vector regression Novel hybrid model Spatial-temporal analysis

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Aeromagnetic gradient compensation method for helicopter based on ε-support vector regression algorithm

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JOURNAL OF APPLIED REMOTE SENSING 2017年第2期11卷 025012-1-025012-12页

作者： Wu, Peilin Zhang, Qunying Fei, Chunjiao Fang, Guangyou Chinese Acad Sci Inst Elect Key Lab Electromagnet Radiat & Sensing Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Aeromagnetic gradients are typically measured by optically pumped magnetometers mounted on an aircraft. Any aircraft, particularly helicopters, produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is essential, and least square (LS) is the conventional method used for reducing interference levels. However, the LSs approach to solving the aeromagnetic interference model has a few difficulties, one of which is in handling multicollinearity. Therefore, we propose an aeromagnetic gradient compensation method, specifically targeted for helicopter use but applicable on any airborne platform, which is based on the epsilon-support vector regression algorithm. The structural risk minimization criterion intrinsic to the method avoids multicollinearity altogether. Local aeromagnetic anomalies can be retained, and platform-generated fields are suppressed simultaneously by constructing an appropriate loss function and kernel function. The method was tested using an unmanned helicopter and obtained improvement ratios of 12.7 and 3.5 in the vertical and horizontal gradient data, respectively. Both of these values are probably better than those that would have been obtained from the conventional method applied to the same data, had it been possible to do so in a suitable comparative context. The validity of the proposed method is demonstrated by the experimental result. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： aeromagnetic compensation helicopter optically pumped magnetometer epsilon-support vector regression

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ε-SVR-based Estimation for the Motion Trajectory of Human Lower Limb Using Acceleration Signals

ε-SVR-based Estimation for the Motion Trajectory of Human L...

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IEEE International Conference on Information and Automation (ICIA)

作者： Zhang Dongxu Hou Zengguang Cheng Long Bian Guibin Xie Xiaoliang Peng Liang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781509041022

Active training mode has good clinical effect for patients who need lower limb rehabilitation, and estimation for the motion trajectory of human lower limb is one of the most important and fundamental work for active training. In this study, a novel estimation method is proposed based on e-support vector regression (epsilon-SVR) using acceleration signals of lower limb. Firstly, some qualitative variation regularities between acceleration signals of lower limb superficial muscle group and angle values of hip, knee and ankle joints were obtained through the preliminary analysis of the data, which were acquired in the experiments designed and performed in different motion trajectories that were cycling, walk and pedaling. Then, according to the regularities, the input vectors and the output vectors of epsilon-SVR were determined for the estimation model. After that, experimental data were divided into training data and test data. The estimation model was built based on epsilon-SVR using training data. At last, the estimation model was verified through the test data. The results were that the absolute mean error between predicted values and measured values were respectively 6.26 degrees,4.86 degrees and 5.75 degrees for three kinds of motion trajectories that were cycling, walk and pedaling. The effectiveness and applicability of the estimation model for active training of rehabilitation were confirmed.

关键词： rehabilitation training estimation method acceleration signal epsilon-support vector regression motion trajectory

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A Study on SMO Algorithm for Solving E-SVR with Non-PSD Kernels

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2013年第10期42卷 2175-2196页

作者： Zhou, Xiaojian Ma, Yizhong Nanjing Univ Posts & Telecommun Sch Econ & Management Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Dept Management Sci & Technol Nanjing Jiangsu Peoples R China

Sequential minimal optimization (SMO) algorithm is effective in solving large-scale support vector machine (SVM). The existing algorithms all assume that the kernels are positive definite (PD) or positive semi-definite (PSD) and should meet the Mercer condition. Some kernels, however, such as sigmoid kernel, which originates from neural network and then is extensively used in SVM, are conditionally PD in certain circumstances;in addition, practically, it is often difficult to prove whether a kernel is PD or PSD or not except some well-known kernels. So, the applications of the existing algorithm of SMO are limited. Considering the deficiency of the traditional ones, this algorithm of solving E-SVR with nonpositive semi-definite (non-PSD) kernels is proposed. Different from the existing algorithms which must consider four Lagrange multipliers, the algorithm proposed in this article just need to consider two Lagrange multipliers in the process of implementation. The proposed algorithm simplified the implementation by expanding the original dual programming of E-SVR and solving its KKT conditions, thus being easily applied in solving E-SVR with non-PSD kernels. The presented algorithm is evaluated using five benchmark problems and one reality problem. The results show that E-SVR with non-PSD provides more accurate prediction than that with PD kernel.

关键词： Non-positive semi-definite kernel Sequential minimal optimization algorithm support vector machine epsilon-support vector regression

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Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty

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ECOLOGICAL INFORMATICS 2012年 12卷 31-42页

作者： Hajek, Petr Olej, Vladimir Univ Pardubice Fac Econ & Adm Inst Syst Engn & Informat Pardubice 53210 Czech Republic

The article presents modeling of daily average ozone level prediction by means of neural networks, support vector regression and methods based on uncertainty. Based on data measured by a monitoring station of the Pardubice micro-region, the Czech Republic, and optimization of the number of parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, c-support vector regression, fuzzy inference systems and Takagi-Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Takagi-Sugeno intuitionistic fuzzy inference system and adaptation of fuzzy logic-based systems using evolutionary algorithms. Based on data obtained, the daily average ozone level prediction in a certain time is characterized by a root mean squared error. The best possible results were obtained by means of an c-support vector regression with polynomial kernel functions and Takagi-Sugeno intuitionistic fuzzy inference systems with adaptation by means of a Kalman filter. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Ozone prediction Neural networks epsilon-support vector regression Takagi-Sugeno fuzzy inference systems Takagi-Sugeno intuitionistic fuzzy inference systems Evolutionary fuzzy logic-based systems

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