Stable and safe operation of power grids is an important guarantee for economy development.Support Vector Machine(SVM)based stability analysis method is a significant method started in the last century.However,the SVM...Stable and safe operation of power grids is an important guarantee for economy development.Support Vector Machine(SVM)based stability analysis method is a significant method started in the last century.However,the SVM method has several drawbacks,e.g.low accuracy around the hyperplane and heavy computational burden when dealing with large amount of data.To tackle the above problems of the SVM model,the algorithm proposed in this paper is optimized from three aspects.Firstly,the gray area of the SVM model is judged by the probability output and the corresponding samples are processed.Therefore the clustering of the samples in the gray area is improved.The problem of low accuracy in the training of the SVM model in the gray area is improved,while the size of the sample is reduced and the efficiency is improved.Finally,by adjusting the model of the penalty factor in the SVM model after the clustering of the samples,the number of samples with unstable states being misjudged as stable is reduced.Test results on the IEEE 118-bus test system verify the proposed method.展开更多
This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression an...This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique （SVR-GA） for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the lit- erature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.展开更多
To investigate whether the expression of exogenous heme oxygenase-1 (HO-1) gene within vascular smooth muscle cells (VSMC) could protect the cells from free radical attack and inhibit cell proliferation, we establishe...To investigate whether the expression of exogenous heme oxygenase-1 (HO-1) gene within vascular smooth muscle cells (VSMC) could protect the cells from free radical attack and inhibit cell proliferation, we established an in vitro transfection of human HO-1 gene into rat VSMC mediated by a retroviral vector. The results showed that the profound expression of HO-1 protein as well as HO activity was 1.8- and 2.0-fold increased respectively in the transfected cells compared to the non-transfected ones. The treatment of VSMC with different concentrations of H2O2 led to the remarkable cell damage as indicated by survival rate and LDH leakage. However, the resistance of the HO-1 transfected VSMC against H2O2 was significantly raised. This protective effect was dramatically diminished when the transfected VSMC were pretreated with ZnPP-IX, a specific inhibitor of HO, for 24 h. In addition, we found that the growth potential of the transfected cells was significantly inhibited directly by increased activity of HO-1, and this effect might be related to decreased phosphorylation of MAPK. These results suggest that the overexpression of introduced hHO-1 is potentially able to reduce the risk factors of atherosclerosis, partially due to its cellular protection against oxidative injury and to its inhibitory effect on cellular proliferation.展开更多
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a...Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.展开更多
A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Th...A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.展开更多
Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass i...Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.展开更多
Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learn...Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).展开更多
With the decrease of agricultural labor and the increase of production cost,the researches on citrus harvesting robot(CHR)have received more and more attention in recent years.For the success of robotic harvesting and...With the decrease of agricultural labor and the increase of production cost,the researches on citrus harvesting robot(CHR)have received more and more attention in recent years.For the success of robotic harvesting and the safety of robot,the identification of mature citrus fruit and obstacle is the priority of robotic harvesting.In this work,a machine vision system,which consisted of a color CCD camera and a computer,was developed to achieve these tasks.Images of citrus trees were captured under sunny and cloudy conditions.Due to varying degrees of lightness and position randomness of fruits and branches,red,green,and blue values of objects in these images are changed dramatically.The traditional threshold segmentation is not efficient to solve these problems.Multi-class support vector machine(SVM),which succeeds by morphological operation,was used to simultaneously segment the fruits and branches in this study.The recognition rate of citrus fruit was 92.4%,and the branch of which diameter was more than 5 pixels,could be recognized.The results showed that the algorithm could be used to detect the fruits and branches for CHR.展开更多
Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificia...Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.展开更多
Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on f...Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.展开更多
基金This work was supported by China’s National key research and development program 2017YFB0902201National Natural Science Foundation of China under Grant 51777104Science and Technology Project of the State Grid Corporation of China.
文摘Stable and safe operation of power grids is an important guarantee for economy development.Support Vector Machine(SVM)based stability analysis method is a significant method started in the last century.However,the SVM method has several drawbacks,e.g.low accuracy around the hyperplane and heavy computational burden when dealing with large amount of data.To tackle the above problems of the SVM model,the algorithm proposed in this paper is optimized from three aspects.Firstly,the gray area of the SVM model is judged by the probability output and the corresponding samples are processed.Therefore the clustering of the samples in the gray area is improved.The problem of low accuracy in the training of the SVM model in the gray area is improved,while the size of the sample is reduced and the efficiency is improved.Finally,by adjusting the model of the penalty factor in the SVM model after the clustering of the samples,the number of samples with unstable states being misjudged as stable is reduced.Test results on the IEEE 118-bus test system verify the proposed method.
文摘This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique （SVR-GA） for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the lit- erature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.
基金This work was kindly supported by Na-tional Natural Science Foundation of China(No.39670308)
文摘To investigate whether the expression of exogenous heme oxygenase-1 (HO-1) gene within vascular smooth muscle cells (VSMC) could protect the cells from free radical attack and inhibit cell proliferation, we established an in vitro transfection of human HO-1 gene into rat VSMC mediated by a retroviral vector. The results showed that the profound expression of HO-1 protein as well as HO activity was 1.8- and 2.0-fold increased respectively in the transfected cells compared to the non-transfected ones. The treatment of VSMC with different concentrations of H2O2 led to the remarkable cell damage as indicated by survival rate and LDH leakage. However, the resistance of the HO-1 transfected VSMC against H2O2 was significantly raised. This protective effect was dramatically diminished when the transfected VSMC were pretreated with ZnPP-IX, a specific inhibitor of HO, for 24 h. In addition, we found that the growth potential of the transfected cells was significantly inhibited directly by increased activity of HO-1, and this effect might be related to decreased phosphorylation of MAPK. These results suggest that the overexpression of introduced hHO-1 is potentially able to reduce the risk factors of atherosclerosis, partially due to its cellular protection against oxidative injury and to its inhibitory effect on cellular proliferation.
基金Supported by the State Key Development Program for Basic Research of China （No.2002CB312200） and the National Natural Science Foundation of China （No.60574019）.
文摘Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
基金Project 06KJD470182 supported by the Jiangsu Educational Natural Science Foundation of china
文摘A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
基金Supported by the National Natural Science Foundation of China （No.20476007）.
文摘Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.
文摘Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of'IF-THEN' rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).
基金International Science&Technology Cooperation Program of China(2013DFA11470)the National Natural Science Foundation of China(30771243)+1 种基金International Science&Technology Cooperation Program of Chongqing(cstc2011gjhz80001)Fundamental Research Funds for the Central Universities(XDJK2013C102).
文摘With the decrease of agricultural labor and the increase of production cost,the researches on citrus harvesting robot(CHR)have received more and more attention in recent years.For the success of robotic harvesting and the safety of robot,the identification of mature citrus fruit and obstacle is the priority of robotic harvesting.In this work,a machine vision system,which consisted of a color CCD camera and a computer,was developed to achieve these tasks.Images of citrus trees were captured under sunny and cloudy conditions.Due to varying degrees of lightness and position randomness of fruits and branches,red,green,and blue values of objects in these images are changed dramatically.The traditional threshold segmentation is not efficient to solve these problems.Multi-class support vector machine(SVM),which succeeds by morphological operation,was used to simultaneously segment the fruits and branches in this study.The recognition rate of citrus fruit was 92.4%,and the branch of which diameter was more than 5 pixels,could be recognized.The results showed that the algorithm could be used to detect the fruits and branches for CHR.
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN.
基金supported by the Major State Basic Research Development of China (Grant No. 2011CB706803)National Natural Science Foundation of China (Grant No. 50875098)Important National Science & Technology Specific Projects of China (Grant No. 2009ZX04014-024)
文摘Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
