To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identific...To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.展开更多
For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction model....For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction model.Firstly,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate.Secondly,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying IPSO.Finally,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP model.The results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction models.The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.展开更多
Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the d...Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.展开更多
Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to th...Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to the analysis with support vector machine method, the drawback of determining the parameters only by experts' experience should be improved. After a detailed description of the methodology of SVM and simulated annealing, an improved algorithm was proposed for the automatic optimization of parameters using SVM method. An example has proved that the proposed method can efficiently select the parameters of the SVM method. And by optimizing the parameters, the forecasting accuracy of the max wind velocity increases by 34.45%, which indicates that the new SASVM model improves the forecasting accuracy.展开更多
Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-r...Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.展开更多
针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vecto...针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.展开更多
基金Sponsored by the National Natural Science Foundation of China(60472110)
文摘To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.
基金Key Project of Science and Technology of Education Department of Henan Province(19B120002)Key Laboratory of Control Engineering of Henan Province(KG2016-17).
文摘For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction model.Firstly,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate.Secondly,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying IPSO.Finally,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP model.The results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction models.The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2006AA020804)Fundamental Research Funds for the Central Universities(No.NJ20120007)+2 种基金Jiangsu Province Science and Technology Support Plan(No.BE2010652)Program Sponsored for Scientific Innovation Research of College Graduate in Jangsu Province(No.CXLX11_0218)Shanghai University Scientific Selection and Cultivation for Outstanding Young Teachers in Special Fund(No.ZZGCD15081)
文摘Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.
基金Project(71071052) supported by the National Natural Science Foundation of ChinaProject(JB2011097) supported by the Fundamental Research Funds for the Central Universities of China
文摘Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to the analysis with support vector machine method, the drawback of determining the parameters only by experts' experience should be improved. After a detailed description of the methodology of SVM and simulated annealing, an improved algorithm was proposed for the automatic optimization of parameters using SVM method. An example has proved that the proposed method can efficiently select the parameters of the SVM method. And by optimizing the parameters, the forecasting accuracy of the max wind velocity increases by 34.45%, which indicates that the new SASVM model improves the forecasting accuracy.
文摘Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.
文摘针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.
