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
Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have...Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.展开更多
为了解决直推式支持向量机(transductive support vector machines,TSVM)在样本选择自动化程度低和特征学习充分性不足的问题,提出了一种融合深度空间特征与传统影像对象特征的TSVM自动高分遥感影像变化检测方法。首先,采用基于分形网...为了解决直推式支持向量机(transductive support vector machines,TSVM)在样本选择自动化程度低和特征学习充分性不足的问题,提出了一种融合深度空间特征与传统影像对象特征的TSVM自动高分遥感影像变化检测方法。首先,采用基于分形网络演化算法的叠置分割获取多时相高分遥感影像的影像对象,通过卷积神经网络提取遥感影像的深度空间特征,并与灰度、指数和纹理等传统影像对象特征联合构建特征空间;然后,利用卡方变换计算多维特征的加权特征差异度,采用最大期望算法和贝叶斯最小错误判别规则得到二值分割结果,依据变化概率自动将分割结果中准确率较高的部分标记为训练样本;最后,采用标记训练样本获得TSVM的多维特征空间二值分割超平面,进而完成自动变化检测。选择武汉市的两组高分数据集作为实验数据。实验结果表明,该方法能够实现样本自动选择,并且通过融合深度空间特征可以有效提高特征学习的充分性,平均准确率达到了88.84%,平均漏检率较仅利用传统影像对象特征的TSVM法降低了3.29个百分点,在定性和定量的变化检测有效性评价中均得到了提高。展开更多
为了能够精确预测短期电价为市场参与者提供有效的决策指导,首先对电价数据进行水平处理,然后建立BP神经网络(BPNN)和最小二乘支持向量机(LSSVM)组合变权模型(BP LSSVM),同时提出采用遗传算法(GA)对该组合变权模型的权重进行优化,最后...为了能够精确预测短期电价为市场参与者提供有效的决策指导,首先对电价数据进行水平处理,然后建立BP神经网络(BPNN)和最小二乘支持向量机(LSSVM)组合变权模型(BP LSSVM),同时提出采用遗传算法(GA)对该组合变权模型的权重进行优化,最后将权重优化之后的GA BP LSSVM模型应用于美国PJM电力市场的边际电价预测,并与传统的LSSVM与BPNN的预测结果进行比较,结果表明,该组合变权模型能够提供更加精确的预测电价。展开更多
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Projects(61621062,61563015)supported by the National Natural Science Foundation of ChinaProject(2016zzts056)supported by the Central South University Graduate Independent Exploration Innovation Program,China
文摘Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.
文摘为了解决直推式支持向量机(transductive support vector machines,TSVM)在样本选择自动化程度低和特征学习充分性不足的问题,提出了一种融合深度空间特征与传统影像对象特征的TSVM自动高分遥感影像变化检测方法。首先,采用基于分形网络演化算法的叠置分割获取多时相高分遥感影像的影像对象,通过卷积神经网络提取遥感影像的深度空间特征,并与灰度、指数和纹理等传统影像对象特征联合构建特征空间;然后,利用卡方变换计算多维特征的加权特征差异度,采用最大期望算法和贝叶斯最小错误判别规则得到二值分割结果,依据变化概率自动将分割结果中准确率较高的部分标记为训练样本;最后,采用标记训练样本获得TSVM的多维特征空间二值分割超平面,进而完成自动变化检测。选择武汉市的两组高分数据集作为实验数据。实验结果表明,该方法能够实现样本自动选择,并且通过融合深度空间特征可以有效提高特征学习的充分性,平均准确率达到了88.84%,平均漏检率较仅利用传统影像对象特征的TSVM法降低了3.29个百分点,在定性和定量的变化检测有效性评价中均得到了提高。
文摘为了能够精确预测短期电价为市场参与者提供有效的决策指导,首先对电价数据进行水平处理,然后建立BP神经网络(BPNN)和最小二乘支持向量机(LSSVM)组合变权模型(BP LSSVM),同时提出采用遗传算法(GA)对该组合变权模型的权重进行优化,最后将权重优化之后的GA BP LSSVM模型应用于美国PJM电力市场的边际电价预测,并与传统的LSSVM与BPNN的预测结果进行比较,结果表明,该组合变权模型能够提供更加精确的预测电价。
