以压力容器气体泄漏展开研究,提出了一种融合黄金正弦的减法平均优化器(subtraction-average-based optimizer with golden sine,GSABO)、优化变分模态分解(variational mode decomposition,VMD)和卷积神经网络(convolutional neural ne...以压力容器气体泄漏展开研究,提出了一种融合黄金正弦的减法平均优化器(subtraction-average-based optimizer with golden sine,GSABO)、优化变分模态分解(variational mode decomposition,VMD)和卷积神经网络(convolutional neural network,CNN)与支持向量机(support vector machine,SVM)联合分类检测的方法。首先,引入了融合黄金正弦的减法平均优化器对变分模态分解的参数模态个数K和惩罚参数α进行寻优,将最小包络熵为适应度函数得到最佳的K和惩罚参数α,计算最佳IMF分量的9种时域指标构建特征向量,输入CNN-SVM联合的分类方法进行特征提取并对气体泄漏情况进行识别。经实验分析,提出的引入融合黄金正弦的减法平均优化器优化后的VMD方法能够有效地自适应获取最优参数组,然后对压力容器气体泄漏声波信号进行特征提取,选取最优的特征组合输入CNNSVM联合分类检测,得到泄漏与否判别准确率高达99.16%,有助于对后续研究进一步开展。展开更多
Due to the spectral and spatial properties of pervious and impervious surfaces,image classification and information extraction in detailed,small-scale mapping of urban surface materials is quite difficult and complex....Due to the spectral and spatial properties of pervious and impervious surfaces,image classification and information extraction in detailed,small-scale mapping of urban surface materials is quite difficult and complex.Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques,which are fundamental to this approach.Consequently,the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods,pixel-based approach and object-based approach,using WorldView-2 satellite imagery to specifically highlight linear features such as roads,building edges,and road dividers.Two applied algorithms,including support vector machines(SVM)and ruled-based,were evaluated using two distinct software.A comparison of the results reveals that the object-based classification has a higher overall resolution than the pixel-based classification.The output of rule-based classificationwas satisfactory,with an overall accuracy of 88.6%(ENVI)and 92.2%(e-Cognition).The SVM classification result contained misclassified impervious surfaces and other urban features,as well as mixed objects.This classification achieved an overall accuracy of 75.1%.Nonetheless,this study provides an excellent overview for understanding the differences in their performances on the same data,as well as a comparison of the software employed.展开更多
文摘Due to the spectral and spatial properties of pervious and impervious surfaces,image classification and information extraction in detailed,small-scale mapping of urban surface materials is quite difficult and complex.Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques,which are fundamental to this approach.Consequently,the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods,pixel-based approach and object-based approach,using WorldView-2 satellite imagery to specifically highlight linear features such as roads,building edges,and road dividers.Two applied algorithms,including support vector machines(SVM)and ruled-based,were evaluated using two distinct software.A comparison of the results reveals that the object-based classification has a higher overall resolution than the pixel-based classification.The output of rule-based classificationwas satisfactory,with an overall accuracy of 88.6%(ENVI)and 92.2%(e-Cognition).The SVM classification result contained misclassified impervious surfaces and other urban features,as well as mixed objects.This classification achieved an overall accuracy of 75.1%.Nonetheless,this study provides an excellent overview for understanding the differences in their performances on the same data,as well as a comparison of the software employed.
