为解决电磁频谱中的未知信号分类和身份识别问题,提出一种基于改进卷积神经网络(CNN) Le Net-5模型的信号分类方法。该方法使用信号全双谱作为CNN的输入,然后通过改进的Le Net-5模型学习信号特征并完成信号分类和身份识别。实验结果表明...为解决电磁频谱中的未知信号分类和身份识别问题,提出一种基于改进卷积神经网络(CNN) Le Net-5模型的信号分类方法。该方法使用信号全双谱作为CNN的输入,然后通过改进的Le Net-5模型学习信号特征并完成信号分类和身份识别。实验结果表明,算法对未知信号调制类型识别率达97%以上,对信号身份属性识别率达96%以上。相比传统方法,该算法对信号身份属性识别率提高6. 5%,具有更好的泛化性能,并有效解决了全双谱应用的二维模板匹配和Loss函数值下降缓慢的问题。展开更多
In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single tria...In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.展开更多
Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classific...Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working facefloor.Cluster analysis,which classifies samples according to data similarity,has remarkable advantages in nonlinear classification.A water inrush early warning method for coal minefloors is proposed in this paper.First,the short time average over long time average(STA/LTA)method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines.Then,ten attributes of microseismic events are extracted,and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events.Clustering results of synthetic andfield data demonstrate the effectiveness of the proposed method.The analysis offield data clustering results shows that thefirst kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working facefloor.展开更多
文摘为解决电磁频谱中的未知信号分类和身份识别问题,提出一种基于改进卷积神经网络(CNN) Le Net-5模型的信号分类方法。该方法使用信号全双谱作为CNN的输入,然后通过改进的Le Net-5模型学习信号特征并完成信号分类和身份识别。实验结果表明,算法对未知信号调制类型识别率达97%以上,对信号身份属性识别率达96%以上。相比传统方法,该算法对信号身份属性识别率提高6. 5%,具有更好的泛化性能,并有效解决了全双谱应用的二维模板匹配和Loss函数值下降缓慢的问题。
基金Natural Science Foundation of Shandong Provincegrant number:Y2007G31
文摘In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.
基金supported in part by the National Natural Science Foundation of China under Grant 41904098in part by the Beijing Nova Program under Grant 2022056in part by the National Natural Science Foundation of China (52174218)。
文摘Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working facefloor.Cluster analysis,which classifies samples according to data similarity,has remarkable advantages in nonlinear classification.A water inrush early warning method for coal minefloors is proposed in this paper.First,the short time average over long time average(STA/LTA)method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines.Then,ten attributes of microseismic events are extracted,and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events.Clustering results of synthetic andfield data demonstrate the effectiveness of the proposed method.The analysis offield data clustering results shows that thefirst kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working facefloor.
