There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods...There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.展开更多
现有跨域人脸活体检测算法,其特征提取过程容易发生过拟合和缺乏特征聚合所导致的泛化性不足问题。针对该问题,提出了跨域人脸活体检测的单边对抗网络算法,将分组卷积与改进的倒残差结构融合替换普通卷积,降低网络参数同时加强人脸细粒...现有跨域人脸活体检测算法,其特征提取过程容易发生过拟合和缺乏特征聚合所导致的泛化性不足问题。针对该问题,提出了跨域人脸活体检测的单边对抗网络算法,将分组卷积与改进的倒残差结构融合替换普通卷积,降低网络参数同时加强人脸细粒度特征的表达能力,并引入自适应特征归一化模块,强调图像中人脸活体信息区域淡化无关背景区域,有效避免人脸活体信息的过拟合并加强来自不同源域的人脸活体检测能力。基于NetVLAD引入通道注意力机制模块,通道注意力机制模块作为特征聚合网络的分支,学习不同源域中人脸局部特征的语义信息,有效增强对不同源域的人脸活体信息分类的泛化能力。设计两模块融合网络以提高未知场景下跨域人脸活体检测精度。在OULU-NPU、CASIA-FASD、MSU-MFSD和Idiap Replay-Attack数据集上的实验结果表明,该算法在跨数据集测试O&C&M to I、O&C&I to M、I&C&M to O、O&M&I to C均有不错的表现,其中,在O&C&I to M及O&M&I to C性能评估指标分别提升了0.99个百分点和0.5个百分点的精度。展开更多
The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate ...The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.展开更多
基金National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2018A0303130188)+1 种基金Guangdong Provincial Science and Technology Special Funds Project of China(Grant No.190805145540361)Special Projects in Key Fields of Colleges and Universities in Guangdong Province of China(Grant No.2020ZDZX2005).
文摘There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.
文摘现有跨域人脸活体检测算法,其特征提取过程容易发生过拟合和缺乏特征聚合所导致的泛化性不足问题。针对该问题,提出了跨域人脸活体检测的单边对抗网络算法,将分组卷积与改进的倒残差结构融合替换普通卷积,降低网络参数同时加强人脸细粒度特征的表达能力,并引入自适应特征归一化模块,强调图像中人脸活体信息区域淡化无关背景区域,有效避免人脸活体信息的过拟合并加强来自不同源域的人脸活体检测能力。基于NetVLAD引入通道注意力机制模块,通道注意力机制模块作为特征聚合网络的分支,学习不同源域中人脸局部特征的语义信息,有效增强对不同源域的人脸活体信息分类的泛化能力。设计两模块融合网络以提高未知场景下跨域人脸活体检测精度。在OULU-NPU、CASIA-FASD、MSU-MFSD和Idiap Replay-Attack数据集上的实验结果表明,该算法在跨数据集测试O&C&M to I、O&C&I to M、I&C&M to O、O&M&I to C均有不错的表现,其中,在O&C&I to M及O&M&I to C性能评估指标分别提升了0.99个百分点和0.5个百分点的精度。
基金financially supported by the National Natural Science Foundation of China,No.61263011,81000554Program in Sun Yat-sen University supported by Fundamental Research Funds for the Central Universities,No.11ykpy07+1 种基金Natural Science Foundation of Guangdong Province,No.S2011010005309Innovation Fund of Xinjiang Medical University,No.XJC201209
文摘The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
