货车运行故障动态图像检测系统通过人工方式对采集的铁路货车关键部位图像进行故障判别,效率低且易发生漏报.非人工故障检测多采用传统图像处理技术和基于深度学习的目标检测网络,存在受图像数据限制的缺点.为解决目前存在的故障图像的...货车运行故障动态图像检测系统通过人工方式对采集的铁路货车关键部位图像进行故障判别,效率低且易发生漏报.非人工故障检测多采用传统图像处理技术和基于深度学习的目标检测网络,存在受图像数据限制的缺点.为解决目前存在的故障图像的采集与标注难题,针对铁路货车故障中发生率最高的车辆异物故障,提出一种车辆异物检测算法.算法基于新型对抗自编码器,所用训练数据集由无标注的非异常图片组成.针对小目标异物,在对抗自编码器结构中引入注意力机制,并比较多种注意力机制在目标场景的应用效果,选择最优配置.使用特征匹配损失优化损失函数,提升对抗性训练的稳定性.结合应用场景和生成模型特点,提出特征向量异常值评分机制,评估整体异常性能.研究结果表明:提出的车辆异物检测算法,在转向架底部和侧部2个场景具有有效性,曲线下面积(Area Under Curve,AUC)指标分别能够达到96.9%和99.3%.展开更多
Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
文摘货车运行故障动态图像检测系统通过人工方式对采集的铁路货车关键部位图像进行故障判别,效率低且易发生漏报.非人工故障检测多采用传统图像处理技术和基于深度学习的目标检测网络,存在受图像数据限制的缺点.为解决目前存在的故障图像的采集与标注难题,针对铁路货车故障中发生率最高的车辆异物故障,提出一种车辆异物检测算法.算法基于新型对抗自编码器,所用训练数据集由无标注的非异常图片组成.针对小目标异物,在对抗自编码器结构中引入注意力机制,并比较多种注意力机制在目标场景的应用效果,选择最优配置.使用特征匹配损失优化损失函数,提升对抗性训练的稳定性.结合应用场景和生成模型特点,提出特征向量异常值评分机制,评估整体异常性能.研究结果表明:提出的车辆异物检测算法,在转向架底部和侧部2个场景具有有效性,曲线下面积(Area Under Curve,AUC)指标分别能够达到96.9%和99.3%.
文摘在航天发射活动中,高效快速地检测干扰信号是保障电磁环境安全的关键环节。针对航天发射场内干扰信号检测效率低、可靠性差的问题,提出了一种新颖的干扰信号检测方法,该方法结合了对抗性自编码器(Adversarial Autoencoder,AAE)与时频注意力机制(Time Frequency Attention Mechanism,TFAM)。首先,通过编码器与时频注意力机制,提取输入频谱数据的潜在特征、时间特征、频率特征,并通过对抗性的训练,使用鉴别器引导潜在特征分布在特定特征空间;其次,通过解码器利用三重特征进行频谱重构;最后,基于重构结构与输入数据之间的均方误差检测干扰信号。通过与经典异常检测算法的对比,所提的方法具备更优越的检测性能。
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
